01-workflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "what_were_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 model (inference)\",\n",
    "    5: \"saving and loading a model\",\n",
    "    6: \"putting it all together\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight = 0.7\n",
    "bias = 0.3\n",
    "\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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 40, 10, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create train/test split\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": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\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\u001b[43mX_train\u001b[49m,\n\u001b[1;32m      2\u001b[0m                      train_labels\u001b[38;5;241m=\u001b[39my_train,\n\u001b[1;32m      3\u001b[0m                      test_data\u001b[38;5;241m=\u001b[39mX_test,\n\u001b[1;32m      4\u001b[0m                      test_labels\u001b[38;5;241m=\u001b[39my_test,\n\u001b[1;32m      5\u001b[0m                      predictions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m      6\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[38;5;66;03m# Plot training data in blue\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "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",
    "\n",
    "    # Plot 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 test data)\n",
    "        plt.scatter(test_data, predictions, c=\"r\", s=4, label=\"Predictions\")\n",
    "    \n",
    "    plt.legend(prop={\"size\": 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegressionModel(nn.Module):\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": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set manual seed since nn.Parameter are randomly initialized\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Create an instance of the model (this is a subclass of nn.Module that contains nn.Parameter(s))\n",
    "model_0 = LinearRegressionModel()\n",
    "\n",
    "list(model_0.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('weights', tensor([0.3367])), ('bias', tensor([0.1288]))])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_0.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with Model\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_0(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of testing samples: 10\n",
      "Number of predictions made: 10\n",
      "Prediction 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"
     ]
    }
   ],
   "source": [
    "print(f\"Number of testing samples: {len(X_test)}\")\n",
    "print(f\"Number of predictions made: {len(y_preds)}\")\n",
    "print(f\"Prediction values:\\n{y_preds}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test - y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the loss function\n",
    "loss_fn = nn.L1Loss()\n",
    "\n",
    "# Create the optimizer\n",
    "optimizer = torch.optim.SGD(params=model_0.parameters(), lr=0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | MAE Train Loss: 0.31288138031959534 | MAE Test Loss: 0.48106518387794495\n",
      "Epoch: 10 | MAE Train Loss: 0.1976713240146637 | MAE Test Loss: 0.3463551998138428\n",
      "Epoch: 20 | MAE Train Loss: 0.08908725529909134 | MAE Test Loss: 0.21729660034179688\n",
      "Epoch: 30 | MAE Train Loss: 0.053148526698350906 | MAE Test Loss: 0.14464017748832703\n",
      "Epoch: 40 | MAE Train Loss: 0.04543796554207802 | MAE Test Loss: 0.11360953003168106\n",
      "Epoch: 50 | MAE Train Loss: 0.04167863354086876 | MAE Test Loss: 0.09919948130846024\n",
      "Epoch: 60 | MAE Train Loss: 0.03818932920694351 | MAE Test Loss: 0.08886633068323135\n",
      "Epoch: 70 | MAE Train Loss: 0.03476089984178543 | MAE Test Loss: 0.0805937647819519\n",
      "Epoch: 80 | MAE Train Loss: 0.03132382780313492 | MAE Test Loss: 0.07232122868299484\n",
      "Epoch: 90 | MAE Train Loss: 0.02788739837706089 | MAE Test Loss: 0.06473556160926819\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Set the number of epochs (how many times the model will pass over the training data)\n",
    "epochs = 100\n",
    "\n",
    "train_loss_values = []\n",
    "test_loss_values = []\n",
    "epoch_count = []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "\n",
    "    # Put model in training mode\n",
    "    model_0.train()\n",
    "\n",
    "    # 1. Forward pass on train data using the forward() method inside\n",
    "    y_pred = model_0(X_train)\n",
    "\n",
    "    # 2. Calculate the loss (how different are our models predictions to the ground truth)\n",
    "    loss = loss_fn(y_pred, y_train)\n",
    "\n",
    "    # 3. Zero grad of the optimizer\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Progress the optimizer\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "\n",
    "    # Put the model in evaluation mode\n",
    "    model_0.eval()\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        # 1. Forward pass on test data\n",
