02_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "02. Linear Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPaUXoGDCCWnGcnEf5lYSqw",
      "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/tanaymukherjee/Deep-Learning-with-PyTorch/blob/master/02_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7zHP0uYAe-E",
        "colab_type": "text"
      },
      "source": [
        "# Linear Regression with PyTorch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCJkYSs6Alpb",
        "colab_type": "text"
      },
      "source": [
        "## Import packages and libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYq7ivPy_6A-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qj7GQp3LApK8",
        "colab_type": "text"
      },
      "source": [
        "## Hyper-parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ykahp3rADOz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_size = 1\n",
        "output_size = 1\n",
        "num_epochs = 60\n",
        "learning_rate = 0.001"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyyG6j2gAuqM",
        "colab_type": "text"
      },
      "source": [
        "## Toy dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKKeUhycAGeb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], \n",
        "                    [9.779], [6.182], [7.59], [2.167], [7.042], \n",
        "                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)\n",
        "\n",
        "y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], \n",
        "                    [3.366], [2.596], [2.53], [1.221], [2.827], \n",
        "                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onxqJ8AGA3wL",
        "colab_type": "text"
      },
      "source": [
        "## Linear regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhClfz-yAHql",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = nn.Linear(input_size, output_size)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_C93YRRuA6t0",
        "colab_type": "text"
      },
      "source": [
        "## Loss and optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFvtxygjAKtd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  "
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ohgEoOnA-K1",
        "colab_type": "text"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmG0X3plAOi8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "4dc58841-2e91-4753-8caa-b93f3f9ed9bb"
      },
      "source": [
        "for epoch in range(num_epochs):\n",
        "    # Convert numpy arrays to torch tensors\n",
        "    inputs = torch.from_numpy(x_train)\n",
        "    targets = torch.from_numpy(y_train)\n",
        "\n",
        "    # Forward pass\n",
        "    outputs = model(inputs)\n",
        "    loss = criterion(outputs, targets)\n",
        "    \n",
        "    # Backward and optimize\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    if (epoch+1) % 5 == 0:\n",
        "        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [5/60], Loss: 0.3858\n",
            "Epoch [10/60], Loss: 0.3642\n",
            "Epoch [15/60], Loss: 0.3552\n",
            "Epoch [20/60], Loss: 0.3512\n",
            "Epoch [25/60], Loss: 0.3494\n",
            "Epoch [30/60], Loss: 0.3483\n",
            "Epoch [35/60], Loss: 0.3476\n",
            "Epoch [40/60], Loss: 0.3471\n",
            "Epoch [45/60], Loss: 0.3466\n",
            "Epoch [50/60], Loss: 0.3461\n",
            "Epoch [55/60], Loss: 0.3457\n",
            "Epoch [60/60], Loss: 0.3452\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0y1uEB89BAVJ",
        "colab_type": "text"
      },
      "source": [
        "## Plot the graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHahG9sDAQ-j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "0bc84b7e-b0f4-424d-8490-ed05526f46d9"
      },
      "source": [
        "predicted = model(torch.from_numpy(x_train)).detach().numpy()\n",
        "plt.plot(x_train, y_train, 'ro', label='Original data')\n",
        "plt.plot(x_train, predicted, label='Fitted line')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "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": "markdown",
      "metadata": {
        "id": "EsfT5kwDBBUM",
        "colab_type": "text"
      },
      "source": [
        "## Save the model checkpoint"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O4iJvDIATRt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(model.state_dict(), 'model.ckpt')"
      ],
      "execution_count": 9,
      "outputs": []
    }
  ]
}