{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "\n",
    "torch.set_printoptions(edgeitems=2, linewidth=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([11, 1])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_c = [0.5,  14.0, 15.0, 28.0, 11.0,  8.0,  3.0, -4.0,  6.0, 13.0, 21.0]\n",
    "t_u = [35.7, 55.9, 58.2, 81.9, 56.3, 48.9, 33.9, 21.8, 48.4, 60.4, 68.4]\n",
    "t_c = torch.tensor(t_c).unsqueeze(1) # <1>\n",
    "t_u = torch.tensor(t_u).unsqueeze(1) # <1>\n",
    "\n",
    "t_u.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([5, 6, 1, 4, 9, 0, 3, 2, 8]), tensor([ 7, 10]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_samples = t_u.shape[0]\n",
    "n_val = int(0.2 * n_samples)\n",
    "\n",
    "shuffled_indices = torch.randperm(n_samples)\n",
    "\n",
    "train_indices = shuffled_indices[:-n_val]\n",
    "val_indices = shuffled_indices[-n_val:]\n",
    "\n",
    "train_indices, val_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_u_train = t_u[train_indices]\n",
    "t_c_train = t_c[train_indices]\n",
    "\n",
    "t_u_val = t_u[val_indices]\n",
    "t_c_val = t_c[val_indices]\n",
    "\n",
    "t_un_train = 0.1 * t_u_train\n",
    "t_un_val = 0.1 * t_u_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6018],\n",
       "        [0.2877]], grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "linear_model = nn.Linear(1, 1) # <1>\n",
    "linear_model(t_un_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[-0.0674]], requires_grad=True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([0.7488], requires_grad=True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.6814], grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.ones(1)\n",
    "linear_model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6814],\n",
       "        [0.6814],\n",
       "        [0.6814],\n",
       "        [0.6814],\n",
       "        [0.6814],\n",
       "        [0.6814],\n",
       "        [0.6814],\n",
       "        [0.6814],\n",
       "        [0.6814],\n",
       "        [0.6814]], grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.ones(10, 1)\n",
    "linear_model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = nn.Linear(1, 1) # <1>\n",
    "optimizer = optim.SGD(\n",
    "    linear_model.parameters(), # <2>\n",
    "    lr=1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object Module.parameters at 0x7f94b4a8a750>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([[0.7398]], requires_grad=True), Parameter containing:\n",
       " tensor([0.7974], requires_grad=True)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(linear_model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_loop(n_epochs, optimizer, model, loss_fn, t_u_train, t_u_val,\n",
    "                  t_c_train, t_c_val):\n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "        t_p_train = model(t_u_train) # <1>\n",
    "        loss_train = loss_fn(t_p_train, t_c_train)\n",
    "\n",
    "        t_p_val = model(t_u_val) # <1>\n",
    "        loss_val = loss_fn(t_p_val, t_c_val)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss_train.backward() # <2>\n",
    "        optimizer.step()\n",
    "\n",
    "        if epoch == 1 or epoch % 1000 == 0:\n",
    "            print(f\"Epoch {epoch}, Training loss {loss_train.item():.4f},\"\n",
    "                  f\" Validation loss {loss_val.item():.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Training loss 158.3422, Validation loss 209.0985\n",
      "Epoch 1000, Training loss 4.9330, Validation loss 4.9653\n",
      "Epoch 2000, Training loss 3.0409, Validation loss 3.0661\n",
      "Epoch 3000, Training loss 2.8581, Validation loss 3.8942\n",
      "\n",
      "Parameter containing:\n",
      "tensor([[5.4288]], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([-17.9516], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "def loss_fn(t_p, t_c):\n",
    "    squared_diffs = (t_p - t_c)**2\n",
    "    return squared_diffs.mean()\n",
    "\n",
    "linear_model = nn.Linear(1, 1) # <1>\n",
    "optimizer = optim.SGD(linear_model.parameters(), lr=1e-2)\n",
    "\n",
    "training_loop(\n",
    "    n_epochs = 3000, \n",
    "    optimizer = optimizer,\n",
    "    model = linear_model,\n",
    "    loss_fn = loss_fn,\n",
    "    t_u_train = t_un_train,\n",
    "    t_u_val = t_un_val, \n",
    "    t_c_train = t_c_train,\n",
    "    t_c_val = t_c_val)\n",
    "\n",
    "print()\n",
    "print(linear_model.weight)\n",
    "print(linear_model.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Training loss 134.9599, Validation loss 183.1707\n",
      "Epoch 1000, Training loss 4.8053, Validation loss 4.7307\n",
      "Epoch 2000, Training loss 3.0285, Validation loss 3.0889\n",
      "Epoch 3000, Training loss 2.8569, Validation loss 3.9105\n",
      "\n",
      "Parameter containing:\n",
      "tensor([[5.4319]], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([-17.9693], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "linear_model = nn.Linear(1, 1)\n",
