01_pytorch_workflow_fundamentals.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have a workflow that we will be using:\n",
    "what_we_are_covering = {\n",
    "    1: \"data (prepare and load)\",\n",
    "    2: \"build model\",\n",
    "    3: \"fitting the model to data (training)\",\n",
    "    4: \"making predictions and evaluating a model (inference)\",\n",
    "    5: \"saving and loading a model\",\n",
    "    6: \"putting it all together\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.1+cu121'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the libraries\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Check your PyTorch version\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0.0000],\n",
       "         [0.0200],\n",
       "         [0.0400],\n",
       "         [0.0600],\n",
       "         [0.0800],\n",
       "         [0.1000],\n",
       "         [0.1200],\n",
       "         [0.1400],\n",
       "         [0.1600],\n",
       "         [0.1800]]),\n",
       " tensor([[0.3000],\n",
       "         [0.3140],\n",
       "         [0.3280],\n",
       "         [0.3420],\n",
       "         [0.3560],\n",
       "         [0.3700],\n",
       "         [0.3840],\n",
       "         [0.3980],\n",
       "         [0.4120],\n",
       "         [0.4260]]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are going to learn to estimate a straight line\n",
    "# Create *known* parameters\n",
    "weight = 0.7\n",
    "bias = 0.3\n",
    "\n",
    "# Create data\n",
    "start = 0\n",
    "end = 1\n",
    "step = 0.02\n",
    "X = torch.arange(start, end, step).unsqueeze(dim=1)\n",
    "y = weight * X + bias\n",
    "\n",
    "X[:10], y[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've gotten our data we will build a model.\n",
    "\n",
    "this model will learn the relationship between X (features) and y (labels).\n",
    "\n",
    "We will separate our data into training (80%) and test (20%) data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 40, 10, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_split = int(0.8 * len(X))\n",
    "X_train, y_train = X[:train_split], y[:train_split]\n",
    "X_test, y_test = X[train_split:], y[train_split:]\n",
    "\n",
    "len(X_train), len(y_train), len(X_test), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a function to visualize the data\n",
    "\n",
    "def plot_predictions(train_data=X_train,\n",
    "                     train_labels=y_train,\n",
    "                     test_data=X_test,\n",
    "                     test_labels=y_test,\n",
    "                     predictions=None):\n",
    "    '''\n",
    "    Plots training data, test data and compares predictions.\n",
    "    '''\n",
    "    plt.figure(figsize=(10, 7))\n",
    "\n",
    "    # Plot the training data in blue\n",
    "    plt.scatter(train_data, train_labels, c='b', s=4, label=\"Training data\")\n",
    "\n",
    "    # Plot test data in green\n",
    "    plt.scatter(test_data, test_labels, c='g', s=4, label=\"Testing data\")\n",
    "\n",
    "    if predictions is not None:\n",
    "        # Plot the predictions in red (predictions were made on the test data)\n",
    "        plt.scatter(test_data, predictions, c='r', s=4, label=\"Predictions data\")\n",
    "\n",
    "    # Show the legend\n",
    "    plt.legend(prop={\"size\": 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will make a linear regression model clss\n",
    "class LinearRegressionModel(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.weights = nn.Parameter(torch.randn(1, dtype=torch.float), requires_grad=True)\n",
    "        self.bias = nn.Parameter(torch.randn(1, dtype=torch.float), requires_grad=True)\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        return self.weights * x + self.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([0.3367], requires_grad=True),\n",
       " Parameter containing:\n",
       " tensor([0.1288], requires_grad=True)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's initialize a model and check the parameters\n",
    "torch.manual_seed(42)\n",
    "model_0 = LinearRegressionModel()\n",
    "list(model_0.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('weights', tensor([0.3367])), ('bias', tensor([0.1288]))])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can get the state of the model using .state_dict()\n",
    "model_0.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make some predictions\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_0(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of testing samples: 10\n",
      "Number of predictions made: 10\n",
      "Predicted values:\n",
      "tensor([[0.3982],\n",
      "        [0.4049],\n",
      "        [0.4116],\n",
      "        [0.4184],\n",
      "        [0.4251],\n",
      "        [0.4318],\n",
      "        [0.4386],\n",
      "        [0.4453],\n",
      "        [0.4520],\n",
      "        [0.4588]])\n",
      "Actual values:\n",
      "tensor([[0.8600],\n",
      "        [0.8740],\n",
      "        [0.8880],\n",
      "        [0.9020],\n",
      "        [0.9160],\n",
      "        [0.9300],\n",
      "        [0.9440],\n",
      "        [0.9580],\n",
      "        [0.9720],\n",
      "        [0.9860]])\n"
     ]
    }
   ],
   "source": [
    "# Let's check how well the predictions compare\n",
    "print(f\"Number of testing samples: {len(X_test)}\") \n",
    "print(f\"Number of predictions made: {len(y_preds)}\")\n",
    "print(f\"Predicted values:\\n{y_preds}\")\n",
    "print(f\"Actual values:\\n{y_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(predictions=y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.4618],\n",
       "        [0.4691],\n",
       "        [0.4764],\n",
       "        [0.4836],\n",
       "        [0.4909],\n",
       "        [0.4982],\n",
       "        [0.5054],\n",
       "        [0.5127],\n",
       "        [0.5200],\n",
       "        [0.5272]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test - y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's train our model\n",
    "# Create a loss function\n",
    "loss_fn = nn.L1Loss() # Mean Absolute Error\n",
    "\n",
    "# Create an optimizer\n",
    "optimizer = torch.optim.SGD(params=model_0.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | MAE Train Loss: 0.31288138031959534 | MAE Test Loss: 0.4931890368461609 \n",
      "Epoch: 10 | MAE Train Loss: 0.3013603389263153 | MAE Test Loss: 0.4797181189060211 \n",
      "Epoch: 20 | MAE Train Loss: 0.28983938694000244 | MAE Test Loss: 0.4662471413612366 \n",
      "Epoch: 30 | MAE Train Loss: 0.2783183455467224 | MAE Test Loss: 0.4527761936187744 \n",
      "Epoch: 40 | MAE Train Loss: 0.26679736375808716 | MAE Test Loss: 0.43930521607398987 \n",
      "Epoch: 50 | MAE Train Loss: 0.2552763819694519 | MAE Test Loss: 0.4258342385292053 \n",
      "Epoch: 60 | MAE Train Loss: 0.24375538527965546 | MAE Test Loss: 0.41236335039138794 \n",
      "Epoch: 70 | MAE Train Loss: 0.232234388589859 | MAE Test Loss: 0.398892343044281 \n",
      "Epoch: 80 | MAE Train Loss: 0.22071340680122375 | MAE Test Loss: 0.38542139530181885 \n",
      "Epoch: 90 | MAE Train Loss: 0.2091923952102661 | MAE Test Loss: 0.3719504475593567 \n",
      "Epoch: 100 | MAE Train Loss: 0.19767141342163086 | MAE Test Loss: 0.35847947001457214 \n",
      "Epoch: 110 | MAE Train Loss: 0.18615040183067322 | MAE Test Loss: 0.34500852227211 \n",
      "Epoch: 120 | MAE Train Loss: 0.17462942004203796 | MAE Test Loss: 0.33153754472732544 \n",
      "Epoch: 130 | MAE Train Loss: 0.16310855746269226 | MAE Test Loss: 0.3180667459964752 \n",
      "Epoch: 140 | MAE Train Loss: 0.15158770978450775 | MAE Test Loss: 0.304595947265625 \n",
      "Epoch: 150 | MAE Train Loss: 0.14006686210632324 | MAE Test Loss: 0.2911251187324524 \n",
      "Epoch: 160 | MAE Train Loss: 0.12854602932929993 | MAE Test Loss: 0.2776543200016022 \n",
      "Epoch: 170 | MAE Train Loss: 0.11702518165111542 | MAE Test Loss: 0.26418352127075195 \n",
      "Epoch: 180 | MAE Train Loss: 0.10654274374246597 | MAE Test Loss: 0.251315176486969 \n",
      "Epoch: 190 | MAE Train Loss: 0.09747617691755295 | MAE Test Loss: 0.2392154484987259 \n",
      "Epoch: 200 | MAE Train Loss: 0.08973254263401031 | MAE Test Loss: 0.22795839607715607 \n",
      "Epoch: 210 | MAE Train Loss: 0.08306284993886948 | MAE Test Loss: 0.21740305423736572 \n",
      "Epoch: 220 | MAE Train Loss: 0.07729282230138779 | MAE Test Loss: 0.20750825107097626 \n",
      "Epoch: 230 | MAE Train Loss: 0.07242877781391144 | MAE Test Loss: 0.19840054214000702 \n",
      "Epoch: 240 | MAE Train Loss: 0.06828799843788147 | MAE Test Loss: 0.18992407619953156 \n",
      "Epoch: 250 | MAE Train Loss: 0.06476505100727081 | MAE Test Loss: 0.18208928406238556 \n",
      "Epoch: 260 | MAE Train Loss: 0.06174134090542793 | MAE Test Loss: 0.17478716373443604 \n",
      "Epoch: 270 | MAE Train Loss: 0.05915876477956772 | MAE Test Loss: 0.16802480816841125 \n",
      "Epoch: 280 | MAE Train Loss: 0.0569603331387043 | MAE Test Loss: 0.161748006939888 \n",
      "Epoch: 290 | MAE Train Loss: 0.05514555424451828 | MAE Test Loss: 0.15608695149421692 \n",
      "Epoch: 300 | MAE Train Loss: 0.05357731133699417 | MAE Test Loss: 0.15086300671100616 \n",
      "Epoch: 310 | MAE Train Loss: 0.05218071490526199 | MAE Test Loss: 0.14595307409763336 \n",
      "Epoch: 320 | MAE Train Loss: 0.05105169489979744 | MAE Test Loss: 0.1415499746799469 \n",
      "Epoch: 330 | MAE Train Loss: 0.050030313432216644 | MAE Test Loss: 0.1374681293964386 \n",
      "Epoch: 340 | MAE Train Loss: 0.049160152673721313 | MAE Test Loss: 0.13370750844478607 \n",
      "Epoch: 350 | MAE Train Loss: 0.04836020991206169 | MAE Test Loss: 0.13020753860473633 \n",
      "Epoch: 360 | MAE Train Loss: 0.04769856110215187 | MAE Test Loss: 0.12709848582744598 \n",
      "Epoch: 370 | MAE Train Loss: 0.04703690856695175 | MAE Test Loss: 0.12398938834667206 \n",
      "Epoch: 380 | MAE Train Loss: 0.04650093987584114 | MAE Test Loss: 0.12147434055805206 \n",
      "Epoch: 390 | MAE Train Loss: 0.045996345579624176 | MAE Test Loss: 0.11902527511119843 \n",
      "Epoch: 400 | MAE Train Loss: 0.04549176245927811 | MAE Test Loss: 0.11657620966434479 \n",
      "Epoch: 410 | MAE Train Loss: 0.045014552772045135 | MAE Test Loss: 0.11432783305644989 \n",
      "Epoch: 420 | MAE Train Loss: 0.04461454600095749 | MAE Test Loss: 0.11254779994487762 \n",
      "Epoch: 430 | MAE Train Loss: 0.04421453922986984 | MAE Test Loss: 0.11076776683330536 \n",
      "Epoch: 440 | MAE Train Loss: 0.043814532458782196 | MAE Test Loss: 0.1089877337217331 \n",
      "Epoch: 450 | MAE Train Loss: 0.04341452196240425 | MAE Test Loss: 0.10720770061016083 \n",
      "Epoch: 460 | MAE Train Loss: 0.043014515191316605 | MAE Test Loss: 0.10542766749858856 \n",
      "Epoch: 470 | MAE Train Loss: 0.04265119880437851 | MAE Test Loss: 0.10419009625911713 \n",
      "Epoch: 480 | MAE Train Loss: 0.04230218380689621 | MAE Test Loss: 0.1030881255865097 \n",
      "Epoch: 490 | MAE Train Loss: 0.04195316880941391 | MAE Test Loss: 0.10198615491390228 \n"
     ]
    }
   ],
   "source": [
