projectbook_v6 UNET.ipynb

projectbook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "projectbook_v6 UNET.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "swift",
      "display_name": "Swift"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/magnuskahr/cdaadb17193e21404faedfa74c8e8dc9/projectbook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZRlD4utdPuX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import Foundation"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uthWm9IJvG_Z",
        "colab_type": "text"
      },
      "source": [
        "# Setup\n",
        "\n",
        "First we will need to set up a bunch of different structures.\n",
        "\n",
        "* A `Point` contains coordinates for a corner in an image.\n",
        "* A `Mask` is a collection of points, representing a every corner"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUrYwzxlFuJy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "struct Point: Codable {\n",
        "  let x, y: Double\n",
        "}\n",
        "\n",
        "struct Mask: Codable {\n",
        "  let nv, ne, se, sv: Point\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-Pj9_jUwHos",
        "colab_type": "text"
      },
      "source": [
        "# Import python and tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "au63cjnstSaD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import TensorFlow\n",
        "import Python\n",
        "\n",
        "%include \"EnableIPythonDisplay.swift\"\n",
        "IPythonDisplay.shell.enable_matplotlib(\"inline\")\n",
        "let plt = Python.import(\"matplotlib.pyplot\")\n",
        "\n",
        "let np = Python.import(\"numpy\")\n",
        "let pil = Python.import(\"PIL\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LcIrKwiyiE5v",
        "colab_type": "text"
      },
      "source": [
        "# Endable shell scripts\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AO83EmkiJRn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let subprocess = Python.import(\"subprocess\")\n",
        "\n",
        "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": {
        "id": "eIQjMWYpv3yz",
        "colab_type": "text"
      },
      "source": [
        "# Download dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGmiL1-Tiodb",
        "colab_type": "code",
        "outputId": "66f26ffe-58fb-4cf4-cbe5-3a20490745e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "source": [
        "let command = \"git clone https://github.com/magnuskahr/BingoTicketDataset\"\n",
        "subprocess.call(command, shell: true)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'BingoTicketDataset'...\n",
            "remote: Enumerating objects: 3215, done.\u001b[K\n",
            "remote: Counting objects: 100% (3215/3215), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3211/3211), done.\u001b[K\n",
            "remote: Total 8061 (delta 6), reused 3213 (delta 4), pack-reused 4846\n",
            "Receiving objects: 100% (8061/8061), 663.24 MiB | 48.87 MiB/s, done.\n",
            "Resolving deltas: 100% (16/16), done.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NFQPHd3FyLu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let base = URL(fileURLWithPath:  \"BingoTicketDataset/324/points.json\")\n",
        "let jsonData = try! Data(contentsOf: base)\n",
        "let data: [String: Mask] = try! JSONDecoder().decode([String: Mask].self, from: jsonData)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sk-cwlD-jrRa",
        "colab_type": "text"
      },
      "source": [
        "We also wonna decode the json to be used as objects"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TeUh85HajvTM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let files = Array(data.keys) // names of the image files\n",
        "let masks = Array(data.values) // points of the images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OeMKI49hwXCX",
        "colab_type": "text"
      },
      "source": [
        "# Prepare data\n",
        "\n",
        "In `MaskImageGenerator` we convert a mask to an image, where the points of the the mask is represented with radial gradients."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnTHbMIotO0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "struct MaskImageGenerator {\n",
        "\n",
        "  struct Constants {\n",
        "    static let white = PythonObject(tupleOf: 255, 255, 255)\n",
        "    static let black = PythonObject(tupleOf: 0, 0, 0)\n",
        "\n",
        "    static let imageSide = 324\n",
        "    static let imageSize = PythonObject(tupleOf: imageSide, imageSide)\n",
        "\n",
        "    static let gradientSide = 40\n",
        "    static let gradientSize = PythonObject(tupleOf: gradientSide, gradientSide)\n",
        "  }\n",
        "\n",
        "  // Lazy create the gradient once\n",
        "  private let gradient: PythonObject = {\n",
        "    let image = pil.Image.new(\"RGBA\", Constants.gradientSize, Constants.white)\n",
        "\n",
        "    // Create the radial gradient\n",
        "    for y in 0 ..< Constants.gradientSide {\n",
        "      for x in 0 ..< Constants.gradientSide {\n",
        "        let distanceToCenter = sqrt(pow(Double(x) - Double(Constants.gradientSide) / 2, 2) + pow(Double(y) - Double(Constants.gradientSide) / 2, 2))\n",
        "\t\t\t\tvar alpha = distanceToCenter / (sqrt(2.0) * (Double(Constants.gradientSide) / 2)) + 0.3\n",
        "        alpha = 255.0 - alpha * 255\n",
        "\n",
        "        let position = PythonObject(tupleOf: x, y)\n",
        "        let color = PythonObject(tupleOf: 0, 0, 0, Int(alpha))\n",
        "        image.putpixel(position, color)\n",
        "      }\n",
        "    }\n",
        "\n",
        "    image.putpixel(PythonObject(tupleOf: 19, 19), Constants.black)\n",
        "    image.putpixel(PythonObject(tupleOf: 20, 19), Constants.black)\n",
        "    image.putpixel(PythonObject(tupleOf: 19, 20), Constants.black)\n",
        "    image.putpixel(PythonObject(tupleOf: 20, 20), Constants.black)\n",
        "    return image\n",
        "  }()\n",
        "\n",
        "  // Create an image with at gradient at `point` \n",
        "  private func imageGradient(for point: Point) -> Tensor<Float> {\n",
        "    let image = pil.Image.new(\"RGBA\", Constants.imageSize, Constants.white)\n",
        "    image.paste(gradient, PythonObject(tupleOf: Int(point.x) - 20, Int(point.y) - 20), gradient)\n",
        "    let nimage = np.array(image.convert(\"L\"), dtype: np.float32) * (1.0 / 255)\n",
        "    return Tensor<Float>(numpy: nimage)!\n",
        "  }\n",
        "\n",
        "  // Return a tensor containing images for the corners\n",
        "  func images(for mask: Mask) -> Tensor<Float> {\n",
        "    \n",
        "    let images = [\n",
        "      imageGradient(for: mask.nv).expandingShape(at: 2),\n",
        "      imageGradient(for: mask.ne).expandingShape(at: 2),\n",
        "      imageGradient(for: mask.se).expandingShape(at: 2),\n",
        "      imageGradient(for: mask.sv).expandingShape(at: 2)\n",
        "    ]\n",
        "\n",
        "    return Tensor(concatenating: images, alongAxis: 2).expandingShape(at: 0)\n",
        "  }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDGPgcVOsiTa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "/*\n",
        "var generator = MaskImageGenerator()\n",
        "let mask = Mask(nv: Point(x: 10, y: 20), ne: Point(x: 60, y: 60), se: Point(x: 33, y: 33), sv: Point(x: 100, y: 20))\n",
        "let t = generator.images(for: mask)\n",
        "\n",
        "plt.imshow(np.squeeze(t.makeNumpyArray(), 0))\n",
        "plt.show()\n",
        "*/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACr7mB15xL7E",
        "colab_type": "text"
      },
      "source": [
        "Next we need to be able to download an image and convert it to a tensor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eubuGwhSxLYM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "// download the ticket image and convert to tensor\n",
        "func tensor(for imagePath: String) -> Tensor<Float> {\n",
        "\n",
        "  let img = pil.Image.open(\"BingoTicketDataset/324/\\(imagePath)\")\n",
        "  let image = np.array(img, dtype: np.float32) * (1.0 / 255)\n",
        "\n",
        "  let imageTensor = Tensor<Float>(numpy: image)!\n",
        "  return imageTensor.expandingShape(at: 0)\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZN3yZFdmxmRW",
        "colab_type": "text"
      },
      "source": [
        "Finaly we are able to collect all of our data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdXeZoldIgil",
        "colab_type": "code",
        "outputId": "202c4791-73d5-42ac-8c8b-7edc62a558a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "var generator = MaskImageGenerator()\n",
        "\n",
        "print(\"Creating dataset, hold on!\")\n",
        "let start = Date().timeIntervalSince1970\n",
        "let x = Tensor(concatenating: files.map(tensor), alongAxis: 0)\n",
        "let y = Tensor(concatenating: masks.map { generator.images(for: $0) }, alongAxis: 0)\n",
