pytorch_workflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMCnFptXaFWpT7TbTVei1wD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/redom13/ML/blob/main/pytorch_workflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the dependencies"
      ],
      "metadata": {
        "id": "FmCQUpdIQSas"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Z47yVwXYP3hy"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data (preparing and loading)"
      ],
      "metadata": {
        "id": "5gVXU3X5Qij0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 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]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYkJPiekQMAA",
        "outputId": "43cfd926-2489-4431-dd78-37be4ee47a32"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Test Split"
      ],
      "metadata": {
        "id": "UHwNxZwuRTN6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create train/test split\n",
        "train_split = int(0.8 * len(X)) # 80% of data used for training set, 20% for testing\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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PcG8ZRfQrUl",
        "outputId": "04d3a64e-1a8c-4d48-9476-980bf6defdfa"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 40, 10, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting train and test data"
      ],
      "metadata": {
        "id": "HSD5voT4RlMK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_predictions(train_data=X_train,\n",
        "                     test_data=X_test,\n",
        "                     train_label=y_train,\n",
        "                     test_label=y_test,\n",
        "                     predictions=None):\n",
        "  plt.figure(figsize=(12,8))\n",
        "  plt.scatter(train_data,train_label,c='b',s=4,label='Training Data')\n",
        "  plt.scatter(test_data,test_label,c='c',s=4,label='Test Data')\n",
        "  if predictions is not None:\n",
        "    plt.scatter(test_data,predictions,c='r',s=4,label='Predictions')\n",
        "  plt.xlabel('Feature')\n",
        "  plt.ylabel('Target')\n",
        "  plt.legend( prop={'size':14})"
      ],
      "metadata": {
        "id": "5YBpO7QvRXin"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "wI2_Ev8Fhepr",
        "outputId": "c9089d32-9ea3-42af-e95d-7a6402ef309a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build Model"
      ],
      "metadata": {
        "id": "InP_ioaVi77s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LinearRegressionModel(nn.Module): # Inherites nn.Module class\n",
        "  def __init__(self): # Like the constructor\n",
        "    super().__init__() # Calling the constructor for parent class\n",
        "    self.weights=nn.Parameter(torch.randn(1,dtype=torch.float),requires_grad=True) # Can we update this value with grad descent\n",
        "    self.bias=nn.Parameter(torch.randn(1,dtype=torch.float),requires_grad=True)\n",
        "\n",
        "  # Forward defines the computation in the model\n",
        "  def forward(self,x:torch.Tensor)->torch.Tensor:\n",
        "    return self.weights*x+self.bias\n"
      ],
      "metadata": {
        "id": "adBxWkMciAeB"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking the contents of a Pytorch Model"
      ],
      "metadata": {
        "id": "q-HME0C59Yez"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(12)\n",
        "\n",
        "model_0=LinearRegressionModel()\n",
        "\n",
        "print(list(model_0.parameters()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDVEVO5xkjDD",
        "outputId": "29ebb06a-de2c-4fcd-8c7b-d16be426bd00"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Parameter containing:\n",
            "tensor([-0.2138], requires_grad=True), Parameter containing:\n",
            "tensor([-1.3780], requires_grad=True)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7-btkgO93kV",
        "outputId": "7ad4ed02-e4b1-41dd-ee4b-1d95f28e3a7e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('weights', tensor([-0.2138])), ('bias', tensor([-1.3780]))])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making prediction using ```torch.inference_mode()```\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4pLyGCay-zYR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.inference_mode():\n",
