S4TF_Tutorial_4.ipynb

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  "metadata": {
    "colab": {
      "name": "S4TF Tutorial 4",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "language_info": {
      "file_extension": ".swift",
      "mimetype": "text/x-swift",
      "name": "swift",
      "version": ""
    },
    "kernelspec": {
      "display_name": "Swift",
      "language": "swift",
      "name": "swift"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QyCcF45zBQ3E"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors. [Licensed under the Apache License, Version 2.0](#scrollTo=y_UVSRtBBsJk)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKs3EDAWo_11",
        "colab_type": "code",
        "outputId": "41ff035e-26b1-4700-e30e-ca7cbd80c933",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "%install-location $cwd/swift-install\n",
        "%install '.package(url: \"https://github.com/mxcl/Path.swift\", from: \"0.16.1\")' Path\n",
        "%install '.package(url: \"https://github.com/JustHTTP/Just\", from: \"0.7.1\")' Just"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing packages:\n",
            "\t.package(url: \"https://github.com/mxcl/Path.swift\", from: \"0.16.1\")\n",
            "\t\tPath\n",
            "\t.package(url: \"https://github.com/JustHTTP/Just\", from: \"0.7.1\")\n",
            "\t\tJust\n",
            "With SwiftPM flags: []\n",
            "Working in: /tmp/tmpuaw0xwkb/swift-install\n",
            "Fetching https://github.com/mxcl/Path.swift\n",
            "Fetching https://github.com/JustHTTP/Just\n",
            "Completed resolution in 2.00s\n",
            "Cloning https://github.com/JustHTTP/Just\n",
            "Resolving https://github.com/JustHTTP/Just at 0.8.0\n",
            "Cloning https://github.com/mxcl/Path.swift\n",
            "Resolving https://github.com/mxcl/Path.swift at 0.16.3\n",
            "[1/11] Compiling Path Extensions.swift\n",
            "[2/11] Compiling Path Path+Attributes.swift\n",
            "[3/11] Compiling Path Path+Codable.swift\n",
            "[4/11] Compiling Path Path+CommonDirectories.swift\n",
            "[5/11] Compiling Path Path+FileManager.swift\n",
            "[6/11] Compiling Path Path+StringConvertibles.swift\n",
            "[7/11] Compiling Path Path+ls.swift\n",
            "[8/11] Compiling Path Path->Bool.swift\n",
            "[9/11] Compiling Path Path.swift\n",
            "[10/12] Merging module Path\n",
            "[12/13] Merging module Just\n",
            "[13/14] Compiling jupyterInstalledPackages jupyterInstalledPackages.swift\n",
            "[14/15] Merging module jupyterInstalledPackages\n",
            "[15/15] Linking libjupyterInstalledPackages.so\n",
            "Initializing Swift...\n",
            "Installation complete!\n"
          ],
          "name": "stdout"
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CPII1rGR2rF9",
        "scrolled": true,
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "// you may not use this file except in compliance with the License.\n",
        "// You may obtain a copy of the License at\n",
        "//\n",
        "// https://www.apache.org/licenses/LICENSE-2.0\n",
        "//\n",
        "// Unless required by applicable law or agreed to in writing, software\n",
        "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "// See the License for the specific language governing permissions and\n",
        "// limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YHI3vyhv5p85"
      },
      "source": [
        "# Image Classification with Convolutional Neural Networks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OVi775ZJ2bsy"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/drive/1M4EVMOSb0TwcAIcHMTExEYEauJdc6LGx\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"Link to be updated\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />GitHub link to be updated accordingly</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "F8YVA_634OFk"
      },
      "source": [
        "In this tutorial, we'll build and train a neural network to classify images of clothing, like sneakers and shirts.\n",
        "\n",
        "It's okay if you don't understand everything. This is a fast-paced overview of a complete S4TF program, with explanations along the way. The goal is to get the general sense of a TensorFlow project, not to catch every detail."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KaGgOzPQbGj3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import TensorFlow\n",
        "import Foundation\n",
        "import Just\n",
        "import Path\n",
        "import Python\n",
        "\n",
        "%include \"EnableIPythonDisplay.swift\"\n",
        "IPythonDisplay.shell.enable_matplotlib(\"inline\")\n",
        "let plt = Python.import(\"matplotlib.pyplot\")\n",
        "let subprocess = Python.import(\"subprocess\")\n",
        "let os = Python.import(\"os\")\n",
        "let np = Python.import(\"numpy\")  // Make numpy available using np."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H68btGfnpF6s",
        "colab_type": "text"
      },
      "source": [
        "We will need to gunzip, untar or unzip files we download, so instead of grabbing one library for each, we implement a function that can execute any shell command."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KJdCdcvpFW_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "public extension String {\n",
        "    @discardableResult\n",
        "    func shell(_ args: String...) -> String {\n",
        "        let (task,pipe) = (Process(),Pipe())\n",
        "        task.executableURL = URL(fileURLWithPath: self)\n",
        "        (task.arguments,task.standardOutput) = (args,pipe)\n",
        "        do    { try task.run() }\n",
        "        catch { print(\"Unexpected error: \\(error).\") }\n",
        "\n",
        "        let data = pipe.fileHandleForReading.readDataToEndOfFile()\n",
        "        return String(data: data, encoding: String.Encoding.utf8) ?? \"\"\n",
        "    }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wwJGmDrQ0EoB"
      },
      "source": [
        "This guide uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset, which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 $\\times$ 28 pixels), as seen here:\n",
        "\n",
        "<table>\n",
        "  <tr><td>\n",
        "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