        "let end = Date().timeIntervalSince1970\n",
        "print(\"Did finish!\")\n",
        "print(\"It took: \\(end - start) seconds\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating dataset, hold on!\n",
            "tcmalloc: large alloc 2015543296 bytes == 0x7bf10000 @  0x7f331d080b6b 0x7f331d0a0379 0x7f3304906ab7 0x7f33048b3afe 0x7f33047f633b 0x7f33047c5feb 0x7f33047c6f1b 0x7f33047c70ae 0x7f32ff5888db 0x7f32ff433ca5 0x7f32ff4346f8 0x7f32ff415ffb 0x7f32ff4166af 0x7f32ff411f6c 0x7f32ff413a10 0x7f32fcc24725 0x7f3317b5c382 0x7f3317b5beb8 0x7f3317b5c454 0x7f3317b65074 0x7f331d4ba8a0 0x7f331d4b83bb 0x400490 0x2\n",
            "tcmalloc: large alloc 2687385600 bytes == 0x11d284000 @  0x7f331d080b6b 0x7f331d0a0379 0x7f3304906ab7 0x7f33048b3afe 0x7f33047f633b 0x7f33047c5feb 0x7f33047c6f1b 0x7f33047c70ae 0x7f32ff5888db 0x7f32ff433ca5 0x7f32ff4346f8 0x7f32ff415ffb 0x7f32ff4166af 0x7f32ff411f6c 0x7f32ff413a10 0x7f32fcc24725 0x7f3317b5c382 0x7f3317b5beb8 0x7f3317b5c454 0x7f3317b65074 0x7f331d4ba8a0 0x7f331d4b860c 0x400490 0x2\n",
            "Did finish!\n",
            "It took: 16.218261241912842 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ky7JV33dpmqb",
        "colab_type": "code",
        "outputId": "0a59a2be-9ea4-4b5d-9562-c205ed4dc306",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1600, 324, 324, 3]\r\n",
            "[1600, 324, 324, 4]\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjMBrhO0uGNI",
        "colab_type": "code",
        "outputId": "628bb671-20e5-476c-ff5d-de5ad2448e15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "if let randomIndex = (0 ..< x.shape[0]).randomElement() {\n",
        "  let img = x[randomIndex].makeNumpyArray()\n",
        "  let mask = y[randomIndex].makeNumpyArray()\n",
        "  \n",
        "  plt.figure(figsize: [20, 20])\n",
        "  plt.subplot(1, 5, 1)\n",
        "  plt.imshow(img)\n",
        "  plt.xlabel(\"Ticket\")\n",
        "\n",
        "  let corners = np.dsplit(mask, 4)\n",
        "\n",
        "  plt.subplot(1, 5, 2)\n",
        "  plt.imshow(np.squeeze(corners[0]), cmap: \"gray\")\n",
        "  plt.xlabel(\"North West\")\n",
        "\n",
        "  plt.subplot(1, 5, 3)\n",
        "  plt.imshow(np.squeeze(corners[1]), cmap: \"gray\")\n",
        "  plt.xlabel(\"North East\")\n",
        "\n",
        "  plt.subplot(1, 5, 4)\n",
        "  plt.imshow(np.squeeze(corners[2]), cmap: \"gray\")\n",
        "  plt.xlabel(\"South East\")\n",
        "\n",
        "  plt.subplot(1, 5, 5)\n",
        "  plt.imshow(np.squeeze(corners[3]), cmap: \"gray\")\n",
        "  plt.xlabel(\"South West\")\n",
        "\n",
        "  plt.show()\n",
        "}"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KirOzJW3bog3s7+9T12u8d/R9x2xesl6vsYDyDu17FvOCvm+oUsty+YhZVqC1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\n9Q7nFFrl4DQnD85QymDN6H4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309QNbjQEba9xdIGF7WcnOsLSIqdtO1Sa4JXDhIucqr7v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\nZmZmZg3jASEzMzMzMzMzs4b5Pzax7Wo5Mqv2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3YNXy8i60Oi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "func split(tensor t: Tensor<Float>, trainingPercentage p: Double) -> (Tensor<Float>, Tensor<Float>) {\n",
        "  let length = Double(t.shape[0])\n",
        "  let trainingP = Int32(ceil(length * p))\n",
        "  let testP = Int32(ceil(length * (1 - p)))\n",
        "  let splitted = t.split(sizes: Tensor([trainingP, testP]))\n",
        "  return (splitted[0], splitted[1])\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV0rMUfQD_mm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let (trainingImages, testImages) = split(tensor: x, trainingPercentage: 0.8)\n",
        "let (trainingCorners, testCorners) = split(tensor: y, trainingPercentage: 0.8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpOFyLpNEb_n",
        "colab_type": "code",
        "outputId": "d9a44bce-0fba-490d-d3ac-923c9e298177",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "print(trainingImages.shape)\n",
        "print(trainingCorners.shape)\n",
        "print(testImages.shape)\n",
        "print(testCorners.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1280, 324, 324, 3]\r\n",
            "[1280, 324, 324, 4]\r\n",
            "[320, 324, 324, 3]\r\n",
            "[320, 324, 324, 4]\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UR3X5u39EmxK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "// This cell is just boilerplate code..\n",
        "struct TicketsBatch {\n",
        "  let images: Tensor<Float>\n",
        "  let corners: Tensor<Float>\n",
        "}\n",
        "\n",
        "extension TicketsBatch: TensorGroup {\n",
        "    public static var _typeList: [TensorDataType] = [\n",
        "        Float.tensorFlowDataType,\n",
        "        Float.tensorFlowDataType\n",
        "    ]\n",
        "    public static var _unknownShapeList: [TensorShape?] = [nil, nil]\n",
        "    public var _tensorHandles: [_AnyTensorHandle] {\n",
        "        fatalError(\"unimplemented\")\n",
        "    }\n",
        "    public func _unpackTensorHandles(into address: UnsafeMutablePointer<CTensorHandle>?) {\n",
        "        address!.advanced(by: 0).initialize(to: images.handle._cTensorHandle)\n",
        "        address!.advanced(by: 1).initialize(to: corners.handle._cTensorHandle)\n",
        "    }\n",
        "    public init(_owning tensorHandles: UnsafePointer<CTensorHandle>?) {\n",
        "        images = Tensor(handle: TensorHandle(_owning: tensorHandles!.advanced(by: 0).pointee))\n",
        "        corners = Tensor(handle: TensorHandle(_owning: tensorHandles!.advanced(by: 1).pointee))\n",
        "    }\n",
        "    public init<C: RandomAccessCollection>(_handles: C) where C.Element: _AnyTensorHandle {\n",
        "        fatalError(\"unimplemented\")\n",
        "    }\n",
        "}\n",
        "\n",
        "extension Dataset where Element == TicketsBatch {\n",
        "  init(images: Tensor<Float>, corners: Tensor<Float>) {\n",
        "    self.init(elements: TicketsBatch(images: images, corners: corners))\n",
        "  }\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRRj8GlIGixD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let batchSize = 32\n",
        "let trainingDataset = Dataset<TicketsBatch>(images: trainingImages, corners: trainingCorners).batched(batchSize)\n",
        "let testDataset = Dataset<TicketsBatch>(images: testImages, corners: testCorners).batched(batchSize)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EKABlfCmTd9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@differentiable\n",
        "func depthwiseSoftmax<S: TensorFlowFloatingPoint>(_ input: Tensor<S>) -> Tensor<S> {\n",
        "  softmax(input, alongAxis: 3)\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcSOg_DojOOJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LatestResult {\n",
        "  var value: Tensor<Float>!\n",
        "}\n",
        "\n",
        "public struct DownBlock: Layer {\n",
        "\n",
        "  var conv1: Conv2D<Float>\n",
        "  var conv2: Conv2D<Float>\n",
        "\n",
        "  init(inputSize: Int, outputSize: Int) {\n",
        "    conv1 = Conv2D<Float>(filterShape: (3, 3, inputSize,  outputSize), padding: .same, activation: relu)\n",
        "    conv2 = Conv2D<Float>(filterShape: (3, 3, outputSize, outputSize), padding: .same, activation: relu)\n",
        "  }\n",
        "\n",
        "  @noDerivative\n",
        "  var latestResult = LatestResult()\n",
        "\n",
        "  @differentiable\n",
        "  public func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {\n",
        "    let result = input.sequenced(through: conv1, conv2)\n",
        "    self.latestResult.value = result\n",
        "    return result\n",
        "  }\n",
        "}\n",
        "\n",
        "struct UNet: Layer {\n",
        "\n",
        "  var down1 = DownBlock(inputSize: 3, outputSize: 8)\n",
        "  var down2 = DownBlock(inputSize: 8, outputSize: 16)\n",
        "  var down3 = DownBlock(inputSize: 16, outputSize: 32)\n",
        "  var down4 = DownBlock(inputSize: 32, outputSize: 64)\n",
        "  \n",
        "  var pool = MaxPool2D<Float>(poolSize: (2, 2), strides: (2, 2))\n",
        "\n",
        "  var trans1 = TransposedConv2D<Float>(filterShape: (3, 3, 32, 64), strides: (2, 2), padding: .valid)\n",
        "  var trans2 = TransposedConv2D<Float>(filterShape: (3, 3, 16, 32), strides: (2, 2), padding: .same)\n",
        "  var trans3 = TransposedConv2D<Float>(filterShape: (3, 3, 8, 16), strides: (2, 2), padding: .same)\n",
        "\n",
        "  var up1 = DownBlock(inputSize: 64, outputSize: 32)\n",
        "  var up2 = DownBlock(inputSize: 32, outputSize: 16)\n",
        "  var up3 = DownBlock(inputSize: 16, outputSize: 8)\n",
        "\n",
        "  var output = Conv2D<Float>(filterShape: (3, 3, 8, 4), padding: .same, activation: depthwiseSoftmax)\n",
        "\n",
        "  @differentiable\n",
        "  public func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {\n",
        "    let down = input\n",
