Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Linear Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashishpatel26/Pytorch-Learning/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": "markdown",
      "metadata": {
        "id": "oBLfi1mp1JnR"
      },
      "source": [
        "### Linear Regression\n",
        "\n",
        "Simple Linear Regression Basics¶\n",
        "* Allows us to understand relationship between two continuous variables\n",
        "* **Example**\n",
        "* x: independent variable\n",
        "  * weight\n",
        "* y: dependent variable\n",
        "  * height\n",
        "* y = α x + β"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJbYXou6chZf",
        "outputId": "4ac4e7ba-a9c2-40df-bf2c-f97e61e706a5"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Mar 18 08:30:47 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.56       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNsa0yBj1d-k"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqPdGB4v1lzS"
      },
      "source": [
        "np.random.seed(2021)\n",
        "n = 50"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlIWKyxW1ozH"
      },
      "source": [
        "x = np.random.randn(n)\n",
        "y = x * np.random.randn(n)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vy-V4ilw10oU"
      },
      "source": [
        "colors = np.random.rand(n)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "QE8gTd2513Py",
        "outputId": "743bc27d-a2b6-435a-bfd6-8b03e2339af0"
      },
      "source": [
        "plt.plot(np.unique(x), np.poly1d(np.polyfit(x,y,1))(np.unique(x)))\n",
        "plt.scatter(x,y, c=colors, alpha = 0.5)\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": "markdown",
      "metadata": {
        "id": "_YPn8f912b4z"
      },
      "source": [
        "### Building Linear regression with Pytorch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKSJgYxL2Kii"
      },
      "source": [
        "x_values = [i for i in range(11)]"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5H9Vr5h2jRq",
        "outputId": "51a458af-0179-48a3-f869-a18db35f4f61"
      },
      "source": [
        "x_values"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZqI2V7Y2j7D",
        "outputId": "0d69a04e-1b65-4118-fec3-9d6ea4cfade3"
      },
      "source": [
        "x_train = np.array(x_values, dtype=np.float32)\n",
        "x_train.shape"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3K8Wlii2tXi",
        "outputId": "89e164e0-700d-41b7-aae1-c465260b0dee"
      },
      "source": [
        "x_train = x_train.reshape(-1,1)\n",
        "x_train.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8uKlU2n3rB3"
      },
      "source": [
        "### Formula\n",
        "\n",
        "$y = 2x + 1$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sr-l_q6_25dk"
      },
      "source": [
        "y_values = []\n",
        "for i in x_values:\n",
        "  result = 2 * i + 1\n",
        "  y_values.append(result)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtIPG4Uj4L8R",
        "outputId": "a9bd57dd-eda6-4b96-a3f6-1e509c7673e3"
      },
      "source": [
        "y_train = np.array(y_values, dtype = np.float32)\n",
        "y_train  = y_train.reshape(-1,1)\n",
        "y_train.shape"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZEkV5vB4ipS"
      },
      "source": [
        "## Building Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EU_L1jS4S4a"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JbFt3p264o8m"
      },
      "source": [
        "class LinearRegression(nn.Module):\n",
        "  def __init__(self, input_dim, output_dim):\n",
        "    super(LinearRegression, self).__init__()\n",
        "    self.linear = nn.Linear(input_dim, output_dim)\n",
        "\n",
        "  def forward(self, x):\n",
        "    out = self.linear(x)\n",
        "    return out"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdWVT-NL5bVV"
      },
      "source": [
        "* input: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "* desired output: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiCFEyrB5CFQ"
      },
      "source": [
        "input_dim = 1\n",
        "output_dim = 1"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhBne1BL5GLy"
      },
      "source": [
        "model = LinearRegression(input_dim, output_dim)\n",
        "\n",
        "criterion = nn.MSELoss()\n",
        "\n",
        "learning_rate = 0.01\n",
        "\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm6RHw5F9MLF",
        "outputId": "adc4cb2a-71bb-461c-a148-4ebc3d40c173"
      },
      "source": [
        "!pip install pkbar"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pkbar\n",
