jkrukowski/my-mnist.ipynb

## my-mnist.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "my-mnist.ipynb",
      "version": "0.3.2",
      "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/1b40ef7fd3c12cd9c70fa44477644f48/my-mnist.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": [
        "# MNIST SGD 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",
        "import Foundation\n",
        "import FoundationNetworking\n",
        "%include \"EnableIPythonDisplay.swift\"\n",
        "IPythonDisplay.shell.enable_matplotlib(\"inline\")\n",
        "let plt = Python.import(\"matplotlib.pyplot\")\n",
        "let gzip = Python.import(\"gzip\")\n",
        "let pickle = Python.import(\"pickle\")\n",
        "let dataPath = \"/content/sample_data/mnist.pkl.gz\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9aaSkrQVw0u",
        "colab_type": "text"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvW5qNmBNX5_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "struct MnistData {\n",
        "  var x: Tensor<Float>\n",
        "  var y: Tensor<Int32>\n",
        "}\n",
        "\n",
        "extension MnistData {\n",
        "  init(x: PythonObject, y: PythonObject) {\n",
        "    self.x = Tensor<Float>(numpy: x)!\n",
        "    self.y = Raw.cast(Tensor<Int64>(numpy: y)!)\n",
        "  }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bi9j1YCglJ2T",
        "colab_type": "text"
      },
      "source": [
        "## Utility functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VEHLo6jPCw1i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "// taken from https://www.tensorflow.org/swift/tutorials/model_training_walkthrough\n",
        "\n",
        "func download(from sourceString: String, to destinationString: String) {\n",
        "    let source = URL(string: sourceString)!\n",
        "    let destination = URL(fileURLWithPath: destinationString)\n",
        "    let data = try! Data.init(contentsOf: source)\n",
        "    try! data.write(to: destination)\n",
        "}\n",
        "\n",
        "func loadData(from path: String) -> PythonObject {\n",
        "  let f = gzip.open(path)\n",
        "  defer {\n",
        "    f.close()\n",
        "  }\n",
        "  return pickle.load(f, encoding: \"latin-1\")\n",
        "}\n",
        "\n",
        "func parse(data: PythonObject) -> (train: MnistData, vaild: MnistData) {\n",
        "  let (trainData, validData, _) = data.tuple3\n",
        "  let (trainX, trainY) = trainData.tuple2\n",
        "  let (validX, validY) = validData.tuple2\n",
        "  return (\n",
        "    MnistData(x: trainX, y: trainY),\n",
        "    MnistData(x: validX, y: validY)\n",
        "  )\n",
        "}\n",
        "\n",
        "func randomImage(_ data: MnistData) -> (Int, Tensor<Float>) {\n",
        "  let index = Int.random(in: 0..<data.x.shape[0])  \n",
        "  return (index, data.x[index].reshaped(to: [28, 28]))\n",
        "}\n",
        "\n",
        "func plot(_ tensor: Tensor<Float>) {\n",
        "  plt.imshow(tensor.makeNumpyArray(), cmap: \"gray\")\n",
        "  plt.show()\n",
        "}\n",
        "\n",
        "func plot(loss: [Float]) {\n",
        "  plt.plot(loss)\n",
        "  plt.show()\n",
        "}\n",
        "\n",
        "// taken from https://github.com/tensorflow/swift-models/blob/master/MNIST/main.swift\n",
        "\n",
        "func minibatch<Scalar>(\n",
        "  in x: Tensor<Scalar>, \n",
        "  at index: Int, \n",
        "  batchSize: Int\n",
        ") -> Tensor<Scalar> {\n",
        "  let start = index * batchSize\n",
        "  return x[start..<start+batchSize]\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWzrsNEGnmSG",
        "colab_type": "text"
      },
      "source": [
        "## Get the data\n",
        "\n",
        "Get the 'pickled' MNIST dataset from http://deeplearning.net/data/mnist/mnist.pkl.gz."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWcmkZnTocK5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "download(\n",
        "  from: \"http://deeplearning.net/data/mnist/mnist.pkl.gz\", \n",
        "  to: dataPath\n",
        ")\n",
        "\n",
        "let data = parse(data: loadData(from: dataPath))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "310logT4T-mZ",
        "colab_type": "code",
        "outputId": "02dbaa94-93d7-4496-cd43-930616f483f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "let (index, image) = randomImage(data.train)\n",
        "plot(image)\n",
        "print(\"Recognized as: \\(data.train.y[index])\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ERfiBoAg/EBThB4Ii/EBQhB8IivADQf0/7omI4C7FifcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Recognized as: 5\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVpVjR7xlggl",
        "colab_type": "text"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrbIs7W9YIDo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "struct MnistModel: Layer {\n",