    "        test_pred = model_0(X_test)\n",
    "\n",
    "        # 2. Calculate loss on test data\n",
    "        test_loss = loss_fn(test_pred, y_test.type(torch.float))\n",
    "\n",
    "        # Print out whats 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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tTpvc7pbq27dvtnNeevdKbndLXVpnbu8TFRVl3HLLLUZgYKARFBRk3HrrrcaSJUsMwPj2229z+1UYhmEYKSkpxqOPPmpUqlTJ8PX1NZo3b24sWrQo1ztpXn311WznAIxJkyY57Xv//feN2rVrG97e3kadOnWMDz/8MNs5c3Pw4EGjZ8+eRlBQkAE4vebcuXPGxIkTjcjISMPb29sICQkxGjVqZIwbN86IiYkxDMMwfvjhB6N3795GpUqVDG9vb6N8+fJGnz59jNWrVzu9z7x584y6desaXl5eOX6GrHK6RoZhGIsWLTJat25t+Pr6GgEBAUa3bt2MP/74w/F8cnKyMXr0aKNx48ZGcHCw4efnZ0RGRhqTJk0yzp8/bxiGYfzzzz/GoEGDjJo1axp+fn5GSEiI0apVK+Ojjz667O/KMAxjyZIlRqdOnYyAgADD39/fqF+/vvHyyy87HfPxxx8b9erVM3x9fY369esbCxYsuKJrnCkuLs7w8/MzAOO9997L8ZiTJ08aDz30kBEREWF4eXkZZcqUMVq0aGFMmDDBOHfunKOeLl26GGFhYYa3t7cRHh5u3H777cbff/+dr88sksliGHm0W4qIS3nxxReZOHEiUVFRBT7gVESkuFC3lIiLyux+qFu3Lmlpafz666/MmjWLIUOGKNiIiEtTuBFxUf7+/syYMYODBw+SkpJC1apVeeKJJ5g4caLZpYmIFCp1S4mIiIhL0a3gIiIi4lIUbkRERMSlKNyIiIiISzF9QPHs2bN59dVXiY6OpkGDBsycOZMOHTrkeOyKFSvo0qVLtv27du2ibt26+Xo/m83GsWPHCAoKKtBF5URERKTwGIZBQkIC4eHhl52I1NRws2DBAsaOHcvs2bNp164d77zzDr1792bnzp1OKyZfavfu3U6zaGaupZIfx44do0qVKtdUt4iIiJjj8OHDl53OwtS7pVq3bk3z5s2dFuerV68eAwYMYNq0admOz2y5OXv2LKVKlbqq94yLi6NUqVIcPnz4qqYZFxERkaIXHx9PlSpViI2NJSQkJM9jTWu5SU1NZdOmTdlWtu3Zs+dlF25r1qwZycnJ1K9fn4kTJ+bYVZUpJSXFaWG4zFWNg4ODFW5ERERKmPwMKTFtQPGpU6fIyMggLCzMaX9YWBgxMTE5vqZixYq8++67fP311yxcuJDIyEi6devGqlWrcn2fzEXXMh/qkhIREXFtpg8ovjSBGYaRayqLjIx0Wm22TZs2HD58mNdee42OHTvm+JqnnnqK8ePHO37ObNYSERER12Ray03ZsmXx8PDI1kpz4sSJbK05ebn++uvZu3dvrs/7+Pg4uqDUFSUiIuL6TGu58fb2pkWLFixfvpybb77ZsX/58uXcdNNN+T7P5s2bqVixYmGUKCIiJVBGRgZpaWlmlyFXwdvb+7K3eeeHqd1S48ePZ+jQobRs2ZI2bdrw7rvvEhUVxejRowF7l9LRo0f55JNPAJg5cybVq1enQYMGpKam8tlnn/H111/z9ddfm/kxRESkGDAMg5iYGGJjY80uRa6S1WolIiICb2/vazqPqeFm4MCBnD59milTphAdHU3Dhg1ZsmQJ1apVAyA6OpqoqCjH8ampqTz66KMcPXoUPz8/GjRowOLFi+nTp49ZH0FERIqJzGBTvnx5/P39NVFrCZM5yW50dDRVq1a9puvndquCx8fHExISQlxcnMbfiIi4iIyMDPbs2UP58uUJDQ01uxy5SnFxcRw7doxatWrh5eXl9NyVfH9rbSkRESnxMsfY+Pv7m1yJXIvM7qiMjIxrOo/CjYiIuAx1RZVsBXX9FG5ERETEpSjciIiIuJDOnTszduxY089hJtNnKBYREXFHl+uCGT58OB999NEVn3fhwoXZBuO6G4WbgpR4Bs7sh8otza5ERESKuejoaMf2ggULePbZZ9m9e7djn5+fn9PxaWlp+QotZcqUKbgiSyh1SxWUw+thZiP4cjikp5pdjYiIFHMVKlRwPEJCQrBYLI6fk5OTKVWqFF9++SWdO3fG19eXzz77jNOnTzNo0CAqV66Mv78/jRo1Yt68eU7nvbRLqXr16rz44ovcfffdBAUFUbVqVd59990rqvXs2bMMGzaM0qVL4+/vT+/evZ2WPjp06BD9+/endOnSBAQE0KBBA5YsWeJ47R133EG5cuXw8/Ojdu3azJ079+p/cfmglpuCUqEReAdC/BHY/Clcd4/ZFYmIuC3DMEhKu7bbia+Wn5dHgd3188QTTzB9+nTmzp2Lj48PycnJtGjRgieeeILg4GAWL17M0KFDqVGjBq1bt871PNOnT2fq1Kk8/fTTfPXVV9x333107NiRunXr5quOO++8k7179/Ldd98RHBzME088QZ8+fdi5cydeXl488MADpKamsmrVKgICAti5cyeBgYEAPPPMM+zcuZMff/yRsmXLsm/fPpKSkgrk95MbhZuC4uUHHcbDj4/D6teh2RDw9DG7KhERt5SUlkH9Z38y5b13TumFv3fBfL2OHTuWW265xWnfo48+6th+8MEHWbp0Kf/73//yDDd9+vTh/vvvB+yBacaMGaxYsSJf4SYz1Pzxxx+0bdsWgM8//5wqVaqwaNEi/vOf/xAVFcWtt95Ko0aNAKhRo4bj9VFRUTRr1oyWLe1DNqpXr56/D38N1C1VkJoPh8AKF1pvPjO7GhERKeEyA0GmjIwMXnjhBRo3bkxoaCiBgYEsW7bMaaminDRu3Nixndn9deLEiXzVsGvXLjw9PZ3CU2hoKJGRkezatQuAhx56iOeff5527doxadIk/v77b8ex9913H/Pnz6dp06Y8/vjjrFmzJl/vey3UclOQvHyztN5MV+uNiIhJ/Lw82Dmll2nvXVACAgKcfp4+fTozZsxg5syZNGrUiICAAMaOHUtqat5jPS8diGyxWLDZbPmqIbdVmgzDcHS/jRgxgl69erF48WKWLVvGtGnTmD59Og8++CC9e/fm0KFDLF68mJ9//plu3brxwAMP8Nprr+Xr/a+GWm4KWvPhEFQR4o/ax96IiEiRs1gs+Ht7mvIozFmSV69ezU033cSQIUNo0qQJNWrUcBrYWxjq169Peno6f/75p2Pf6dOn2bNnD/Xq1XPsq1KlCqNHj2bhwoU88sgjvPfee47nypUrx5133slnn33GzJkzr3hA85VSuCloXr7Qfrx9e/XrkJ5ibj0iIuIyatWqxfLly1mzZg27du1i1KhRxMTEFOp71q5dm5tuuomRI0fy+++/s3XrVoYMGUKlSpW46aabAPvYoJ9++okDBw7w119/8euvvzqCz7PPPsu3337Lvn372LFjBz/88INTKCoMCjeFofkwCAq3t9789YnZ1YiIiIt45plnaN68Ob169aJz585UqFCBAQMGFPr7zp07lxYtWtCvXz/atGmDYRgsWbLE0d2VkZHBAw88QL169bjhhhuIjIxk9uzZgH0xzKeeeorGjRvTsWNHPDw8mD9/fqHWazFy60xzUVeyZPo1Wf8eLHnUHnIe3qKxNyIihSg5OZkDBw4QERGBr6+v2eXIVcrrOl7J97dabgpLs6H2YJNwTK03IiIiRUjhprBk3jkF9rE3acnm1iMiIuImFG4KU/NhEFxJrTciIiJFSOGmMHn6XGy9+V2tNyIiIkVB4aawNRsKwZUhIVqtNyIiIkVA4aawqfVGRESkSCncFIVmQ7K03nxsdjUiIiIuTeGmKGRtvVn9OqQV7lLvIiIi7kzhpqhkjr05FwOb1HojIiJSWBRuioqnN3R8xL79u1pvRETEfBaLhUWLFpldRoFTuClKTYdASBU4dxw2fWR2NSIiYiKLxZLn484777zqc1evXp2ZM2cWWK0ljcJNUfL0hg6ZrTcz1HojIuLGoqOjHY+ZM2cSHBzstO+NN94wu8QSS+GmqDW9A0Kq2ltvNs41uxoRETFJhQoVHI+QkBAsFovTvlWrVtGiRQt8fX2pUaMGzz33HOnp6Y7XT548mapVq+Lj40N4eDgPPfQQAJ07d+bQoUOMGzfO0QqUX9u2baNr1674+fkRGhrKvffey7lz5xzPr1ixglatWhEQEECpUqVo164dhw4dAmDr1q106dKFoKAggoODadGiBRs3biyg39aV8TTlXd1Z5tib7x+GP2ZCy7vAy8/sqkREXIthQFqiOe/t5Q9XEChy8tNPPzFkyBBmzZpFhw4d+Pfff7n33nsBmDRpEl999RUzZsxg/vz5NGjQgJiYGLZu3QrAwoULadKkCffeey8jR47M93smJiZyww03cP3117NhwwZOnDjBiBEjGDNmDB999BHp6ekMGDCAkSNHMm/ePFJTU1m/fr0jPN1xxx00a9aMOXPm4OHhwZYtW/Dy8rqm38PVUrgxQ5PBsGo6xEXBxg+hzQNmVyQi4lrSEuHFcHPe++lj4B1wTad44YUXePLJJxk+fDgANWrUYOrUqTz++ONMmjSJqKgoKlSoQPfu3fHy8qJq1aq0atUKgDJlyuDh4UFQUBAVKlTI93t+/vnnJCUl8cknnxAQYK//zTffpH///rz88st4eXkRFxdHv379qFmzJgD16tVzvD4qKorHHnuMunXrAlC7du1r+h1cC3VLmcHTGzo+at/+fSakmvSvCxERKZY2bdrElClTCAwMdDxGjhxJdHQ0iYmJ/Oc//yEpKYkaNWowcuRIvvnmG6cuq6uxa9cumjRp4gg2AO3atcNms7F7927KlCnDnXfeSa9evejfvz9vvPEG0dHRjmPHjx/PiBEj6N69Oy+99BL//vvvNdVzLdRyY5Ymg2D1axAbBZvmqvVGRKQgefnbW1DMeu9rZLPZeO6557jllluyPefr60uVKlXYvXs3y5cv5+eff+b+++/n1VdfZeXKlVfdFWQYRq7jczL3z507l4ceeoilS5eyYMECJk6cyPLly7n++uuZPHkygwcPZvHixfz4449MmjSJ+fPnc/PNN19VPddCLTdm8fSGDmq9EREpFBaLvWvIjMc1jrcBaN68Obt376ZWrVrZHlar/avbz8+PG2+8kVmzZrFixQrWrl3Ltm3bAPD29iYjI+OK3rN+/fps2bKF8+fPO/b98ccfWK1W6tSp49jXrFkznnrqKdasWUPDhg354osvHM/VqVOHcePGsWzZMm655RbmzjXnxhmFGzM1HQylqsL5E/axNyIiIsCzzz7LJ598wuTJk9mxYwe7du1ytJQAfPTRR3zwwQds376d/fv38+mnn+Ln50e1atUA+zw3q1at4ujRo5w6dSpf73nHHXfg6+vL8OHD2b59O7/99hsPPvggQ4cOJSwsjAMHDvDUU0+xdu1aDh06xLJly9izZw/16tUjKSmJMWPGsGLFCg4dOsQff/zBhg0bnMbkFCWFGzN5eEHHx+zbf8yE1PN5Hi4iIu6hV69e/PDDDyxfvpzrrruO66+/ntdff90RXkqVKsV7771Hu3btaNy4Mb/88gvff/89oaGhAEyZMoWDBw9Ss2ZNypUrl6/39Pf356effuLMmTNcd9113HbbbXTr1o0333zT8fw///zDrbfeSp06dbj33nsZM2YMo0aNwsPDg9OnTzNs2DDq1KnD7bffTu/evXnuuecK5xd0GRbDMAxT3tkk8fHxhISEEBcXR3BwsNnlQEYa/F8LiD0EPZ+Htg+aXZGISImTnJzMgQMHiIiIwNfX1+xy5CrldR2v5PtbLTdmc2q9eUOtNyIiItdI4aY4aPJfKF0dzp+EDR+YXY2IiEiJpnBTHKj1RkREpMAo3BQXjQfaW28ST8GG982uRkREpMRSuCkuPLyg4+P2bbXeiIhcFTe7R8blFNT1U7gpThoPhNIRkHga1r9ndjUiIiVG5qy8iYmaELUkS01NBcDDw+OazqPlF4oTD0/72Jtv74c1s+C6EeATaHZVIiLFnoeHB6VKleLEiROAfU6W3JYSkOLJZrNx8uRJ/P398fS8tniicFPcNB4Iq16FswfsY2/ajzW7IhGREiFzBezMgCMlj9VqpWrVqtccTBVuihsPT+j0OCy6T603IiJXwGKxULFiRcqXL09aWprZ5chV8Pb2dqyddS0UboqjRrfbW2/O7IcN70H7cWZXJCJSYnh4eFzzmA0p2TSguDjy8Mxy59QsSEkwtx4REZESROGmuGr0HyhTE5LO6M4pERGRK6BwU1xljr0BWPN/ar0RERHJJ4Wb4qzhbVlab941uxoREZESQeGmOPPwhE5P2LfVeiMiIpIvCjfFXcNbIbQWJJ2FP98xuxoREZFiT+GmuMvaerP2TUiON7ceERGRYk7hpiTI2nqjsTciIiJ5UrgpCawezmNv1HojIiKSK4WbkqLhrRBaG5JjYb3G3oiIiORG4aakcGq9eROS48ytR0REpJhSuClJGt4CZevYW2/+1NgbERGRnCjclCRZW2/W/p9ab0RERHKgcFPSNLgZykbag43mvREREclG4aaksXpcXHNqrcbeiIiIXErhpiTK2nqz7m2zqxERESlWFG5KIqsHdL4w9mbdW5AUa2o5IiIixYnp4Wb27NlERETg6+tLixYtWL16db5e98cff+Dp6UnTpk0Lt8Diqv4AKFf3wtgbtd6IiIhkMjXcLFiwgLFjxzJhwgQ2b95Mhw4d6N27N1FRUXm+Li4ujmHDhtGtW7ciqrQYchp7M1utNyIiIheYGm5ef/117rnnHkaMGEG9evWYOXMmVapUYc6cOXm+btSoUQwePJg2bdoUUaXFVP2b7a03KWq9ERERyWRauElNTWXTpk307NnTaX/Pnj1Zs2ZNrq+bO3cu//77L5MmTcrX+6SkpBAfH+/0cBlWa5Z5b9R6IyIiAiaGm1OnTpGRkUFYWJjT/rCwMGJiYnJ8zd69e3nyySf5/PPP8fT0zNf7TJs2jZCQEMejSpUq11x7sVJ/AJSrZ2+9WZd3i5eIiIg7MH1AscVicfrZMIxs+wAyMjIYPHgwzz33HHXq1Mn3+Z966ini4uIcj8OHD19zzcWK1ZrlzqnZkHTW3HpERERMlr/mj0JQtmxZPDw8srXSnDhxIltrDkBCQgIbN25k8+bNjBkzBgCbzYZhGHh6erJs2TK6du2a7XU+Pj74+PgUzocoLurdBOXrw4md9tabLk+bXZGIiIhpTGu58fb2pkWLFixfvtxp//Lly2nbtm2244ODg9m2bRtbtmxxPEaPHk1kZCRbtmyhdevWRVV68ZN17M26OWq9ERERt2Zayw3A+PHjGTp0KC1btqRNmza8++67REVFMXr0aMDepXT06FE++eQTrFYrDRs2dHp9+fLl8fX1zbbfLdW7Eco3gBM77IOLu04wuyIRERFTmBpuBg4cyOnTp5kyZQrR0dE0bNiQJUuWUK1aNQCio6MvO+eNXJA59ubLYfbbwq+/D/zLmF2ViIhIkbMYhmGYXURRio+PJyQkhLi4OIKDg80up2DZbPB2e3vrTcfHoOtEsysSEREpEFfy/W363VJSgJzunHobEs+YW4+IiIgJFG5cTd3+ENYQUhNg7VtmVyMiIlLkFG5cTdY7p/58R603IiLidhRuXFHdfhDWSK03IiLilhRuXFHWsTd/auyNiIi4F4UbV1W3H1RoBKnnYO2bZlcjIiJSZBRuXJXFAp2etG//+Q6cP21uPSIiIkVE4caV1e2r1hsREXE7CjeuzGKBzk/Zt9e/q9YbERFxCwo3ri6yD1RofKH15v/MrkZERKTQKdy4uqytN3+q9UZERFyfwo07iOxtb71JOw9rZpldjYiISKFSuHEHTmNv3oPzp8ytR0REpBAp3LiLyN5QscmF1huNvREREdelcOMu1HojIiJuQuHGndS5ASo21dgbERFxaQo37uTS1ptzJ82tR0REpBAo3LibOr0gvBmkJar1RkREXJLCjbvJ2nqz4X213oiIiMtRuHFHtXtCePMLrTdvmF2NiIhIgVK4cUdOY2/UeiMiIq5F4cZd1e5hb71JT4I/ZppdjYiISIFRuHFXTmNvPoBzJ8ytR0REpIAo3Liz2j2gUosLrTcaeyMiIq5B4cadXdp6k3Dc3HpEREQKgMKNu6vVHSq1tLfeaN4bERFxAQo37k6tNyIi4mIUbgRqdbvYeqOxNyIiUsIp3Ii99abLhdabjR9AQoy59YiIiFwDhRuxq9kNKl8H6clqvRERkRJN4Ubsso692fihWm9ERKTEUriRi2p2hcqt7K03v880uxoREZGronAjF1ks0PlJ+/bGDyH+mLn1iIiIXAWFG3FWsytUuR4yUmDVq2ZXIyIicsUUbsSZxQLdnrVv//UJnNlvbj0iIiJXSOFGsqvezn73lC0dVrxkdjUiIiJXROFGctbtGfuff38Jx3eaW4uIiMgVULiRnIU3g3o3Agb89oLZ1YiIiOSbwo3krutEsFjhnx/gyCazqxEREckXhRvJXblIaDLIvv3rFHNrERERySeFG8lbpyfA6gX7V8D+lWZXIyIiclkKN5K30tWg5V327V+ngmGYW4+IiMhlKNzI5XV4FDz94MgG2LPU7GpERETypHAjlxcUBtePtm//MhVsNnPrERERyYPCjeRP24fAJwRO7IAdC82uRkREJFcKN5I//mWg3YP27V+fh4w0c+sRERHJhcKN5F/r+8C/LJw9AJs/M7saERGRHCncSP75BELHR+3bK1+BtCRz6xEREcmBwo1cmRZ3QXBlSDgGGz4wuxoREZFsFG7kynj5Qucn7Nurp0NyvLn1iIiIXELhRq5ck8FQpiYknYF1c8yuRkRExInCjVw5D0/oOsG+veb/IPGMufWIiIhkoXAjV6f+zRDWCFIT4PcZZlcjIiLioHAjV8dqhW7P2LfXvwvx0ebWIyIicoHCjVy92j2hSmtIT4ZVr5pdjYiICKBwI9fCYoFuz9q3//oYzhwwtx4REREUbgqMYRgs2RbNJ2sPml1K0areHmp2BVs6rHjJ7GpEREQUbgrKmn9Pc//nf/HC4l0cOZtodjlFq+uFsTd/L4ATu8ytRURE3J7CTQFpWzOUNjVCSUm38cJiN/uCr9Qc6vUHDPuimiIiIiZSuCkgFouFSTfWx8Nq4cftMfyx75TZJRWtLhMBC/zzAxzdZHY1IiLixhRuClDdCsEMvb4aAM99v4O0DJvJFRWh8nWhyX/t279MNbcWERFxawo3BWxc9zqU9vdiz/FzfLbukNnlFK3OT4LVC/b/BgdWmV2NiIi4KYWbAhbi78VjveoC8PryPZw+l2JyRUWodHVoMdy+/ctUMAxTyxEREfdkeriZPXs2ERER+Pr60qJFC1avXp3rsb///jvt2rUjNDQUPz8/6taty4wZxW/q/4HXVaFhpWASktN5bdlus8spWh0fA08/OLIe9vxkdjUiIuKGTA03CxYsYOzYsUyYMIHNmzfToUMHevfuTVRUVI7HBwQEMGbMGFatWsWuXbuYOHEiEydO5N133y3iyvPmYbUwuX8DAOZvOMzfR2LNLagoBVWA1qPs279OBZsbjTsSEZFiwWIY5vUdtG7dmubNmzNnzhzHvnr16jFgwACmTZuWr3PccsstBAQE8Omnn+br+Pj4eEJCQoiLiyM4OPiq6s6vcQu28M3mozSvWoqvRrfFarUU6vsVG4ln4I0mkBIPt34AjW4zuyIRESnhruT727SWm9TUVDZt2kTPnj2d9vfs2ZM1a9bk6xybN29mzZo1dOrUKddjUlJSiI+Pd3oUlSd718Xf24O/omJZtOVokb2v6fzLQNuH7Nu/vQAZaebWIyIibsW0cHPq1CkyMjIICwtz2