S4TF_Tutorial_3.ipynb

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  "metadata": {
    "colab": {
      "name": "S4TF Tutorial 3",
      "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": "y_hlPngd-bN1",
        "colab_type": "code",
        "outputId": "95d1a962-1728-417a-82b4-03368bf91987",
        "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/saeta/Just\", from: \"0.7.2\")' 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/saeta/Just\", from: \"0.7.2\")\n",
            "\t\tJust\n",
            "With SwiftPM flags: []\n",
            "Working in: /tmp/tmpnhi4vmqo/swift-install\n",
            "Fetching https://github.com/mxcl/Path.swift\n",
            "Fetching https://github.com/saeta/Just\n",
            "Completed resolution in 12.78s\n",
            "Cloning https://github.com/saeta/Just\n",
            "Resolving https://github.com/saeta/Just at 0.7.3\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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    {
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        "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": [
        "## **Classifying Images of Clothing**"
      ]
    },
    {
      "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/1XfT75glapWdCp0Zj_4mkiMrjT6odD-Gc\"><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": "NomMxD_UCkX6",
        "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": "tilH8TTZ_i2e",
        "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": "b9f815ae-a0f4-4d99-a186-dbcc0be8b5bf",
        "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": "f2df035c-4eea-46f9-d491-df0b488d47bb",
        "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 `trainImages` and `trainNumericLabels`.\n",
        "* The model is tested against `testImages` and `testNumericLabels`.\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",
        "* 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": "8aa3ee4c-5b18-4515-93f1-cba5ef24280c",
        "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": "7fb7e8b0-d9b6-4826-d009-9fd2c4cf07ae",
        "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": "872aa91c-c936-4400-d5b9-c956493c1223",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        }
      },
      "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 flatten = Flatten<Float>()\n",
        "    var layer1a = Dense<Float>(inputSize: 28 * 28 * 1, 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",
        "        return input.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",
        "* **input** `Flatten` — This layer transforms the images from a 3d-array of 28 $\\times$ 28 $\\times$ 1 pixels, to a 1d-array of 784 pixels (28\\*28\\*1). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn, as it only reformats the data.\n",
        "\n",
        "* **\"hidden\"** `Dense`— A densely connected layer of 128 neurons. Each neuron (or node) takes input from all 784 nodes in the previous layer, weighting that input according to hidden parameters which will be learned during training, and outputs a single value to the next layer.\n",
        "\n",
        "* **output** `Dense` — A 10-node *softmax* layer, with each node representing a class of clothing. As in the previous layer, each node takes input from the 128 nodes in the layer before it. Each node weights the input according to learned parameters, and then 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.\n",
        "* *Metrics* —Used to monitor the training and testing steps. The following example uses *accuracy*, the fraction of the images that are correctly classified."
      ]
    },
    {
      "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": "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": "d8825c2b-50db-4f7d-934d-0c4e979960b9",
        "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>(\n",
        "            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": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Beginning training...\n",
            "[Epoch 1] Training Loss: 794.0864, Training Accuracy: 46924/59904 (0.78331995), Test Loss: 128.02048, Test Accuracy: 8276/9984 (0.82892627)\n",
            "[Epoch 2] Training Loss: 760.638, Training Accuracy: 50553/59904 (0.84390026), Test Loss: 126.92258, Test Accuracy: 8389/9984 (0.8402444)\n",
            "[Epoch 3] Training Loss: 754.5387, Training Accuracy: 51226/59904 (0.8551349), Test Loss: 126.92353, Test Accuracy: 8359/9984 (0.83723956)\n",
            "[Epoch 4] Training Loss: 751.242, Training Accuracy: 51547/59904 (0.8604935), Test Loss: 126.49781, Test Accuracy: 8397/9984 (0.8410457)\n",
            "[Epoch 5] Training Loss: 748.6346, Training Accuracy: 51835/59904 (0.86530113), Test Loss: 125.7942, Test Accuracy: 8483/9984 (0.84965944)\n",
            "[Epoch 6] Training Loss: 746.29614, Training Accuracy: 52126/59904 (0.8701589), Test Loss: 125.53842, Test Accuracy: 8519/9984 (0.8532652)\n",
            "[Epoch 7] Training Loss: 744.5181, Training Accuracy: 52339/59904 (0.8737146), Test Loss: 125.327805, Test Accuracy: 8550/9984 (0.8563702)\n",
            "[Epoch 8] Training Loss: 742.8283, Training Accuracy: 52545/59904 (0.87715346), Test Loss: 125.05981, Test Accuracy: 8583/9984 (0.85967547)\n",