        "  var layer1 = Dense<Float>(inputSize: 784, outputSize: 10)\n",
        "\n",
        "  @differentiable\n",
        "  func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {\n",
        "    return layer1.call(input)\n",
        "  }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-GehLP0bWd5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "func train(\n",
        "  mnistData: MnistData,\n",
        "  model: inout MnistModel,\n",
        "  epochCount: Int = 1,\n",
        "  larningRate: Float = 2e-2,\n",
        "  batchSize: Int = 64\n",
        ") -> [Float] {\n",
        "  let maxBatch = Int(mnistData.x.shape[0]) / batchSize\n",
        "  var modelLoss = [Float]()\n",
        "  for epoch in 0..<epochCount {\n",
        "    for i in 0..<maxBatch {\n",
        "      let x = minibatch(in: mnistData.x, at: i, batchSize: batchSize)\n",
        "      let y = minibatch(in: mnistData.y, at: i, batchSize: batchSize)\n",
        "      let (loss, grad) = model.valueWithGradient { m -> Tensor<Float> in\n",
        "        let predicted = m.callAsFunction(x)\n",
        "        return softmaxCrossEntropy(logits: predicted, labels: y)\n",
        "      }\n",
        "      model.layer1.weight -= larningRate * grad.layer1.weight\n",
        "      model.layer1.bias -= larningRate * grad.layer1.bias\n",
        "      modelLoss.append(loss.scalarized())\n",
        "    }\n",
        "  }\n",
        "  return modelLoss\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHmquk4yzUxG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "func trainWithWeightDecay(\n",
        "  mnistData: MnistData,\n",
        "  model: inout MnistModel,\n",
        "  epochCount: Int = 1,\n",
        "  larningRate: Float = 2e-2,\n",
        "  batchSize: Int = 64,\n",
        "  weightDecay: Float = 1e-5\n",
        ") -> [Float] {\n",
        "  let maxBatch = Int(mnistData.x.shape[0]) / batchSize\n",
        "  var modelLoss = [Float]()\n",
        "  for epoch in 0..<epochCount {\n",
        "    for i in 0..<maxBatch {\n",
        "      var w2: Float = 0\n",
        "      for p in model.recursivelyAllWritableKeyPaths(to: Tensor<Float>.self) {\n",
        "        w2 += pow(model[keyPath: p], 2).sum().scalarized()\n",
        "      }\n",
        "      let x = minibatch(in: mnistData.x, at: i, batchSize: batchSize)\n",
        "      let y = minibatch(in: mnistData.y, at: i, batchSize: batchSize)\n",
        "      let (loss, grad) = model.valueWithGradient { m -> Tensor<Float> in\n",
        "        let predicted = m.callAsFunction(x)\n",
        "        return softmaxCrossEntropy(logits: predicted, labels: y) + w2 * weightDecay\n",
        "      }\n",
        "      model.layer1.weight -= larningRate * grad.layer1.weight\n",
        "      model.layer1.bias -= larningRate * grad.layer1.bias\n",
        "      modelLoss.append(loss.scalarized())\n",
        "    }\n",
        "  }\n",
        "  return modelLoss\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dScxOOME2LZK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "var mnistModel = MnistModel()\n",
        "var mnistModelWithWeightDecay = MnistModel()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEr5JA272QXz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let loss = train(\n",
        "  mnistData: data.train, \n",
        "  model: &mnistModel,\n",
        "  epochCount: 1\n",
        ")\n",
        "\n",
        "let lossWithWeightDecay = trainWithWeightDecay(\n",
        "  mnistData: data.train, \n",
        "  model: &mnistModelWithWeightDecay,\n",
        "  epochCount: 1\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14sham-o5JmP",
        "colab_type": "code",
        "outputId": "9d048664-2dfb-43ed-fbba-acbd8a86c625",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "plot(loss: loss)\n",
        "plot(loss: lossWithWeightDecay)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r2RFOusgSKiz8jMElxx6KLm2LUBAKYNwRnY0fR9il7o9XHLcoHDQyCGTv3uJw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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OP769CWv2HDfZKDx3zs2DUaR6M6L2ztq95mvK/PLfa3WCGo2Pttbhisfsa/Jb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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxM_NmHf2bdo",
        "colab_type": "code",
        "outputId": "5208fa85-2b0d-4737-9c41-65620dba5fa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "let item = data.vaild.x[0]\n",
        "let prediction = mnistModel(item.reshaped(to: [1, 784]))\n",
        "plot(item.reshaped(to: [28, 28]))\n",
        "prediction.argmax(squeezingAxis: 1)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXGStUmRfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3]\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWBLzSbw-tik",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}