        "      .sequenced(through: down1, pool)\n",
        "      .sequenced(through: down2, pool)\n",
        "      .sequenced(through: down3, pool)\n",
        "      .sequenced(through: down4, trans1)\n",
        "    guard let d3val = down3.latestResult.value,\n",
        "          let d2val = down2.latestResult.value,\n",
        "          let d1val = down1.latestResult.value else {\n",
        "            fatalError()\n",
        "          }\n",
        "          \n",
        "    let up = down\n",
        "      .concatenated(with: d3val, alongAxis: 3).sequenced(through: up1, trans2)\n",
        "      .concatenated(with: d2val, alongAxis: 3).sequenced(through: up2, trans3)\n",
        "      .concatenated(with: d1val, alongAxis: 3).sequenced(through: up3, output)\n",
        "\n",
        "    return up\n",
        "  }\n",
        "}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQYSrBQSuyse",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let firstTrainExamples = trainingDataset.first(where: { _ in true })!\n",
        "let firstTrainFeatures = firstTrainExamples.images\n",
        "let firstTrainLabels = firstTrainExamples.corners"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LpXd_nkWBBPf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "let epochCount = 2\n",
        "var trainAccuracyResults: [Float] = []\n",
        "var trainLossResults: [Float] = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJHdmM3Rqm2D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "func IoUAccuracy(predictions: Tensor<Float>, truths: Tensor<Float>) -> Float {\n",
        "  let axes = [1, 2, 3]\n",
        "  let intersection = (predictions * truths).sum(alongAxes: axes)\n",
        "  let union = predictions.sum(alongAxes: axes) + truths.sum(alongAxes: axes) - intersection\n",
        "  let iou = ((intersection + 1) / (union + 1)).mean(alongAxes: 0)\n",
        "  return iou.flattened().scalarized()\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8GtCgEUkRXO",
        "colab_type": "text"
      },
      "source": [
        "# Problemer \n",
        "\n",
        "Her begynder jeg at træne, og der er nogle problemer:\n",
        "\n",
        "* Det er ikke lykkedes mig at få en accuracy på over 0.25, forslag modtages med kyshånd.\n",
        "* Kan det gøres hurtigere?\n",
        "\n",
        "Nogen gange så fejler den bare lige pludselig, se følgende output: (tror dette problem er løst)\n",
        "\n",
        "```\n",
        "epoch: 1, batch: 0, with accuracy: 0.24982814, is nan: false\n",
        "epoch: 1, batch: 1, with accuracy: 0.24983129, is nan: false\n",
        "epoch: 1, batch: 2, with accuracy: -nan, is nan: true\n",
        "epoch: 1, batch: 3, with accuracy: -nan, is nan: true\n",
        "epoch: 1, batch: 4, with accuracy: -nan, is nan: true\n",
        "```\n",
        "\n",
        "Pludselig begynder den bare at blive `nan`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0w_bC83NBEa2",
        "colab_type": "code",
        "outputId": "d48e1947-9cea-4f3e-af82-8b94a46ef0b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "var model = UNet()\n",
        "let optimizer = Adam(for: model, learningRate: 0.1)\n",
        "let train = true\n",
        "guard train else { fatalError() }\n",
        "\n",
        "var lastAcc: Float = 0\n",
        "for epoch in 1...epochCount {\n",
        "    var epochLoss: Float = 0\n",
        "    var epochAccuracy: Float = 0\n",
        "    var batchCount: Int = 0\n",
        "    \n",
        "    Context.local.learningPhase = .training\n",
        "    for batch in trainingDataset {\n",
        "      let (loss, grad) = model.valueWithGradient { (model: UNet) -> Tensor<Float> in\n",
        "        let logits = model(batch.images)\n",
        "        let thisloss = sigmoidCrossEntropy(\n",
        "          logits: logits,//.reshaped(to: [batchSize * 324 * 324, 4]), \n",
        "          labels: batch.corners//.reshaped(to: [batchSize * 324 * 324, 4])\n",
        "        )\n",
        "        return thisloss\n",
        "      }\n",
        "      optimizer.update(&model, along: grad)\n",
        "        \n",
        "      let logits = model(batch.images)\n",
        "      let accuracy = IoUAccuracy(predictions: logits, truths: batch.corners)\n",
        "      \n",
        "      print(\"epoch: \\(epoch), batch: \\(batchCount), with accuracy: \\(accuracy), is nan: \\(accuracy.isNaN)\")\n",
        "      epochAccuracy += accuracy\n",
        "      epochLoss += loss.scalarized()\n",
        "      batchCount += 1\n",
        "    }\n",
        "    epochAccuracy /= Float(batchCount)\n",
        "    epochLoss /= Float(batchCount)\n",
        "    trainAccuracyResults.append(epochAccuracy)\n",
        "    trainLossResults.append(epochLoss)\n",
        "    print(\"Epoch \\(epoch): Loss: \\(epochLoss), Accuracy: \\(epochAccuracy), deltaAcc: \\(lastAcc - epochAccuracy)\")\n",
        "    lastAcc = epochAccuracy\n",
        "}\n",
        "print(\"trained!\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tcmalloc: large alloc 1934925824 bytes == 0x1bdd68000 @  0x7f331d09e1e7 0x7f32fcd51c62 0x7f33032ae2f8 0x7f33032e51e2 0x7f33032e7401 0x7f33032e7d87 0x7f3303339e35 0x7f330333a4d3 0x7f330333b8f8 0x7f32ff433ca5 0x7f32ff4346f8 0x7f32ff415ffb 0x7f32ff4166af 0x7f32ff411f6c 0x7f32ff413a10 0x7f32fcc24725 0x7f3317b5c382 0x7f3317b5beb8 0x7f3317b5c454 0x7f3317b65074 0x7f3317b935e4 0x7f3317d3f173 0x7f3317de78b8 0x7f3317de7a90 0x7f3317e9c69c 0x7f3317e24e40 0x7f3317d491d9 0x7f3317e9595a 0x7f3317e17c8e 0x7f32e0de39ca 0x7f32e0dcdc64\n",
            "tcmalloc: large alloc 1934925824 bytes == 0x1bdd68000 @  0x7f331d09e1e7 0x7f32fcd51c62 0x7f33032ae2f8 0x7f33032e51e2 0x7f33032e7401 0x7f33032e7d87 0x7f3303339e35 0x7f330333a4d3 0x7f330333b8f8 0x7f32ff433ca5 0x7f32ff4346f8 0x7f32ff415ffb 0x7f32ff4166af 0x7f32ff411f6c 0x7f32ff413a10 0x7f32fcc24725 0x7f3317b5c382 0x7f3317b5beb8 0x7f3317b5c454 0x7f3317b65074 0x7f3317b935e4 0x7f3317d3f173 0x7f3317d4518c 0x7f3317d48e52 0x7f3317d3d0d3 0x7f32e0dcba06 0x7f32e0dcda47 0x7f3317d3d0d3 0x7f32e0dd3150 0x7f32dfdbe012 0x400490\n",
            "epoch: 1, batch: 0, with accuracy: 0.24983045, is nan: false\n",
            "tcmalloc: large alloc 1934925824 bytes == 0x1bdd68000 @  0x7f331d09e1e7 0x7f32fcd51c62 0x7f33032ae2f8 0x7f33032e51e2 0x7f33032e7401 0x7f33032e7d87 0x7f3303339e35 0x7f330333a4d3 0x7f330333b8f8 0x7f32ff433ca5 0x7f32ff4346f8 0x7f32ff415ffb 0x7f32ff4166af 0x7f32ff411f6c 0x7f32ff413a10 0x7f32fcc24725 0x7f3317b5c382 0x7f3317b5beb8 0x7f3317b5c454 0x7f3317b65074 0x7f3317b935e4 0x7f3317d3f173 0x7f3317de78b8 0x7f3317de7a90 0x7f3317e9c69c 0x7f3317e24e40 0x7f3317d491d9 0x7f3317e9595a 0x7f3317e17c8e 0x7f32e0de39ca 0x7f32e0dcdc64\n",
            "tcmalloc: large alloc 1934925824 bytes == 0x1bdd68000 @  0x7f331d09e1e7 0x7f32fcd51c62 0x7f33032ae2f8 0x7f33032e51e2 0x7f33032e7401 0x7f33032e7d87 0x7f3303339e35 0x7f330333a4d3 0x7f330333b8f8 0x7f32ff433ca5 0x7f32ff4346f8 0x7f32ff415ffb 0x7f32ff4166af 0x7f32ff411f6c 0x7f32ff413a10 0x7f32fcc24725 0x7f3317b5c382 0x7f3317b5beb8 0x7f3317b5c454 0x7f3317b65074 0x7f3317b935e4 0x7f3317d3f173 0x7f3317d4518c 0x7f3317d48e52 0x7f3317d3d0d3 0x7f32e0dcba06 0x7f32e0dcda47 0x7f3317d3d0d3 0x7f32e0dd3150 0x7f32dfdbe012 0x400490\n",
            "epoch: 1, batch: 1, with accuracy: 0.24982975, is nan: false\n",
            "epoch: 1, batch: 2, with accuracy: 0.24983144, is nan: false\n",
            "epoch: 1, batch: 3, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 1, batch: 4, with accuracy: 0.2498321, is nan: false\n",
            "epoch: 1, batch: 5, with accuracy: 0.2498312, is nan: false\n",
            "epoch: 1, batch: 6, with accuracy: 0.24982932, is nan: false\n",
            "epoch: 1, batch: 7, with accuracy: 0.24983111, is nan: false\n",
            "epoch: 1, batch: 8, with accuracy: 0.24983059, is nan: false\n",
            "epoch: 1, batch: 9, with accuracy: 0.24983193, is nan: false\n",
            "epoch: 1, batch: 10, with accuracy: 0.24983108, is nan: false\n",
            "epoch: 1, batch: 11, with accuracy: 0.2498306, is nan: false\n",
            "epoch: 1, batch: 12, with accuracy: 0.2498319, is nan: false\n",
            "epoch: 1, batch: 13, with accuracy: 0.2498317, is nan: false\n",
            "epoch: 1, batch: 14, with accuracy: 0.2498318, is nan: false\n",
            "epoch: 1, batch: 15, with accuracy: 0.24983126, is nan: false\n",
            "epoch: 1, batch: 16, with accuracy: 0.24983075, is nan: false\n",
            "epoch: 1, batch: 17, with accuracy: 0.24983096, is nan: false\n",
            "epoch: 1, batch: 18, with accuracy: 0.24982989, is nan: false\n",
            "epoch: 1, batch: 19, with accuracy: 0.24983257, is nan: false\n",
            "epoch: 1, batch: 20, with accuracy: 0.2498317, is nan: false\n",
            "epoch: 1, batch: 21, with accuracy: 0.24983154, is nan: false\n",
            "epoch: 1, batch: 22, with accuracy: 0.2498293, is nan: false\n",
            "epoch: 1, batch: 23, with accuracy: 0.24983037, is nan: false\n",
            "epoch: 1, batch: 24, with accuracy: 0.24983212, is nan: false\n",