    "# Let's make the training and testing loop:\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Set the number of epoch (how many times the model will pass through the loop)\n",
    "EPOCHS = 500\n",
    "\n",
    "# Create lists to track values\n",
    "train_loss_values = []\n",
    "test_loss_values = []\n",
    "epoch_count = []\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    # Start with the training - put it in training mode\n",
    "    model_0.train()\n",
    "\n",
    "    # Forward pass on train data\n",
    "    y_pred = model_0(X_train)\n",
    "\n",
    "    # Calculate the loss\n",
    "    loss = loss_fn(y_pred, y_train)\n",
    "\n",
    "    # Set the gradients of the optimizer to zero\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Calculate the loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # Progress the optimizer\n",
    "    optimizer.step()\n",
    "\n",
    "    # Now test the model - put it in testing mode\n",
    "    model_0.eval()\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        # Do a forward pass\n",
    "        test_pred = model_0(X_test)\n",
    "\n",
    "        # Calculate the loss on test data\n",
    "        test_loss = loss_fn(test_pred, y_test)\n",
    "\n",
    "        # Print every 10 epochs what is happening\n",
    "        if epoch % 10 == 0:\n",
    "            epoch_count.append(epoch)\n",
    "            train_loss_values.append(loss.detach().numpy())\n",
    "            test_loss_values.append(test_loss.detach().numpy())\n",
    "            print(f\"Epoch: {epoch} | MAE Train Loss: {loss} | MAE Test Loss: {test_loss} \")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a09e687710>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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///wzjRs35qWXXuL333+nXbt2lCtXjiNHjvDxxx9TpkwZmjdvflU1STFn3olaIoXLpU4Fr1Wr1kXXX7x4sXHDDTcYvr6+RqlSpYz//Oc/rlNjzz9d+VKngl/sFF/+cVrxpU7L7d+//wXPLVeunNGrV68c8+bPn280aNDAsNlsRqVKlYxPP/3UeOqppwwfH59LbIVzNm3aZMTHxxsBAQFGeHi40bdvX9cp5+eftt2rVy/D39//gudfrPbjx48bPXr0MIKCgozg4GCjR48exurVq6/oVHDDMIyxY8caFStWNDw8PC7YzgsWLDBat25tBAcHGz4+PkalSpWM3r17GytWrDAMwzCOHTtm9O/f36hevbrh7+9vBAcHG7GxscaUKVNyvEdCQoLRrl07IzAw0AAue1r4P39mhmEYq1atMlq3bm0EBAQYfn5+xi233GL8+eefOdZ55ZVXjKZNmxohISGGr6+vUb16dePVV181MjMzr6reS9mwYYPRsWNHIyQkxPDx8TGqVatmvPDCCznWmTt3rlG7dm3DZrMZ1apVMyZOnHhVv3NnORwOIyYmxgCMV1555aLrZGZmGm+88YZRq1Ytw9vb2wgNDTUaNWpkvPjii0ZiYqJhGM7f17vvvtsoVaqUYbPZjFKlShndunUztm3bdkWfWeQsi2EUoj81RSTfdejQIddTkEVEijqNuRFxY/9slbB9+3ZmzZp1TS0FRESKCu25EXFj0dHR9O7dm4oVK7Jnzx5GjRpFRkYGq1evpkqVKmaXJyKSLzSgWMSNtWnThm+++YaEhAS8vb2Ji4vjtddeU7AREbemPTciIiLiVjTmRkRERNyKwo2IiIi4lWI35sbhcHDw4EECAwOv6zLrIiIiUnAMwyA5OZlSpUphtea+b6bYhZuDBw/m6O0iIiIiRce+ffsoU6ZMrusUu3ATGBgIODfO2R4vIiIiUrglJSURExPj+h7PTbELN2cPRQUFBSnciIiIFDFXMqREA4pFRETErSjciIiIiFtRuBERERG3UuzG3IiIiHuz2+1kZWWZXYZcA5vNdtnTvK+Ewo2IiLgFwzBISEjg1KlTZpci18hqtVKhQgVsNtt1vU6hCDcfffQRb731FgkJCdSrV48RI0bQtGnTi647fvx4+vTpk2Oet7c36enpBVGqiIgUUmeDTcmSJfHz89OFWouYsxfZPXToEGXLlr2un5/p4Wby5MkMGjSI0aNHExsby/vvv0/r1q3ZunUrJUuWvOhzgoKC2Lp1q+uxfoFFRIo3u93uCjYlSpQwuxy5RhERERw8eJDs7Gy8vLyu+XVMH1D87rvv0rdvX/r06UPNmjUZPXo0fn5+jBs37pLPsVgsREVFuW6RkZEFWLGIiBQ2Z8fY+Pn5mVyJXI+zh6Psdvt1vY6p4SYzM5OVK1cSHx/vmme1WomPj2fJkiWXfF5KSgrlypUjJiaGu+++m40bN15y3YyMDJKSknLcRETEPWlPftGWVz8/U8PNsWPHsNvtF+x5iYyMJCEh4aLPqVatGuPGjWPmzJlMnDgRh8PBjTfeyP79+y+6/vDhwwkODnbd1FdKRETEvZl+WOpqxcXF0bNnT+rXr89NN93EtGnTiIiIYMyYMRddf/DgwSQmJrpu+/btK+CKRURECk758uV5//33TX8NM5k6oDg8PBwPDw8OHz6cY/7hw4eJioq6otfw8vKiQYMG7Nix46LLvb298fb2vu5aRURE8tLlDsEMHTqUYcOGXfXrLl++HH9//2usyj2YuufGZrPRqFEj5s+f75rncDiYP38+cXFxV/Qadrud9evXEx0dnV9lXrl9yyDlqNlViIhIEXDo0CHX7f333ycoKCjHvKefftq1rmEYZGdnX9HrRkREFPuB1aYflho0aBBjx47liy++YPPmzTz22GOkpqa6rmXTs2dPBg8e7Fr/pZdeYu7cufz999+sWrWKBx54gD179vDwww+b9RGcdvwCX7SHrzpBugYti4hI7s4/6zc4ODjHmcBbtmwhMDCQ2bNn06hRI7y9vVm0aBE7d+7k7rvvJjIykoCAAJo0acIvv/yS43X/eUjJYrHw6aef0rFjR/z8/KhSpQrff//9VdW6d+9e7r77bgICAggKCqJz5845jrqsXbuWW265hcDAQIKCgmjUqBErVqwAYM+ePbRv357Q0FD8/f2pVasWs2bNuvYNdwVMv85Nly5dOHr0KEOGDCEhIYH69eszZ84c1yDjvXv35rgU88mTJ+nbty8JCQmEhobSqFEj/vzzT2rWrGnWR3AKKQ82fzi0FibdD92/BS8fc2sSESmmDMPgdNb1nU58rXy9PPLsrJ9nn32Wt99+m4oVKxIaGsq+ffu44447ePXVV/H29ubLL7+kffv2bN26lbJly17ydV588UXefPNN3nrrLUaMGEH37t3Zs2cPYWFhl63B4XC4gs1vv/1GdnY2/fv3p0uXLixcuBCA7t2706BBA0aNGoWHhwdr1qxxXaemf//+ZGZm8vvvv+Pv78+mTZsICAjIk+1zKaaHG4ABAwYwYMCAiy47u+HOeu+993jvvfcKoKqrFF4ZHvgOxt8Ju/+AaQ/DfV+A1cPsykREip3TWXZqDvnZlPfe9FJr/Gx58/X60ksvcdttt7keh4WFUa9ePdfjl19+menTp/P9999f8nsUoHfv3nTr1g2A1157jQ8//JBly5bRpk2by9Ywf/581q9fz65du1xnHH/55ZfUqlWL5cuX06RJE/bu3cszzzxD9erVAahSpYrr+Xv37qVTp07UqVMHgIoVK17FFrg2ph+WciulGkDXr8HDBpt/gB//DYZhdlUiIlJENW7cOMfjlJQUnn76aWrUqEFISAgBAQFs3ryZvXv35vo6devWdU37+/sTFBTEkSNHrqiGzZs3ExMTk+NSKjVr1iQkJITNmzcDziEmDz/8MPHx8bz++uvs3LnTte4TTzzBK6+8QrNmzRg6dCjr1q27ove9HoViz41bqXgTdPoUpvaGVV+Afzi0GmJ2VSIixYqvlwebXmpt2nvnlX+e9fT0008zb9483n77bSpXroyvry/33nsvmZmZub7OP1sZWCwWHA5HntU5bNgw7r//fn766Sdmz57N0KFDmTRpEh07duThhx+mdevW/PTTT8ydO5fhw4fzzjvvMHDgwDx7/3/Snpv8UPNuuPPMobM/3oElH5tbj4hIMWOxWPCzeZpyy8+rJC9evJjevXvTsWNH6tSpQ1RUFLt378639wOoUaMG+/bty3GduE2bNnHq1Kkc412rVq3Kv//9b+bOncs999zD559/7loWExNDv379mDZtGk899RRjx47N15oVbvJLo95w6wvO6Z8Hw9rJppYjIiJFX5UqVZg2bRpr1qxh7dq13H///Xm6B+Zi4uPjqVOnDt27d2fVqlUsW7aMnj17ctNNN9G4cWNOnz7NgAEDWLhwIXv27GHx4sUsX76cGjVqAPDkk0/y888/s2vXLlatWsWCBQtcy/KLwk1+avEU3PC4c3rm47Btrrn1iIhIkfbuu+8SGhrKjTfeSPv27WndujUNGzbM1/e0WCzMnDmT0NBQWrZsSXx8PBUrVmTyZOcf7R4eHhw/fpyePXtStWpVOnfuTNu2bXnxxRcB5/Xo+vfvT40aNWjTpg1Vq1bl44/z94iGxTCK14jXpKQkgoODSUxMJCgoKP/f0OGAGf1g3WTw9IWeM6DsDfn/viIixUh6ejq7du2iQoUK+PjoMhxFVW4/x6v5/taem/xmtcLdH0GV2yH7NHzdGQ5fuou5iIiIXB+Fm4Lg4eW85k1MLKQnwoR74ORus6sSERFxSwo3BcXmB/dPhpI1ISUBJnSElCu7xoCIiIhcOYWbguQbCg9Mg5CycOJvmKg+VCIiInlN4aagBUVDjxngHwEJ65x9qLLSza5KRETEbSjcmKFEJWcfKlugsw/Vdw+B/cpa2YuIiEjuFG7MEl0Pun0DHt6w5Uf48Un1oRIREckDCjdmqtAC7h0HFiusngDzXzS7IhERkSJP4cZsNe6E9h84pxe9B3+ONLceERGRIk7hpjBo2BNaDXVOz30O1nxjbj0iIuL2du/ejcViYc2aNWaXkucUbgqL5v+GuAHO6Zn9Yescc+sREZF8ZbFYcr0NGzbsul57xowZeVZrUeNpdgFyhsUCt70Macdh7TcwtZfzlPFycWZXJiIi+eDQoUOu6cmTJzNkyBC2bt3qmhcQEGBGWW5Be24KE6sV7hoBVdtCdjp83QUSNphdlYiI5IOoqCjXLTg4GIvFkmPepEmTqFGjBj4+PlSvXj1HJ+3MzEwGDBhAdHQ0Pj4+lCtXjuHDhwNQvnx5ADp27IjFYnE9vhK//fYbTZs2xdvbm+joaJ599lmys89dquTbb7+lTp06+Pr6UqJECeLj40lNTQVg4cKFNG3aFH9/f0JCQmjWrBl79uy5/g11DbTnprDx8IL7Pne2Z9i7BCbeAw/+DGEVzK5MRKToMAzISjPnvb38nHvjr8NXX33FkCFDGDlyJA0aNGD16tX07dsXf39/evXqxYcffsj333/PlClTKFu2LPv27WPfvn0ALF++nJIlS/L555/Tpk0bPDw8rug9Dxw4wB133EHv3r358ssv2bJlC3379sXHx4dhw4Zx6NAhunXrxptvvknHjh1JTk7mjz/+wDAMsrOz6dChA3379uWbb74hMzOTZcuWYbnO7XCtFG4KIy9f6DYJxreDwxucQefBnyEw0uzKRESKhqw0eK2UOe/9v4Ng87+ulxg6dCjvvPMO99xzDwAVKlRg06ZNjBkzhl69erF3716qVKlC8+bNsVgslCtXzvXciIgIAEJCQoiKirri9/z444+JiYlh5MiRWCwWqlevzsGDB/nvf//LkCFDOHToENnZ2dxzzz2u96tTpw4AJ06cIDExkTvvvJNKlSoBUKNGjevaBtdDh6UKK98Q51WMQ8rByV1n+lAlml2ViIjks9TUVHbu3MlDDz1EQECA6/bKK6+wc+dOAHr37s2aNWuoVq0aTzzxBHPnzr3u9928eTNxcXE59rY0a9aMlJQU9u/fT7169WjVqhV16tThvvvuY+zYsZw8eRKAsLAwevfuTevWrWnfvj0ffPBBjjFFBU17bgqzwCjoMR3GtYHD6+Gbbs7A4+VrdmUiIoWbl59zD4pZ730dUlJSABg7diyxsbE5lp09xNSwYUN27drF7Nmz+eWXX+jcuTPx8fF8++231/XeufHw8GDevHn8+eefzJ07lxEjRvDcc8+xdOlSKlSowOeff84TTzzBnDlzmDx5Ms8//zzz5s3jhhtuyLeaLkV7bgq7s32ovINgz2L49kH1oRIRuRyLxXloyIzbdY4ziYyMpFSpUvz9999Urlw5x61ChXPjL4OCgujSpQtjx45l8uTJfPfdd5w4cQIALy8v7Hb7Vb1vjRo1WLJkCcZ5rYAWL15MYGAgZcqUObNZLTRr1owXX3yR1atXY7PZmD59umv9Bg0aMHjwYP78809q167N119/fT2b4pppz01REF3X2Ydqwj2wdRb88C+4e+R1/wMSEZHC6cUXX+SJJ54gODiYNm3akJGRwYoVKzh58iSDBg3i3XffJTo6mgYNGmC1Wpk6dSpRUVGEhIQAzjOm5s+fT7NmzfD29iY0NPSy7/n444/z/vvvM3DgQAYMGMDWrVsZOnQogwYNwmq1snTpUubPn8/tt99OyZIlWbp0KUePHqVGjRrs2rWLTz75hLvuuotSpUqxdetWtm/fTs+ePfN5S12cwk1RUb658yyqyQ/AmongFwa3v2x2VSIikg8efvhh/Pz8eOutt3jmmWfw9/enTp06PPnkkwAEBgby5ptvsn37djw8PGjSpAmzZs3CanUekHnnnXcYNGgQY8eOpXTp0uzevfuy71m6dGlmzZrFM888Q7169QgLC+Ohhx7i+eefB5x7in7//Xfef/99kpKSKFeuHO+88w5t27bl8OHDbNmyhS+++ILjx48THR1N//79efTRR/NrE+XKYhjFqxV1UlISwcHBJCYmEhQUZHY5V2/1ROcVjAFuewma/cvcekRECoH09HR27dpFhQoV8PHxMbscuUa5/Ryv5vtbY26KmgYPOEMNwLwhzrAjIiIiLgo3RVGzf8GNTzinvx8IW34ytx4REZFCROGmqLrtJaj/ABgOmNoHdi82uyIREZFCQeGmqLJYoP0HUO0OsGfAN13h0DqzqxIRETGdwk1R5uEJ946Dcs0gI8l5FeMTf5tdlYiIaYrZOTJuJ69+fgo3RZ2Xr/MaOJF1IPUIfNkBkhPMrkpEpEB5eXkBkJZmUrNMyROZmZkAV9zs81J0nRt34BPsvIrxuNvh5G7nHpzePzn7U4mIFAMeHh6EhIRw5MgRAPz8/EzrSC3XxuFwcPToUfz8/PD0vL54onDjLgIjnX2oPmvt7CT+TTfoMU19qESk2DjbAftswJGix2q1UrZs2esOprqIn7tJWA+ft4OMRKjaFrpMdI7NEREpJux2O1lZWWaXIdfAZrO5rrL8T1fz/a1vPXcTVQfunwQTOsK22c7r4Nz9EVzil0VExN14eHhc95gNKdr0jeeOyt0I940Hiwes/RrmvQDFawediIgUYwo37qpaW7hrhHN6yUhY/IG59YiIiBQQhRt31qA73Hamc/gvQ2HVBHPrERERKQAKN+6u2RPnOof/8ARs/tHcekRERPKZwk1xEP+is5u44YBvH4Rdf5hdkYiISL5RuCkOLBa48wOofueZPlTd4NBas6sSERHJFwo3xYWHJ3T6DMo1h8xk51WMj+80uyoREZE8p3BTnHj5QLevndfCST0KEzpA0iGzqxIREclTCjfFjU8wPDANQivAqb0w8R44fdLsqkRERPKMwk1xFFDS2YcqIBKObIKvu0KmOumKiIh7ULgprsIqOPfgeAfDvr9gam+wqxeLiIgUfQo3xVlUbbh/Mnj6wPafYeYAcDjMrkpEROS6KNwUd+XioPOXzj5U6ybB3OfVh0pERIo0hRuBqq2hw8fO6b8+gkXvmluPiIjIdVC4Ead6XaH1a87p+S/ByvGmliMiInKtFG7knLj+0Pzfzukf/w2bvje3HhERkWugcCM5tRoKDXs6+1B99xDs+t3sikRERK6Kwo3kZLFAu/fO9KHKhG/uh4NrzK5KRETkiincyIXO9qEq3+JcH6pjO8yuSkRE5Ioo3MjFeflA168huh6kHYMJHSHpoNlViYiIXJbCjVyaTxB0/w7CKkHiXphwD6SdMLsqERGRXCncSO4CIpx9qAKj4ehm+LoLZKaaXZWIiMglKdzI5YWWc/ah8gmB/ctgSi/1oRIRkUJL4UauTGRNuH8KePrCjnkw43H1oRIRkUJJ4UauXNlYZx8qqyesnwI//099qEREpNBRuJGrU/V26DDKOb10FPzxtrn1iIiI/EOhCDcfffQR5cuXx8fHh9jYWJYtW3ZFz5s0aRIWi4UOHTrkb4GSU93O0OZ15/Svr8CKcebWIyIich7Tw83kyZMZNGgQQ4cOZdWqVdSrV4/WrVtz5MiRXJ+3e/dunn76aVq0aFFAlUoONzwGLZ52Tv84CDbOMLUcERGRs0wPN++++y59+/alT58+1KxZk9GjR+Pn58e4cZfeG2C32+nevTsvvvgiFStWLMBqJYdbn4dGfQADpvWFvxeaXZGIiIi54SYzM5OVK1cSHx/vmme1WomPj2fJkiWXfN5LL71EyZIleeihhy77HhkZGSQlJeW4SR6xWKDdO1Dzbmcfqknd4cAqs6sSEZFiztRwc+zYMex2O5GRkTnmR0ZGkpCQcNHnLFq0iM8++4yxY8de0XsMHz6c4OBg1y0mJua665bzWD3gnrFQ4SbITIGv7oVj282uSkREijHTD0tdjeTkZHr06MHYsWMJDw+/oucMHjyYxMRE123fvn35XGUx5OkNXb+CUg0g7bizD1XiAbOrEhGRYsrTzDcPDw/Hw8ODw4cP55h/+PBhoqKiLlh/586d7N69m/bt27vmOc5cSM7T05OtW7dSqVKlHM/x9vbG29s7H6qXHLwDofu3MK4NHN8OE++BPrPBL8zsykREpJgxdc+NzWajUaNGzJ8/3zXP4XAwf/584uLiLli/evXqrF+/njVr1rhud911F7fccgtr1qzRISez+Yef6UNVCo5uga87qw+ViIgUOFP33AAMGjSIXr160bhxY5o2bcr7779Pamoqffr0AaBnz56ULl2a4cOH4+PjQ+3atXM8PyQkBOCC+WKSkBhnwBnXGvYvh8k9oNsk8LSZXZmIiBQTpoebLl26cPToUYYMGUJCQgL169dnzpw5rkHGe/fuxWotUkODpGR15yGqL++CnfNhxmPOQcf6OYqISAGwGEbxag6UlJREcHAwiYmJBAUFmV2Oe9v+C3zTBRzZ0PRRaPuG8/RxERGRq3Q139/6U1ryT5V46DgGsMCyMfD7W2ZXJCIixYDCjeSvOvc699gALHgVln9qbj0iIuL2FG4k/8U+Ci3/45z+6WnYON3cekRExK0p3EjBuOV/0PhBwIDv+sLOBWZXJCIibkrhRgqGxQJ3vA21OoIj60wfqpVmVyUiIm5I4UYKjtXDOcC44s2QlQoT74Wj28yuSkRE3IzCjRQsT2/o8hWUaginT5zpQ7Xf7KpERMSNKNxIwfMOcF7kr0QVSNoPE+6BtBNmVyUiIm5C4UbM4V/C2aYhqDQc2wpf3QsZKWZXJSIibkDhRsxztg+Vb6hzcPGUHpCdaXZVIiJSxCnciLkiqjkPUXn5w85fYUY/cDjMrkpERIowhRsxX5nG0GUCWL1gw3cw+z9QvFqeiYhIHlK4kcKhciu450wfquVj4bc3zK5IRESKKIUbKTxqd4I7zjTXXDgclo01tx4RESmSFG6kcGnaF24e7Jye9Qys/9bcekREpMhRuJHC56b/QpO+gAHT+8GO+WZXJCIiRYjCjRQ+Fgu0fRNq3ePsQzW5B+xfYXZVIiJSRCjcSOFktTr7UFW61dmH6qt74ehWs6sSEZEiQOFGCi9PG3SeAKUbw+mTzj5Up/aZXZWIiBRyCjdSuHkHQPepEF4Nkg44A07qcbOrEhGRQkzhRgo/vzDoMQ2CysDx7Wf6UCWbXZWIiBRSCjdSNASXOdOHKgwOroLJD0B2htlViYhIIaRwI0VHRNVzfaj+XgjTHgGH3eyqRESkkFG4kaKlTCPo+pWzD9WmGTDrafWhEhGRHBRupOipdAt0GgtYYMU4WPCa2RWJiEghonAjRVOtjtDuHef072/C0jHm1iMiIoWGwo0UXU0egluec07P/o/6UImICKBwI0Vdy2eg6aPO6emPwvZfzK1HRERMp3AjRZvFAm1ehzr3gSMbpvSAfcvMrkpEREykcCNFn9UKd38MleMhKw2+ug+ObDa7KhERMYnCjbgHTxt0/hLKNIH0UzDhHji11+yqRETEBAo34j5s/nD/FIioDskHz/ShOmZ2VSIiUsAUbsS9+IXBA9MgOAaO74CJndSHSkSkmFG4EfcTXBp6zAC/EnBoDUy6H7LSza5KREQKiMKNuKfwyvDAd2ALgF2/w7SH1YdKRKSYULgR91WqAXT9GjxssPkH+PHf6kMlIlIMKNyIe6t4E3T6FLDAqi/g11fMrkhERPKZwo24v5p3w53vOaf/eBv+GmVuPSIikq8UbqR4aNwHbn3eOT3nWVg72dx6REQk3yjcSPHR4mmIfcw5PfNx2DbX3HpERCRfKNxI8WGxQOvXoE7nM32oesLepWZXJSIieUzhRooXqxU6fAyVb4Ps0/D1fXB4k9lViYhIHlK4keLHwws6fwFlmkJ6Iky8B07uMbsqERHJIwo3UjzZ/OH+yRBRA5IPwYQOkHLU7KpERCQPKNxI8eUXBj2mQXBZOPG3cw9OepLZVYmIyHVSuJHiLagU9JgOfuGQsE59qERE3IDCjUh4ZXjgW7AFwu4/1IdKRKSIU7gRAWcfqm7n96F6Un2oRESKKIUbkbMqtIROn4HFCqu+hPkvmV2RiIhcA4UbkfPVvOtcH6pF78KSj8ytR0RErprCjcg/NeoNrYY4p3/+H6z5xtRyRETk6ijciFxM80FwQ3/n9Mz+sHWOufWIiMgVU7gRuRiLBW5/Bep2AcMOU3vBniVmVyUiIldA4UbkUqxWuPsjqNIastPh6y6QsMHsqkRE5DIUbkRy4+EF942HmBsgIxEmdoKTu82uSkREcqFwI3I5Nj+4fxKUrAUpCfBlB0g5YnZVIiJyCQo3eeh4SgaGLvzmnnxD4YHvIKQsnNx1pg9VotlViYjIRSjc5JHDSel0+Hgxz363HrtDAcctBUVDjxngHwEJ6+Eb9aESESmMFG7yyKo9Jzlw8jSTV+xj4DeryMhWbyK3VKKScw+OdxDsWQTfPgj2bLOrEhGR8yjc5JG2daL5uHtDbB5WZq1P4OEvVpCWqS89txRdD7p+DR7esPUn+PFf6kMlIlKIKNzkoTa1o/msd2P8bB78sf0YD3y6lMS0LLPLkvxQoQXcO87Zh2r1RPhlmNkViYjIGQo3eaxFlQgmPhxLsK8Xq/aeossnSziSrHEZbqnGndD+A+f04vfhzxGmliMiIk4KN/mgYdlQJj96AxGB3mxJSKbz6CXsO5FmdlmSHxr2hPhhzum5z8Oar00tR0RECkm4+eijjyhfvjw+Pj7ExsaybNmyS647bdo0GjduTEhICP7+/tSvX58JEyYUYLVXpnpUEN/2iyMmzJfdx9O4d/SfbD+cbHZZkh+aPQlxA5zTMwfAllmmliMiUtyZHm4mT57MoEGDGDp0KKtWraJevXq0bt2aI0cufpG0sLAwnnvuOZYsWcK6devo06cPffr04eeffy7gyi+vXAl/vu13I1UjAziclEHnMUtYu++U2WVJXjvbh6re/c4+VN/2gd2Lza5KRKTYshgmX3UuNjaWJk2aMHLkSAAcDgcxMTEMHDiQZ5999opeo2HDhrRr146XX375susmJSURHBxMYmIiQUFB11X7lTqZmknv8ctZu+8U/jYPxvZqzI2VwgvkvaUA2bNh8gOwbbbzVPE+syCqjtlViYi4hav5/jZ1z01mZiYrV64kPj7eNc9qtRIfH8+SJZfvwGwYBvPnz2fr1q20bNkyP0u9LqH+Nr56OJYbK5UgNdNO78+XM2/TYbPLkrzm4Qn3fQ5lb4SMJJhwD5z42+yqRESKHVPDzbFjx7Db7URGRuaYHxkZSUJCwiWfl5iYSEBAADabjXbt2jFixAhuu+22i66bkZFBUlJSjpsZArw9Gde7CbfXjCQz20G/iSuZtmq/KbVIPvLyhW7fQGQdSD0CEzpC8qV/l0VEJO+ZPubmWgQGBrJmzRqWL1/Oq6++yqBBg1i4cOFF1x0+fDjBwcGuW0xMTMEWex4fLw8+7t6QTg3LYHcYDJqylvGLd5lWj+QT3xDnVYxDyzs7iE+8F06fMrcmEZFixNRwEx4ejoeHB4cP5zxEc/jwYaKioi75PKvVSuXKlalfvz5PPfUU9957L8OHD7/ouoMHDyYxMdF127dvX55+hqvl6WHlrXvr0qdZeQCG/bCJD+dvV8NNdxMYCT2mg39JOLwevukGWafNrkpEpFgwNdzYbDYaNWrE/PnzXfMcDgfz588nLi7uil/H4XCQkZFx0WXe3t4EBQXluJnNarUw5M6a/Du+KgDvztvGyz9uxqGGm+4lrCL0mOYcXLz3T/WhEhEpIKYflho0aBBjx47liy++YPPmzTz22GOkpqbSp08fAHr27MngwYNd6w8fPpx58+bx999/s3nzZt555x0mTJjAAw88YNZHuCYWi4V/xVdhaPuaAIxbvIv/fLeObLvD5MokT0XVgW6TwNMHts6CH55QHyoRkXzmaXYBXbp04ejRowwZMoSEhATq16/PnDlzXIOM9+7di9V6LoOlpqby+OOPs3//fnx9falevToTJ06kS5cuZn2E69KnWQWCfLz4z3fr+HblfpLTs/igawN8vDzMLk3ySvlmcO/nztPE13wFfmHO6+KIiEi+MP06NwXNjOvcXIm5GxMY8M1qMrMdNKtcgjE9GhPgbXr2lLy0+iuY+bhzOv5FaP6kqeWIiBQlReY6N3LO7bWiGN+nCf42DxbvOE73T5dyKi3T7LIkLzXoDredudDkL0NhVeFrGyIi4g4UbgqRGyuF81XfGwjx82LtvlN0HrOEw0nqKO5Wmj0Bzf7lnP7hCdj8o7n1iIi4IYWbQqZ+TAhTH40jMsibbYdTuHf0n+w5nmp2WZKX4l+EBg+A4XCeQbV7kdkViYi4FYWbQqhKZCDf9ruRciX82HfiNPeOXsLWBHUUdxsWC9z5AVS/E+wZzmvgHFprdlUiIm5D4aaQignzY+qjcVSPCuRosrOj+Kq9J80uS/KKhyd0+gzKNXf2oZrYCY7vNLsqERG3oHBTiJUM8mHyI3E0LBtC4uksHvh0KYu2HzO7LMkrXj7Q7WvntXBSj6oPlYhIHrmmcLNv3z727z/X9HHZsmU8+eSTfPLJJ3lWmDgF+3kx8eFYWlQJJy3TzoPjlzNnwyGzy5K84hMMD0xzXs341B5nJ/HT2kMnInI9rinc3H///SxYsACAhIQEbrvtNpYtW8Zzzz3HSy+9lKcFCvjZPPm0V2PuqBNFpt3B41+tYsoKc3tkSR4KKOnsQxUQCUc2wtddITPN7KpERIqsawo3GzZsoGnTpgBMmTKF2rVr8+eff/LVV18xfvz4vKxPzvD29GBEt4Z0aRyDw4D/fLuOT//42+yyJK+ElnfuwfEOhn1/wdTeYM8yuyoRkSLpmsJNVlYW3t7eAPzyyy/cddddAFSvXp1Dh3TIJL94WC283qkOfVtUAOCVnzbzztyt6ijuLqJqw/2TnX2otv8MMweAQ73GRESu1jWFm1q1ajF69Gj++OMP5s2bR5s2bQA4ePAgJUqUyNMCJSeLxcL/7qjBM62rATDi1x0M+36jOoq7i3Jx0PlLsHjAukkw7wU12hQRuUrXFG7eeOMNxowZw80330y3bt2oV68eAN9//73rcJXkH4vFQv9bKvPy3bWwWOCLJXt4aupastRR3D1UbQ0dPnZOLxkJi94ztx4RkSLmmhtn2u12kpKSCA0Ndc3bvXs3fn5+lCxZMs8KzGuFtXHmtZq55gCDpqzF7jCIr1GSkfc3VEdxd7HkI/j5f87p9h9Co17m1iMiYqJ8b5x5+vRpMjIyXMFmz549vP/++2zdurVQBxt3dHf90nzSoxHenlZ+2XyEXuOWkZyugahuIa4/NB/knP7xSdj0vanliIgUFdcUbu6++26+/PJLAE6dOkVsbCzvvPMOHTp0YNSoUXlaoFxeqxqRfPFgUwK8PVm66wTdP13KiVR1FHcLrYZAw57OPlTfPQS7fje7IhGRQu+aws2qVato0aIFAN9++y2RkZHs2bOHL7/8kg8//DBPC5Qrc0PFEnzT9wbC/G2s259I5zFLOJR42uyy5HpZLNDuvTN9qDLhm/vh4BqzqxIRKdSuKdykpaURGBgIwNy5c7nnnnuwWq3ccMMN7NmzJ08LlCtXp0wwUx6NIzrYhx1HUrh31BJ2HVNH8SLvbB+q8i0gM1l9qERELuOawk3lypWZMWMG+/bt4+eff+b2228H4MiRI24xSLcoq1wygKn94qgQ7s+BU6e5b/QSNh1MMrssuV5ePtD1a4iqC2nHYEIHSNI1pURELuaaws2QIUN4+umnKV++PE2bNiUuLg5w7sVp0KBBnhYoV69MqB9THo2jZnQQx1Iy6PrJElbuOWF2WXK9fILgge/O9KHaCxPVh0pE5GKu+VTwhIQEDh06RL169bBanRlp2bJlBAUFUb169TwtMi+526nguUk8ncVD45ezYs9JfLysjOnRmJuqRphdllyvk3vgs9shJQFiYp19qWz+ZlclIpKvrub7+5rDzVlnu4OXKVPmel6mwBSncANwOtNOv4kr+W3bUbw8LLzfpQHt6kabXZZcr8Ob4PM2kJ4IFW+GbpOdh65ERNxUvl/nxuFw8NJLLxEcHEy5cuUoV64cISEhvPzyyzjUC6dQ8bV5MLZnY+6sG02W3WDgN6uYtGyv2WXJ9YqsCd2/BS9/+HshTO0F2Tr9X0QErjHcPPfcc4wcOZLXX3+d1atXs3r1al577TVGjBjBCy+8kNc1ynWyeVr5oGsDujUti8OAZ6et55PfdbZNkRfT9FyjzW1zYFpfsGebXZWIiOmu6bBUqVKlGD16tKsb+FkzZ87k8ccf58CBA3lWYF4rboelzmcYBm/M2cro35zB5vGbK/FM62pYLBaTK5Prsv0X+KYrOLKgblfoMAqs1/R3i4hIoZXvh6VOnDhx0UHD1atX58QJnZVTWFksFp5tW53/tnH+7D5euJPnZ2xQR/Girko83Df+XCfxn/6tTuIiUqxdU7ipV68eI0eOvGD+yJEjqVu37nUXJfnrsZsr8VrHOlgs8NXSvTw5eY06ihd1Ne6Eez4BLLByPMwZrIAjIsWW57U86c0336Rdu3b88ssvrmvcLFmyhH379jFr1qw8LVDyx/2xZQn08WTQlDV8v/YgyelZfNy9Eb42dRQvsurcC9npMLM/LB0FNj9nbyoRkWLmmvbc3HTTTWzbto2OHTty6tQpTp06xT333MPGjRuZMGFCXtco+aR9vVKM7dkYHy8rC7Yepde4ZSSpo3jR1uABuONt5/Qf78Dvb5lbj4iICa77OjfnW7t2LQ0bNsRut+fVS+a54jyg+FKW7z7Bg+OXk5yeTa1SQXzxYFPCA7zNLkuux+IPYd6ZMxdbvwZx/c2tR0TkOuX7gGJxL03KhzHpkRsID7Cx8WASnUcv4cApdRQv0po9ATf/zzn98/9g6Sfm1iMiUoAUbgSAWqWcHcVLh/jy97FU7hv1JzuPpphdllyPm/4DzZ50Ts9+BpZ8ZGo5IiIFReFGXCpGODuKV4rw52BiOp1HL2HDgUSzy5JrZbFA/DBoPsj5+Of/waL3zaxIRKRAXNXZUvfcc0+uy0+dOnU9tUghUCrElymPxtH78+WsP5BIt0/+4rPeTWhaIczs0uRaWCzOM6Y8vOC3N+CXoWDPgpueMbsyEZF8c1V7boKDg3O9lStXjp49e+ZXrVJASgR483XfWJpWCCM5I5seny1lwZYjZpcl18pigVv+B7c873y84BVY8JqugyMibitPz5YqCnS21JVLz7LT/6tVzN9yBE+rhXc61+Pu+qXNLkuux6L34Jdhzunmg5x7ddR+Q0SKAJ0tJXnCx8uD0T0a0aF+KbIdBk9OXsPEv/aYXZZcj+b/dp4aDrDoXefp4sXr7xsRKQYUbiRXXh5W3u1cn55x5TAMeH7GBj5asINitsPPvcT1h7ZnLu735wi1ahARt6NwI5dltVp48a5aDLy1MgBv/byV12dvUcApymIfgTvfc04vHQU/PQUO9RcTEfegcCNXxGKx8NTt1Xi+XQ0Axvz+N4OnrceujuJFV+MH4a6RgAVWfAbfDwR7ttlViYhcN4UbuSoPt6jIm53qYrXApOX7eOKb1WRkF952G3IZDXtAh1FgscKaiTClB2Tp6tQiUrQp3MhV69wkho/ub4iXh4Wf1h/i4S9WkJapv/iLrPrdoPME8PCGrbNgQkc4fdLsqkRErpnCjVyTtnWiGde7Cb5eHvyx/Rg9PltGYpo6ihdZNe6EHtPBOxj2LoHP74CkQ2ZXJSJyTRRu5Jq1qBLBxIdjCfLxZOWek3T5ZAlHktPNLkuuVflm0GcWBETCkU3w2e1wbIfZVYmIXDWFG7kujcqFMqVfHBGB3mxJSKbz6CXsO5FmdllyraJqw0NzIawiJO6FcbfDgVVmVyUiclUUbuS6VY8KYuqjcZQJ9WX38TTuG72E7YeTzS5LrlVoeXhwLkTXg7Tj8EV72LnA7KpERK6Ywo3kifLh/nzb70aqlAwgISmdzmOWsHbfKbPLkmsVEAG9foQKLSEzBb66DzZ8Z3ZVIiJXROFG8kxUsA9THo2jXplgTqZlcf/Yv1iy87jZZcm18gmC7t9CzQ7gyIJvH4KlY8yuSkTkshRuJE+F+tv4qu8N3FipBKmZdnp9vox5mw6bXZZcK09vuHccNHkYMGD2f5ztGhy6tpGIFF4KN5LnArw9Gde7CbfVjCQz20G/iSuZtmq/2WXJtbJ6wB1vw60vOB//9TFM6g4ZKebWJSJyCQo3ki98vDwY1b0h9zQsjd1hMGjKWsYv3mV2WXKtLBZo+TTc+7nzYn/bZsPnbSHpoNmViYhcQOFG8o2nh5W3761H7xvLAzDsh018OH+7Gm4WZbXvgd4/gV84JKyDsbfCobVmVyUikoPCjeQrq9XC0PY1eTK+CgDvztvGyz9uxqGGm0VXTBPoOx/Cq0HyIRjXFrbONrsqEREXhRvJdxaLhSfjqzLkzpoAjFu8i2e+XUe23WFyZXLNQss7L/ZX8WbISoVJ98Nfo0B75USkEFC4kQLzYPMKvHNfPTysFr5btZ/+X68iPUtn3RRZviHOU8Ub9gLDAXOehVnPgF1NVEXEXAo3UqA6NSrDx90bYvOw8vPGwzz0xXJSMvRlWGR5eEH7D+C2lwELLB8L33SB06fMrkxEijGFGylwrWtFMb5PE/xtHizecZzuny7lVFqm2WXJtbJYoNkT0PlL8PSFHb84Bxof2WJ2ZSJSTCnciClurBzOV31vIMTPi7X7TtF5zBIOJ6mjeJFW8y54cA4Ex8CJnfBpK9j8g9lViUgxpHAjpqkfE8KUR+OIDPJm2+EU7h39J3uPq6N4kVaqPjyyEMq3cPakmvwA/PoqODR4XEQKjsKNmKpqZCDf9ruRciX82HfiNPeO/pOtCeooXqT5h0OPGXDD487Hv78J33TVOBwRKTAKN2K6mDA/pj4aR/WoQI4kZ9B5zBJW7T1pdllyPTw8oc1w6DgGPH1g+8/OcThHt5pdmYgUAwo3UiiUDPJh0iM30KBsCImns3jg06Us2n7M7LLketXr6hyHE1TGOQ5nbCvY/KPZVYmIm1O4kUIjxM/GVw/H0qJKOGmZdh4cv5w5Gw6ZXZZcr1IN4NHfzozDSYbJ3c+Mw9E1jkQkfyjcSKHiZ/Pk016NaVs7iky7g8e/WsXUFfvMLkuul3849JgOsY85H//+JkzoCMkJ5tYlIm5J4UYKHW9PD0Z0a0DnxmVwGPDMt+v4bJE6ihd5Hl7Q9nXnOBwvP9j1G4xu7rwujohIHioU4eajjz6ifPny+Pj4EBsby7Jlyy657tixY2nRogWhoaGEhoYSHx+f6/pSNHl6WHmjU136tqgAwMs/buLduVvVUdwd1OsKj/wGkbUh9ShM7ATzhoA9y+zKRMRNmB5uJk+ezKBBgxg6dCirVq2iXr16tG7dmiNHjlx0/YULF9KtWzcWLFjAkiVLiImJ4fbbb+fAgQMFXLnkN4vFwv/uqMHTt1cF4MNfd/DiD5vUUdwdRFSFh3+BJg87Hy/+AD5vCyf3mFuXiLgFi2Hyn8KxsbE0adKEkSNHAuBwOIiJiWHgwIE8++yzl32+3W4nNDSUkSNH0rNnz8uun5SURHBwMImJiQQFBV13/VIwJizZzQszNwJwT4PSvHFvXbw8TM/mkhc2zYSZAyEjEbyD4e4RUPNus6sSkULmar6/Tf12yMzMZOXKlcTHx7vmWa1W4uPjWbJkyRW9RlpaGllZWYSFhV10eUZGBklJSTluUvT0iCvP+13q42G1MG31AR6bqI7ibqPm3dDvDyjTxBlwpvSEHwdB1mmzKxORIsrUcHPs2DHsdjuRkZE55kdGRpKQcGVnUfz3v/+lVKlSOQLS+YYPH05wcLDrFhMTc911izk6NCjNJz0a4e1p5ZfNh+nzuTqKu43QctBnNjR70vl4xWfwaTwc2WxqWSJSNBXp/fqvv/46kyZNYvr06fj4+Fx0ncGDB5OYmOi67dun04qLslY1IvniwaYEeHuy5O/j3D/2L06kqqO4W/DwgttehAe+A79wOLwBxtwEf47QNXFE5KqYGm7Cw8Px8PDg8OHDOeYfPnyYqKioXJ/79ttv8/rrrzN37lzq1q17yfW8vb0JCgrKcZOi7YaKJfim7w2E+dtYtz+RLmOWkJCojuJuo3I8PLYYqtwO9gyY+zx80R5O7ja7MhEpIkwNNzabjUaNGjF//nzXPIfDwfz584mLi7vk8958801efvll5syZQ+PGjQuiVClk6pQJZsqjNxAd7MP2Iyl0GvUnu4+lml2W5JXAKLh/CrT/ALz8Yc9iGNUMVn0JuhyAiFyG6YelBg0axNixY/niiy/YvHkzjz32GKmpqfTp0weAnj17MnjwYNf6b7zxBi+88ALjxo2jfPnyJCQkkJCQQEpKilkfQUxSuWQgU/vFUb6EHwdOnebe0UvYfEgDxt2GxQKNejv34pS9ETJT4PuB8HUXSD582aeLSPFlerjp0qULb7/9NkOGDKF+/fqsWbOGOXPmuAYZ7927l0OHzvUXGjVqFJmZmdx7771ER0e7bm+//bZZH0FMVCbUj6n9bqRGdBDHUjLoMmYJK/ecMLssyUthFaD3j3Dby+Bhc3YY//gG2DjD7MpEpJAy/To3BU3XuXFPiaezeGj8clbsOYmvlwdjejSiZdUIs8uSvHZ4E0x/FBLWOR/XuQ/avgl+F78UhIi4jyJznRuRvBLs68WEh2K5qWoEp7PsPPTFcmatV0dxtxNZEx6eDy2fAYsHrJ8KH8XChu80FkdEXBRuxG342jwY27Mx7epGk2U3GPD1KiYv32t2WZLXPG1w6/Pw0FwIrwqpR+DbB51jcU7pUg8ionAjbsbmaeXDrg3o1jQGhwH//W49n/y+0+yyJD+UaQz9FsHNg8+NxfkoFv4apeviiBRzCjfidjysFl7rWIdHb6oIwGuztvDWz1vUUdwdeXrDzc86Q07ZOMhKhTnPwqet4NA6s6sTEZMo3IhbslgsDG5bg/+0qQbARwt28sLMDeoo7q4iqkHvWXDn+87mmwdXwyc3w7whkJlmdnUiUsAUbsStPX5zZV7tWBuLBSb+tZcnJ68hy+4wuyzJD1YrNO4DA5ZBzQ5g2GHxB87Txnf8YnZ1IlKAFG7E7XWPLceHXRvgabXw/dqDPDphJaczNSbDbQVGQecvoNskCCoNp/bAxE4wqbtaOIgUEwo3Uiy0r1eKsb0a4+Nl5dctR+g1bhlJ6VlmlyX5qVpb6L8UbujvPG18y4/OAccLhkPWabOrE5F8pHAjxcYt1Ury5YOxBHp7smz3Cbp98hfHUzLMLkvyk3cgtHnN2cKhfAvIToffXoeRTWHzD7o2joibUriRYqVphTC+eeQGSvjb2HgwifvGLOHAKf0V7/ZK1oBeP8B94yGoDCTuhckPwISOcHSb2dWJSB5TuJFip3bpYKb2i6NUsA9/H03lvlF/svOoGq+6PYsFanV0Djhu8bTz2jh/L4BRcfDzc5Cupqsi7kLhRoqlihEBfPvYjVSM8OdgYjqdRy9hw4FEs8uSgmDzh1YvOMfjVG0LjmxYMhJGNIIV48CebXaFInKdFG6k2CoV4svUR+OoXTqI46mZdPvkL5btUkfxYiOsItw/Ce6f6pxOPQI//tu5J2fLTxqPI1KEKdxIsVYiwJuv+95A0wphJGdk0+OzpSzYcsTssqQgVb0dHl/q7C7uGwbHtsGk++HzO2D/CrOrE5FroHAjxV6QjxdfPtiUW6uXJCPbQd8vV/D92oNmlyUFydMGsY/Cv9ZA80Hg6QN7/3S2cZjSC46rP5lIUaJwIwL4eHkwpkcj7q5fimyHwb8mrWbiX3vMLksKmk8wxA+Fgaug/gOABTbNgI+awqz/QOoxsysUkSugcCNyhpeHlfc61+eBG8piGPD8jA18tGCHGm4WR8GlocNHzoaclW9zDjpeNgY+qA+/vgqnT5ldoYjkwmIUs/+5k5KSCA4OJjExkaCgILPLkULIMAzembuNkQt2APBoy4o827Y6FovF5MrENH8vdDbhPLTW+dg7GG4cALH9wEf/j4gUhKv5/la4EbmEsb//zauzNgPQtUkMr3asg4dVAafYcjhgyw/O9g1Hnb8X+IRAsyeg6SPOqyGLSL5RuMmFwo1cjcnL9zJ42nocBrSrE817Xepj89TR3GLN4YBN02Hh684zqwD8SkCzf0GTh53X0RGRPKdwkwuFG7las9Yf4l+TVpNlN2hRJZwxPRrhZ/M0uywxm8MOG75zhpwTZ86m8o+A5v+GRr0VckTymMJNLhRu5Fr8vu0oj05YyeksO43KhTKuVxOC/bzMLksKA3s2rJsMv70Bp86cYecbCk36Og9XBUSYW5+Im1C4yYXCjVyrlXtO0ufzZSSlZ1M9KpAJD8USEehtdllSWNizYM3X8Mc750KOpw/Uvx/iBkCJSubWJ1LEKdzkQuFGrsfmQ0n0+GwZx1IyKF/CjwkPxRIT5md2WVKYOOyw+XtY/AEcXH1mpgVq3AnNnoQyjc2sTqTIUrjJhcKNXK/dx1J54LOl7D95mqggHyY+3JTKJXWmjPyDYcDuRfDnh7B97rn5ZW90nmFVpTVYNThd5Eop3ORC4UbyQkJiOj0+W8r2IymE+nnxxYNNqVsmxOyypLA6shn+HAHrpoAjyzkvrBI07es8bOUTbG59IkWAwk0uFG4kr5xMzaT358tYuz+RAG9PxvZsTFylEmaXJYVZ0kH4axSsHA8ZSc55Xv5Qr6sz6JSsYWp5IoWZwk0uFG4kL6VkZNP3ixUs+fs4Nk8rH9/fkPiakWaXJYVdRrLzDKtlY+HolnPzK7R0nmFVtS146HIDIudTuMmFwo3ktfQsOwO/Wc28TYfxsFp4+766dGxQxuyypCgwDNj9Byz7BLb8BIbDOT+oDDR5EBr2Av9wc2sUKSQUbnKhcCP5Idvu4D/frWPaqgMAvHR3LXrGlTe3KClaTu2DFeNg1ReQdtw5z+oFNdo7LwpYvoUGIEuxpnCTC4UbyS8Oh8FLP25i/J+7AXjqtqoMuLWyGm7K1clKh43TnXtzDq46Nz+0AjTqBfW7Q0BJ8+oTMYnCTS4UbiQ/GYbBB/O38/4v2wF4qHkFnm9XQwFHrs2htbDyC1g/9dwAZKsnVLvDuTen4i3amyPFhsJNLhRupCCMW7SLl37cBMB9jcow/J46eHroS0iuUWaqc2/OyvGwf/m5+SHloEEPqHsfhJY3qzqRAqFwkwuFGyko367cz3+/W4fdYdCmVhQfdKuPt6eH2WVJUXd4o3NvztpJkJF4bn5MLNS5D2rdA/66JIG4H4WbXCjcSEH6eWMCA79eTabdQfPKzo7i/t46xVfyQGYabJoJ6ybB378BZ/4rt3pC5Xhn0Kl2B9jUHkTcg8JNLhRupKAt3nGMvl+uIC3TTv2YEMb3aUKIn83sssSdJB2CDd/B+inOcTpn2QKg+p3Ow1YVbgIPdbKXokvhJhcKN2KGNftO0fvzZZxKy6JaZCATHmpKySAfs8sSd3R0q7PNw/opcGrvufm+oVC9HdTsCBUVdKToUbjJhcKNmGVrQjI9PlvKkeQMyob5MfGhWMqW0CEDySeGAfuWOUPOxhmQduzcMp+QM0GnA1S8GTy1J1EKP4WbXCjciJn2nUij+6dL2XsijZKB3kx4KJZqUeooLvnMng17/3SGnM0/QOqRc8u8g6H6HeeCjpf2KErhpHCTC4UbMduRpHR6fLaMrYeTCfb1YnyfJjQoG2p2WVJcOOywd4lzMPKm7yEl4dwyL3+odItzIHLV1mr9IIWKwk0uFG6kMDiVlkmf8ctZvfcUfjYPxvZsTLPK+iKRAuZwwL6lsGmGc49O0oHzFlqcp5dXa+sMO+FVQBejFBMp3ORC4UYKi9SMbB6dsJJFO45h87DyYbcGtKkdZXZZUlwZBiSsg62zYeusnGddAYRVdIacKrdD2TiN05ECp3CTC4UbKUwysu3865s1zNmYgNUCb3Sqy32NY8wuSwQSD8C22bB1Duz6DeyZ55Z5+TvPuKrcCirfBqHlzKtTig2Fm1wo3Ehhk213MHjaeqau3A/AC3fW5KHmFUyuSuQ8Gcmwc4Fzr86OX3IOSAYoUQWq3OYMO+Waa1Cy5AuFm1wo3Ehh5HAYvDprM58t2gXAE62q8O/4Kmq4KYWPwwGH1ztDzvZfnGN2DPu55Z6+UC4Oyrdw3krV1zV1JE8o3ORC4UYKK8MwGPnrDt6Ztw2AXnHlGNq+FlarAo4UYumJ8PfCc2En+WDO5bYAKHvDubATXQ881IJErp7CTS4UbqSw++LP3Qz9fiMA9zQozRv31sVLHcWlKDAMOLIZdv0Ou/+APYvh9Mmc69gCz+zZaX4u7FjVUFYuT+EmFwo3UhRMX72fp6c6O4rH14hk5P0N8PHSF4AUMQ4HHNkIu/6A3YtgzyLnnp7zeQdBuRvPhZ2oOgo7clEKN7lQuJGi4pdNh3n861VkZju4oWIYn/ZqQoA6iktR5rDD4Q3nhZ0/IeOfYSfYGXYqtIByzSCylsbsCKBwkyuFGylKluw8Tt8vV5CSkU3dMsGM79OUMH9dX0TchMMOCeudh7BcYScp5zoe3s69OaUanLtFVNPenWJI4SYXCjdS1Kzbf4pe45ZxMi2LyiUDmPhQLFHBOtVW3JA923khwd2LnIFn79IL9+wAePlBVN3zAk99KFFZgcfNKdzkQuFGiqIdR5J54NNlJCSlUzrEl68ejqV8uL/ZZYnkL8OAE3/DwdVnbmvg0BrITLlwXS9/iK7rHKAcXd8ZeMKrKvC4EYWbXCjcSFG1/2QaD3y6lN3H0wgP8GbCQ02pEa3fYSlmHHY4vuO8wLPaeWgrK+3Cdb38ILK2M+i4Ak81nYpeRCnc5ELhRoqyo8kZ9By3jM2Hkgjy8eTzPk1oVC7M7LJEzOWww7Htzr06Z/fuHFoHWakXruvpc17gObOXp2QNDVouAhRucqFwI0Vd4uksHhq/nBV7TuLr5cGH3RpwW81Is8sSKVwcdji+87zAs9Z5y0y+cF0Pb+dZWdF1ncEnsrbzsY++IwoThZtcKNyIO0jLzOaxiav4bdtRLBb4X9saPNyigto1iOTG4XCO4Tm05rzQs+7ig5YBQspCZB2IOhN2ImtDaAWw6qKaZlC4yYXCjbiLLLuDod9v5OulewHo1rQsL91dS1czFrkahgEndzmDzuENkLABDm+EpP0XX9/TF8IqQFhFKFHJeR9WyTkdGA36AyPfKNzkQuFG3IlhGHy2aBevztqMYUCzyiX4+P5GBPtp/IDIdUk74Qw5hzc6G4UmbICjWyA7/dLP8fI7E3YqnAs8Z+8DIhV8rpPCTS4UbsQd/bLpME9MWk1app1KEf6M692EciV0qrhInrJnQ+JeOP43nNjpHNNz9v7U3pzd0f/Jy//M3p6KFwYf/wgFnyugcJMLhRtxVxsPJvLwFys4lJhOqJ8Xn/RsTJPyOpNKpEDYs5wB5/zAc/Y+cR8Yjks/1xb4j9Bz3rRfCQWfMxRucqFwI+7sSFI6D32xgvUHErF5WHnj3jp0bFDG7LJEirfsTDi15yLB529n8CGXr2Hv4Ivv7QmrCH7F648XhZtcKNyIu0vLzGbQ5LXM2ZgAwMBbK/Pv+KpYrfrrT6TQyUqHk7tzhp4TfzuDz6UGNZ/lE/KPwFPpXBDyDSmA4guWwk0uFG6kOHA4DN78eSujf9sJwK3VS/LWvXUpEeBtcmUicsWyTsOJXRfu7TnxNyQfzP25fiWcIefsWV2h5SEwynlGV2AUeAcWyEfIS0Uq3Hz00Ue89dZbJCQkUK9ePUaMGEHTpk0vuu7GjRsZMmQIK1euZM+ePbz33ns8+eSTV/V+CjdSnExZsY/nZ2wgM9tBZJA373dpQFylEmaXJSLXKzP1wuBzYpdzOiXh8s/38j8TdqLOhZ6ASAgoCf7h4F/SOe0XXmjaVVzN97epFU+ePJlBgwYxevRoYmNjef/992ndujVbt26lZMmSF6yflpZGxYoVue+++/j3v/9tQsUiRUvnxjHUKR3MgK9XsfNoKvd/+hcDb63CE7dWxlPXwxEpumz+zosLRtW+cFlGinPvjiv4/O0c7JxyGJITICPJ2ZrixJlQlCuLc2yP/5nQE1DyTPCJODMvIue0l0++fNyrZeqem9jYWJo0acLIkSMBcDgcxMTEMHDgQJ599tlcn1u+fHmefPJJ7bkRuQJpmdkM+34jU1Y4j+E3LR/G+13rUyrE1+TKRKTAZaY6Q05yAiQfOhN6DkHyYUg9AilHnfdpx3M/y+tivIOcIad0I+g0Nk/LLhJ7bjIzM1m5ciWDBw92zbNarcTHx7NkyRKzyhJxS342T968tx7NKofz3PQNLNt9gjs+/IO37q2nvlQixY3N3zkOp0Sl3Ndz2J0XM0w9AilHIPXouXvX9NkwdBQcWc69QhlJzkNdJjIt3Bw7dgy73U5kZM7/WCMjI9myZUuevU9GRgYZGRmux0lJSXn22iJFzd31S1M/JoSB36xm3f5E+n65gt43lufZttXx8fIwuzwRKUysHs5DTgERzt5auTEMSD91bq+Ph61ASrwUtz/oPnz4cIKDg123mJgYs0sSMVW5Ev582+9G+raoAMD4P3dzz8d/suPIRboli4hcCYsFfEMhoiqUbw4xFz8xqKCYFm7Cw8Px8PDg8OHDOeYfPnyYqKi82501ePBgEhMTXbd9+/bl2WuLFFU2TyvPtavJ572bEOZvY9OhJNp+8AfvzttGelYul5AXESkCTAs3NpuNRo0aMX/+fNc8h8PB/PnziYuLy7P38fb2JigoKMdNRJxuqV6S2f9qwa3VS5JlN/hw/nbu+OAP/tx5zOzSRESumamHpQYNGsTYsWP54osv2Lx5M4899hipqan06dMHgJ49e+YYcJyZmcmaNWtYs2YNmZmZHDhwgDVr1rBjxw6zPoJIkRcZ5MNnvRrz0f0NiQj05u9jqdw/dilPTVnLidRMs8sTEblqpl/Eb+TIka6L+NWvX58PP/yQ2NhYAG6++WbKly/P+PHjAdi9ezcVKlS44DVuuukmFi5ceEXvp1PBRS4tKT2Lt+ZsZeLSPRgGhPp58Xy7mtzTsDQWNe8TERMVqSsUFzSFG5HLW7nnJM9NX8+WBOcg4xsrleCVDrWpGBFgcmUiUlxdzfe3258tJSJXr1G5UH4Y2Jz/tqmOj5eVP3cep82ZAccpGdlmlycikivtuRGRXO09nsbzMzfw+7ajAIQH2HiiVRW6NimLzVN/H4lIwdBhqVwo3IhcPcMwmL0hgbd+3squY6kAlCvhx9O3V6NdnWisVo3HEZH8pXCTC4UbkWuXZXcwafk+PvhlO8dSnFf+rlM6mGfbVqdZ5XCTqxMRd6ZwkwuFG5Hrl5qRzWeLdjHmt52kZjov+teiSjj/bVOd2qWDTa5ORNyRwk0uFG5E8s6xlAxG/rqDr5buIcvu/K+kfb1SPH5zJWpE69+XiOQdhZtcKNyI5L09x1N5Z+42vl970DXvlmoRPHZzZZqUD9U1ckTkuinc5ELhRiT/bDyYyMcLdzJ7/SEcZ/5naVg2hH43VSK+RqQGHovINVO4yYXCjUj+230slU/++JtvV+wn0+4AoHLJAB5tWZG765fWKeQictUUbnKhcCNScI4kpTNu8W6++msPyWcu/hcd7MNDzStwX6MYgv28TK5QRIoKhZtcKNyIFLyk9Cy++msv4xbv4miy8xRym6eVO2pH0bVpWWIrhGlcjojkSuEmFwo3IuZJz7IzbdUBvlyy29W3CqBiuD9dmsTQqVEZwgO8TaxQRAorhZtcKNyImM8wDNbuT2TSsr18v/YgaWeuleNptXBbzUi6Ni1Li8rhGoAsIi4KN7lQuBEpXFIysvlx7UG+Wb6PtftOueaXDvHlrvqlaFcnmlqlgnTYSqSYU7jJhcKNSOG1+VASk5fvY9qq/SSln+s+Xq6EH+3qRNOubjQ1oxV0RIojhZtcKNyIFH7pWXZ+2XyYWesP8euWI6RnOVzLKoT7c0edKNrVKUWN6EAFHZFiQuEmFwo3IkVLakY2v245wk/rDrFg6xEyss8FnYrh/txWM5KbqkXQuFyYrp8j4sYUbnKhcCNSdKVkZDN/82F+WneIhduOknle0PG3edCscjg3VyvJzdUiKBXia2KlIpLXFG5yoXAj4h6S07NYsPUoC7cc4bdtRzmempljedXIAGfQqRpB4/LaqyNS1Cnc5ELhRsT9OBwGGw8msXDrERZuO8rqvSddva0AvD2tNCwbSmzFMJpWCKNh2VB8vDzMK1hErprCTS4UbkTc36m0TP7YfoyFW4/y27ajHEvJyLHc5mGlXkwwTSuEEVuhBI3KheLv7WlStSJyJRRucqFwI1K8GIbBzqMpLN11gqV/n2DpruMcTsoZdjysFmqVCqJemRDqlgmmfkwIFSMC8NBFBEUKDYWbXCjciBRvhmGw90QaS/8+wV+7jrNs1wn2nzx9wXr+Ng/qlAk+E3hCqBcTTOkQX516LmIShZtcKNyIyD8dOHWa1XtPsnbfKdbuT2T9/kROZ9kvWC/M30aN6ECqRQZRPTqQGlFBVIkM0PgdkQKgcJMLhRsRuRy7w2DHkZQzYcd523IomWzHhf9dWi1QPtyf6lGBVI8KolpUIJUi/IkJ88PbU6FHJK8o3ORC4UZErkV6lp2tCclsTUhmc0KS8/5QEifTsi66vtUCpUN9qRAeQMVwf8qX8KNCRAAVSvhTOtRX43lErpLCTS4UbkQkrxiGwdHkDLYkJLMlIYktZ8LP7mOppGZeeFjrLJuHldKhvpQJ9aVMqB9lQn2JCTtzH+pHeIBNY3tE/uFqvr917qOIyDWyWCyUDPKhZJAPLatGuOafDT27jqVecNtzIo3MbIfr8cX4eFkpE+pHqRBfSgX7EB3sS3SID6XOu/e16ZCXyKVoz42ISAGyOwwOnjrNvpNp7D95mv0nztyfdM5LSErnSv5XDvHzIjrYl6ggbyICvQkPOHd/djoiwJsgX0/tBRK3oD03IiKFlIfVQkyYHzFhfhddnpntcIWfQ6fSOZh4+tx9YjqHTp0mNdPOqbQsTqVlsflQ7u9n87ASHmAjLMBGmL83JfxthPnbKBFgOzPt7XzsbyPUz0agjydWjQeSIk7hRkSkELF5Wikf7k/5cP+LLjcMg6T0bA6dCT2Hk9I5lpLBsZRMjiZncDQlg2MpGRxNziA5PZtMu4ODiekcTEy/ove3WCDY14sQXy+C/WyE+HoR4nfmsa8XQWfuLzbtb/PQXiIpFBRuRESKEIvF4goU1aNy3zWfnmXneGomx5IzOJGaybEU5/2J1EyO57jP4HhKJmmZdgwD114hjqddVW2eVgtBvl4E+njib/MkwMeTQG/nvb/3mWlv5/TZZedPn13P3+aps8nkuijciIi4KR8vD0qH+FI6xPeK1s/MdpB4OovE05mugHPqdBan0jJJPJ3FybRMkk5nn1kni6T0LJLOTGfZDbIdhis8XS9/mwf+3p742TzwtTnv/Wwe+Hp5XDjP5oGfl/Pex8sDP5snvmcen1v/3GMvD3WId3cKNyIiAjgPiUUEOgcjXw3DMEjPcrhCT0pGFikZdlLSsy8+nZFNcnoWqRnZZx5nnVmeTZbdOZo6NdOe6+n018PTaskRfHzOD0Bezseue9u5x75eVnzOzPf2tOLtZcXb0wOfM/fenv947GXF5mHVGCYTKNyIiMh1sVgsrj0jUcE+1/VaGdnOIJSaYSc5I4vTmXbSztxOZznnu+ZlZbumT2fZST9v+vSZ+7RMO6czszmdZefsBaazHQbJ6dkkp2fnwae/PJun1RV8vD2tOcKPz5l75/wLA9L56/5zHWfAuvA1zwUta7EdA6VwIyIihYa3pwfeAR6UCMjb1zUMg0y7g/RMR45QlJ51LgSlnxeK0rMcZ+7PzTsboDLtDtKz7GRkO8jIcpCR7Vw/I9s5L/28IAXOw32Z2Q6SKZgwdb6zIcfbywObh9W1N8nbywPvHI+d97YzwcnmaXWFMpvnmXX+seyfzzs/xPnZPCgRcHV7APOSwo2IiLg9i8VyZo+GB8F45et7GYZz/NHZoOMMQRcGoIxsxwXrnH+fnnU2SJ15XtY/1v9HqMrIcpCebc9xnaSz70EB7aU6q16ZYGYOaF6g73k+hRsREZE8ZLFY8PKw4OVhJcC7YL9mDcMgy26cCzxnw1CWg0z7ueCUeWZZpv1caMrItrv2MmXYHa5p17rZztc4N+/Ma9kvXM/sK2gr3IiIiLgJi8WCzdOCzdNKoNnFmEjnw4mIiIhbUbgRERERt6JwIyIiIm5F4UZERETcisKNiIiIuBWFGxEREXErCjciIiLiVhRuRERExK0o3IiIiIhbUbgRERERt6JwIyIiIm5F4UZERETcisKNiIiIuBWFGxEREXErnmYXUNAMwwAgKSnJ5EpERETkSp393j77PZ6bYhdukpOTAYiJiTG5EhEREblaycnJBAcH57qOxbiSCORGHA4HBw8eJDAwEIvFkqevnZSURExMDPv27SMoKChPX1supO1dsLS9C5a2d8HS9i5Y17K9DcMgOTmZUqVKYbXmPqqm2O25sVqtlClTJl/fIygoSP84CpC2d8HS9i5Y2t4FS9u7YF3t9r7cHpuzNKBYRERE3IrCjYiIiLgVhZs85O3tzdChQ/H29ja7lGJB27tgaXsXLG3vgqXtXbDye3sXuwHFIiIi4t6050ZERETcisKNiIiIuBWFGxEREXErCjciIiLiVhRu8shHH31E+fLl8fHxITY2lmXLlpldUpH0+++/0759e0qVKoXFYmHGjBk5lhuGwZAhQ4iOjsbX15f4+Hi2b9+eY50TJ07QvXt3goKCCAkJ4aGHHiIlJaUAP0XRMXz4cJo0aUJgYCAlS5akQ4cObN26Ncc66enp9O/fnxIlShAQEECnTp04fPhwjnX27t1Lu3bt8PPzo2TJkjzzzDNkZ2cX5EcpEkaNGkXdunVdFy6Li4tj9uzZruXa1vnn9ddfx2Kx8OSTT7rmaXvnrWHDhmGxWHLcqlev7lpeoNvbkOs2adIkw2azGePGjTM2btxo9O3b1wgJCTEOHz5sdmlFzqxZs4znnnvOmDZtmgEY06dPz7H89ddfN4KDg40ZM2YYa9euNe666y6jQoUKxunTp13rtGnTxqhXr57x119/GX/88YdRuXJlo1u3bgX8SYqG1q1bG59//rmxYcMGY82aNcYdd9xhlC1b1khJSXGt069fPyMmJsaYP3++sWLFCuOGG24wbrzxRtfy7Oxso3bt2kZ8fLyxevVqY9asWUZ4eLgxePBgMz5Sofb9998bP/30k7Ft2zZj69atxv/+9z/Dy8vL2LBhg2EY2tb5ZdmyZUb58uWNunXrGv/6179c87W989bQoUONWrVqGYcOHXLdjh496lpekNtb4SYPNG3a1Ojfv7/rsd1uN0qVKmUMHz7cxKqKvn+GG4fDYURFRRlvvfWWa96pU6cMb29v45tvvjEMwzA2bdpkAMby5ctd68yePduwWCzGgQMHCqz2ourIkSMGYPz222+GYTi3r5eXlzF16lTXOps3bzYAY8mSJYZhOAOp1Wo1EhISXOuMGjXKCAoKMjIyMgr2AxRBoaGhxqeffqptnU+Sk5ONKlWqGPPmzTNuuukmV7jR9s57Q4cONerVq3fRZQW9vXVY6jplZmaycuVK4uPjXfOsVivx8fEsWbLExMrcz65du0hISMixrYODg4mNjXVt6yVLlhASEkLjxo1d68THx2O1Wlm6dGmB11zUJCYmAhAWFgbAypUrycrKyrHNq1evTtmyZXNs8zp16hAZGelap3Xr1iQlJbFx48YCrL5osdvtTJo0idTUVOLi4rSt80n//v1p165dju0K+t3OL9u3b6dUqVJUrFiR7t27s3fvXqDgt3exa5yZ144dO4bdbs/xwwCIjIxky5YtJlXlnhISEgAuuq3PLktISKBkyZI5lnt6ehIWFuZaRy7O4XDw5JNP0qxZM2rXrg04t6fNZiMkJCTHuv/c5hf7mZxdJjmtX7+euLg40tPTCQgIYPr06dSsWZM1a9ZoW+exSZMmsWrVKpYvX37BMv1u573Y2FjGjx9PtWrVOHToEC+++CItWrRgw4YNBb69FW5EBHD+hbthwwYWLVpkdilurVq1aqxZs4bExES+/fZbevXqxW+//WZ2WW5n3759/Otf/2LevHn4+PiYXU6x0LZtW9d03bp1iY2NpVy5ckyZMgVfX98CrUWHpa5TeHg4Hh4eF4z4Pnz4MFFRUSZV5Z7Obs/ctnVUVBRHjhzJsTw7O5sTJ07o55GLAQMG8OOPP7JgwQLKlCnjmh8VFUVmZianTp3Ksf4/t/nFfiZnl0lONpuNypUr06hRI4YPH069evX44IMPtK3z2MqVKzly5AgNGzbE09MTT09PfvvtNz788EM8PT2JjIzU9s5nISEhVK1alR07dhT477fCzXWy2Ww0atSI+fPnu+Y5HA7mz59PXFyciZW5nwoVKhAVFZVjWyclJbF06VLXto6Li+PUqVOsXLnStc6vv/6Kw+EgNja2wGsu7AzDYMCAAUyfPp1ff/2VChUq5FjeqFEjvLy8cmzzrVu3snfv3hzbfP369TlC5bx58wgKCqJmzZoF80GKMIfDQUZGhrZ1HmvVqhXr169nzZo1rlvjxo3p3r27a1rbO3+lpKSwc+dOoqOjC/73+6qHQ8sFJk2aZHh7exvjx483Nm3aZDzyyCNGSEhIjhHfcmWSk5ON1atXG6tXrzYA49133zVWr15t7NmzxzAM56ngISEhxsyZM41169YZd99990VPBW/QoIGxdOlSY9GiRUaVKlV0KvglPPbYY0ZwcLCxcOHCHKdvpqWludbp16+fUbZsWePXX381VqxYYcTFxRlxcXGu5WdP37z99tuNNWvWGHPmzDEiIiJ0uuxFPPvss8Zvv/1m7Nq1y1i3bp3x7LPPGhaLxZg7d65hGNrW+e38s6UMQ9s7rz311FPGwoULjV27dhmLFy824uPjjfDwcOPIkSOGYRTs9la4ySMjRowwypYta9hsNqNp06bGX3/9ZXZJRdKCBQsM4IJbr169DMNwng7+wgsvGJGRkYa3t7fRqlUrY+vWrTle4/jx40a3bt2MgIAAIygoyOjTp4+RnJxswqcp/C62rQHj888/d61z+vRp4/HHHzdCQ0MNPz8/o2PHjsahQ4dyvM7u3buNtm3bGr6+vkZ4eLjx1FNPGVlZWQX8aQq/Bx980ChXrpxhs9mMiIgIo1WrVq5gYxja1vntn+FG2ztvdenSxYiOjjZsNptRunRpo0uXLsaOHTtcywtye1sMwzCueZ+TiIiISCGjMTciIiLiVhRuRERExK0o3IiIiIhbUbgRERERt6JwIyIiIm5F4UZERETcisKNiIiIuBWFGxEpliwWCzNmzDC7DBHJBwo3IlLgevfujcViueDWpk0bs0sTETfgaXYBIlI8tWnThs8//zzHPG9vb5OqERF3oj03ImIKb29voqKictxCQ0MB5yGjUaNG0bZtW3x9falYsSLffvttjuevX7+eW2+9FV9fX0qUKMEjjzxCSkpKjnXGjRtHrVq18Pb2Jjo6mgEDBuRYfuzYMTp27Iifnx9VqlTh+++/dy07efIk3bt3JyIiAl9fX6pUqXJBGBORwknhRkQKpRdeeIFOnTqxdu1aunfvTteuXdm8eTMAqamptG7dmtDQUJYvX87UqVP55ZdfcoSXUaNG0b9/fx555BHWr1/P999/T+XKlXO8x4svvkjnzp1Zt24dd9xxB927d+fEiROu99+0aROzZ89m8+bNjBo1ivDw8ILbACJy7a6zCaiIyFXr1auX4eHhYfj7++e4vfrqq4ZhOLuV9+vXL8dzYmNjjccee8wwDMP45JNPjNDQUCMlJcW1/KeffjKsVquRkJBgGIZhlCpVynjuuecuWQNgPP/8867HKSkpBmDMnj3bMAzDaN++vdGnT5+8+cAiUqA05kZETHHLLbcwatSoHPPCwsJc03FxcTmWxcXFsWbNGgA2b95MvXr18Pf3dy1v1qwZDoeDrVu3YrFYOHjwIK1atcq1hrp167qm/f39CQoK4siRIwA89thjdOrUiVWrVnH77bfToUMHbrzxxmv6rCJSsBRuRMQU/v7+Fxwmyiu+vr5XtJ6Xl1eOxxaLBYfDAUDbtm3Zs2cPs2bNYt68ebRq1Yr+/fvz9ttv53m9IpK3NOZGRAqlv/7664LHNWrUAKBGjRqsXbuW1NRU1/LFixdjtVqpVq0agYGBlC9fnvnz519XDREREfTq1YuJEyfy/vvv88knn1zX64lIwdCeGxExRUZGBgkJCTnmeXp6ugbtTp06lcaNG9O8eXO++uorli1bxmeffQZA9+7dGTp0KL169WLYsGEcPXqUgQMH0qNHDyIjIwEYNmwY/fr1o2TJkrRt25bk5GQWL17MwIEDr6i+IUOG0KhRI2rVqkVGRgY//vijK1yJSOGmcCMippgzZw7R0dE55lWrVo0tW7YAzjOZJk2axOOPP050dDTffPMNNWvWBMDPz4+ff/6Zf/3rXzRp0gQ/Pz86derEu+++63qtXr16kZ6eznvvvcfTTz9NeHg499577xXXZ7PZGDx4MLt378bX15cWLVowadKkPPjkIpLfLIZhGGYXISJyPovFwvTp0+nQoYPZpYhIEaQxNyIiIuJWFG5ERETErWjMjYgUOjpaLiLXQ3tuRERExK0o3IiIiIhbUbgRERERt6JwIyIiIm5F4UZERETcisKNiIiIuBWFGxEREXErCjciIiLiVhRuRERExK38Hx93rg/oVFS/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the loss curves\n",