            "  Downloading https://files.pythonhosted.org/packages/95/8f/28e0a21b27f836a8903315050db17dd68e55bf477b6fde52d1c68da3c8a6/pkbar-0.5-py3-none-any.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pkbar) (1.19.5)\n",
            "Installing collected packages: pkbar\n",
            "Successfully installed pkbar-0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ECYJDUp5OSA",
        "outputId": "993c73e4-2923-4a21-977e-5ffa669d02fb"
      },
      "source": [
        "import pkbar\n",
        "train_per_epoch = 100\n",
        "num_epochs = 100\n",
        "for epoch in range(num_epochs):\n",
        "\n",
        "  kbar = pkbar.Kbar(target=train_per_epoch, epoch=epoch, num_epochs=num_epochs, width=8, always_stateful=False)\n",
        "\n",
        "  epoch += 1\n",
        "\n",
        "  # Convert nparray into torch variable\n",
        "  inputs = torch.from_numpy(x_train).requires_grad_()\n",
        "  labels = torch.from_numpy(y_train)\n",
        "\n",
        "  # training\n",
        "  for i in range(train_per_epoch):\n",
        "    # clear gradient w.r.t parameters\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # forward to get output\n",
        "    outputs = model(inputs)\n",
        "\n",
        "    # calculate loss\n",
        "    loss = criterion(outputs, labels)\n",
        "    rmse = torch.sqrt(loss)\n",
        "\n",
        "    # getting gradient wrt parameter\n",
        "    loss.backward()\n",
        "\n",
        "    # updating parameters\n",
        "    optimizer.step()\n",
        "\n",
        "    ############################# Update after each batch ##################################\n",
        "    kbar.update(i, values=[(\"loss\", loss), (\"rmse\", rmse)])\n",
        "    ########################################################################################\n",
        "\n",
        "  # print(f'epoch: {epoch}, loss: {loss.item()}')\n",
        "  # validation\n",
        "  outputs = model(inputs)\n",
        "  val_loss = criterion(outputs, labels)\n",
        "  val_rmse = torch.sqrt(val_loss)\n",
        "\n",
        "  ################################ Add validation metrics ###################################\n",
        "  kbar.add(1, values=[(\"val_loss\", val_loss), (\"val_rmse\", val_rmse)])\n",
        "  ###########################################################################################"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/100\n",
            "100/100 [========] - 0s 617us/step - loss: 1.5779e-04 - rmse: 0.0124 - val_loss: 8.5524e-05 - val_rmse: 0.0092\n",
            "Epoch: 2/100\n",
            "100/100 [========] - 0s 427us/step - loss: 5.1332e-05 - rmse: 0.0071 - val_loss: 2.7825e-05 - val_rmse: 0.0053\n",
            "Epoch: 3/100\n",
            "100/100 [========] - 0s 418us/step - loss: 1.6700e-05 - rmse: 0.0040 - val_loss: 9.0508e-06 - val_rmse: 0.0030\n",
            "Epoch: 4/100\n",
            "100/100 [========] - 0s 414us/step - loss: 5.4327e-06 - rmse: 0.0023 - val_loss: 2.9447e-06 - val_rmse: 0.0017\n",
            "Epoch: 5/100\n",
            "100/100 [========] - 0s 425us/step - loss: 1.7673e-06 - rmse: 0.0013 - val_loss: 9.5771e-07 - val_rmse: 9.7863e-04\n",
            "Epoch: 6/100\n",
            "100/100 [========] - 0s 452us/step - loss: 5.7496e-07 - rmse: 7.4874e-04 - val_loss: 3.1161e-07 - val_rmse: 5.5822e-04\n",
            "Epoch: 7/100\n",
            "100/100 [========] - 0s 429us/step - loss: 1.8705e-07 - rmse: 4.2705e-04 - val_loss: 1.0137e-07 - val_rmse: 3.1838e-04\n",
            "Epoch: 8/100\n",
            "100/100 [========] - 0s 431us/step - loss: 6.0866e-08 - rmse: 2.4362e-04 - val_loss: 3.3019e-08 - val_rmse: 1.8171e-04\n",
            "Epoch: 9/100\n",
            "100/100 [========] - 0s 417us/step - loss: 1.9837e-08 - rmse: 1.3908e-04 - val_loss: 1.0718e-08 - val_rmse: 1.0353e-04\n",
            "Epoch: 10/100\n",
            "100/100 [========] - 0s 389us/step - loss: 6.4641e-09 - rmse: 7.9397e-05 - val_loss: 3.5160e-09 - val_rmse: 5.9296e-05\n",
            "Epoch: 11/100\n",
            "100/100 [========] - 0s 401us/step - loss: 2.1149e-09 - rmse: 4.5416e-05 - val_loss: 1.1651e-09 - val_rmse: 3.4133e-05\n",
            "Epoch: 12/100\n",
            "100/100 [========] - 0s 413us/step - loss: 6.9972e-10 - rmse: 2.6138e-05 - val_loss: 3.8752e-10 - val_rmse: 1.9685e-05\n",
            "Epoch: 13/100\n",
            "100/100 [========] - 0s 413us/step - loss: 2.3514e-10 - rmse: 1.5157e-05 - val_loss: 1.3132e-10 - val_rmse: 1.1459e-05\n",
            "Epoch: 14/100\n",
            "100/100 [========] - 0s 405us/step - loss: 8.0994e-11 - rmse: 8.9116e-06 - val_loss: 4.9867e-11 - val_rmse: 7.0617e-06\n",
            "Epoch: 15/100\n",