h8WFkZMTEyer61cuTI+Pj60bNmSBx54gBEjRuR67LRp0wgJCXE8qlSpUiD150dYsC8Pdq1tr+PHfziXkl5k722660eDf1k4sx+2fGF2NSIi4kZMH1BssTh31RiGkW3fpVavXs3GjRt5++23mTlzJvPmzcv12Keeeoq4uDjH4/DhwwVSd37d3b461UP9OZmQwv/9urdI39tUPkHQ4RH79sqXIS3Z3HpERMRtmBZuypYti4eHR7ZWmhMnTmRrzblUREQEjRo1YuTIkYwbN47JkyfneqyPjw/BwcFOj6Lk4+nBs/3rA/Dh7wfYf/Jckb6/qVreDcGVIP4obPzQ7GpERMRNmBZuvL29adGiBcuXL3fav3z5ctq2bZvv8xiGQUpK8Z5LpmvdMLpEliMtw2DqDzvNLqfoePlCpyfs26unQ0qCufWIiIhbMLVbavz48bz//vt8+OGH7Nq1i3HjxhEVFcXo0aMBe5fSsGHDHMe/9dZbfP/99+zdu5e9e/cyd+5cXnvtNYYMGWLWR8i3Z/s3wMvDwm+7T/LLruNml1N0mg6GMjUh8RSse9vsakRExA14mvnmAwcO5PTp00yZMoXo6GgaNmzIkiVLqFbNvj5TdHS005w3NpuNp556igMHDuDp6UnNmjV56aWXGDVqlFkfId8iygZwT/savL3yX6b8sJP2tcvi4+lhdlmFz8MLujwNX98Da2bBdffY76YSEREpJKbOc2OGopzn5lLnUtLp+toKTiSk8PgNkdzfuVaRvr9pbDZ4pwMc3w7txkKP58yuSERESpgSMc+NOwr08eSpPvZ1p978dR8xcW5yB5HVCl2fsW//+Q4k5H2rv4iIyLVQuCliA5pWonnVUiSmZvDSj7vMLqfo1OkFlVtBehKses3sakRExIUp3BQxi8XCczc2xGKBRVuOsfHgGbNLKhoWC3R71r696SM4e9DMakRExIUp3JigUeUQ/nudfabkZ7/dQYbNTYY9RXSAGl3AlgYrXjK7GhERcVEKNyZ5tGckwb6e7IyOZ/6GnFdBd0ndLoy92TofTrhRt5yIiBQZhRuThAb6ML5HHQBe+2k3sYmpJldURCq1gLr9AMO+qKaIiEgBU7gx0ZDrq1EnLJCziWnMWL7H7HKKTteJgAV2fQ9HN5ldjYiIuBiFGxN5eliZ3L8BAJ+uO8Q/MfEmV1REyteDxgPt278+b24tIiLichRuTNa2Vln6NKqAzYDJ3+3AbeZU7PwkWD3h31/hwGqzqxEREReicFMMPN2nHj6eVtbtP8OSbW4ywV2ZCGg+3L7961Rwl1AnIiKFTuGmGKhc2p/7OtcE4IXFO0lMTTe5oiLS8THw9IXDf8LeZWZXIyIiLkLhppgY3akmlUr5cSwumbdX/Gt2OUUjuCK0ute+/ctU+xpUIiIi10jhppjw9fLgmX71AHh71X4On0k0uaIi0n4ceAfB8W2w8xuzqxERERegcFOM9GpQgXa1QklNt/H84p1ml1M0/MtA2wft27++ABlu0iUnIiKFRuGmGLFYLEzq3wAPq4Wfdhxn9d6TZpdUNNrcD/6hcOZf2PqF2dWIiEgJp3BTzNQJC2JYm2oAPPf9TtIy3GAcik8QdHjEvr3iZUhLNrceEREp0RRuiqGx3etQJsCbfSfO8fGag2aXUzRa3gPBlSD+CGyaa3Y1IiJSgl1VuPn4449ZvHix4+fHH3+cUqVK0bZtWw4dOlRgxbmrED8vHu8VCcAbP+/lZEKKyRUVAS9f6PS4fXvVa5Byztx6RESkxLqqcPPiiy/i5+cHwNq1a3nzzTd55ZVXKFu2LOPGjSvQAt3Vf1pWoVGlEBJS0nn1p3/MLqdoNL0DytSAxFPw5xyzqxERkRLqqsLN4cOHqVWrFgCLFi3itttu495772XatGmsXq2p9AuCh9XC5Bvt6059ufEIWw7HmltQUfDwgi4T7Nt//B8knjG3HhERKZGuKtwEBgZy+vRpAJYtW0b37t0B8PX1JSkpqeCqc3MtqpXmluaVAPu6UzabGyxR0OAWKN8AUuJgzSyzqxERkRLoqsJNjx49GDFiBCNGjGDPnj307dsXgB07dlC9evWCrM/tPXlDXQK8PdhyOJaFm4+aXU7hs1qh2zP27XVvQ8Jxc+sREZES56rCzVtvvUWbNm04efIkX3/9NaGhoQBs2rSJQYMGFWiB7q58sC8PdasNwEs//kNCcprJFRWBOjdA5esgPQlWv2Z2NSIiUsJYDMO9lmOOj48nJCSEuLg4goODzS4nX1LTbdwwcxX7T51nZIcIJvStb3ZJhe/AKvi4P1i94MFNULqa2RWJiIiJruT7+6pabpYuXcrvv//u+Pmtt96iadOmDB48mLNnz17NKSUP3p5WnulvDzRz/zjIvhNucJt0REeo0RlsabDyZbOrERGREuSqws1jjz1GfHw8ANu2beORRx6hT58+7N+/n/HjxxdogWLXJbI83eqWJ91m8Nz3O3CLBreuz9r/3DoPTu42txYRESkxrircHDhwgPr17S0JX3/9Nf369ePFF19k9uzZ/PjjjwVaoFz0TL/6eHtYWb33FD/vOmF2OYWvcguo2w8MG/z2gtnViIhICXFV4cbb25vExEQAfv75Z3r27AlAmTJlHC06UvCqlw1gRIcIAKb+sJPktAyTKyoCXSYAFtj5LRzbbHY1IiJSAlxVuGnfvj3jx49n6tSprF+/3nEr+J49e6hcuXKBFijOHuhSi7BgH6LOJPLB7wfMLqfwhdWHxrfbt3993txaRESkRLiqcPPmm2/i6enJV199xZw5c6hUyT7R3I8//sgNN9xQoAWKswAfT57uUw+AN3/dx7FYN5g0sfOTYPWEfT/DwT/MrkZERIo53QpeAhmGwX/eXsvGQ2fp3ySc/xvUzOySCt8P42Djh1C1Ddz1I1gsZlckIiJFqNBvBQfIyMjg66+/5vnnn+eFF15g4cKFZGS4wRiQYsBisa87ZbHA91uP8ef+02aXVPg6PgaevhC11t6CIyIikourCjf79u2jXr16DBs2jIULF/LVV18xdOhQGjRowL///lvQNUoOGlYKYVCrqgBM+m4H6Rk2kysqZMHh0GqkffuX58Dm4p9XRESu2lWFm4ceeoiaNWty+PBh/vrrLzZv3kxUVBQRERE89NBDBV2j5OLRnpGE+HnxT0wC8zYcNrucwtduHHgHQcw22LnI7GpERKSYuqpws3LlSl555RXKlCnj2BcaGspLL73EypUrC6w4yVuZAG8e6VkHgOnLdnP2fKrJFRWygFBoO8a+/dsLkJFubj0iIlIsXVW48fHxISEhIdv+c+fO4e3tfc1FSf4NblWVuhWCiE1M4/Xle8wup/Bdfz/4lYHT++wzF4uIiFziqsJNv379uPfee/nzzz8xDAPDMFi3bh2jR4/mxhtvLOgaJQ+eHlYm9W8AwOd/HmLnMRefRNE3GDpcWOJjxUuQnmJuPSIiUuxcVbiZNWsWNWvWpE2bNvj6+uLr60vbtm2pVasWM2fOLOAS5XLa1Aylb+OK2AyY/J0brDt13QgIqgjxR2DjXLOrERGRYuaa5rnZt28fu3btwjAM6tevT61atQqytkLhCvPc5ORobBLdpq8gOc3GrEHNuLFJuNklFa6NH9rnvgkoBw9tAZ9AsysSEZFCdCXf3/kON1ey2vfrr7+e72OLmquGG4BZv+zl9eV7qBDsy6+PdsLf29PskgpPRhq8eR2cPQBdn4GOj5pdkYiIFKIr+f7O97ff5s35W7TQopljTXNvxxr8b9NhDp9JYvZv//Jor0izSyo8Hl72RTUXjoA/ZsF194BfabOrEhGRYkDLL7iYn3bEMOrTTXh7WFk+viPVQgPMLqnw2Gzwdns4sQPaj4fuk8yuSERECkmRLL8gxVPP+mF0qF2W1AwbU3/YZXY5hctqha4T7dt/vg0Jx82tR0REigWFGxdjsViY1L8+nlYLP+86zordJ8wuqXBF9oZKLSEtEVZPN7saEREpBhRuXFCt8kEMb1sdgCk/7CQ13YXXYbJYoNuz9u2NH0JslLn1iIiI6RRuXNTD3WtTNtCb/SfP8/Gag2aXU7hqdIKITmBLgxUvm12NiIiYTOHGRQX7evF4r7oAvPHLXk4kJJtcUSHLbL3Z+gWcdINlKEREJFcKNy7sthaVaVI5hHMp6byydLfZ5RSuyi0hsi8YNvuimiIi4rYUblyY1Wph8o32dae+2nSEzVFnTa6okHWdAFhg5yI4tsXkYkRExCwKNy6uWdXS3NaiMmBfd8pmc+FpjcIaQKP/2Ld/fd7cWkRExDQKN27g8RsiCfTxZOuROL7adMTscgpX5yfB6gn7lsOhNWZXIyIiJlC4cQPlg3x5uFttAF5e+g9xSWkmV1SIQmtCs6H27V+mgHtNwC0iIijcuI3hbatTo1wAp8+nMuuXvWaXU7g6PQ4ePhC1Fvb9YnY1IiJSxBRu3IS3p5VJ/e2Diz9ec5C9xxNMrqgQBYdDq5H27V+es69BJSIibkPhxo10qlOOHvXDSLcZPPf9Tlx6zdT248E7CGL+hh8fU/eUiIgbUbhxM8/0rY+3p5Xf953ipx0uvNBkQCjc+AZggQ3va+4bERE3onDjZqqG+nNvhxoAPL94J8lpGSZXVIga3gp9X7Nvr3oV1rxpbj0iIlIkFG7c0P1dalIh2JcjZ5N4d9V+s8spXNeNgK7P2LeXTYDNn5lbj4iIFDqFGzfk7+3J033rATB7xT6OxiaZXFEh6/AItBlj3/7uQdj1vbn1iIhIoVK4cVP9G1ekVfUyJKfZeHHJLrPLKVwWC/R8HpoNsa899dXdsH+F2VWJiEghUbhxUxaLfd0pqwUW/x3N2n9Pm11S4bJYoP8sqHcjZKTCvMFwZKPZVYmISCFQuHFj9cODuaN1NQCe+34H6RkuPh+M1QNufR9qdIa08/D5bXDCxVutRETckMKNmxvfow6l/L34JyaBz/+MMrucwufpAwM/h0otIeksfHoznD1odlUiIlKATA83s2fPJiIiAl9fX1q0aMHq1atzPXbhwoX06