    "optimizer = optim.SGD(linear_model.parameters(), lr=1e-2)\n",
    "\n",
    "training_loop(\n",
    "    n_epochs = 3000, \n",
    "    optimizer = optimizer,\n",
    "    model = linear_model,\n",
    "    loss_fn = nn.MSELoss(), # <1>\n",
    "    t_u_train = t_un_train,\n",
    "    t_u_val = t_un_val, \n",
    "    t_c_train = t_c_train,\n",
    "    t_c_val = t_c_val)\n",
    "\n",
    "print()\n",
    "print(linear_model.weight)\n",
    "print(linear_model.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=1, out_features=13, bias=True)\n",
       "  (1): Tanh()\n",
       "  (2): Linear(in_features=13, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_model = nn.Sequential(\n",
    "            nn.Linear(1, 13), # <1>\n",
    "            nn.Tanh(),\n",
    "            nn.Linear(13, 1)) # <2>\n",
    "seq_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[torch.Size([13, 1]), torch.Size([13]), torch.Size([1, 13]), torch.Size([1])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[param.shape for param in seq_model.parameters()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.weight torch.Size([13, 1])\n",
      "0.bias torch.Size([13])\n",
      "2.weight torch.Size([1, 13])\n",
      "2.bias torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "for name, param in seq_model.named_parameters():\n",
    "    print(name, param.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (hidden_linear): Linear(in_features=1, out_features=8, bias=True)\n",
       "  (hidden_activation): Tanh()\n",
       "  (output_linear): Linear(in_features=8, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "seq_model = nn.Sequential(OrderedDict([\n",
    "    ('hidden_linear', nn.Linear(1, 8)),\n",
    "    ('hidden_activation', nn.Tanh()),\n",
    "    ('output_linear', nn.Linear(8, 1))\n",
    "]))\n",
    "\n",
    "seq_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hidden_linear.weight torch.Size([8, 1])\n",
      "hidden_linear.bias torch.Size([8])\n",
      "output_linear.weight torch.Size([1, 8])\n",
      "output_linear.bias torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "for name, param in seq_model.named_parameters():\n",
    "    print(name, param.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([-0.0173], requires_grad=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_model.output_linear.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Training loss 182.9724, Validation loss 231.8708\n",
      "Epoch 1000, Training loss 6.6642, Validation loss 3.7330\n",
      "Epoch 2000, Training loss 5.1502, Validation loss 0.1406\n",
      "Epoch 3000, Training loss 2.9653, Validation loss 1.0005\n",
      "Epoch 4000, Training loss 2.2839, Validation loss 1.6580\n",
      "Epoch 5000, Training loss 2.1141, Validation loss 2.0215\n",
      "output tensor([[-1.9930],\n",
      "        [20.8729]], grad_fn=<AddmmBackward>)\n",
      "answer tensor([[-4.],\n",
      "        [21.]])\n",
      "hidden tensor([[ 0.0272],\n",
      "        [ 0.0139],\n",
      "        [ 0.1692],\n",
      "        [ 0.1735],\n",
      "        [-0.1697],\n",
      "        [ 0.1455],\n",
      "        [-0.0136],\n",
      "        [-0.0554]])\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(seq_model.parameters(), lr=1e-3) # <1>\n",
    "\n",
    "training_loop(\n",
    "    n_epochs = 5000, \n",
    "    optimizer = optimizer,\n",
    "    model = seq_model,\n",
    "    loss_fn = nn.MSELoss(),\n",
    "    t_u_train = t_un_train,\n",
    "    t_u_val = t_un_val, \n",
    "    t_c_train = t_c_train,\n",
    "    t_c_val = t_c_val)\n",
    "    \n",
    "print('output', seq_model(t_un_val))\n",
    "print('answer', t_c_val)\n",
    "print('hidden', seq_model.hidden_linear.weight.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94b3a07990>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "t_range = torch.arange(20., 90.).unsqueeze(1)\n",
    "\n",
    "fig = plt.figure(dpi=600)\n",
    "plt.xlabel(\"Fahrenheit\")\n",
    "plt.ylabel(\"Celsius\")\n",
    "plt.plot(t_u.numpy(), t_c.numpy(), 'o')\n",
    "plt.plot(t_range.numpy(), seq_model(0.1 * t_range).detach().numpy(), 'c-')\n",
    "plt.plot(t_u.numpy(), seq_model(0.1 * t_u).detach().numpy(), 'kx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Training loss 202.7462, Validation loss 250.2893\n",
      "Epoch 1000, Training loss 48.0192, Validation loss 113.9049\n",
      "Epoch 2000, Training loss 40.0076, Validation loss 93.4053\n",
      "Epoch 3000, Training loss 32.3877, Validation loss 71.1377\n",
      "Epoch 4000, Training loss 24.6268, Validation loss 49.4235\n",