            "100/100 [========] - 0s 399us/step - loss: 3.1033e-11 - rmse: 5.4971e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 16/100\n",
            "100/100 [========] - 0s 408us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 17/100\n",
            "100/100 [========] - 0s 410us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 18/100\n",
            "100/100 [========] - 0s 423us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 19/100\n",
            "100/100 [========] - 0s 410us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 20/100\n",
            "100/100 [========] - 0s 404us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 21/100\n",
            "100/100 [========] - 0s 409us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 22/100\n",
            "100/100 [========] - 0s 416us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 23/100\n",
            "100/100 [========] - 0s 410us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 24/100\n",
            "100/100 [========] - 0s 422us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 25/100\n",
            "100/100 [========] - 0s 403us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 26/100\n",
            "100/100 [========] - 0s 440us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 27/100\n",
            "100/100 [========] - 0s 438us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 28/100\n",
            "100/100 [========] - 0s 449us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 29/100\n",
            "100/100 [========] - 0s 420us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 30/100\n",
            "100/100 [========] - 0s 455us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 31/100\n",
            "100/100 [========] - 0s 419us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 32/100\n",
            "100/100 [========] - 0s 412us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 33/100\n",
            "100/100 [========] - 0s 395us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 34/100\n",
            "100/100 [========] - 0s 421us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 35/100\n",
            "100/100 [========] - 0s 417us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 36/100\n",
            "100/100 [========] - 0s 384us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 37/100\n",
            "100/100 [========] - 0s 406us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 38/100\n",
            "100/100 [========] - 0s 404us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 39/100\n",
            "100/100 [========] - 0s 411us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 40/100\n",
            "100/100 [========] - 0s 442us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 41/100\n",
            "100/100 [========] - 0s 422us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 42/100\n",
            "100/100 [========] - 0s 413us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 43/100\n",
            "100/100 [========] - 0s 426us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 44/100\n",
            "100/100 [========] - 0s 421us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 45/100\n",
            "100/100 [========] - 0s 405us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 46/100\n",
            "100/100 [========] - 0s 398us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 47/100\n",
            "100/100 [========] - 0s 407us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 48/100\n",
            "100/100 [========] - 0s 422us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 49/100\n",
            "100/100 [========] - 0s 420us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 50/100\n",
            "100/100 [========] - 0s 425us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 51/100\n",
            "100/100 [========] - 0s 419us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 52/100\n",
            "100/100 [========] - 0s 399us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 53/100\n",
            "100/100 [========] - 0s 424us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 54/100\n",
            "100/100 [========] - 0s 436us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 55/100\n",
            "100/100 [========] - 0s 426us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 56/100\n",
            "100/100 [========] - 0s 419us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 57/100\n",
            "100/100 [========] - 0s 441us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 58/100\n",
            "100/100 [========] - 0s 446us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 59/100\n",