            "[Epoch 9] Training Loss: 741.52106, Training Accuracy: 52719/59904 (0.8800581), Test Loss: 124.91615, Test Accuracy: 8603/9984 (0.86167866)\n",
            "[Epoch 10] Training Loss: 740.76526, Training Accuracy: 52785/59904 (0.88115984), Test Loss: 124.79718, Test Accuracy: 8616/9984 (0.8629808)\n",
            "[Epoch 11] Training Loss: 739.2509, Training Accuracy: 52984/59904 (0.88448185), Test Loss: 125.01356, Test Accuracy: 8594/9984 (0.86077726)\n",
            "[Epoch 12] Training Loss: 738.731, Training Accuracy: 53035/59904 (0.8853332), Test Loss: 124.90804, Test Accuracy: 8598/9984 (0.86117786)\n",
            "[Epoch 13] Training Loss: 737.3019, Training Accuracy: 53229/59904 (0.88857174), Test Loss: 124.60047, Test Accuracy: 8629/9984 (0.86428285)\n",
            "[Epoch 14] Training Loss: 736.9235, Training Accuracy: 53233/59904 (0.8886385), Test Loss: 124.573814, Test Accuracy: 8638/9984 (0.8651843)\n",
            "[Epoch 15] Training Loss: 735.81165, Training Accuracy: 53405/59904 (0.8915098), Test Loss: 124.585335, Test Accuracy: 8639/9984 (0.86528444)\n",
            "[Epoch 16] Training Loss: 734.90594, Training Accuracy: 53512/59904 (0.89329594), Test Loss: 124.364815, Test Accuracy: 8662/9984 (0.86758816)\n",
            "[Epoch 17] Training Loss: 734.2842, Training Accuracy: 53584/59904 (0.8944979), Test Loss: 124.221924, Test Accuracy: 8689/9984 (0.8702925)\n",
            "[Epoch 18] Training Loss: 734.0758, Training Accuracy: 53605/59904 (0.8948484), Test Loss: 124.10627, Test Accuracy: 8688/9984 (0.8701923)\n",
            "[Epoch 19] Training Loss: 732.5294, Training Accuracy: 53782/59904 (0.8978031), Test Loss: 124.03502, Test Accuracy: 8709/9984 (0.8722957)\n",
            "[Epoch 20] Training Loss: 732.2189, Training Accuracy: 53825/59904 (0.89852095), Test Loss: 124.12582, Test Accuracy: 8689/9984 (0.8702925)\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 0.90 (or 90%) on the training data and 0.87 (or 87%) 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": "1e7e92fc-bbbe-4e8c-a4ba-e15d6a539190",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(x.shape)\n",
        "print(y.shape)\n",
        "print(predictions.shape)"
      ],
      "execution_count": 17,
      "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": "36807ce2-720f-4991-b5fd-881f478808a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "print(predictions[2])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 1.645529e-23, 2.0737465e-26, 2.4399716e-16, 5.5744185e-25, 1.0433484e-14, 4.2173477e-20,\r\n",
            " 4.6086103e-17,  5.915549e-22,           1.0,  4.084331e-30]\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": "138658b1-cdc5-4465-b4ae-4e47bfe54044",
        "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": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUiJd3aEJf9c",
        "colab_type": "code",
        "outputId": "95b12c12-79b8-43c9-f069-d8cbea08875a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predictions[2].argmax()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "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": "c2c5c539-99e6-48d9-c1b2-54c7eb7ed3c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y[2]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "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": "95e7e5d8-5238-453c-fbc5-17da1839f890",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "var i = 1\n",
        "plotImageStatistics(index: i, images: x, predictions: predictions, correctLabels: y)"
      ],
      "execution_count": 23,
      "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\npnf4yzMiUhc9O2DWjfW/DMNHkx5Pf+qFwzQtqGTFolle5Yu4PDAv/QR+cltSbZssaNwAq9+Q8sNY\nUDfmbE3AvpivI15ZXFAXkY24q3nOOeec+Ec6/IIbT08iqI9PKDd9ayu/vXoRt783iYOqz/8d+NUP\n4Ym/mv19TXa84kMW1I3JJlXdBGwC6OjoiE++s/oN8Ol97gCLWXqx7zjHT4/Rta8/uc51fADa35/c\nfU2WpOdELAvqxpytB4gdY2v2ypJTXpPU3aLBvHfgFH1Dp1k8L34YbUZFie9zMPnDJkqNOdtm4P3e\nKpjLgIGkxtNT1B0ZiLmd5NW6KUgW1E1B8TueUURuEpGbvCoPAy8Be4CvAx/ORj+7I/20NddSJNAV\nE+CNmcmMwy8icidwLXBIVS/yyj4P/CHQ51X7rKo+nKlOziVBq1927doVVzY6OppSW1VVVb7lfrnT\nx8b8U61WVFTElfmtnikU0xzPGP2+Ah8JqTu+RsYm2L1/iA9cvpxToxN2pW5mJZEr9bvwX9f7JVVd\n730UREA3Jgy/PDDIyPgErc11tDbX0h0ZCDwExZipZgzqqvokcDSEvhhjODPc0tpcS2tLHUdPjBA5\n5r9nwZipUhlTv9nbRn2niCwIqiQiG0Vki4hs6evrC6pmjPF07+unvrqM5gWVtDXXujIbVzcJSjao\n3w6sAtbjNmX8fVBFVd2kqh2q2rF48eIkmzOmcHR5k6Qiwppl8ykrLrJxdZOwpNapq+rB6G0R+Trw\nUNp6NMddeeWVvuXd3d1xZbPJce6noaHBt9wv/UBQmoD9+0NfjWdScOL0GHsOHedNF7mffVlJEWsb\n57Mt2U1IpuAkdaUuIrHR5m2A5fg0Jg129AwwodDWUjtZ1tZcy46eAcYnbLLUzGzGoO63rhe4TUS2\ni0g38Drg4xnupzEFoXtykrRusqy1uY4TI+O81Hc8W90yOWTG4ZeAdb3fyEBfjCl4XZF+muoqWVRz\nJi1AdLK0KzLA6qWFu8fAJMZ2lBozh3RHBmhtrj2rbOXiGqrLim2y1CTEgroxc8SxEyP85ujJs4Ze\nAIqLhIuaai1dgEmIZWmcpbVr1/qW+6UECEoTsGTJkoTams3qmaC2GhsbE2rLZF93jwvabVOu1AHa\nWuq467/2MjI2QVmJXYuZYPbbYcwc0e0tW7zIJ6i3NtcyMj7B8weGwu6WyTEW1I2ZI7oiA6xcXM38\nitK477V5QzJdNq5uZmBB3Zg5QFXpivSzfsp4elTzgkoWVJXaZKmZkQV1Y+aAA4PuhKOpK1+iRITW\n5jrLAWNmZBOlsxSJRHzLZzN5WVoa//baT3+//1WZX0qAoNQDlrI1N3Tt8zYdtfhfqYObLP3q4y9w\ncmSMqjL70zX+7ErdmDmgO9JPSZGwrmF+YJ225lomFHb0DIbYM5NrLKgbMwd0Rwa4YNk8KkqDD4uO\nrl+3cXUzHQvqxmSZqtId6Y/bdDTV4nnlNNZW2CYkMy0L6sZk2d4jJxk8Nea76WgqN1lqV+ommAV1\nY7IsGqRnulIHaG2p5eUjJ+k/OZLpbpkcZVPos1RTU