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/611ba422838305eb54b756119699a989/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": "dd1bd0b1-e7a7-4bca-a906-038be9bce728",
        "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": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IDyV9Q9JOSe9JekHSpR3U27+rNprzW6oFbWZFvS1Q7ZD+LUl7s5/eqrddoq9Kthuf8AOC\n4g0/ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANB/T+ftHGIP6EW+QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Recognized as: 2\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 = train(\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": "4bd7bbea-4c78-46ad-a847-08637db275b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "plot(loss: loss)\n",
        "plot(loss: lossWithWeightDecay)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qZFi2tQZV172V8oncb3pjJS57Rpk/QT7DLO4MgGh45aZZw1Dk92iiVZznTfih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\nz3ovDoTqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sce8IhPDQR5tw4YML4x77WJyViX+ZvzFqWyIentVgFwgJy0bnPYYMy3QyluwL\nhPDzF1dHiV8nL5sUdtS3YtZ9n+irvHuSVK4uZnHPApwmcS8rcMesXdMWw3M3N/U2c9cFE3Dk0HK4\nnQ7LrB1zX1Lp2Rd67KtFOpWwjOxvKkMnUtzVmL1Z2KRWqRPIvmAIy7cfwpx/LscljxgFQp7bynM3\n3620+YJYvTOStbLrUFvc+PO2uha8tnI3gK6FI6IzjiyOSUAs2/1BhEICt7wSaZIWCKZ2IZO8UrxF\nbGakjV1d/CQH/FASBrK/L9iCdXsa8eqK3d0+Vzx4QpUxIPVY1n4/b+JA/HzWaJQVhCdgh/c1Llr6\n5dnj9MdWoZxbzxtveP77bx6OmooCfPfoIbhk2hC8eNVxAKJXsFohhdAfEvjRicNxyph+hv2hkNDL\nKQRDAvlS3LVJT+lJq5kfZo9bCkFtU8RzVwewxZuNcU0p7iERPVCYw0w3PL8C585dgIZWP+5+az2m\n3/UBJt72TtTfeaC5Qz/X6X/+GD9V2wnGw/Q2tiaQDpeIQLf5gtjf3IEnF23Tt8lJ60SRd15djV3L\nmHtnRUveAXZZmk2L67pDKtIqpZ/EE6qMJR4t8yXP7YTX5cSxWpOQ/zpmqOG4EUq6o5Wn/oPjagwD\nwrkTB+LDn56E33/TWKbArsbNjBF9cfSwPgCAjfubIYSAPxiC1+nAfZdONtwthAQM2TJyn5wX0MVd\nyRAye7bS21a9vMY2+/i7KvzmGKc5LCMncv2hkKEWjzqhWd/iw9Tb38Pdb6832NNV2hIoB51IzL3N\nH0KTKR0y2MmwTHcLunW1vHJQqXAai6/2NuH8uQvwygpjGQr5qkQ89y0HWvDoJ5tt98szJFPbzd8z\ndd4pVbC4ZxFu7QuSpwmk9ILd2vP+Jfadmsyowm2uDhlv+/QRffFbzfu/9pkvEAwJCBFuCl7kdWHu\npZP1Y4MhYYi5S7HVxT0Y/l8VKbN3I1P61HLD9RZpgBK1Ro5ZJOWPTnpSkRBO9CTuz19YhfoWHw5q\n13pnzT7ba8bC/C4m0qUrGBKYftcH+M7frFeiAuG7H3PNnoAyoZoIaoaOFat2HorZeEZmrXQ2CylR\nz/2Mez/Gyp0NuO7ZFfrf1dDqx5YD4Yn4RDz3ix9ehNvfWGfb+jKSVpk8eTcP4PLc7LkzAICBZeFm\nGubuR7Lfql6PXQgsuvlkvPOTE/VjfnPuOEwfYV922KUUlrErd3DIIt8eCHeZKldSKr/z6BIAkUHG\nrXjuQo25h0JKZyeT566IlPkH+/MXVwMw/mAOxhJ3Q8tB6zkCGXLyWUzoAsA/F2/Dc0t34J53v9IH\nuVhe15e7GrCnwbrLklk0EvXU1VeiAAAgAElEQVTcdx1qw8JNdZjz1DIcavVFzYG0+YOGOx4g/Pcm\na5Vqmy+I8+Z+imv+tdz2GNFFzz2ge+6Jv+be98NrId5ZGyllkYjnLh0Hu7elJ9IqW0wDif4dYnFn\nAODeiyfjvksmYZgWQpFfZDkpKT33dn8IVaX5hqX6P5w+DE9faV1ZEojE7wF7cberg1Hf6tPj/QD0\nXF65yMrtMIZl5LV8gZAe0mgzhWWaOwKR9ng2XqDqeVkt4JGonrvZU9I9d9PfbPY8m7TBhhBZpRvr\njvqcBxZgxh/m2x+goA4Sdl52ULH7rTV78cTCrVE2t/mD+oK3syYM0M5t9NxrbnoDD7wfuz48YC1s\n8n1U1yCYkVfqbLihK7Hy99aG75xUPe/MaYI2wtoTnrv57kx+dByWYQAApQVunD8pUs1RfjdleYF8\nU+ZJZyhSSvrahV/OnTjQcnt9sw95bmfUZK1HE3E15BMUQo+5f/OvC/XbaZl1I0VVCOgrV608z4c/\n2qT3egWAgy2Jrag0/5jMYRmJeUCRaYZOB+nefzwv0U6wzG+vei27cIY55VQIRMUwWn1BfSL67AkD\ndRvM799DH8Wu62+HzGCKlT7amZx6lX1KWmuiQi+dBPVzSGRlLcUR1p6IuZtDQHJg5rAMY4n8QUhP\nXop8V4oR9S0Kx+eJor1YyQNK7Fxl8pAyAMBNs8YYtuueuyL6+W6nIZwgFy29tWYvbn5pFeatjtxi\nX/TQIizdWm+Zwnjnm+v1mjpAbM9dxXwbHEm9DGGR4pH+9tU1huOatbLKTgfpP0gpQua3S+1Tmwjq\nD9yuvs+d89YbngtEi1O7P6jHw0vyw4O1VbZMImEaq8VAiUwcy3N3NhR00UOR9NV/LNya0GvkHaZ6\nqSufTLw3kJ2NPbEswCziDj0sw547Y8FNs8bg8unD9CyZ4ZXhrBi1RV+iSHG389qtGNKnAItuPhn/\nffxwANHZNBFxj2y/4bRRhrDPjvpIXPqZz3ZEXeOihxYZRNcOc11su6wLs2esPv/rh5HFQ+Z0ymYt\nLON0kC6q0mM0z4FY8f3HPsObq8MLrMzvsDrgdAStB+b31hknb/3BUFQIos0X1D1muTgsGApF3WHY\niZqd0Bxs8eFvH2+O6zS0++2zkjpDokXcXNr3S5huYRJ1buwHoOTH3M0DrPwJ7G1sxx/f/iolaxFY\n3LOIKUPKccu54/TY4KzDB+DpK4/Gd6cN6fS5pLh3xuMiAqpK83VP3xwv1idUFc+90OuyXAylosbv\ngfAPIB7mbBm7P0MVUl8gZFjuHquBiAzLOCjiucv3aqhWPnn8wJKo1y3ZXIedB