liniear-regression.ipynb

liniear-regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "liniear-regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "swift",
      "display_name": "Swift"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/jkrukowski/59ee41d8522daa7d248ea50daa1886f3/liniear-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": "CoEfOhVWQI9T",
        "colab_type": "text"
      },
      "source": [
        "# Linear Regression with Swift for TensorFlow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9oJuFnoUKuS",
        "colab_type": "text"
      },
      "source": [
        "## Imports\n",
        "### Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glNzWvo49gIl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import TensorFlow\n",
        "import Python\n",
        "%include \"EnableIPythonDisplay.swift\"\n",
        "IPythonDisplay.shell.enable_matplotlib(\"inline\")\n",
        "let plt = Python.import(\"matplotlib.pyplot\")\n",
        "let np = Python.import(\"numpy\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9aaSkrQVw0u",
        "colab_type": "text"
      },
      "source": [
        "## Data\n",
        "### Data for linear regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VEHLo6jPCw1i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let n = 100\n",
        "let a: Float = 1.5\n",
        "let b: Float = 4.0\n",
        "let x = Tensor<Float>(randomNormal: [n])\n",
        "let y = x * a + b + Tensor<Float>(randomNormal: [n])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tdNVW4siUNX5",
        "colab_type": "text"
      },
      "source": [
        "### Plotting function\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DS-OLs3aTjvF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "func plotData(\n",
        "  x: PythonObject, \n",
        "  y: PythonObject, \n",
        "  fitLine: PythonObject\n",
        ") {\n",
        "  plt.plot(x, y, \"yo\", x, fitLine, \"--k\")\n",
        "  plt.show()\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFk9Lyd2UyMb",
        "colab_type": "text"
      },
      "source": [
        "## NumPy Linear Regression\n",
        "### [Docs](https://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cHkRZ0jA8JK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "func linearRegression(\n",
        "  x: PythonObject, \n",
        "  y: PythonObject\n",
        ") -> PythonObject {\n",
        "  let fit = np.polyfit(x, y, 1)\n",
        "  let poly = np.poly1d(fit)\n",
        "  print(poly)\n",
        "  return poly(x)\n",
        "}\n",
        "\n",
        "let regressionFunction = linearRegression(\n",
        "  x: x.makeNumpyArray(), \n",
        "  y: y.makeNumpyArray()\n",
        ")\n",
        "\n",
        "plotData(\n",
        "  x: x.makeNumpyArray(), \n",
        "  y: y.makeNumpyArray(), \n",
        "  fitLine: regressionFunction\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcSlAmqpXFQ4",
        "colab_type": "text"
      },
      "source": [
        "## Swift for TensorFlow Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxfk4CYxv39O",
        "colab_type": "text"
      },
      "source": [
        "### Model and Train Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gq2SD-H1v21s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "struct LiniearModel: Differentiable {\n",
        "  var w: Float\n",
        "  var b: Float\n",
        "\n",
        "  func applied(to input: Tensor<Float>) -> Tensor<Float> {\n",
        "    return w * input + b\n",
        "  }\n",
        "}\n",
        "\n",
        "func train(\n",
        "  x: Tensor<Float>, \n",
        "  y: Tensor<Float>, \n",
        "  model: inout LiniearModel,\n",
        "  epoch: Int,\n",
        "  lr: Float\n",
        ") {\n",
        "  for _ in 0..<epoch {    \n",
        "    let grad = model.gradient { m -> Tensor<Float> in\n",
        "      let predictedY = m.applied(to: x)\n",
        "      return (y - predictedY).squared().mean()\n",
        "    }\n",
        "    model.w -= lr * grad.w\n",
        "    model.b -= lr * grad.b\n",
        "  }  \n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Ob1Qu_jvZ_P",
        "colab_type": "text"
      },
      "source": [
        "### Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuNumQaFGFIf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "var model = LiniearModel(w: 0.0, b: 0.0)\n",
        "train(x: x, y: y, model: &model, epoch: 100, lr: 0.1)\n",
        "print(model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_ap6q_i-Gp0",
        "colab_type": "text"
      },
      "source": [
        "### Plot the result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qqq1qHCvIjHX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "func swiftLinearRegression(\n",
        "  x: Tensor<Float>, \n",
        "  model: LiniearModel\n",
        ") -> PythonObject {\n",
        "  let result = model.applied(to: x)\n",
        "  return result.makeNumpyArray()\n",
        "}\n",
        "\n",
        "let swiftRegressionFunction = swiftLinearRegression(\n",
        "  x: x, \n",
        "  model: model\n",
        ")\n",
        "\n",
        "plotData(\n",
        "  x: x.makeNumpyArray(), \n",
        "  y: y.makeNumpyArray(), \n",
        "  fitLine: swiftRegressionFunction\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2OEPxOmlC5X4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}