        "         alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
        "  </td></tr>\n",
        "  <tr><td align=\"center\">\n",
        "    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
        "  </td></tr>\n",
        "</table>\n",
        "\n",
        "Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc) in an identical format to the articles of clothing we'll use here.\n",
        "\n",
        "This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
        "\n",
        "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_XrcNGNbejX",
        "colab_type": "code",
        "outputId": "6173dc9e-1b3d-4c09-d555-d7771318bc98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        }
      },
      "source": [
        "public func downloadFile(url: String, destination: String? = nil, force: Bool = false) {\n",
        "    let destinationName = destination ?? (Path.cwd/url.split(separator: \"/\").last!).string\n",
        "    let urlDestination = URL(fileURLWithPath: (destination ?? (Path.cwd/url.split(separator: \"/\").last!).string))\n",
        "    if !force && Path(destinationName)!.exists { return }\n",
        "\n",
        "    print(\"Downloading \\(url)...\")\n",
        "\n",
        "    if let content = Just.get(url).content {\n",
        "        do    { try content.write(to: URL(fileURLWithPath:destinationName)) }\n",
        "        catch { print(\"Can't write to \\(urlDestination).\\n\\(error)\") }\n",
        "    } else {\n",
        "        print(\"Can't reach \\(url)\")\n",
        "    }\n",
        "}\n",
        "\n",
        "let files = [\"t10k-images-idx3-ubyte\", \"t10k-labels-idx1-ubyte\", \"train-images-idx3-ubyte\", \"train-labels-idx1-ubyte\"]\n",
        "\n",
        "for file in files {\n",
        "    downloadFile(url: \"http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/\\(file).gz\")\n",
        "    \"/bin/gunzip\".shell(\"-fq\", \"\\(file).gz\")\n",
        "}"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz...\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz...\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz...\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbPfmnDJ_o0e",
        "colab_type": "code",
        "outputId": "5321701d-3960-4561-8a2a-c29caf7cfd23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "let filelist = try FileManager.default.contentsOfDirectory(atPath: \".\")\n",
        "for filename in filelist {\n",
        "    print(filename)\n",
        "}"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".config\r\n",
            "t10k-labels-idx1-ubyte\r\n",
            "train-images-idx3-ubyte\r\n",
            "swift-install\r\n",
            "train-labels-idx1-ubyte\r\n",
            "t10k-images-idx3-ubyte\r\n",
            "sample_data\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1XJ5nk3tZHJ",
        "colab_type": "text"
      },
      "source": [
        "Loading the dataset returns metadata as well as a *training dataset* and *test dataset*.\n",
        "\n",
        "* The model is trained using `train_dataset`.\n",
        "* The model is tested against `test_dataset`.\n",
        "\n",
        "The images are 28 $\\times$ 28 arrays, with pixel values in the range `[0, 255]`. The *labels* are an array of integers, in the range `[0, 9]`. These correspond to the *class* of clothing the image represents:\n",
        "\n",
        "<table>\n",
        "  <tr>\n",
        "    <th>Label</th>\n",
        "    <th>Class</th> \n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>0</td>\n",
        "    <td>T-shirt/top</td> \n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>1</td>\n",
        "    <td>Trouser</td> \n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>2</td>\n",
        "    <td>Pullover</td> \n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>3</td>\n",
        "    <td>Dress</td> \n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>4</td>\n",
        "    <td>Coat</td> \n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>5</td>\n",
        "    <td>Sandal</td> \n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>6</td>\n",
        "    <td>Shirt</td> \n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>7</td>\n",
        "    <td>Sneaker</td> \n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>8</td>\n",
        "    <td>Bag</td> \n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>9</td>\n",
        "    <td>Ankle boot</td> \n",
        "  </tr>\n",
        "</table>\n",
        "\n",
        "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtRFIk2Q_YGp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let classNames = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGz8KCXh_jPg",
        "colab_type": "text"
      },
      "source": [
        "## Reading the data\n",
        "\n",
        "We need to read the file into an array of bytes which can further be passed into the `readMNIST` function to convert it into a tensor array.\n",
        "\n",
        "The value of each pixel in the image data is an integer in the range `[0, 255]`. For the model to work properly, these values need to be normalized to the range `[0, 1]`. So here we normalize the image by dividing the pixel value by 255, and then apply it to each image in the test and train datasets.\n",
        "\n",
        "The tensor array we create is in the form of NHWC format which is TensorFlow's default.\n",
        "* N refers to the number of images in a batch.\n",
        "* H refers to the number of pixels in the vertical (height) dimension.\n",
        "* W refers to the number of pixels in the horizontal (width) dimension.\n",
        "* C refers to the channels. For example, 1 for black and white or grayscale and 3 for RGB."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiakU6qi_t57",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "/// Reads a file into an array of bytes.\n",
        "func readFile(_ path: String) -> [UInt8] {\n",
        "    let possibleFolders = [\".\", \"MNIST\"]\n",
        "    for folder in possibleFolders {\n",
        "        let parent = URL(fileURLWithPath: folder)\n",
        "        let filePath = parent.appendingPathComponent(path)\n",
        "        guard FileManager.default.fileExists(atPath: filePath.path) else {\n",
        "            continue\n",
        "        }\n",
        "        let data = try! Data(contentsOf: filePath, options: [])\n",
        "        return [UInt8](data)\n",