1vx8Yb9uOpp64lI\nNeSSaDjDahLWFwxPUDsoMuD4g9FhGTshaVeurQrbz15chd/PWxdzkG33Bw01+ruz8jfO10MnEpYx\nbt95sBWHWn341kML8YVSlM6M3WAmx0J1Xcf2ulbU3PSGYYGbSjAksPOgfekE9T1v9QX0cz/+6VbM\nnb8RizfHd2C6C4t7FkNEmD6ir21YJRZVpXndvr75RyZF3G3y6O0mbCUTBpXi+lNH6s8TWTxlDsvY\n1SjxB0N4/NMteH/dPlz7zBf4v7e/0vftb7IfRGSGhdMR8UrlYBYSAmeOH4Czj6iKet3FjyzGGaZG\nJeaJOoPnHggllJduJe7+gIAvGILHFakYettra+OWRAbCf9O6PY2W+w5qE+mxUiBvfW2N3rcXAPY0\nRL+XoZDA7CeXxhUy+XlvrG2KsmnMgEg3MpnhZb5L29vQgWc+24HPtx7EPxdvjzq/fPfX7Lb+eyN1\naiLIO6cXlkXfXQLA3W+tx4w/zMdei78bMM4LjLvl7aj97LkzPYa60ClZSBF0mfr3mZ+bcTsdhsbe\nieSBmydUf/L8Csvjlm6tx62vrcUV/1iKt0zdoGJ57vJW30mki3FjewBN7X74AmFBtQvPtJhznE37\nVS/XFwgZnqt3A2eOH6A/tlrVKj13j9OhD6C7DlmnY5p5+KNN+JYS91ZtjPTLjdjV5gvisQVbdFFa\nu8dYTnhbXXS54kNtfryzdh9mx4mLy7H/1Hs+xqz7PjHsU8NoLptJyUAopNeFiTUgXf30ckMG2Oqd\nDeEsLYvBVZ9ktXE0Pv76AADY1qMxi3dK6/1osLj3Ug6r7Ly4nzrW2Krv4qMGY4jSkFsKottUzyae\n506AoRRxrGbafYvCx5k9dztH6LevrY15bTs6lJRJNYwy/6v9irh37eejZlL4AiGDiP7yrLH6YzlJ\nCtiEZbTUUo/LGTf0ZWZbXYyQgvZedih2/emdr3Db62vxjjZAqterKPRgu8Wdkxy04ularBRE9b2R\nczzmGHtICD11dY8S0qtr7sCLy4zVNmWpaX8whHPnLsCV//hc3ycF+IH3v8bfPg6vaLW6i3z0k81Y\nv7cx6m/bfahNv4M0i7s5hJnI3Wl3YXHvpeTbFPqyY8kvTsHNpuyYQq8LH//sJHz/2HD5AylAbpPQ\nmCdeS5Q0TCAsoGVKjr5VsxLJs7PDufvmCdXB5Z2fVDZz7ckj9MdS3FXPHQgLy+6GdgSCoYTF3fw7\nVu9MOkyeuzoQyraLgLHSp8QfDKEjEITX5Yg7gJoZGGMSXoY91EweeUcg4/mquPcrycMhi5TESI2f\n2OoeS+f8SkGyg61+tPoCUVk8wZCyUEkR0R8/8wVu/PdKw53UV5ooy8yzxZvrdS9dCvKf3t2gz/uY\nbatv8eH2N9bpoq7+bcfd9QGOv3u+4VwSs/ORijr0LO5MQvQvydOzFcxM0+rMjOgXjo+aO0aZhefJ\nK47G45dFOkK5nWSYVN2ueZXfmDQQY6uMk5YVheGJYHNuv7pitqtM11oeAhHv0KHkuQPhlasA8PKK\n3fpKYTvs9FYt1esLhPTQw8TqUsNAWOCNDIJWoSo9LONy2NYBsiPW2ggpWKqd8vpS1NXr9S3yGAbk\ngDbPsV6Ln0ths1uwFSvTRt332ZZ6XPzw4qgmM8GQ0O/21Mlkq5DJrkNh0VYXnEl9DoaEIf8eiP4M\nV5rq99sNXOYwjPk4jrkzPcqLVx2Luy86otvnOeeIgfjkZydhxsiwOEalSJpi7iV5LlQWR+rguBwO\nPZUPCGfLeFwO3HHBBFwxw1jauCjPZen1yB/86P7F0TttKDDdvVQURWySYuY0NSyRcwPXnTIyrufu\ndBD+/O