            "epoch: 1, batch: 25, with accuracy: 0.24983063, is nan: false\n",
            "epoch: 1, batch: 26, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 1, batch: 27, with accuracy: 0.24983189, is nan: false\n",
            "epoch: 1, batch: 28, with accuracy: 0.2498306, is nan: false\n",
            "epoch: 1, batch: 29, with accuracy: 0.2498311, is nan: false\n",
            "epoch: 1, batch: 30, with accuracy: 0.2498306, is nan: false\n",
            "epoch: 1, batch: 31, with accuracy: 0.24983251, is nan: false\n",
            "epoch: 1, batch: 32, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 1, batch: 33, with accuracy: 0.24983203, is nan: false\n",
            "epoch: 1, batch: 34, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 1, batch: 35, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 1, batch: 36, with accuracy: 0.24982977, is nan: false\n",
            "epoch: 1, batch: 37, with accuracy: 0.24983048, is nan: false\n",
            "epoch: 1, batch: 38, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 1, batch: 39, with accuracy: 0.24983051, is nan: false\n",
            "Epoch 1: Loss: 0.5766345, Accuracy: 0.24983104, deltaAcc: -0.24983104\n",
            "epoch: 2, batch: 0, with accuracy: 0.24983114, is nan: false\n",
            "epoch: 2, batch: 1, with accuracy: 0.24982978, is nan: false\n",
            "epoch: 2, batch: 2, with accuracy: 0.24983256, is nan: false\n",
            "epoch: 2, batch: 3, with accuracy: 0.24982977, is nan: false\n",
            "epoch: 2, batch: 4, with accuracy: 0.24982974, is nan: false\n",
            "epoch: 2, batch: 5, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 2, batch: 6, with accuracy: 0.24983196, is nan: false\n",
            "epoch: 2, batch: 7, with accuracy: 0.24983196, is nan: false\n",
            "epoch: 2, batch: 8, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 2, batch: 9, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 2, batch: 10, with accuracy: 0.24983034, is nan: false\n",
            "epoch: 2, batch: 11, with accuracy: 0.24983254, is nan: false\n",
            "epoch: 2, batch: 12, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 2, batch: 13, with accuracy: 0.24983057, is nan: false\n",
            "epoch: 2, batch: 14, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 2, batch: 15, with accuracy: 0.24983105, is nan: false\n",
            "epoch: 2, batch: 16, with accuracy: 0.24983251, is nan: false\n",
            "epoch: 2, batch: 17, with accuracy: 0.24983159, is nan: false\n",
            "epoch: 2, batch: 18, with accuracy: 0.2498311, is nan: false\n",
            "epoch: 2, batch: 19, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 2, batch: 20, with accuracy: 0.24983045, is nan: false\n",
            "epoch: 2, batch: 21, with accuracy: 0.24983196, is nan: false\n",
            "epoch: 2, batch: 22, with accuracy: 0.2498317, is nan: false\n",
            "epoch: 2, batch: 23, with accuracy: 0.24983189, is nan: false\n",
            "epoch: 2, batch: 24, with accuracy: 0.24983129, is nan: false\n",
            "epoch: 2, batch: 25, with accuracy: 0.24983017, is nan: false\n",
            "epoch: 2, batch: 26, with accuracy: 0.24982974, is nan: false\n",
            "epoch: 2, batch: 27, with accuracy: 0.24983156, is nan: false\n",
            "epoch: 2, batch: 28, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 2, batch: 29, with accuracy: 0.2498317, is nan: false\n",
            "epoch: 2, batch: 30, with accuracy: 0.24982975, is nan: false\n",
            "epoch: 2, batch: 31, with accuracy: 0.24983172, is nan: false\n",
            "epoch: 2, batch: 32, with accuracy: 0.24983114, is nan: false\n",
            "epoch: 2, batch: 33, with accuracy: 0.2498331, is nan: false\n",
            "epoch: 2, batch: 34, with accuracy: 0.24983072, is nan: false\n",
            "epoch: 2, batch: 35, with accuracy: 0.24982975, is nan: false\n",
            "epoch: 2, batch: 36, with accuracy: 0.2498317, is nan: false\n",
            "epoch: 2, batch: 37, with accuracy: 0.24983057, is nan: false\n",
            "epoch: 2, batch: 38, with accuracy: 0.24983117, is nan: false\n",
            "epoch: 2, batch: 39, with accuracy: 0.24983123, is nan: false\n",
            "Epoch 2: Loss: 0.5766206, Accuracy: 0.24983113, deltaAcc: -8.940697e-08\n",
            "trained!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4g04z3IEfhve",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "0e2777f3-92e0-43f6-daec-c82045685f04"
      },
      "source": [
        "let output = model(firstTrainFeatures)\n",
        "print(IoUAccuracy(predictions: output, truths: firstTrainLabels))\n",
        "print(output.shape)\n",
        "\n",
        "if let randomIndex = (0 ..< output.shape[0]).randomElement() {\n",
        "  let img = output[randomIndex].makeNumpyArray()\n",
        "  plt.imshow(img)\n",
        "\n",
        "  plt.show()\n",
        "}"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.24983123\r\n",
            "[32, 324, 324, 4]\r\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Al4HD/PoKSvWVlZp0oTl3A3eUu+LrgPdqp8ldd9719a1Ux2O6jo3ljvQKYBg40IX5RLVY\nzbGIeKC2qafH40J19PJ4qJ8rfXXj7mqHd0XXU92N/RnwzR7Oew3VHd5XgSPTc1NdV+0DXgd+BCzu\n8rxPUp1a/h/VNd7mC81Jdcf4H8uxeQ0YabiO75d5DpW/ZEO18d8sdRwHbu5SDddRneofAg6Wx/pe\nH4+L1NGz4wH8MdVKXoeowuZvan9PD1DdfPxnYGHp/1R5PV62XzPXuf2NQbPk+n05YGZ95hAwS84h\nYJacQ8AsOYeAWXIOAbPkHAJmyTkEzJL7f3+cxfDdaAuRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoyVqw0pTZEq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a45a5e2d-f0c7-4dfc-b62d-a2e4f373306a"
      },
      "source": [
        "print(model)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "UNet(down1: __lldb_expr_135.DownBlock(conv1: TensorFlow.Conv2D<Swift.Float>(filter: [[[[   0.20478329,   -0.12418474,    0.21595398,  -0.105370075,   -0.23311165,    0.05528714,\r\n",
            "      -0.13415167,    0.23103398],\r\n",
            "   [   0.07442516,    -0.1812778,    0.21345665,     0.2308124,   -0.04336498,    0.16348852,\r\n",
            "      -0.11407959,    0.20712878],\r\n",
            "   [  -0.21373433,    0.19482604,    0.15398276,    0.18929595,   -0.10235082,  -0.052807942,\r\n",
            "      -0.05088669,    0.15728673]],\r\n",
            "\r\n",
            "  [[ -0.031106906,    0.15631282,     0.2263147,    0.11751081,   -0.22694743,   -0.06479103,\r\n",
            "     -0.029364321,    0.19911492],\r\n",
            "   [  -0.18637994,   -0.12027803,   -0.07908622,   -0.07128482,   0.012730917, -0.0117609305,\r\n",
            "      -0.04835948,   0.100923546],\r\n",
            "   [    0.2443509,   0.044257607,    0.23763712,    0.23457038,    -0.2158802,    0.10495029,\r\n",
            "      -0.21963912,    0.18928868]],\r\n",
            "\r\n",
            "  [[   0.24276033,    0.21257487,    0.12825662,   -0.24136417,    0.13192603,    0.08079335,\r\n",
            "      -0.01666868, -0.0034946178],\r\n",
            "   [   0.03938396,    0.09374842,   -0.22504313,    0.10973049,    0.09550474,  -0.043597527,\r\n",
            "     -0.031427436,   -0.13680537],\r\n",
            "   [  -0.21810468,   -0.11869069,   -0.03328641,    0.05552914,    0.18469988,    0.13010655,\r\n",
            "     -0.053936873,   -0.10249867]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[    0.2411275,    0.12452869,   0.053548843,   -0.07009098,   -0.23701185,        0.0858,\r\n",
            "       0.12700062,  -0.025951875],\r\n",
            "   [  -0.23712777,  -0.106972374,  0.0063298605,   -0.24306573,   -0.22833654,    0.14900452,\r\n",
            "       0.16033597,   -0.12764649],\r\n",
            "   [ -0.025342744,   -0.13113089,  -0.057014585,   0.032446668,  -0.113389224,   0.028658224,\r\n",
            "       0.10170987,   -0.23141713]],\r\n",
            "\r\n",
            "  [[    0.1025299,   -0.18716311,   -0.15176557,    0.11759428,   -0.24572653,   -0.22267029,\r\n",
            "       0.18734477,  -0.044923145],\r\n",
            "   [   0.20530696,    0.21315835, -0.0031570063,    0.13944857,   -0.15914705,  -0.014477962,\r\n",
            "      0.062928945,   -0.13835585],\r\n",
            "   [   0.09301315,     0.1644537,    0.12672257,   -0.11845603,    0.13939686, -0.0060191313,\r\n",
            "       0.19423217,   -0.14654604]],\r\n",
            "\r\n",
            "  [[    0.2317764,   -0.22744621,    0.03062467,   0.048798338,   -0.21985036,  -0.021513449,\r\n",
            "        0.1165707,   -0.06166807],\r\n",
            "   [  -0.23188558,    0.09432152,    0.06805457,   -0.23426946,   -0.04596251,    0.18581067,\r\n",
            "      -0.17957132,    0.13592725],\r\n",
            "   [   0.15622501,    0.21035041,    0.19285737,   -0.12952355,    0.19920707,    0.10304841,\r\n",
            "        -0.217098,     0.2129659]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[  -0.21058707,  -0.014729351,   -0.07313875,   0.072716326,   -0.07968467,     0.1467247,\r\n",
            "       0.21344309,   -0.18977378],\r\n",
            "   [  -0.00499808,   0.038701516,    0.09606181,    0.12796268,    0.12785673,   0.064836286,\r\n",