        "    }\n",
        "    print(\"File not found: \\(path)\")\n",
        "    exit(-1)\n",
        "}\n",
        "\n",
        "/// Reads MNIST images and labels from specified file paths.\n",
        "func readMNIST(imagesFile: String, labelsFile: String) -> (images: Tensor<Float>,\n",
        "                                                           labels: Tensor<Int32>,\n",
        "                                                           number: Int) {\n",
        "    print(\"Reading data.\")\n",
        "    let images = readFile(imagesFile).dropFirst(16).map(Float.init)\n",
        "    let labels = readFile(labelsFile).dropFirst(8).map(Int32.init)\n",
        "    let rowCount = labels.count\n",
        "    let imageHeight = 28, imageWidth = 28\n",
        "\n",
        "    print(\"Constructing data tensors.\")\n",
        "    return (\n",
        "        images: Tensor(shape: [rowCount, 1, imageHeight, imageWidth], scalars: images)\n",
        "            .transposed(withPermutations: [0, 2, 3, 1]) / 255, // NHWC\n",
        "        labels: Tensor(labels),\n",
        "        number: rowCount\n",
        "    )\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Urigd4SAAtQZ",
        "colab_type": "text"
      },
      "source": [
        "### Process the data\n",
        "\n",
        "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, and 10000 images in the test set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mE76dHtHAumU",
        "colab_type": "code",
        "outputId": "f3a9b42f-1268-44e7-d2ec-97930ea35fca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "let (trainImages, trainNumericLabels, numTrainExamples) = readMNIST(\n",
        "                                                imagesFile: \"train-images-idx3-ubyte\",\n",
        "                                                labelsFile: \"train-labels-idx1-ubyte\")\n",
        "let trainLabels = Tensor<Float>(oneHotAtIndices: trainNumericLabels, depth: 10)\n",
        "\n",
        "let (testImages, testNumericLabels, numTestExamples) = readMNIST(\n",
        "                                                imagesFile: \"t10k-images-idx3-ubyte\",\n",
        "                                                labelsFile: \"t10k-labels-idx1-ubyte\")\n",
        "let testLabels = Tensor<Float>(oneHotAtIndices: testNumericLabels, depth: 10)\n",
        "\n",
        "\n",
        "print(\"Number of training examples: \\(numTrainExamples)\")\n",
        "print(\"Number of test examples: \\(numTestExamples)\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading data.\n",
            "Constructing data tensors.\n",
            "Reading data.\n",
            "Constructing data tensors.\n",
            "Number of training examples: 60000\n",
            "Number of test examples: 10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whCyfZgfCGVl",
        "colab_type": "text"
      },
      "source": [
        "### Explore the processed data\n",
        "\n",
        "Let's plot an image to see what it looks like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXyFCHQTCJMY",
        "colab_type": "code",
        "outputId": "7c97a826-d817-413c-f800-1b11c89695d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "let firstTrainImage = trainImages[0]\n",
        "let firstTrainLabel = Int((trainNumericLabels[0].scalars)[0])\n",
        "let numpyArray = firstTrainImage.reshaped(to: [28, 28]).makeNumpyArray()\n",
        "\n",
        "let cmap = plt.cm.binary\n",
        "plt.figure(figsize: [4, 4])\n",
        "plt.imshow(numpyArray, cmap)\n",
        "plt.colorbar()\n",
        "plt.grid(false)\n",
        "plt.xlabel(classNames[firstTrainLabel])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liZMNGVSCLgd",
        "colab_type": "text"
      },
      "source": [
        "Display the first 25 images from the *training set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RHBungPfCLrg",
        "colab_type": "code",
        "outputId": "622fd0c5-014a-4a8d-a0b0-181c6b9d91fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        }
      },
      "source": [
        "plt.figure(figsize: [12, 12])\n",
        "var dis = 0\n",
        "for i in 0...24 {\n",
        "    let trainImage = trainImages[i]\n",
        "    let trainLabel = Int((trainNumericLabels[i].scalars)[0])\n",
        "    let numpyArray = trainImage.reshaped(to: [28, 28]).makeNumpyArray()\n",
        "\n",
        "    let cmap = plt.cm.binary\n",
        "    plt.subplot(5, 5, dis + 1)\n",
        "    plt.imshow(numpyArray, cmap)\n",
        "    plt.colorbar()\n",
        "    plt.grid(false)\n",
        "    plt.xlabel(classNames[trainLabel])\n",
        "    dis += 1\n",
        "}\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AXALgwzLLlCYj4y/KqpUcoj5FCUV9ihJCLD5FB8o5xSPA7iIymf7OoFX6AOCf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GECeipYh140k0YLsFU6+c1vfpPa//RP/5TafsUpP7yljD+lPG/evNTeb7/9Unvu3LmZdh/60IdS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\nGIZhGIZhNMAWyoZhGIZhGIbRgP8fRvTl3+lFpAQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x864 with 50 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkDQbw2xCU9R",
        "colab_type": "text"
      },
      "source": [
        "## Build the model\n",
        "\n",
        "Building the neural network requires configuring the layers of the model, then compiling the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBUGUSziCXWR",
        "colab_type": "text"
      },
      "source": [
        "### Setup the layers\n",
        "\n",
        "The basic building block of a neural network is the *layer*. A layer extracts a representation from the data fed into it. Hopefully, a series of connected layers results in a representation that is meaningful for the problem at hand.\n",
        "\n",
        "Much of deep learning consists of chaining together simple layers. Most layers, like `Dense`, have internal parameters which are adjusted (\"learned\") during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXcMhonbCYqv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "/// A classifier.\n",
        "struct Classifier: Layer {\n",
        "    typealias Input = Tensor<Float>\n",
        "    typealias Output = Tensor<Float>\n",
        "\n",
        "    var conv1a = Conv2D<Float>(filterShape: (3, 3, 1, 32), activation: relu)\n",
        "    var pool1 = MaxPool2D<Float>(poolSize: (2, 2), strides: (2, 2))\n",