        "  y_preds=model_0(X_test)\n",
        "\n",
        "y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "urhrLMs7-pzO",
        "outputId": "16541006-783b-4060-903c-2ee5b8cc0143"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-1.5490],\n",
              "        [-1.5533],\n",
              "        [-1.5576],\n",
              "        [-1.5618],\n",
              "        [-1.5661],\n",
              "        [-1.5704],\n",
              "        [-1.5747],\n",
              "        [-1.5789],\n",
              "        [-1.5832],\n",
              "        [-1.5875]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(predictions=y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "iDIjIRvE_RaV",
        "outputId": "df2fcf69-20b9-4544-dc34-f492f05156f1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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muro6NTU1aw1j77zzTmbMmJG+ffumsrKyGSukCA4++OBMmTIlNTU12WqrrVq6nGbldwMAoHXb0Jnl332FflmyWUxmty4NzaGu0EMTeu2119KjR496Yz/96U/z2GOP5fDDD291YR4AgNatKdrlN8dW+aYi0EMT2m233bLXXnvlAx/4QMrLyzN16tRMnjw5W2+9dS677LKWLg8AAJpVU7XLb26t8k2lULPcw6bujDPOyOuvv56f/OQnueaaazJt2rSceOKJefLJJ9O/f/+WLg8AABplQ2eX3xxnlt+UuId+HdxDDxvG7wYAQDE11b3rE+bN0y7fSO6hBwAAYL1pl9/0abkHAADYDGmX3/y5Qg8AALCZMbt86yDQAwAAbGa0y7cOWu4BAAA2IRvaKp9ol28tXKEHAADYRDRFq3yiXb61EOgBAAA2EU3VKp9ol28NtNwDAAA0ETPL05xcoQcAAGgCZpanuQn0AAAATcDM8jQ3LfdssLKyska9mtqYMWNSVlaWyZMnr9d6K1/l5eXp2LFjdtppp3zyk5/MTTfdlIULFzZJjX369EmfPn2aZFsAAGwc2uUpGlfo2WCjR49eZezKK69MTU3Nat/b1Hz84x/PbrvtliSpra3NzJkzM3ny5Nx6660ZNWpU/vu//zsDBw5s2SIBANiotMtTRAI9G2zMmDGrjI0fPz41NTWrfW9T84lPfCKf+tSn6o0tWrQoV155ZS666KJ89KMfzeOPP57dd9+9hSoEAGBj0y5PEWm5p1ktXrw4V1xxRfbee+9sueWW2XrrrXPIIYdkwoQJqyxbU1OTUaNG5QMf+EC22mqrVFVVZccdd8zw4cPz8ssvJ0kGDhyYsWPHJkkGDRpU1z6/oe3tFRUV+epXv5pRo0Zl4cKFueCCC+q9//TTT+eLX/xidtttt1RXV2eLLbZI//798+1vfztLliypW27mzJkpKyvLyy+/nJdffrlei//Kkx2LFy/O1VdfnSFDhqRXr16pqKhI165dc9xxx+WZZ57ZoP0AAGgttMvTGrlCT7NZtGhRjjjiiEyePDl77rlnTj311CxZsiT33ntvjj322Fx99dX54he/mCQplUoZMmRIfv/73+eggw7KEUcckTZt2uTll1/OhAkTctJJJ6V3794ZMWJEkuThhx/O8OHD64J8xyb6A3zuuefm0ksvzf3335+amppUV1cnSW688cbcfffd+fCHP5yjjjoqb731ViZPnpwLL7wwTz31VG677ba6OkaPHp0rr7wySXL22WfXbXtlG/8///nPnH322TnkkENy1FFH5X3ve1+mT5+eCRMm5P/9v/+XRx55JB/60IeaZH8AADZH2uVprQR6ms3FF1+cyZMn5xvf+EbGjh1bN0Hem2++mUMPPTTnnntujjvuuPTs2TPPPvtsfv/732fYsGG544476m1n0aJFdVfBR4wYkZkzZ+bhhx/OiBEjmvxe96222ir77LNPfve73+Xpp5/OoYcemiS56KKLcu2116a8vLxu2VKplM997nP58Y9/nMceeywHHXRQOnbsmDFjxmT8+PFJVn97wvve977MmjUr2267bb3xv/71r9l///1z0UUX5cEHH2zS/QIA2Jxol6e10nJPs1i+fHmuv/767LDDDvXCfJJsvfXWGTVqVBYvXpzbb7+93npbbLHFKtuqqKjIVltttdFrXqlnz55Jknnvat/afvvt64X5ZMVs/2eeeWaS5KGHHmrw9isqKlYJ80nyb//2bxk0aFAeeeSRem38AACbkw1tlU+0y9N6uUJfQBPmzcuk+fMzqECtQNOmTcsbb7yRnj171t3z/m5///vfkyQvvPBCkmTXXXfN7rvvnl/84hd59dVXM2zYsAwcODB77rln2rRp+fNQixcvzjXXXJNf/vKXeeGFF7JgwYKUSqW69//2t781antTp07NpZdemkcffTRz5sxZJcDPmzcvPXr0aJLaAQA2FU3RKp9ol6f1EugLpqn+6DW3f/7zn0lWtJH/9a9/XeNyK5/73rZt2/z2t7/NmDFjctttt+Xcc89NknTp0iVf/OIX87WvfW2VK+Qby8pw3qVLl7qxT3ziE7n77ruz00475YQTTkjXrl3Trl27zJ8/P9/73veyaNGiBm//8ccfr2vlP/zww/P+978/W221VcrKynLnnXfmT3/6U6O2BwBQFE3VKp9ol6d1EugLpin/6DWnqqqqJCue+X7rrbc2aJ1tttkmV199da666qq88MIL+e1vf5urr746o0ePTrt27XLhhRduzJKTJAsWLMjTTz+d8vLy7L333kmSp556KnfffXeGDBmSe++9t96JhSeeeCLf+973GvUZl1xySRYtWpTf/e53Ofjgg+u998QTT+RPf/rThu8IAMBGMGFCMmlSMmhQMnRo49cf1LFjrnz1Va3ysJ5avneZRinq/UG77rprqqqq8oc//KHR94OXlZVl1113zZlnnlk3Ody7H3O3MlAvW7as6Qr+P5dffnneeuutHHnkkXUz3L/00ktJkqOPPnqVLoHf/e53q91OeXn5Gut76aWX0qlTp1XC/FtvvZU//vGPG7oLAAAbxYQJybHHJldfveLrap5CvE4rW+W/tN12hek8hU2JQF8wRf2j17Zt23z+85/Pyy+/nPPOO2+1of7ZZ5/N66+/nmTF89tnzpy5yjJz585NklRWVtaNderUKUnyyiuvNFm9ixYtyqWXXpqLL744W221VcaNG1f3Xu/evZMkjz76aL11/vrXv9Zb7t06deqUefPm5Z133lnlvd69e+eNN96odyvCsmXLct5559XNLQAAsKmZNCkpL0+WLVvxdfLk9dvO0M6dc8WOOxbm37WwKdFyX0BFvT9o7Nix+eMf/5irrroq9957bz784Q+na9eumT17dv7yl7/kT3/6U6ZMmZKuXbtm6tSpOe6447LvvvvmAx/4QLp3757Zs2fnzjvvTJs2bXLOOefUbXfQoEEpKyvLRRddlL/+9a+prq5Ox44d655pvy633npr3WR8CxYsyIwZM/LII49k3rx56dWrV376059mt912q1t+3333zb777ptf/epXee2117L//vtn1qxZmTBhQo4++ujV3lJw6KGH5g9/+EOOPPLIHHLIIWnfvn0+/OEP58Mf/nDOOuusPPDAAzn44INz/PHHp7KyMpMnT87s2bMzcODATF7f/3cEAFiLDW6XH5RceeW/Qn0TPz0YaIgSa1VTU1NKUqqpqVnrcm+//XbpueeeK7399tvNVNmmrXfv3qXVHV5Lly4tff/73y8ddNBBpaqqqlJFRUVp++23Lx1xxBGl66+/