            "100/100 [========] - 0s 429us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 60/100\n",
            "100/100 [========] - 0s 449us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 61/100\n",
            "100/100 [========] - 0s 466us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 62/100\n",
            "100/100 [========] - 0s 464us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 63/100\n",
            "100/100 [========] - 0s 464us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 64/100\n",
            "100/100 [========] - 0s 455us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 65/100\n",
            "100/100 [========] - 0s 445us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 66/100\n",
            "100/100 [========] - 0s 440us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 67/100\n",
            "100/100 [========] - 0s 438us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 68/100\n",
            "100/100 [========] - 0s 432us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 69/100\n",
            "100/100 [========] - 0s 453us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 70/100\n",
            "100/100 [========] - 0s 438us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 71/100\n",
            "100/100 [========] - 0s 463us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 72/100\n",
            "100/100 [========] - 0s 450us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 73/100\n",
            "100/100 [========] - 0s 448us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 74/100\n",
            "100/100 [========] - 0s 446us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 75/100\n",
            "100/100 [========] - 0s 442us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 76/100\n",
            "100/100 [========] - 0s 473us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 77/100\n",
            "100/100 [========] - 0s 425us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 78/100\n",
            "100/100 [========] - 0s 442us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 79/100\n",
            "100/100 [========] - 0s 436us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 80/100\n",
            "100/100 [========] - 0s 440us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 81/100\n",
            "100/100 [========] - 0s 429us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 82/100\n",
            "100/100 [========] - 0s 494us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 83/100\n",
            "100/100 [========] - 0s 451us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 84/100\n",
            "100/100 [========] - 0s 441us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 85/100\n",
            "100/100 [========] - 0s 428us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 86/100\n",
            "100/100 [========] - 0s 424us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 87/100\n",
            "100/100 [========] - 0s 450us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 88/100\n",
            "100/100 [========] - 0s 434us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 89/100\n",
            "100/100 [========] - 0s 454us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 90/100\n",
            "100/100 [========] - 0s 477us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 91/100\n",
            "100/100 [========] - 0s 435us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 92/100\n",
            "100/100 [========] - 0s 444us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 93/100\n",
            "100/100 [========] - 0s 416us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 94/100\n",
            "100/100 [========] - 0s 425us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 95/100\n",
            "100/100 [========] - 0s 431us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 96/100\n",
            "100/100 [========] - 0s 429us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 97/100\n",
            "100/100 [========] - 0s 426us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 98/100\n",
            "100/100 [========] - 0s 476us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 99/100\n",
            "100/100 [========] - 0s 460us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n",
            "Epoch: 100/100\n",
            "100/100 [========] - 0s 432us/step - loss: 1.5594e-11 - rmse: 3.9489e-06 - val_loss: 1.5594e-11 - val_rmse: 3.9489e-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSultuM86CN_",
        "outputId": "058c0fcd-22ed-4fc5-a1ae-564471f660a0"
      },
      "source": [
        "predicted = model(torch.from_numpy(x_train).requires_grad_()).data.numpy()\n",