+Nb7ndwRUmJ/8s7MJDY2+eggy/80gcEtTU0\nZDsQ57qufQNUlBZx/lL/361YbZPj6gO85nw7SczEsyt1Y7KsO9LPhY21lBTP/Od4UVP0zFIbgjH+\nLKgbk0Vj4xPs6B2YvAKfSW1lKSsXVdtkqQlkQd2YLPrVweOcGp046/i6mbQ219qVuglkQd2YLJrN\nJGlUa3MdBwdPc3DwVKa6ZXKYTZTOUktLi2/5bLbjDw8Pp1SvuDh+g0pQSoKg/pq5oSsywPyKEpYv\nrEr4PtGr+q59/bzxwmWZ6prJUYkcPN0iIk+IyC4R2SkiH/XK60XkURF5wfu8IPPdNSa/RDcdBeXu\n8XNhYy3FRWLJvYyvRIZfxoBPquo64DLgIyKyDvg08JiqrgYe8742Zk4TkatF5HkR2SMicb+zInKj\niPSJyDbv40OZ6sup0XGePzAUmJkxSEVpMRcsnWe51Y2vGYO6qu5X1U7v9hCwG2gCrgPu9qrdDbw1\nU500Jh1EpBj4GvAmYB3wHu8CZar7VXW993FHpvqza/8gYxM6q/H0qLaWWrojA5aF08SZ1USpiCwH\nNgDPAktVdb/3rQPA0oD7bBSRLSKypa+vL4WuGpOyS4E9qvqSqo4A9+EuTrIienzdbFa+RLU21zEw\nPMrLR06mu1smxyUc1EWkBvgu8DFVPSv3p7rLBd9LBlXdpKodqtqxeLHtgDNZ1QTsi/k64pVN9Xsi\n0i0iD4hI4Exzqhcs3ZEBFs8rZ9n8ilnfNzpkY0MwZqqEVr+ISCkuoN+jqg96xQdFpEFV94tIA3Ao\nU52cSxYtWpRw3aDJr9OnT6d0f7+33GNjY751m5ubE2oL/NMP+K20yXP/BtyrqqdF5H/ihhav9Kuo\nqpuATQAdHR2zHgfpivTT1lw7q0nSqPOXzqO8pIjuyADXrff7v2QKVSKrXwT4BrBbVb8Y863NwA3e\n7RuAH6S/e8akVQ8Qe+Xd7JVNUtUjqhr9r3sHcEkmOjJ4apQX+04kvJN0qtLiIi5snG+bkEycRIZf\nLgfeB1wZsyLgzcCtwBtE5AXg9d7XxsxlvwBWi8gKESkDrsddnEzy3nVGvQW3MCDtdkRmPr5uJq3N\ndezoGWRs3D/xmylMMw6/qOpTQND7w6vS2x1jMkdVx0TkZuARoBi4U1V3isgXgC2quhn4ExF5C24p\n71Hgxkz0JZq7pbVp9pOkUW0ttdz1s73s6TvOmmXBx+CZwmI7Sk1BUdWHgYenlH0u5vZngM9kuh/d\nkX7Oqa9iQXVZ0o8xebzdvgEL6maSBfVZqq6u9i3PxIRiUI50v4m1oDQBK1euTLi9ZCbsTHK6IwNs\nOCf5oReAFQurmVdeQlekn3e9wtJBGMcSehkTssPHT9PTP5z0JGlUUZFwcXOtpQswZ7GgbkzIzmRm\nTH48PaqtpY5fHhjk9Fj8clRTmCyoGxOyrn0DFMmZU4xS0dZcy+i4snu/HVtoHAvqxoSsO9LPeUtq\nqC5PfUorOlnatc/WqxvHgroxIVJVuiMDSSXx8tNQW8GimnJLF2Am2eqXWSotLU24bqoZ9Py27c+2\n7mxWv1jGv8yLHBvmyIkR2lLYdBRLRGizyVITw67UjQlRNPi2pWGSNKq1uY4X+45z/LR//h9TWCyo\nGxOi7kg/ZcVFad0s1NpSiypst6t1gwV1Y0LVFelnbcM8ykrS96cXXe9uyb0MWFA3JjQTE8qOnsG0\nTZJG1VeX0byg0sbVDWATpbMWdNBHUVH8/8eJidSy5wXlSPdrK0hQWgMTvpcOu3HvdGw6mqqtuc5W\nwBjArtSNCU3XPm+SNE0rX2K1Nte6lTXHEzuAxeQvC+rGhKQ70k9VWTGrFtek/bEnMzb22BBMobOg\nbkxIuiIDXNRUS3FR+rNhXtxci4hLw2sKmwV1Y0IwMjbBrv2DaV2fHqumvITzFtfYChhjQd2YMDx/\nYIiRsYmMjKdHtXqTpbYzuLDNuPpFRFqAbwJLAQU2qeqXReTzwB8CfV7Vz3qnyuS1ujr/P8rKysq4\nshMnTqTUVlOT/ynxfo9bU+M/Trt8+fKE27NDMjInujIl1Rzq02lrqeW7nRF6B07RVBf/+2gKQyJL\nGseAT6pqp4jMA7aKyKPe976kqn+Xue4Zkx+6I/0sqCqleUHmgu2Z4+36LagXsBmHX1R1v6p2ereH\ncKer+19CGmN8RTMzZvLd0NqGeZQWy+Sh1qYwzWpMXUSWAxuAZ72im0WkW0TuFJEFAffZKCJbRGRL\nX1+fXxVj8trJkTF+dXAoY5OkUeUlxaxZNt8mSwtcwkFdRGqA7wIfU9VB4HZgFbAe2A/8vd/9VHWT\nqnaoakfQbkxj8tnO3kEmlLSnB/DT2lzL9sgAExM2WVqoEkoTICKluIB+j6o+CKCqB2O+/3XgoYz0\ncI4ZHR31Lffb0p9qmgC/yVfwz50+PDzsWzeo3I+tmsiM6KlErS2ZvVIHNxF7z7O/4ddHTmRkk5OZ\n+2a8Uhc3CPgNYLeqfjGmvCGm2tuAHenvnjG5rzsyQENtBUvmVWS8reg/DhuCKVyJDL9cDrwPuFJE\ntnkfbwZuE5HtItINvA74eCY7akyu6o70ZySJl5/zFtdQWVo8mWfGFJ4Zh19U9SnAb8o+79ekG5Oq\n/pMj7D1yknd2tITSXklxERc12WRpIbMdpcZk0Jnj6zI/SRrV1lzHzt5BRsdTm9MxucmCujEZFL1i\nvjik4ReA1pY6To9N8KuDQ6G1aeYOOyRjlkpLS33L/bb0Hz9+3LfukiVLEmrr/PPPT7itqqoq37r1\n9fUJtVUoRORq4MtAMXCHqt465fvluLQYlwBHgHer6t5k2+uKDLByUTW1lf6/N5kQXQ/ftW+ACxvD\n+2di5ga7UjcFQ0SKga8BbwLWAe8RkXVTqn0QOKaq5wFfAv5vKm2GOUkadU59FXVVpTauXqDsSt0U\nkkuBPar6EoCI3AdcB+yKqXMd8Hnv9gPAV0VENIlF/AcHT3Fw8HQom45iiQgXN9Xy9EtHeLAzEmrb\nJnkrFlWz4RzfjfmzYkHdFJImYF/M1xHglUF1VHVMRAaAhcDhqQ8mIhuBjQDnnHNOXGOTk6QhbDqa\n6lWrFvLTFw7ziX/tCr1tk5z3XXauBXVjsklVNwGbADo6OuKu5K9as4QffeK1tNSHnzHxptes4ndb\nG5mwXcI5o6Y8PeE41KC+devWwyLysvflInyufvJAUs8rU9n7Ojo60vVQufDzOneG7/cAsQvGm70y\nvzoRESkBanETprNWVCSctyQ7W/WLioSWev/Jc5PfQg3qqjqZ0UtEtqhq2iLOXGHPa077BbBaRFbg\ngvf1wO9PqbMZuAF4GngH8Hgy4+nGZIsNv5iC4Y2R3ww8glvSeKeq7hSRLwBbVHUzLs/Rv4jIHuAo\nLvAbkzMsqJuC4h25+PCUss/F3D4FvDPsfhmTLtlcp74pi21nkj0vY0zWZC2oeysH8o49L2NMNonN\nARmTOhHpA172+VY2Vw1Z2/nT7rmxC02mY0HdmAzK5qoha7sw2p0q9OEXEblaRJ4XkT0i8umw208n\n78DtQyKyI6asXkQeFZEXvM+pbxELmYi0iMgTIrJLRHaKyEe98px/bsbku1CDeoIJlXLJXcDVU8o+\nDTymqquBx7yvc80Y8ElVXQdcBnzE+znlw3MzJq+FfaU+mVBJVUeAaEKlnKSqT+LWMse6Drjbu303\n8NZQO5UGqrpfVTu920PAblxOlJx/blmQzQlma7sw2j1L2EHdL6FSfHLw3LZUVfd7tw8AS7PZmVSJ\nyHJgA/AsefbcwpDNVUPWdmG0O5XlU88gb3t5zs5Ei0gN8F3gY6o6GPu9XH9uxuSrsIN6IgmVct1B\nEWkA8D4fynJ/kiIipbiAfo+qPugV58VzMyafhR3UJxMqiUgZLq/G5pD7kGnRhFB4n3+Qxb4kRVzK\nyG8Au1X1izHfyvnnFqZsrfQKWr0UYvvFIvKciDwUcrt1IvKAiPxSRHaLyKtCbPvj3mu9Q0TuFZGK\nsNqOo6qhfgBvBn4FvAj8Wdjtp/m53AvsB0Zx8wMfxB2o8BjwAvAjoD7b/Uzief0WbmilG9jmfbw5\nH55biK9hsfc7vhIoA7qAdSG13QC0e7fneX9vobTttfkJ4NvAQyG/5ncDH/JulwF1IbXbBPwaqPS+\n/lfgxjCfe+xH6Am91CehUq5S1fcEfOuqUDuSZqr6FBCU4D2nn1uIEjk6LyPUTWbv924PiUh09VLG\n2xaRZuAa4K9wwT0UIlILvAa4EUDd6rqRsNrHJUesFJFRoAroDbHts9hEqTGZMSdWek1ZvRSG/wf8\nL2AipPaiVgB9wP/3hn7uEJHqMBpW1R7g74Df4P6ZDqjqf4bRth8L6sbkqelWL2WovWuBQ6q6NdNt\n+SgB2oHbVXUDcIKQNsd5O6uvw/1jaQSqReS9YbTtx4K6MZmR1ZVeAauXMu1y4C0ishe3sfBKEflW\nSG1HgIiqRt+RPIAL8mF4PfBrVe1T1VHgQeDVIbUdx4K6MZmRtZVe06xeyihV/YyqNqvqctzzfVxV\nQ7liVdUDwD4RucAruooQ5hA8vwEuE5Eq77W/CrcLOyvs5CNjMkADjs4LqfnLgfcB20Vkm1f2WW+R\nQj77Y+Ae75/oS8AHwmhUVZ/CFxhqAAAID0lEQVQVkQeATlzepOfIYsqAgk+9KzK5TA9gGTCOm3AB\nuFQ11Bn0QCLcCrwXqFGlLqa8AvgWsB7X73er8hvve3+OWw0wBtysyo98HncVbmlmPfBz4AZVRkX4\nOG6J5l7gbV7ZFcA1qnwqU8/TGJOagh9+UeWIKutVWQ/8E/Cl6NfRgC6CiIT3Won4voP6AS5j4lQb\ngQOqnIfLgPk33mO0Am/HZcO8Brg94Dn8LXCbd/+TeEvCgHcDrcAW4PXefT+LW6pmjJmjCj6oBxHh\nPBF2iXAPsBNoEOG9ImwXYYcIf+3VKxGhP+Z+14twR8ztHSJ0ifBETP0vivBzEbpF+JBX/noRfizC\nQ8D2qf1R5WlcEq2pYjMn/ivwOzHl96oyosqLuHG/S6Y8x2Lc2t7veUWxmRcFNzxXhdtcdQOwWfXM\nczXGzD02pj69NcD7VdkiQjPwl0AHMAD8SIRrgf+Y5v5/AVyhykGRySGTjcAhVS4VoRx4RoTomtYO\nYF10+CRBk+uhVRkR4YTXVhPw45h60XXSv4gpWwwcVmV8Sh2A23Frm7txwzKf4cw/DGPMHGVX6tN7\nUZUt3u1XAo+rcliVUdw26NfMcP//Ar7pXY1HX+s3Ah8QYRsuaNYBq73vPT3LgJ4xqtylygZVbgD+\nFPgScK0I3xHh70UCd5waY7LIgvr0TiRQZ4Kzt9THJvL5Q9zV+nKgU4QFXt0Px4zbr1CdnKhNpL2p\nJtdDi1AGVHtDJImsk+4DFnnDML51vHco61V5CLft+924sfcrkuirMSbDLKgn7lngdSIs9CYyrwd+\nosoEcEyE1d5k4tti7rNSlWeA/w0cww1tPAJ8ODoZKsIFIlSm0K/YzInvgsmhnM3Ae0Qo81a4nAuc\ntdPPG3b5aUyf/TIv/iXw597tClyirwncWHvBEpFxEdnmZeX7jogk/XqIyBXRjIYi8pbpMjp6mQg/\nnEQbnxeRP020fJrHOZ6OdlMhIpeKyJNeBsxoSoDQfh9F5K1Bx3DOhdfTgnqCVInggvOPcVkLn1Hl\n371v34IL1j/DjUtHfUmE7biJzydU2QH8My7L4TYRduDGrmec2xDhi7jlhfNFiHjLFcGth20QYQ9w\nM26FCqp0Ad/HbYJ4GPfuYMJ7rEdEWOLd/1PALd79a3DnrkbbfAUwokq3V3Q/sAN4BfDoTH3Oc8Oq\nul5VL8Iljrop9pvizPrvS1U3q+qt01SpA2Yd1POFiCwFvgPcoqoXeCkB/gOXjTKR+5dM93WC3opb\nVTY3ZSs9pH3YRy5/AMdjbt8E/CNumO154Ju4FVPn4uZQnsZtTPkOUOPd52rgl175V/DS1OKWlH7V\nu70UtzKpy/t4NW77/TDuwuJvvXqfwk2AdwP/J6Zff4ZLu/sUbi/Cn/o8j88HlH8f985uJ7Ax9nnj\n5ld24vZ3LPbKV+GC61bcu781QY8P1AIvA0Xe19W4yf5S4E9wO0G7gft8+vUF4AsBP5N6r9/dwDNA\na0wf/gU3x3Wv9xpvBh4HfjLDa/h+r6zLe4xX484l/rX3M1iV7dczrq1s/3HYh33k4gdeUMe9y/oB\n8Ee4oD4BXOZ9bxHwJFDtfX0L8DncMNY+3AS54Jai+gX1+3HJuMDtSq312tgR04834t6tCe6d90O4\nCfxLcO8Qq4D5wJ6AYBMUhOq9z5W4d2cLva8V+APv9udi+voYsNq77S0qmPbxfwC8zrv9buAO73Yv\nUO7djsuHjsurcl3Az+QfgL/wbl8JbIvpw1bO5Du/EfeOOvocg17DC3H/FBdNeU3uAt4R0IesvJ6x\nH7ak0ZjkVMZswf8pLtdKI/Cyqj7jlV+Ge5v+Xy4lCGW4q/Y1uARQLwB4Sa82+rRxJe5KEVUdBwa8\njICx3uh9POd9XYP7ZzEP+J6qnvTamG3emT8RkehcS4v3mEdw/7Tu98q/BTzoZYN8NfAd73kClM/w\n+PfjgvkTuPmpf/TKu3Fb/b+Pu7qdjd8Cfg9AVR8XkYUiMt/73mZVHY6p+6iqHvVuB72GbcB3VPWw\n95hHSV6mX89JFtSNSc6wqq6PLfD+AGNXMAkueLxnSr2z7pciAf5GVf95ShsfS/oBRa7AZR58laqe\nFJEfc/aqrliKu7rtn/p6zGAz8NciUo97V/G4V34N7ir5d4E/E5GLVXUs5n47vfqzPUpx6sqyqT8n\nv9fwj2fZhq+QXs9JNlFqTOY8A1wuIucBiEi1iJyPG0tfLiKrvHpBJ2g9hhvWiZ77WQsMcfak4CPA\n//Cu7hCRJhFZghv2eauIVIrIPFyQTFQtcMwLQGs4Oz1FEfAO7/bvA0+py9X+axF5p9cHEZG26RpQ\n1eO4Mewv44aexr2J5RZVfQI3VFWLu2qO9VXgBhF5ZbRARN7uTaD+FPgDr+wK4LAmlkc+6DV8HHin\niCz0yuu9+lN/BjPJ+OsZy4K6MRmiqn248dt7RaQbb+hFVU/hhlv+XUQ6gUMBD/FR4HUish03JrxO\nVY/ghnN2iMjfqjth59vA0169B4B5qtqJe1vfBfyQs3cST/XnIhKJfuAm6ErEHYN3K+6fU9QJ4FIR\n2YEbHvqCV/4HwAdFpAt3NX1dAi/R/bgkddHhh2LgW97zeA74iqqelZZCVQ/ihmv+zlvSuBu303kI\nN958ifda38qZpb7TmuY13InLdfQT73lF0xjfB3zKW065yuchs/V6Apal0Rhj8opdqRtjTB6xoG6M\nMXnEgroxxuQRC+rGGJNHLKgbY0wesaBujDF5xIK6Mcbkkf8GdK4aqGOxV3UAAAAASUVORK5CYII=\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": "94f1f276-145c-4a78-cae5-b5e5f6c2e145",
        "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": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ksudxt9VcHPb0BA3nQ/8+6PljfGOWIKbUDcNIpZDldqOwJKS8YErdKCtE5GYR2S0iGyM+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\nRK5CYII=\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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\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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OCegz8A+PtKZ8jKvPmWFB3ZhcUtW7gLsAFi5cGHcnf9aME3jhhsXZLlZJKxOh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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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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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AeurGGLcDW73Jy2xOksbVNELXDjg06lZHjzkW1I0xblFMksbZJqSMsaBujHHraPaOnJt5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\npAAAAABJRU5ErkJggg==\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\ngeuINlR4ANgC/D9gTqXbOYTrejnR0EoT8Hj8eF01XFuGn+H4+Gf8BUAN0Agsy+jci4AXx89nxP/f\nMjl3fM6/A74L3JfxZ34H8Dfx8xpgdkbnrQWeAabE338fuDbLa89/ZJ7QywMJlUYrd78q5aWLM21I\nibn7I0Dartej+toyVMjWeWXh0WT2jvh5u5n1Ry+V/dxmthj4S+CzRJ17JsxsFvBK4FoAj6LrurI6\nP1FyxClm1g1MBVoyPPcxNFEqUh4jItJrQPRSFv4d+AegL6Pz9TsZ2AP8Rzz0c4uZZbJDibs3A58H\n/kT0y7TN3X+RxblD1KmLVKnBopfKdL5Lgd3uvr7c5wqYALwY+Lq7nwt0ktHiuHhl9WVEv1hOAKaZ\n2V9nce4Qdeoi5VHRSK+U6KVyuwB4vZltI1pYeJGZfSejc+eAnLv3/0VyL1Enn4VXA8+4+x537wZ+\nBLwso3MnqFMXKY+KRXoNEr1UVu7+cXdf7O51RNf7oLtncsfq7juB7WZ2Wlx0MRnMIcT+BJxvZlPj\nz/5iolXYFaGdj0TKwFO2zsvo9BcAbwM2mNnjcdkNcZBCNftb4K74l+jTwNuzOKm7/97M7gUaiPIm\nPUYFUwaMmdS7ZrwB+DHwIneeLKD+NqDe/dj8yGZ0uDO9iPMWVX+Q41wL/MI9OatuxpuAG4EXAee5\nsy7vtY8ThVr2Ah9w5+dx+UrgS0Qdzi3u3BQ47iTgTmAFsA94szvbzLgA+DpRdMFV7mwxYzZRKNdK\n98wnyUQkNpaGX64CHom/jkbXEk3ChGwE/gp4OL/QjGVEfwafQZRN8mtmjDcjkC2TULbM64Dn3Hkh\n8EXgX+PyDxPFrX8IeHdc9kngn9Whi1TWmOjUzZhOtKDmOvJ2hzfjQjN+Zca9Zjxpxl1mx8ZnmzHF\njJ+Z8c7AcT9ixlozmsz4p0HO/0UzNpnxgBnz47JzzHg0fu+PzTgurdyMK4B64C4zHjdjSv7x3dns\nzlOBU18GfM+dI+48A2wlip+OY6h52p3BsmXmZ2W8F7g4/nz6Y3GnAt1mnAIscedXaZ+BiGRjTHTq\nRJ3T/e78Adhnxoq8184luuNcRrT674K816YD/wnc7c638g9oxmuApUQd5DnACjNeGTj3NGCdO2cA\nvwY+HZffCXzUneXAhsHK3bkXWAe81Z1z3DlU4HWnxUoXGkN9tJ47PUAb0arSf4nb+XHgZqKFJp8s\nsE0iUkZjpVO/iuhulPhr/hDMf7uTi4cNHgfq8l77KfAf7twZOOZr4sdjRBMkpxN18gP1AffEz78D\nvNyMWcBsd34dl98BvDKtvOA/Qfl+AAAGSUlEQVSrzIg7j7tzvjuvIvpFuAMwM+4x4ztmSskrUilV\nH/1ixhzgIuAsM5xoYtDN+Ehc5Uhe9V6O/Ux+C6w047vuiTSzBvyLO98ssklZzkwPFitdSAx1//tz\nZkwAZhFNmAIQD8V8kmhI6ytEKwnrgA8AnyjJFYhIUcbCnfoVwLfdOcmdOneWECXfeUUB7/0U8BzR\npOJAPwfeEY/XY0atGccH6o2L2wDwFuARd9qA58yOtuFtwK/TyuPn7UTJmYqxGrjSjElmnEz0l8R/\nczSGmpPNGCyGOj8r4xXAgwN+uV0NrHFnP9H4el/8mFpkO0cdM+s1s8fjrHw/MLMhX7OZXdif0dDM\nXj9YRsc4E+F7h3COG83s7wstH+Q4HaU473CY2Xlm9nCcAbM/JUBmP3Nm9oa0bThHwuc5Fjr1q4hC\nGfP9kMKjYD4ITDHj/+YXuvMLokx0vzNjA9FEYqjT7QTOM2Mj0V8Mn4nLrwE+Z0YT0Zj885XfDnwj\nNFFqxuVm5ICXAv9lFoUturOJKMzwCeB+4H3u9Mbj4/0x1JuB78d1MeMzZrw+PvStwFwzthIlZ/pY\n3jmnEkXk9P/C+wJRorZ/B76R8llWk0Pufo67n0kU2vnu/BctUvT/L3df7e6J8NI8s4GiO/VqYWYL\ngB8AH3X30+KUAPdT4A2PmU0Y7PsCvQGC0WIjQ6XSQ+qhx2h+AB15z98NfI1o6OkpoknkTcBJRPMu\nvyOad/kBMD1+z0rgybj8y8Rpaol+Ud4cP19AdEPSGD9eRjQndIho/udzcb2PEP311QT8U167PkGU\ndvcRojTRfx+4jhtTyn8CrI+v4/r86yYKb91ElIZ5flx+ClHnuh74DXB62vGJhvGeBcbF308jmpCf\nSDR090R8Ld8LtOszwGdS/k3mxO1uAh4Flue14dtEw6l3x5/xauBB4NfP8xleHZc1xsd4GdG+xM/E\n/wanVPrzTJyr0v859NBjND6IO3WiOZifAu8h6tT7gPPj1+YRrR2YFn//UaIhvclxJ7aUaG7m+4Q7\n9XuIknFBNBc0Kz7Hxrx2vIZo9aIR/eV9H9Hk+gqi6KmpwEyicNZiOvU58dcpROsg5sbfO/DW+Pmn\n8tr6ALA0fv4SohQBgx3/p8Cr4udvBm6Jn7cAk+LniXzoRHlVLkv5N/kK8On4+UXA43ltWM+f851f\nSxTx1X+NaZ/hGUS/FOcN+ExuB65IaUNFPs/8R9VPlIqUyZS8Jfi/IRqqOgF41t0fjcvPJ/oz/bdR\nShBqiO7aTydKALUFIE56dX3gHBcR3Sni7r1AW5wRMF9+FBZEYbhLiYYjfuzuB+NzFJt35gNmdnn8\nfEl8zH0ko7l+FGeDfBnwg/g6ASY9z/HvIerMHyKa0/laXN5EtNT/J0R3t8V4OfBGAHd/0MzmmtnM\n+LXV7p4fCvxLd98fP0/7DM8GfuDue+Nj7mfoyv15HqVOXWRoDrn7OfkF8X/Azvwios7jqgH1jnnf\nMMVRWH5MFJaZfWjIBzS7kCjz4Evd/aCZ/Yror4sQJ7q7bR34eTyP1cA/m9kcor8qHozL/5LoLvl/\nAZ8ws7PcvSfvfZvi+sVupdg5yPdpn+HfFnmOoIw+z6PGwkSpSKU8ClxgZi8EMLNpZnYq0Vh6nZmd\nEtdLm7R/gGhYp3/fz1kko6DiKCyLo7Cs1syOJxr2eYOZTTGzGUSdZKFmAc/FHdDpRH9x9AtEc/kB\n4Bkze1PcBjOzswc7gbt3EI1hf4lo6Kk3nlhe4u4PEQ1VzYJE3qSbgWvM7CX9BWb2V/EE6m+At8Zl\nFwJ7vbA88mmf4YPAm8xsblw+J65fbCRa2T/PfOrURcrE3fcQjd/ebWZNxEMv7n6YaLjlv8ysAdid\ncogPAq8ysw1EY8LL3H0f0XDORjP7nEc77MRRWHY0CsvdG4j+rG8EfkbUgab5pJnl+h9EE3QTLNoG\n7yaiX0794mguGxjN9VbgOjNrJLqbDqWdGOge4K/58/DDeOA78XU8BnzZ3Vvz3+Duu4iGaz4fhzRu\nBv4nUUd7I7Ai/qxv4s/huIMa5DPcRLRa+tfxdfWnMf4e8JE4nPKUwCEr9XkCYyhLo4jIWKA7dRGR\nKqJOXUSkiqhTFxGpIurURUSqiDp1EZEqok5dRKSKqFMXEaki/x+sitd6HQAoAQAAAABJRU5ErkJg\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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O0zfgxjm0tvVMPKgDhFR1ZkNulMKY3LEO+FPfC+ZCoDeh+nSTV1rbek5st7SFt+HkC7tT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\nG2NMAbGgbowxBeT/A+tb9lFTAD+DAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gC+wvuejocYcpdcMwwhTCSZrEFiHlDFPqhmGE6e10JedmnZv/vua1gVSYszQHmFI3DCNM\nT6crOVdZANfbpDqYebbN1HNAWThKn3zyyYgstBQ/Th5ycgK0t6f/2rl169aILM6pWlER/S4NOUpr\namrS7j8u93rIoRnnRD7jjDPS7m/btm0RWZxjuKenJ+3rGiXC8UFX7Wjp1YXrs7kdnvqFc9Dmog7q\nBMVm6oZhRNmx0ZWaK4Q9PUnThXBwF/Q/X7g+yxBT6oZhRMlnut04bBFSTjClbkwoRORWEdkuIuti\njouI3CIim0SkW0QKqNVKiJ5OmNTgSs4VijmLoaLa7OpZYkrdmGh8m2h5xlReByzy27XA1wswptKj\nt9PlTw/4f/JG1SSYuxh6bbFaNpSFo3T79uiChVBxZxhbzvBTTjklItu3b1+wbcgBG8o5HjeG0OrR\nuALRlZWVEVlo5SiEHaU7d+4Mtl24cGFEduqppwbbfutb34rIQitSITzeYqGqD4nIghGaXAF8xxd/\neVREGpO1A8bc2e4/wNofZjbQYqLqSsy95EMZnb76+T1UV1awuDmD/OvNS2HNHfCbz2fU97im6UJY\ndFnWlykLpW4YOaQZ2JLyOeFlEaUuItfiZvPhL789m+HBz+ZjjPmnohoWZpaI9fofdlE/uZp7rnvp\n2E9eeBmsvG38PrdsuOj9ptQNo5io6nJgOUBHR0f0VeuMS+CTw2u2jxckI9PLwKFjPLvjADWVFRwd\nHKKmaozXOPty+KeJWss4N2GcptQN42R6gPkpn1u8bOyIgJSO6akQrOtxhS6OHh9i49a9tLY0jv0i\nFRPrmeUac5QaxsmsAN7to2CWAQMZ2dMnKF2J/pR9q2RUDGymbkwofHnGS4CZIpIAPgVUA6jqN4D7\ngNcDm4CDwHuLM9LxSfeWAU6bUcu+w4N0b+mHZacVe0gTjlGVuojcCrwB2K6qi73s08CfA0nj1w2q\nel++BjkaoWXvcdEvIeLyltfV1UVkoZQEAFOmRIsIDA4OBtumO7aqqvS/c+PyqYeicuLSF/T1RSek\nF1wQrngTum5cqoKx3Ee+GaE8Y/K4AtcVaDhlR3ein6ULprPv8DG6baZeFNIxv3ybcFzvl1V1id+K\nptANwygNduw7Qu/AYdpaGmhtaeTp7fs4eDQ8sTHyx6hKXVUfAsarC98wjALR7e3prS2NtLU0MKSw\nriecLM/IH9k4Sj/ol1HfKiLRVToeEblWRFaKyModOyZqqJJhlD9diQEqBBY3TzsR9dKd4jg1CkOm\nSv3rwJnAEtyijC/FNVTV5araoaods2bNyrA7wzBKne5EP4tm11NbU8Ws+kk0NUxmzRZT6oUmIw+W\nqp5Ipi0i/wH8LGcjyoDe3t6ILM5JGXLmxS1jDzkDBwbCzp/6+vqILM5BeOTIkYgslDs9lHc9blyh\na0LY2RuXUmDLli0R2bnnhqvehMYWN95QGoa4dAuh52iUPqpK15Z+LjtvzglZ2/xGc5YWgYxm6iIy\nL+XjlUAw451hGBODxJ5D7Dl47KTFRq0tjTy/+yB7DoQjs4z8kE5IYyiu9xIRWQIosBn4izyO0TCM\nEie56KgtRam3tbiEXt09A7zyLDO9FopRlXpMXG80RZ9hGBOW7sQANZUVnD33BfPZ4qRS39JvSr2A\nWJoAwzCypmtLP+c2TTspgde0ydWcMWuqpQsoMKbUDcPIiuNDyrqegRPmllTaWhotrLHAlM767Szo\n7u6OyOIiT0IpAeIiLkLFN+KiakL9xUWDhKJtQhEtcUUyQmkG4lIPhAp1hCJiALZu3RqRPf7448G2\noYIYcekWQve2cePGYNuLLrooKDdKl2d37OfA0ePBjIytLQ38ZHUPWwcOM7dhchFGN/GwmbphGFmR\nNK+EZupJRd9ls/WCYUrdMIys6E70M7WmkjNmRd8Az2+aRlWFmAmmgJhSNwwjK7oSAyxubqCyIrqw\nb3J1JWfNqadrizlLC4UpdcMwMubo4BAbevfSNj++wlHb/Aa6E/2xPhcjt5SFozRE3FL4kOOwoSFc\n9TyUgGzSpEnBtnFOzRCh3OuhZf5x6QtC8jjH8P79+9O+buje1q9fH2w7ffr0iCzunzbkxN21a1ew\nrTG+eHLrPo4eHzpp0dFw2loauePxLWzedZDTZ4b/L43cYTN1wzAyZs2JdLvhiZE7ZhkbC4kpdcMw\nMqZ7Sz/Tp9bQckr07TPJWXPqmFxdYXb1AmFK3TCMjOlODNDa0hBbyhCgqrKC85sabKZeIEypG4aR\nEQePDvL09n3BRUfDaW1pYF3vAIPHowvRjNxiSt0wjIxY17OXIQ0vOhpOW0sjh48N8fT2qOPeyC1l\nEf1SW1ubdttQ5Edcmb3+/ujr4imnxFbuixCKtIFwpExcSoEQoWX3cekLQsS9KoeeTVwxi8mTo0u+\nQ4U+IHxvoXswxhepNUlHI+lI7U70c+68aIoJI3fYTN0wjIzoSgzQ1DCZWfXhMN9UFsyYSv3kKsvY\nWABMqRuGkRHdif60ZukAFRVCa4s5SwuBKXXDMMZM/8GjPLfrIK3zR7enJ2ltaWRj3z4OHwubJY3c\nYErdMIwx030iM2N6M3XXtoHBIeWJvr35GpZBmThKE4lERBbnpAw5CeNykYeWzcc5+EJOwjiHZCgl\nQKivQ4cOBc8PpRmIaxtKl3D0aLgQcMiBG5d+IPQc4u431F8od7sxfkiaURY3j22mDm7BUvup6Qcc\nGGNj1Jm6iMwXkQdF5AkRWS8iH/by6SLyaxF52v+035JhTBC6EgOcMWsqDVPCE6IQ87xTtducpXkl\nHfPLIHC9qp4HLAOuE5HzgI8B96vqIuB+/9kwShoRuVxEnhSRTSIS+ZsVkatFZIeIrPHb+4sxzlKn\nO9E/JtMLuDe5tpYGK5iRZ0ZV6qrap6qdfn8fsAFoBq4AbvfNbgfenK9BGkYuEJFK4KvA64DzgHf6\nCcpw7lLVJX77ZkEHOQ7YOnCYbXuPjJjEK47Wlkae3XmAfYfTz2pqjI0xOUpFZAFwIfAYMEdV+/yh\nrcCcmHOuFZGVIrIybpGPYRSIi4FNqvqsqh4F7sRNTowx0DWGRUfDaW1pQBXW9pgJJl+krdRFpA74\nEfARVT3Jfa0ukXYwmbaqLlfVDlXtmDVrVlaDNYwsaQa2pHxOeNlw3iIi3SJyt4jMj7vYRJ2wdCf6\nqaoQzm8a+8rQF9LwmlLPF2lFv4hINU6hf09Vf+zF20Rknqr2icg8YHu+BjkaoWXocREtY4l+yVfb\nkDxUYCIU5RJ3flyUSih9QFxBj1AETygdQBxx1w3dR29vb9rXLTD/BdyhqkdE5C9wpsVLQw1VdTmw\nHKCjo2PClPXpTgxw1px6JleHi62MxPSpNcyfPsUWIeWRdKJfBPgWsEFVb0o5tAJ4j99/D3BP7odn\nGDmlB0idebd42QlUdZeqJmNOvwksLdDYxgWqSndigLYxLDoaTmtLo+VWzyPpmF9eClwFXJoSEfB6\n4EbgMhF5GniN/2wYpczvgUUicrqI1ADvwE1OTuDfOpO8CRcYYHie23WQgUPHMrKnJ2lraaCn/xA7\n90fXaxjZM6r5RVUfAeIy4L86t8MxjPyhqoMi8kHgl0AlcKuqrheRzwArVXUF8CEReRMulHc3cHXR\nBlyCdKVRvm40UsvbXXpOML7CyIKyWFFqGOmiqvcB9w2TfTJl/+PAxws9rvFCd2KASVUVnDWnPuNr\nLG5uQAS6tgyYUs8DZaHU6+ujf2BxS9YPHjyY9nWfe+65iCwugmcsjtKQUzO0lD6UOgDCKRDGcr9j\nyae+f3+4qMHMmTMjsric7iEncFwaB6O06U70c37TNKorM08bVTepioWz6sxZmicsoZdhGGkxeHyI\ndT17s7KnJ2ltaaQ7MRD8wjeyw5S6YRhpsWnHfg4dO86S+dkr9SXzG9h14Cg9/eFEdEbmmFI3DCMt\nun0YYjZO0iS2CCl/mFI3DCMt1iT6qZ9cxYIZ0XTOY+WcefVUV4ol98oDptQNw0gLV76ugYqKuAjn\n9JlUVcm586admP0buaMsol92794dkcUVswhFg7S3twfbPvXUUxHZ1772tWDbUPRKXV1dsG3IORRa\nYt/d3R08/yUveUlEFlckIxS9EpdSIDTe+fPDqU+uuuqqtM6H8P2GUjsYpcvhY8fZ2LePP3/FGTm7\nZmtLA/es7mVoSHPyRWE47D/LMIxR2dC3l8EhpS0H9vQkrS2N7DsyyLM7D+TsmoYpdcMw0iDp0MxF\nOGOSZJGNri1mV88lptQNwxiVrkQ/M+smMa8h/aydo7Fwdh21NZW2CCnHmFI3DGNUuhMDtLU0xK5G\nzoTKCmFxUwNdFtaYU8rCURpash7ntBsYiP4B9feHZwqhPODXX3/9GEdXnoRSAsQt/Z8zJ5rfI+Tc\nNkqT/UcGeWbHft7Y2pTza7e2NPCdR5/j6OAQNVU2x8wF9hQNwxiRtYkBVKE1ixzqcbTOb+To4BBP\nbduX82tPVEypG4YxIkmbd1sOnaRJktE0tggpd5hSNwxjRLoTA8yfPoXpU6PlDrPl1Om1NNZW2yKk\nHGJK3TCMEelK9Oc0lDEVEXHl7WymnjNMqRuGEcuu/UdI7DmU00VHw2lraeDp7fs5dNRy7OeCUaNf\nRGQ+8B1gDqDAclW9WUQ+Dfw5sMM3vcFXlSk4oZQAcQUqQvK4YhQh4vI/x6UlCBEKC0tXFtdXXOTJ\nWELQ4tIHhFi2bFlE1tfXF2w7eXI0tjmu+IZRWuRj0dFwWlsaOT6krO8doGPB9Lz1M1FI5794ELhe\nVTtFpB5YJSK/9se+rKpfzN/wDMMoJl2JfkRcCbp8kXwLWLOl35R6Dkin8HQf0Of394nIBqA53wMz\nDKP4dCcGWDirjrpJ+VvSMnvaZOZOm2y51XPEmGzqIrIAuBB4zIs+KCLdInKriJwSc861IrJSRFbu\n2LEj1MQwjBJEVX263fyZXpK0tjRYuoAckbZSF5E64EfAR1R1L/B14ExgCW4m/6XQeaq6XFU7VLUj\nrmizYRilR+/AYXbuP0pbHhYdDadtfiObdx1k4GA0BbUxNtJ6pxKRapxC/56q/hhAVbelHP8P4Gd5\nGWEaPPzwwxFZXH7xffuiK9fiHHwh4hyPlZWVaV8jW0K5yAudnzzk6Ny6dWuwbShlw7333pvzMRm5\npdtnTyzUTB2gu6efly+yyV82jKoJxGmxbwEbVPWmFPm8lGZXAutyPzzDMIpFV2KA6krh3Hn1ee+r\ntdlqluaKdGbqLwWuAtaKyBovuwF4p4gswYU5bgb+Ii8jNAyjKHQn+jln7jQmVeX/LbShtpoFM2ot\nt3oOSCf65REgZHMoSky6YRj5Z2hIWZsY4E1Lcp+ZMY7WlkYe/4Nl78wWW1FqGEaEP+w6wL4jg7TN\nz789PUnb/Ea27j3M9r2HC9ZnOWJK3TCMCPnMzBjHCxkbza6eDWVRJOPzn/98RLZx48Zg21D0ywUX\nXJDzMZU711xzTUQWKpwBUF8fdbQ1NOQ/TC6EiFwO3AxUAt9U1RuHHZ+ES4uxFNgFvF1VNxd6nMWm\na8sAtTWVLJwdLjaTD85vaqCyQuja0s9l50ULqxjpYTN1Y8IgIpXAV4HXAefhnP3nDWv2PmCPqi4E\nvgz8a2FHWRp0JfpZ7JVsoZhSU8mi2XWWsTFLymKmbhhpcjGwSVWfBRCRO4ErgCdS2lwBfNrv3w18\nRURE4zK5jcD2vYd5ZNPO7EZcBFThid69XLXstIL33dbSyM/X9fHjzkTB+y42p8+cyoWnBhfmjwlT\n6sZEohnYkvI5Abworo2qDorIADADiGhnEbkWuBbg1FNPjXT21Lb9/O0PunIy8GKw7IwZBe/zxWfO\n4K6VW8b1c8uUq5adZkrdMIqJqi4HlgN0dHREZvJLTzuF33z0kkIPKyfUVFUwryFaeD3fXLGkiYtO\nn87g8fRTWZcLuUqaVlClvmrVqp0i8pz/OJPA7KeUuOqqqzI5reTvK0PGw32NZi/oAeanfG7xslCb\nhIhUAQ04h+mYmVJTyWkzpmZy6oRFRGhuLPyXSTlRUKWuqieSOojISlXtKGT/hcDuq6T5PbBIRE7H\nKe93AO8a1mYF8B7gd8BbgQcysacbRrEw84sxYfA28g8Cv8SFNN6qqutF5DPASlVdgctz9F0R2QTs\nxil+wxg3mFI3JhS+5OJ9w2SfTNk/DLyt0OMyjFxRzDj15UXsO5/YfRmGUTSKptR95EDZYfdlGEYx\nEfMBGUb2iMgO4LnAoWJGDVmZNkXNAAAN5ElEQVTf5dPvaamBJiNhSt0w8kgxo4as74nR73AKbn4R\nkctF5EkR2SQiHyt0/7nEF9zeLiLrUmTTReTXIvK0/5n9ErECIyLzReRBEXlCRNaLyIe9fNzfm2GU\nOwVV6mkmVBpPfBu4fJjsY8D9qroIuN9/Hm8MAter6nnAMuA6/3sqh3szjLKm0DP1EwmVVPUokEyo\nNC5R1YdwscypXAHc7vdvB95c0EHlAFXtU9VOv78P2IDLiTLu760IFNPBbH1PjH5PotBKPZRQqbnA\nY8g3c1S1z+9vBcZ1YmgRWQBcCDxGmd1bIShm1JD1PTH6HY7lU88jfnn5uPVEi0gd8CPgI6q6N/XY\neL83wyhXCq3U00moNN7ZJiLzAPzP7UUeT0aISDVOoX9PVX/sxWVxb4ZRzhRaqZ9IqCQiNbi8GisK\nPIZ8k0wIhf95TxHHkhEiIrgcKBtU9aaUQ+P+3gpJsSK94qKXCth/pYisFpGfFbjfRhG5W0Q2isgG\nEXlxAfv+G/+s14nIHSIyuVB9R1DVgm7A64GngGeATxS6/xzfyx1AH3AM5x94H66gwv3A08B/A9OL\nPc4M7utlONNKN7DGb68vh3sr4DOs9H/jZwA1QBdwXoH6nge0+/16//9WkL59n38LfB/4WYGf+e3A\n+/1+DdBYoH6bgT8AU/znHwBXF/LeU7eCJ/TSQEKl8YqqvjPm0KsLOpAco6qPAHHFKcf1vRWQdErn\n5QV1zuw+v79PRJLRS3nvW0RagD8CPotT7gVBRBqAVwBXA6iLrjtaqP5xyRGniMgxoBboLWDfJ2GO\nUsPIDyUR6TUseqkQ/Bvw90ChSxedDuwAbvOmn2+KSEEqlKhqD/BF4Hncl+mAqv6qEH2HMKVuGGXK\nSNFLeervDcB2VV2V774CVAHtwNdV9ULgAAVaHOdXVl+B+2JpAqaKyJ8Vou8QptQNIz8UNdIrJnop\n37wUeJOIbMYtLLxURP6zQH0ngISqJt9I7sYp+ULwGuAPqrpDVY8BPwZeUqC+I5hSN4z8ULRIrxGi\nl/KKqn5cVVtUdQHufh9Q1YLMWFV1K7BFRM72oldTAB+C53lgmYjU+mf/atwq7KJglY8MIw9oTOm8\nAnX/UuAqYK2IrPGyG3yQQjnz18D3/Jfos8B7C9Gpqj4mIncDnbi8SaspZsqAYoXdRMOCdC7onaDP\ngK4CvQ/0rAyu0wj6VyMc/zDoOtD1oB9JkbeB/g50Leh/gU7Lxfmg1aC3e/kG0I+nnPM3/jrrQO8A\nnezlAvpZ0Kf8OR/y8o+CrvHbOtDjoNP9sc2+jzWgK1P6eJvvYwi0I0U+A/RB0P2gX0mR16f0sQZ0\nJ+i/DXsGbwHV5PVAa0Bv8/13gV7i5bWg94Ju9GO4MeUaX07p4ynQfi8/2//+u0Ff7GVVoP8NWlvs\nv1PbbCv1regDUD2hxH4H+oEUWRvoyzO41gLQdTHHFntlWJuiKBb6Y78HfaXfvwb0n3NxPui7QO/0\n+7Ve+S4AbQb9A6iPbdUfgF7t998L+h3QCv95dmAsbwR9IOXzZtCZgXbnekX5P8OU+lTQl4F+IFWp\nB85fBfqKlM/1oA+BPpqi1K8DvS05Vn9Ohb/fV3l5DejDoK8L9PHXoLf6/Zv8uFpAf5Ry/Opi/53a\nZtt42ErFpv4q4Jgq30gKVOlS5WERRIQviLBOhLUivB1AhDoR7heh08uT2R5vBM4UYY0IXxjWz7nA\nY6ocVGUQ+A3wx/7YWcBDfv/XwFsC48zkfAWmirg4VlzsbDISwce2UsXJsa1/CXxG1YWFqQaX478T\nt/hpRFTZoMqTAfkBVR4BDsedK8JZwGzg4RTxPwP/Ouy884AHUsbaD3T45/Sglx/FvZ62jHIvyTjf\nWuCYCI3AG4HvjHavhmGUjqN0MRAXBvXHwBKgDedl/oII83BK5UpV2nFfCl8SQXBhTM+oskSVjw67\n1jrg5SLMEKEWt0oyGaGwnhfSAL+NkyMXsjn/blx4VR/OofJFVXarEohtJRnbeibwdhFWivBzERal\nDsL3fTkuuiGJAr8SYZUI1wbGngnvAO5SdYm7RGgH5qty77B2XcCbRKgS4XRgKcOeX4pyvn+Y/DRc\nKNgDXvRV4Abc6sDPAf8EfC75BWcYxsiUilIfiZcBd6hyXJVtuNnxRbgVj58ToRu3ZL2ZUVLBqrIB\nN8v8FfAL3PL34/7wNcBfibAKt7Q6shotw/Mv9m2acMrrehHOECEQ20oyUmAScFiVDuA/gFuHDeWN\nwG9VT8rl/jL/Bfc64DoRXjHSs0iTd+Bn0CJUADcB1wfa3YoLKVuJW3zy/3jhueDfRO4AblHl2UAf\nd6u69qo8r8olqrwYOIib2W8Q4bsi3OXfHgzDiKFUlPp63OxuLPwpMAtYqsoSYBswahIdVb6lylJV\nXgHsweXFQJWNqrxWlaU4BfRMjs5/F/ALVY5508RvgQ5OxLayQ5Xhsa0J/xngJ0DrsGGcULYp4+rx\nP7f7cy4e7VmMhAhtQJXqiTeoetwb1f+IsBlXEWmFCB2qDKryN/7t6AqgMflcPMuBp1X5t0BXkXtJ\n4bPAPwIfAr6JW6n4qWzuyzDKnVJR6g8Ak1LNBiK0ivBynD337SJUijALl9/hcaAB2K7KMRFeBZzm\nT92HU0BBRJjtf56KM+18f5i8AqdIvpGj858HLvXHpuKU4UZOxLZS681GqbGtP8WZlABeSYqCFKHB\ny+5JkU0Vcffs+3gtzlSUDSfZ7FUZUGWmKgtUWQA8CrxJlZX+Hqb6/i8DBlVdjLAI/wf3u/rI8A5E\nOAc4Bfhd4NgrgV5VnsbZ14f8VpvlfeUEETkuImt8Vr4fikjG4xKRS5IZDUXkTSNldPSZCP8qgz4+\nLSJ/l658hOvsz0W/2SAiF4vIQz4DZjIlQMH+LkTkzXFlOEvheZaEUvc22yuB14jwjAjrgX/BVdf5\nCS5bYBdO+f+9KluB7wEdIqwF3o1TlKiyC/itd6wOd5QC/EiEJ4D/Aq5Tpd/L3ynCU/46vcBtACI0\niZyUgGxM5+NsxHX+nn4P3KZKtyqP4eztncBa3O8iGdt6I/AWf2//Arw/pf8rgV+pciBFNgd4RIQu\n3Bfevar8wo//ShESwIuBe0X4ZfIkP+O+CbhahIQIqX+of0IajljPbKBThA3AP+BipBGhBfgEzpHa\n6Z3XqffyDuDOpM0+ZVyC+2L8Zy9aDtwM3IvzQ5QCh1R1iaouxpnaPpB6UBxj/v9S1RWqeuMITRqB\nMSv1ckFE5gA/BP5BVc9WlxLgF4wwkRt2ftVIn9PkzUBQqZcExQ6/sc228bgB+1P2PwB8DVgAPImL\n1FmPe3t8Le5NpBOnjOr8OZfjJgCdwC34NLW4LINf8ftzcJOaLr+9BLf8/hDOn/MF3+6juAlDN/C/\nU8b1Cdxb3iO4L+i/C9zHp2PkP8UFL6wHrk29b+DLXn4/MMvLz8Qp11W4t+tz4q6Pe3N7DvAhu0zF\nJT+rxpnanvD3cmdgXJ8BPhPzO5nux92Ne5NsTRnDd3Gmzzv8M16BmyT+ZpRn+G5emFR+1/8OduNS\n7a4Bziz284z0Vex/DttsG48bXqnjwlLvwYWhLsCZiJb5YzNxYa5T/ed/AD6J8/1sARbhHP4/IKzU\n78Il4wK3KrXB97EuZRyvxb3JCO5t72c4E+VS3BtgLTAN2BSjbOKUkF/UxhScKW+G/6zAn/r9T6aM\n9X5gkd9/ES5FwEjXvwfwaxh4O/BNv98LTPL7kXzoOF/TFTG/k38HPuX3LwXWpIxhFS/kO78a57dK\n3mPcMzwf96U4c9gz+Tbw1pgxFOV5pm6WJsAwMmNKyhL8h3G5VpqA51T1US9fhntN/61LCUINbtZ+\nDi4B1NMAPulVKAz1UtxMEVU9Dgz4jICpvNZvq/3nOtyXRT3wE1U96PsYa96ZD4nIlX5/vr/mLtyX\n1l1e/p/Aj302yJcAP/T3CS6CayTuwinzB3FmuK95eTduqf9PcbPbsfAy/PoQVX1ARGaIyDR/bIWq\nHkpp+2tVTUaPxT3DNuCHqrrTXzM12mys5Pt5nsCUumFkxiFVXZIq8P+Aqb4OwSmPdw5rd9J5WSLA\nv6jq/x3WR8QxnfYFRS7BRWe9WFUPisj/EB9ZprjZbf/w5zEKK4DPich03FtFcp3CH+FmyW8EPiEi\nF6jqYMp5yUi5sZZSPDDC57hn+Ndj7CNIgZ7nCUrCUWoYZcqjwEtFZCGAiEwVkbNwtvQFInKmbxdX\nQet+nFknWfezgWh01y+Ba/zsDhFpFpHZOLPPm0VkiojU45RkujQAe7wCOgf3xpGkAnir338X8Ii6\nXO1/EJG3+TGIiLSN1IGq7sfZsG/GmZ6Oe8fyfFV9EGeqasDNmlP5CvAeEXlRUiAif+wdqA/jQp2T\ninSnppdHPu4ZPgC8TURmePl0337ECLsAeX+eqZhSN4w8oao7cPbbO0SkG296UdXDOHPLvSLSCcE0\nEAAfBl4lImtxNuHzVNVHd8k6EfmCugo73wd+59vdDdSraifutb4L+DlOgcbxjyKSSG44B12VuDJ4\nN+K+nJIcAC4WkXU489BnvPxPgfeJSBcnr64eibuAP+MF80Ml8J/+PlYDt6hqf+oJqroNZ675og9p\n3AD8L5yi/TSw1D/rG3mhSPqIjPAM1+PWSvzG31cyjfGdwEd9OOWZgUsW63kCIN74bhiGYZQBNlM3\nDMMoI0ypG4ZhlBGm1A3DMMoIU+qGYRhlhCl1wzCMMsKUumEYRhlhSt0wDKOM+P+PwQA1xxK6FAAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n5Zw5c9I+biidvr29Pdj2wIEDKbK+vvByWVE12YcTqoUO0N/fn3bb3t7eFFl3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\nMKNuGIYRI/4HqDQfs/6p1z4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JLJdRsiO7GbPNeNyM+81YasZdZgeOiTZjmBn/ZRb++Ts99xkznjOj1Yy/76L8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\nRBqIgrqISANRUBcRaSD/H6NaaRUSk+A5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9YUrdMIyCYW1rJ6rpqcwYxTFnaY7Y1U2pGyMKEXmniGwUkc0icl3g/VEicq9/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\nhmEYBYQpdcMwjALClLphGEYB8f8BjjTfUbHx6IcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CLNUvKxPaGpIHvk3xizSoq+pkRi8R2ayqXVGePwr2uma1XwCrRWQVLnjfAPze\nlDqbgBuBx4G3AQ9n0p9uTKFY94spGb6P/GbgR7gpjXeo6g4R+QKwWVU34fIc/ZOI7AIO4wK/MXOG\nBXVTUvyWiw9OKftcwv1TQHFNzjclpZDz1DcW8Nz5ZK/LGFMwBQvqfuZA0bHXZYwpJLExIGOyJyID\nwIsBDxVy1pCdu3jOe27iRJPpWFA3Jo8KOWvIzl0a550q8u4XEblGRJ4RkV0i8umoz59LfsPtAyKy\nPaGsQUR+LCLP+a/ZLxGLmIi0icgjIvK0iOwQkY/48jn/2owpdpEG9RQTKs0ldwLXTCn7NPCQqq4G\nHvLfzzVjwCdUdS1wBfBh/3MqhtdmTFGL+kp9MqGSqo4A8YRKc5KqPoqby5zoeuAuf/8u4C2RNioH\nVHWvqnb7+0eBnbicKHP+tRVAIQeY7dylcd6zRB3UgxIqtUTchnxbrqp7/f19wPJCNiZbIrISWA88\nSZG9tigUctaQnbs0zjuV5VPPI7+8fM6ORIvIQuC7wEdVdTjxsbn+2owpVlEH9VQSKs11+0WkCcB/\nPVDg9mRERCpxAf1uVb3fFxfFazOmmEUd1CcTKolIFS6vxqaI25Bv8YRQ+K8/KGBbMiIigsuBslNV\nv5Tw0Jx/bVEq1EyvsNlLEZ6/XESeEpEHIj5vvYjcJyK/FJGdIvLyCM/9Mf9ebxeRe0RkXlTnTqKq\nkd6ANwHPAs8Dfxz1+XP8Wu4B9gKjuPGB9+M2VHgIeA74CdBQ6HZm8Lpeieta6QW2+tubiuG1Rfge\nlvvf8fOAKqAHWBvRuZuATn+/1v+9RXJuf86PA98CHoj4Pb8L+IC/XwXUR3TeFuBXQI3//p+B90b5\n2hNvkSf00oCESnOVqr4z5KHXRtqQHFPVxwAJeXhOv7YIpbJ1Xl6oG8ze6+8fFZH47KW8n1tEWoFr\ngT/HBfdIiEgd8CrgvQDqZteNRHV+XHLEGhEZBeYD/RGe+yw2UGpMfsyKmV5TZi9F4f8C/xOYiOh8\ncauAAeD/+a6f20VkQRQnVtU+4K+BX+P+mQ6p6r9Hce4gFtSNKVLTzV7K0/muAw6o6pZ8nytABdAJ\n3Kaq64HjRLQ4zq+svh73j6UZWCAi74ri3EEsqBuTHwWd6RUyeynfrgTeLCK7cQsLrxaRb0Z07hgQ\nU9X4J5L7cEE+Cq8DfqWqA6o6CtwPvCKicyexoG5MfhRsptc0s5fySlU/o6qtqroS93ofVtVIrlhV\ndR+wR0Qu9EWvJYIxBO/XwBUiMt+/96/FrcIuCNv5yJg80JCt8yI6/ZXAu4FtIrLVl33WT1IoZv8D\nuNv/E30BeF8UJ1XVJ0XkPqDI97kdAAAIAUlEQVQblzfpKQqYMqDkU++KTE7TA1gBjOMGXAAuV410\nBD2UCLcA7wIWqlKfUD4P+CawDtfud6jya//Yn+BmA4wBN6vyk4Djno+bmtkA/BdwoyqjInwMN0Vz\nN/BWX3YVcK0qn8zX6zTGZKfku19UOaTKOlXWAf8AfDn+fTygiyAi0b1XIoGfoH6Ay5g41QZgnyoX\n4DJg/qU/Rjvw27hsmNcCt4W8hr8CvuiffwI/JQx4B9AObAZe55/7WdxUNWPMLFXyQT2MCBeI8LQI\ndwM7gCYR3iXCNhG2i/AXvl6FCIMJz7tBhNsT7m8XoUeERxLqf0mE/xKhV4QP+PLXifBTER4Atk1t\njyqP45JoTZWYOfGfgd9MKL9HlRFVnsf1+1025TWW4+b2fs8XJWZeFFz33Hzc4qobgU2qZ16rMWb2\nsT716a0B3qPKZhFagT8DuoAh4CciXAf