      "Epoch 5000, Training loss 17.9837, Validation loss 31.9612\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94b15a0910>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XxQkJujghQRf5X5fFxys+OnpKNUYKQggAAABmpdLSUu3YsUPV1dUqKSkJGESGBpDCwkKVlpZO6NoN/f16rbNTr3V16TV/4Kjq7lbnFJ5sLF6wQJckJKgoMfFs4Lg4IUG5CxbIRMj0qWAjhAAAAGBWKigoUHl5+dmQMTyIDA8ggUJKt9erSn/QeK2rS691durVri41ud2TrmdJXJwuSUg4GzgG/53qcATl884nhBAAAADMWqMFEUnnBJDnnntOJitLPz99Wq90demVzk690tmpo/6GfpO6Z2ysihMTdWlioooTE1WckKBLEhOVOM+nUAUTIQQAAACz2vAgsmzZMllJHrdbC51OZX/rW7rK5VLLiROTum6Ww6HLEhN1WVKSLvWHjqKEBCXH8H+RQ43/hAEAADBrdXo8eqWrS3+MitKK735XJz7wAbkHp1LFxKjt61/X/vh4yeMZ9RrxUVEqTkz0BY7ERF2elKTLEhPnZH+NSEEIAQAAwKxwxu3Wy52dqujo0MudnfpjR4feHDqdqqlp3Gs4Y2N1RVKSrkhM9L0mJWlpfHzE9NeIFIQQAAAAhN3QwFHR0aEjHR063ts7+oCGBmnLFt8Tj8HpUh6Pkh94QPfu3q1rL7pIVyQlKZ1F4nMCIQQAAAAh1eX16uWODh3u6NCh9nZVdHbqWE/PhMcnnD6tgfvuU+/Jk8o87zzt/OUvdUFCgj5w/fWqrq7Wrttu093l5UpPSwvhp0AwEUIAAAAQNO6BAf1vV5cOdXTocHu7DnV0qLKrSxNt9ZcWE6Plycm6KilJVyUnK7u1VXfffbeq6+pGbMM71va9mN0IIQAAAJgSa63e7u3VwY4OHWhv18H2dv2xs1O9E+wunhoTo+VJSVqRnKzl/q8lcXFnG/zV1NSo5IMfHLUPyHh9RDB7EUIAAAAwIR0ej44MCRwH2tvVMMGmf0nR0VqRnKyrk5O1wv81NHAEsnfv3jEbEUojg8jevXu1adOmKX9GhAchBAAAACNYa3Wsp0cvtbdrf1ubXmpvn/C0KocxuiIpSSv9oWNlSoouSkiY9A5Vg2GitLR0zKcbg0GEADJ3GGsn20MSk2GMqSwqKiqqrKyc6VIAAABG1eP1qqKjQy+1t+slf+homuBTjqVxcVqdkqJV/q8rkpIUGxUV4ooxk4qLi1VVVVVlrS2eyniehAAAAMxDjf39erGtTS+0temltja93Nkp9wT+OJ0SHX02bKxOSdHK5GRl0vQPk0QIAQAAiHDWWh3v6dEf/KHjD21tenOCW+RekpCga1JStGbhQq1JSdHFCQmKovEfpokQAgAAEGEGrNWrnZ36vT9w/KGtTfX9/eOOS4iK0qqUFF2TkqJrFi7U6pQUmv8hJAghAAAAc5xnYEB/7OzU78+c0fP+px1nPJ5xx+UuWKB3L1yod/q/Lk9MVAxrORAGhBAAAIA5xj0woCMdHfr9mTP6fVubXmxrU4fXO+64SxIS9K6FC/XuhQv1roULdf44W+QCoUIIAQAAmOUGn3SUnzmj51pb9Ye2NnWN0xAwWtJVycl6z8KFendqqt6ZkqJFLCDHLEEIAQAAmGUG13SUnzmj586c0fNnzqh9nCcdDmN0dXKy3puaqvempuqalBQlx/B/9TA78b9MAACAGTa4e9Wzra16trVV5WfOqGWcNR2xxmh1SsrZ0LE6JUUJ0dFhqhiYHkIIAADADGjo79dz/tDxbGuravr6xjw/xhitSk5WSVqaSlJTtSYlRfGEDsxRhBAAAIAw6PJ69fyZM3rGHzpe6+oa8/xoSSuGhI53LlyoREIHIgQhBAAAIAQGrNUrnZ36TWurftPSoj+0tal/nI7klyYm6vrUVL0vLU3vSU1VCms6EKH4XzYAAECQnOrr0zP+0PFMa6sa3e4xz3fGxup9aWm6Pi1N16WmanFsbJgqBWYWIQQAAGCK3AMDerGtTb9qadHTLS16dZwpVinR0bouLU3v9wePC+Lj6dOBeYkQAgAAMAmu3l493dKiX7W06NnW1jGbBEZJWpmSog+kpen96elamZxMR3JAhBAAAIAx9Q8M6A9tbXq6pUVPNzersrt7zPPPi43VB9LT9f70dF2Xmqo0hyNMlQJzByEEAABgmIb+fj3d3KyfNzfrN+M87Yg1RtempurGjAz9VXq6LmSKFTAuQggAAJhTysrKVFpaqoKCgjHPq6mp0d69e7Vp06Zxrzlgrf7Y2alfNDfrF83NOtTRMeb5y+LjdWN6um5MT9d7U1NpEghMEiEEAADMGWVlZdq8ebN27Nih8vLyUYNITU2NSkpKVF1dLUkBg0i316tnW1u1zx88TvX3j3rfuKgolaSmng0eyxISgvOBgHmKEAIAAOaM0tJS7dixQ9XV1SopKQkYRIYGkMLCQpWWlp49Vt/Xp583N+tnzc16prVVvQMDo97LGRurmzIydFN6uq5LS+NpBxBEhBAAADBnFBQUqLy8/GzIGB5EhgeQ5557Tu3p6fry22/rZ6dP6+AY06yiJK1JSfEFj4wMXZaYyNoOIEQIIQAAYE4ZLYhIOvte7vnn69of/EAlp07pxIkTo14rJTpaN6an668zMnRjRoYy2MkKCIuICSHGmOWSbpC0UtIqSbmS+qy1caOc/6CkrWNc8qvW2i8Eu04AQGRq6ujTU4drdPBEizr7PEqKjdHqwgytX+FUZjJdsINteBBZtmyZrCSP262o3Fyd/MpX9NjAgNTbO2LsebGx+ptFi/Q3GRl6T2qqFtC3Awi7iAkhkr4o6UNTGPeipGMB3q+YXjkAgPmg1+3Vtn2V2lNRK7fXnnPshaOn9eizb2rdcqe2ri1SnIM1BcGUsnixPrd7t+5dvVput9v3ZkyMBrZvl7Kzzzn36uRk/U1Ghv5m0SKmWQGzQCSFkP2SXpF02P9VP8Fx37PWPh6qogAAkavX7dWdjx3SwRMto57j9lo9eahG1U2d2rlhJUFkmhr7+/XT06f146YmPXfmjDz1gX/dO4zRdampKs3M1NqMDOXG8jQKmE0iJoRYa7869Hv+wgEACLVt+yrHDCBDHTzRom37qvTQLZeFuKrIU9fXp580NenHTU16oa1NZ/ezamiQtmyRPB4pxv9/aTweZX3+8/rtc8/p0sLCmSoZwDiYBAkAwBQ0dvRqT0XtpMbsqXCpqaMvRBVFlrd6evQNl0vXvPyy8vfv1+eOHdPvAwWQkyeV7HTqsSNH9Mabb6qwsFCNb7+tD91wg2pqambyIwAYQ8Q8CZmG64wxV0qKk1Qr6WlrLetBAABj2n3YNWINyHjcXqvdR1zaWLIsRFXNbSd6evTfTU3a3diois7OUc/LaW1V9wMPqP3kSRUWFp6zRe9Y2/cCmD0IIdIdw77/V2PMjyXdZa0d/SfgMMaYylEOLZ1yZQCAWWui07CGO1DdTAgZYjB4/HdTk46M0cOjMC5OH87M1Lt6e3Xv3Xer3uUaEUCk8fuIAJgd5nMIOSbpfklPS3pbUpqk90j6mqQPS4qWVDrqaADAvNbZ5wnruEjy1pDgcXiM4HFJQoI+nJmpDy9apCuSkmSMUVlZ2dlGhKOFi+FBZO/evdq0aVMoPxKASZq3IcRau2vYW12SfmiMKZf0mqSbjTHXWGtfmuD1igO9739CUjStYgEAs05S7NR+hU513FxX19en3Y2N+lFjow6NETyKEhK0PitLH8nMVFFi4ojjg2GitLR0zKcbg0GEAALMTvPzJ+EYrLWnjDE/kO8pyQckTSiEAADml1VL0vXC0dOTHre6MCME1cxOTf39+nFTk55sbNQLbW0abQXNJQkJWp+ZqY9kZak4QPAYbqKhoqCggAACzFKEkMCO+l8Xz2gVAIBZa/3VTpX99uikFqc7oo3Wr3CGsKqZ1+bxaG9Tk37U2KhnW1vlHeW8i/3BY/0EgweAyEIICSzN/zrhhekAgPklKzlO65bn68lDrgmPWbfcqczkyGua1+P16ufNzfphY6N+2dysfhs4mBXGxeljWVn6WFaWLqVrOTCvEUKGMb6fiIML0tmqFwAwqq1ri1Xd1DWhnbJWLUnX1rWRs0TQa62ea23VDxsb9eOmJnV4Az/zyF2wQB/NytKtWVlakZxM8AAgKcghxBiTLekiSW9YaxuGvL9E0pclXSbfTlTbrLWHgnnvyTDGLJL0QUlPWWv7hryfJOlhSask1UvaOzMVAgBCqamjT08drtHBEy3q7PMoKTZGqwsztH7F5J5UxDmitXPDSm3bV6U9FYH7hjiijdYtd2rr2iLFOaKD+THCzlqrIx0d+qF/gXl9f3/A8xY5HPpIZqY+lpWldy1cqCiCB4BhjB3lkemULmbMI5I+J6nIWvuG/70kSW9IypE0+FOoS9IV1trqIN77JklfHPLWKklW0tCw86/W2l8YY86XdEJSu6Q/S6qRlCrpKkkZks5I+mtr7YtBqKuyqKioqLJytDYiAIBw6XV7tW1fpfZU1AY9MDR19Gn3EZcOVDdPK9jMRsd7erSroUE/bGjQmz09Ac9Jio5W6aJF+nhWlq5PS5MjKirMVQIIp+LiYlVVVVWNtkPseII9HetaSX8eDCB+d8m3wPuHkrbJ9wTiEfl2n/pMEO+dKV/wGMoMey/T/9os6auSVktaJulKSV75gsnjkh6x1tYFsTYAwAzrdXt152OHxpw65fZaPXmoRtVNndq5YeWkgkhmcqw2liyLmEaELW63nmps1BMNDdrf3h7wnBhjdGN6um7LztbajAwlRM/tJz0AwifYISRP0oFh7/21JI+kTdbaZkllxpg7JZUE88bW2sflCxATObdD0heCeX8AwOy2bV/lhLucHzzRom37qvTQLZeFuKrZpW9gQL9sbtYTDQ36eXOz3KPMlnj3woW6LTtb6zIzleFwhLlKAJEg2CEkWdLZDkT+Rd6rJFX4A8igN+QLJwAAhFxjR6/2VNROasyeCpe23HDhnJ9KNR5rrfa3t+uJhgY91dioVk/gju7FCQm6PTtbt2Zn67y4uDBXCSDSBDuE1ElaMuT7FZIWSvpdgPsGXs0GAECQ7T4ceNH4WNxeq91HXBEzvWq4mt5e/Wd9vXY