    "plt.plot(epoch_count, train_loss_values, label=\"Train loss\")\n",
    "plt.plot(epoch_count, test_loss_values, label=\"Test loss\")\n",
    "plt.title(\"Training and test loss curves\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model learned the following values for weights and bias:\n",
      "OrderedDict([('weights', tensor([0.4922])), ('bias', tensor([0.3840]))])\n",
      "\n",
      "And the original values for weights and bias are:\n",
      "weights: 0.7, bias: 0.3\n"
     ]
    }
   ],
   "source": [
    "# Let's look at our model's learned parameters\n",
    "print(\"The model learned the following values for weights and bias:\")\n",
    "print(model_0.state_dict())\n",
    "print(\"\\nAnd the original values for weights and bias are:\")\n",
    "print(f\"weights: {weight}, bias: {bias}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.7777],\n",
       "        [0.7876],\n",
       "        [0.7974],\n",
       "        [0.8072],\n",
       "        [0.8171],\n",
       "        [0.8269],\n",
       "        [0.8368],\n",
       "        [0.8466],\n",
       "        [0.8565],\n",
       "        [0.8663]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look a bit more at the testing setup\n",
    "model_0.eval()\n",
    "\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_0(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(predictions=y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving model to: models\\01_pytorch_workflow_model_0.pth\n"
     ]
    }
   ],
   "source": [
    "# We might want to save our model\n",
    "from pathlib import Path\n",
    "# Create models directory\n",
    "MODEL_PATH = Path(\"models\")\n",
    "MODEL_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Create the model svae path\n",
    "MODEL_NAME = \"01_pytorch_workflow_model_0.pth\"\n",
    "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
    "\n",
    "# Save the models parameters\n",
    "print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n",
    "torch.save(obj=model_0.state_dict(), f=MODEL_SAVE_PATH)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's load the model we saved\n",
    "loaded_model_0 = LinearRegressionModel()\n",
    "\n",
    "# Load the state \n",
    "loaded_model_0.load_state_dict(torch.load(f=MODEL_SAVE_PATH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's test our model by predicting some values\n",
    "loaded_model_0.eval()\n",
    "\n",
    "with torch.inference_mode():\n",
    "    loaded_model_preds = loaded_model_0(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds == loaded_model_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.1+cu121'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's go through it once more but with the device being agnostic\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "torch.__version__ # if the version include +cu then you have GPU available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device is: cuda\n"
     ]
    }
   ],
   "source": [
    "# Let's make the device agnostic\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(f\"Device is: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0.0000],\n",
       "         [0.0200],\n",
       "         [0.0400],\n",
       "         [0.0600],\n",
       "         [0.0800],\n",
       "         [0.1000],\n",
       "         [0.1200],\n",
       "         [0.1400],\n",
       "         [0.1600],\n",
       "         [0.1800]]),\n",
       " tensor([[0.3000],\n",
       "         [0.3140],\n",
       "         [0.3280],\n",
       "         [0.3420],\n",
       "         [0.3560],\n",
       "         [0.3700],\n",
       "         [0.3840],\n",
       "         [0.3980],\n",
       "         [0.4120],\n",
       "         [0.4260]]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create some starting values again (same as before)\n",
    "weight = 0.7\n",
    "bias = 0.3\n",
    "\n",
    "start = 0\n",
    "end = 1\n",
    "step = 0.02\n",
    "\n",
    "X = torch.arange(start, end, step).unsqueeze(dim=1)\n",
    "y = weight * X + bias\n",
    "X[:10], y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 40, 10, 10)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data\n",
    "train_split = int(0.8 * len(X))\n",
    "X_train, y_train = X[:train_split], y[:train_split]\n",
    "X_test, y_test = X[train_split:], y[train_split:]\n",
    "\n",
    "len(X_train), len(y_train), len(X_test), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot them\n",
    "plot_predictions(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(LinearRegressionModelV2(\n",
       "   (linear_layer): Linear(in_features=1, out_features=1, bias=True)\n",
       " ),\n",
       " OrderedDict([('linear_layer.weight', tensor([[0.7645]])),\n",
       "              ('linear_layer.bias', tensor([0.8300]))]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We'll build the model again but now by using nn.Linear\n",
    "class LinearRegressionModelV2(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.linear_layer = nn.Linear(in_features=1, out_features=1)\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        return self.linear_layer(x)\n",
    "    \n",
    "torch.manual_seed(42)\n",
    "model_1 = LinearRegressionModelV2()\n",
    "model_1, model_1.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check which device the model is on\n",
    "next(model_1.parameters()).device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.to(device)\n",
    "next(model_1.parameters()).device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make the training and testing loop\n",
    "loss_fn = nn.L1Loss()\n",
    "optimizer = torch.optim.SGD(params=model_1.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 0.5551779866218567 | Test loss: 0.5739762187004089\n",
      "Epoch: 100 | Train loss: 0.006215683650225401 | Test loss: 0.014086711220443249\n",
      "Epoch: 200 | Train loss: 0.0012645035749301314 | Test loss: 0.013801801018416882\n",
      "Epoch: 300 | Train loss: 0.0012645035749301314 | Test loss: 0.013801801018416882\n",
      "Epoch: 400 | Train loss: 0.0012645035749301314 | Test loss: 0.013801801018416882\n",
      "Epoch: 500 | Train loss: 0.0012645035749301314 | Test loss: 0.013801801018416882\n",
      "Epoch: 600 | Train loss: 0.0012645035749301314 | Test loss: 0.013801801018416882\n",
      "Epoch: 700 | Train loss: 0.0012645035749301314 | Test loss: 0.013801801018416882\n",
      "Epoch: 800 | Train loss: 0.0012645035749301314 | Test loss: 0.013801801018416882\n",
      "Epoch: 900 | Train loss: 0.0012645035749301314 | Test loss: 0.013801801018416882\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "EPOCHS = 1000\n",
    "\n",
    "# Put the data on the available same agnostic device\n",
    "X_train = X_train.to(device)\n",
    "X_test = X_test.to(device)\n",
    "y_train = y_train.to(device)\n",
    "y_test = y_test.to(device)\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "\n",
    "    model_1.train()\n",
    "    y_pred = model_1(X_train)\n",
    "    loss = loss_fn(y_pred, y_train)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # Testing\n",
    "    model_1.eval()\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        test_pred = model_1(X_test)\n",
    "        test_loss = loss_fn(test_pred, y_test)\n",
    "\n",
    "    if epoch % 100 == 0:\n",
    "        print(f\"Epoch: {epoch} | Train loss: {loss} | Test loss: {test_loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model learned the following values for weights and bias:\n",
      "OrderedDict([('linear_layer.weight', tensor([[0.6968]], device='cuda:0')),\n",
      "             ('linear_layer.bias', tensor([0.3025], device='cuda:0'))])\n",
      "\n",
      "And the original values for weights and bias are:\n",
      "weights: 0.7, bias: 0.3\n"
     ]
    }
   ],
   "source": [
    "# Find our model's learned parameters\n",
    "from pprint import pprint # pprint = pretty print, see: https://docs.python.org/3/library/pprint.html \n",
    "print(\"The model learned the following values for weights and bias:\")\n",
    "pprint(model_1.state_dict())\n",
    "print(\"\\nAnd the original values for weights and bias are:\")\n",
    "print(f\"weights: {weight}, bias: {bias}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.8600],\n",
       "        [0.8739],\n",
       "        [0.8878],\n",
       "        [0.9018],\n",
       "        [0.9157],\n",
       "        [0.9296],\n",
       "        [0.9436],\n",
       "        [0.9575],\n",
       "        [0.9714],\n",
       "        [0.9854]], device='cuda:0')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's make some predictions\n",
    "model_1.eval()\n",
    "\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_1(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the results\n",
    "plot_predictions(predictions=y_preds.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving model to: models\\01_pytorch_workflow_model_1.pth\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# 1. Create models directory \n",
    "MODEL_PATH = Path(\"models\")\n",
    "MODEL_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# 2. Create model save path \n",
    "MODEL_NAME = \"01_pytorch_workflow_model_1.pth\"\n",
    "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
    "\n",
    "# 3. Save the model state dict \n",
    "print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n",
    "torch.save(obj=model_1.state_dict(), # only saving the state_dict() only saves the models learned parameters\n",
    "           f=MODEL_SAVE_PATH) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model:\n",
      "LinearRegressionModelV2(\n",
      "  (linear_layer): Linear(in_features=1, out_features=1, bias=True)\n",
      ")\n",
      "Model on device:\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a fresh instance of LinearRegressionModelV2\n",
    "loaded_model_1 = LinearRegressionModelV2()\n",
    "\n",
    "# Load model state dict \n",
    "loaded_model_1.load_state_dict(torch.load(MODEL_SAVE_PATH))\n",
    "\n",
    "# Put model to target device (if your data is on GPU, model will have to be on GPU to make predictions)\n",
    "loaded_model_1.to(device)\n",
    "\n",
    "print(f\"Loaded model:\\n{loaded_model_1}\")\n",
    "print(f\"Model on device:\\n{next(loaded_model_1.parameters()).device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True]], device='cuda:0')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate loaded model\n",
    "loaded_model_1.eval()\n",
    "with torch.inference_mode():\n",
    "    loaded_model_1_preds = loaded_model_1(X_test)\n",