82zTP/1PgKlX2i0x2mWwADqhyuQjVwBMixOe0dgFr490n\nKZqcD63KiAjH/blagJ8m1IvPk/5FQtlS4KAq41PqANyGm9vci+uW+Qxn/mEYY2Ypu1Kf3vOqbPb3\nXwY8rMpBVUZxy6BfNcPz/wP4hr8aj7/XbwDeJ8JWXNCsB1b7xx5PM6DnjSp3qrJelRuBPwK+DFwn\nwndE+BuR0BWnxpgCsqA+veMp1Jng7CX1iYl8Poi7Wl8JdIuw2Nf9UEK//SrVyYHaVM431eR8aBGq\ngAW+iySVedIDQKPvhgms4z+hrFPlAdyy73fg+t6vyqCtxpg8s6CeuieB14iwxA9k3gD8TJUJ4IgI\nq/1g4lsTnnOeKk8A/ws4guva+BHwofhgqAgXilCTRbsSMyf+Lkx25WwC3ilClZ/hci5w1ko/3+3y\n84Q2B2Ve/DPgT/z9ebhEXxO4vvaSJSLjIrLVZ+X7johk/H6IyFXxjIYi8ubpMjr6TIQfyuAcnxeR\nP0q1fJrjHMvFebMhIpeLyKM+A2Y8JUBkv48i8pawbThnw/tpQT1FqsRwwfmnuKyFT6jyQ//wp3DB\n+j9x/dJxXxZhG27g8xFVtgP/iMtyuFWE7bi+6xnHNkT4Em564SIRYn66Irj5sE0i7AJuxs1QQZUe\n4Pu4RRAP4j4dTPhj/UiEZf75nwQ+5Z+/ELfvavycLwVGVOn1RfcC24GXAj+eqc1F7qSqrlPVS3CJ\no25KfFCctP++VHWTqt4yTZV6IO2gXixEZDnwHeBTqnqhTwnwb7hslKk8v2K671P0FtysstmpUOkh\n7Wa3uXwDjiXcvwn4e1w32zPAN3Azps7FjaE8jluY8h1goX/ONcAvffmt+DS1uCmlX/X3l+NmJvX4\n2ytwy+9P4i4s/srX+yRuALwX+N8J7fpjXNrdx3BrEf4o4HV8PqT8+7hPdjuADYmvGze+sgO3vmOp\nLz8fF1y34D79rQk7PlAHvAiU+e8X4Ab7K4E/xK0E7QW+HdCuLwBfCPmZNPh29wJPAO0Jbfgn3BjX\nPf493gQ8DPxshvfwPb6sxx/jFbh9iX/lfwbnF/r9TDpXof847Ga3uXjDB3Xcp6wfAH+AC+oTwBX+\nsUbgUWCB//5TwOdw3Vh7cAPkgpuKGhTU78Ul4wK3KrXOn2N7QjvegPu0JrhP3g/gBvAvw31CnA8s\nAnaFBJuwINTgv9bgPp0t8d8r8Pv+/ucS2voQsNrf95MKpj3+D4DX+PvvAG739/uBan8/KR86Lq/K\n9SE/k78F/tTfvxrYmtCGLZzJd/5e3Cfq+GsMew8vxv1TbJzyntwJvC2kDQV5PxNvNqXRmMzUJCzB\n/zku10oz8KKqPuHLr8B9TP8PlxKEKtxV+xpcAqjnAHzSqw0B57gad6WIqo4DQz4jYKI3+NtT/vuF\nuH8WtcD3VPWEP0e6eWf+UETiYy1t/piHcP+07vXl3wTu99kgXwF8x79OgOoZjn8vLpg/ghuf+ntf\n3otb6v993NVtOl4J/A6Aqj4sIktEZJF/bJOqnkyo+2NVPezvh72HHcB3VPWgP+ZhMpfv93OSBXVj\nMnNSVdclFvg/wMQZTIILHu+cUu+s52VJgL9U1X+cco6PZnxAkatwmQdfrqonROSnnD2rK5Hirm4H\np74fM9gE/IWINOA+VTzsy6/FXSX/FvDHInKpqo4lPG+Hr5/uVopTZ5ZN/TkFvYf/I81zBIro/Zxk\nA6XG5M8TwJUicgGAiCwQkZfg+tJXisj5vl7YDloP4bp14vt+1gFHOXtQ8EfAf/NXd4hIi4gsw3X7\nvEVEakSkFhckU1UHHPEBaA1np6coA97m7/8e8Ji6XO2/EpG3+zaIiHRMdwJVPYbrw/4Krutp3A8s\nt6nqI7iuqjrcVXOirwI3isjL4gUi8tt+APXnwO/7squAg5paHvmw9/Bh4O0issSXN/j6U38GM8n7\n+5nIgroxeaKqA7j+23tEpBff9aKqp3DdLT8UkW7gQMghPgK8RkS24fqE16rqIVx3znYR+St1O+x8\nC3jc17sPqFXVbtzH+h7gXzl7JfFUfyIisfgNN0BXIW4bvFtw/5zijgOXi8h2XPfQF3z57wPvF5Ee\n3NX09Sm8RffiktTFux/KgW/61/EUcKuqnpWWQlX347pr/tpPadyJW+l8FNfffJl/r2/hzFTfaU3z\nHu7A5Tr6mX9d8TTG3wY+6adTnh9wyEK9n4BlaTTGmKJiV+rGGFNELKgbY0wRsaBujDFFxIK6McYU\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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iUgJUAHvHUpgJZcXMm566VQJNdpk1ZXymiwCkOair6vEVvURkraouS+f108Fe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\nWFA3xpg88v8B9YD0yeJlNQ8AAAAASUVORK5CYII=\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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bhFTipDJTPxt4D7BKRFZ62aeAy0RkIS7McSMQjiE0DCNjOny63flN+Yt8iael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\nUTcMw4gQ/x8Wq221YTbqPQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pGT/1UFc3j61q47K6GoqLMlwErDqps/SYszK+dn/DNqgbUyDVQHPS/RaflhLUReRqXG2e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\nBRxviP76GRacAAAAAElFTkSuQmCC\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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Wv3nCp7GgbszRGoBtMY8jPi0hqIvIFbjWPEuWLEk8064XXH96GkF9eES58rZ1/MXyedzw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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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pXwV8DLhuxPTF/yEi20WkCXgH8McZa5UxxuTA9tZuVN0gZhRqq8o4eXqI144c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\nxkwhFtSNMWYK+f9GtO4PAlKSpgAAAABJRU5ErkJggg==\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": "7dd98cec-4d52-4fb3-de49-eb95ae74e2f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        }
      },
      "source": [
        "// Making a new Image minibatch instead of modifying the originalal 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": 25,
      "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": 25
        }
      ]
    },
    {
      "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": "8faa6d13-28b1-4def-9366-66dd779b0af5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "let predictionsSingle = classifier(image)\n",
        "print(predictionsSingle[0])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.0175981e-12, 1.7299041e-15,  4.724373e-12, 2.7910443e-11, 2.0506177e-10,  0.0021678507,\r\n",
            "  1.622685e-11,  0.0013498362, 2.7563726e-10,    0.99648225]\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKFvNUgjzE-A",
        "colab_type": "code",
        "outputId": "38a85b70-e251-46c8-9875-ec08350dcab0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "plotImageStatistics(index: 0, images: image, predictions: predictionsSingle, correctLabels: lbl)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+IC1tqYOOjo6i6pVWVlK90kaTJE3df+ONNxLzzpgxoyAtbQROUh0OOih5ukLScgevvZY8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\n51wV8aDunHNVxIO6c85Vkf8PNY22aCn2HJcAAAAASUVORK5CYII=\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": "f72da670-6421-44c1-f5fc-3559763753d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predictionsSingle[0].argmax()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "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 first 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"
      ]
    }
  ]
}