            "      0.060165368,  -0.094038844],\r\n",
            "   [ -0.121002436,    0.08221928,  -0.032518744,   -0.17642969,   -0.19418633,   0.072129145,\r\n",
            "       0.23459932,   -0.18850645]],\r\n",
            "\r\n",
            "  [[   0.22325659,    0.17169796,    0.21942107,   -0.19795951,    0.10652723,   -0.02813825,\r\n",
            "       0.21500465,    0.07290292],\r\n",
            "   [ -0.073617876,   -0.21374062,   -0.23583561,    -0.1717496,    0.17063218,   -0.18849853,\r\n",
            "      -0.05895796,     0.1829138],\r\n",
            "   [  0.016667211,     0.2182875,    0.22973742,    0.14848876,   -0.22861958,    0.14241517,\r\n",
            "       -0.1963683,    0.07680652]],\r\n",
            "\r\n",
            "  [[  -0.05463845,   -0.18705148,    0.07532842,   -0.08497939,   -0.16973956,   -0.11358608,\r\n",
            "        0.1380885,    -0.2143498],\r\n",
            "   [   0.17529887,  -0.032273225,   -0.01113818,    0.06247142,   -0.08109351,     0.2052306,\r\n",
            "     -0.030409731,   -0.13009189],\r\n",
            "   [   0.22507031,   0.023678165, -0.0055376594,    0.09580068,  -0.028112952,   0.036651198,\r\n",
            "        0.1026973,   -0.19337393]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), conv2: TensorFlow.Conv2D<Swift.Float>(filter: [[[[    0.15304995,    0.018127577,     0.07617085,    -0.18177491,   -0.060884256,\r\n",
            "         0.0691592,    -0.07726746,   -0.037002828],\r\n",
            "   [    0.12075842,    -0.07557867,    0.107322834,     0.08215718,      0.1035671,\r\n",
            "       0.028396312,    -0.19995889,    -0.17917697],\r\n",
            "   [     -0.152137,    0.056719676,      0.0907762,     0.16291256,    -0.08706773,\r\n",
            "      -0.061693978,     0.07790714,     0.15246317],\r\n",
            "   [    0.19601993,     0.10569599,     0.07356094,   0.0014922273,     0.10878313,\r\n",
            "        0.05187103,     0.04595726,    0.082690276],\r\n",
            "   [    0.16712634,      0.1997049,    0.035664678,     0.12496744,   -0.049684178,\r\n",
            "        0.17767525,     0.18811403,     -0.1913989],\r\n",
            "   [   0.111441225,   -0.075124554,     0.02525369,     -0.1370877,     -0.1942814,\r\n",
            "       0.010325335,     0.15031384,   -0.079462536],\r\n",
            "   [   -0.12208601,    -0.17130557,    -0.13240965,   -0.041384365,    -0.14627618,\r\n",
            "        0.06840155,    -0.18433785,    0.117226176],\r\n",
            "   [    0.13967577,   -0.027487116,     0.20011482,     0.15392089,     0.17330986,\r\n",
            "        -0.1303833,    0.029260833,    0.034623496]],\r\n",
            "\r\n",
            "  [[     -0.163161,    -0.14482138,     0.14287718,    0.036341395,    -0.08489046,\r\n",
            "      -0.047825247,   -0.071828485,     0.14504218],\r\n",
            "   [    0.13732661,    0.012496515,    -0.16435772,    -0.19037105,    0.082287945,\r\n",
            "       0.069628835,    0.008141744,      0.1946444],\r\n",
            "   [    0.17525406,     -0.2001829,    -0.13640904,   -0.080068976,    0.061123356,\r\n",
            "       -0.08463058,    -0.02527788,     0.18461637],\r\n",
            "   [   0.015635926,  -0.0040052445,   -0.095227964,    -0.15966603,   -0.088941455,\r\n",
            "        0.18421546,    0.029445574,     0.10086404],\r\n",
            "   [   -0.16688228,    -0.04604564,     -0.1853238,    0.028059147,     0.11857415,\r\n",
            "        -0.0682337,    0.054073505,    -0.14739178],\r\n",
            "   [    0.19142601,    -0.13510993,     0.14018151,   -0.026002482,    0.106986985,\r\n",
            "       -0.13613115,   -0.022973496,     -0.0680894],\r\n",
            "   [    0.17026637,    -0.20403534,    0.108324446,   -0.056002468,    -0.18841815,\r\n",
            "       0.047891043,     0.07081607,    -0.08084527],\r\n",
            "   [   0.060526114,   -0.010658606,    -0.05046767,     0.11000628,    -0.18502104,\r\n",
            "        -0.0952138,    -0.03518195,   -0.014855356]],\r\n",
            "\r\n",
            "  [[   0.037377954,    0.030392973,     0.16435038,    -0.16763933,   -0.099137776,\r\n",
            "       -0.15545127,   -0.059833094,     0.13380322],\r\n",
            "   [   -0.12541844,    0.057924863,    -0.18539977,     0.04254069,   0.0011639685,\r\n",
            "        -0.0725583,     0.13297997,     0.10454618],\r\n",
            "   [   -0.14563179,    -0.11034077,    0.049662035,   -0.113487184,    -0.13880877,\r\n",
            "        0.07343635,     0.16995136,     -0.1927353],\r\n",
            "   [    0.06391484,    0.011708597,      0.0860901,    -0.14196147,    -0.14284045,\r\n",
            "       -0.11152756,    0.014384843,     0.15948844],\r\n",
            "   [   0.029507866,    -0.17096563,     0.07378076,    -0.17502445,     0.05107625,\r\n",
            "       0.053441077,     -0.0398898,   -0.092792764],\r\n",
            "   [    0.07152271,   -0.014701957,    -0.19189374,    -0.19132566,     -0.2005519,\r\n",
            "        0.07399061,     0.03347233,    0.021701438],\r\n",
            "   [   -0.19704135,   -0.041742604,     -0.0652037,    0.020969925,     0.13440192,\r\n",
            "         0.0910006,     0.20308983,     0.17973664],\r\n",
            "   [   -0.15333796,     -0.1632888,     0.09712642,     0.11889122,    0.054715957,\r\n",
            "       0.052484185,     0.10918201,    0.034271635]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[  -0.059759803,     0.12082198,   -0.017149566,     0.07238266,     0.15268227,\r\n",
            "        -0.0575366,    -0.11054789,      0.1471827],\r\n",
            "   [    0.18059571,     0.17652291,     0.19742183,    0.042106096,   -0.042561036,\r\n",
            "       -0.13398334,    -0.13739216,     0.12549898],\r\n",
            "   [    -0.1945313,     0.19303879,   -0.020952161,     0.08712175,    -0.15702838,\r\n",
            "        0.19161703,    -0.15452203,    0.022019623],\r\n",
            "   [   -0.12241909,     0.16696082,     0.09132341,    0.040523104,   -0.010355411,\r\n",
            "       0.049412277,     0.19808453,    -0.10158918],\r\n",
            "   [    0.10165167,     0.06440935,    -0.20170195,     0.16188905,    -0.06872373,\r\n",
            "         0.1471879,     0.13364501,   -0.055877734],\r\n",
            "   [   -0.14903794,   -0.049899045,     0.11640477,      0.0577793,    0.083337404,\r\n",
            "        0.09867588,  -0.0042041955,    -0.13538821],\r\n",
            "   [   0.056744736,    0.046834193,      0.0207609,    -0.12475919,      0.1176121,\r\n",
            "        0.06614214,    -0.13143241,     0.15450232],\r\n",
            "   [   -0.15954056,     0.11737222,    -0.17145327,     0.12059281,     0.13321981,\r\n",
            "       -0.16460271,    -0.10986899,      0.1583491]],\r\n",
            "\r\n",
            "  [[ -0.0012510823,   -0.036104873,     0.13010974,     0.12204459,   -0.021596026,\r\n",
            "      -0.050400704,    -0.07276158,   -0.002964501],\r\n",
            "   [    0.08998746,    -0.14643459,    -0.14316924, -0.00012921086,   -0.015216367,\r\n",
            "        0.07935055,    -0.09866731,     0.20283604],\r\n",
            "   [    0.12765862,    0.091080956,        0.17921,  0.00036748443,     0.09955855,\r\n",
            "       -0.03436308,  -2.068347e-05,   -0.010543947],\r\n",
            "   [   -0.15855661,     0.13692798,    -0.07131451,     -0.0654935,     0.20132254,\r\n",
            "       -0.19078107,    -0.06830198,     -0.1693714],\r\n",
            "   [     0.0911288,      0.0514815,  -0.0081537645,    -0.13496417,   -0.016132863,\r\n",
            "         0.1974652,     0.16323663,      -0.126241],\r\n",
            "   [     -0.070435,    -0.04532021,    -0.03993988,    -0.00841097,   -0.043048777,\r\n",
            "    -9.2564616e-05,    0.007547131,      0.1865471],\r\n",
            "   [   0.013331397,     -0.1033919,     0.12637314,    0.104419455,     0.17799918,\r\n",
            "       -0.19350068,    -0.13503732,    -0.17929435],\r\n",
            "   [  -0.102562614,  -0.0038541823,    -0.07508504,    0.037554953,     0.19003682,\r\n",
            "         0.1162924,     0.12151822,     -0.1362192]],\r\n",
            "\r\n",
            "  [[   -0.14840727,      0.0668682,    -0.15133132,    -0.15097007,    0.078005545,\r\n",
            "        0.12259994,    -0.18906114,     0.05731677],\r\n",
            "   [   0.122925125,  0.00089595927,   -0.078506134,   -0.050362937,     0.01652877,\r\n",
            "      -0.052172426,    0.115764365,     0.15055756],\r\n",
            "   [   -0.11027697,    -0.08336656,     0.16784018,    -0.10280551,     0.08803766,\r\n",
            "       -0.13862374,    -0.12041328,     0.10796144],\r\n",
            "   [    -0.1020447,      0.0146292,     -0.1849496,    0.031815752,     0.08502926,\r\n",
            "       -0.15194409,    -0.09236863,    -0.08090902],\r\n",
            "   [  -0.011333958,      0.1555231,    -0.09194075,     0.05162565,    0.057370156,\r\n",
            "        0.04413142,  -0.0095842825,     -0.1449648],\r\n",
            "   [   0.049432132,    -0.14205247,     0.20106095,     0.10419666,    -0.10523371,\r\n",
            "       -0.11880537,   0.0023151373,     0.15140183],\r\n",
            "   [  -0.096349694,   -0.007649283,    -0.13996364,     0.08929454,     0.12616606,\r\n",
            "       -0.18156242,    -0.20408687,     0.09317003],\r\n",
            "   [   -0.13540086,   0.0155152315,    -0.15141702,     0.18415336,    -0.06313685,\r\n",
            "        0.13368243,     -0.0349044,     0.06767213]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[  -0.062041022,    -0.12609933,    -0.13219634,    -0.10190921,     0.19821462,\r\n",
            "       -0.06928754,     0.18566588,    0.059297223],\r\n",
            "   [    0.16275415,    0.104363345,    0.024004117,  -0.0070799277,     0.06229253,\r\n",
            "      -0.026112517,    -0.09273369,     0.19839084],\r\n",
            "   [    0.03294765,     0.07533275,    -0.11001105,     0.17776538,     0.06323876,\r\n",
            "        0.12881607,    -0.15295826,     0.19696802],\r\n",
            "   [   -0.18022944,     0.13022926,    -0.10592867,   -0.036017075,    -0.11342105,\r\n",
            "       0.028255569,     0.09320532,   0.0009357689],\r\n",
            "   [    0.13451785,    0.010258661,   -0.032655843,    -0.16478707,   -0.012971359,\r\n",
            "        0.03826982,     0.14853516,     0.05241294],\r\n",
            "   [   -0.10438262,      0.1820032,    -0.18585373,    -0.06788753,    -0.15304011,\r\n",
            "       -0.12181903,    0.103737585,     0.16166668],\r\n",
            "   [   -0.15245825,    -0.15935543,     0.03566132,   0.0033256589,    0.041899163,\r\n",
            "        0.16747236,   -0.053265437,     -0.1345799],\r\n",
            "   [   -0.07698135,     0.06574185,     0.18174021,     -0.0266754,    0.083610915,\r\n",
            "       -0.08247979,    0.051287513,    -0.17360936]],\r\n",
            "\r\n",
            "  [[   0.027990138,    -0.11191154,    0.026649605,     0.05851529,     0.06821915,\r\n",
            "       -0.17095979,   -0.031736083,    -0.04971265],\r\n",
            "   [  -0.025406748,      0.2000568,    -0.17043366,     0.14904591,  -0.0061495122,\r\n",
            "        0.07003676,    0.078515425,    0.023665443],\r\n",
            "   [    0.07524194,    -0.10678613,    -0.05691483,   -0.043829102,   -0.006849149,\r\n",
            "       -0.08967073,    -0.17814942,    -0.15600155],\r\n",
            "   [    0.09092833,     0.09768142,     -0.1357247,    -0.08150368,    0.023841228,\r\n",
            "       -0.15115261,    -0.08756783,    -0.04962213],\r\n",
            "   [   -0.10072247,    -0.04870782,     0.09521049,    -0.15073958,   -0.014177424,\r\n",
            "        0.06830777,   -0.084853135,     -0.1376468],\r\n",
            "   [   -0.10158426,      0.1826494,    0.026804416,    -0.10020577,    0.017276976,\r\n",
            "        0.15829605,    -0.07867847,     0.14074202],\r\n",
            "   [    0.19505943,   -0.080527514,     0.15963396,   -0.060216885,    -0.14583136,\r\n",
            "       -0.16627583,      0.1109475,   -0.056502666],\r\n",
            "   [    0.18383338,       0.198865,     0.13316008,    -0.19393733,     0.13695967,\r\n",
            "        0.17180143,      0.0668647,    -0.20365185]],\r\n",
            "\r\n",
            "  [[ -0.0015904859,    -0.19926076,    0.022490378,     0.04534104,   -0.030223222,\r\n",
            "        0.09055793,    -0.20068671,    0.039106164],\r\n",
            "   [    0.18994872,     0.14646725,     0.14343004,   -0.049273867,    -0.20059969,\r\n",
            "      0.0144565785,    0.010217489,     0.16000651],\r\n",
            "   [    0.19632342,    -0.17534946,    0.015948806,    -0.04443296,     0.04636777,\r\n",
            "        0.14209905,    -0.09520203,    -0.06359593],\r\n",
            "   [   0.025684394,    -0.19445382,    -0.08940175,     0.12688594,    -0.09158451,\r\n",
            "       -0.15177585,    -0.13925928,     0.06876656],\r\n",
            "   [   -0.16476993,   -0.053970817,    0.053787585,     0.16117193,   -0.088823825,\r\n",
            "       0.054637894,     0.13181075,     0.05275755],\r\n",
            "   [   0.057052847,   -0.032738283,    -0.16064754,      0.1099399,   -0.028321754,\r\n",
            "         0.1145564,    -0.07697361,    0.061141215],\r\n",
            "   [    0.18307568,    -0.07062281,     0.15227084,     0.13007742,    -0.17355393,\r\n",
            "       0.021989694,    -0.16942152,    -0.19536789],\r\n",
            "   [    -0.0869956,    -0.11173678,    -0.08717255,     0.15767789,    -0.11365767,\r\n",
            "        0.19200106,    -0.07199118,     0.19975477]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), latestResult: __lldb_expr_135.LatestResult), down2: __lldb_expr_135.DownBlock(conv1: TensorFlow.Conv2D<Swift.Float>(filter: [[[[   -0.13546836,     0.13832831,     0.06531291, ...,   -0.029306095,\r\n",
            "       0.084732495,    -0.12594604],\r\n",
            "   [    0.15147087,    -0.03910339,    0.034314435, ...,     0.05461526,\r\n",
            "      -0.055752598,     0.16642079],\r\n",
            "   [   0.045378648,      0.1391987,   -0.006619255, ...,      0.0969313,\r\n",
            "      -0.045448266,    -0.16135573],\r\n",
            "   ...,\r\n",
            "   [  -0.047430158,     0.06019485,   -0.039632242, ...,    -0.15210323,\r\n",
            "       -0.16037893,    0.026165128],\r\n",
            "   [   -0.11662328,    0.012631377,    0.066163704, ...,     0.03496691,\r\n",
            "       -0.06734173,    0.102118894],\r\n",
            "   [   -0.11201473,    -0.10147917,   -0.085565016, ...,   -0.083459616,\r\n",
            "        0.06523657,    0.084995985]],\r\n",
            "\r\n",
            "  [[   0.021450281,   -0.085692845,    0.066296935, ...,    0.018190425,\r\n",
            "       -0.16608986,      0.1428934],\r\n",
            "   [  0.0048650107,     0.15793371,    0.106103145, ...,   0.0018411081,\r\n",
            "        -0.1576891,    -0.06724846],\r\n",
            "   [    0.15122104,    0.084876105,     0.16462438, ...,     0.06742239,\r\n",
            "        0.09675952,    -0.13987684],\r\n",
            "   ...,\r\n",
            "   [   -0.07602601,    0.005434513,    -0.14771168, ...,     -0.0511506,\r\n",
            "        0.07872999,    -0.13394038],\r\n",
            "   [  -0.110750005,   -0.028255105,    -0.09195372, ...,   0.0035291116,\r\n",
            "       0.078127146,     0.11480685],\r\n",
            "   [   -0.09423252,    -0.14362308,    -0.15789497, ...,    -0.14876053,\r\n",
            "       -0.04216091,    -0.09279072]],\r\n",
            "\r\n",
            "  [[   0.041279554,     0.09233471,    -0.12091637, ...,     0.12453298,\r\n",
            "      -0.032402437,    -0.15439987],\r\n",
            "   [    0.11981817,    0.092898175,     0.02615738, ...,     0.09818666,\r\n",
            "       0.031256877,     0.12563352],\r\n",
            "   [    0.13792968,   -0.070357524,     -0.1305362, ...,   -0.086546585,\r\n",
            "       -0.13086379,    0.054932833],\r\n",
            "   ...,\r\n",
            "   [  -0.013801655,    -0.07729479,   -0.028854728, ...,     0.11657441,\r\n",
            "         0.1619064,    0.098563954],\r\n",
            "   [  0.0070006847,     0.12996253,    -0.13614139, ...,     0.07241838,\r\n",
            "         0.1560725,  -0.0022989116],\r\n",
            "   [   0.009341359,    -0.07374358,   -0.056569856, ...,    -0.14833304,\r\n",
            "       -0.11337125,   -0.078129135]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[  -0.066685006,    -0.09406491,     0.10533409, ...,   -0.009281675,\r\n",
            "       -0.09422966,    0.024978718],\r\n",
            "   [    0.11241031,     0.14651677,    0.124482796, ...,   -0.095457554,\r\n",
            "        0.06385481,     0.14617383],\r\n",
            "   [    0.05355398,    -0.12438866,   0.0060151024, ...,   0.0117811365,\r\n",
            "        0.11771413,    0.050446987],\r\n",
            "   ...,\r\n",
            "   [    0.15982485,    0.122147046,    -0.07848959, ...,     0.12899244,\r\n",
            "       -0.08669107,     -0.0520527],\r\n",
            "   [    0.10310733,    -0.16090629,   -0.047962945, ...,    -0.07903473,\r\n",
            "        0.13418233,     0.16492626],\r\n",
            "   [   0.040331006,    0.043272298,     0.06216725, ...,     0.11292156,\r\n",
            "       -0.06566648,    -0.15418641]],\r\n",
            "\r\n",
            "  [[  -0.018531045,      0.0621204,    -0.06410313, ...,    0.110189125,\r\n",
            "       -0.04230281,     -0.1622082],\r\n",
            "   [    0.14285259,   -0.061037146,   -0.026803374, ...,    0.026696285,\r\n",
            "        0.12686066,   -0.019288462],\r\n",
            "   [    0.10073932,  -0.0082280245,    -0.12582032, ...,     0.16071133,\r\n",