2NOjYKOs8shwOfTw7W5/IztaVSUnsbAUgaIIdQvZLutUY8yFJ5ZL+Wb7F4fuGnXeJfIEFAICQm+g0rOEOVDdHVAjp8nr1k6Ym7ayv13NnzgTsYB4fFaWbFy3SHdnZuiEtTTEsMAcQAsEOIf9X0i2SfuL/3kgqt9a+NHiCf2eqIknfD/K9AQAIqLMv8BSjUI2bTay1eqGtTTvr67W7qUmdAfp5GEnXpqbqE9nZuiUzUykxtBEDEFpB/SljrX3dGPMuSZvk24mqQtLXh532AUmvSPppMO8NAMBokmKn9utuquNmg5reXu2sr9fj9fWq7u0NeM6y+HjdmZ2tO3JyWOcBIKyC/tPVWvtH+bblHe34v0v692DfFwCA0axakq4Xjp6e9LjVhRkhqGZsZWVlKi0tVUFBwZjn1dTUaO/evdq0adPZ93q9Xv309Gk9Vl+vZ1tbA063SomO1kezsnRnTo6uSUlhnQeAGTF3/8QDAMAErb/aqbLfHp3U4nRHtNH6Fc4QVjVSWVmZNm/erB07dqi8vHzUIFJTU6OSkhJVV1fLWqv33H23Hjt1Sv/V2KgzAXa3MpJuSEvTXTk5unnRIsXTzwPADAtqCDHGjP1nm2GstTXBvD8AAIFkJcdp3fJ8PXnINeEx65aHv9N5aWmpduzYoerqapWUlAQMIkMDSEZBgf5j6VLdW1ER8HrL4uP1yZwcfSI7W/lMtwIwiwT7SchbUsCnv4HYENwfAICAtq4tVnVT14R2ylq1JF1b1xaFoapzFRQUqLy8/GzIGB5E3nr7bV1z7bU69dZbUm6umr/2NTUnJ59zjYSoKK3PytLdOTl658KFTLcCMCsFOwQ8r8AhJEqSU1KB/9/7RZ8QAEAYxTmitXPDSm3bV6U9FYH7hjiijdYtd2rr2iLFOWZmytLwILJ89bu0/L4dOh7v1vEvbZY9dVLKzZW2b5eys8+OuyYlRRsWL9b6zEwls7sVgFnOWDu55k3TupkxF8m3Na+VdIO1NvB2HRHEGFNZVFRUVFlZOdOlAAD8mjr6tPuISweqm9XZ54bULFcAACAASURBVFFSbIxWF2Zo/YrwT8EKpNft1ebHntH3/mmDvM2npMFQ4fGcE0CyHQ7dmZOjT+bk6OLExJktGsC8UlxcrKqqqiprbfFUxoc1hEiSMSZT0huSvmut/XxYbz4DCCEAgMmoau/Uh5/5o44muuVta5Juv90XPiRfGHniCcVHLVJSrUfvTUzRExtWzdhTGwDz13RDSNjboFprmyQdkPSxcN8bAIDZqH9gQHsaG3XDK6+o+OUjej3DK29cgF/RVso+0Kusl/uU0OjV4ROt2ravKvwFA8A0hT2EDJE9/ikAAMxeZWVlqqkZf6PHmpoalZWVjXj/eE+P/k91tZz79+sjVVV6trX1LwcbGqR77/U9BYmOkaJiJK9HzTv/jzztjWdP21PhUlNHX1A+DwCES9hDiDHmHZKulfR2uO8NAECwDPb0KCkpGTOIDG6pu3nzZpWVlZ3z1GPZwYP6Sk2NGt3uc8ZEV59S1GfvlU6dUkxqjvL+9j+Ud89/KCY1R54z9Wp48h/PBhG312r3kYlvPQwAs0Gw+4T8yxiHkyRdKOlG/33pmg4AmLMm29OjYMkSvbl8uZz7948IHZIUa4xy26PU8VK1znznC/KeqVdMao6yb/2yYlKyJEnZt37ZF0D8QWTw2IHqZm0sWRaWzw0AwRDsPfwelG/nq7E2Je+W9JC1dnuQ7w0AQNiM19NjaACJz89XzUMP6dsBuplfkpCge3JzdUd2tm796s/03He+IE+AACJJMSlZAYNIZ19q2D43AARDsEPIJ8c41i/plKTD1tquIN8XAICwGy2INPT16QPve59aa2qk3Fz1PPzwOT09Yo3RR7KydM/ixec0FGypfHHUADJoeBDpfnO/kpaHv7EiAExHUEOItXZnMK8HAMBsNzyInL90qa9r77CeHpJ0YXy8/i43V3fm5Cjd4RhxrTs//fc63tSphAvXBAwggwaDSPeb+5Wy4kNaXZgRok8HAKFBS1UAAKah1e3Wj43RwPbt0rp1skN7emzfrpicHJUuWqS/z83VtampZ596BLL+aqfKVt0csJv7cDEpWUpZ8SE5oo3Wr3AG6+MAQFjM5Ba9AADMmOlur/tyR4c+9frrytu/X1uOH9dbvb0jznnA6ZRr9WrtLi5WSVramAFEkrKS47Ruef7EP4SkdctnR5d3AJiMaYUQY0y1Mea4MWbJkO8n+nU8OB8BAIDxDQ0dY22vOzR0DN9et9fr1RP19Vrz8staXlGh79fXq2dgwNfTY8sWyeORiYlRjMMheTz68R13qL+hYVJ1bl1brFVL0id07qol6dq6lvUgAOae6T4JOV/SEkmOId9P9GvJNO8NAMCEDA8dpaWlKiwsPLuYfDCIDA0dDz744Dnb675+1VXK379fn3j9dR1ob//LxRsaFLVli3TypAqWLNFbx4/r+LFjAa8/EXGOaO3csFK3riyQIzrwkxNHtNGtKwu0c8NKxTmip/WfDQDMBGPt+PNOMXXGmMqioqKiysrKmS4FAOatodvlFhYWqry8XJLOeW/Xrl26/fbbfaFjyDa7Cfn56h62u9Wgd3R1qW7jRjW+/fbZ6wbaonf4sYlq6ujT7iMuHahuVmefR0mxMVpdmKH1K5iCBWBmFRcXq6qqqspaWzyV8YSQECOEAMDsMF4QiYmJkcfjUX5Bgbq8XrXW1Y3Y3UqS4qOidFt2tm72evW5tWvHDBnBCCIAMBtNN4SwMB0AMC8MbqU7dJqUJO3atetsAJGk2rq6gAFkWXy8Hlm6VHVr1ui7F12kY888M264GH7PvXv3hu8DA8AsFtQteo0xF0haI+kFa+2JIe+vlFQm6TJJb0v6R2vt/wTz3gAAjGd4T49ly5bJSmcDiCTJ6z27va7JztZNGRn6bF6ebkhLU9SQ3a02bdokSSotLR3z6cbgPffu3Xt2DADMd0GdjmWM+Y6kT0laYq11+d/LlHRUUookK8lI8ki62lr7StBuPksxHQsAZp9Xjh/X8ksukdft9r0R7V/c7fX6XmNi9LfPPafPr1ihwvj4mSkSAGax2TYd612SXh0MIH4b5Asg35AUL6lUUrSk+4J8bwAAxvRqZ6c+/cYbWvXyy/IO/SOc1yt5vTIxMYr2b6/77F13KaapaeaKBYAIFuwQsli+6VZD3SipT9I2a22/fxrWAUmrg3xvAABG8AwM6CdNTbr2j3/UFUeO6Ht/+pP6Nm+WPJ6/PAGRFB0Toz/8/veqnsb2ugCAiQl2CImTdLZlrDEmWtIKSQestZ1DzntLUl6Q7w0AwFktbre+VlOjpQcP6sOVlfp9W9tfmgqePCmTlaUU/6LzmJgYeT0e3XHHHZI0YgE7QQQAgivYIcQl6eIh379bUoKk8mHnxUvqCvK9AQBQZVeX7nnjDeXv36/PV1erpq/Pd2BIAEnPy1N+XJzaT55UYWGhnn/++RG7ZhFEACB0gh1CfivpcmPMJmPM5ZK+JN9i9OE7YV0mX2ABAGDaBqzVz0+f1g2vvKJLDx/Wf5w6pZ6BgbPHoxsblfjAA5I/dPzDpz4lV03N2e1116xZE3D7XrbXBYDQCOoWvZIekrRe0nb/90bSU0N3wTLGFEtaKulbQb43AGCe6fB49Hh9vb5ZV6ejPT0jji9yOHTP4sVyVFToQZfrnJ4eaWlp52yvO3z73sEtddleFwCCL6ghxFpba4y5UtKnJWVKqpD0+LDT3iHfk5Hdwbw3AGD+qO7p0Tfr6vTYqVNqH9xWd4grEhO1KT9ft2ZlKS46WrrvPqXGxJwTOoaGiqaOPj11uEYHT7Ro2Se/pvTKFxVz+U1q6uhTQUEBAQQAgiyofUIwEn1CACA4rLV6oa1Nj9bW6n9On9bAsONRkj60aJE25efrPQsXygxpLDiaXrdX2/ZVak9Frdzekb8PHdFG65Y7tXVtkeIc0QGuAADz03T7hAR7OhYAAEHVPzCgpxob9WhtrV7u7BxxfGF0tO5evFifzcvTkkk0Fux1e3XnY4d08ETLqOe4vVZPHqpRdVOndm5YSRABgCCZVggxxhRMZ7y1lq1GAAABNfX3699PntT/O3lS9f39I45fEB+vTfn5ujM7W0kxk/91tm1f5ZgBZKiDJ1q0bV+VHrrlsknfBwAw0nSfhLwl3+5XU2GDcH8AQISp7OrSo7W12tXQoN6B4ZOupOtTU3Wv06kb09MVNYEpV4E0dvRqT0XtpMbsqXBpyw0XKjM5dkr3BAD8xXRDwPOaeggBAECSb73Hb1pb9YjLpV+3to44HmuMbsvO1ub8fF2WlDTt++0+7Aq4BmQsbq/V7iMubSxZNu37A8B8N60QYq29Nkh1AADmoV6vV7saGvRoba0qu7tHHM92OPSZvDz9XW6ushYsCNp9JzoNa7gD1c2EEAAIAqZDAQDCrqG/X/9WV6dvnzypJrd7xPErEhN1r9Opj2VlKTYq2H11pc4+T1jHAQDOFdIQYoyJlZQuqc9aO7U/OwEAIsb/dnbqkdpa/VdDg/oCbBH/1xkZujc/XyWpqRPaYneqkmKn9utvquMAAOcKyU9TY8zfS7pH0qXydU3fKWmD/9h6SR+T9Hlr7dFQ3B8AMHtYa/VMa6u2j7LeIz4qSnfm5Ghzfr4uSkgIS02rlqTrhaOnJz1udWFGCKoBgPknqCHEGBMjaa+kD0rql1QlXxAZ6s+Sbpb0sqQvBfP+AIDZo29gQE82NGh7ba1e6+oacXzxggX6bF6e7snNVYbDEdba1l/tVNlvj05qcboj2mj9CmcIqwKA+SPYT0I2SbpJ0j5Jn7bWNhpjztlf0Vr7mjHmhKQbRQgBgIjT7HbrOydP6lt1dQH7e1yRmKgt/vUeC0Kw3mMispLjtG55vp485JrwmHXLnWzPCwBBEuwQ8glJpyR9zFrbM8Z51ZIuCfK9AQAz6Gh3tx6prdXj9fXqCdDf44Pp6brP6Qz