        "predicted"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.99999267],\n",
              "       [ 2.9999938 ],\n",
              "       [ 4.999995  ],\n",
              "       [ 6.9999967 ],\n",
              "       [ 8.999997  ],\n",
              "       [10.999998  ],\n",
              "       [13.        ],\n",
              "       [15.000001  ],\n",
              "       [17.000002  ],\n",
              "       [19.000004  ],\n",
              "       [21.000004  ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-ps5EfM7SFv",
        "outputId": "6d8fc741-38b0-4c1b-9f28-fb005e78bb9d"
      },
      "source": [
        "y_train"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.],\n",
              "       [ 3.],\n",
              "       [ 5.],\n",
              "       [ 7.],\n",
              "       [ 9.],\n",
              "       [11.],\n",
              "       [13.],\n",
              "       [15.],\n",
              "       [17.],\n",
              "       [19.],\n",
              "       [21.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "QQ6a4TAt7VVZ",
        "outputId": "ef0e4ba3-5178-4dc3-8b0d-aaf6c2f6058a"
      },
      "source": [
        "plt.clf()\n",
        "\n",
        "predicted = model(torch.from_numpy(x_train).requires_grad_()).data.numpy()\n",
        "\n",
        "plt.plot(x_train, y_train, 'go', label='True data', alpha = 0.5)\n",
        "\n",
        "plt.plot(x_train, predicted, '--', label='Predicted data', alpha = 0.5)\n",
        "\n",
        "plt.legend(loc = 'best')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 65,
      "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": {
        "id": "L2EZa66i7yhc"
      },
      "source": [
        "save_model = False\n",
        "if save_model is True:\n",
        "  model.load_state_dict(torch.load('awesome.pkl'))"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jc2UyW5--1ga"
      },
      "source": [
        "### Linear Regression on GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDp53WUk-y4Y",
        "outputId": "19385cd5-e6a7-4912-9b34-8ec9fdd2adf5"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "'''\n",
        "Step:1 Create Class Model\n",
        "'''\n",
        "class LinearRegression(nn.Module):\n",
        "\n",
        "  def __init__(self, input_dim, output_dim):\n",
        "    super(LinearRegression, self).__init__()\n",
        "    self.linear = nn.Linear(input_dim, output_dim)\n",
        "\n",
        "  def forward(self, x):\n",
        "    out = self.linear(x)\n",
        "    return out\n",
        "\n",
        "'''\n",
        "Step:2 Initalize the class\n",
        "'''\n",
        "input_dim = 1\n",
        "output_dim = 1\n",
        "\n",
        "model = LinearRegression(input_dim, output_dim)\n",
        "\n",
        "#######################\n",
        "#  USE GPU FOR MODEL  #\n",
        "#######################\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "'''\n",
        "Step:3 Instatiate loss\n",
        "'''\n",
        "criterion = nn.MSELoss()\n",
        "\n",
        "'''\n",
        "Step:4 Instatiate Optimizer class\n",
        "'''\n",
        "learning_rate = 0.01\n",
        "optimzer = torch.optim.SGD(model.parameters(), lr = learning_rate)\n",
        "\n",
        "'''\n",
        "Step:5 Train the Model\n",
        "'''\n",
        "epochs = 100\n",
        "for epoch in range(epochs):\n",
        "\n",
        "  epoch += 1\n",
        "\n",
        "  inputs = torch.from_numpy(x_train).to(device)\n",
        "  labels = torch.from_numpy(y_train).to(device)\n",
        "\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  outputs = model(inputs)\n",
        "\n",
        "  loss = criterion(outputs, labels)\n",
        "  loss.backward()\n",
        "\n",
        "  optimzer.step()\n",
        "\n",
        "  print(f\"Epoch  : {epoch}, loss : {loss.item()}\")"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch  : 1, loss : 65.81813049316406\n",
            "Epoch  : 2, loss : 6.080535888671875\n",
            "Epoch  : 3, loss : 26.803136825561523\n",
            "Epoch  : 4, loss : 79.30482482910156\n",
            "Epoch  : 5, loss : 40.29248046875\n",
            "Epoch  : 6, loss : 1.2602595090866089\n",
            "Epoch  : 7, loss : 53.73297882080078\n",
            "Epoch  : 8, loss : 74.43798828125\n",
            "Epoch  : 9, loss : 14.657421112060547\n",
            "Epoch  : 10, loss : 14.583102226257324\n",
            "Epoch  : 11, loss : 74.25627136230469\n",
            "Epoch  : 12, loss : 53.473724365234375\n",
            "Epoch  : 13, loss : 0.8807759284973145\n",
            "Epoch  : 14, loss : 39.79227066040039\n",