vrRgwYJSqVQqvfLKK6ULLrigtP/++5e6du1aat++fWn77bcvHXfccaUpU6asst3x48eX+vfvX6qoqCglKfXu3XudNY4ePbqUpO7Vpk2bUlVVVWnHHXcsfeITnyjddNNNpYULF6523ddff7307//+76WePXuWKisrS/379y9de+21penTp5eSlIYPH15v+TfffLN02mmnlXr06FEqLy8vJSmNHj267v1bb721tPfee5c6dOhQ6ty5c+n4448vvfTSS6Xhw4eXkpRmzJixzv3ZVPndAIBNz113lUpJqVRevuLrXXet/3bOOWf91wdWr6E5tKxUeteztlhFbW1tqqurU1NTUzex2+q88847mTFjRvr27VuvHRxaO78bALDpOeecFfe+r2yX/9KXkiuuaOmqgJUamkPdQw8AAAUzYcKKUL4+E9ElK9rlV4Z57fJQXO6hBwCAAlk5u3x5+Yp72O+6q/H3wA8dumK9yZNXhPn1uYceaHkCPQAAFMjqZpdfn0A+dKggD0Wn5R4AAJrJhrbKJ9rlgX9xhR4AAJpBU7TKJ9rlgX8R6AEAoBk0Vat8ol0eWEHLPQAANICZ5YFNjSv0AACwDmaWBzZFAj0AAKyDmeWBTZGWewAANnva5YHNkSv0AABs1rTLA5srgR4AgM2adnlgc6XlHgCATZp2eYDVE+jZrM2cOTNlZWUZMWJEvfGBAwemrKxso31unz590qdPn422fQBoLVa2y1999Yqv6xPqV7bLf+lL69duD7CpEuhpMivD87tf7du3T69evXLiiSfmz3/+c0uX2GRGjBiRsrKyzJw5s6VLAYDN2ura5dfH0KHJFVcI88DmxT30NLkddtghn/3sZ5MkCxYsyBNPPJFf/OIXuf322zNx4sQcdNBBLVxh8pOf/CRvvfXWRtv+xIkTN9q2AaA1GTRoxUR22uUBViXQ0+R23HHHjBkzpt7Y17/+9VxyySX52te+lsnre2q9CW2//fYbdfs77LDDRt0+ABTBhAkrrrAPGrT+V8bNLg+wZlruaRZnnXVWkuSpp55KkpSVlWXgwIGZPXt2Tj755HTv3j1t2rSpF/YfeeSRHHPMMencuXMqKiry/ve/P1//+tdXe2V92bJl+c53vpMdd9wxlZWV2XHHHTNu3LgsX758tfWs7R76u+66K4cffni22WabVFZWpk+fPjnppJPy7LPPJllxf/zNN9+cJOnbt2/d7QUD33XJYE330C9cuDCjR4/OLrvsksrKynTq1ClHH310HnvssVWWHTNmTMrKyjJ58uT8/Oc/z5577pktttgiPXr0yJe//OW8/fbbq6xz2223ZcCAAenatWsqKyvTs2fPDB48OLfddttq9xUANpamuPd9Je3yAKvnCj3N6t0h+h//+EcOOOCAdOrUKZ/61KfyzjvvpKqqKkly/fXX58wzz0zHjh1zzDHHpGvXrvnDH/6QSy65JJMmTcqkSZPSvn37um2dfvrp+fGPf5y+ffvmzDPPzDvvvJMrrrgijz/+eKPqO/fcc3PFFVekU6dOGTZsWLp27ZpXXnklDz30UPbZZ5/stttuOfvsszN+/Pj86U9/ype//OV07NgxSdY5Cd4777yTQw89NE8++WT23nvvnH322Zk7d25uueWW3H///fnFL36RT37yk6usd8011+S+++7Lsccem0MPPTT33XdfrrrqqsybNy8/+9nP6pa7/vrr84UvfCE9evTIxz72sWyzzTaZM2dOnnzyydxxxx35+Mc/3qifBQBsiKZ6VBwAa1FirWpqakpJSjU1NWtd7u233y4999xzpbfffruZKtv0zJgxo5SkNGTIkFXeGzVqVClJadCgQaVSqVRKUkpSOuWUU0pLly6tt+xf//rXUtu2bUt77LFHad68efXeGzduXClJ6bLLLqsbmzRpUilJaY899igtWLCgbvzVV18tde7cuZSkNHz48HrbGTBgQOm9h//dd99dSlLq37//Kp+7ZMmS0pw5c+q+Hz58eClJacaMGav9WfTu3bvUu3fvemNjx44tJSl95jOfKS1fvrxu/I9//GOpffv2pY4dO5Zqa2vrxkePHl1KUqquri698MILdeNvvfVWaaeddiq1adOmNHv27Lrxvffeu9S+ffvS3LlzV6nnvfvTnPxuABTTXXeVSmefveLr+q6flErl5Su+ru92AFqjhuZQLfc0uRdffDFjxozJmDFj8pWvfCUf/vCHc/HFF6eysjKXXHJJ3XLt27fPpZdemvLy8nrrf//738/SpUtz9dVXZ5tttqn33vnnn58uXbrkF7/4Rd3YT37ykyTJqFGjsuWWW9aNb7vttvnyl7/c4Lqvu+66JMn3vve9VT63bdu26datW4O3tTo333xz2rVrl29/+9v1OhX22muvDB8+PPPnz8+dd965ynpf/vKXs/POO9d9v8UWW+TTn/50li9fnqeffrresu3atUu7du1W2cZ79wcA1saj4gCKQct9ETXFDDMb0UsvvZSxY8cmWREwu3XrlhNPPDEXXHBB+vfvX7dc375907lz51XWf+KJJ5Ik999//2pni2/Xrl1eeOGFuu//9Kc/JUkOOeSQVZZd3diaPPnkk6moqMiAAQMavE5D1dbWZvr06dl1112z3XbbrfL+oEGDcuONN2bq1Kk56aST6r23zz77rLL8ym3Mnz+/buxTn/pUzj///Oy222458cQTM2jQoBx88MF1tzEAQEM1Vbv80KGb5D9VADYbAn3RrDxlXl6+4hkum+Ap7yFDhuS+++5b53JruuL9z3/+M0nqXc1fm5qamrRp02a1Jwcac1W9pqYm2267bdq0afrGldra2rXW06NHj3rLvdvqAnnbtit+dZctW1Y3dt5552WbbbbJ9ddfn8svvzyXXXZZ2rZtm6OPPjrf/e5307dv3w3eDwCKYUPP/XtUHEAxaLkvmtWdMi+oNc0yvzLA1tbWplQqrfG1UnV1dZYvX5558+atsq25c+c2uJ6OHTtmzpw5a5wZf0Os3Kc11TNnzpx6y62PsrKy/Pu//3ueeuqp/P3vf88dd9yR4447LnfddVc++tGP1gv/AGy+tMsDtB4CfdEMGvSvML+ZnjLfb7/9kvyr9X5d9thjjyTJ7373u1XeW93Ymuy7775ZtGhRHn744XUuu/K+/4aG5KqqqvTr1y8vvvhiZs+evcr7Kx/Xt+eeeza43rXZZpttMmzYsNxyyy059NBD89xzz+XFF19skm0DsGlrqnP/HhUHsOkT6IumFZwy/8IXvpC2bdvmrLPOyqxZs1Z5f/78+XnmmWfqvl95z/nFF1+chQsX1o3Pnj073/ve9xr8uWeeeWaSFZPQrWz7X2np0qX1rq536tQpSfLKK680ePvDhw/PkiVLcuGFF9brMPjzn/+c8ePHp7q6OsOGDWvw9t5r8uTJ9babJEuWLKnbl8rKyvXeNgDNZ8KE5Jxz1v+57a3g3D8A/8c99EW0mc8ws9tuu+W6667L5z//+ey888456qijssMOO+TNN9/M9OnT8/DDD2fEiBG54YYbkqyYUO6UU07JTTfdlP79++djH/tYFi1alFtuuSX7779/7rnnngZ97lFHHZXzzjsvl112Wd7//vfnYx/7WLp27ZrZs2dn4sSJOe+883L22WcnSQ499NBcdtllOf300/Pxj388W265ZXr37r3KhHbvdv755+fee+/Nf//3f+f555/PYYcdltdffz233HJLli5dmhtvvDFbb731ev/chg0blqqqquy///7p3bt3lixZkgcffDDPPfdcPvGJT6R3797rvW0AmkdTTJWz8tz/5Mkrwvxm/E8GgFZPoGeTdNppp2XPPffMFVdckUceeSR33313qqurs/322+ecc87J8OHD6y1/4403ZqeddsqNN96Ya665Jtttt11GjhyZ448/vsGBPkn+67/+KwcccECuueaa3HrrrXnnnXfSo0ePHHroofnIRz5St9yRRx6ZSy+9NDfeeGMuv/zyLFmyJAMGDFhroK+srMxvf/vbfOc738ktt9yS7373u+nQoUMGDBiQiy66KAcffHDjf1DvMm7cuNx333158sknc/fdd2fLLbfMDjvskOuvvz6nnnrqBm0bgOZhdnkAGqOs9N4eXeqpra1NdXV1ampq1jph2TvvvJMZM2akb9++WpvhXfxuAK1FUzxV9t1X6Jct22zvrgNgHRqaQ12hBwDYQE31VFnt8gA0hkAPALCBmqpVPtEuD0DDmeUeAGj1zCwPQBG5Qg8AtGpmlgegqAR6AKBVM7M8AEWl5R4AKDTt8gC0Vq7QAwCFpV0egNZMoG9ipVKppUuATYrfCWBj0i4PQGum5b6JtGvXLmVlZVm4cGFLlwKblIULF6asrCzt2rVr6VKATZB2eQBYf67QN5Hy8vJUV1fn73//exYtWpSqqqq0bds2ZWVlLV0aNLtSqZSlS5emtrY2tbW16dixY8rLy1u6LGATo10eADaMQN+Eunfvni222CKvv/56amtrW7ocaHHl5eXp0aNHqqurW7oUYBOkXR4ANoxA34TKysrSsWPHVFdXZ9myZVm6dGlLlwQtpm3btikvL9elApupCRNWBPJBg9Y/TA8atOLKvHZ5AFg/ZSUzVq1VbW1tqqurU1NTk6qqqpYuBwBa3Ltb5ZctW79W+XdvS7s8ANTX0BzqCj0A0ChN1SqfaJcHgA1hlnsAaGXMLA8Amwct9+ug5R6AzUlTtctrlQeAjUfLPQCwCjPLA8DmQ8s9ABSIdnkAYCUt9+ug5R6ATYV2eQBoHbTcA8BmRrs8APBuWu4BoJlolwcAmpKW+3XQcg9AU9AuDwA0lJZ7ANiEaJcHAJqalnsAWIcNbZVPtMsDAE1Py/06aLkHaN2aqlV+5ba0ywMA66LlHgCaQFO1yifa5QGApqXlHoDNmpnlAYDNlZb7ddByD1BcZpYHAIpIyz0ArZ6Z5QGAzZmWewA2WdrlAQDWTMv9Omi5B2gZ2uUBgNZKyz0AhaZdHgBg7bTcA9DkNrRVPtEuDwCwLlru10HLPUDjNFWr/MptaZcHAFobLfcAtIimapVPtMsDAKyNlnsA6jGzPABAMWi5Xwct90BrYmZ5AICWp+UegEYzszwAQHEUruX+2muvTZ8+fVJZWZn99tsvTz755BqXHT9+fMrKyuq9Kisrm7FagOalXR4AoPUo1BX6W265JSNHjswNN9yQ/fbbL1deeWWGDBmSadOmpWvXrqtdp6qqKtOmTav7vqysrLnKBWhW726Xv/LK9WuXHzp0xXra5QEANn2FukJ/xRVX5LTTTsspp5ySD3zgA7nhhhvSoUOH/PjHP17jOmVlZenevXvdq1u3bs1YMUDzWV27/PoYOjS54gphHgBgU1eYQL948eI8/fTTGTx4cN1YmzZtMnjw4EyZMmWN6y1YsCC9e/dOr169cuyxx+avf/3rWj9n0aJFqa2trfcCaA7a5QEAaIzCBPp58+Zl2bJlq1xh79atW+bMmbPadXbeeef8+Mc/zl133ZWf/vSnWb58eQ488MC8+uqra/yccePGpbq6uu7Vq1evJt0PgNVZ2S5/9dUrvq5PqF/ZLv+lL63/7PQAABRHYQL9+jjggANy8sknZ88998yAAQNy++23p0uXLvn+97+/xnUuvPDC1NTU1L1eeeWVZqwYaK20ywMA0FiFCfSdO3dOeXl55s6dW2987ty56d69e4O20a5du+y111558cUX17hMRUVFqqqq6r0A1mZDW+UT7fIAADReYQJ9+/bts88++2TixIl1Y8uXL8/EiRNzwAEHNGgby5Yty1/+8pf06NFjY5UJtDJN0SqfaJcHAKDxCvXYupEjR2b48OH54Ac/mH333TdXXnllFi5cmFNOOSVJcvLJJ2fbbbfNuHHjkiQXX3xx9t9//+y4446ZP39+/uu//isvv/xyPve5z7XkbgCbkdW1yq9vGB86VJAHAKDhChXoTzjhhPz973/PqFGjMmfOnOy5556577776ibKmzVrVtq0+VfTwRtvvJHTTjstc+bMyfve977ss88+efzxx/OBD3ygpXYB2MRMmLAilA8atH5hetCgFc981yoPAEBzKyuVSqWWLmJTVltbm+rq6tTU1LifHjYzK9vlV4bx9W11nzBhxZX5gQNdYQcAYMM1NIcW6go9QFNqqnZ5rfIAALSEwkyKB/BeGzq7vJnlAQAoMi3366DlHjZN2uUBANhcabkHNmva5QEAaO203AOFpF0eAIDWzhV6oEVs6OPihg5d0WavXR4AgNbKPfTr4B56aHpNdf87AABsjhqaQ7XcA81udfe/AwAAjSPQA42yoY+KS9z/DgAATUHL/TpouYd/acpWeY+LAwCA1fPYOqDJNdWj4hKPiwMAgA2l5R5akQ1tl9cqDwAAmw4t9+ug5Z7NRVO1y2uVBwCAjUvLPVBPU7XLa5UHAIBNg5Z7KAjt8gAAwLtpuV8HLfdsCrTLAwBA66HlHjYj2uUBAID30nIPzUC7PAAA0NS03K+Dlns2lHZ5AACgMbTcwyZCuzwAALAxaLmHtdjQVvlEuzwAALBxaLlfBy33rVdTtcqv3JZ2eQAAoCG03MMGaqpW+US7PAAA0PS03LPZMrM8AACwOdNyvw5a7ovJzPIAAEBRabmnVTOzPAAAsLnTcs8mSbs8AADA2mm5Xwct981PuzwAANCaabmnsLTLAwAArJuWe5qcdnkAAICNT8v9Omi5bxzt8gAAABtGyz0tQrs8AABA89ByT50NbZVPtMsDAAA0Fy3369BaWu6bqlV+5ba0ywMAAKwfLfc0SlO1yifa5QEAAJqDlvvNhJnlAQAAWhct9+tQhJZ7M8sDAABsPrTctyJmlgcAAGh9tNxvBrTLAwAAtD6u0G8Ghg5d0WavXR4AAKD1EOg3E9rlAQAAWhct9wAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAhQv01157bfr06ZPKysrst99+efLJJ9e6/K9//evssssuqaysTP/+/fOb3/ymmSoFAACAjadQgf6WW27JyJEjM3r06Pzxj3/MHnvskSFDhuT1119f7fKPP/54Pv3pT+fUU0/NM888k2HDhmXYsGF59tlnm7lyAAAAaFplpVKp1NJFNNR+++2XD33oQ7nmmmuSJMuXL0+vXr1y1lln5YILLlhl+RNOOCELFy7MPffcUze2//77Z88998wNN9zQoM+sra1NdXV1ampqUlVV1TQ7AgAAAGvQ0BxamCv0ixcvztNPP53BgwfXjbVp0yaDBw/OlClTVrvOlClT6i2fJEOGDFnj8kmyaNGi1NbW1nsBAADApqYwgX7evHlZtmxZunXrVm+8W7dumTNnzmrXmTNnTqOWT5Jx48alurq67tWrV68NLx4AAACaWGECfXO58MILU1NTU/d65ZVXWrokAAAAWEXbli6goTp37pzy8vLMnTu33vjcuXPTvXv31a7TvXv3Ri2fJBUVFamoqNjwggEAAGAjKswV+vbt22efffbJxIkT68aWL1+eiRMn5oADDljtOgcccEC95ZPkwQcfXOPyAAAAUBSFuUKfJCNHjszw4cPzwQ9+MPvuu2+uvPLKLFy4MKecckqS5OSTT862226bcePGJUm+/OUvZ8CAAbn88stz9NFH55e//GX+8Ic/5Ac/+EFL7gYAAABssEIF+hNOOCF///vfM2rUqMyZMyd77rln7rvvvrqJ72bNmpU2bf7VdHDggQfm5z//eb7+9a/noosuyvvf//7ceeed2W233VpqFwAAAKBJFOo59C3Bc+gBAABoTpvdc+gBAACAfxHoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACigRgf6fv365R//+Mcq4/Pnz0+/fv2apCgAAABg7Rod6GfOnJlly5atMr5o0aLMnj27SYoCAAAA1q5tQxecMGFC3X/ff//9qa6urvt+2bJlmThxYvr06dOkxQEAAACr1+BAP2zYsCRJWVlZhg8fXu+9du3apU+fPrn88subtDgAAABg9Roc6JcvX54k6du3b5566ql07tx5oxUFAAAArF2DA/1KM2bMqPvvd955J5WVlU1aEAAAALBujZ4Ub/ny5fnmN7+ZbbfdNltttVWmT5+eJPnGN76RH/3oR01eIAAAALCqRgf6b33rWxk/fnwuvfTStG/fvm58t912yw9/+MMmLQ4AAABYvUYH+p/85Cf5wQ9+kM985jMpLy+vG99jjz3ywgsvNGlxAAAAwOo1OtDPnj07O+644yrjy5cvz5IlS5qkKAAAAGDtGh3oP/CBD+R3v/vdKuO33npr9tprryYpCgAAAFi7Rs9yP2rUqAwfPjyzZ8/O8uXLc/vtt2fatGn5yU9+knvuuWdj1AgAAAC8R6Ov0B977LG5++6789BDD2XLLbfMqFGj8vzzz+fuu+/ORz7ykY1RIwAAAPAeZaVSqdTSRWzKamtrU11dnZqamlRVVbV0OQAAAGzmGppDG32FHgAAAGh5jb6H/n3ve1/KyspWGS8rK0tlZWV23