        "    \n",
        "    var conv1b = Conv2D<Float>(filterShape: (3, 3, 32, 64), activation: relu)\n",
        "    var pool2 = MaxPool2D<Float>(poolSize: (2, 2), strides: (2, 2))\n",
        "    \n",
        "    var flatten = Flatten<Float>()\n",
        "    var layer1a = Dense<Float>(inputSize: 1600, outputSize: 128, activation: relu)\n",
        "    var layer1b = Dense<Float>(inputSize: 128, outputSize: 10, activation: softmax)\n",
        "\n",
        "    @differentiable\n",
        "    func callAsFunction(_ input: Input) -> Output {\n",
        "        let convolved = input.sequenced(through: conv1a, pool1, conv1b, pool2)\n",
        "        return convolved.sequenced(through: flatten, layer1a, layer1b)\n",
        "    }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmMZIupiCzt3",
        "colab_type": "text"
      },
      "source": [
        "This network has three layers:\n",
        "\n",
        "* **\"convolutions\"** `Conv2D and MaxPool2D`— Network start with two pairs of Conv/MaxPool. The first layer is a Conv2D filters (3, 3) being applied to the input image, retaining the original image size by using padding, and creating 32 output (convoluted) images (so this layer creates 32 convoluted images of the same size as input). The next Conv2D also has a (3, 3) kernel, takes the 32 images as input and creates 64 outputs which are again reduced in size by a MaxPooling2D layer. So far in the course, we have described what a Convolution does, but we haven't yet covered how you chain multiples of these together. We will get back to this in lesson 4 when we use color images. At this point, it's enough if you understand the kind of operation a convolutional filter performs\n",
        "\n",
        "* **output** `Dense` — A 128-neuron, followed by 10-node *softmax* layer. Each node represents a class of clothing. As in the previous layer, the final layer takes input from the 128 nodes in the layer before it, and outputs a value in the range `[0, 1]`, representing the probability that the image belongs to that class. The sum of all 10 node values is 1.\n",
        "\n",
        "\n",
        "### Compile the model\n",
        "\n",
        "Before the model is ready for training, it needs a few more settings. These are added during the model's *compile* step:\n",
        "\n",
        "\n",
        "* *Loss function* — An algorithm for measuring how far the model's outputs are from the desired output. The goal of training is this measures loss.\n",
        "* *Optimizer* —An algorithm for adjusting the inner parameters of the model in order to minimize loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sIsB1v-DNZy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "var classifier = Classifier()\n",
        "let optimizer = Adam(for: classifier)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFkvqIt_IFjW",
        "colab_type": "code",
        "outputId": "c385663b-8bff-4ad9-c207-c0a6807dbebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "let tensor = Tensor<Float>(zeros:[128, 28, 28, 1])\n",
        "classifier(tensor).shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "▿ [128, 10]\n",
              "  ▿ dimensions : 2 elements\n",
              "    - 0 : 128\n",
              "    - 1 : 10\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MA--dvSEDV4s",
        "colab_type": "text"
      },
      "source": [
        "### Training loop\n",
        "\n",
        "With all the pieces in place, the model is ready for training! A training loop feeds the dataset examples into the model to help it make better predictions. The following code block sets up these training steps:\n",
        "\n",
        "1. The `epochCount = 20` parameter limits training to 20 iterations of the training dataset based on minibatches.\n",
        "2. Within an epoch, iterate over each example in the training `Dataset` grabbing minibatches of images and labels.\n",
        "3. Using the example's features, make a prediction and compare it with the label. Measure the inaccuracy of the prediction and use that to calculate the model's loss and gradients.\n",
        "4. Use the Adam optimizer to update the model's variables.\n",
        "5. Keep track of some stats for visualization.\n",
        "6. Repeat for each epoch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6p9HPtDDczo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let epochCount = 20\n",
        "let batchSize = 128\n",
        "\n",
        "/// Extract a batch of size batchSize.\n",
        "func minibatch<Scalar>(in x: Tensor<Scalar>, at index: Int) -> Tensor<Scalar> {\n",
        "    let start = index * batchSize\n",
        "    return x[start..<start + batchSize]\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDH1upB5Dkfn",
        "colab_type": "code",
        "outputId": "2c3aca11-d67d-4f51-9172-c2d48a86f009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        }
      },
      "source": [
        "print(\"Beginning training...\")\n",
        "\n",
        "struct Statistics {\n",
        "    var correctGuessCount: Int = 0\n",
        "    var totalGuessCount: Int = 0\n",
        "    var totalLoss: Float = 0\n",
        "}\n",
        "\n",
        "// The training loop.\n",
        "for epoch in 1...epochCount {\n",
        "    var trainStats = Statistics()\n",
        "    var testStats = Statistics()\n",
        "    Context.local.learningPhase = .training\n",
        "    for i in 0 ..< Int(trainLabels.shape[0]) / batchSize {\n",
        "        let x = minibatch(in: trainImages, at: i)\n",
        "        let y = minibatch(in: trainNumericLabels, at: i)\n",
        "        // Compute the gradient with respect to the model.\n",
        "        let 𝛁model = classifier.gradient { classifier -> Tensor<Float> in\n",
        "            let ŷ = classifier(x)\n",
        "            let correctPredictions = ŷ.argmax(squeezingAxis: 1) .== y\n",
        "            trainStats.correctGuessCount += Int(\n",
        "                Tensor<Int32>(correctPredictions).sum().scalarized())\n",
        "            trainStats.totalGuessCount += batchSize\n",
        "            let loss = softmaxCrossEntropy(logits: ŷ, labels: y)\n",
        "            trainStats.totalLoss += loss.scalarized()\n",
        "            return loss\n",
        "        }\n",
        "        // Update the model's differentiable variables along the gradient vector.\n",