6GqNi+ISwTDKKlI4jDKgvx/Jxj9QVfgLEWvyyaps5N+AORmHu8eQ3zJGSs2LSMQjUp4TF5\nfGRxV+R6fYu86AiE9AHx3bX7cOtra/EzrXJmvPK8sXr5+oIhXHRktf589a6GqOPDYZloz92q/0GH\nPxgl4DK10h8M4eg73jfsM4dPzCWK7STaPJDFa1fZE7C492KOHNoH3546OCnnGqzE3s2eu1l38txO\nQxNvl5NwWGURbjxtlL7t6GF9UOBxRf1AXQ5CkccY1gEiK15luGnMgGKM6h97XqGiyOjtVxZFF14L\nCWEIy0gRmXPiYTHPDYRjyfdZLP3/z/JIhUNfIISmdj+qSvPhNcXOVXHfebANbicZJlx9wUi2TLyY\nu3mS13wn0OYP4nt/X4L562t1EVZDLdLTl+KuXk/Og0jvXRZ4a9KO1T1jG3Fvj9Hu0R8MoaLQo6ef\nAtHtIYMhodfYURd6WfUcbg8EozN7NLO2WsxDmNNKO0x3PB+ur8Xxd38Q1TjF/DrzwJaspjGxYHFn\nko75R2X+TcuSxfrxDgeICD8+ZaTu0cvSBh7TXQARYYBFGqf0LGV9Ha/bidvOPzymnYWmQaI4L3rQ\nCAaFQTAOtfpAFF4dHG+VrJ3e7lbEpSMQQnNHAEVaCEa96zEPbCP7FaNEmZs40NyBz7bUw+NywBkn\nLGP2OM2e+77GDnzy9QH88InP9c9LLc4mPdjm9khZBom845Hibo6JS8/YrqyKuQGMij8o4HY6TPMe\n5pi70FcUq5pqKe7+UFRpAPmS9RapoeYCdeZr/33BFuyob8O81caOYGYxj6pxxJ47k27mXXu8IT6e\nCOYYu9mLyXc7DV6pKmiyo5NVKQOJ1WSg9JykQDspUmDNDvN+q/UCQSEMP8yGNj8K3E4QEcZVlRqO\nfe+GEw3P5fxALDoC4ZrsstaP6hGba/eMrSqxrC/jsemapSLF/WCLD/UtPoPnXuBxGgYiKUz1Srqp\nWuDNbGeFFiq69bU12FbXYulZq+dVcTkoKvvlYa0WTlArpeB2OgwDhtX5ZfMXdSC2Cpu1+4NRmUcy\nZLV8e3Q/3JeW79KPf2zBFizYeMCwf0hFuBRInSn0Fi+mnooaMyzuTEzGDSzBSabuSokw58TD8MKc\nYwGEm1+rE6NeU464KhQFHqMHaxWbjCXu0rN1Osg2Jt6/xIsFPz9JL7wWi083HtAnUQFN3DUve0J1\nKZ7570jlzcMqIw1QhvUtjApHWeELhNDcHtAHJY8zYpPXbTzB4YNKdNFQ3zN3Atky9S0+CCEw+Xfv\n4sx7PzaUpJ05utLg8X5dG25irt6ZyOPl/+rkerGW1fPpxjpc/PDiqHZ10uu3Csvku51R3vCdb4br\n6uzWMnRcTjJ8D8zNvINCRDJztMNqm9rxyddGIQY0cTcNJvF0Vn63bnt9LT7asN+wT9plLokgP6cr\nTXNGEg7LMFnLTbPGYGpNOIvG43LgzeuO1/c5HGQQrm8pcf9Cb1jcpNfa1yIOrq5aBIB+xV7dE5Xp\ng0Rk67n3KfSiurxAj/vfeNooLLzp5KjjHAR8vvUg1u+NLNjxB4VhIlbaK68pKfK6YoYbJO3+IJp9\nARRrA4aaoqoK/ZQhZbjwyGpL77fDH4yatDZzsNWnpzPWNnWgtUPps2uTBaXecUnvVaYmqoOLGs7a\n29hueL+AyEAtLN6OAq8zyhMHgA/W79PTCvPdToO4r93TiMF9IgN8uz+oD067DrXhlRW78NaXxgVr\nkWNDUfMNMj3Sjlhles2rlyXyc7JLOeYJVSZnkYJy0uhKHD4oEt4we+5Ta/rgjPHGxVOyKuW1J4/A\n5jvOgstBFmEZMkzaqkiPXv7wKou9+t