NGDcuXKERwcTJs2bfjpp5+KsFrXUzrAm0d6RgIwfdluzpxPNbmiIuATCHf8D8rXh4Ro+GQAJMSYXZWIiBQQU8PNggULGDt2LBMmTGDz5s106NCB3r17ExWVcwvCqlWr6NGjB0uWLGHTpk106dKF/v37s3nz5iKu3LUMblWVehWDiU9O57Vlu80up2j4l4EhC6FUNTh7AD69xd6SIyIiJZ7FMHEO/tatW9O8eXPmzJnj2FevXj0GDBjAtGnT8nWOBg0aMHDgQJ599tl8HR8fH09ISAhxcXEEBwdfVd2u6M/9pxn47josFvh+THsaVgoxu6SicWY/fHgDnDsOlVvBsEXgHWB2VSIicokr+f42reUmNTWVTZs20bNnT6f9PXv2ZM2aNfk6h81mIyEhgTJlyuR6TEpKCvHx8U4Pya51jVD6NwnHMGDydztce92prMrUgKHfgG8IHFkPC4ZAeorZVYmIyDUwLdycOnWKjIwMwsLCnPaHhYURE5O/8Q/Tp0/n/Pnz3H777bkeM23aNEJCQhyPKlWqXFPdruzpPnXx8/Jg46GzfLf1mNnlFJ2wBnDHV+DlD//+CgvvBZsLL0shIuLiTB9QbLFYnH42DCPbvpzMmzePyZMns2DBAsqXL5/rcU899RRxcXGOx+HDh6+5ZldVMcSPMV1rAfDikl2cS0k3uaIiVKUV/PdzsHrBzkXwwzitJC4iUkKZFm7Kli2Lh4dHtlaaEydOZGvNudSCBQu45557+PLLL+nevXuex/r4+BAcHOz0kNzd0z6CaqH+HI9PYbq7DC7OVLOrfR4cixX++hh+nmx2RSIichVMCzfe3t60aNGC5cuXO+1fvnw5bdu2zfV18+bN48477+SLL76gb9++hV2m2/H18mDqTQ0B+HjNQbYdiTO5oiLWYAD0f8O+/cdM+H2GmdWIiMhVMLVbavz48bz//vt8+OGH7Nq1i3HjxhEVFcXo0aMBe5fSsGHDHMfPmzePYcOGMX36dK6//npiYmKIiYkhLs7NvoALWcc65ejfJBybAU9/s40Mm5t1zzQfBj2m2rd/ngybPjKzGhERuUKmhpuBAwcyc+ZMpkyZQtOmTVm1ahVLliyhWjX7kgDR0dFOc9688847pKen88ADD1CxYkXH4+GHHzbrI7isZ/rVI8jXk21H4/hk7UGzyyl67R6C9uPt29+Phe0LTS1HRETyz9R5bsygeW7y77N1h5i4aDuBPp4sH9+RiiF+ZpdUtAzDPrB401z7QONB86F23mO8RESkcJSIeW6k+BvcqirNqpbiXEo6z3230+xyip7FAn2nQ4NbwJZmnwMnap3ZVYmIyGUo3EiurFYLL97cCA+rhaU7Yvhl13GzSyp6Vg+4+R2o1R3Sk+Dz2yFmm9lViYhIHhRuJE/1KgYzon0EAM9+u4PEVDea+yaTpzfc/ilUuR5S4uzrUJ3+1+yqREQkFwo3clkPd69NpVJ+HI1NYubPe80uxxze/jB4AYQ1gvMn4NMBEO9GsziLiJQgCjdyWf7enky5qQEAH/x+gJ3H3HR9Lr9SMHShfT2q2Cj49GZIPGN2VSIicgmFG8mXbvXC6N2wAhk2g6e/2YbN3ea+yRRYHoYugqBwOPkPfH4bpCSYXZWIiGShcCP5Nql/AwJ9PNlyOJbP10dd/gWuqnQ1+0rifmXg6CaYPxjSks2uSkRELlC4kXyrEOLLIz3rAPDK0n84keDGX+jl68KQr8A7EA6sgq/vgQw3HGwtIlIMKdzIFRnWpjqNKoWQkJzO1B92mV2OuSq1gEHzwMMH/vkBvn8IbDazqxIRcXsKN3JFPKwWpt3SCKsFvt96jJV7TppdkrkiOsJ/5oLFA7Z8Dssm2Gc2FhER0yjcyBVrWCmE4W2rA/DMou0kp2WYW5DZ6vaFm960b6+bDateM7ceERE3p3AjV+WRnpFUCPYl6kwi//erm859k1XTwXDDS/bt356H9e+ZW4+IiBtTuJGrEujjyeQb7XPfvLtqP3uO63Zorr8POj1h317yKPz9pbn1iIi4KYUbuWq9GoTRvV550jIMJrjz3DdZdX4KWo2yb38zGnYvNbceERE3pHAjV81isfDcTQ3x9/Zgw8Gz/G/TYbNLMp/FYu+eajwQjAz433A4+IfZVYmIuBWFG7kmlUr5Ma67fe6bF5f8w6lzKSZXVAxYrXDTW1CnN6Qnw7z/wrEtZlclIuI2FG7kmt3Vrjr1KgYTl5TGi4vdfO6bTB5e9lvEq7WHlHj47FY4pYHXIiJFQeFGrpmnh5UXb26IxQILNx9lzb5TZpdUPHj52Sf5q9gEEk/BJwMgVl13IiKFTeFGCkSzqqUZ0roaABM1981FvsEwZCGE1ob4I/DpADjn5hMfiogUMoUbKTCP3RBJuSAf9p86z5wV/5pdTvERUBaGLYLgynB6H3x2CyTHmV2ViIjLUriRAhPs68Wk/vUBmLPiX/49ec7kioqRkMr2gONfFmL+hnmDIC3J7KpERFySwo0UqL6NKtKpTjlSM2xM/GY7htZZuqhsbRjyNfgEw6E/4H93Qkaa2VWJiLgchRspUBaLhak3NcTH08ra/adZ+NdRs0sqXsKbwqD54OkLe5bCovu1kriISAFTuJECVzXUn4e71wbghSW7OHs+1eSKipnq7eD2T8DqCdu+hKVPaCVxEZECpHAjhWJkhxrUCQvkzPlUXvrxH7PLKX7q9IKb3wEssP5dWDHN7IpERFyGwo0UCi8PKy/e3AiABRsPs/7AGZMrKoYa3QZ9X7Nvr3wZ1s42tx4RERehcCOFpmX1Mvz3uioAPP3NNlLTNbYkm+tGQNeJ9u2fnoLNn5tbj4iIC1C4kUL1ZO+6hAZ4s+/EOd5bvd/scoqnDo9CmzH27e/GwK4fzK1HRKSEU7iRQlXK35uJ/eoBMOuXvRw6fd7kioohiwV6Pg9Nh4Bhg6/ugv0rzK5KRKTEUriRQjegaSXa1QolJd3GxEWa+yZHFgv0fwPq9oOMVJg3GI5sMrsqEZESSeFGCl3m3DfenlZW7z3F939Hm11S8eThCbd+ABGdIO08fH4rnNCdZiIiV0rhRopEjXKBPNC5FgBTvt9JXJJm5s2Rly/893Oo1AKSztoX2jx7yOyqRERKFIUbKTKjO9egRrkATp1L4ZWlapHIlU8Q3PEVlKsLCdH2gJNw3OyqRERKDIUbKTI+nh68MMA+980X66P4K+qsyRUVY/5lYOg3UKoqnNkPn94MhzeYXZWISImgcCNFqk3NUG5tXhnDgKcXbiMtQ3Pf5Co4HIZ9C4FhcGIHfNAdPuwNu3/UelQiInlQuJEiN6FvPUr5e/FPTAIf/n7A7HKKtzI14J5l9tvErV4QtQbm/RdmXw9/fQrpKWZXKCJS7CjcSJErE+DN033sc9/M/HkvR84mmlxRMVe6Ogx4C8b+De0eBp9gOLXbPuHfzMbw+wxIijW7ShGRYkPhRkzxnxaVaRVRhqS0DJ79dofmvsmP4HDoMQXGbYceUyGoIpyLgZ8nw4yGsGwixB01u0oREdMp3IgpLBYLL97cEC8PC7/+c4Kl22PMLqnk8A2Bdg/Bw3/DTbPtd1WlJsCa/4M3GsM398HxnWZXKSJiGoUbMU2t8kGM7lQTgMnf7yAhWXPfXBFPb2h2B9y3FgZ/CdXagy0dtn4Bc9rA5/+BA6tBrWIi4mYUbsRUD3SpRbVQf47HpzB92R6zyymZrFao0wvuWgwjfoF6NwIW2LsMPu4H73WFHYvAlmF2pSIiRULhRkzl6+XB8wMaAvDJ2oP8fSTW3IJKusotYeCn8OAmaHk3ePrCsb/gf8Ph/1rAhvchLcnsKkVECpXCjZiuQ+1y3NQ0HJsBT3+zjXTNfXPtQmtCvxkwdjt0fBz8SsPZA7D4EZjRAFa8DIlnzK5SRKRQKNxIsTCxb32CfT3ZfjSeT9ZqLaUCE1gOuk6AcTug9yv2GY8TT8OKF+H1+rDkMTh70OwqRUQKlMKNFAvlgnx4onddAKYv2010nLpOCpR3ALQeBQ9utq88XqExpCfB+ndhVjP4311wbLPZVYqIFAiFGyk2Bl1XleZVS3E+NYPJ3+0wuxzX5OEJjW6DUavsSzvU7AqGDXYshHc7w8f9Yd/PusNKREo0hRspNqxWCy/e0ghPq4Wfdhzn551aCbvQWCxQo7N9cc7Rv0Oj28HiAQdWwWe3wtvtYesCyNDt+SJS8ijcSLFSt0Iw93SIAGDSdzs4n5JuckVuoEIjuPU9eHgLXH8/eAXA8e3wzb3wRlNY+xakJJhdpYhIvincSLHzcLfaVCrlx9HYJGb+rLlvikypqnDDNBi/A7o+AwHlIP4I/PS0/Q6rn5+DBLWmiUjxp3AjxY6/t6dj7psP/zjIzmPxJlfkZvxKQ8dH7beR938DQmtBchz8/jrMbAjfPQin9ppdpYhIrhRupFjqUrc8fRpVIMNm8PQ328iwaYBrkfPyhRZ3wgMbYODnULkVZKTCX5/Am9fBvMEQ9afZVYqIZKNwI8XWpP4NCPTxZMvhWL74U3PfmMZqhXr9YMRyuPsniOwDGLB7MXzYEz7oBf8sBpsmXxSR4kHhRoqtsGBfHusVCcArS3dzIj7Z5IqEqtfDoHnwwHpoNhQ8vOHwOpg/GN5qZW/VSU8xu0oRcXMKN1KsDbm+Go0rh5CQks6UH3aaXY5kKhcJN70JY7dB+3HgEwKn99rH48xsBKunQ9JZs6sUETelcCPFmofVwos3N8JqgR/+jmbF7hNmlyRZBVWA7pNh3Hbo+QIEV4Jzx+GXKTCjIfw0AeKOmF2liLgZhRsp9hpWCuGudva5b575djtJqRkmVyTZ+AZD2zHw0Ba4+R0oXx9Sz8HaN+GNJrBwFMRsN7tKEXETFsNwr3nW4+PjCQkJIS4ujuDgYLPLkXw6n5JO99dXEh2XzP2da/L4DXXNLknyYhj2ZRz+eAMOrr64v0YXqNkFKrWAik3BJ9C0EkWkZLmS72+FGykxftoRw6hPN+FptbDk4Q7UCQsyuyTJj6N/wZpZsPNb+zpWmSxWewtPpeZQqaU98JSvB1YP82oVkWJL4SYPCjcl28hPNrJ853FaVivNl6PaYLVazC5J8uvMftj5HRzdZH/EH81+jFcAhDezB57KFwJPcCX7Wlgi4tYUbvKgcFOyHYtNovvrK0lMzeClWxrx31ZVzS5JrlZ89IWgs/HCn5shNYc1rAIr2ENO5Rb2Fp7wZvYxPiLiVhRu8qBwU/K9v3o/zy/eRYifF7880omygT5mlyQFwZZhX9bh6EY4ciHwHN8BxqUDyC32W9ErtbgQelrau7c8vEwpW0SKhsJNHhRuSr70DBs3vvkHO6PjublZJWYMbGp2SVJYUhMheqtzC09sVPbjPP2gYhPnFp5SVdWdJeJCFG7yoHDjGrYcjuXm2X9gGPD5iNa0q1XW7JKkqJw7cXHczpGN9gHLKXHZjwsod7F1p1IL+zgev9JFX6+IFIgSFW5mz57Nq6++SnR0NA0aNGDmzJl06NAhx2Ojo6N55JFH2LRpE3v37uWhhx5i5syZV/R+Cjeu49lvt/PJ2kNElA3gx4c74Oulu2zcks0GZ/7NEnY22ufUsaVlPza01sU7syq3gLBG4Old9DWLyBW7ku9vzyKqKUcLFixg7NixzJ49m3bt2vHOO+/Qu3dvdu7cSdWq2QeKpqSkUK5cOSZMmMCMGTNMqFiKk0d7RbJ0ewwHTp1n9op/Gd+jjtkliRmsVihb2/5o8l/7vrRkiNl2sSvryEY4ewBO77M//p5vP87DGyo0vnhnVqUWUKaGurNESjhTW25at25N8+bNmTNnjmNfvXr1GDBgANOmTcvztZ07d6Zp06ZquXFzi/+O5oEv/sLbw8qPYztQs5wmhZNcJJ7J0rpzYQxPTutf+ZW+EHSyBJ6A0KKvV0SclIiWm9TUVDZt2sSTTz7ptL9nz56sWbOmwN4nJSWFlJSLqxTHx8cX2LnFfH0aVaBzZDlW7D7JhG+2MW/k9Vj0r27JiX8ZqN3D/gD7LMpnD8CRLIOVo/+2B559P9sfmUpXz9Kd1dLe2uPla8rHEJHLMy3cnDp1ioyMDMLCwpz2h4WFERMTU2DvM23aNJ577rkCO58ULxaLhak3NaTHjJWs23+Gr/86ym0tKptdlpQEFou9C6pMDWj8H/u+9FQ4vt25hef0Xjh70P7Y/pX9OKunPfCE1oaytS78WRvK1gH/UHVriZjM1DE3QLZ/ZRuGUaD/8n7qqacYP3684+f4+HiqVKlSYOcX81Up48/D3erw8tJ/eHHJLrrVLU/pAA0Slavg6X1hOYjm0GqkfV/SWTi2+WILz5GNkHjq4vidPZecw7eUPehcGnzK1ABPzckkUhRMCzdly5bFw8MjWyvNiRMnsrXmXAsfHx98fPQ/FFc3okMEizYfZffxBKb9uItXbmtidkniKvxKQ82u9gfYu7Pij9lbdE5deJzeC6f2QdxhSI6FIxvsj6wsVihVzTn4lK1j3w4sr9YekQJkWrjx9vamRYsWLF++nJtvvtmxf/ny5dx0001mlSUllJeHlRdvacitc9by5cYj3Nq8Mq1raBCoFAKLBUIq2R81Ojs/l5YEp/+9GHZO7bm4nZpgH+Nz9gDsXeb8Op9g+23qlwafMjU1tkfkKpjaLTV+/HiGDh1Ky5YtadOmDe+++y5RUVGMHj0asHcpHT16lE8++cTxmi1btgBw7tw5Tp48yZYtW/D29qZ+/fpmfAQpRlpUK8OgVlWZtz6KCYu2s+ShDnh7Ws0uS9yJlx9UaGh/ZGUYcO64cytPZvCJjYKUeDj2l/3hxAKlqlzs2soMQGXrQFBFtfaI5KJYTOL3yiuvEB0dTcOGDZkxYwYdO3YE4M477+TgwYOsWLHCcXxO43GqVavGwYMH8/V+uhXctcUlptHt9RWcOpfKoz3rMKZrbbNLEslbeop9xfScgk9yDjMvZ/IOhNCalwSfOvY/vf2Lrn6RIlKiZiguago3rm/R5qOMXbAFH08rP43tSPWyAWaXJHLlDAPOn7o4tifrGJ+zB3NYUDSL4MqX3MV1obsruJJ90kOREkjhJg8KN67PMAyGfrCe3/edol2tUD4Yfp2WZhDXkp5qDzin92Zv8Uk6k/vrPP0utPBkCT6lqtkXGQ0MU/CRYk3hJg8KN+7h4Knz9Jy5itR0GxVDfBnbvTa3Nq+Mp4f+5y0uLvGMc0vP6X32P8/sz3m9rUwe3vaWnVJV7eN8Qqpm2a5if87D9NlDxI0p3ORB4cZ9LN95nEnfbudYXDIANcsF8FivSHo1qKBZjMX9ZKRD7KFLgs+/9gHN8Ufz7uYCsHhAcLg96GQNPaUuhKCQyprHRwqVwk0eFG7cS3JaBp+tO8Rbv+3jbKL9X61NKofwxA11aVurrMnViRQTGemQcAxiD9vn6omNsj8yt+OOQEbq5c8TWOGS0HNJC5C3xr/J1VO4yYPCjXuKT07j/VX7ef/3AySm2v+F2qF2WR7vVZdGlUNMrk6kmLPZ4PyJ7KEnaxhKS7z8efzK5N7tVaoq+JUq9I8iJZfCTR4UbtzbyYQU3vptH5//eYi0DPtf/b6NK/JIjzrU0IriIlfHMOxjfWIPXQg7h7OEoAvbKXnc1p7JJ/hC0LkQdpy2q0JAWc3t48YUbvKgcCMAh88kMmP5Hr7ZchTDAA+rhdtbVuHhbrWpEKIZYUUKXHLcJaEnyjkAJZ66/Dk8/bK09GQJPZnbgRV0x5cLU7jJg8KNZLUrOp7XftrNL/+cAMDH08qd7apzX6ealPLX4psiRSb1vH1sT+zhLC1AWbq+EqIvfw6rl31ZjEtDT2YYCq4EHl6F/1mkUCjc5EHhRnKy4eAZXv7xHzYeOgtAkK8nozvV5K521fH31u2vIqZLT7GHn5zG+8QezucdX1YICr8k9GQZAxRSWWt5FWMKN3lQuJHcGIbBb7tP8MrS3fwTkwBAuSAfHu5Wm4HXVcFLc+SIFF8FdsdXWA63u1e7uO2jsXlmUbjJg8KNXE6GzeD7rceYvnw3h88kAVAt1J9HekbSr1FFrFYNaBQpcQr0jq8cQk9mGPItpUHPhUThJg8KN5Jfqek25q2P4v9+3cupc/Z/8dWvGMzjN0TSqU45TQQo4koy7/iKi8q52ysuKu+FTDN5B+UwyWGWW991x9dVU7jJg8KNXKnzKel8+PsB3lm1n3Mp6QC0jijDE73r0rxqaZOrE5Eik3nHV07dXldyx1dI5dy7vYIqgFVr4eVE4SYPCjdytc6cT2X2b/v4ZN0hUtNtAPSsH8ajvSKpExZkcnUiYrqsd3zl1AKUEANc5ivX6glBFe13dgWH2+/+Cq584c9w+3ZAObe85V3hJg8KN3KtjsYm8cbPe/hq0xFsBlgtcEvzyoztXpvKpf3NLk9Eiqv0FPtdXbl1e8Xl444vsN/yHlwxS+ipZG8NCg6/uO0f6nLdXwo3eVC4kYKy70QCr/20h6U7YgDw9rAy5PpqPNClJqGBWkBQRK5QRjqcOw7xxyD+iD3sxF94ZG7np/UHwMPnQstP5QuBJ0vLT2Yg8itdogKQwk0eFG6koG2OOssrS3ezdv9pAAJ9PBnZoQb3dIgg0Edz5IhIAcpIswec+KP2LrD4o/YwlLkdd9R+V1h+ePk7t/Y4bV8IQ74hxSYAKdzkQeFGCoNhGKzee4pXfvqH7UfjAQgN8GZM11oMbl0VH08NEBSRIpKeap/zJ6eWn7gj9jCUn8HPAN6Bubf8ZO73KZoxhwo3eVC4kcJksxks2R7N9GV7OHDqPACVSvkxvkcdBjSrhIfmyBGR4iAt+WLwubTlJ7NbLOls/s7lE5Il/Fxo+QmpDE0GFWirj8JNHhRupCikZdj438YjzPx5DycSUgCIDAvi0V6RdK9XXnPkiEjxl3oe4qNzH/8TdzT31d79y8Lj/xZoOQo3eVC4kaKUlJrBx2sPMvu3fcQn2+fIaVGtNE/cUJdWEWVMrk5E5BqlJDi3/GRue3hDv9cL9K0UbvKgcCNmiEtM4+1V/zL3jwMkp9nnyOkSWY7HetWlfrj+HoqIXI7CTR4UbsRMx+OTmfXLXuZvOEyGzcBigRubhDO+Rx2qhQaYXZ6ISLGlcJMHhRspDg6cOs/0Zbv54e9oADytFga1qsqD3WpRPsjX5OpERIofhZs8KNxIcbL9aByv/LSbVXtOAuDn5cE97SO4t1MNgn29TK5ORKT4ULjJg8KNFEdr/j3FK0t3s+VwLACl/L24v3NNhrWpjq+X5sgREVG4yYPCjRRXhmHw047jvLZsN/tOnAOgQrAvY7vX5rYWlfH0cL+F8kREMinc5EHhRoq79AwbCzcfZebyPRyLSwYgPMSXWmFBVCrlS8UQPyqG+BJe6uKfat0REVencJMHhRspKZLTMvhs3SHe+m0fZxPT8jy2TIA3FUPswadSKV8qlnIOQGHBvnip5UdESjCFmzwo3EhJcy4lnc1RZ4mOTeZYXJLjz2OxSUTHJZOYmnHZc1gtUD7Il4qlfAnP0vITntkSVMqXsgE+WLU8hIgUU1fy/a0li0WKuUAfTzrULpfjc4ZhEJ+U7gg7x+KSib4Qeo7GJhEdl0RMXDJpGQYx8cnExCezmdgcz+XtYaVCiG+2Lq/MABQe4kewn6eWjhCRYk/hRqQEs1gshPh7EeLvRb2KOf9LxmYzOHU+xd7ik0sAOpGQQmqGjagziUSdScz1/fy9PS4GnwstPuGl/C5uh/jh563xPyJiLoUbERdntVooH+RL+SBfmlQpleMxaRk2jscnEx13IQDFJhMdZ//z2IUAdDYxjcTUDPadOOe4mysnpfy9CA9x7vKy/+xHhWBfgnw9CfT11BggESk0CjcigpeHlcql/alc2j/XY5JSM4iOy9LikxmALgSi6NgkzqdmEJuYRmxiGjuj4/N8T18vK4E+XgT5etoDj8+Fh68nwb5eju1AH88sx3g5/Rzgo5AkItkp3IhIvvh5e1CjXCA1ygXm+LxhGMQnp9sDUOzFLq+LA6CTOZGQ7Fg4NDnNRnJaCqfOpVxTXb5eVoJ8vQjKEobsAcjrYmjKEqAyQ5Lzz56aR0jEhSjciEiBsFgshPh5EeLnRd0Kud/JkJZh43xKOgnJ9se5lHTOpaQ5/5ycTkJyGgkXts+lXHzO/mdatpB0MuHaQpKfl4c9BF0ShrK1LmUJUAE+2bd9vawadC1iMoUbESlSXh5WSvl7U8rf+5rOk5puD0nnHEEp7UJQyhqc0uxBKTMUOcLUxdekpNtDUlJaBklpGdcckjysFgK8PRyBJ+BC61CAd2YA8rD/eSEkZe7P7GbLfD7gwnMeuj1f5Iop3IhIieTtacXb05vSAQUXkuKT05wCULwjEF0ISReC0rnkdM6nXgxL51PSOX9hvqEMm717Lj45vSA+Jv7eHo5WoQAfD6eWogAfe0tTgM8lwemS1qTMP7091fUm7kHhRkTcWkGFJJvN4HxqOudTMi4GnhR7GMrcPpeS4QhS5xz7sgSkLK9Nt9nnV01MzSAx9dpblMA+l1FArgHIwykMOcKS96X7Lhzr7alJH6XYUrgRESkAVqvlwiBmr2s+l2EYpKTbnAKQPfikOQJS5rgle6tRluCU2ZqUevG1meOTUjNspCbaLrucR35d2qqUGYT8swQj58Dkka1FKXOfn5eHxipJgVG4EREpZiwWC75eHvh6eVA20Oeaz5eeYbOHoyyB51xy1uBk71ZzDlPZW6HOXTguoxBalawWHGEoawjK1rKUz8Dk46nJJN2Zwo2IiIvz9LAS4m8lxL/wWpWyhiLHvtT07OHokmMzxyrZDOwDv1MKZqzSpV1wl3a1OY9H8rgkSGlgd0mncCMiIvlW0K1KNptBYlou4Sjl0nB0YV9q9n2Zx2be/VbQXXB+Xh55B6FL7nrLPUhpuoCioHAjIiKmsVotjsHNYQVwvpy64HIa0J0tHKXmPbA7c6qAU7mvPJJvVgv5HLydR4tSlmN1F1x2CjciIuIyCqMLzinwZOtqy7ik2y33wHQ+NR3DuNAFd2FqgYKgLrjsFG5ERERykLULLjTnVUeuiM1mkOTUBZdlwHZqDuEo25QBxasLLq9B3YE+npQLuvZuy6ulcCMiIlIErFaLIyyUL4DzFecuuNL+Xmx+tmcBfMqro3AjIiJSAhXXLrhzyekFMt/TtVC4ERERcXMF3QVnGMa1n+QaaIi1iIiIFCizb3VXuBERERGXonAjIiIiLkXhRkRERFyKwo2IiIi4FIUbERERcSkKNyIiIuJSFG5ERETEpSjciIiIiEtRuBERERGXonAjIiIiLkXhRkRERFyKwo2IiIi4FIUbERERcSmeZhdQ1DKXYY+Pjze5EhEREcmvzO/tzO/xvLhduElISACgSpUqJlciIiIiVyohIYGQkJA8j7EY+YlALsRms3Hs2DGCgoKwWCwFeu74+HiqVKnC4cOHCQ4OLtBzy5XT9ShedD2KH12T4kXXI2+GYZCQkEB4eDhWa96jatyu5cZqtVK5cuVCfY/g4GD9xSxGdD2KF12P4kfXpHjR9cjd5VpsMmlAsYiIiLgUhRsRERFxKQo3BcjHx4dJkybh4+NjdimCrkdxo+tR/OiaFC+6HgXH7QYUi4iIiGtTy42IiIi4FIUbERERcSkKNyIiIuJSFG5ERETEpSjcFJDZs2cTERGBr68vLVq0YPXq1WaX5BamTZvGddddR1BQEOXLl2fAgAHs3r3b6RjDMJg8eTLh4eH4+fnRuXNnduzYYVLF7mXatGlYLBbGjh3r2KfrUfSOHj3KkCFDCA0Nxd/fn6ZNm7Jp0ybH87omRSc9PZ2JEycSERGBn58fNWrUYMqUKdhsNscxuh4FwJBrNn/+fMPLy8t47733jJ07dxoPP/ywERAQYBw6dMjs0lxer169jLlz5xrbt283tmzZYvTt29eoWrWqce7cOccxL730khEUFGR8/fXXxrZt24yBAwcaFStWNOLj402s3PWtX7/eqF69utG4cWPj4YcfduzX9ShaZ86cMapVq2bceeedxp9//mkcOHDA+Pnnn419+/Y5jtE1KTrPP/+8ERoaavzwww/GgQMHjP/9739GYGCgMXPmTMcxuh7XTuGmALRq1coYPXq00766desaTz75pEkVua8TJ04YgLFy5UrDMAzDZrMZFSpUMF566SXHMcnJyUZISIjx9ttvm1Wmy0tISDBq165tLF++3OjUqZMj3Oh6FL0nnnjCaN++fa7P65oUrb59+xp33323075bbrnFGDJkiGEYuh4FRd1S1yg1NZVNmzbRs2dPp/09e/ZkzZo1JlXlvuLi4gAoU6YMAAcOHCAmJsbp+vj4+NCpUyddn0L0wAMP0LdvX7p37+60X9ej6H333Xe0bNmS//znP5QvX55mzZrx3nvvOZ7XNSla7du355dffmHPnj0AbN26ld9//50+ffoAuh4Fxe0Wzixop06dIiMjg7CwMKf9YWFhxMTEmFSVezIMg/Hjx9O+fXsaNmwI4LgGOV2fQ4cOFXmN7mD+/Pn89ddfbNiwIdtzuh5Fb//+/cyZM4fx48fz9NNPs379eh566CF8fHwYNmyYrkkRe+KJJ4iLi6Nu3bp4eHiQkZHBCy+8wKBBgwD9N1JQFG4KiMVicfrZMIxs+6RwjRkzhr///pvff/8923O6PkXj8OHDPPzwwyxbtgxfX99cj9P1KDo2m42WLVvy4osvAtCsWTN27NjBnDlzGDZsmOM4XZOisWDBAj777DO++OILGjRowJYtWxg7dizh4eEMHz7ccZyux7VRt9Q1Klu2LB4eHtlaaU6cOJEteUvhefDBB/nuu+/47bffqFy5smN/hQoVAHR9isimTZs4ceIELVq0wNPTE09PT1auXMmsWbPw9PR0/M51PYpOxYoVqV+/vtO+evXqERUVBei/kaL22GOP8eSTT/Lf//6XRo0aMXToUMaNG8e0adMAXY+ConBzjby9vWnRogXLly932r98+XLatm1rUlXuwzAMxowZw8KFC/n111+JiIhwej4iIoIKFSo4XZ/U1FRWrlyp61MIunXrxrZt29iyZYvj0bJlS+644w62bNlCjRo1dD2KWLt27bJNj7Bnzx6qVasG6L+RopaYmIjV6vzV6+Hh4bgVXNejgJg4mNllZN4K/sEHHxg7d+40xo4dawQEBBgHDx40uzSXd9999xkhISHGihUrjOjoaMcjMTHRccxLL71khISEGAsXLjS2bdtmDBo0SLdVFqGsd0sZhq5HUVu/fr3h6elpvPDCC8bevXuNzz//3PD39zc+++wzxzG6JkVn+PDhRqVKlRy3gi9cuNAoW7as8fjjjzuO0fW4dgo3BeStt94yqlWrZnh7exvNmzd33IoshQvI8TF37lzHMTabzZg0aZJRoUIFw8fHx+jYsaOxbds284p2M5eGG12Povf9998bDRs2NHx8fIy6desa7777rtPzuiZFJz4+3nj44YeNqlWrGr6+vkaNGjWMCRMmGCkpKY5jdD2uncUwDMPMliMRERGRgqQxNyIiIuJSFG5ERETEpSjciIiIiEtRuBERERGXonAjIiIiLkXhRkRERFyKwo2IiIi4FIUbEXE7K1aswGKxEBsba3YpIlIIFG5ERETEpSjciIiIiEtRuBGRImcYBq+88go1atTAz8+PJk2a8NVXXwEXu4wWL15MkyZN8PX1pXXr1mzbts3pHF9//TUNGjTAx8eH6tWrM336dKfnU1JSePzxx6lSpQo+Pj7Url2bDz74wOmYTZs20bJlS/z9/Wnbtq3T6tlbt26lS5cuBAUFERwcTIsWLdi4cWMh/UZEpCB5ml2AiLifiRMnsnDhQubMmUPt2rVZtWoVQ4YMoVy5co5jHnvsMd544w0qVKjA008/zY033siePXvw8vJi06ZN3H777UyePJmBAweyZs0a7r//fkJDQ7nzzjsBGDZsGGvXrmXWrFk0adKEAwcOcOrUKac6JkyYwPTp0ylXrhyjR4/m7rvv5o8//gDgjjvuoFmzZsyZMwcPDw+2bNmCl5dXkf2OROQamLxwp4i4mXPnzhm+vr7GmjVrnPbfc889xqBBg4zffvvNAIz58+c7njt9+rTh5+dnLFiwwDAMwxg8eLDRo0cPp9c/9thjRv369Q3DMIzdu3cbgLF8+fIca8h8j59//tmxb/HixQZgJCUlGYZhGEFBQcZHH3107R9YRIqcuqVEpEjt3LmT5ORkevToQWBgoOPxySef8O+//zqOa9OmjWO7TJkyREZGsmvXLgB27dpFu3btnM7brl079u7dS0ZGBlu2bMHDw4NOnTrlWUvjxo0d2xUrVgTgxIkTAIwfP54RI0bQvXt3XnrpJafaRKR4U7gRkSJls9kAWLx4MVu2bHE8du7c6Rh3kxuLxQLYx+xkbmcyDMOx7efnl69asnYzZZ4vs77JkyezY8cO+vbty6+//kr9+vX55ptv8nVeETGXwo2IFKn69evj4+NDVFQUtWrVcnpUqVLFcdy6desc22fPnmXPnj3UrVvXcY7ff//d6bxr1qyhTp06eHh40KhRI2w2GytXrrymWuvUqcO4ceNYtmwZt9xyC3Pnzr2m84lI0dCAYhEpUkFBQTz66KOMGzcOm81G+/btiY+PZ82aNQQGBlKtWjUApkyZQmhoKGFhYUyYMIGyZcsyYMAAAB555BGuu+46pk6dysCBA1m7di1vvvkms2fPBqB69eoMHz6cu+++2zGg+NChQ5w4cYLbb7/9sjUmJSXx2GOPcdtttxEREcGRI0fYsGEDt956a6H9XkSkAJk96EdE3I/NZjPeeOMNIzIy0vDy8jLKlStn9OrVy1i5cqVjsO/3339vNGjQwPD29jauu+46Y8uWLU7n+Oqrr4z69esbXl5eRtWqVY1XX33V6fmkpCRj3LhxRsWKFQ1vb2+jVq1axocffmgYxsUBxWfPnnUcv3nzZgMwDhw4YKSkpBj//e9/jSpVqhje3t5GeHi4MWbMGMdgYxEp3iyGkaWjWkTEZCtWrKBLly6cPXuWUqVKmV2OiJRAGnMjIiIiLkXhRkRERFyKuqVERETEpajlRkRERFyKwo2IiIi4FIUbERERcSkKNyIiIuJSFG5ERETEpSjciIiIiEtRuBERERGXonAjIiIiLkXhRkRERFzK/wNiOSBpLSOLdgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model learned the following values for weights and bias:\n",