    "y_preds == loaded_model_1_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exericses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device is: {device}\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"device is: {device}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.0000],\n",
      "        [0.0100],\n",
      "        [0.0200],\n",
      "        [0.0300],\n",
      "        [0.0400],\n",
      "        [0.0500],\n",
      "        [0.0600],\n",
      "        [0.0700],\n",
      "        [0.0800],\n",
      "        [0.0900]])\n",
      "tensor([[0.9000],\n",
      "        [0.9030],\n",
      "        [0.9060],\n",
      "        [0.9090],\n",
      "        [0.9120],\n",
      "        [0.9150],\n",
      "        [0.9180],\n",
      "        [0.9210],\n",
      "        [0.9240],\n",
      "        [0.9270]])\n",
      "80\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[77], line 18\u001b[0m\n\u001b[0;32m     14\u001b[0m X_test, y_test \u001b[38;5;241m=\u001b[39m X[train_split:], y[train_split:]\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(X_train))\n\u001b[1;32m---> 18\u001b[0m \u001b[43mplot_predictions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[76], line 8\u001b[0m, in \u001b[0;36mplot_predictions\u001b[1;34m(train_data, train_labels, test_data, test_labels, predictions)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_predictions\u001b[39m(train_data \u001b[38;5;241m=\u001b[39m X_train,\n\u001b[0;32m      3\u001b[0m                  train_labels \u001b[38;5;241m=\u001b[39m y_train,\n\u001b[0;32m      4\u001b[0m                  test_data \u001b[38;5;241m=\u001b[39m X_test,\n\u001b[0;32m      5\u001b[0m                  test_labels \u001b[38;5;241m=\u001b[39m y_test,\n\u001b[0;32m      6\u001b[0m                  predictions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m      7\u001b[0m   plt\u001b[38;5;241m.\u001b[39mfigure(figsize \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m7\u001b[39m))\n\u001b[1;32m----> 8\u001b[0m   \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtrain_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mc\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTraining data\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      9\u001b[0m   plt\u001b[38;5;241m.\u001b[39mscatter(test_data,test_labels,c \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg\u001b[39m\u001b[38;5;124m'\u001b[39m,s \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m,label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     11\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m predictions \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\alexa\\Documents\\Uni\\Personal Projects\\Pytorch-Fundamentals\\Pytorch-Course\\Lib\\site-packages\\matplotlib\\pyplot.py:3699\u001b[0m, in \u001b[0;36mscatter\u001b[1;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[0;32m   3680\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[0;32m   3681\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[0;32m   3682\u001b[0m     x: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3697\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   3698\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PathCollection:\n\u001b[1;32m-> 3699\u001b[0m     __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3700\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3701\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3702\u001b[0m \u001b[43m        \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3703\u001b[0m \u001b[43m        \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3704\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3705\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3706\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3707\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3708\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3709\u001b[0m \u001b[43m        \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3710\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlinewidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlinewidths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3711\u001b[0m \u001b[43m        \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3712\u001b[0m \u001b[43m        \u001b[49m\u001b[43mplotnonfinite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplotnonfinite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3713\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3714\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3715\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3716\u001b[0m     sci(__ret)\n\u001b[0;32m   3717\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n",
      "File \u001b[1;32mc:\\Users\\alexa\\Documents\\Uni\\Personal Projects\\Pytorch-Fundamentals\\Pytorch-Course\\Lib\\site-packages\\matplotlib\\__init__.py:1478\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1475\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1476\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1477\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1478\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1480\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1481\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1482\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32mc:\\Users\\alexa\\Documents\\Uni\\Personal Projects\\Pytorch-Fundamentals\\Pytorch-Course\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:4652\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[0;32m   4649\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_unit_info([(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m, x), (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, y)], kwargs)\n\u001b[0;32m   4650\u001b[0m \u001b[38;5;66;03m# np.ma.ravel yields an ndarray, not a masked array,\u001b[39;00m\n\u001b[0;32m   4651\u001b[0m \u001b[38;5;66;03m# unless its argument is a masked array.\u001b[39;00m\n\u001b[1;32m-> 4652\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4653\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(y)\n\u001b[0;32m   4654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39msize:\n",
      "File \u001b[1;32mc:\\Users\\alexa\\Documents\\Uni\\Personal Projects\\Pytorch-Fundamentals\\Pytorch-Course\\Lib\\site-packages\\numpy\\ma\\core.py:6875\u001b[0m, in \u001b[0;36m_frommethod.__call__\u001b[1;34m(self, a, *args, **params)\u001b[0m\n\u001b[0;32m   6872\u001b[0m     args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(args)\n\u001b[0;32m   6873\u001b[0m     a, args[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m], a\n\u001b[1;32m-> 6875\u001b[0m marr \u001b[38;5;241m=\u001b[39m \u001b[43masanyarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6876\u001b[0m method_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[0;32m   6877\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mtype\u001b[39m(marr), method_name, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\alexa\\Documents\\Uni\\Personal Projects\\Pytorch-Fundamentals\\Pytorch-Course\\Lib\\site-packages\\numpy\\ma\\core.py:8299\u001b[0m, in \u001b[0;36masanyarray\u001b[1;34m(a, dtype)\u001b[0m\n\u001b[0;32m   8297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(a, MaskedArray) \u001b[38;5;129;01mand\u001b[39;00m (dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m a\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[0;32m   8298\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m a\n\u001b[1;32m-> 8299\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmasked_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeep_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\alexa\\Documents\\Uni\\Personal Projects\\Pytorch-Fundamentals\\Pytorch-Course\\Lib\\site-packages\\numpy\\ma\\core.py:2820\u001b[0m, in \u001b[0;36mMaskedArray.__new__\u001b[1;34m(cls, data, mask, dtype, copy, subok, ndmin, fill_value, keep_mask, hard_mask, shrink, order)\u001b[0m\n\u001b[0;32m   2811\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   2812\u001b[0m \u001b[38;5;124;03mCreate a new masked array from scratch.\u001b[39;00m\n\u001b[0;32m   2813\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2817\u001b[0m \n\u001b[0;32m   2818\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   2819\u001b[0m \u001b[38;5;66;03m# Process data.\u001b[39;00m\n\u001b[1;32m-> 2820\u001b[0m _data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(data, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m   2821\u001b[0m                  order\u001b[38;5;241m=\u001b[39morder, subok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, ndmin\u001b[38;5;241m=\u001b[39mndmin)\n\u001b[0;32m   2822\u001b[0m _baseclass \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(data, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_baseclass\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mtype\u001b[39m(_data))\n\u001b[0;32m   2823\u001b[0m \u001b[38;5;66;03m# Check that we're not erasing the mask.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\alexa\\Documents\\Uni\\Personal Projects\\Pytorch-Fundamentals\\Pytorch-Course\\Lib\\site-packages\\torch\\_tensor.py:1062\u001b[0m, in \u001b[0;36mTensor.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   1060\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(Tensor\u001b[38;5;241m.\u001b[39m__array__, (\u001b[38;5;28mself\u001b[39m,), \u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m   1061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1062\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1063\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1064\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[1;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight = 0.3\n",
    "bias = 0.9\n",
    "\n",
    "start = 0\n",
    "end = 1\n",
    "step = 0.01\n",
    "\n",
    "X = torch.arange(start, end, step).unsqueeze(dim=1)\n",
    "y = weight * X + bias\n",
    "print(X[:10]), print(y[:10])\n",
    "\n",
    "train_split = int(0.8 * len(X))\n",
    "X_train, y_train = X[:train_split], y[:train_split]\n",
    "X_test, y_test = X[train_split:], y[train_split:]\n",
    "\n",
    "\n",
    "print(len(X_train))\n",