1AkZJ+aBUSUm0E7FffFue5oibfrDjQ7IMQkTsOdeDwuBxY\n8POT8PqPZ+Clq6ejJM8daWuniEkwJGxLUHxLyzSpb/EZwh8tvgCOGd4Hr/3PjIQKoUmP3R8Mobax\n3VDDxWquQqXA48KqnYewcJPRk55YXYphfQsts2UufyKyqjXf44wSQ3W+xNxq8LpnV9iW720PBKPC\nQPJORfLYZVMNz/0xmn8HTHWH9O3aczsHIxXiHvtTYZgkcsc3J2D0gHAGi8NB+PB/Z6J/iXFyVIqb\netv/4HePxPceW4LaxnDc+IjqMrz2PzMwfmAJHA6C00l61yg5aDgcQJ4iWv+ec6y+3P4Hx4UXXckf\nnpru9uWtZ+glfF0OgtXi8kJFgM11dCTFeS5DYS4rCjxOPLloKwBguBbSUUNFXpcD1eUFqC6PvKaf\n1krRQZHJwylDlQNMHFFdijdW78H/vf2VQVwPtfoxc3QlJlSXWqYMmpF3If6QwDRTuqA6IFrhD4Rw\n3txPo7bPGNkXa3Y32jaFkYQ9d9O8jfIZyIHd43ToE5V2q147LDx3882Q+W7RFwzZ5ujLuw67Fal2\nob/u9uFNBBZ3JmV852hj9coapUm3RHrFaqjA4aCorlITqiPevsvh0H+wUmwdprBMVWkeTh3bHzNH\nV+Kbk8PerPzhWYUF5HWtUGP+dgu7ivPcceOqF0wZpBe6GqXl56vXtBLdX541FpOqy/Ds59uxfPsh\nnDG+P25QUkjNuJ0OlBd40Oprw6cbIwW8mjsCuvdrnpso8rp0wSwrcBtSIq3uRgrjiLtdZojX5USe\nyxm3ZK+V96uKpszgKfQ64WuV4m59LqsJVTNqRzMA2HqgBQu+3m95rFzDYB58pNinMyzD4s5kFDKG\n3ZlaKepkohRbhyksU+R14dEfGG+343WysrOhRsm5tjsmXqjinm9PNPQ/HVASnd5pJe6FXhe+fdRg\nlOS7MOefy3H3hRNjZgW5nQ5UFHksC4pJ4TFP3BbnKeKebxR38wIgwHiHZIWdl+pxOZDndqDdH4qZ\nXVLgceJXZ4/F7W+sU7ZF3l9pa4HHpZelsGo+AlhPqJoxD1aJNAQJmuLyUuvtPHeOuTO9DvmjNRcf\ni4UqsGOrwh7wqWP7GW7NrbxLKYptPusfml0GyugBkcwfu2X/al0YK4rz3KguVwYJizuAWCtgzzy8\nClvvOhulBbGvc7DVZ1uaodATnaEDGAcVdSUxAOw4GD1ImAeHRJFZUx2BIJZurY/aJ8n3OHHl8cPx\nA62Gkfma//liV9Q2K8/9iOpStPmDUXnuZuJlUVkN6NGee/g7pQ686vs6qhMrqbtKQp8KEZ1JRF8R\n0UYiusli/2VEtJ+IVmj/rky+qUxvQMbcO1MpRRXYsVUlWP7r0/Bfxww1HGM1OSo9V7sVknb1rmYd\nHinFKydUzbH3eJ67x+VAVVnsmvpdrTqp0tjmN5QsUCnwWnvuakjMHH+2io931U7Vc7/4kcWGfaq3\nL8XW8BlafDax7iCOqinHWROqEBL2jd/188QR92MPq4jaFlXiVzNfDcvI7/bZR1QZeiP3FHE/FSJy\nAvgLgFkAxgG4lIjGWRz6nBBikvbv0STbyfQSpFB0pkOUKsJelxN9Cj0JdbK/cEo1hlYU4NKjrDtZ\n2dWVt4rFF5s89Xieu8fpiPKK452zs/xweg2umDHcVtztYu5OB+mDk7lEgxWJvNcq8txtviDy3M6Y\nVUCBSK9gtZCa1R2Taqt5b2m+R7/uvsboaXL1s3A6CDfNGoPfnT/e0h6rEJpduYE85b3VM3xSVNo9\nkSF3GoCNQojNQggfgGcBnN+zZjG9FemddWaRh6wIOPuE4VGhlMMHRbfCkwwozcNHPz0JQ5QYuopV\nbNZcrrii0IOLjqyOWsVb4I3t/REBfWzCJX++eCJ+943DozKJOsOvzh6L35w7HqUFbnvPXfMkZbhA\nZr3Utfhw9oRwl6myfE+nywnHY6TWKGZvQ3vcyppApEhZ/5KIAF93ysio4/oVR96vZz43dlCqKPTo\ng2WtRQcu8yTqnBMPM6ToqliVv1Arh9Y2tltOqMrH5gVPPUUiE6qDAKjv1E4AR1scdyERnQBgA4Cf\nCCGs+1MxTAyk0HSm9oac8Jui5b+rvHjVcUkrr/rOT06IClM4HIQ/fmui/rzQ40SLLxg3d9wXCNlm\n2shsnq6y+OZTDEJYbVPrXgqabJI+fUQF3l6zD60dAT3v3utyoMjrsvSuS/JcePV/ZujPxwwoxv2X\nTsa+xnas3HEIf3xnA/oVe6MaYF8wpRrLtx/CzNH9sGqXccl/aX44y0gdWAuVYnAAcPXMwzC8sgib\n7zgLw38xTz/O44oMQit3GM9bXujRF4rVKp57gceJVl/Q0E9AYrfOoZ/FoKt+x6bd8T6unhkuLKfG\n76VjkKqmTMnKlnkNwDNCiA4i+hGAfwCIWvJHRLMBzAaAIUM639SZyX08XfDc5SKWCovVrIks0DHz\n0H9NgYMIs59aZtieyCTYW9efgHV7Gm3j0DIXW+bWj6sqwQQbD7GrmD1Lu/CPnIyV2SZVpfn49Tnj\ncOTQcr3jElG4I5SVuPcvydPTWZf/+jTku53I9zgxqn8xZozoi1H9i7GtrhW/n7fO8Lojh5Zj611n\nA4iuGHrXBRPw+3nrsPNgG04d2w/XnTJKF9Mjh/bBU1dMw1Haymc1PFZW4LYVzbOPqMKcE4djo7ZY\nqSMQQk1FAWaO7oft9a34YH0tqsrygW3G5tp24l5pEarymxyIv364CQ4yTqLKie14zUuSRSJhmV0A\n1Lqw1do2HSFEnRBCDoePAjjS6kRCiEeEEFOFEFMrKyu7Yi+T48gfQ1dSxaxKFXSFMw+vwulK/9If\nTq/Bc7OPifGKCIP7FOD08QOiJuWGVxbid+ePx+e/PBU3zxqD47RJuXnXHY8/JKGmfizGDLAOTcl5\nATk4luS5cMWMYZg0uEwXNn9QoNhrHftXV9P2KfQYQhBEhNPHD7AUMrWy5XGH9TWsCC3wuvQ7iuI8\nt2E9AwAcP7LScsJz8c2n2IrmX74zBWUFHsMcxpgBJfjteeP1omdWi9HsFqiV5keLe8Di++p0kGGA\nkINpCvp0AEjMc/8cwEgiGoawqF8C4DvqAURUJYSQNS/PA2AcqhkmQSKi0nlxT2Tyryv84NgaywVX\nsVCzZeb/70xUlebpovSjBGrBJ5M+hZ6oPHEgIrKyWfrkIZGVrm6lcJtd5k+8rBLAemLcPG9x8pj+\nenikwOPUu3LZ1byxwutyxA13FCnXlQOTnI9wOxy45ZxxhgHCznMvs0g/tcrldzrI4LnLsFDGhGWE\nEAEi+h8AbwNwAnhMCLGGiG4DsFQI8SqAa4noPAABAPUALutBm5kcRgqJudZ6Qq+Ns1Kys1w6bQie\n+Wx7lwaNMQOK8fD3jkSbL4hhnRwYegKriUsZyz7niCqMG1hiaJou5wMCIWGbtWPuZGWF1XyH1Wcr\nRbXA49Q9d7cr8YlcIorrEauDVJ5mu2zM7g+GcLmpgmPnxD168t1JRnHP90hxz5wJVQgh5gGYZ9p2\ni/L4ZgA3J9c0pjdy/Mi++PU54/CtqZ2fVOxsSl487vjm4bjhtFFdSkkkIpyhhHbSjdr05MzxA/Dh\nhlr9/SIig7ADxjsos6ctSaTJutlznzykzDKVVC7wLPC49LCHeXFVPOJloRR5wi0ahYhMdBZ5ZQmK