        "        optimizer.update(&classifier.allDifferentiableVariables, along: 𝛁model)\n",
        "    }\n",
        "\n",
        "    Context.local.learningPhase = .inference\n",
        "    for i in 0 ..< Int(testLabels.shape[0]) / batchSize {\n",
        "        let x = minibatch(in: testImages, at: i)\n",
        "        let y = minibatch(in: testNumericLabels, at: i)\n",
        "        // Compute loss on test set.\n",
        "        let ŷ = classifier(x)\n",
        "        let correctPredictions = ŷ.argmax(squeezingAxis: 1) .== y\n",
        "        testStats.correctGuessCount += Int(Tensor<Int32>(correctPredictions).sum().scalarized())\n",
        "        testStats.totalGuessCount += batchSize\n",
        "        let loss = softmaxCrossEntropy(logits: ŷ, labels: y)\n",
        "        testStats.totalLoss += loss.scalarized()\n",
        "    }\n",
        "\n",
        "    let trainAccuracy = Float(trainStats.correctGuessCount) / Float(trainStats.totalGuessCount)\n",
        "    let testAccuracy = Float(testStats.correctGuessCount) / Float(testStats.totalGuessCount)\n",
        "    print(\"\"\"\n",
        "          [Epoch \\(epoch)] \\\n",
        "          Training Loss: \\(trainStats.totalLoss), \\\n",
        "          Training Accuracy: \\(trainStats.correctGuessCount)/\\(trainStats.totalGuessCount) \\ \n",
        "          (\\(trainAccuracy)), \\\n",
        "          Test Loss: \\(testStats.totalLoss), \\\n",
        "          Test Accuracy: \\(testStats.correctGuessCount)/\\(testStats.totalGuessCount) \\\n",
        "          (\\(testAccuracy))\n",
        "          \"\"\")\n",
        "}"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Beginning training...\n",
            "[Epoch 1] Training Loss: 807.5927, Training Accuracy: 44387/59904 (0.7409689), Test Loss: 130.19128, Test Accuracy: 7931/9984 (0.794371)\n",
            "[Epoch 2] Training Loss: 768.6304, Training Accuracy: 49170/59904 (0.8208133), Test Loss: 126.730354, Test Accuracy: 8370/9984 (0.83834136)\n",
            "[Epoch 3] Training Loss: 752.5269, Training Accuracy: 51201/59904 (0.85471755), Test Loss: 125.62386, Test Accuracy: 8514/9984 (0.8527644)\n",
            "[Epoch 4] Training Loss: 744.4304, Training Accuracy: 52236/59904 (0.8719952), Test Loss: 124.23053, Test Accuracy: 8674/9984 (0.8687901)\n",
            "[Epoch 5] Training Loss: 740.2535, Training Accuracy: 52765/59904 (0.880826), Test Loss: 123.30669, Test Accuracy: 8806/9984 (0.88201123)\n",
            "[Epoch 6] Training Loss: 735.8317, Training Accuracy: 53344/59904 (0.8904914), Test Loss: 123.35861, Test Accuracy: 8792/9984 (0.880609)\n",
            "[Epoch 7] Training Loss: 733.89197, Training Accuracy: 53580/59904 (0.8944311), Test Loss: 122.69644, Test Accuracy: 8882/9984 (0.8896234)\n",
            "[Epoch 8] Training Loss: 730.95703, Training Accuracy: 53948/59904 (0.90057427), Test Loss: 122.80226, Test Accuracy: 8848/9984 (0.88621795)\n",
            "[Epoch 9] Training Loss: 729.4532, Training Accuracy: 54150/59904 (0.90394634), Test Loss: 122.7962, Test Accuracy: 8848/9984 (0.88621795)\n",
            "[Epoch 10] Training Loss: 727.0126, Training Accuracy: 54462/59904 (0.90915465), Test Loss: 122.43109, Test Accuracy: 8900/9984 (0.89142627)\n",
            "[Epoch 11] Training Loss: 725.0182, Training Accuracy: 54699/59904 (0.913111), Test Loss: 122.089745, Test Accuracy: 8951/9984 (0.89653444)\n",
            "[Epoch 12] Training Loss: 723.95917, Training Accuracy: 54850/59904 (0.91563165), Test Loss: 122.32231, Test Accuracy: 8917/9984 (0.893129)\n",
            "[Epoch 13] Training Loss: 722.5477, Training Accuracy: 55002/59904 (0.9181691), Test Loss: 122.022835, Test Accuracy: 8953/9984 (0.8967348)\n",
            "[Epoch 14] Training Loss: 720.9302, Training Accuracy: 55234/59904 (0.92204195), Test Loss: 121.68592, Test Accuracy: 8991/9984 (0.9005409)\n",
            "[Epoch 15] Training Loss: 719.49506, Training Accuracy: 55392/59904 (0.92467946), Test Loss: 121.72478, Test Accuracy: 8990/9984 (0.9004407)\n",
            "[Epoch 16] Training Loss: 718.6124, Training Accuracy: 55525/59904 (0.92689973), Test Loss: 121.65978, Test Accuracy: 8993/9984 (0.90074116)\n",
            "[Epoch 17] Training Loss: 717.6021, Training Accuracy: 55643/59904 (0.92886955), Test Loss: 121.98085, Test Accuracy: 8954/9984 (0.8968349)\n",
            "[Epoch 18] Training Loss: 717.11774, Training Accuracy: 55702/59904 (0.92985445), Test Loss: 122.09777, Test Accuracy: 8945/9984 (0.8959335)\n",
            "[Epoch 19] Training Loss: 715.68475, Training Accuracy: 55884/59904 (0.9328926), Test Loss: 121.7241, Test Accuracy: 8995/9984 (0.9009415)\n",
            "[Epoch 20] Training Loss: 715.1231, Training Accuracy: 55942/59904 (0.93386084), Test Loss: 121.67378, Test Accuracy: 9006/9984 (0.9020433)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAc0Y-RVEBFf",
        "colab_type": "text"
      },
      "source": [
        "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.93 (or 93%) on the training data and 0.91 (or 91%) on the testing data. \\\n",
        "As it turns out, the accuracy on the test dataset is smaller than the accuracy on the training dataset. This is completely normal, since the model was trained on the `trainImages`. When the model sees images it has never seen during training, (that is, from the `testImages`), we can expect performance to go down. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jP_tiy9SGDwZ",
        "colab_type": "text"
      },
      "source": [
        "## Make predictions and explore\n",
        "\n",
        "With the model trained, we can use it to make predictions about some images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9YfY8gzGFwT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "var x = minibatch(in: trainImages, at: 1)\n",
        "var y = minibatch(in: trainNumericLabels, at: 1)\n",
        "let predictions = classifier(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a__DPZtOJQtr",
        "colab_type": "code",
        "outputId": "65dd6105-626c-4eab-d143-e3a20d5b3d7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(x.shape)\n",
        "print(y.shape)\n",
        "print(predictions.shape)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[128, 28, 28, 1]\r\n",
            "[128]\r\n",
            "[128, 10]\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j81zr5DrJZWY",
        "colab_type": "text"
      },
      "source": [