            "       0.024072886,    -0.07068837],\r\n",
            "   ...,\r\n",
            "   [   -0.12571594,      0.0936081,    -0.05446192, ...,    -0.15141651,\r\n",
            "       -0.08779816,   -0.041898333],\r\n",
            "   [    0.16247332,     0.15816331,       0.085085, ...,     0.13978052,\r\n",
            "       0.123162076,    0.104525805],\r\n",
            "   [  -0.023260474,     0.09730701,    -0.16350365, ...,    0.050332747,\r\n",
            "       -0.15487449,     0.09363314]],\r\n",
            "\r\n",
            "  [[   -0.13189983,     0.07072648,    0.068571016, ...,    -0.09948385,\r\n",
            "        0.10188671,     0.06971733],\r\n",
            "   [    0.08902876,    -0.03247261,    0.081945896, ...,     -0.1255916,\r\n",
            "         0.1025885,    -0.13266787],\r\n",
            "   [   0.123417735,     0.03230139,    0.120422326, ...,    -0.05892428,\r\n",
            "      -0.026699146,    0.026310127],\r\n",
            "   ...,\r\n",
            "   [    0.14880002,    0.033047836,    0.057370346, ...,    0.054219842,\r\n",
            "       -0.09245174,    -0.06380308],\r\n",
            "   [   0.065824114,    0.055481236,   -0.081164084, ...,     0.11758761,\r\n",
            "      -0.018273473,    0.042700768],\r\n",
            "   [   0.100234315,     0.11059638,    0.090039775, ...,    -0.06270711,\r\n",
            "      -0.049970787,     0.15520105]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[   0.105218455,    -0.04419915,   -0.067853495, ...,       0.129319,\r\n",
            "       -0.10387846,    -0.09511459],\r\n",
            "   [   0.069036804,     0.15610898,    -0.09589724, ...,      0.1627028,\r\n",
            "       0.015286287,    -0.08622714],\r\n",
            "   [    0.03120017,   -0.030820092,   -0.047261357, ...,     0.14112091,\r\n",
            "          0.082461,    0.102620564],\r\n",
            "   ...,\r\n",
            "   [   -0.14870822,   -0.030570984,    -0.04907314, ...,    0.112296104,\r\n",
            "      -0.029660385,     0.01887266],\r\n",
            "   [   -0.06213407,   -0.028902253,     0.08522217, ...,     0.07163914,\r\n",
            "      -0.044064842,    -0.09742916],\r\n",
            "   [   0.090635896,     0.13307576,    -0.16648006, ...,    -0.05305783,\r\n",
            "       -0.07078274,   -0.090402484]],\r\n",
            "\r\n",
            "  [[   0.070009515,    -0.07265588,     0.15035316, ...,  -0.0009935697,\r\n",
            "       0.067446075,     0.08738732],\r\n",
            "   [   0.004138112,   -0.013342977,    0.101557896, ...,     0.16340733,\r\n",
            "       0.095493004,    -0.07937475],\r\n",
            "   [  -0.061257802,    0.109794855,   -0.006967624, ...,     0.15115854,\r\n",
            "        0.15749797,   -0.040511172],\r\n",
            "   ...,\r\n",
            "   [  -0.030404568,    -0.09101276,     0.16515589, ...,    0.057152074,\r\n",
            "       -0.13061552,    -0.11194452],\r\n",
            "   [   -0.14668028,    0.058323383,   -0.013896903, ...,    0.012315433,\r\n",
            "         0.0946459,   -0.060418963],\r\n",
            "   [  0.0050187907,    -0.12725954,    -0.14871733, ...,    -0.05737233,\r\n",
            "        0.14877836,     0.13153856]],\r\n",
            "\r\n",
            "  [[  -0.091744505,    -0.15650961,    -0.13000695, ...,    -0.04947754,\r\n",
            "        0.10612341,     0.09566331],\r\n",
            "   [   0.054189485,   -0.039607167,     0.11624086, ...,    0.107912384,\r\n",
            "      -0.008643627,   -0.015463233],\r\n",
            "   [    0.05669574,     0.08610078,    -0.14313218, ...,    -0.08502551,\r\n",
            "       -0.10928035,      0.1091284],\r\n",
            "   ...,\r\n",
            "   [  -0.020147284,    -0.09888788,    -0.09929224, ...,     -0.0936337,\r\n",
            "       -0.09495497,    -0.08251735],\r\n",
            "   [   -0.11062281,     0.15137021,    0.097859144, ...,    -0.02035141,\r\n",
            "        0.08018963,     0.03602318],\r\n",
            "   [   -0.13324122,     0.15007913,     0.05579416, ...,     0.15273973,\n",
            "       0.036892854,    0.025504073]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), conv2: TensorFlow.Conv2D<Swift.Float>(filter: [[[[    0.10332258,    -0.06092414,     0.10880839, ...,    -0.11477522,\n",
            "       -0.08446202,     0.11080316],\n",
            "   [     -0.132459,     0.10247235,     0.10976782, ...,    0.008854024,\n",
            "       -0.08464079,      0.0371154],\n",
            "   [    0.04926069,    0.027477933,    -0.13134517, ...,     0.13369997,\n",
            "        0.06284881,    -0.10437558],\n",
            "   ...,\n",
            "   [   0.021005547,     0.06753015,    -0.12967563, ...,    0.083594196,\n",
            "        0.13138595,    0.067695156],\n",
            "   [     0.1266342,      0.1095611,   -0.079505585, ...,     0.06476391,\n",
            "      -0.038892336,   -0.048358113],\n",
            "   [    0.05664109,    0.019220935,    -0.06497307, ...,    -0.07425172,\n",
            "      -0.016143369,    -0.07455961]],\n",
            "\n",
            "  [[    0.13164862,     0.13058963,    -0.12832934, ...,     0.12684022,\n",
            "        0.07483267,    -0.05883057],\n",
            "   [   0.048586372,    0.044895846,    0.102023326, ...,    -0.04894671,\n",
            "        0.07122818,      0.1027407],\n",
            "   [   0.068600245,     0.08643989,   -0.074651666, ...,   -0.030679248,\n",
            "        0.11365935,   -0.033612523],\n",
            "   ...,\n",
            "   [  -0.017391484,    -0.08994737,    -0.10909491, ...,    -0.14129768,\n",
            "        0.09020854,     0.10323314],\n",
            "   [   -0.08749722,   -0.002033037,     0.14085655, ...,   -0.016616303,\n",
            "        0.09855975,    0.096449904],\n",
            "   [    0.06074609,    -0.05885225,   -0.065371886, ...,    -0.10156193,\n",
            "        0.14152347,    0.116117455]],\n",
            "\n",
            "  [[  -0.003206615,    0.039641295,     0.11681923, ...,     0.08152224,\n",
            "      -0.020121723,    -0.00215885],\n",
            "   [    -0.0224663,     0.14325722,    -0.07446377, ...,     0.07190622,\n",
            "        0.11806724,     0.09393971],\n",
            "   [   0.045491014,     0.12085726,    -0.08142365, ...,    -0.02557064,\n",
            "       -0.04148726,     0.13782154],\n",
            "   ...,\n",
            "   [    0.09683846,     0.09278391,     0.10516387, ...,   -0.102050446,\n",
            "        0.12699474,    0.107737534],\n",
            "   [ -0.0002605734,     0.14416151,     0.12538315, ...,   -0.023359207,\n",
            "       -0.02406484,   -0.108132936],\n",
            "   [   0.024429273,    -0.10089751,    -0.05182365, ...,   -0.063702144,\n",
            "       0.093320414,    -0.02254803]]],\n",
            "\n",
            "\n",
            " [[[   0.012216838,  -0.0010935342,     0.12106239, ...,    -0.07813134,\n",
            "      -0.116501845,     0.08803079],\n",
            "   [   -0.10191201,    -0.14372739,    -0.02294302, ...,     -0.0780108,\n",
            "      -0.051843543,    -0.13128343],\n",
            "   [   0.057812124,     0.14169419,   -0.061975688, ...,     0.08789018,\n",
            "      -0.110529885,    -0.06457777],\n",
            "   ...,\n",
            "   [    0.04763242,     0.13858956,     0.08241339, ...,   -0.090418346,\n",
            "       -0.13270853,    -0.07575937],\n",
            "   [       0.07496,     0.12896456,     0.02015913, ...,     0.14293511,\n",
            "      -0.095571004,    -0.04752622],\n",
            "   [   -0.13748209,  -0.0027294133,    -0.13632618, ...,    0.059884805,\n",
            "        0.11389904,    0.015928771]],\n",
            "\n",
            "  [[    0.13553843,   -0.114782445,   -0.062242147, ...,    -0.13418043,\n",
            "        -0.0523194,    0.071207084],\n",
            "   [   -0.10089538,   -0.019722192,    0.039887242, ...,      0.0667857,\n",
            "       -0.06983918,    -0.11154685],\n",
            "   [  -0.024370804,   -0.050967805,     0.12080082, ...,   -0.043894745,\n",
            "      -0.041595113,    -0.12131629],\n",
            "   ...,\n",
            "   [   -0.14391969,    -0.08175398,    0.009516297, ...,   -0.117010534,\n",
            "        0.07084372,     -0.0645052],\n",
            "   [    0.09117756,    -0.10195523,     0.03821681, ...,    -0.12894729,\n",
            "        -0.1311607,   -0.015472664],\n",
            "   [    -0.0780854,     0.14261968,     0.07174757, ...,    0.037953038,\n",
            "       -0.13453537,    -0.09133861]],\n",
            "\n",
            "  [[   -0.13994598,     0.13529254,    0.005732924, ...,   -0.005235075,\n",
            "      0.0143825365,    0.090533495],\n",
            "   [ -0.0092959525,    -0.07826139,    -0.05694857, ...,    0.013410066,\n",
            "        -0.1269624,     0.14157236],\n",