HHHjBgxIqecckqTFAgAAACsar0mxbvkkkty5JFHZt99902SPPnkk7nvvvty5plnZsaMGfn85z+fpUuX5rTTTmvyggEAAID1CPSPPvpovvWtb+WMM86oN/79738/DzzwQG677bbsvvvuueqqqwR6AAAA2EgafQ/9/fffn8GDB68yfthhh+X+++9Pkhx11FGZPn36hlcHAAAArFajA32nTp1y9913rzJ+9913p1OnTkmShQsXZuutt97w6gAAAIDVanTL/Te+8Y18/vOfz6RJk+ruoX/qqafym9/8JjfccEOS5MEHH8yAAQOatlIAAACgzno9h/6xxx7LNddck2nTpiVJdt5555x11lk58MADm7zAluY59AAAADSnhubQRl2hX7JkSf7jP/4j3/jGN/KLX/xig4sEAAAA1k+j7qFv165dbrvtto1VCwAAANBAjZ4Ub9iwYbnzzjs3QikAAABAQzV6Urz3v//9ufjii/PYY49ln332yZZbblnv/S996UtNVhwAAACweo2eFK9v375r3lhZ2Wb3/HmT4gEAANCcNsqkeEkyY8aMDSoMAAAA2HCNvoceAAAAaHmNvkKfJK+++momTJiQWbNmZfHixfXeu+KKK5qkMAAAAGDNGh3oJ06cmKFDh6Zfv3554YUXsttuu2XmzJkplUrZe++9N0aNAAAAwHs0uuX+wgsvzHnnnZe//OUvqayszG233ZZXXnklAwYMyCc/+cmNUSMAAADwHo0O9M8//3xOPvnkJEnbtm3z9ttvZ6uttsrFF1+c73znO01eIAAAALCqRgf6Lbfcsu6++R49euSll16qe2/evHlNVxkAAACwRg0O9BdffHEWLlyY/fffP48++miS5Kijjsq5556bSy65JP/+7/+e/ffff6MVCgAAAPxLWalUKjVkwfLy8rz22mtZsGBBFixYkN133z0LFy7Mueeem8cffzzvf//7c8UVV6R3794bu+ZmVVtbm+rq6tTU1KSqqqqlywEAAGAz19Ac2uBZ7lfm/n79+tWNbbnllrnhhhs2oEwAAABgfTTqHvqysrKNVQcAAADQCI16Dv1OO+20zlD/z3/+c4MKAgAAANatUYF+7Nixqa6u3li1AAAAAA3UqED/qU99Kl27dt1YtQAAAAAN1OB76N0/DwAAAJuOBgf6Bj7dDgAAAGgGDW65X758+casAwAAAGiERj22DgAAANg0CPQAAABQQAI9AAAAFJBADwAAAAUk0AMAAEABCfQAAABQQAI9AAAAFJBADwAAAAUk0AMAAEABCfQAAABQQAI9AAAAFJBADwAAAAUk0AMAAEABCfQAAABQQAI9AAAAFFBhAv0///nPfOYzn0lVVVU6duyYU089NQsWLFjrOgMHDkxZWVm91xlnnNFMFQMAAMDG07alC2ioz3zmM3nttdfy4IMPZsmSJTnllFNy+umn5+c///la1zvttNNy8cUX133foUOHjV0qAAAAbHSFCPTPP/987rvvvjz11FP54Ac/mCS5+uqrc9RRR+Wyyy5Lz54917huhw4d0r179+YqFQAAAJpFIVrup0yZko4dO9aF+SQZPHhw2rRpk9///vdrXfdnP/tZOnfunN122y0XXnhh3nrrrbUuv2jRotTW1tZ7AQAAwKamEFfo58yZk65du9Yba9u2bTp16pQ5c+ascb0TTzwxvXv3Ts+ePfPnP/85X/3qVzNt2rTcfvvta1xn3LhxGTt2bJPVDgAAABtDiwb6Cy64IN/5znfWuszzzz+/3ts//fTT6/67f//+6dGjRw477LC89NJL2WGHHVa7zoUXXpiRI0fWfV9bW5tevXqtdw0AAACwMbRooD/33HMzYsSItS7Tr1+/dO/ePa+//nq98aVLl+af//xno+6P32+//ZIkL7744hoDfUVFRSoqKhq8TQAAAGgJLRrou3Tpki5duqxzuQMOOCDz58/P008/nX322SdJ8tvf/jbLly+vC+kNMXXq1CRJjx491qteAAAA2FQUYlK8XXfdNUcccUROO+20PPnkk3nsscfyxS9+MZ/61KfqZrifPXt2dtlllzz55JNJkpdeeinf/OY38/TTT2fmzJmZMGFCTj755Hz4wx/O7rvv3pK7AwAAABusEIE+WTFb/S677JLDDjssRx11VA4++OD84Ac/qHt/yZIlmTZtWt0s9u3bt89DDz2Uww8/PLvsskvOPffcfPzjH8/dd9/dUrsAAAAATaasVCqVWrqITVltbW2qq6tTU1OTqqqqli4HAACAzVxDc2hhrtADAAAA/yLQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEVJtBfcsklOfDAA9OhQ4d07NixQeuUSqWMGjUqPXr0yBZbbJHBgwfnf//3fzduoQAAANAMChPoFy9enE9+8pP5/Oc/3+B1Lr300lx11VW54YYb8vvf/z5bbrllhgwZknfeeWcjVgoAAAAbX1mpVCq1dBGNMX78+Jx99tmZP3/+WpcrlUrp2bNnzj333Jx33nlJkpqamnTr1i3jx4/Ppz71qQZ9Xm1tbaqrq1NTU5OqqqoNLR8AAADWqqE5tDBX6BtrxowZmTNnTgYPHlw3Vl1dnf322y9TpkxZ43qLFi1KbW1tvRcAAABsajbbQD9nzpwkSbdu3eqNd+vWre691Rk3blyqq6vrXr169dqodQIAAMD6aNFAf8EFF6SsrGytrxdeeKFZa7rwwgtTU1NT93rllVea9fMBAACgIdq25Iefe+65GTFixFqX6dev33ptu3v37kmSuXPnpkePHnXjc+fOzZ577rnG9SoqKlJRUbFenwkAAADNpUUDfZcuXdKlS5eNsu2+ffume/fumThxYl2Ar62tze9///tGzZQPAAAAm6LC3EM/a9asTJ06NbNmzcqyZcsyderUTJ06NQsWLKhbZpdddskdd9yRJCkrK8vZZ5+db33rW5kwYUL+8pe/5OSTT07Pnj0zbNiwFtoLAAAAaBoteoW+MUaNGpWbb7657vu99torSTJp0qQMHDgwSTJt2rTU1NTULXP++edn4cKFOf300zN//vwcfPDBue+++1JZWdmstQMAAEBTK9xz6Jub59ADAADQnFr9c+gBAABgcybQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABSTQAwAAQAEJ9AAAAFBAAj0AAAAUkEAPAAAABVSYQH/JJZfkwAMPTIcOHdKxY8cGrTNixIiUlZXVex1xxBEbt1AAAABoBm1buoCGWrx4cT75yU/mgAMOyI9+9KMGr3fEEUfkpptuqvu+oqJiY5QHAAAAzaowgX7s2LFJkvHjxzdqvYqKinTv3n0jVAQAAAAtpzAt9+tr8uTJ6dq1a3beeed8/vOfzz/+8Y+WLgkAAAA2WGGu0K+PI444Iscdd1z69u2bl156KRdddFGOPPLITJkyJeXl5atdZ9GiRVm0aFHd97W1tc1VLgAAADRYi16hv+CCC1aZtO69rxdeeGG9t/+pT30qQ4cOTf/+/TNs2LDcc889eeqppzJ58uQ1rjNu3LhUV1fXvXr16rXenw8AAAAbS4teoT/33HMzYsSItS7Tr1+/Jvu8fv36pXPnznnxxRdz2GGHrXaZCy+8MCNHjqz7vra2VqgHAABgk9Oigb5Lly7p0qVLs33eq6++mn/84x/p0aPHGpepqKgwEz4AAACbvMJMijdr1qxMnTo1s2bNyrJlyzJ16tRMnTo1CxYsqFtml112yR133JEkWbBgQb7yla/kiSeeyMyZMzNx4sQce+yx2XHHHTNkyJCW2g0AAABoEoWZFG/UqFG5+eab677fa6+9kiSTJk3KwIEDkyTTpk1LTU1NkqS8vDx//vOfc/PNN2f+/Pnp2bNnDj/88Hzzm990BR4AAIDCKyuVSqWWLmJTVltbm+rq6tTU1KSqqqqlywEAAGAz19AcWpiWewAAAOBfBHoAAAAoIIEeAABoORMmJOecs+Ir0CgCPQAA0DImTEiOPTa5+uoVXzck1DsxQCsk0AMAAC1j0qSkvDxZtmzF18mT1287TXViwEkBCkagBwAAWsagQf8K88uWJf/3OOpGa4oTA04KUEACPQAA0DKGDk3uuiv50pdWfB06dP220xQnBpwUoIAEegAAoOUMHZpcccX6h/mV29jQEwOb20mBldtyYmCzJtADAADFt6EnBjankwKJboFWQqAHAABINp+TAsmm1S3gpMBGI9ADAAA0lU3hpECy6XQLOCmwUQn0AAAAmxLzCtRnXoE1EugBAAA2R5tCt8CmclIgadoTA5sIgR4AAIDV21xOCiRNd2JgE9K2pQsAAABgMzZ0aNPcPjB58oowvyHzClx55YafGNiElJVKpVJLF7Epq62tTXV1dWpqalJVVdXS5QAAALC+JkzY8BMDzaChOdQVegAAAFqHDe0W2MS4hx4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIAEegAAACgggR4AAAAKSKAHAACAAhLoAQAAoIDatnQBm7pSqZQkqa2tbeFKAAAAaA1W5s+VeXRNBPp1ePPNN5MkvXr1auFKAAAAaE3efPPNVFdXr/H9stK6In8rt3z58vztb3/L1ltvnbKyspYuZ41qa2vTq1evvPLKK6mqqmrpcmCdHLMUieOVonHMUiSOV4qmOY7ZUqmUN998Mz179kybNmu+U94V+nVo06ZNtttuu5Yuo8Gqqqr8IaRQHLMUieOVonHMUiSOV4pmYx+za7syv5JJ8QAAAKCABHoAAAAoIIF+M1FRUZHRo0enoqKipUuBBnHMUiSOV4rGMUuROF4pmk3pmDUpHgAAABSQK/QAAABQQAI9AAAAFJBADwAAAAUk0AMAAEABCfQFcu2116ZPnz6prKzMfvvtlyeffHKty//617/OLrvsksrKyvTv3z+/+c1vmqlSWKExx+yNN96YQw45JO973/vyvve9L4MHD17nMQ5NqbF/Y1f65S9/mbKysgwbNmzjFgjv0dhjdv78+TnzzDPTo0ePVFRUZKeddvJvA5pNY4/XK6+8MjvvvHO22GKL9OrVK+ecc07eeeedZqqW1uyRRx7JMccck549e6asrCx33nnnOteZPHly9t5771RUVGTHHXfM+PHjN3qdKwn0BXHLLbdk5MiRGT16dP74xz9mjz32yJAhQ/L666+vdvnHH388n/70p3PqqafmmWeeybBhwzJs2LA8++yzzVw5rVVjj9nJkyfn05/+dCZNmpQpU6akV69eOfzwwzN79uxmrpzWqLHH60ozZ87Meeedl0MOOaSZKoUVGnvMLl68OB/5yEcyc+bM3HrrrZk2bVpuvPHGbLvtts1cOa1RY4/Xn//857ngggsyevToPP/88/nRj36UW265JRdddFEzV05rtHDhwuyxxx659tprG7T8jBkzcvTRR2fQoEGZOnVqzj777Hzuc5/L/fffv5Er/T8lCmHfffctnXnmmXXfL1u2rNSzZ8/SuHHjVrv88ccfXzr66KPrje23336l//iP/9iodcJKjT1m32vp0qWlrbfeunTzzTdvrBKhzvocr0uXLi0deOCBpR/+8Iel4cOHl4499thmqBRWaOwxe/3115f69etXWrx4cXOVCHUae7yeeeaZpUMPPbTe2MiRI0sHHXTQRq0T3itJ6Y477ljrMueff37p3/7t3+qNnXDCCaUhQ4ZsxMr+xRX6Ali8eHGefvrpDB48uG6sTZs2GTx4cKZMmbLadaZMmVJv+SQZMmTIGpeHprQ+x+x7vfXWW1myZEk6deq0scqEJOt/vF588cXp2rVrTj311OYoE+qszzE7YcKEHHDAATnzzDPTrVu37LbbbvnP//zPLFu2rLnKppVan+P1wAMPzNNPP13Xlj99+vT85je/yVFHHdUsNUNjtHTuatssn8IGmTdvXpYtW5Zu3brVG+/WrVteeOGF1a4zZ86c1S4/Z86cjVYnrLQ+x+x7ffWrX03Pnj1X+QMJTW19jtdHH300P/rRjzJ16tRmqBDqW59jdvr06fntb3+bz3zmM/nNb36TF198MV/4wheyZMmSjB49ujnKppVan+P1xBNPzLx583LwwQenVCpl6dKlOeOMM7Tcs0laU+6qra3N22+/nS222GKjfr4r9MAm59vf/nZ++ctf5o477khlZWVLlwP1vPnmmznppJNy4403pnPnzi1dDjTI8uXL07Vr1/zgBz/IPvvskxNOOCFf+9rXcsMNN7R0abCKyZMn5z//8z9z3XXX5Y9//GNuv/323HvvvfnmN7/Z0qXBJscV+gLo3LlzysvLM3fu3Hrjc+fOTffu3Ve7Tvfu3Ru1PDSl9TlmV7rsssvy7W9/Ow899FB23333jVkmJGn88frSSy9l5syZOeaYY+rGli9fniRp27Ztpk2blh122GHjFk2rtj5/Y3v06JF27dqlvLy8bmzXXXfNnDlzsnjx4rRv336j1kzrtT7H6ze+8Y2cdNJJ+dznPpck6d+/fxYuXJjTTz89X/va19KmjWuSbDrWlLuqqqo2+tX5xBX6Qmjfvn322WefTJw4sW5s+fLlmThxYg444IDVrnPAAQfUWz5JHnzwwTUuD01pfY7ZJLn00kvzzW9+M/fdd18++MEPNkep0OjjdZdddslf/vKXTJ06te41dOjQutlte/Xq1Zzl0wqtz9/Ygw46KC+++GLdyack+Z//+Z/06NFDmGejWp/j9a233loltK88GVUqlTZesbAeWjx3NcvUe2ywX/7yl6WKiorS+PHjS88991zp9NNPL3Xs2LE0Z86cUqlUKp100kmlCy64oG75xx57rNS2bdvSZZddVnr++edLo0ePLrVr1670l7/8paV2gVamscfst7/97VL79u1Lt956a+m1116re7355psttQu0Io09Xt/LLPc0t8Yes7NmzSptvfXWpS9+8YuladOmle65555S165dS9/61rdaahdoRRp7vI4ePbq09dZbl37xi1+Upk+fXnrggQdKO+ywQ+n4449vqV2gFXnzzTdLzzzzTOmZZ54pJSldccUVpWeeeab08ssvl0qlUumCCy4onXTSSXXLT58+vdShQ4fSV77yldLzzz9fuvbaa0vl5eWl++67r1nqFegL5Oqrry5tv/32pfbt25f23Xff0hNPPFH33oABA0rDhw+vt/yvfvWr0k477VRq37596d/+7d9K9957bzNXTGvXmGO2d+/epSSrvEaPHt38hdMqNfZv7LsJ9LSExh6zjz/+eGm//fYrVVRUlPr161e65JJLSkuXLm3mqmmtGnO8LlmypDRmzJjSDjvsUKqsrCz16tWr9IUvfKH0xhtvNH/htDqTJk1a7b9JVx6jw4cPLw0YMGCVdfbcc89S+/btS/369SvddNNNzVZvWamkbwUAAACKxj30AAAAUEACPQAAABSQQA8AAAAFJNADAABAAQn0AAAAUEACPQAAABSQQA8AAAAFJNADAABAAQn0ANBKjRgxImVlZau8XnzxxQ3e9vjx49OxY8cNLxIAWKO2LV0AANByjjjiiNx00031xrp06dJC1azekiVL0q5du5YuAwA2Oa7QA0ArVlFRke7du9d7lZeX56677sree++dysrK9OvXL2PHjs3SpUvr1rviiivSv3//bLnllunVq1e+8IUvZMGCBUmSyZMn55RTTklNTU3dVf8xY8YkScrKynLnnXfWq6Fjx44ZP358kmTmzJkpKyvLLbfckgEDBqSysjI/+9nPkiQ//OEPs+uuu6aysjK77LJLrrvuuo3+8wGATZkr9ABAPb/73e9y8skn56qrrsohhxySl156KaeffnqSZPTo0UmSNm3a5Kqrrkrfvn0zffr0fOELX8j555+f6667LgceeGCuvPLKjBo1KtOmTUuSbLXVVo2q4YILLsjll1+evfbaqy7Ujxo1Ktdcc0322muvPPPMMznttNOy5ZZbZvjw4U37AwCAghDoAaAVu+eee+qF7SOPPDJvvPFGLrjggrqg3K9fv3zzm9/M+eefXxfozz777Lp1+vTpk29961s544wzct1116V9+/aprq5OWVlZunfvvl51nX322TnuuOPqvh89enQuv/zyurG+ffvmueeey/e//32BHoBWS6AHgFZs0KBBuf766+u+33LLLbP77rvnscceyyWXXFI3vmzZsrzzzjt566230qFDhzz00EMZN25cXnjhhdTW1mbp0qX13t9QH/zgB+v+e+HChXnppZdy6qmn5rTTTqsbX7p0aaqrqzf4swCgqAR6AGjFttxyy+y44471xhYsWJCxY8fWu0K+UmVlZWbOnJmPfvSj+fznP59LLrkknTp1yqOPPppTTz01ixcvXmugLysrS6lUqje2ZMmS1db17nqS5MYbb8x+++1Xb7ny8vJ17yQAbKYEegCgnr333jvTpk1bJeiv9PTTT2f58uW5/PLL06bNivl1f/WrX9Vbpn379lm2bNkq63bp0iWvvfZa3ff/+7//m7feemut9XTr1i09e/bM9OnT85nPfKaxuwMAmy2BHgCoZ9SoUfnoRz+a7bffPp/4xCfSpk2b/OlPf8qzzz6bb33rW9lxxx2zZMmSXH311TnmmGPy2GOP5YYbbqi3jT59+mTBggWZOHFi9thjj3To0CEdOnTIoYcemmuuuSYHHHBAli1blq9+9asNeiTd2LFj86UvfSnV1dU54ogjsmjRovzhD3/IG2+8kZEjR26sHwUAbNI8tg4AqGfIkCG555578sADD+RDH/pQ9t9//3z3u99N7969kyR77LFHrrjiinznO9/Jbrvtlp/97GcZN25cvW0ceOCBOeOMM3LCCSekS5cuufTSS5Mkl19+eXr16pVDDjkkJ554Ys4777wG3XP/uc99Lj/84Q9z0003pX///hkwYEDGjx+fvn37Nv0PAAAKoqz03hvZAAAAgE2eK/QAAABQQAI9AAAAFJBADwAAAAUk0AMAAEABCfQAAABQQAI9AAAAFJBADwAAAAUk0AMAAEABCfQAAABQQAI9AAAAFJBADwAAAAUk0AMAAEAB/X8FsbN5m3SgVgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Model"