5eo+J2rq2WNVNXRPaKWvVknRtXVsUhqoAYH4Idgi5QNKvxgkgknRa0qJg3tgYs1zSDZJWSlolKVe+BfFx44z7hKTPSiqSbwrZAUlfsta+FMz6ACASWWv1YlubHna59LPm5hGNo2KN0R05Obo3P19FiYkzUuNo4hzR2rlhpbbtq9KeisB9QxzRRuuWO7V1bZHiHNEzUCUARKZgh5A+SRPpIlUgqS3I9/6ipA9NZoAxZrukeyX1SPqNpDj5gsz7jTEfsdbuDXKNABARPAMD+snp0/qGy6VDHR0jji9yOLQxN1efycsLan+PYItzROuhWy7Tlhsu1O4jLh2oblZnn0dJsTFaXZih9SuYggUAoRDsEPKapKuNMRnW2uZAJxhjCiRdJem5IN97v6RXJB32f9WPdbIx5jr5AkizpDWDO3UZY9ZI+p2kHxhjfmetHbmVCwDMUx0ejx6rr9ejtbV6q7d3xPGLExK0JT9ft2dnKz567jw5yEyO1caSZTQiBIAwCfaKwO9LWihplzEmbfhBY0ySpO9JWuB/DRpr7VettVuttT+31jZMYMh9/tcvDd0q2Fq7X9J35PscG4JZIwDMpLKyMtXU1Ix7Xk1NjcrKys55r66vT184flwFBw5o87FjIwLItamp2nfppaq8+mp9Ojd3TgUQAED4BfVJiLV2pzHmJknrJJ0wxvzBf+gaY8weSSWS0iT9cCanOhlj4iRd7/92T4BT9kj6nKS1kr4RrroAIFTKysq0efNm7dixQ+Xl5SooKAh4Xk1NjUpKSlRdXS1Juu7uu/UNl0s/bGyUe1hzwWhJ67OydJ/TqeXJyaH+CACACBKKvRE/KukL8i3y/qD/vQsl3eK/3xcl3RGC+07GxZJiJTVZa2sDHH/Z/3p5+EoCgOAZ/tSjtLRUhYWFqq6uVklJydljQ596DA0gi88/Xz+56CJdfuSIdjY0nBNAkqOjdV9+vqpXr9YPi4oIIACASQt6x3RrrZX0NWPMNyS9Q9L58v3BrFbSYWvtyE3jw2/wT4CBAoistV3GmDOS0owxydbakasuhzHGVI5yaOkUawSAKQn01KOgoEDl5eVnQ0ZJSYl27dql22+/XdXV1WptbdUTTzyh6upqLcjL06mvfEWn4s7dXDA/Nlab8/P1qcWLtTAm6L8+AADziLF24t1iA17At8A7X9IRa23VOOcWSVohyWWtLZ/Wjcevy2qULXqNMR+X9F+SXrTWvmuU8bWS8iTlWmtPTeB+o4aQoqKi2MrK0Q4DQHANfaJRWFh4zvSrocdiYmLk8XiUX1CgTo9HZ06elHJzpe3bpezss9e7MilJDzid+khmphwz1FwQADC7FBcXq6qqqspaWzyV8dP6U5YxxinpF5JckpZPYIhL0l5J+caYC6y1J6dz/2kY7JI1VgKbVCet0f4L8IcTOlwBCJtATz2GPhHZtWuX3vOe98jj8UiSauvqJK93RAD5q/R03e906rpZ0lwQABA5pvsnrU/Jt9PV/zeRKUv+cx6QFC/p7mneezoGax2rc1aC/7UzxLUAQNANBpHh60Bqamq0/rbbzgYQSb4AEhMjbd8uR06O7srJ0asrVujpyy/X9WlpBBAAQNBNd1LvDfIt7v7pRAdYa39mjGmQdKOkf53m/adqcLVmfqCDxphESamSzkwkXAHAbDT8icjSZcvktVbW45GysqTmZl8A8fv73Fz908qVyoulOR8AILSm+yTkYvkaA07WEUkXTfPe0/GGfN3dM40xgYLIVf7XV8NXEgAE3+L8fH1u926ZmBh53G5fABns4eF/AhLtcEgej359113yNkykzRIAANMz3RCSKKltCuPaJCVN895TZq3t0V86tq8LcMrgez8PT0UAEFwdHo8ecbm09OBBbT527NwFcF6v1NiorPPO07GjR1V97FjA7XsBAAiV6YaQVknZ4541UrZ/7Eza7n/9Z2PMBYNvGmPWSLpHUrt8HeABYM446e9s7ty/X1uOH5erpkbaskUa+gREUkxMjH765JNaev75o64fAQAgVKYbQqokrTbGxE90gDEmQdIa/9igMcbcZIw5MPjlf3vB0Pf83dwlSdbaZyWVScqQ9CdjzE+NMb+U9Lwkh6QN1tqWYNYIAKFS2dWlT77+us4/cEBfdbnU5vVKDQ2+AHLypBIXL1ZObq4knd2a9/bbbz8bNggiAIBwmm4I2SfflKx/nsSYf5Zvd6x907z3cJmSVg35knzb7A59L3PoAGvtZkmflPRn+RbZXyPpt5Lea639cZDrA4Cgstbqd62tuunVV3Xp4cN6vL7+L53NGxpk/AGkYMkS3f+3f6t6l0uFhYV6/vnnA4aN4UFk7969M/jpAACRbFrNCv1PNY7JN71qq6QvW2sHRjk3StI/SdomqV7SMmtt95RvPkcYYyqLioqKaFYIIFg8AwP68enTetjl0pGOkRv45bS2yr15s5pras5pVlhWVqbS0lIVFBSM29Bw79692rRpU7g/GgBgjphus8JgdExfLd/TgzhJtZL+W9LLkpr8p2TKt9vUR+TbErdP0vXW2v3TuvEcQQgBECxdXq8eO3VKj9TW6kRv74jjlyUm6gGnU40/+pHuv/feEeFiuKFB5NFHHyV0AAAmbEY7pkuStfaAfzH3LkmXSro3wGmDna4qJd1urX1luvcFgPmisb9f36yr07fr6tQytMmg3/vS0nS/06n3DzYW3LxZMcacfeoxmsHpVzz1AACE27SfhJxzMWM+IOkmSe+Qb8G3kXRa0p8k/cJa+6ug3WyO4EkIgKl6s7tb33C5tLO+Xn3DflZHS/poVpbudzr1juTkmSkQADBvzfiTkKGstb+W9OtgXhMA5pv9bW36msul/zl9WsP/TJQYFaVP5+Zqc36+zouLm5H6AACYrqCGEADA1AxYq5+dPq2vu1x6qb19xPGcBQv0ubw8/V1urtIcjhmoEACA4CGEAMAM6vF69Z8NDfqGy6WjPT0jjl+ckKD7nU7dnp2t2Kjp7qoOAMDsQAgBgBnQ7Hbr23V1+mZdnZrc7hHH371woR5wOnVTRoaijAlwBQAA5i5CCACE0YmeHm2vrdVjp06pe+DctkpRkm7JzNT9TqdWpaTMTIEAAIQBIQQAwuBwe7sedrm0p6lJwzu6xkdF6ZM5OdridGppfPyM1AcAQDgRQgAgRAas1S+bm/Wwy6Xft7WNOL7I4dBn8/K0MTdXixYsCGttTR19eupwjQ6eaFFnn0dJsTFaXZih9SucykyODWstAID5hxACAEHWNzCg/2po0MMul/7c3T3i+NK4ON3ndOrOnBwlREeHtbZet1fb9lVqT0Wt3N5zNwB+4ehpPfrsm1q33Kmta4sU5whvbQCA+YMQAgBB0up26zsnT2pHXZ3q+/tHHF+dkqIHnE59aNEiRc/AYvNet1d3PnZIB0+0jHqO22v15KEaVTd1aueGlQQRAEBIEEIAYJre6ulRWV2dvnfqlDq93nOOGUl/k5GhBwoK9M6FC2emQL9t+yrHDCBDHTzRom37qvTQLZeFuCoAwHxECAGAKaro6NDDLpf+u7FR3mHHYo3Rnf7F5hclJMxIfUM1dvRqT0XtpMbsqXBpyw0XskYEABB0hBAAmIQBa/WrlhY97HKp/MyZEcfTY2K0MS9PG/PylB3mxeZj2X3YNWINyHjcXqvdR1zaWLIsRFUBAOYrQggATMDgYvNvuFyqCrDYvDAuTlucTt2Vk6PEMC82n4iJTsMa7kB1MyEEABB0hBAAGEOLf7H5N0dZbL4qOVkPFBTo5hlabD5RnX2esI4DAGAshBAACKC6p0eP1tbq+wE6mw8uNr/f6dQ7Fy6UmcXhY1BS7NR+3E91HAAAY+G3CwAMcdDf2fwnATqbxxqju3JydO8sWWw+GauWpOuFo6cnPW51YUYIqgEAzHeEEADzntda7Tt9Wg+7XHqxvX3E8UUOhzbm5uozeXnKmkWLzSdj/dVOlf326KQWpzuijdavcIawKgDAfEUIATBvdXu92llfr0dqa3W0p2fE8Qvi47UlP1+fmIHO5sGWlRyndcvz9eQh14THrFvuZHteAEBIEEIAzDsN/f36Vl2d/q2uTs2ekQuv37Vwoe53OrU2I0NRc2C9x0RtXVus6qauCe2UtWpJurauLQpDVQCA+YgQAmDeqOzq0naXS7saGtRvz52WFCXplsxM3Zefr9Uz3Nk8VOIc0dq5YaW27avSnorAfUMc0Ubrlju1dW2R4hxz++kPAGD2IoQAiGjWWj135owedrn0q5aRTwASo6K0YfFibc7PV2F8/AxUGF5xjmg9dMtl2nLDhdp9xKUD1c3q7PMoKTZGqwsztH4FU7AAAKFHCAEQkfoHBvRUY6O+4XLpla6uEcdzFyzQP+Tl6Z7cXKU5HDNQ4czKTI7VxpJlNCIEAMwIQgiAiNLiduvf/c0FTwVoLnh5YqLuczr1sawsLYiKmoEKAQAAIQRARDja3a2y2lr9oL5+RHNBSfqr9HTdl5+v69PS5kRzQQAAIhkhBMCcZa3VC21t2u5y6WfNzRq+zHqBMbotO1tb8vN1aVLSjNQIAABGIoQAmHPcAwPa09Sk7bW1OtLRMeL4IodDn/E3F8yeo80FAQCIZIQQAHNGq9ut/zh1St+qq1NtX9+I4xcnJOje/HzdkZ2t+DneXBAAgEhGCAEw64233uP61FRtcTr1V+npEdVcEACASEUIATArWWv1+zNntL22Vj8PsN7DYYxuzcrSvfn5ujI5eUZqBAAAU0MIATCrDPb32F5bqz91do44nhETo7/Py9NncnO1OJamegAAzEWEEACzQlN/v/795En9v5MnVR+gv8clCQnanJ+v27OzlcB6DwAA5jRCCIAZ9b+dnXq0tla7GhrUZ4dPupJuSEvTlvx8vZ/1HgAARAxCCICwG7BWT7e06NHaWj3b2jrieKy/v8fm/HxdRn+PaWvq6NNTh2t08ESLOvs8SoqN0erCDK1f4VRmMlPaAADhRwgBEDadHo/+s6FBZbW1erOnZ8TxbIdDG/PydE9urrLo7zFtvW6vtu2r1J6KWrm95z5leuHoaT367Jtat9yprWuLFOdgihsAIHwIIQBC7q2eHn2rrk7fO3VKbV7viONXJiXp3vx8fTQrS7FRUTNQYeTpdXt152OHdPBEy6jnuL1WTx6qUXVTp3ZuWEkQAQCEDSEEQEhYa/VCW5vKamv109OnNby7h5H0oUWLdG9+vt69cKEM6z2Catu+yjEDyFAHT7Ro274qPXTLZSGuCgAAH0IIgKDq9Xr1o8ZGldXVBdxiNzk6WhtycvS5/HwVxsfPQIWRr7GjV3sqaic1Zk+FS1tuuJA1IgCAsGDeAwBJUllZmWpqasY9r6amRmVlZSPer+/r09YTJ3TegQP65BtvjAggS+PiVLZsmWrXrNGjF1xAAAmh3YddI9aAjMfttdp9xBWiigAAOBdPQgCorKxMmzdv1o4dO1ReXq6CgoKA59XU1KikpETV1dWSpE2bNulQe7t21NZqd1OT3AG22H1fWpo25eXpgxkZbLEbJhOdhjXcgepmbSxZFuRqAAAYiRACQKWlpdqxY4eqq6tVUlISMIgMDSBLCgtl3vUura6o0MGOjhHXi4uK0h3Z2fpcXp4uZYvdsOvs84R1HAAAk0UIAaCCggKVl5efDRnDg8jQAJJWUKDOr39dmwKEj/zYWG3MzdWnc3OV4XCE+2PALyl2aj/apzoOAIDJ4jcOAEmjBxFJuua971XdW29Jublq/drXpPT0c8a+e+FCfS4vTzcvWqQYttidcauWpOuFo6cnPW51YUYIqgEAYCRCCICzhgeRwmXLNGCtrMcj5eZK27dL2dmSfF3NP56drX/Iy9M7kpNnuHIMtf5qp8p+e3RSi9Md0UbrVzhDWBUAAH/BnywBnCMmO1t/vXOnFBMjr9vtCyAxMWcDSN6CBfq/S5bItWaNHrv4YgLILJSVHKd1y/MnNWbdcifb8wIAwoYQAkSA6W6va63VH86c0UcrK3XegQPaUVc34pwVSUn6UVGRTqxerX887zxlLlgQlNoRGlvXFmvVkvTxT5Rv+tbWtUUhrggAgL+Y1yHEGPM7Y4wd4+uvZrpGYDyD2+uWlJSMGUQGF5dv3rz5bBDp8Xr12KlTuqqiQu/+05+0u6lJnvp6acsWyf8EJMrx/7d37/F1lWWix39P05DQNtALbZNKawkVpR0UBUvxgtTbOCpH0VoFBSmo4DCKAzrjHD1yGOcMc1FGmfl4F6h3KlgVHdFRi8Ao5aKCtAgcUk4LTdq0hd5oQ5K+54+1UkO6d5rmsnLZv+/nsz+Lvdb7rjx5kob17PW+76qGjg62ffCDnLp3L9XO+RgVaqurWH7+Qs5aOIfqqtJLI1dXBWctnMPy8xdSW11VcISSpEoWqcS6/pUiIm4BXgHcCBz4aGf4dErpDwP8Gmvmz58/f82aNQM5jVRW95WrGhsbD7q8bmNjI8t/8hN+UFXFV5ubeaKj27KsmzZlBcjGjUydM4df/OIXTD3ssIOeXyNb6842Vty9gTuatrKrrYNJNeNZ1DiNpSc7BEuS1D8LFixg7dq1a1NKC/rT3yIkK0KOSSk9OkRfwyJEQ663QqT7sfq5c5n/hS+wqqaGA/7lb9pE7Yc/zN7HHuv1HBYikiRpoEWI4yqkMaBrVavGxsb9y+uuX7+e9evX84q8eBj/rGfR8k//xC97FCATx43jncDRf/d3JQuQ3s4vSZLUHxYh0hjRs1A4dt48jpk3j0ebmmDWLDo+/en9y+sCPPfww7l63jwef8lLePG99/LYunW93uXoef6VK1cW+e1JkqQxxOeEZC6IiGnAPuAh4PspJT/m1agz41nP4gPXX8+lp55KR3t7trPb8rrjgDOmTeOvnvUsXjVlChHZhOVLLrkEgDPPPLPXYVZdhcjKlSv395EkSTpUFiGZj/d4/6mI+GRK6ZN9PUFElJv0cWz/w5L6Zt2ePXxx40a+2tLClsceO+D4lPHjuWjOHC6cogxoywAAIABJREFUNYtn19aWPEdfi4o5c+ZYgEiSpAGp9CLkVuArwK+BZmA2sISsKPn7iNiRUjrwoQrSCLAvJX66bRuf27iRH2/dms3z6FrdKl9eN4DU0cHkv/kbLlq1ijllChBJkqQiVfTqWOVExGuBnwLbgYaU0p4BnMvVsTSoWp9+mmtbWvjixo007d37pwPdltetmz2bb998MydMmuSqVpIkadC5OtYQSCn9DLgbOBJYNMzhSPufaP7OtWs5+je/4W+bmg4oQKovuww2bmRuYyP33347b5g/31WtJEnSiGQRUt7D+bZhWKNQRdvR0cHnHn+c5999Ny///e/51ubNPN3t7uU44LXt7TR89KO0P/44jY2N/MrldSVJ0ghnEVLelHxb6knq0pC6d9cuLnrwQWb9+tdc/PDD3L979zOO1x92GB9/9rNZt2gRr1+7luZHH3V5XUmSNGpU+sT0kiJiOvDy/O1vhzMWVY49nZ2saG3lCxs3cseOHSXbvHLyZN4/axZvOuooqsdlnyG4vK4kSRptKrYIiYhFwOHALanb7PyImAt8A5gI/DCldOB6p9IgemD3br64cSPLN23iyY6OA45PHj+e8+rruWjWLJ47YULJc7i8riRJGk0qtggBngdcCzRHxENAC3A0cBJQC6wB3jt84Wksa9u3jxtbW/nixo3cun17yTYvrqvj/bNm8fYZM5hQVVVwhJIkSUOnkouQ1cDngVOA+cBLgd3A74HvAp8fyNK8UikPP/UUX25u5tqWFrZ0PdG8m0lVVbxzxgwunDWLF9bVDUOEkiRJQ69ii5CU0gPAXw53HBr72vbt4/tbtvCljRv55ZNPlmxz4qRJXDRrFmfPmEHd+Ir9ZylJkiqEVzvSEDnYXY/Dx43jHTNmcNGsWby4ro6IGIYoJUmSimcRIg2itn37WNnaypeam1lV5q7HggkTeN+sWZw7cyaTq6sLjlCSJGn4WYRIg+DBp57iy/kKV6XuetSOG8fbp0/nfbNmceoRR3jXQ5IkVTSLEKmf9nR2cmNrK19ubi67wtWCCRO4cNYs3jVzJlO86yFJkgRYhEiH7A+7dvHl5ma+Xua5Ht71kCRJ6p1FiNQHuzo6uL61lS9v3MjqnTtLtjlh4kTe29DgXQ9JkqSDsAiRykgpcffOnXyluZlvb97Mzs7OA9pMzFe4ep8rXEmSJPWZRYjUw7b2dr65aRNfaW7mvt27S7Y5ua6O9zY0cJbP9ZAkSTpkXj1JwL6U+NWTT/KV5mZubG2lLaUD2hxRVcW7Zs7kvQ0NnDiCn2beurON6+9az+p129jV1sGkmvEsapzG0pNnM72uZrjDkyRJsghRZdvY1sbylha+2tzMI3v3lmxz2pFH8p6GBt46fToTqqoKjrDv9rZ3csVNa7jhnsdo73xmEXXbw1v4zM8fYslJs7n8jPnUVo/c70OSJI19FiGqOO379vGjrVv5anMzP9m2jX0l2syorua8+nrOb2jguRMmFB7jodrb3sm7r7mT1eu2lW3T3pn49p3raWrdxfLzF1qISJKkYWMRoorxwO7dXNPSwtdaWthc4oGC44DXTZ3KexoaeOO0aVSPG1d8kP10xU1rei1Aulu9bhtX3LSWK99ywhBHJUmSVJpFiMa0nR0drGht5avNzfxmx46SbebW1nJ+fT3n1dczu7a24AgHbvPOvdxwz2OH1OeGezZw6WuOc46IJEkaFhYhGnNSSvx6xw6uaW7m+s2b2b3vwAFXNRG8Zfp0LmhoYPHkyYwbxUvrrrhrwwFzQA6mvTOx4u4NXLx43hBFJUmSVJ5FiMaM5rY2vrZpE9c0N/PQnj0l25w4aRIX1Ndz9syZTB0jDxTs6zCsnu5o2moRIkmShoVFiEa1p/ft48dbt3JNSws/2bqVAx8nCEdWVfHOmTO5oKGBF43gpXX7a1dbR6H9JEmSBsoiRKPS/bt2cW1LC1/ftInWEpPMAV41eTLnNzRw5lFHcfgIXlp3oCbV9O+fcX/7SZIkDZRXIRo1nmhv5zubN3NNSwt379xZss2cmhqW5ZPM5x5+eMERDo9TjpnKbQ9vOeR+ixqnDUE0kiRJB2cRohGtMyV++cQTXNPSwsoyTzLvmmR+fn09r5wyZVRPMu+PpS+ezWd/8fAhTU6vrgqWnjx7CKOSJEkqzyJEI9Ije/ZwXUsLy1ta2NDWVrLNSZMmsayhgbNnzGDKGJlk3h8z6mpZctLRfPvODX3us+Sk2S7PK0mSho1FiEaMXR0d3NDaynUtLfxq+/aSbY6qruacmTNZVl/PCZMmFRzhyHX5GQtoat3dp5WyTjlmKpefMb+AqCRJkkqzCNGwSilx+/btXNvSwooyz/SoAl4/bRrL6ut5w7RpHDaKnmRelNrqKpafv5ArblrLDfeUfm5IdVWw5KTZXH7GfGqrx+5EfUmSNPJZhGhYbNi7l+UtLVzX0sIje/eWbHP8hAksq6/nnJkzqa9x6NDB1FZXceVbTuDS1xzHirs3cEfTVna1dTCpZjyLGqex9GSHYEmSpJHBIkSF2dPZyfe3bOHalhZ+/sQTlJpGfWRVFe+YMYNlDQ0srKsjKmyS+WCYXlfDxYvn+SBCSZI0YlmEaEillFi9YwfXtrTwnc2b2dF54OMEA3j1lCksq6/nzWP8mR6SJEmyCNEQebytja/nw60e3LOnZJt5hx/OefX1nDtzJrNrawuOUJIkScPFIkT7ffazn+XMM89kzpw5vbZbv349K1eu5JJLLnnG/r2dnfxg61aua2nhZ9u2ceAUc5hUVcXS6dNZVl/PS4880uFWkiRJFcgiREBWgHzoQx/i6quvZtWqVWULkfXr17N48WKampoA+OAHP8hdO3dyXUsL3968mSc7Okr2Wzx5MufV1/PW6dOZ6HArSZKkimYRIgDOPPNMrr76apqamli8eHHJQqR7AfLsY45h08KF/Nldd7H2qadKnnNube3+4VbHHH