\naM/bLixTlu/BTbPGoNDjxK9fWRN+vUXxszZ/0HD3Uahny6QGLj/AZBREFNU3NR6PXTZVb3ycbFvi\n5aJnC6oH+dfvTok7qTdIW2BVVZpnGyL7wbE1ca972XE1uF9pNThzlHVvAFnrvbCLnjsQP9zh0GLg\nvkBIz46RK5etxN0umynP7cCcEw+DEEIXd6uFcCFhDC0VeFPruXP5ASbrOXlMf3zPtCKVMaJ6wQ4H\n2QqX5IzxA/Do96fiihnDDXVcJFvvOhtHD49eqWmmT6EHl0+PDNZ25RrUvHB5A+bs5J1YIqIps7Bk\nZpX826zCKuaS0UVeF5b96lTDHY9E9dwHKYXl1LsUOWgVJDl8aAeLO8P0AvI9nfupExFOHdcfTgfp\nMeaJ1V1L2VTvGuwKrcnFZgUely7qRw3r06nrdCYLRc6jFMXw3L0uJzbcPkt/nu9xWqbbAsaa8MdZ\nlCcAwh2vfnrGaNwep7dvsuCwDMPkCBdMGWSo4KjSnTTRwVqBswGleVi5s6HTr1dj13Z3DE9dfjQ2\n7GuC00G4amZ4dejMUZ1Ll+5M/nhfTdzloGe3rkIdmNwJrtSdUF2K8kJPVA2aQo8L15w0ImEbuwuL\nO8PkCPd8e5Ltvu6UMpCTrZ4uLAgDjN2q7CYpyws9epin0OvCmYd3fjLaStpvNNW6v+y4GjyxcCsG\nlYUHLLnILZFFc4lWKj33iIEotyj5YNcZrKdgcWeYXkCFTX2ZRBg3sAT3XjwJJ46qxGsrd8d/gfna\nSippMipd2mFVqvfHpho0vzl3HH504nB9Fa+0x65hi0oiPQYe/f5US2EHkp/NFQ8Wd4bpBchwyAVT\nulZq9huTu16itp+ScdSd88Tj1+eMwwvLdsY8hohQVRqZ8JSrRkcPiF9a4tyJA+MeU2SRNvr29Seg\nPE7d/Z6AxZ1heglb7jwr5d4jEGlLeNq4/l2q9ROL2ScMx1CtqmdpvhvlBW69G1Mi9CvOwwtzjsXY\nKvvqoUC4Ps+1J0dXojRjtZo3kYGjJ2BxZ5heQjqEHQDGDyzB7d84vEcWdf3irLGG550RdsnUmthZ\nOStvOR1et8Ny8dXsE4ZjWN9C3PzSagCwrcOTDljcGYZJmPsumdTp2ihEFNUZq6eZOLgsquxvV4nV\nylAOLlLcrcIy6SJzLGEYJuNJRXu47vDyNdOxZHMdLpteY9uYuycpStECpUTIHEsYhmG6yaTBZZg0\nONy0Jdnx/VjMu/Z4fLih1pAXn25Y3BmGYbrJuIElGDcw9qRsqsmcYYZhGIZJGizuDMMwOQiLO8Mw\nTA7C4s4wDJODsLgzDMPkICzuDMMwOQiLO8MwTA7C4s4wDJODUKqatUZdmGg/gG1dfHlfAAeSaE4y\nYdu6RibbBmS2fWxb18hW24YKIeK2qUqbuHcHIloqhJiabjusYNu6RibbBmS2fWxb18h12zgswzAM\nk4OwuDMMw+Qg2Sruj6TbgBiwbV0jk20DMts+tq1r5LRtWRlzZxiGYWKTrZ47wzAME4OsE3ciOpOI\nviKijUR0Uxqu/xgR1RLRl8q2PkT0LhF9rf1frm0nIrpfs3UVEU3pYdsGE9F8IlpLRGuI6LpMsY+I\n8ojoMyJaqdl2q7Z9GBEt0Wx4jog82nav9nyjtr+mp2xTbHQS0RdE9Hom2UZEW4loNRGtIKKl2ra0\nf6ba9cqI6AUiWk9E64jo2EywjYhGa++X/NdIRNdngm3a9X6i/Q6+JKJntN9Hcr9vQois+QfACWAT\ngOEAPABWAhiXYhtOADAFwJfKtrsB3KQ9vgnAH7THZwF4EwABOAbAkh62rQrAFO1xMYANAMZlgn3a\nNYq0x24AS7RrPg/gEm37QwCu0h5fDeAh7fElAJ5LwWd7A4B/AXhde54RtgHYCqCvaVvaP1Ptev8A\ncKX22AOgLFNsU2x0AtgLYGgm2AZgEIAtAPKV79llyf6+9fgbm+Q35VgAbyvPbwZwcxrsqIFR3L8C\nUKU9rgLwlfb4YQCXWh2XIjtfAXBaptkHoADAcgBHI7xQw2X+fAG8DeBY7bFLO4560KZqAO8DOBnA\n69qPPFNs24pocU/7ZwqgVBMpyjTbTPacDuDTTLENYXHfAaCP9v15HcAZyf6+ZVtYRr4pkp3atnTT\nXwixR3u8F0B/7XHa7NVu3SYj7CFnhH1a2GMFgFoA7yJ8F3ZICBGwuL5um7a/AUBFT9kG4F4APwMg\nuypXZJBtAsA7RLSMiA0t+OMAAAKrSURBVGZr2zLhMx0GYD+Ax7Vw1qNEVJghtqlcAuAZ7XHabRNC\n7ALwRwDbAexB+PuzDEn+vmWbuGc8Ijy8pjUFiYiKALwI4HohRKO6L532CSGCQohJCHvJ0wCMSYcd\nZojoHAC1Qohl6bbFhhlCiCkAZgG4hohOUHem8TN1IRyifFAIMRlAC8KhjkywDQCgxa3PA/Bv8750\n2abF+c9HeHAcCKAQwJnJvk62ifsuAIOV59XatnSzj4iqAED7v1bbnnJ7iciNsLA/LYR4KdPsAwAh\nxCEA8xG+9SwjItmoXb2+bpu2vxRAXQ+ZNB3AeUS0FcCzCIdm7ssQ26SnByFELYD/IDwwZsJnuhPA\nTiHEEu35CwiLfSbYJpkFYLkQYp/2PBNsOxXAFiHEfiGEH8BLCH8Hk/p9yzZx/xzASG1W2YPw7dar\nabYJCNvwA+3xDxCOdcvt39dm4o8B0KDcEiYdIiIAfwewTghxTybZR0SVRFSmPc5HeC5gHcIif5GN\nbdLmiwB8oHlaSUcIcbMQoloIUYPwd+oDIcR3M8E2IiokomL5GOH48ZfIgM9UCLEXwA4iGq1tOgXA\n2kywTeFSREIy0oZ027YdwDFEVKD9ZuX7ltzvW09PZvTAZMRZCGeBbALwyzRc/xmE42R+hD2XKxCO\nf70P4GsA7wHoox1LAP6i2boawNQetm0GwreZqwCs0P6dlQn2ATgCwBeabV8CuEXbPhzAZwA2Inzr\n7NW252nPN2r7h6fo852JSLZM2m3TbFip/Vsjv/OZ8Jlq15sEYKn2ub4MoDyDbCtE2MMtVbZlim23\nAliv/RaeAuBN9veNV6gyDMPkINkWlmEYhmESgMWdYRgmB2FxZxiGyUFY3BmGYXIQFneGYZgchMWd\nYRgmB2FxZxiGyUFY3BmGYXKQ/weRuSOJLNRgeAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxM_NmHf2bdo",
        "colab_type": "code",
        "outputId": "e5155caa-b826-45df-d328-4a10f105312c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "let item = data.vaild.x[0]\n",
        "let prediction = model(item.reshaped(to: [1, 784]))\n",
        "plot(item.reshaped(to: [28, 28]))\n",
        "prediction.argmax(squeezingAxis: 1)"
      ],
      "execution_count": 0,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWBLzSbw-tik",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}