            "Epoch  : 15, loss : 78.74667358398438\n",
            "Epoch  : 16, loss : 26.190624237060547\n",
            "Epoch  : 17, loss : 5.37883186340332\n",
            "Epoch  : 18, loss : 65.06869506835938\n",
            "Epoch  : 19, loss : 65.07728576660156\n",
            "Epoch  : 20, loss : 5.325362205505371\n",
            "Epoch  : 21, loss : 26.02469253540039\n",
            "Epoch  : 22, loss : 78.5689468383789\n",
            "Epoch  : 23, loss : 39.62467575073242\n",
            "Epoch  : 24, loss : 0.6238564848899841\n",
            "Epoch  : 25, loss : 53.151119232177734\n",
            "Epoch  : 26, loss : 73.97216796875\n",
            "Epoch  : 27, loss : 14.292315483093262\n",
            "Epoch  : 28, loss : 14.287351608276367\n",
            "Epoch  : 29, loss : 74.075927734375\n",
            "Epoch  : 30, loss : 53.438682556152344\n",
            "Epoch  : 31, loss : 0.9433753490447998\n",
            "Epoch  : 32, loss : 39.94208908081055\n",
            "Epoch  : 33, loss : 79.0319595336914\n",
            "Epoch  : 34, loss : 26.5960693359375\n",
            "Epoch  : 35, loss : 5.84687614440918\n",
            "Epoch  : 36, loss : 65.61461639404297\n",
            "Epoch  : 37, loss : 65.72844696044922\n",
            "Epoch  : 38, loss : 6.027714252471924\n",
            "Epoch  : 39, loss : 26.736650466918945\n",
            "Epoch  : 40, loss : 79.3230972290039\n",
            "Epoch  : 41, loss : 40.41315460205078\n",
            "Epoch  : 42, loss : 1.3773980140686035\n",
            "Epoch  : 43, loss : 53.86180114746094\n",
            "Epoch  : 44, loss : 74.67262268066406\n",
            "Epoch  : 45, loss : 14.941096305847168\n",
            "Epoch  : 46, loss : 14.83036994934082\n",
            "Epoch  : 47, loss : 74.54058837890625\n",
            "Epoch  : 48, loss : 53.84040451049805\n",
            "Epoch  : 49, loss : 1.224683403968811\n",
            "Epoch  : 50, loss : 40.08771514892578\n",
            "Epoch  : 51, loss : 79.09022521972656\n",
            "Epoch  : 52, loss : 26.5567684173584\n",
            "Epoch  : 53, loss : 5.662394046783447\n",
            "Epoch  : 54, loss : 65.31483459472656\n",
            "Epoch  : 55, loss : 65.359619140625\n",
            "Epoch  : 56, loss : 5.558589458465576\n",
            "Epoch  : 57, loss : 26.15188980102539\n",
            "Epoch  : 58, loss : 78.68423461914062\n",
            "Epoch  : 59, loss : 39.743499755859375\n",
            "Epoch  : 60, loss : 0.640339195728302\n",
            "Epoch  : 61, loss : 53.08271408081055\n",
            "Epoch  : 62, loss : 73.91741943359375\n",
            "Epoch  : 63, loss : 14.200963973999023\n",
            "Epoch  : 64, loss : 14.082270622253418\n",
            "Epoch  : 65, loss : 73.84071350097656\n",
            "Epoch  : 66, loss : 53.23031997680664\n",
            "Epoch  : 67, loss : 0.6695551872253418\n",
            "Epoch  : 68, loss : 39.59086990356445\n",
            "Epoch  : 69, loss : 78.71441650390625\n",
            "Epoch  : 70, loss : 26.30157470703125\n",
            "Epoch  : 71, loss : 5.4849162101745605\n",
            "Epoch  : 72, loss : 65.24476623535156\n",
            "Epoch  : 73, loss : 65.43820190429688\n",
            "Epoch  : 74, loss : 5.744562149047852\n",
            "Epoch  : 75, loss : 26.41513442993164\n",
            "Epoch  : 76, loss : 79.06739807128906\n",
            "Epoch  : 77, loss : 40.24708557128906\n",
            "Epoch  : 78, loss : 1.2014254331588745\n",
            "Epoch  : 79, loss : 53.69757843017578\n",
            "Epoch  : 80, loss : 74.6209487915039\n",
            "Epoch  : 81, loss : 14.951683044433594\n",
            "Epoch  : 82, loss : 14.823701858520508\n",
            "Epoch  : 83, loss : 74.59577941894531\n",
            "Epoch  : 84, loss : 54.00812530517578\n",
            "Epoch  : 85, loss : 1.4041521549224854\n",
            "Epoch  : 86, loss : 40.25672149658203\n",
            "Epoch  : 87, loss : 79.34833526611328\n",
            "Epoch  : 88, loss : 26.880521774291992\n",
            "Epoch  : 89, loss : 5.947338581085205\n",
            "Epoch  : 90, loss : 65.6060562133789\n",
            "Epoch  : 91, loss : 65.72991943359375\n",
            "Epoch  : 92, loss : 5.920708656311035\n",
            "Epoch  : 93, loss : 26.44583511352539\n",
            "Epoch  : 94, loss : 79.00042724609375\n",
            "Epoch  : 95, loss : 40.093074798583984\n",
            "Epoch  : 96, loss : 0.9121671319007874\n",
            "Epoch  : 97, loss : 53.28836441040039\n",
            "Epoch  : 98, loss : 74.14947509765625\n",
            "Epoch  : 99, loss : 14.402846336364746\n",
            "Epoch  : 100, loss : 14.170138359069824\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}