      "OrderedDict({'weights': tensor([0.5784]), 'bias': tensor([0.3513])})\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",
    "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.8141],\n",
       "        [0.8256],\n",
       "        [0.8372],\n",
       "        [0.8488],\n",
       "        [0.8603],\n",
       "        [0.8719],\n",
       "        [0.8835],\n",
       "        [0.8950],\n",
       "        [0.9066],\n",
       "        [0.9182]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_0.eval()\n",
    "\n",
    "# Setup the inference mode context manager\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_0(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Model to: models/01_pytorch_workflow_model_0.pth\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "MODEL_PATH = Path(\"models\")\n",
    "MODEL_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "MODEL_NAME = \"01_pytorch_workflow_model_0.pth\"\n",
    "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
    "\n",
    "print(f\"Saving Model to: {MODEL_SAVE_PATH}\")\n",
    "torch.save(obj=model_0.state_dict(), f=MODEL_SAVE_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 madaghafuri madaghafuri 1680 Apr  2 17:04 models/01_pytorch_workflow_model_0.pth\n"
     ]
    }
   ],
   "source": [
    "!ls -l models/01_pytorch_workflow_model_0.pth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model_0 = LinearRegressionModel()\n",
    "\n",
    "loaded_model_0.load_state_dict(torch.load(f=MODEL_SAVE_PATH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model_0.eval()\n",
    "\n",
    "with torch.inference_mode():\n",
    "    loaded_model_preds = loaded_model_0(X_test)\n",
    "\n",
    "y_preds == loaded_model_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 40, 10, 10)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splot 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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: If you've reset your runtime, this function won't work, \n",
    "# you'll have to rerun the cell above where it's instantiated.\n",
    "plot_predictions(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class LinearRegressionModelV2(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        # nn.Linear() for creating the model parameters\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",
    "# Set manual seed \n",
    "torch.manual_seed(42)\n",
    "model_1 = LinearRegressionModelV2()\n",
    "model_1, model_1.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(model_1.parameters()).device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.to(device)\n",
    "next(model_1.parameters()).device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.L1Loss()\n",
    "\n",
    "optimizer = torch.optim.SGD(params=model_1.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 100 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 200 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 300 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 400 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 500 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 600 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 700 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 800 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n",
      "Epoch: 900 | Train loss: 0.0012645035749301314 | Test loss: 0.013801807537674904\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "epochs = 1000\n",
    "\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",
    "    ### Training\n",
    "    model_1.train()\n",
    "\n",
    "    y_pred = model_1(X_train)\n",
    "\n",
    "    loss = loss_fn(y_pred, y_train)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    loss.backward()\n",
    "\n",
    "    optimizer.step()\n",
    "\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": 48,
   "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]])),\n",
      "             ('linear_layer.bias', tensor([0.3025]))])\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": 50,
   "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]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.eval()\n",
    "\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_1(X_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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) # -> won't work... data not on CPU\n",
    "\n",
    "# Put data on the CPU and plot it\n",
    "plot_predictions(predictions=y_preds.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 55,
   "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",
      "cpu\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": 56,
   "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": 56,
     "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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}