Linear_regression_pytorch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Linear_regression_pytorch.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "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/jaspreet145/pytorch/blob/master/Linear_regression_pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zO6uhzQ0aVPf",
        "colab_type": "code",
        "outputId": "b5fa139f-600c-47c7-d654-e18cd7913071",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "import torch\n",
        "print(torch.__version__)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STHeOhpsakVS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "class lr(torch.nn.Module):\n",
        "  \n",
        "  def __init__(self,input_size,output_size):\n",
        "    super().__init__()\n",
        "    self.inp = input_size\n",
        "    self.out = output_size\n",
        "    self.model = torch.nn.Linear(input_size,output_size)\n",
        "  def forward(self,x):\n",
        "    return self.model(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EphhW5NEc666",
        "colab_type": "code",
        "outputId": "8f80eebd-ecd5-4a20-b19a-b0e60cf8f101",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "torch.manual_seed(1)\n",
        "x = torch.randn(100,1)\n",
        "y = x + torch.randn(100,1)\n",
        "plt.plot(x.numpy(),y.numpy(),'o')\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AjjQEdhQyqt2xWfXf/cEs71F022YP6sSpwdn3MpVHWZulygZh9jZgGKVy7Gb2bjM7aman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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yl_Fcxrcd-BL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = lr(1,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JB_c78dnfZLs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_para():\n",
        "  w1 , b1 = model.parameters()\n",
        "  return (w1[0][0].item(),b1[0].item())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4LNlH66ed8H",
        "colab_type": "code",
        "outputId": "be3b706b-62d9-4fbd-b045-f2867bfdf4ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "def plot_fit():\n",
        "  w1 , b1 = get_para()\n",
        "  x1 = torch.tensor([-3,3])\n",
        "  y1 = w1*x1 + b1\n",
        "  plt.scatter(x.numpy(),y.numpy())\n",
        "  plt.plot(x1.numpy(),y1.numpy())\n",
        "  plt.show()\n",
        "plot_fit()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JLrjskseg7s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = torch.nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters() , lr = 0.01)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-M4gLGNhMZs",
        "colab_type": "code",
        "outputId": "493cc8d3-8117-44e4-b644-358304d7f06b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        }
      },
      "source": [
        "epoch  = 50\n",
        "for _ in range(epoch):\n",
        "  y_pred = model(x)\n",
        "  loss = criterion(y_pred,y)\n",
        "  print(\"loss :{} epoch:{}\".format(loss,_))\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "  \n",
        "  "
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss :1.1886200904846191 epoch:0\n",
            "loss :1.1860820055007935 epoch:1\n",
            "loss :1.183625340461731 epoch:2\n",
            "loss :1.181247591972351 epoch:3\n",
            "loss :1.1789461374282837 epoch:4\n",
            "loss :1.1767184734344482 epoch:5\n",
            "loss :1.1745623350143433 epoch:6\n",
            "loss :1.1724753379821777 epoch:7\n",
            "loss :1.1704550981521606 epoch:8\n",
            "loss :1.1684997081756592 epoch:9\n",
            "loss :1.1666069030761719 epoch:10\n",
            "loss :1.164774775505066 epoch:11\n",
            "loss :1.1630011796951294 epoch:12\n",
            "loss :1.1612844467163086 epoch:13\n",
            "loss :1.1596226692199707 epoch:14\n",
            "loss :1.158013939857483 epoch:15\n",
            "loss :1.156456708908081 epoch:16\n",
            "loss :1.1549491882324219 epoch:17\n",
            "loss :1.1534899473190308 epoch:18\n",
            "loss :1.1520771980285645 epoch:19\n",
            "loss :1.1507097482681274 epoch:20\n",
            "loss :1.1493858098983765 epoch:21\n",
            "loss :1.148104190826416 epoch:22\n",
            "loss :1.1468634605407715 epoch:23\n",
            "loss :1.1456623077392578 epoch:24\n",
            "loss :1.1444995403289795 epoch:25\n",
            "loss :1.143373727798462 epoch:26\n",
            "loss :1.1422839164733887 epoch:27\n",
            "loss :1.1412289142608643 epoch:28\n",
            "loss :1.1402074098587036 epoch:29\n",
            "loss :1.1392184495925903 epoch:30\n",
            "loss :1.1382609605789185 epoch:31\n",
            "loss :1.1373341083526611 epoch:32\n",
            "loss :1.1364367008209229 epoch:33\n",
            "loss :1.1355677843093872 epoch:34\n",
            "loss :1.134726643562317 epoch:35\n",
            "loss :1.133912205696106 epoch:36\n",
            "loss :1.1331236362457275 epoch:37\n",
            "loss :1.1323602199554443 epoch:38\n",
            "loss :1.13162100315094 epoch:39\n",
            "loss :1.1309053897857666 epoch:40\n",
            "loss :1.1302125453948975 epoch:41\n",
            "loss :1.1295416355133057 epoch:42\n",
            "loss :1.1288920640945435 epoch:43\n",
            "loss :1.1282631158828735 epoch:44\n",
            "loss :1.1276541948318481 epoch:45\n",
            "loss :1.12706458568573 epoch:46\n",
            "loss :1.1264936923980713 epoch:47\n",
            "loss :1.1259409189224243 epoch:48\n",
            "loss :1.1254056692123413 epoch:49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJtgDk1ejJA3",
        "colab_type": "code",
        "outputId": "6467dc56-d00e-4c37-c7df-ce5a2da813f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "plt.clf()\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58gUKOL8mLDS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "760e452c-34e3-49d4-e861-e1b51dcf8e98"
      },
      "source": [
        "plot_fit()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J3zz0Vdfw94+38+jbRew/epwJp5/KtIn5DDi1XVzPK4nnujV2EbeKtJrD3/svGNil0deP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\nw0/P70eH1rHVovv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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwuff5Xjzw6B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}