54Ed+GJEmSRgGLEAEwZ84cVq1atb/I6FmIrF+/ntMXL2ZdUxMTjj6a9VdeyZUlnmQ+Ydw43jZ9OufV13Pa5MmMc7iVJEmSerAI0X6lCpFf/vKX3L97N2e/7nXs2LABZs3iqU99CmbOfEbf0448kvPq61kyfTp14/21kiRJUnleLeoZugqR004/naamJo6ZNy97nkdHB8yaBVddtb8AmVNTw7vr63l3fT3HOtxKkiRJfWQRov2e3rePH2/dyrXbt7PhyivhXe8idU00Hz8errqKwxsaeGs+3Gqxw60kSZLUDxYh4vf56lbf3LyZLe3tJdsEcGVjI+9/4Qs5wuFWkiRJGgCvJivUlqef5lubN3NtSwu/37XrmQc3bYJLL4WODsZVV1MFtLe386V3vIOzVq3iiIM8R0SSJEnqjUVIBenYt4+fPvEE1zY388OtW2lP6YA2Na2tVH/kI+zauJHGxkZWrVoFUHbVLEmSJOlQWYRUgAd27+balha+vmkTLU8/XbLNKXV1vGnfPr50wQU8umHD/gKkq9jobfleSZIk6VBYhIxR2zs6+M7mzVzb3MzqnTtLtqk/7DDOnTmT8+rrmbh1K4sXL+bRpqYDChA4+HNEJEmSpL4aN9wBaGh8/vHHueihhw4oQKojeOtRR/GjE05gw6JF/POxx3L8xImsXLmSpjIFSJeuQqSxsZGmpiZWrlxZ1LcjSZKkMcQ7IWPUOfX1fGzdOvbl70+cNIll9fWcPWMGRx122AHtL7nkEgDOPPPMXu9udBUiK1eu3N9HkiRJOhSRSkxO1uCJiDXz58+fv2bNmsK/9jkPPMDU8eNZVl/PiXV1hX99SZIkjU0LFixg7dq1a1NKC/rT3zshY9jXjz9+uEPQKNW6s43r71rP6nXb2NXWwaSa8SxqnMbSk2czva5muMOTJEmjnEWIpP32tndyxU1ruOGex2jvfOZd0tse3sJnfv4QS06azeVnzKe2umqYopQkSaOdRYgkICtA3n3Nnaxet61sm/bOxLfvXE9T6y6Wn7/QQkSSJPWLq2NJAuCKm9b0WoB0t3rdNq64ae0QRyRJksYqixBJbN65lxvueeyQ+txwzwZad7YNUUSSJGksswiRxIq7NhwwB+Rg2jsTK+7eMEQRSZKksazii5CIqI2IKyLioYjYGxEbI+KaiDh6uGOTitLXYVg93dG0dZAjkSRJlaCii5CIqAV+AXwCmAT8ANgALAN+GxHHDmN4UmF2tXUU2k+SJFW2ii5CgP8JvAT4DXBcSuntKaVTgMuA6cA1wxmcVJRJNf1bKK+//SRJUmWr2CIkIqqBD+RvL04p7eo6llK6CrgPOC0iThqO+KQinXLM1H71W9Q4bZAjkSRJlaBiixDgZcBk4JGU0u9KHL8h355RXEjS8Fj64tlUV8Uh9amuCpaePHuIIpIkSWNZJRchL8i3vy1z/Lc92klj1oy6WpacdGhrMSw5aTbT62qGKCJJkjSWVfKA7jn5ttzDER7r0a5XEbGmzCEnt2tUuPyMBTS17u7TSlmnHDOVy8+YX0BUkiRpLKrkOyGT8u1TZY7v7tFOGtNqq6tYfv5Czlo4p+zQrOqq4KyFc1h+/kJqq6sKjlCSJI0VlXwnpOsqq9wT2g5pgHxKaUHJk2R3SPzIWKNCbXUVV77lBC59zXGsuHsDdzRtZVdbB5NqxrOocRpLT3YIliRJGrhKLkJ25tuJZY5PyLe7yhyXxqzpdTVcvHgeFy+eN9yhSJKkMaiSh2Otz7flZuMe3aOdJEmSpEFQyUXIvfn2RWWOd+2/r4BYJEmSpIpRyUXIfwPbgWMj4oUlji/Jtz8qLiRJkiRp7KvYIiSl9DTwH/nb/4iI/XNDIuJS4PnA7Smlu4YjPkmSJGmsquSJ6QD/ALwaeAnwcETcBjwbOAXYCiwbxtgkSZKkMali74QApJT2AouBT5I9L+TNwFxgOfDClNL/Hb7oJEmSpLGp0u+EkFLaA3wif0mSJEkaYhV9J0SSJElS8SxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBXKIkSSJElSoSxCJEmSJBVq/HAHoJGpdWcb19+1ntXrtrGrrYNJNeNZ1DiNpSfPZnpdzXCHJ0mSpFHMIkTPsLe9kytuWsMN9zxGe2d6xrHbHt7CZ37+EEtOms3lZ8yntrpqmKKUJEnSaGYRov32tnfy7mvuZPW6bWXbtHcmvn3neppad7H8/IUWIpIkSTpkzgnRflfctKbXAqS71eu2ccVNa4c4IkmSJI1FFiECYPPOvdxwz2OH1OeGezbQurNtiCKSJEnSWGURIgBW3LXhgDkgB9PemVhx94YhikiSJEljlUWIAPo8DKunO5q2DnIkkiRJGussQgTArraOQvtJkiSpclmECIBJNf1bKK2//SRJklS5LEIEwCnHTO1Xv0WN0wY5EkmSJI11FiECYOmLZ1NdFYfUp7oqWHry7CGKSJIkSWOVRYgAmFFXy5KTjj6kPktOms30upohikiSJEljlUWI9rv8jAV9HpZ1yjFTufyM+UMckSRJksaiii1CIuL0iEi9vO4Y7hiLVltdxfLzF3LWwjllh2ZVVwVnLZzD8vMXUltdVXCEkiRJGgtc2ggeAW4vs7/i1FZXceVbTuDS1xzHirs3cEfTVna1dTCpZjyLGqex9GSHYEmSJGlgLELg9pTSecMdxEgzva6GixfP4+LF84Y7FEmSJI0xFTscS5IkSdLwsAiRJEmSVCiHY8FzIuJKYBqwhWx+yM0ppX3DG5YkSZI0NlmEwEvyV3d/iIi3ppQe7utJImJNmUPH9jsySZIkaQyq5OFY24F/BRaR3QWZBrwKuAM4AfiviDhy+MKTJEmSxqZReyckIm4A/uwQu52bUroTIKX0O+B3PY7/MiJeBqwCXg5cDPxjX06cUlpQJs41gE/1kyRJknKjtggB5gLPPcQ+Ew7WIKXUGRH/TFaE/Dl9LEIkSZIk9c2oLUJSSicP4em75oI0DOHXkCRJkipSJc8J6c2UfLtrWKOQJEmSxiCLkNLemm/vGdYoJEmSpDGoYouQiLgwIqb12BcRcSHw10ACvjAswUmSJEljWKSUhjuGYRERjwKzgLXA/8t3nwAcA+wDPpRS+vdB+Do7ampq6o491seFSJIkaWx45JFHaGtr25lSOqI//Su5CPkA8FpgATADqAaagduAq1NKdw3S12khW5Vrw2Cc7xB1VT6PDMPXrkTmu1jmu1jmu1jmu1jmu1jmu1hDle/ZwFMppfr+dK7YIqQSdD3FvdwzTDS4zHexzHexzHexzHexzHexzHexRmq+K3ZOiCRJkqThYREiSZIkqVAWIZIkSZIKZREiSZIkqVAWIZIkSZIK5epYkiRJkgrlnRBJkiRJhbIIkSRJklQoixBJkiRJhbIIkSRJklQoixBJkiRJhbIIkSRJklQoixBJkiRJhbIIkSRJklQoi5BRKCImRMSbI+KrEXFfROyIiN0RcW9EfCIiJvXS99yIuDMidkXEtoj4z4h4SZHxj0YRcWlEfC8iHo6I7RHRFhH/LyKWR8SCXvqZ7wGKiKkRsTkiUkT88SBtzfchiohb8tyWe72uTD9zPQARUR8R/xYRD0XEnjyH90TEv5Rpb74PUUScfpDf7a7XJ0r0Nd/9FBGLIuLGiGiJiPY8f7+IiCW99DHf/ZTn+wcRsSUi9uZ/U/4hIib00mdE5Nsnpo9CEfEe4Mv52zXAWuAI4CVAHfBH4BUppc09+l0F/DWwB/gZUAu8CgjgbSmllYV8A6NQRGwBJgL3AY/nuxcAxwFPA29OKf2kRx/zPQgi4jrgXLK8PZhSel6Zdua7HyLiFuAVwI3ArhJNPp1S+kOPPuZ6ACLiVOA/gclkf7/vJ/vbPR84OqU0vkd7890PEfE84KNlDlcB78r/+5UppVXd+pnvfoqItwHfIfuQ+27gEWAW8NJ83z+nlD7ao4/57qeIeCewnOz3+R5gPXAyMBu4F3h5Smlnjz4jJ98pJV+j7EV2QfY54Dk99jcAvwUS8K0ex16Z79/SvR9wKtAGPAlMGe7vbaS+yP6A1pbY//48r48DVeZ70PP+qjyPX8y3fyzTznz3P8e35Lmb28f25npg+Z4FPAE8BZxZ4vhC813Iz+Ev8ryuB8aZ70HJ6Xhgc56/t/c4dirZRe8+4FjzPSj5PjrPaQKWddtfA6zI93++R58Rle9hT6KvQf6BZr9ICdgLHNZt/4/z/R8q0eez+bHLhjv+0fgCHs7zN998D2peD89zuwZ4Dr0XIea7/3k+1CLEXA8s31/Lc/RX5ntYfw7fzHN3pfketJz+WZ6fB8oc/35+fKn5HpR8fzzPz89KHJsO7CYbqTFtpObbOSFjz735tgaYBhARXbfaAG4o0adr3xlDG9qY1ZlvnwbzPYguB44lu9vUXq6R+S6OuR6YiJgCLAW2A1/pQ3vzPQQiYiLwpvztN7rtN98D09bHdtvAfA+Ck/LtLT0PpJRayYZ6VgOvh5GZ7/EHb6JRpjHftpP/QweeR1aUtKaUHivR57f59vlDHNuYExHnAs8FHgKa8t3me4Ai4vnAZcC1KaVbI2JuL83N9+C4ICKmkQ2XeAj4fkppfY825npgXkqWv58D7flE3ZeRXSj8EViRUtrUrb35HhpvIZvj97uU0ppu+833wDTlr+dFxNKU0oquA/k8qD8H1gG35rvN98BMzLdPlDnedQ34AuDrjMB8W4SMPZfk25tTSl2fSszJt6V+6Ugp7Y6IJ4EpEVGXekxi0p9ExEfIJqRPBI7P/3sjcHZKaV/ezHwPQESMI1t44Ungb/rQxXwPjo/3eP+piPhkSumT3faZ64HpWklvE3Ab2fDZ7q6MiGUppe/m78330OiakP71HvvN9wCklDoj4jzgJuD6/P+Xj5DNV30ZcCdwTkrp6byL+R6Y1nz77DLHu/bPzbcjLt8OxxpDIuL1wAVkd0H+V7dDXUv2PtVL99092qq0PwfeDSwhu6DYQFaA3NOtjfkemA8AC4GPpJS29qG9+R6YW4FzyIa+TSC7s/cxoAP4+4i4pFtbcz0wU/LtuWSfNl5ANnb7GOAqsg83vpHfCQTzPegiop5sSEon8O0eh833AKWUbiNbbW8d2SpNbwdOI8vbz8k+tOtivgfmV/n2rIg4rPuBiFhE9rccspX3YATm2yJkjIiI48nGtgbZxdu93Q/n297WY45ejimXUnp1SinILiZOAx4EbomIj3VrZr77KSJmA/8A/CqldF1fu+Vb890PKaVPpJS+kVJqSintSSk9lFL6R+DNeZMrIuLw/L/N9cBU5dvxwKUppWtSSltSSo+mlC4jG5N9GH+6A2i+B9/ZZD+H/0optfQ4Zr4HKCLOAlaTrTp2CtnF7HFkBd/HgZ9HRHVX83xrvvvnm2R5ngP8ICIWRERdZM92+i7ZB0mQDbGFEZhvi5AxICKOBm4muzC+KqX02R5Num6pTaS8rofalHpOgHpIKT2Zf+LzerK1uT8ZES/OD5vv/vsc2UXY+w+hj/keAimln5Gt838ksCjfba4Hpit/+8jW9u/pmnx7eo/25nvwlBuKBeZ7QCLiOWS/163AG1JKd6aUdqeUHk4pXUg2TOtUYFnexXwPQEppN/BGskLkdWTPG9oB/ITsb8xVedOuOSMjLt8WIaNcRBwF/BdZJXwt8OESzbomlx5d5hwTyR6a9aRjLg9NSqkduJ7s04OuFSXMd/+9kexW8ecje5L3LfnD9L6TH5/TbX/X7WLzPXQezrcN+dZcD8yj+bal25y9Usdn5FvzPYjyEQMvJLvA+n6JJuZ7YN5BtsjCzfkFck9dE9VPz7fme4BS9iDZ55ENE/934PPARWTLJXfpWnxhxOXbiemjWETUkVW8zwO+B7w35Ys99/Ag2dJ50yPi6BKrIrwo3943ZMGObVvy7fR8a74HZjLZmOJSDu92rOvvl/keOl1zGLo+FTPXA/O7fDslIqLE3+tp+dZ8D41z8u33UkqlxsWb74HpurjdUeZ41/6p+dZ8D4KU0h6y5w99rfv+iHh1/p+35NsRl2/vhIxSEVED/IBs4tdPgbNSSp2l2ua/oL/M3y4p0aRr348GO84K0XVR/AiY74FIKUWpF9nEXYAHu+1/Mu9jvodAREwHXp6//S2Y64HKP7VcR1ZMn1Kiyen51nwPsogIsvkgUHoolvkeuK45NieXOd41ZPlRMN9DKSJeQVZUrEkp/TeM0HwP1lMPfRX3IptU9z2yyUW3AhP60OfVefstwHO67T+V7Onq24Gpw/29jcQX2YXY24HxPfZXk63k1Ek2hGi2+R6yn8Fcen9iuvnuX14XAYuBKJHv2/Oc/sBcD2rOL8zzdydwVLf9J5GN3U7AEvM96Hk/Lc/j48C4XtqZ7/7n+EV57hLw/h7HFpHd4UvAq833oOX8xBLXJi/Kf8/3AYt7HBtR+R72BPrqxw8texZI1z/07wHXlXkd1aPfZ/I+u8nGw/4n2XK+ncBbh/v7Gqkv4Lw8b61kCwB8k+zu08Z8/x5gaYl+5nvwfgZz6aUIMd/9zmvX7/ZGslv23yErPvbk++8HZpjrQc35OLKx8QnYSjZZdxXZMIkEfMl8D0nev5Tn8F/60NZ89z/P/9rt+uT+/Hf99jx3Cfii+R7UfN8CbAZ+BnwL+HWet3ayIfql+oyYfA97An3144cG/7vbP/LeXnNL9D2PbMWb3WQPg7sZeNlwf08j+UU2FOj/5H9INwJPk32icz9wNTCvl77me3B+BnM5SBFivvuV1+PJViS7J/8fWXuet98AlwKHm+shyfs44C/Jhl3tzv+e/DfZg9zM9+Dnu4bs6dEJeH4f+5jv/uf7TLIP6rbkf1O2kQ0DOtt8D3qu38OfCpGnye6AfBM48SD9RkS+Iw9GkiRJkgrhxHRJkiRJhbIIkSRJklQoixBJkiRJhbIIkSRJklQoixBJkiRJhbIIkSRJklQoixBJkiRJhbIIkSRJklQoixBJkiRJhbIIkSRJklQoixBJkiRJhbIIkaQKFBHpIK9bBnj+9+Tn+fgghVyoiLg9j//osfB1JGmkGT/cAUiShtXyMvv/WGgU2i8ixgPtwCMppXnDHY8kDQWLEEmqYCml84Y7hgp3NjABaBnuQCSpSBYhkiQNk5TS+uGOQZKGg3NCJEkHFRFnRMS1EfFAROyMiN0R8fuI+GhEHHaQvnMj4jsRsSUi9kTEnRHxhhLtXp3Pj/hKRMyKiK9GxOMR0RkRf9Wt3biIWBYRt0XE9vyc90bEX+dDmXqe97GI6Mj/+30R8YeI2BsRLRHx+Yg48iDxvzUiVkfEUxGxNSK+FRGzyrQ91NieMSckIt5DNhQL4Nge83R+3luckjSaeCdEktQX1wI1wBrgD8ARwCnAlcArI+J1KaV9Jfo1AncBu4FbgQZgEfDDiHhtSukXJfrMzPsEcDvZcKWnACKiClgBvAXYDtyZHzsFuApYHBFvLhVLRFwFXJz3+QnwUuAi4HkR8cqUUioRyweBy/J4fgIsBM4CXhQRJ6aU9nY7f79j6+Yh4GvAucBO4Hvdjq3ppZ8kjSoWIZKkvngv8NOU0lNdOyLiCOA7wF8A7wC+VaLfMuAzwIdTSp15v8uATwEfA0oVIW8EbgDelVJq63Hsb8ku8m8GzkkpbcnPWZfHckYe6xd79KsClgIvTindl/eZDqwGTgdeTlYk9XQR8KoJLcS+AAADEElEQVSU0i15n4l5zKfk5/vaIMS2X0rp1oj4NVkRstk5O5LGKodjSVIF62WJ3snd26WUVnYvQPJ9O4BL87dvKvMlHgY+0lWA5K4mu1PwklJDlIC9wAd6FiD5sK/L8r7v7LrIz2PZCXQNZbqwTCwf6ypA8j6twBfyt6eV6fPprgIk77Ob7K7GM/oMQmySVFG8EyJJla3cEr1P99wREccBrweOBSaRDZfq+jDrOWXOsyql1NF9R0qpPSIeBV4ATAFae/S5K6VUarWok4CpwE0ppW09D6aUmiPiEeD5EXFYSqnn9/CzEud8KN82lIm/r30GGpskVRSLEEmqYH0Z7hMRAfwb2fyIKNOsrsz+x8rs35Vva0ocK7di1Nx8e0ZElJq/0d0UYFO39/tSSs2HGAeUjr9Un4HEJkkVxyJEknQw7wQuISsO/hq4A2jN72hMIJt0Xq44OdgFeSl7y+yvyrcP5jH0puedhv7EcSj9BhKbJFUcixBJ0sGcmW8vTCnd3ONYY4FxdN2VuG8ETtgeybFJ0ojjxHRJ0sFMybcbShxbWmAcq8mWrX1VREwq8Ov2xaDFls+h2YcfFEoawyxCJEkH0zUR+8J8fggAEXE62YpQhUgp7SFbmWoqcGNEzO7ZJiJeEBFvKyqmIYytGWjIl/eVpDHHT1kkSQfzWeAc4ANkDyb8A3A02cP+Pg18uMBYPgkcT3YH5qGI+B3ZXJWjyFbtmgvcCHy3wJiGIrYfAu8Hfh8RvyGbJ7M2pXRV790kaXTwTogkqVcppQfInhT+I2AG8D/InmL+XuDvCo6lM6X0drIL/V8Bx5E9IHA+2d2Dy4uOaYhi+1vgc8BhwNuBC8iWR5akMSFS6u+CIZIkSZJ06LwTIkmSJKlQFiGSJEmSCmURIkmSJKlQFiGSJEmSCmURIkmSJKlQFiGSJEmSCmURIkmSJKlQFiGSJEmSCmURIkmSJKlQFiGSJEmSCmURIkmSJKlQFiGSJEmSCmURIkmSJKlQFiGSJEmSCmURIkmSJKlQFiGSJEmSCmURIkmSJKlQ/x/wxL/I/28wuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercises here!\n",
    "\n",
    "neuron_count = 20\n",
    "\n",
    "seq_model = nn.Sequential(OrderedDict([\n",
    "    ('hidden_linear', nn.Linear(1, neuron_count)),\n",
    "    ('hidden_activation', nn.Tanh()),\n",
    "    ('output_linear', nn.Linear(neuron_count, 1))\n",
    "]))\n",
    "\n",
    "optimizer = optim.SGD(seq_model.parameters(), lr=1e-4)\n",
    "\n",
    "training_loop(\n",
    "    n_epochs = 5000, \n",
    "    optimizer = optimizer,\n",
    "    model = seq_model,\n",
    "    loss_fn = nn.MSELoss(),\n",
    "    t_u_train = t_un_train,\n",
    "    t_u_val = t_un_val, \n",
    "    t_c_train = t_c_train,\n",
    "    t_c_val = t_c_val)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "t_range = torch.arange(20., 90.).unsqueeze(1)\n",
    "\n",
    "fig = plt.figure(dpi=150)\n",
    "plt.xlabel(\"Fahrenheit\")\n",
    "plt.ylabel(\"Celsius\")\n",
    "plt.plot(t_u.numpy(), t_c.numpy(), 'o')\n",
    "plt.plot(t_range.numpy(), seq_model(0.1 * t_range).detach().numpy(), 'c-')\n",
    "plt.plot(t_u.numpy(), seq_model(0.1 * t_u).detach().numpy(), 'kx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}