Linear_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Linear Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNMoQ07Yg/AJPWvwb8Mu8tm",
      "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/sancoap/pytorch1/blob/main/Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fVtBz-fjbj4p"
      },
      "source": [
        "import torch \n",
        "import torch.nn as nn #module that contains a lot of useful stuff in pytorch\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__-UY2ZSb0d0"
      },
      "source": [
        "#generate a data set of 20 random points\n",
        "N = 20\n",
        "X = np.random.random(N)*10 - 5\n",
        "Y = 0.5 * X - 1 + np.random.randn(N)\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "BYc8DFBlc4DV",
        "outputId": "29a23a00-2446-4902-e749-4447e9b40d7a"
      },
      "source": [
        "#generate plot\n",
        "plt.scatter(X,Y)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f91f36c38d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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o+2Xbnym7oEnYesD7lKRlbT7o+6emhPiWY5I+IumDW//dX94amSIRRuQAkBwjcgBIjiAHgOQIcgBIjiAHgOQIcgBIjiAHgOQIcgBI7v8BTrsQ1GcNSNQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pe2jnJBCdP8l"
      },
      "source": [
        "#Now the Pytorch code starts\n",
        "#linear model with 1 input and 1 output parameter, 1 X-Axis and 1 Y-Axis\n",
        "model = nn.Linear(1,1)\n"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jmb9hth8dhGm"
      },
      "source": [
        "#Define training process by defining the Loss and Optimizer\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouvVCIOzd42G"
      },
      "source": [
        "#Transform X and Y into data types that is relevant for PyTroch\n",
        "#We have to shape our data (Num_of_samples * Num_of_dims)\n",
        "X = X.reshape(N,1)\n",
        "Y = Y.reshape(N,1)\n",
        "\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKZAVKQqeZJH"
      },
      "source": [
        "#PyTorch requires you to create Torch Tensors from Numpy Arrays\n",
        "#PyTorch uses float32 by default and numpy uses float64\n",
        "#You have to cast down for converting Numpy Arrays into Torch Tensors\n",
        "inputs = torch.from_numpy(X.astype(np.float32))\n",
        "targets = torch.from_numpy(Y.astype(np.float32))\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_PgF_iQe9Mk",
        "outputId": "7dd41938-b08d-412a-9a6a-6affeb9bbf95"
      },
      "source": [
        "type(inputs)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNclptZse_Dv",
        "outputId": "190b5ab1-6ad2-407f-ffe2-e327ccbd78bf"
      },
      "source": [
        "#Train the model\n",
        "#As gradient descent is an iterative process, we have to define a number of iterations or epocs\n",
        "#Since the data is random, this might NOT work every time. I saw that losses were not converging in some cases!!!\n",
        "\n",
        "n_epochs = 30\n",
        "losses = []\n",
        "for it in range(n_epochs):\n",
        "  #zero the parameter gradients\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  #forward pass\n",
        "  outputs = model(inputs)\n",
        "  loss = criterion(outputs,targets)\n",
        "\n",
        "  #keep the losses so we can plot it later\n",
        "  #note that item() is python function, not numpy \n",
        "  #since loss is a single value, we don't use a numpy array to store it \n",
        "  losses.append(loss.item())\n",
        "\n",
        "  #backward pass\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "  print(f'Epoch {it+1}/{n_epochs}, Loss: {loss.item():.4f}')\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30, Loss: 0.6068\n",
            "Epoch 2/30, Loss: 0.5628\n",
            "Epoch 3/30, Loss: 0.5340\n",
            "Epoch 4/30, Loss: 0.5150\n",
            "Epoch 5/30, Loss: 0.5025\n",
            "Epoch 6/30, Loss: 0.4943\n",
            "Epoch 7/30, Loss: 0.4889\n",
            "Epoch 8/30, Loss: 0.4854\n",
            "Epoch 9/30, Loss: 0.4830\n",
            "Epoch 10/30, Loss: 0.4815\n",
            "Epoch 11/30, Loss: 0.4804\n",
            "Epoch 12/30, Loss: 0.4798\n",
            "Epoch 13/30, Loss: 0.4793\n",
            "Epoch 14/30, Loss: 0.4790\n",
            "Epoch 15/30, Loss: 0.4788\n",
            "Epoch 16/30, Loss: 0.4787\n",
            "Epoch 17/30, Loss: 0.4786\n",
            "Epoch 18/30, Loss: 0.4786\n",
            "Epoch 19/30, Loss: 0.4785\n",
            "Epoch 20/30, Loss: 0.4785\n",
            "Epoch 21/30, Loss: 0.4785\n",
            "Epoch 22/30, Loss: 0.4785\n",
            "Epoch 23/30, Loss: 0.4785\n",
            "Epoch 24/30, Loss: 0.4785\n",
            "Epoch 25/30, Loss: 0.4785\n",
            "Epoch 26/30, Loss: 0.4785\n",
            "Epoch 27/30, Loss: 0.4785\n",
            "Epoch 28/30, Loss: 0.4785\n",
            "Epoch 29/30, Loss: 0.4785\n",
            "Epoch 30/30, Loss: 0.4785\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ma8UGkHkhPu9",
        "outputId": "62654421-41a6-44d3-a023-2e0bf3ebb0c3"
      },
      "source": [
        "plt.plot(losses)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f91f36af850>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "JZ8S8xutixCA",
        "outputId": "1af514c0-df28-4881-8e61-37713e88ebcc"
      },
      "source": [
        "#use the trained model to make predictions and plot the result\n",
        "#here were detach the torch tensor as we want to use a numpy array to plot the data\n",
        "\n",
        "predicted = model(inputs).detach().numpy()\n",
        "plt.scatter(X,Y, label= \"orignal data\")\n",
        "plt.plot(X,predicted, label = 'Fitted Line')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnTrzcKrjxlX",
        "outputId": "a4d39cbb-5218-47e8-fd10-0cfa512f143a"
      },
      "source": [
        "#This is an important step for synthetic data\n",
        "w = model.weight.data.numpy()\n",
        "b = model.bias.data.numpy()\n",
        "print(w,b)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.45273742]] [-0.33605134]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJWoXzEblE5J"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}