      ],
      "metadata": {
        "id": "qu3Y4WOo_f1p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a loss function and optimizer in Pytorch"
      ],
      "metadata": {
        "id": "Qx7tjP3EA7wo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn=nn.L1Loss() # L1Loss --> Mean Absolute Error(MAE)\n",
        "optimizer=torch.optim.SGD(params=model_0.parameters(),lr=0.01) # SGD --> Stochastic Gradient Descent"
      ],
      "metadata": {
        "id": "SQjQjmHb_ZNQ"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Optimizer Loop"
      ],
      "metadata": {
        "id": "r2JzESFeBwjL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(12)\n",
        "\n",
        "epochs=500\n",
        "\n",
        "train_loss_values=[]\n",
        "test_loss_values=[]\n",
        "epoch_count=[]\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  ### Training\n",
        "\n",
        "  # Put the model in training mode\n",
        "  model_0.train()\n",
        "\n",
        "  # Forward propagation (uses the forward method implicitly)\n",
        "  y_pred=model_0(X_train)\n",
        "\n",
        "  # Calculate the loss\n",
        "  loss=loss_fn(y_pred,y_train)\n",
        "\n",
        "  # Zero grad to the optimizer\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  # Loss Backwards\n",
        "  loss.backward()\n",
        "\n",
        "  # Progress the Optimizer\n",
        "  optimizer.step()\n",
        "\n",
        "\n",
        "  ### Testing\n",
        "\n",
        "  # Put the model in evaluation mode\n",
        "  model_0.eval()\n",
        "\n",
        "  with torch.inference_mode():\n",
        "    # Forward pass on test data\n",
        "    test_pred = model_0(X_test)\n",
        "\n",
        "    # Calculate loss on test data\n",
        "    test_loss = loss_fn(test_pred,y_test.type(torch.float))\n",
        "\n",
        "    # Print out what's 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} \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrN47iQdBhAW",
        "outputId": "9431ef60-a40d-483c-bb3a-448cf76e3e63"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | MAE Train Loss: 0.8822507858276367 | MAE Test Loss: 1.1306867599487305 \n",
            "Epoch: 10 | MAE Train Loss: 0.7670409083366394 | MAE Test Loss: 0.9959767460823059 \n",
            "Epoch: 20 | MAE Train Loss: 0.6518309116363525 | MAE Test Loss: 0.8612667322158813 \n",
            "Epoch: 30 | MAE Train Loss: 0.5366209149360657 | MAE Test Loss: 0.7265567779541016 \n",
            "Epoch: 40 | MAE Train Loss: 0.4214109480381012 | MAE Test Loss: 0.5918468236923218 \n",
            "Epoch: 50 | MAE Train Loss: 0.3062010407447815 | MAE Test Loss: 0.45713692903518677 \n",
            "Epoch: 60 | MAE Train Loss: 0.19099099934101105 | MAE Test Loss: 0.322426974773407 \n",
            "Epoch: 70 | MAE Train Loss: 0.08029498159885406 | MAE Test Loss: 0.1917971670627594 \n",
            "Epoch: 80 | MAE Train Loss: 0.045205287635326385 | MAE Test Loss: 0.1215946301817894 \n",
            "Epoch: 90 | MAE Train Loss: 0.03887280821800232 | MAE Test Loss: 0.09516765177249908 \n",
            "Epoch: 100 | MAE Train Loss: 0.035264354199171066 | MAE Test Loss: 0.08279164135456085 \n",
            "Epoch: 110 | MAE Train Loss: 0.03180491551756859 | MAE Test Loss: 0.07383222877979279 \n",
            "Epoch: 120 | MAE Train Loss: 0.028373941779136658 | MAE Test Loss: 0.06555967777967453 \n",
            "Epoch: 130 | MAE Train Loss: 0.02493513934314251 | MAE Test Loss: 0.05728715658187866 \n",
            "Epoch: 140 | MAE Train Loss: 0.021502980962395668 | MAE Test Loss: 0.04970148950815201 \n",