        "Here, the model has predicted the label for each image in the minibatch created. Let's take a look at the third prediction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79Fypi-yJRAU",
        "colab_type": "code",
        "outputId": "c38d908a-6aad-48c9-e896-3fc8cfe0cfaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "print(predictions[2])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[7.8713885e-26,  4.717218e-24, 1.5302585e-25, 1.4119528e-24, 4.2719587e-22,  4.234288e-21,\r\n",
            " 4.9816932e-23,  4.532809e-21,           1.0, 1.3271081e-20]\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4BUa7M5JcIh",
        "colab_type": "text"
      },
      "source": [
        "A prediction is an array of 10 numbers. These describe the \"confidence\" of the model that the image corresponds to each of the 10 different articles of clothing. We can see which label has the highest confidence value:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSeHE8wrKwZz",
        "colab_type": "code",
        "outputId": "d120cf06-27cb-45bf-cb1f-dfaab3403caa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "let thirdTrainImage = x[2]\n",
        "let thirdTrainLabel = Int((y[2].scalars)[0])\n",
        "let numpyArray = thirdTrainImage.reshaped(to: [28, 28]).makeNumpyArray()\n",
        "\n",
        "let cmap = plt.cm.binary\n",
        "plt.figure(figsize: [4, 4])\n",
        "plt.imshow(numpyArray, cmap)\n",
        "plt.colorbar()\n",
        "plt.grid(false)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u+he/+MWW69UqBwGzinMQMKs4BwGzinMQMKswTypiZr4SMKu6vg4CkhYA3wbmU1tsZCIi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\nQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUiJd3aEJf9c",
        "colab_type": "code",
        "outputId": "cc993e64-d397-4498-9a9d-6e66a3e56960",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predictions[2].argmax()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XEUygeGGKW0S",
        "colab_type": "text"
      },
      "source": [
        "So the model is most confident that this image is a Bag, or `classNames[8]`. And we can check the test label to see this is correct:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RL0L6U0YKdUU",
        "colab_type": "code",
        "outputId": "2492fc59-cbba-4798-ddf7-819464605407",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y[2]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6BoElWELOIk",
        "colab_type": "text"
      },
      "source": [
        "We can graph this to look at a set of class predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQBY-NBlLOuY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "func plotImageStatistics(index: Int, images: Tensor<Float>, predictions: Tensor<Float>, correctLabels: Tensor<Int32>) {\n",
        "    let numpyImage = images[index].reshaped(to: [28, 28]).makeNumpyArray()\n",
        "    \n",
        "    let predictedLabel = predictions[index].makeNumpyArray()\n",
        "    let predictedLabelIndex = Int((predictions[index].argmax().scalars)[0])\n",
        "    \n",
        "    // Correct Label Array with only correct label index as 2, all others as 1.\n",
        "    // This will help in creating a non-overlapping graph with the Predicted Label Array.\n",
        "    let correctLabelIndex = Int((correctLabels[index].scalars)[0])\n",
        "    let correctLabelArray = np.ones([10], dtype: np.float32)\n",
        "    correctLabelArray[correctLabelIndex] += 1.0\n",
        "    \n",
        "    let cmap = plt.cm.binary\n",
        "    var color = \"blue\"\n",
        "    \n",
        "    if correctLabelIndex != predictedLabelIndex\n",
        "    {\n",
        "        color = \"red\"\n",
        "    }\n",
        "    \n",
        "    plt.figure(figsize: [6, 3])\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.imshow(numpyImage, cmap)\n",
        "    plt.xlabel(\"\\(classNames[correctLabelIndex]) \\(predictedLabel[correctLabelIndex]*100.0)%\").set_color(color)\n",
        "    \n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(predictedLabel)\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(correctLabelArray)\n",
        "    plt.xlabel(\"Predicted Label vs Correct Label\")\n",
        "    plt.show()\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHERLS9Cx6lr",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the \"1\" indexed image, predictions, and prediction vs correct label graph. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3JLzDp_nxYqK",
        "colab_type": "code",
        "outputId": "97442ca0-43e4-4335-9b6b-e92f3f0cdbc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "var i = 1\n",
        "plotImageStatistics(index: i, images: x, predictions: predictions, correctLabels: y)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \r\n",
            "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\r\n",
            "  \"Adding an axes using the same arguments as a previous axes \"\r\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pU/Hjl0VFif/P9JvQHB4e9q07b968uLKJiQnfuuXl5XFly5cvT7hf+UZVnxSR5dNUuQ74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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Q5spqVHyFqa",
        "colab_type": "text"
      },
      "source": [
        "Let's plot several images with their predictions. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percent (out of 100) for the predicted label. Note that it can be wrong even when very confident. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQS2d1bjyGor",
        "colab_type": "code",
        "outputId": "4c0a7dd6-c0b0-485f-94b3-25d7e627e8bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for imgNumber in 0...24 {\n",
        "    plotImageStatistics(index: imgNumber, images: x, predictions: predictions, correctLabels: y)\n",
        "}"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZgKxRN8YkymW43TC2CSkrrFE3RUVE7hCR/SKyLeR5EZFbRGSHiHSKSISt2jTS3Q6Vte7I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\nMQXEGnVjjCkg1qgbY0wBsUbdGGMKyP8HSAgf+nT/6f0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pU/Hjl0VFif/P9JvQHB4e9q07b968uLKJiQnfuuXl5XFly5cvT7hf+UZVnxSR5dNUuQ74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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RDBbo6D00xFM7+6ZWYhOZx5/vRQRW10ZcUw++GbR39tOUhfXbLaib2aQW6Ih7\n3AlcEpZHVUdEZABYAiRFZxFZB6wDOP3005NO9uLew/z5v7RlpeAmGk11lVTMiTYs1lbNZcn8Mto6\nB7ghC8ezoG5MhlR1PbAeoKWlJakmv/aMRfzyk1dGXSwzBactyO3SAD4iwv0fuoy6RXOzcrxIg/qm\nTZv6ROSV4OFSPLWfAmDXlT9nTPJ8F1Af97guSPPl6RSREqAS2J9JYeaWFXNGjjZZMIXlnNMqsnas\nSIO6qp5c0UtENqpqS5Tnj4Jd17T2O2CliJyJC97XAdcn5NkA3AQ8DVwLPJ5Je7ox+WLNL2bWCNrI\nbwF+DhQDd6rqNhH5IrBRVTfg1jm6R0R2Agdwgd+YGcOCuplVgh26Hk1I+3zc/SHg3VGXy5hsyec4\n9fV5PHcu2XUZY/Imb0E9GDlQcOy6jDH5JNYHZMzUicg+4BXPU/kcNWTnLpzznhE/0GQiFtSNyaF8\njhqyc8+O8yaKvPllsgWVZpJgw+1eEdkal7ZYRH4hIjuCn94NuaczEakXkSdE5DkR2SYitwbpM/7a\njCl0kQb1FBdUmknuAq5OSPs08JiqrgQeCx7PNCPAJ1R1FXAp8LHg91QI12ZMQYu6pn5yQSVVPQHE\nFlSakVT1SdxY5njXAHcH9+8G3hFpobJAVXtUtTW4Pwhsx62JMuOvLQ/y2cFs554d5x0n6qDuW1Cp\nNuIy5Fq1qvYE9/cA1fkszFQF64lfBPyGAru2KORz1JCde3acN5Gtp55DwfTyGdsTLSIVwI+Aj6vq\nofjnZvq1GVOoog7qqSyoNNPtFZEVAMHP3jyXJyMiUooL6Peq6kNBckFcmzGFLOqgfnJBJREpw62r\nsSHiMuRabEEogp8P57EsGRERwa2Bsl1VvxL31Iy/tijla6RX2OilCM9fLCLPisgjEZ+3SkQeFJHn\nRWS7iFwW4bn/LHivt4rIfSIS/Rq+Maoa6Q14C/Ai8Hvgr6M+f5av5T6gBxjG9Q98ALehwmPADuD/\nAYvzXc4Mrus1uKaVdmBzcHtLIVxbhO9hcfA3fhZQBrQBqyI69wqgObi/IPh/i+TcwTn/HPgB8EjE\n7/ndwB8H98uAqojOWwu8DMwNHv8LcHOU1x5/i3xBL/UsqDRTqep7Q556faQFyTJVfQqQkKdn9LVF\nKJWt83JCXWd2T3B/UERio5dyfm4RqQP+APhbXHCPhIhUAq8FbgZQN7ruRFTnxy2OOFdEhoF5QHeE\n5x7HOkqNyY1pMdIrYfRSFP438JfAWETnizkT2Ad8N2j6+baIRLJDiap2Af8E7MZ9mA6o6r9HcW4f\nC+rGFKiJRi/l6HxvBXpVdVMpo7pLAAAIdElEQVSuz+VRAjQD31DVi4AjRDQ5LphZfQ3ug6UGmC8i\n74/i3D4W1I3JjbyO9AoZvZRrVwBvF5FduImFV4nI9yM6dyfQqaqxbyQP4oJ8FN4AvKyq+1R1GHgI\nuDyicyexoG5MbuRtpNcEo5dySlU/o6p1qtqAu97HVTWSGquq7gE6ROS8IOn1RNCHENgNXCoi84L3\n/vW4Wdh5YTsfGZMDGrJ1XkSnvwK4AdgiIpuDtL8KBikUsj8B7g0+RF8C/iiKk6rqb0TkQaAVt27S\ns+RxyQBbejdFIowCW3CjQkaBW1T5jywc97W4zqVG4DpVHox77ibgs8HD/6nq1l0RYS1uMbG5uJFE\nt6qOn90pggD/jBuKeBS4WZVWEc7DDTcrBT6kytMilAD/BrxdlaNTvSZjTP5Y80vqjqmyRpUm4DPA\n32fpuLtxw7B+EJ8owmLgC8AluOFxXxAhttTtN4APAiuDW+JKkeBWwow9vy54DcCHgFtxwf4vgrSP\nAN+3gG7MzGdBPTMLgYMAIlSI8JgIrSJsETm16qQInxPhBRGeEuE+kZNB9CRVdqnSTvIQsP8K/EKV\nA6ocBH4BXC3CCmChKs8EtfPv4V8t8Rrge8F8hGeAquC1sXG084BhEaqAtwXHMcbMcNamnrq5ImwG\nynEz9q4K0oeAd6pySISlwDMibABagHcBTbimjlYgnaFeYeOca4P7iempvv4OXACfg6u1fw74O9XI\nxxUbY3LAgnrqjqmyBkCEy4DvibAa18b+d0Hb+BgucFbjOqseVmUIGBLhp3kq9ziq7AauBBDhHNxQ\nu+0i3IObWv05VV7MXwmNMVNhzS8ZUOVp3Cazy4D3BT/XBkF/L642P1Vh45y7gvuJ6am+Pt7f4jpi\n/xT4Nm4m4BemVGpjTF5ZUM+ACK/CDVPbD1QCvaoMi/A64Iwg26+Bt4lQLkIF8NY0T/Nz4E0iLAo6\nSN8E/FyVHuCQCJcGI1xuxL9a4gbgRhFEhEuBgeC1sWv4L0C3Kjtw7etjwW1emuWclURkVEQ2B6vy\nPSAiGb9vInJlbEVDEXn7RCs6BisRfjSDc9wmIkl9OmHpExzncDbOOxUicrGIPBmsgBlbEiCyv1sR\neUfYNpzT4f205pfUxdrUwTW53KTKqAj3Aj8VYQuwEXgeQJXfBW3r7bja+xZgIPGgIrwa+DGwCPch\n8DeqXKDKARH+B24SC8AXVU9unfdRTg1p/FlwQ4QPB+f+Jm6o41uAnbghjSfH7AYfBp8F3hMkrQfu\nxf09fCTjd2h2OaaqQXOc3At8GDg50SeYhCKqmlZfhapuYOJJSlW43///SbvEBUBEqoEHgOtU9ekg\n7VrcapSTjt4SkRJVHQl7nKJ3AI8Q3eSm9ORrecjZcAOtCH7OA90I2pzvMtktW79bDsfd/zAuyDYA\nL+A6orfhvrW9CXga11H+ABD8TXA1rgLQCtxOsEwtbnjr14P71bgP/Lbgdjlu+v0x3HLI/xjk+yTu\nw78d+Ju4cv01btndp3DLRP+F5zpuC0n/Ca5jfxuwLv66ga8G6Y8By4L0s3FzHTYBvwJeFXZ83Lfb\nV4Ci4PF8XKd+Ka4p8LngWn7oKdcXgS+G/E4WB+VuB54BGuPKcA/u2/N9wXu8AXgc+OUk7+GNQVpb\ncIzLcfsSvxz8Ds7O9/uZdK58/3MU8g30B6CbQZ8H/Uy+y2O3bP5uXVDHfbt5GPcNpwHXhHVp8NxS\n4ElgfvD4U8DncX0uHbg5BIJbf9sX1O/HLcYFrrmvMjjH1rhyvAn3TUtwzamP4JagXYv7djgPNwR3\nZ0iwCQtCi4Ofc4GtwJLgsQLvC+5/Pq6sjwErg/uX4JYImOj4DwOvC+6/B/h2cL8bmBPcT1oPHbeu\nyjUhv5OvAV8I7l8FbI4rwyZOrXd+M240WOwaw97DC3AfiksT3pO7gGtDypCX9zP+Zs0vOaTK9fku\ng8mZuXFT8H+FW2ulBnhFVZ8J0i8FVgG/dq0xlOFq7a/CLQC1AyBY9Gqd5xxX4WqKqOooMBCsCBjv\nTcHt2eBxBe7DYgHwY1U9Gpwj3XVn/lRE3hncrw+OuR/3oXV/kP594KFgNcjLgQeC6wQ3ZHYi9+OC\n+RO4dWJizUntuKn+P8HVbtPxGtwwYlT1cRFZIiILg+c2qOqxuLy/UNVYc2bYe9gEPKCqfcExD5C5\nXL+fJ1lQNyYzJ9vUY4J/wCPxSbjg8d6EfONeN0UC/L2q/t+Ec3w84wOKXIlbefAyVT0qIv+f8BFd\niqvd9ie+H5PYAPydiCzGfat4PEj/A1wt+W3AX4vIhTq+zXtbkD/drRSPTPA47D38kzTP4RXR+3mS\njX4xJneeAa4QkXMARGS+iJyLa0tvEJGzg3xhO2g9RtBxHez7WQkM4mrhMT8H/ntQu0NEakXkNFyz\nzztEZK6ILMAFyVRVAgeDAPQq3DeOmCLg2uD+9cBT6tZqf1lE3h2UQUSkaaITqOphXBv2P+OankZF\npAioV9UncE1Vlbhac7yvAzeJyCWxBBH5w6AD9Ve4IcaxQNqnqa0jH/YePg68W0SWBOmLg/yJv4PJ\n5Pz9jGdB3ZgcUdV9uPbb+0SknaDpRVWHcM0t/yoirUBvyCFuBV4nIltwbcKrVHU/rjlnq4j8o7od\ndn4APB3kexBYoKqtuK/1bbjRUb/znwKAz4pIZ+yG66ArEbcN3pdwH04xR4CLRWQrrnnoi0H6+4AP\niEgbrjZ9DZO7H3g/p5ofioHvB9fxLHC7qvbHv0BV9+Kaa/4pGNK4HbekxiCuvXlt8F5/iVObpE9o\ngvdwG24uxy+D64qNbvoh8MlgOOXZnkPm6/0EbJVGY4wpKFZTN8aYAmJB3RhjCogFdWOMKSAW1I0x\npoBYUDfGmAJiQd0YYwqIBXVjjCkg/wlc2Yi2waOTWQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YG/FUIWcN+bnL57xL4yeajMeDunM5VMhZQ37uyjjvWHnvfpkooVIpCTfcPiBp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\nG7ZFN/cv++hjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7gXsBVq9eHXclf8mSc3juM1dku1klrUyEhnMyPz891qXnzeHF267i5PBo0seY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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ctB00OGfS+JeZlUIW1I0Zqg2YH/O43pelTgQkveA2UbVGuv3XLt67uj71A9hnbhOlxgyz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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GzRjWqLrZ83lX5b6v6kZoezr3/YwAbKZuGEaYvS8652UunaQJahqhayscHHLR0cMOU+qG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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QGV9iMVHa0dGRUhY2Sekrv+yyy9K+V1dXl7c8bKIyl7qF2k89H9c999xzU8rC9mn3Tcz6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\nRiyoG2NMjPx/Wx4YKU0cxNwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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22cxemuG5/0/8WW80s7vNbHJW505w90wfwOuAPwB/BD6R9flLfC13AzuAbqL5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\ngg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LrBOR1bjgfR0we9nQFuAG4DHgjcCDyYynG5MtNvxiioY3Rn4T8DPcksbbVHWn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\ntPrXXW0EEgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9nDlQcfx9OeeKST4G5Fz2JO0DXo54qpizhvzclXPeZfETTSbiQd25PCrmrCE/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\ngnhQd865CuJB3TnnKogHdeecqyD/H6nlUd3kjsEoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Xnpqi1E0723dxxvv7+Cuj5/W6oibaLp3yWXGlBHMfP0DqnYfjHvhlBEf8fxm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\n1A3DMLKI/w/4Uv9iipZLFwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7WK5c6ADDw8NJ1z1y5EigrK/PfVXS0JB8r9p5550XKPvlL3/prHvppZcmfVxj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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4JbBZ4pyy184mu+I8hezuvwvKXpsI/BtwRwT/VL5DiTcC88gC5FnAORKvSRx7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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g5cqV4Y+Gi1bDbcP3bDcZl8SVsqrSuq+Ho6cG6Oo9RW21P49RpNrl8Nn9jLCzZwLSk6fG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\nA/RVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CLNUvKxPaGpIHvk3xizSoq+pkRi8R2ayqXVGePwr2uma1XwCrRWQVLnjfAPze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\nEQvqxhhTRCyoG2NMEbGgbowxReT/A5KkW+WitRyJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jXwujjCYsp78Zimel/bHfbt3L3z7QzNJ5M7n4jDRnos5dCvOqYP8LaZ/XjwV1YzKkquuA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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YhfzPbrCgbkw2qepKYCXA0qVLo8fgzlsGt0Tu2W7SLoE7ZVWldXsPfYcH6Oo9TE118G5b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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g7yurPQMsFpGjccH7QuAfxuRZBVwKPAl8CHgwkf50YzLFul9MwfD6yK8A7sNN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\n6tE1KAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z+tnAO4G1IrLGyz4JXCYii3FpjluAcA6hYRgjptWX211Yn7vMl3gaG2r5TWsH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\nCGFG3TAMI0KYUTcMw4gQZtQNwzAixP8HCW1d0KVR26wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+Jd6wWF05jTkzymhq7eHmHBzPgroxWVLVNcAagIaGhpSa/Mqzz+LXn7s67mKZ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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+dGNSYZcBJm+4PvJrgCcJpjTerqovi8gcYLGqziPIc3SXiKwFdhA0/MYMGtao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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Jkdj+Q62sb9jD++dOLdgx8+l+uY/wvN7vuPv8+NYvGnQRkUJaU99Ma5sXbJAU8mjU3f15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\nMvL/ARj70IpVcEdwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G4HHgbcDDmfSnG1Mo1v1iKobXR34T8GPclMbbVXWbiHwB2KSqG3F5jv5JRHYA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\ngg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/uJFXutth3/bc2dNjVDfCjg1wODl/ZSSYUjeONX5M8vaM8VwELPKv64CbczAnYyzQnqXt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\nKXXDMIwJhCl1wzCMCcT/ApJhnQfshfKeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9DYtNOFyaVc",
        "colab_type": "text"
      },
      "source": [
        "Finally, use the trained model to make a prediction about a single image. \n",
        "Since our classifier takes minibatches as input, we change the first value of our generated minibatch and predict the label for the entire minibatch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZ_HbyPAycbW",
        "colab_type": "code",
        "outputId": "b53fc753-35af-4b80-f3b2-022c99702c37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        }
      },
      "source": [
        "// Making a new Image minibatch instead of modifying the original minibatch created.\n",
        "var image = x\n",
        "image[0] = testImages[0]\n",
        "\n",
        "// Making a new Label minibatch instead of modifying the original minibatch created.\n",
        "let minibatchArray = y.makeNumpyArray()\n",
        "minibatchArray[0] = PythonObject(Int((testNumericLabels[0].scalars)[0]))\n",
        "let lbl = Tensor<Int32>(numpy: minibatchArray)!\n",
        "\n",
        "let cmap = plt.cm.binary\n",
        "\n",
        "let imgArray = testImages[0].reshaped(to: [28, 28]).makeNumpyArray()\n",
        "plt.figure(figsize: [4, 4])\n",
        "plt.imshow(imgArray, cmap)\n",
        "plt.xlabel(\"testImages[0]\")\n",
        "plt.colorbar()\n",
        "plt.grid(false)\n",
        "plt.show()\n",
        "\n",
        "let img2Array = image[0].reshaped(to: [28, 28]).makeNumpyArray()\n",
        "plt.figure(figsize: [4, 4])\n",
        "plt.imshow(img2Array, cmap)\n",
        "plt.xlabel(\"Modified image[0]\")\n",
        "plt.colorbar()\n",
        "plt.grid(false)\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tioCICdNy-S9",
        "colab_type": "text"
      },
      "source": [
        "Now predict the image:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kumHy8w21E_E",
        "colab_type": "code",
        "outputId": "aed3a99a-607a-46a8-abef-be86fdf46ff5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "let predictionsSingle = classifier(image)\n",
        "print(predictionsSingle[0])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 2.355352e-21, 1.1594732e-20,  8.679765e-17, 1.3507437e-15,   4.86021e-22,  8.736438e-09,\r\n",
            " 1.3886261e-13, 6.4994185e-13, 1.3135623e-16,           1.0]\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKFvNUgjzE-A",
        "colab_type": "code",
        "outputId": "6d38ad89-3931-4703-ce54-14c19b6c25f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "plotImageStatistics(index: 0, images: image, predictions: predictionsSingle, correctLabels: lbl)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vwB0RfL30gBJvBxaQBMiFwDkSb8poewKwJILTgJ8Dn03LvwlcH8GZwDN9lUdw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\nzcwaiIO6mVkD+f8QSaCAUwVFiAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izts55dr2zyB",
        "colab_type": "text"
      },
      "source": [
        "`classifier(x)` returns a list of lists of probabilities, one for each image in the batch of data. Grab the predictions for our (first) image in the batch:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHkYkjbf3Mo_",
        "colab_type": "code",
        "outputId": "70daea73-7bd5-47e8-8e2c-8652ec4c9c13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predictionsSingle[0].argmax()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YBHHIFO3RS7",
        "colab_type": "text"
      },
      "source": [
        "And, as before, the model predicts a label of 9 (Ankle Boot)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVdvw_DI3akc",
        "colab_type": "text"
      },
      "source": [
        "# Exercises\n",
        "\n",
        "Experiment with different models and see how the accuracy results differ. In particular change the following parameters:\n",
        "*   Set training epochs set to 1.\n",
        "*   Adjust the number of neurons in the Dense layer. For example, go really low (e.g. 10) in ranges up to 512 and see how accuracy changes.\n",
        "*   Add additional Dense layers between the Flatten layer and the final Dense layer, experiment with different units in these layers.\n",
        "*   Don't normalize the pixel values, and see the effect that has.\n",
        "\n",
        "\n",
        "Remember to enable GPU acceleration to make everything run faster (Runtime -> Change runtime type -> Hardware accelerator -> GPU).\n",
        "Also, if you run into trouble, simply reset the entire environment and start from the beginning:\n",
        "*   Edit -> Clear all outputs\n",
        "*   Runtime -> Reset all runtimes"
      ]
    }
  ]
}