Linear_Regression_with_a_Simple_Line_Example.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Linear Regression with a Simple Line Example.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/IoT-master/PyTorchMLTemplates/blob/master/Linear_Regression_with_a_Simple_Line_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41P_JZngACkp",
        "colab_type": "code",
        "outputId": "44edc4ae-d4b4-4f47-844d-cce654cba29e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!pip3 install torch"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.1.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch) (1.16.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xiq3cP5SAEsF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_FEVmDlDFAO",
        "colab_type": "code",
        "outputId": "7ecd331b-1753-4015-a7e0-ae4a1583470e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "X = torch.randn(100, 1)*10\n",
        "y = X + torch.randn(100, 1)*3\n",
        "# plt.axis([min(X), max(X), min(y), max(y)])\n",
        "plt.plot(X.numpy(), y.numpy(), 'o')\n",
        "plt.ylabel('y')\n",
        "plt.xlabel('x')\n",
        "plt.title('Visualization of the scatter X and y values')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Visualization of the scatter X and y values')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
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6rpVEdLMeTe7iWh2uUZhZ1zVLRNcCyeS6DQRbA0m21uHnWFeHA4WZdV2zRHQ3Aon1jwOF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\nzAo5UJiZWaH/Dw50DHzNct3CAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEo7eDGlAKaW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LR(nn.Module):\n",
        "  def __init__(self, input_size, output_size):\n",
        "    super().__init__()\n",
        "    self.linear = nn.Linear(input_size, output_size) \n",
        "  def forward(self, x):\n",
        "    pred = self.linear(x)\n",
        "    return pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I74ElLyHBOL7",
        "colab_type": "code",
        "outputId": "0927acde-54bf-4516-dd3b-122b4316e510",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#This will seed the data, so the results will always be the same on every run\n",
        "torch.manual_seed(1)\n",
        "model = LR(1, 1)\n",
        "print(model)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR(\n",
            "  (linear): Linear(in_features=1, out_features=1, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlAqrLc5EpB2",
        "colab_type": "code",
        "outputId": "83ebbab0-67ed-4560-e55f-3d1e7051d631",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "[w, b] = model.parameters()\n",
        "w1 = w[0][0].item()\n",
        "b1 = b[0].item()\n",
        "print(w1, b1)\n",
        "# This should always show 0.5152631998062134 -0.44137823581695557, where w1 is the first value and b1 is the second value.\n",
        "def get_params():\n",
        "  return (w[0][0].item(), b[0].item())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5152631998062134 -0.44137823581695557\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3OVE8zDFEA_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_fit(title):\n",
        "  plt.title(title)\n",
        "  w1, b1 = get_params()\n",
        "  x1 = np.array([-30,30])\n",
        "  y1 = w1*x1+b1\n",
        "  plt.plot(x1,y1, 'r')\n",
        "  plt.scatter(X, y)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XmnA5xRTF1Pe",
        "colab_type": "code",
        "outputId": "fe63c442-a8f9-4fc9-c07c-54967e54191b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plot_fit('Initial Guess of y=mx+b to fit Scatter Plot')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/vT2fjdvKasxzXNkG+N3v4MUXoayMVaf04Pb805m+/8Fuhs07rC7epKy47uGr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\nwDfGmAzx/7Dl6lwv5yxUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8stqXL7FGDvg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbqVLghEIU5Y",
        "colab_type": "code",
        "outputId": "2750be7a-bfda-4025-e32c-067f71bffbf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "epochs = 100\n",
        "losses = []\n",
        "for i in range(epochs):\n",
        "  y_pred = model.forward(X)\n",
        "  loss = criterion(y_pred, y)\n",
        "  print(\"epoch:\", i, \"loss:\", loss.item())\n",
        "  \n",
        "  losses.append(loss)\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 0 loss: 29.492820739746094\n",
            "epoch: 1 loss: 23.270309448242188\n",
            "epoch: 2 loss: 19.004030227661133\n",
            "epoch: 3 loss: 16.078929901123047\n",
            "epoch: 4 loss: 14.073341369628906\n",
            "epoch: 5 loss: 12.698162078857422\n",
            "epoch: 6 loss: 11.755189895629883\n",
            "epoch: 7 loss: 11.108538627624512\n",
            "epoch: 8 loss: 10.66504192352295\n",
            "epoch: 9 loss: 10.36082935333252\n",
            "epoch: 10 loss: 10.152109146118164\n",
            "epoch: 11 loss: 10.008858680725098\n",
            "epoch: 12 loss: 9.910494804382324\n",
            "epoch: 13 loss: 9.842905044555664\n",
            "epoch: 14 loss: 9.796415328979492\n",
            "epoch: 15 loss: 9.76439094543457\n",
            "epoch: 16 loss: 9.74228572845459\n",
            "epoch: 17 loss: 9.726981163024902\n",
            "epoch: 18 loss: 9.716340065002441\n",
            "epoch: 19 loss: 9.70889663696289\n",
            "epoch: 20 loss: 9.703645706176758\n",
