LinearRegression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LinearRegression.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/anujdutt9/PyTorch-DeepLearning/blob/master/LinearRegression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsIo1SpcxKiX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import Linear\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6L1kSs6pyyqv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d9a338ab-e83c-4e0e-f47c-290e27150645"
      },
      "source": [
        "torch.manual_seed(101)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f4c7c8f8130>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BiJ95Iyy9t4",
        "colab_type": "text"
      },
      "source": [
        "**Basic Linear Regression Equation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GX3GroEfxTie",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Weight\n",
        "W = torch.tensor(3.0, requires_grad=True)\n",
        "\n",
        "# Bias\n",
        "b = torch.tensor(1.0, requires_grad=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLqsBmZjx6ZQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Forward Pass\n",
        "def forward(X):\n",
        "  # Linear Regression\n",
        "  y = W * X + b\n",
        "  return y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4U_c2loyD35",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3e9c97e4-abd6-4cab-e3c1-633b50a7d4d9"
      },
      "source": [
        "x = torch.tensor(2)\n",
        "\n",
        "# Make Prediction\n",
        "print(forward(X=x))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(7., grad_fn=<AddBackward0>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mOFrlv4yR7g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "927fa0cc-b352-4af5-8618-05ec2b3cc2d2"
      },
      "source": [
        "x = torch.tensor([[4], [7]])\n",
        "\n",
        "# Make Prediction\n",
        "print(forward(X=x))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[13],\n",
            "        [22]], grad_fn=<AddBackward0>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KfZbvzozIhX",
        "colab_type": "text"
      },
      "source": [
        "**Creating a Linear Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcPNJLmCyes5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Linear Model with 1 input and 1 output\n",
        "model = Linear(in_features=1, out_features=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0MFpnVTzOyN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f8d1a360-dcf9-41a9-908b-b56433f92a51"
      },
      "source": [
        "model.bias"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([-0.0994], requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVSegr9XzR4N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "275b012d-2aed-4033-b2c6-431dcd947c78"
      },
      "source": [
        "model.weight"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([[-0.6039]], requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MIDV0zKzTKk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b5e35427-413b-4897-f10e-9ebbc6a0b90d"
      },
      "source": [
        "x = torch.tensor([2.0])\n",
        "\n",
        "# Make a Prediction\n",
        "print(model(x))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([-1.3072], grad_fn=<AddBackward0>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVdC7IS6zf4v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "982ce3ab-a7b4-49ee-bef1-10a6884696d2"
      },
      "source": [
        "x = torch.tensor([[3.0], [5.0]])\n",
        "\n",
        "# Make a Prediction\n",
        "print(model(x))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-1.9111],\n",
            "        [-3.1189]], grad_fn=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYOW41CN4_1g",
        "colab_type": "text"
      },
      "source": [
        "**Create a Dummy Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_YcOBxB5C09",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a Dummy Datatset\n",
        "x = torch.randn(100, 1)*10\n",
        "x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbSKN4xn5Fbn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = x + 3*torch.randn(100, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDPbLPWw5IaN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "1869eeef-cf9c-4783-f34e-79ab6f7b5f55"
      },
      "source": [
        "plt.plot(x.numpy(), y.numpy(), 'o')\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('y')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'y')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nnSuj2RIVVZlcR9Eo1lEgbXFqHsVZe5G1NQ9sStTd2OEOaFEjD8k4lVtrr8nLmgfKc0Ai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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zj5-TNhfztmK",
        "colab_type": "text"
      },
      "source": [
        "**Custom Modules**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LfLIMgV1zvU9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Linear Regression Model Class\n",
        "class LinearRegression(nn.Module):\n",
        "  \n",
        "  # Constructor\n",
        "  def __init__(self, input_size, output_size):\n",
        "    super().__init__()\n",
        "    \n",
        "    # Linear Model\n",
        "    self.linear = nn.Linear(in_features=input_size, out_features=output_size)\n",
        "    \n",
        "  # Forward Pass\n",
        "  def forward(self, x):\n",
        "    pred = self.linear(x)\n",
        "    return pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5PRHE-L0doo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Model Initialization\n",
        "lr_model = LinearRegression(input_size=1, output_size=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qnvu59X00nYF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a3a33917-9983-4c1a-d96f-9a2e7ba83dc8"
      },
      "source": [
        "lr_model"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(\n",
              "  (linear): Linear(in_features=1, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrtEYCSM0oOE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get Model Parameters\n",
        "def get_params():\n",
        "  [w,b] = lr_model.parameters()\n",
        "  w = w[0][0].item()\n",
        "  b = b[0].item()\n",
        "  return (w,b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzwSoFnr4kSt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Plotting Function\n",
        "def plot_fit(title):\n",
        "  plt.title(title)\n",
        "  W, b = get_params()\n",
        "  x_in = np.array([-30, 30])\n",
        "  y_pred = W * x_in + b\n",
        "  plt.plot(x_in, y_pred, 'r')\n",
        "  plt.scatter(x, y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pw9Kwmy97a2y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "60b3028a-bbeb-4dbd-fc17-bf4691c5af2a"
      },
      "source": [
        "plot_fit(title='Initial Model')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WSt2Uq5yaCZaqycvNYdXws5nV/1QFe8k46uFLwlQe535Kq0ZVVhQ5ZEMhJ/Yd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\nIf4fH7r0Ho0ykPEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXIdnfrF7dy0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loss Criterion\n",
        "criterion = nn.MSELoss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GtXceQtx-Bk1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Optimizer\n",
        "optimizer = torch.optim.SGD(lr_model.parameters(), lr=0.01)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dy__0W8l-S1P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Epochs\n",
        "epochs = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tnajrH0g-ZrT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "b3886847-b32b-4913-eb96-c514cda5e74e"
      },
      "source": [
        "losses = []\n",
        "\n",
        "for e in range(epochs):\n",
        "  y_pred = lr_model.forward(x)\n",
        "  \n",
        "  loss = criterion(y, y_pred)\n",
        "  \n",
        "  losses.append(loss)\n",
        "  \n",
        "  optimizer.zero_grad()\n",
        "  \n",
        "  loss.backward()\n",
        "  \n",
        "  optimizer.step()\n",
        "  \n",
        "  if (e%10 == 0):\n",
        "    print('epoch: {}'.format(e),\n",
        "       'loss: {}'.format(loss.item()))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 0 loss: 13.375500679016113\n",
            "epoch: 10 loss: 8.300289154052734\n",
            "epoch: 20 loss: 8.240790367126465\n",
            "epoch: 30 loss: 8.223700523376465\n",
            "epoch: 40 loss: 8.212434768676758\n",
            "epoch: 50 loss: 8.204909324645996\n",
            "epoch: 60 loss: 8.199878692626953\n",
            "epoch: 70 loss: 8.196520805358887\n",
            "epoch: 80 loss: 8.194273948669434\n",
            "epoch: 90 loss: 8.192773818969727\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3NF2RYl_Y_t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "9191da0f-865f-492c-d5e5-ce8816ebf33f"
      },
      "source": [
        "plt.plot(range(epochs), losses)\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "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": "1iElJSJ7_ov5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "a20c2f9f-cfae-4d96-fc7f-708757c4b2f0"
      },
      "source": [
        "plot_fit(title='Trained Model')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2rlVzj566Jk6J7EkPbSUmyj4kzc3LZ9y8DRQUlY7wBUUufCqkc/D889CyJbz1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\nIpIiFPBFRFKEAr6ISIr4f9yu5lQ8njYfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}