    "plot_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dqueee05dunTRxx9/rEsuuaSFPx0AAIDGGkoSZoycoYKbC0JyfZA7FqO5nuwQ4nA4FBMTI7vd3uRfZE+ePKndu3erT58+io6O9vMIEQquuOIKFRcXy263q1OnToEejs/xOwEAAE7XXElCKPEkG0jcGYJJlZeXN9r28ssv6+OPP1ZqaqopghAAAMDpwrUkoTmsGYIp/fSnP9WQIUN04YUXOp+PVFRUpM6dO+vhhx8O9PAAAAD8zl1JQijfHfIEd4ZgStOmTdPBgwf14osv6vHHH9f27dt1yy23aOPGjRo0aFCghwcAAOB3KX1SnEEonEoSmsOaIcDE+J0AAACnK9xeqKLSIo3uPTqk7wp5umaIaXIAAACAiTRXkpA+ID2kQ5C3mCYHAAAAmIQZSxKaQxgCAAAATMJdSYKZEYYAAAAAkzBjSUJzWDMEAAAAmET6gHQV3FwQFiUJbYEwBAAAAIQZShI8wzQ5AAAAIIxQkuA5whAAAAAQRihJ8BxhCAAAAAgjlCR4jjCEkDZv3jxZLBYVFRUFeigAAABBoaEkYcbIGSq4uYD1Qc0gDJmIxWLx6tXWgjW45Ofny2KxKD8/P9BDAQAA8Fjh9kLNXDHT7Zqg9AHpWpS2iCB0BrTJmUhOTk6jbbm5ubLb7W7fAwAAQHBqKEmItEQq95Nc7gC1EGHIRObNm9doW35+vux2u9v3AAAAEJzclSQQhrzHNDm4VV1drUWLFmno0KH6yU9+os6dO2vUqFEqLGx8G9Zut2vu3Lm68MIL1alTJ9lsNp1//vnKzMzUnj17JEmjR4/W/PnzJUkpKSnOqXi9e/f2aDz79u3TpEmT1LVrV3Xq1ElXXnml1q5d2+TYFy9erLS0NCUkJMhqtapHjx668cYbtWXLFpd9p0yZoqlTp0qSpk6d6naa4KZNm3TnnXfqpz/9qWJiYtShQwcNGjRICxcuVE1NjUfjBwAAaEuUJLQN7gyhkaqqKl133XUqKirS4MGDddttt6mmpkbLly9XRkaGFi9erDvvvFOSZBiG0tLS9Mknn+jyyy/Xddddp4iICO3Zs0eFhYWaPHmyEhMTNWXKFEnSmjVrlJmZ6QxBXbp0OeN4ysvLlZycrLKyMqWlpWno0KHaunWrrrnmGqWkpDTa/9///rfuvvtujRo1SmPGjNFZZ52lXbt2qbCwUP/85z+1du1aDR8+XJI0fvx4fffddyooKFBGRoYGDx7c6HxPP/203n77bf3sZz/TmDFj9P3336uoqEjZ2dn617/+pTfffLNFP2cAAIAzaerhqQ0lCUWlRRrdezR3hVrKCBN2u92QZNjt9ib3OXHihPHVV18ZJ06c8OPIgltiYqLx48vg3nvvNSQZ999/v1FXV+fc7nA4jEsuucSIiooyysrKDMMwjP/3//6fIckYP358o3OfPHnSOHr0qPPrnJwcQ5KxevVqr8aYmZlpSDL++Mc/umx/6qmnDEmNznny5Elj//79jc7zxRdfGJ06dTJSU1Ndtj///POGJOP55593+/337NljnDp1ymVbXV2dceuttxqSjHXr1nn1eYIJvxMAAASvgm0FhubJiJwfaWiejIJtBYEeUsjwJBsYhmEwTc6Hmmv4CFZ1dXV64okndN5552n+/Pku08U6d+6suXPnqrq6WkuXLnU5rkOHDo3OZbVa1alTp1aNp7q6Wq+++qp69Oih3//+9y7v3X777erXr5/b79urV69G2y+66CKlpKRo7dq1Xk1vO/fccxUZGemyzWKxaPr06ZKkDz74wONzAQAAeIqHp/oe0+R8JFQbPrZv364jR44oPj7eucbndIcOHZIkbdu2TZJ0wQUX6OKLL9Y//vEP7d+/X+PHj9fo0aM1ePBgRUS0Pmtv375dJ0+e1FVXXaXo6GiX9yIiInT55Zfrm2++aXRcSUmJ/vKXv2jdunWqqKhoFH4OHz6snj17ejSG6upqPf7441qyZIm2bdumY8eOyTAM5/sHDhxowScDAABoXkqfFOV+ksu6IB8iDPlIqDZ8/Pvf/5Ykffnll/ryyy+b3O/48eOSpHbt2unDDz/UvHnz9Oabbzrv3nTv3l133nmn7rvvvkZ3Vbxht9slST169HD7fmxsbKNt69ev11VXXSVJuvbaa9WvXz916tRJFotFy5Yt02effaaqqiqPx/Dzn/9cb7/9tvr376+JEyeqR48eat++vb777js9+uijXp0LAADAU6wL8j3CkI+EapK32WySpJtuuklvvPGGR8ecffbZWrx4sR577DFt27ZNH374oRYvXqycnBy1b99e2dnZLR5PTEyMJOngwYNu36+srGy07aGHHlJVVZU++ugjXXHFFS7vbdiwQZ999pnH3/9f//qX3n77baWlpWn58uUuwW7Dhg169NFHPT4XAACAO02VJEj1gYgQ5DusGfKRhiQ/Y+SMkJkiJ9VPe7PZbPr000+9ro22WCy64IILNH36dK1cuVKSXKq4G4JEbW2tx+fs37+/oqOj9emnn+rkyZMu79XV1Wn9+vWNjtm5c6e6du3aKAh9//332rx5c6P9mxvXzp07JUljx45tdIfro48+8vhzAAAAuNOwtGLxxsXKWJIRUmvNwwFhyIfSB6RrUdqikAlCUv20t9/85jfas2eP7rnnHreB6IsvvnDeqSktLVVpaWmjfRru2Jy+zqdr166S6p8Z5Cmr1aoJEybo4MGDeuSRR1zee+aZZ/T11183OiYxMVFHjhxxmeZXW1ure+65x7nm6XTNjSsxMVGStG7dOpftX375pRYsWODx5wAAAHCHkoTAYpocGpk/f742b96sxx57TMuXL9fPfvYz9ejRQ2VlZfr888/12Wefqbi4WD169FBJSYluvPFGjRgxQhdeeKHi4uJUVlamZcuWKSIiQjNnznSet+Fhq/fee6++/PJLxcTEqEuXLs5nFjVl4cKFWrVqlebMmaN169ZpyJAh2rp1q959911de+21ev/99132v+uuu/T+++/riiuu0IQJExQdHa2ioiKVlZVp9OjRKioqctk/OTlZHTp0UG5uro4cOaLu3btLkubMmaMRI0ZoxIgReu2111ReXq5LL71Ue/fuVWFhocaOHevxVEIAAAB3QnVpRdjwT9O37/GcoZZx95whwzCMU6dOGU899ZRx+eWXGzabzbBarca5555rXHfddcYTTzxhHDt2zDAMw9i3b58xe/Zs49JLLzV69OhhREVFGeeee65x4403GsXFxY3Om5+fbwwaNMiwWq2GJCMxMdGjce7Zs8eYOHGi0aVLF6Njx47GqFGjjDVr1jT57KI33njDGDp0qNGxY0ejW7duxoQJE4ydO3c6n1m0e/dul/2XL19uDB8+3OjQoYPz2UUNDh48aNx6661GfHy8ER0dbQwaNMjIy8szdu3aZUgyMjMzPfoMwYjfCQAAAq9gW4Exc8VMniPUhjx9zpDFME7rCA5hDodDMTExstvtzhKAHzt58qR2796tPn36NKppBsyI3wkAAHyvuYIE+IYn2UBizRAAAADgMxQkBDfCEAAAAOAjFCQEN8IQAAAA4CMpfVKcQYiChOBDmxwAAADgIw3PniwqLdLo3qNZMxRkCEMAAABAKzVXkpA+IJ0QFKSYJgcAAAC0AiUJocuUYShM2sSBVuN3AQCA1qMkIXSZKgxFRkZKkmpqagI8EiA4NPwuNPxuAAAA71GSELpMtWaoffv2slqtstvt6ty5sywWS6CHBASMYRiy2+2yWq1q3759oIcDAEDIoiQhdFmMMJkn4+lTZh0Oh8rKytSpUyfFxMSoffv2hCKYimEYqqmpkd1u17Fjx9SrV69mf2cAAEC95koSEFw8zQamC0MN+x4+fFhVVVV+Gh0QfKxWq7p160YQAgDAAw0lCQ1T4QpuLiAQBTFPs4Gppsk1sNlsstlsqqmpUW1tbaCHA/hdZGQkU+MAAPCCu5IEwlDoM2UYatC+fXv+QggAAIAzSumTotxPcilJCDOmDkMAAACAJyhJCE+mXDMEAAAAuENJQnjwNBuY6jlDAAAAQFMaShIWb1ysjCUZKtxeGOghwccIQwAAAIDclyQgvBGGAAAAANWXJDQEIUoSzIECBQAAAECUJJgRBQoAAAAwFUoSwh8FCgAAAMCPUJKA0xGGAAAAYBqUJOB0hCEAAACYBiUJOB0FCgAAADANShJwOgoUAAAAEHYoSTA3ChQAAABgSpQkwFOEIQAAAIQVShLgKcIQAAAAwgolCfAUBQoAAAAIK5QkwFMUKAAAACAkUZKAplCgAAAAgLBFSQLaAmEIAAAAIYeSBLQFwhAAAABCDiUJaAsUKAAAACDkUJKAtkCBAgAAAIIWJQloCQoUAAAAENIoSYCveR2G1q5dq3Hjxik+Pl4Wi0XLli1rdv/y8nLdcsst6t+/vyIiInT33Xc32ic/P18Wi8XlFR0d7e3QAAAAEEYoSYCveR2Gjh8/rqSkJOXl5Xm0f1VVlbp37645c+YoKSmpyf1sNpvKy8udrz179ng7NAAAAIQRShLga14XKFx//fW6/vrrPd6/d+/eevTRRyVJzz33XJP7WSwWxcXFeXzeqqoqVVVVOb92OBweHwsAAIDgR0kCfC1o1gwdO3ZMiYmJSkhIUEZGhr788stm91+wYIFiYmKcr4SEBD+NFAAAAG2pcHuhZq6Y6XZNUPqAdC1KW0QQgk8ERRgaMGCAnnvuORUUFOjll19WXV2dLrvsMu3fv7/JY7Kzs2W3252vffv2+XHEAAAAaAuUJCCQguI5Q8nJyUpOTnZ+fdlll+mCCy7QU089pQcffNDtMVarVVar1V9DBAAAgA+4K0ngLhD8JSjuDP1Y+/btNWTIEO3YsSPQQwEAAIAPUZKAQAqKO0M/Vltbq88//1xjxowJ9FAAAADgQ5QkIJC8DkPHjh1zuWOze/dulZSUqGvXrjr33HOVnZ2tsrIyvfjii859SkpKnMceOnRIJSUlioqK0oUXXihJeuCBB3TppZfq/PPP13fffaf/+Z//0Z49e3T77be38uMBAAAg0Aq3F2r17tVK6ZPiNuykD0gnBCEgvA5Dn376qVJSUpxfZ2VlSZIyMzOVn5+v8vJy7d271+WYIUOGOP9506ZNeuWVV5SYmKjS0lJJ0pEjR3THHXeooqJCZ511loYNG6b169c7wxIAAABCU0NBQqQlUrmf5Krg5gKCD4KGxTAMI9CDaAsOh0MxMTGy2+2y2WyBHg4AAAAkzVwxU4s3LnauC5oxcoYWpS0K9LAQ5jzNBkFZoAAAAIDwQEECgllQFigAAAAgPFCQgGDGNDkAAAC02plKEgB/YpocAAAA/KKhJGHxxsXKWJKhwu2FgR4S4BHCEAAAAFpl9e7VzjVBkZZIFZUWBXpIgEcIQwAAAGgVShIQqihQAAAAQKtQkoBQRYECAAAAPEJJAkIFBQoAAABoM5QkIBwRhgAAAHBGlCQgHBGGAAAAcEaUJCAcUaAAAACAM6IkAeGIAgUAAAA4FRZKq1dLKSlSOnkHIYoCBQAAAHilsFDKyJAWL67/s5COBIQ5whAAAAAk1d8RioyUamvr/ywqCvSIAN8iDAEAAEBS/dS4hiBUWyuNHh3oEQG+RYECAAAAJNWvESooqL8jNHo0a4YQ/ghDAAAAJtNcSUJ6OiEI5sE0OQAAABOhJAH4AWEIAADARChJAH5AGAIAADARShKAH7BmCAAAwEQoSQB+QBgCAAAIQ5QkAGfGNDkAAIAwQ0kC4BnCEAAAQJihJAHwDGEIAAAgzFCSAHiGNUMAAABhhpIEwDOEIQAAgBBFSQLQOkyTAwAACEGUJACtRxgCAAAIQZQkAK1HGAIAAAhBlCQArceaIQAAgBBESQLQeoQhAACAIEZJAuA7TJMDAAAIUpQkAL5FGAIAAAhSlCQAvkUYAgAACFKUJAC+xZohAACAIEVJAuBbhCEAAIAAaq4gQaIkAfAlpskBAAAECAUJQGARhgAAAAKEggQgsAhDAAAAAUJBAhBYrBkCAADwsabWBVGQAASWxTAMI9CDaAsOh0MxMTGy2+2y2WyBHg4AAICkH9YFNdz9KSgg9AC+5mk2YJocAACAD7EuCAhehCEAAAAfYl0QELxYMwQAAOBDrAsCghdhCAAAoA009/BUHpwKBCemyQEAALQSD08FQhNhCAAAoJUoSQBCE2EIAACglShJAEITa4YAAABaiZIEIDQRhgAAADxESQIQXpgmBwAA4AFKEoDwQxgCAADwACUJQPghDAEAAHiAkgQg/LBmCAAAwAOUJADhhzAEAABwGkoSAPNgmhwAAMD/oSQBMBfCEAAAwP+hJAEwF8IQAADA/6EkATAX1gwBAAD8H0oSAHMhDAEAANOhJAGAxDQ5AABgMpQkAGhAGAIAAKZCSQKABoQhAABgKpQkAGjAmiEAAGAqlCQAaEAYAgAAYYmSBABnwjQ5AAAQdihJAOAJwhAAAAg7lCQA8ARhCAAAhB1KEgB4gjVDAAAg7FCSAMATXt8ZWrt2rcaNG6f4+HhZLBYtW7as2f3Ly8t1yy23qH///oqIiNDdd9/tdr/XX39dAwcOVHR0tAYNGqR3333X26EBAACTKSyUZs50vyYoPV1atIggBKBpXoeh48ePKykpSXl5eR7tX1VVpe7du2vOnDlKSkpyu8/69es1adIk3XbbbdqyZYvGjx+v8ePH64svvvB2eAAAwCQoSQDQWhbDMIwWH2yx6K233tL48eM92n/06NEaPHiwcnNzXbZPnDhRx48f1zvvvOPcdumll2rw4MF68sknPTq3w+FQTEyM7Ha7bDabpx8BAACEqJkz64NQw9qgGTPq7wQBgKfZICgKFIqLi5WamuqyLS0tTcXFxU0eU1VVJYfD4fICAADmQUkCgNYKijBUUVGh2NhYl22xsbGqqKho8pgFCxYoJibG+UpISPD1MAEAQBBpKEmYMaP+T9YGAfBWUIShlsjOzpbdbne+9u3bF+ghAQAAH6AkAYCvBEW1dlxcnCorK122VVZWKi4ursljrFarrFarr4cGAAACqKEkITJSys3lDhCAthUUd4aSk5O1atUql20rV65UcnJygEYEAACCwerVP6wJioysf24QALQVr+8MHTt2TDt27HB+vXv3bpWUlKhr164699xzlZ2drbKyMr344ovOfUpKSpzHHjp0SCUlJYqKitKFF14oSfrd736nK6+8Uo888ojGjh2rJUuW6NNPP9Xf//73Vn48AAAQylJS6u8IUZIAwBe8rtYuKipSSkpKo+2ZmZnKz8/XlClTVFpaqqLT/q8bi8XSaP/ExESVlpY6v3799dc1Z84clZaWql+/fvrLX/6iMWPGeDwuqrUBAAhPhYX1d4RGj2aKHADPeJoNWvWcoWBCGAIAIDQVFtZPh0tJIewAaBsh9ZwhAABgTg0FCYsX1//prjEOAHyFMAQAAAKGggQAgUQYAgAAAZOS8kMQoiABgL8FxXOGAACAOaWn1z87iIIEAIFAGAIAAD7XXElCejohCEBgME0OAAD4FCUJAIIVYQgAAPgUJQkAghVhCAAA+BQlCQCCFWuGAACAT1GSACBYEYYAAECboCQBQKhhmhwAAGg1ShIAhCLCEAAAaDVKEgCEIsIQAABoNUoSAIQi1gwBAIBWoyQBQCgiDAEAAI9RkgAgnDBNDgAAeISSBADhhjAEAAA8QkkCgHBDGAIAAB6hJAFAuGHNEAAA8AglCQDCDWEIAAC4oCQBgFkwTQ4AADhRkgDATAhDAADAiZIEAGZCGAIAAE6UJAAwE9YMAQAAJ0oSAJgJYQgAABOiJAEAmCYHAIDpUJIAAPUIQwAAmAwlCQBQjzAEAIDJUJIAAPVYMwQAgMlQkgAA9QhDAACEKUoSAKB5TJMDACAMUZIAAGdGGAIAIAxRkgAAZ0YYAgAgDFGSAABnxpohAADCECUJAHBmhCEAAEIYJQkA0HJMkwMAIERRkgAArUMYAgAgRFGSAACtQxgCACBEUZIAAK3DmiEAAEIUJQkA0DqEIQAAglhzBQkSJQkA0BpMkwMAIEhRkAAAvkUYAgAgSFGQAAC+RRgCACBIUZAAAL7FmiEAAIIUBQkA4FuEIQAAAqy5kgQKEgDAd5gmBwBAAFGSAACBQxgCACCAKEkAgMAhDAEAEECUJABA4LBmCACAAKIkAQAChzAEAIAfUJIAAMGHaXIAAPgYJQkAEJwIQwAA+BglCQAQnAhDAAD4GCUJABCcWDMEAICPUZIAAMGJMAQAQBuhJAEAQgvT5AAAaAOUJABA6CEMAQDQBihJAIDQQxgCAKANUJIAAKGHNUMAAHihqXVBlCQAQOixGIZhBHoQbcHhcCgmJkZ2u102my3QwwEAhKGGdUENd38KCgg9ABCMPM0GTJMDAMBDrAsCgPBCGAIAwEOsCwKA8MKaIQAAPMS6IAAIL4QhAAB+hIenAoA5ME0OAIDT8PBUADAPwhAAAKehJAEAzIMwBADAaShJAADzYM0QAACnoSQBAMyDMAQAMCVKEgAATJMDAJgOJQkAAIkwBAAwIUoSAAASYQgAYEKUJAAApBaEobVr12rcuHGKj4+XxWLRsmXLznhMUVGRhg4dKqvVqvPPP1/5+fku78+bN08Wi8XlNXDgQG+HBgCARxpKEmbMqP+T9UEAYE5eh6Hjx48rKSlJeXl5Hu2/e/dujR07VikpKSopKdHdd9+t22+/Xe+9957LfhdddJHKy8udr3Xr1nk7NAAAXBQWSjNnul8TlJ4uLVpEEAIAM/O6Te7666/X9ddf7/H+Tz75pPr06aNHHnlEknTBBRdo3bp1+utf/6q0tLQfBtKuneLi4jw+b1VVlaqqqpxfOxwOj48FAIS/hpKEyEgpN5c7QACAxny+Zqi4uFipqaku29LS0lRcXOyy7ZtvvlF8fLz69u2rX/ziF9q7d2+z512wYIFiYmKcr4SEhDYfOwAgdFGSAAA4E5+HoYqKCsXGxrpsi42NlcPh0IkTJyRJI0eOVH5+vlasWKEnnnhCu3fv1qhRo3T06NEmz5udnS273e587du3z6efAwAQWihJAACcSVA8dPX0aXcXX3yxRo4cqcTERL322mu67bbb3B5jtVpltVr9NUQAQIhpKEkoKqoPQkyRAwD8mM/DUFxcnCorK122VVZWymazqUOHDm6P6dKli/r3768dO3b4engAgBBXWFg/JS4lpXHgSU8nBAEAmubzaXLJyclatWqVy7aVK1cqOTm5yWOOHTumnTt3qmfPnr4eHgAghDWUJCxeXP+nu9Y4AACa4nUYOnbsmEpKSlRSUiKpvjq7pKTEWXiQnZ2tX/3qV879p02bpl27dmnWrFnatm2b/va3v+m1117TzJkznfvcc889WrNmjUpLS7V+/XrdcMMNioyM1KRJk1r58QAA4YySBABAa3gdhj799FMNGTJEQ4YMkSRlZWVpyJAhmjt3riSpvLzcpQmuT58+Wr58uVauXKmkpCQ98sgjeuaZZ1xqtffv369JkyZpwIABmjBhgs4++2xt2LBB3bt3b+3nAwCEMUoSAACtYTEMwwj0INqCw+FQTEyM7Ha7bDZboIcDAPCTwkJKEgAArjzNBkHRJgcAQFOaK0iQKEkAALSczwsUAABoKQoSAAC+RBgCAAQtChIAAL5EGAIABC0KEgAAvsSaIQBA0EpPlwoKKEgAAPgGYQgAEHDNlSRQkAAA8BWmyQEAAoqSBABAoBCGAAABRUkCACBQCEMAgICiJAEAECisGQIABBQlCQCAQCEMAQD8gpIEAECwYZocAMDnKEkAAAQjwhAAwOcoSQAABCPCEADA5yhJAAAEI9YMAQB8jpIEAEAwIgwBANoMJQkAgFDCNDkAQJugJAEAEGoIQwCANkFJAgAg1BCGAABtgpIEAECoYc0QAKBNUJIAAAg1hCEAgFcoSQAAhAumyQEAPEZJAgAgnBCGAAAeoyQBABBOCEMAAI9RkgAACCesGQIAeIySBABAOCEMAQAaoSQBAGAGTJMDALigJAEAYBaEIQCAC0oSAABmQRgCALigJAEAYBasGQIAuKAkAQBgFoQhADApShIAAGbHNDkAMCFKEgAAIAwBgClRkgAAAGEIAEyJkgQAAFgzBACmREkCAACEIQAIa5QkAADQNKbJAUCYoiQBAIDmEYYAIExRkgAAQPMIQwAQpihJAACgeawZAoAwRUkCAADNIwwBQAhrriBBoiQBAIDmME0OAEIUBQkAALQOYQgAQhQFCQAAtA5hCABCFAUJAAC0DmuGACBEUZAAAEDrEIYAIMg1V5JAQQIAAC3HNDkACGKUJAAA4DuEIQAIYpQkAADgO4QhAAhilCQAAOA7rBkCgCBGSQIAAL5DGAKAIEBJAgAA/sc0OQAIMEoSAAAIDMIQAAQYJQkAAAQGYQgAAoySBAAAAoM1QwAQYJQkAAAQGIQhAPATShIAAAguTJMDAD+gJAEAgOBDGAIAP6AkAQCA4EMYAgA/oCQBAIDgw5ohAPADShIAAAg+hCEAaEOUJAAAEDqYJgcAbYSSBAAAQgthCADaCCUJAACEFsIQALQRShIAAAgtrBkCgDZCSQIAAKGFMAQAXqIkAQCA8MA0OQDwAiUJAACED8IQAHiBkgQAAMIHYQgAvEBJAgAA4YM1QwDgRlPrgihJAAAgfFgMwzACPYi24HA4FBMTI7vdLpvNFujhAAhhDeuCGu7+FBQQegAACCWeZgOvp8mtXbtW48aNU3x8vCwWi5YtW3bGY4qKijR06FBZrVadf/75ys/Pb7RPXl6eevfurejoaI0cOVIbN270dmgA0CZYFwQAgDl4HYaOHz+upKQk5eXlebT/7t27NXbsWKWkpKikpER33323br/9dr333nvOfV599VVlZWUpJydHmzdvVlJSktLS0nTw4EFvhwcArca6IAAAzKFV0+QsFoveeustjR8/vsl9/vu//1vLly/XF1984dx2880367vvvtOKFSskSSNHjtTw4cP1+OOPS5Lq6uqUkJCgu+66S7Nnz/ZoLEyTA9CWCgtZFwQAQKjy2TQ5bxUXFys1NdVlW1pamoqLiyVJ1dXV2rRpk8s+ERERSk1Nde7jTlVVlRwOh8sLALxRWCjNnOn+WUHp6dKiRQQhAADCmc/DUEVFhWJjY122xcbGyuFw6MSJEzp8+LBqa2vd7lNRUdHkeRcsWKCYmBjnKyEhwSfjBxCeeHgqAAAI2ecMZWdny263O1/79u0L9JAAhBBKEgAAgM/DUFxcnCorK122VVZWymazqUOHDurWrZsiIyPd7hMXF9fkea1Wq2w2m8sLADxFSQIAAPB5GEpOTtaqVatctq1cuVLJycmSpKioKA0bNsxln7q6Oq1atcq5DwC0tYaHp86YwXOEAAAwq3beHnDs2DHt2LHD+fXu3btVUlKirl276txzz1V2drbKysr04osvSpKmTZumxx9/XLNmzdKtt96qDz/8UK+99pqWL1/uPEdWVpYyMzN1ySWXaMSIEcrNzdXx48c1derUNviIAMyssLB+SlxKSuPAk55OCAIAwMy8DkOffvqpUlJSnF9nZWVJkjIzM5Wfn6/y8nLt3bvX+X6fPn20fPlyzZw5U48++qjOOeccPfPMM0pLS3PuM3HiRB06dEhz585VRUWFBg8erBUrVjQqVQAAbzSUJERGSrm53AECAACuWvWcoWDCc4YA/NjMmfVtcQ1rg2bMqK/LBgAA4S1onjMEAIFCSQIAAGiO19PkACBUNJQkFBXVByGmyAEAgNMRhgCEtOYKEiRKEgAAQNOYJgcgZDUUJCxeXP9nYWGgRwQAAEIJYQhAyFq9+of1QJGR9dPhAAAAPEUYAhCyKEgAAACtwZohACGLggQAANAahCEAQa+5kgQKEgAAQEsxTQ5AUKMkAQAA+AphCEBQoyQBAAD4CmEIQFCjJAEAAPgKa4YABDVKEgAAgK8QhgAEBUoSAACAvzFNDkDAUZIAAAACgTAEIOAoSQAAAIFAGAIQcJQkAACAQGDNEICAoyQBAAAEAmEIgN9QkgAAAIIJ0+QA+AUlCQAAINgQhgD4BSUJAAAg2BCGAPgFJQkAACDYsGYIgF9QkgAAAIINYQhAm6IkAQAAhAqmyQFoM5QkAACAUEIYAtBmKEkAAAChhDAEoM1QkgAAAEIJa4YAtBlKEgAAQCghDAHwGiUJAAAgHDBNDoBXKEkAAADhgjAEwCuUJAAAgHBBGALgFUoSAABAuGDNEACvUJIAAADCBWEIgFuUJAAAgHDHNDkAjVCSAAAAzIAwBKARShIAAIAZEIYANEJJAgAAMAPWDAFohJIEAABgBoQhwMQoSQAAAGbGNDnApChJAAAAZkcYAkyKkgQAAGB2hCHApChJAAAAZseaIcCkKEkAAABmRxgCwhwlCQAAAO4xTQ4IY5QkAAAANI0wBIQxShIAAACaRhgCwhglCQAAAE1jzRAQxihJAAAAaBphCAhxzRUkSJQkAAAANIVpckAIoyABAACg5QhDQAijIAEAAKDlCENACKMgAQAAoOVYMwSEMAoSAAAAWo4wBISA5koSKEgAAABoGabJAUGOkgQAAADfIAwBQY6SBAAAAN8gDAFBjpIEAAAA32DNEBDkKEkAAADwDcIQECQoSQAAAPAvpskBQYCSBAAAAP8jDAFBgJIEAAAA/yMMAUGAkgQAAAD/Y80QEAQoSQAAAPA/whDgR5QkAAAABA+myQF+QkkCAABAcCEMAX5CSQIAAEBwIQwBfkJJAgAAQHBhzRDgJ5QkAAAABBfCENDGKEkAAAAIDUyTA9oQJQkAAAChgzAEtCFKEgAAAEIHYQhoQ5QkAAAAhA7WDAFtiJIEAACA0NGiO0N5eXnq3bu3oqOjNXLkSG3cuLHJfWtqavTAAw/ovPPOU3R0tJKSkrRixQqXfebNmyeLxeLyGjhwYEuGBvhFYaE0c6b7NUHp6dKiRQQhAACAYOd1GHr11VeVlZWlnJwcbd68WUlJSUpLS9PBgwfd7j9nzhw99dRTWrx4sb766itNmzZNN9xwg7Zs2eKy30UXXaTy8nLna926dS37RICPUZIAAAAQHrwOQ4sWLdIdd9yhqVOn6sILL9STTz6pjh076rnnnnO7/0svvaR7771XY8aMUd++ffWb3/xGY8aM0SOPPOKyX7t27RQXF+d8devWrdlxVFVVyeFwuLwAf6AkAQAAIDx4FYaqq6u1adMmpaam/nCCiAilpqaquLjY7TFVVVWKjo522dahQ4dGd36++eYbxcfHq2/fvvrFL36hvXv3NjuWBQsWKCYmxvlKSEjw5qMALUZJAgAAQHjwKgwdPnxYtbW1io2NddkeGxuriooKt8ekpaVp0aJF+uabb1RXV6eVK1dq6dKlKi8vd+4zcuRI5efna8WKFXriiSe0e/dujRo1SkePHm1yLNnZ2bLb7c7Xvn37vPkoQIs1lCTMmFH/J2uDAAAAQpPP2+QeffRR3XHHHRo4cKAsFovOO+88TZ061WVa3fXXX+/854svvlgjR45UYmKiXnvtNd12221uz2u1WmW1Wn09fJhYYWH9lLiUlMaBJz2dEAQAABDqvLoz1K1bN0VGRqqystJle2VlpeLi4twe0717dy1btkzHjx/Xnj17tG3bNnXq1El9+/Zt8vt06dJF/fv3144dO7wZHtBmKEkAAAAIf16FoaioKA0bNkyrVq1ybqurq9OqVauUnJzc7LHR0dHq1auXTp06pTfffFMZGRlN7nvs2DHt3LlTPXv29GZ4QJuhJAEAACD8ed0ml5WVpaefflovvPCCtm7dqt/85jc6fvy4pk6dKkn61a9+pezsbOf+n3zyiZYuXapdu3bpo48+0nXXXae6ujrNmjXLuc8999yjNWvWqLS0VOvXr9cNN9ygyMhITZo0qQ0+IuA9ShIAAADCn9drhiZOnKhDhw5p7ty5qqio0ODBg7VixQpnqcLevXsVEfFDxjp58qTmzJmjXbt2qVOnThozZoxeeukldenSxbnP/v37NWnSJH377bfq3r27rrjiCm3YsEHdu3dv/ScEWqChJKGoqD4IsT4IAAAg/FgMwzACPYi24HA4FBMTI7vdLpvNFujhIEQ0V5IAAACA0ORpNvB6mhwQLihJAAAAMDfCEEyLkgQAAABzIwzBtChJAAAAMDefP3QVCKQzPTiVkgQAAADzokABYathTVDDnZ+CAgIPAACAGVCgANNjTRAAAACaQxhC2GJNEAAAAJrDmiGELdYEAQAAoDmEIYS8M5UkEIIAAADgDtPkENJ4cCoAAABaijCEkEZJAgAAAFqKMISQRkkCAAAAWoo1QwhplCQAAACgpQhDCAmUJAAAAKCtMU0OQY+SBAAAAPgCYQhBj5IEAAAA+AJhCEGPkgQAAAD4AmuGEPQoSQAAAIAvEIYQNChJAAAAgD8xTQ5BgZIEAAAA+BthCEGBkgQAAAD4G2EIQYGSBAAAAPgba4YQFChJAAAAgL8RhuBXlCQAAAAgWDBNDn5DSQIAAACCCWEIfkNJAgAAAIIJYQh+Q0kCAAAAgglrhuA3lCQAAAAgmBCG0OYoSQAAAEAoYJoc2hQlCQAAAAgVhCG0KUoSAAAAECoIQ2hTlCQAAAAgVLBmCG2KkgQAAACECsIQWoSSBAAAAIQ6psnBa5QkAAAAIBwQhuA1ShIAAAAQDghD8BolCQAAAAgHrBmC1yhJAAAAQDggDKFJlCQAAAAgnDFNDm5RkgAAAIBwRxiCW5QkAAAAINwRhuAWJQkAAAAId6wZgluUJAAAACDcEYZMjpIEAAAAmBXT5EyMkgQAAACYGWHIxChJAAAAgJkRhkyMkgQAAACYGWuGTIySBAAAAJgZYSjMNVeQIFGSAAAAAPNimlwYoyABAAAAaBphKIxRkAAAAAA0jTAUxihIAAAAAJrGmqEwRkECAAAA0DTCUBhoriSBggQAAADAPabJhThKEgAAAICWIQyFOEoSAAAAgJYhDIU4ShIAAACAlmHNUIijJAEAAABoGcJQiKAkAQAAAGhbTJMLAZQkAAAAAG2PMBQCKEkAAAAA2h5hKARQkgAAAAC0PdYMhQBKEgAAAIC2RxgKIpQkAAAAAP7DNLkgQUkCAAAA4F+EoSBBSQIAAADgX4ShIEFJAgAAAOBfrBkKEpQkAAAAAP5FGPIzShIAAACA4MA0OT+iJAEAAAAIHoQhP6IkAQAAAAgeLQpDeXl56t27t6KjozVy5Eht3LixyX1ramr0wAMP6LzzzlN0dLSSkpK0YsWKVp0zVFGSAAAAAAQPr8PQq6++qqysLOXk5Gjz5s1KSkpSWlqaDh486Hb/OXPm6KmnntLixYv11Vdfadq0abrhhhu0ZcuWFp8zVDWUJMyYUf8n64MAAACAwLEYhmF4c8DIkSM1fPhwPf7445Kkuro6JSQk6K677tLs2bMb7R8fH6/77rtP06dPd2676aab1KFDB7388sstOqc7DodDMTExstvtstls3nykNtdcSQIAAAAA3/I0G3h1Z6i6ulqbNm1SamrqDyeIiFBqaqqKi4vdHlNVVaXo6GiXbR06dNC6detafM6G8zocDpdXMKAkAQAAAAgNXoWhw4cPq7a2VrGxsS7bY2NjVVFR4faYtLQ0LVq0SN98843q6uq0cuVKLV26VOXl5S0+pyQtWLBAMTExzldCQoI3H8VnKEkAAAAAQoPP2+QeffRR9evXTwMHDlRUVJTuvPNOTZ06VRERrfvW2dnZstvtzte+ffvaaMStQ0kCAAAAEBq8euhqt27dFBkZqcrKSpftlZWViouLc3tM9+7dtWzZMp08eVLffvut4uPjNXv2bPXt27fF55Qkq9Uqq9XqzfD9oqEkoaioPgixZggAAAAITl7dnomKitKwYcO0atUq57a6ujqtWrVKycnJzR4bHR2tXr166dSpU3rzzTeVkZHR6nMGq/R0adEighAAAAAQzLy6MyRJWVlZyszM1CWXXKIRI0YoNzdXx48f19SpUyVJv/rVr9SrVy8tWLBAkvTJJ5+orKxMgwcPVllZmebNm6e6ujrNmjXL43MCAAAAQFvzOgxNnDhRhw4d0ty5c1VRUaHBgwdrxYoVzgKEvXv3uqwHOnnypObMmaNdu3apU6dOGjNmjF566SV16dLF43MCAAAAQFvz+jlDwSqYnjMEAAAAIHB88pwhAAAAAAgXhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAAptQu0ANoK4ZhSJIcDkeARwIAAAAgkBoyQUNGaErYhKGjR49KkhISEgI8EgAAAADB4OjRo4qJiWnyfYtxprgUIurq6nTgwAF17txZFosloGNxOBxKSEjQvn37ZLPZAjoWhA6uG7QE1w1aimsHLcF1g5YIxHVjGIaOHj2q+Ph4RUQ0vTIobO4MRURE6Jxzzgn0MFzYbDb+QwGvcd2gJbhu0FJcO2gJrhu0hL+vm+buCDWgQAEAAACAKRGGAAAAAJgSYcgHrFarcnJyZLVaAz0UhBCuG7QE1w1aimsHLcF1g5YI5usmbAoUAAAAAMAb3BkCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEoRbKy8tT7969FR0drZEjR2rjxo3N7v/6669r4MCBio6O1qBBg/Tuu+/6aaQIJt5cN08//bRGjRqls846S2eddZZSU1PPeJ0hPHn735sGS5YskcVi0fjx4307QAQtb6+d7777TtOnT1fPnj1ltVrVv39//vfKhLy9bnJzczVgwAB16NBBCQkJmjlzpk6ePOmn0SIYrF27VuPGjVN8fLwsFouWLVt2xmOKioo0dOhQWa1WnX/++crPz/f5ON0hDLXAq6++qqysLOXk5Gjz5s1KSkpSWlqaDh486Hb/9evXa9KkSbrtttu0ZcsWjR8/XuPHj9cXX3zh55EjkLy9boqKijRp0iStXr1axcXFSkhI0LXXXquysjI/jxyB5O1106C0tFT33HOPRo0a5aeRIth4e+1UV1frmmuuUWlpqd544w1t375dTz/9tHr16uXnkSOQvL1uXnnlFc2ePVs5OTnaunWrnn32Wb366qu69957/TxyBNLx48eVlJSkvLw8j/bfvXu3xo4dq5SUFJWUlOjuu+/W7bffrvfee8/HI3XDgNdGjBhhTJ8+3fl1bW2tER8fbyxYsMDt/hMmTDDGjh3rsm3kyJHGf/3Xf/l0nAgu3l43P3bq1Cmjc+fOxgsvvOCrISIIteS6OXXqlHHZZZcZzzzzjJGZmWlkZGT4YaQINt5eO0888YTRt29fo7q62l9DRBDy9rqZPn26cdVVV7lsy8rKMi6//HKfjhPBS5Lx1ltvNbvPrFmzjIsuushl28SJE420tDQfjsw97gx5qbq6Wps2bVJqaqpzW0REhFJTU1VcXOz2mOLiYpf9JSktLa3J/RF+WnLd/Nj333+vmpoade3a1VfDRJBp6XXzwAMPqEePHrrtttv8MUwEoZZcO4WFhUpOTtb06dMVGxurn/70p/rTn/6k2tpafw0bAdaS6+ayyy7Tpk2bnFPpdu3apXfffVdjxozxy5gRmoLp78bt/P4dQ9zhw4dVW1ur2NhYl+2xsbHatm2b22MqKirc7l9RUeGzcSK4tOS6+bH//u//Vnx8fKP/eCB8teS6WbdunZ599lmVlJT4YYQIVi25dnbt2qUPP/xQv/jFL/Tuu+9qx44d+u1vf6uamhrl5OT4Y9gIsJZcN7fccosOHz6sK664QoZh6NSpU5o2bRrT5NCspv5u7HA4dOLECXXo0MFvY+HOEBACFi5cqCVLluitt95SdHR0oIeDIHX06FFNnjxZTz/9tLp16xbo4SDE1NXVqUePHvr73/+uYcOGaeLEibrvvvv05JNPBnpoCGJFRUX605/+pL/97W/avHmzli5dquXLl+vBBx8M9NAAj3BnyEvdunVTZGSkKisrXbZXVlYqLi7O7TFxcXFe7Y/w05LrpsHDDz+shQsX6oMPPtDFF1/sy2EiyHh73ezcuVOlpaUaN26cc1tdXZ0kqV27dtq+fbvOO+883w4aQaEl/83p2bOn2rdvr8jISOe2Cy64QBUVFaqurlZUVJRPx4zAa8l1c//992vy5Mm6/fbbJUmDBg3S8ePH9etf/1r33XefIiL4/93RWFN/N7bZbH69KyRxZ8hrUVFRGjZsmFatWuXcVldXp1WrVik5OdntMcnJyS77S9LKlSub3B/hpyXXjST95S9/0YMPPqgVK1bokksu8cdQEUS8vW4GDhyozz//XCUlJc5Xenq6s60nISHBn8NHALXkvzmXX365duzY4QzQkvT111+rZ8+eBCGTaMl18/333zcKPA2B2jAM3w0WIS2o/m7s98qGMLBkyRLDarUa+fn5xldffWX8+te/Nrp06WJUVFQYhmEYkydPNmbPnu3c/+OPPzbatWtnPPzww8bWrVuNnJwco3379sbnn38eqI+AAPD2ulm4cKERFRVlvPHGG0Z5ebnzdfTo0UB9BASAt9fNj9EmZ17eXjt79+41OnfubNx5553G9u3bjXfeecfo0aOH8cc//jFQHwEB4O11k5OTY3Tu3Nn4xz/+Yezatct4//33jfPOO8+YMGFCoD4CAuDo0aPGli1bjC1bthiSjEWLFhlbtmwx9uzZYxiGYcyePduYPHmyc/9du3YZHTt2NP7whz8YW7duNfLy8ozIyEhjxYoVfh87YaiFFi9ebJx77rlGVFSUMWLECGPDhg3O96688kojMzPTZf/XXnvN6N+/vxEVFWVcdNFFxvLly/08YgQDb66bxMREQ1KjV05Ojv8HjoDy9r83pyMMmZu318769euNkSNHGlar1ejbt6/x0EMPGadOnfLzqBFo3lw3NTU1xrx584zzzjvPiI6ONhISEozf/va3xpEjR/w/cATM6tWr3f6dpeFayczMNK688spGxwwePNiIiooy+vbtazz//PN+H7dhGIbFMLiHCQAAAMB8WDMEAAAAwJQIQwAAAABMiTAEAAAAwJQIQwAAAABMiTAEAAAAwJQIQwAAAABMiTAEAAAAwJQIQwAAAABMiTAEAAAAwJQIQwAAAABMiTAEAAAAwJT+P9Jo5lGK3gYDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot the training and testing data \n",
    "def plot_predictions(train_data = X_train,\n",
    "                 train_labels = y_train,\n",
    "                 test_data = X_test,\n",
    "                 test_labels = y_test,\n",
    "                 predictions = None):\n",
    "  plt.figure(figsize = (10,7))\n",
    "  plt.scatter(train_data,train_labels,c = 'b',s = 4,label = \"Training data\")\n",
    "  plt.scatter(test_data,test_labels,c = 'g',s = 4,label = \"Test data\")\n",
    "\n",
    "  if predictions is not None:\n",
    "    plt.scatter(test_data,predictions,c = 'r',s = 4,label = \"Predictions\")\n",
    "  plt.legend(prop = {\"size\" : 14})\n",
    "plot_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(LinearRegressionModelV3(\n",
       "   (linear_layer): Linear(in_features=1, out_features=1, bias=True)\n",
       " ),\n",
       " OrderedDict([('linear_layer.weight', tensor([[0.7645]], device='cuda:0')),\n",
       "              ('linear_layer.bias', tensor([0.8300], device='cuda:0'))]))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2\n",
    "class LinearRegressionModelV3(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.linear_layer = nn.Linear(in_features=1, out_features=1)\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        return self.linear_layer(x)\n",
    "\n",
    "torch.manual_seed(42)   \n",
    "model = LinearRegressionModelV3().to(device)\n",
    "model, model.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.to(device)\n",
    "y_train = y_train.to(device)\n",
    "X_test = X_test.to(device)\n",
    "y_test = y_test.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3\n",
    "loss_fn = nn.L1Loss()\n",
    "\n",
    "optimizer = torch.optim.SGD(params=model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 20 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 40 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 60 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 80 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 100 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 120 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 140 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 160 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 180 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 200 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 220 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 240 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 260 | Train loss: 0.008 | Test loss: 0.007\n",
      "Epoch: 280 | Train loss: 0.008 | Test loss: 0.007\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 300\n",
    "\n",
    "torch.manual_seed(42)\n",
    "for epoch in range(EPOCHS):\n",
    "    model.train()\n",
    "    y_preds = model(X_train)\n",
    "    loss_train = loss_fn(y_preds, y_train)\n",
    "    optimizer.zero_grad()\n",
    "    loss_train.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if epoch % 20 == 0:\n",
    "        model.eval()\n",
    "        y_test_preds = model(X_test)\n",
    "        loss_test = loss_fn(y_test_preds, y_test)\n",
    "        print(f\"Epoch: {epoch} | Train loss: {loss_train:.3f} | Test loss: {loss_test:.3f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4 Visualize predictions\n",
    "torch.manual_seed(42)\n",
    "model.eval()\n",
    "\n",
    "with torch.inference_mode():\n",
    "    y_preds = model(X_test)\n",
    "plot_predictions(predictions = y_preds.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('linear_layer.weight', tensor([[0.3028]], device='cuda:0')),\n",
       "             ('linear_layer.bias', tensor([0.8910], device='cuda:0'))])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 Save the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "MODEL_SAVE_PATH = 'models/01_pytorch_exercises_model.pth'\n",
    "\n",
    "torch.save(obj=model.state_dict(), f=MODEL_SAVE_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegressionModelV3(\n",
       "  (linear_layer): Linear(in_features=1, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_model = LinearRegressionModelV3()\n",
    "new_model.load_state_dict(torch.load(f=MODEL_SAVE_PATH))\n",
    "new_model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True],\n",
       "        [True]], device='cuda:0')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_new_preds = new_model(X_test)\n",
    "y_preds == y_new_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Pytorch-Course",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}