            "epoch: 21 loss: 9.699899673461914\n",
            "epoch: 22 loss: 9.697183609008789\n",
            "epoch: 23 loss: 9.69517707824707\n",
            "epoch: 24 loss: 9.693655967712402\n",
            "epoch: 25 loss: 9.69246768951416\n",
            "epoch: 26 loss: 9.691510200500488\n",
            "epoch: 27 loss: 9.690710067749023\n",
            "epoch: 28 loss: 9.690018653869629\n",
            "epoch: 29 loss: 9.689401626586914\n",
            "epoch: 30 loss: 9.688838005065918\n",
            "epoch: 31 loss: 9.688310623168945\n",
            "epoch: 32 loss: 9.6878080368042\n",
            "epoch: 33 loss: 9.687323570251465\n",
            "epoch: 34 loss: 9.686851501464844\n",
            "epoch: 35 loss: 9.686389923095703\n",
            "epoch: 36 loss: 9.685935020446777\n",
            "epoch: 37 loss: 9.685484886169434\n",
            "epoch: 38 loss: 9.685039520263672\n",
            "epoch: 39 loss: 9.684597969055176\n",
            "epoch: 40 loss: 9.684158325195312\n",
            "epoch: 41 loss: 9.683721542358398\n",
            "epoch: 42 loss: 9.683286666870117\n",
            "epoch: 43 loss: 9.682854652404785\n",
            "epoch: 44 loss: 9.682424545288086\n",
            "epoch: 45 loss: 9.681995391845703\n",
            "epoch: 46 loss: 9.68156909942627\n",
            "epoch: 47 loss: 9.681143760681152\n",
            "epoch: 48 loss: 9.680721282958984\n",
            "epoch: 49 loss: 9.68030071258545\n",
            "epoch: 50 loss: 9.679880142211914\n",
            "epoch: 51 loss: 9.679462432861328\n",
            "epoch: 52 loss: 9.679045677185059\n",
            "epoch: 53 loss: 9.678631782531738\n",
            "epoch: 54 loss: 9.678218841552734\n",
            "epoch: 55 loss: 9.677807807922363\n",
            "epoch: 56 loss: 9.677397727966309\n",
            "epoch: 57 loss: 9.676990509033203\n",
            "epoch: 58 loss: 9.676583290100098\n",
            "epoch: 59 loss: 9.676178932189941\n",
            "epoch: 60 loss: 9.675775527954102\n",
            "epoch: 61 loss: 9.675374984741211\n",
            "epoch: 62 loss: 9.67497444152832\n",
            "epoch: 63 loss: 9.674575805664062\n",
            "epoch: 64 loss: 9.674179077148438\n",
            "epoch: 65 loss: 9.673784255981445\n",
            "epoch: 66 loss: 9.67339038848877\n",
            "epoch: 67 loss: 9.672999382019043\n",
            "epoch: 68 loss: 9.672608375549316\n",
            "epoch: 69 loss: 9.672219276428223\n",
            "epoch: 70 loss: 9.671832084655762\n",
            "epoch: 71 loss: 9.671446800231934\n",
            "epoch: 72 loss: 9.671062469482422\n",
            "epoch: 73 loss: 9.670680046081543\n",
            "epoch: 74 loss: 9.67029857635498\n",
            "epoch: 75 loss: 9.669919967651367\n",
            "epoch: 76 loss: 9.669541358947754\n",
            "epoch: 77 loss: 9.669163703918457\n",
            "epoch: 78 loss: 9.66878890991211\n",
            "epoch: 79 loss: 9.668416023254395\n",
            "epoch: 80 loss: 9.668044090270996\n",
            "epoch: 81 loss: 9.667673110961914\n",
            "epoch: 82 loss: 9.667304039001465\n",
            "epoch: 83 loss: 9.666935920715332\n",
            "epoch: 84 loss: 9.666569709777832\n",
            "epoch: 85 loss: 9.666205406188965\n",
            "epoch: 86 loss: 9.665842056274414\n",
            "epoch: 87 loss: 9.665480613708496\n",
            "epoch: 88 loss: 9.665120124816895\n",
            "epoch: 89 loss: 9.664761543273926\n",
            "epoch: 90 loss: 9.664403915405273\n",
            "epoch: 91 loss: 9.664047241210938\n",
            "epoch: 92 loss: 9.663692474365234\n",
            "epoch: 93 loss: 9.663339614868164\n",
            "epoch: 94 loss: 9.66298770904541\n",
            "epoch: 95 loss: 9.662637710571289\n",
            "epoch: 96 loss: 9.662288665771484\n",
            "epoch: 97 loss: 9.661940574645996\n",
            "epoch: 98 loss: 9.66159439086914\n",
            "epoch: 99 loss: 9.661250114440918\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yadq9L19JxYL",
        "colab_type": "code",
        "outputId": "dc008b9f-f5e6-4929-86e1-dca2ef5f870f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "plt.plot(range(epochs), losses)\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.title('Loss/Error Seen by each epoch')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss/Error Seen by each epoch')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PS5nZqOWEMUw6Wpt41dwXcO3Sta6WMrNRyQljGL3tiOk88uRWbn5g5B/vNTPL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\nDDMzq4gThpmZVeT/AKw/qxACvt6WAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmtf5UfWKUhT",
        "colab_type": "code",
        "outputId": "1a57a55c-4f5e-43e4-96e9-b5924032f139",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "cellView": "both"
      },
      "source": [
        "#@title\n",
        "plot_fit(\"Best Fit Line Found Using Linear Regression\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BLkp06pa64zcFxRzwRaQO8BZwKKDAEFV9SURqAKOBesBqoKOq/hbr/owxxl/7Ztl7B+aZ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\njEkTFvCNMSZNWMA3xpg08f+W97G5sRYvUwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcImQpijKb8X",
        "colab_type": "code",
        "outputId": "093945c7-0034-4036-9e08-62d962942301",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(get_params())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0.9950686693191528, -0.3772332966327667)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}