            "Epoch: 150 | MAE Train Loss: 0.01807033084332943 | MAE Test Loss: 0.04142894595861435 \n",
            "Epoch: 160 | MAE Train Loss: 0.014631524682044983 | MAE Test Loss: 0.033156394958496094 \n",
            "Epoch: 170 | MAE Train Loss: 0.011201046407222748 | MAE Test Loss: 0.025570731610059738 \n",
            "Epoch: 180 | MAE Train Loss: 0.007766709662973881 | MAE Test Loss: 0.017298191785812378 \n",
            "Epoch: 190 | MAE Train Loss: 0.004327903501689434 | MAE Test Loss: 0.009025638923048973 \n",
            "Epoch: 200 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 210 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 220 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 230 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 240 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 250 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 260 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 270 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 280 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 290 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 300 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 310 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 320 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 330 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 340 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 350 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 360 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 370 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 380 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 390 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 400 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 410 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 420 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 430 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 440 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 450 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 460 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 470 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 480 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n",
            "Epoch: 490 | MAE Train Loss: 0.008067700080573559 | MAE Test Loss: 0.0007815062999725342 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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(\"Loss Curves\")\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "efkdY2KNFV5m",
        "outputId": "c0d7c8b0-d16a-434f-b764-a960bd10f143"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LmCe1ZLFGhq2",
        "outputId": "1dd91ac4-c586-4658-a215-85cca9c32d35"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model learned the following values for weights and bias:\n",
            "OrderedDict([('weights', tensor([0.6908])), ('bias', tensor([0.2955]))])\n",
            "\n",
            "And the original values for weights and bias are:\n",
            "weights: 0.7, bias: 0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making Predictions with a trained Pytorch Model(inference)"
      ],
      "metadata": {
        "id": "uJFP4XJRHTOR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Take the model in evaluation mode\n",
        "model_0.eval()\n",
        "\n",
        "with torch.inference_mode():\n",
        "  y_preds=model_0(X_test)\n",
        "\n",
        "y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBA27qlhHJ3g",
        "outputId": "e4307516-497e-428f-b9d2-133ec0efb34f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.8481],\n",
              "        [0.8620],\n",
              "        [0.8758],\n",
              "        [0.8896],\n",
              "        [0.9034],\n",
              "        [0.9172],\n",
              "        [0.9310],\n",
              "        [0.9448],\n",
              "        [0.9587],\n",
              "        [0.9725]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(predictions=y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "S47GRQioH6X8",
        "outputId": "d38ea8fa-c16f-4404-a8e6-97009a1ed3a6"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wCXhf3c2IAtt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}