            "   [   0.038028575,   -0.056363452,  -0.0057093175, ...,    -0.00653247,\n",
            "      -0.052259628,    -0.14267948],\n",
            "   ...,\n",
            "   [  -0.068819426,     0.12516254,   -0.018874193, ...,     -0.1315652,\n",
            "        0.13683803,     0.04266731],\n",
            "   [   0.017995188,    -0.02219141,     0.07468945, ...,     0.00799842,\n",
            "       -0.04810566,    0.035857096],\n",
            "   [    0.11545656,    0.117284216,     0.08594345, ...,    -0.04488184,\n",
            "       0.097938225,    0.089468695]]],\n",
            "\n",
            "\n",
            " [[[  -0.046139628,     0.07581681,   -0.037817314, ...,    -0.05229335,\n",
            "       -0.06681478,   -0.026402809],\n",
            "   [    -0.0672726,     0.09667972,    0.017549887, ...,    0.037660908,\n",
            "       0.066654176,    -0.09961096],\n",
            "   [  -0.030864663,     0.12930474,     0.06055778, ...,    -0.10577759,\n",
            "       -0.09154464,  -0.0013607837],\n",
            "   ...,\n",
            "   [   0.052362554,    -0.10294745,      0.1411476, ...,     -0.1195535,\n",
            "        0.08282745,    0.038383506],\n",
            "   [    0.09487549,     0.01734138,    -0.05953899, ...,    -0.14420219,\n",
            "       0.115551464,   -0.089561336],\n",
            "   [  -0.029451711,   -0.050240938,    -0.11968413, ...,     0.07940231,\n",
            "       0.056513973,   -0.111720875]],\n",
            "\n",
            "  [[    0.08463095,    -0.06706668,   -0.041571885, ...,     0.10042981,\n",
            "       -0.07580979,    0.119644314],\n",
            "   [   -0.06406213,    0.117988504,    -0.01058564, ...,    -0.01313442,\n",
            "       0.082473785,    0.028431889],\n",
            "   [    0.13869928,     0.12425844,     0.07323128, ...,    0.077745646,\n",
            "        0.10755539,     0.04534686],\n",
            "   ...,\n",
            "   [ -0.0079142805,   -0.055730633,    -0.07790277, ...,   -0.048732627,\n",
            "       -0.03470671,   -0.041974925],\n",
            "   [   0.039963845,    0.112009324,    -0.12841436, ...,    0.051295035,\n",
            "        0.03183579,    -0.04647405],\n",
            "   [     0.0979426,    0.083569966,    0.107376784, ...,   -0.063656166,\n",
            "         0.1314897,    0.006332429]],\n",
            "\n",
            "  [[    0.12650323,    -0.02065409,    0.033704888, ...,   -0.115398124,\n",
            "        0.13914461,    0.098767675],\n",
            "   [   -0.07668965,    0.035419468,     0.12684116, ...,   -0.049434476,\n",
            "       0.121011734,   -0.033306386],\n",
            "   [    0.12630436,    0.059290152,    -0.08719673, ...,     0.11479548,\n",
            "       0.010843735,     0.13899134],\n",
            "   ...,\n",
            "   [   0.079787455,    0.020406317,     0.05421193, ...,     0.07071202,\n",
            "       0.094166726,   -0.027093405],\n",
            "   [   -0.08411056,      -0.129649,    -0.11934523, ...,   -0.012365743,\n",
            "       0.120477654,   -0.010478581],\n",
            "   [   -0.14346324,   -0.022877945,   -0.014517985, ...,    0.010584503,\n",
            "          0.038802,   -0.072316416]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), latestResult: __lldb_expr_135.LatestResult), down3: __lldb_expr_135.DownBlock(conv1: TensorFlow.Conv2D<Swift.Float>(filter: [[[[    0.09375319,    -0.11765492,   -0.007979269, ...,    0.066396765,\n",
            "      -0.044913705,  -0.0077821068],\n",
            "   [  -0.042105798,    0.015934234,    -0.08712085, ...,     0.11309183,\n",
            "        -0.0311715,    -0.09682224],\n",
            "   [    0.04979633,     0.03222458,     0.08386998, ...,     0.07120879,\n",
            "        0.04876143,    0.051700782],\n",
            "   ...,\n",
            "   [   -0.07808142,    0.011695666,     0.08763024, ...,    0.017176807,\n",
            "       -0.04814443,     0.07836119],\n",
            "   [   -0.11466913,   -0.096770145,     0.06784426, ...,    0.007878847,\n",
            "      -0.049732916,    0.097074024],\n",
            "   [   0.008125294,   -0.046075467,    0.109056346, ...,   -0.027534565,\n",
            "       -0.04471727,   0.0016309706]],\n",
            "\n",
            "  [[   -0.11094037,    0.108255416,    -0.08462264, ...,     0.10063397,\n",
            "       -0.09935035,    0.023957336],\n",
            "   [   -0.06578423,   -0.036490403,   0.0023497993, ...,  -0.0072479094,\n",
            "        0.07495668,   -0.082975596],\n",
            "   [   0.028534371,     0.09709103,   -0.053869657, ...,   -0.108001634,\n",
            "       -0.08770436,    0.094986014],\n",
            "   ...,\n",
            "   [   -0.08730211,    -0.05377612,    0.061630607, ...,     0.11077574,\n",
            "       -0.11499661,     0.06118857],\n",
            "   [   0.024627274,    -0.04707699,   -0.045395557, ...,     0.11655812,\n",
            "         0.0657835,     0.07339795],\n",
            "   [   0.021361737,    0.064328425,  2.7395452e-05, ...,   -0.085997775,\n",
            "       0.005187659,    0.018226799]],\n",
            "\n",
            "  [[   -0.08185817,   -0.019825287,    -0.11569032, ...,   -0.032256417,\n",
            "       0.026038885,    -0.09418087],\n",
            "   [  -0.039836977,     0.04137719,   -0.016599564, ...,    -0.04548713,\n",
            "      -0.083136685,    -0.09504457],\n",
            "   [  -0.012887607,      0.1144231,    0.094739065, ...,    -0.08795156,\n",
            "      -0.074709676,   -0.035430662],\n",
            "   ...,\n",
            "   [   -0.07678813,   -0.017582934,   -0.032681003, ...,    -0.06711371,\n",
            "        0.08175749,    -0.09851374],\n",
            "   [   -0.10414433,     0.09907794,     0.11055574, ...,    0.039997134,\n",
            "      -0.083439045,  -0.0074224533],\n",
            "   [   -0.08683619,   -0.036334656,   -0.010906621, ...,    -0.03306926,\n",
            "       0.039022308,     0.06572382]]],\n",
            "\n",
            "\n",
            " [[[   -0.02995531,    0.027993599,    -0.06783406, ...,    0.008201327,\n",
            "      -0.078156866,   -0.022343758],\n",
            "   [    0.10449865,    0.042855337,     0.04245067, ...,   -0.049772058,\n",
            "      -0.034668874,     0.08818104],\n",
            "   [   -0.06816497,      0.0852837,    0.049427547, ...,    0.057204682,\n",
            "       0.007459599,   -0.063336685],\n",
            "   ...,\n",
            "   [   -0.07826625,     0.06641045,    -0.07230132, ...,    -0.10330412,\n",
            "      -0.006792892,    0.045670073],\n",
            "   [   -0.10879996,    -0.02247326,     0.09845164, ...,    0.088554494,\n",
            "       -0.10419319,     0.09144515],\n",
            "   [ -0.0080505535,    -0.10528024,   -0.015316923, ...,    0.045243997,\n",
            "       -0.06827191,     0.09666189]],\n",
            "\n",
            "  [[   -0.00904477,    -0.11650269,   -0.022918051, ...,   -0.024190942,\n",
            "        0.08648067,    -0.07689282],\n",
            "   [    0.07365965,    -0.09379194,     0.04390024, ...,    0.099925235,\n",
            "        0.10859313,    -0.09559495],\n",
            "   [ -0.0067912624,     0.07000645,   -0.006026269, ...,    -0.06723049,\n",
            "        0.08766927,    -0.08524459],\n",
            "   ...,\n",
            "   [   -0.08657078,   -0.027115766,    -0.07521403, ...,     0.11294555,\n",
            "        0.09261138,   -0.097931296],\n",
            "   [  -0.112619445,     0.05926521,    0.039289854, ...,   0.0009840446,\n",
            "        0.06540612,    -0.06983694],\n",
            "   [    0.09080685,    -0.09934425,   -0.029551769, ...,    -0.03669198,\n",
            "      -0.093282044,    0.022485316]],\n",
            "\n",
            "  [[  -0.050976727,     -0.0774078,    0.033701856, ...,     0.06669436,\n",
            "     -0.0026099014,     0.04396245],\n",
            "   [   -0.10678992,   -0.079330735,   -0.076638564, ...,    -0.11192236,\n",
            "        -0.1024489,    0.006140487],\n",
            "   [  -0.062349323,    -0.08352949,     0.04680652, ...,   0.0069561126,\n",
            "        0.09718962,    0.022950869],\n",
            "   ...,\n",
            "   [  -0.008559323,    -0.11253315,    0.067861736, ...,   -0.064043626,\n",
            "       0.065741666,   -0.052681144],\n",
            "   [    0.07455628,   -0.010385686,   -0.010424911, ...,     0.06566482,\n",
            "       -0.05340503,     0.09890865],\n",
            "   [    -0.0734306,     0.11335162,    -0.03437292, ...,    -0.09210261,\n",
            "      -0.026487356,    -0.11124006]]],\n",
            "\n",
            "\n",
            " [[[  -0.016928535,    0.024058573,     0.02467299, ...,    0.026659848,\n",
            "      0.0044948207,    0.039044335],\n",
            "   [  -0.025705785,     0.04481213,    -0.08688809, ...,     0.04256576,\n",
            "      -0.032899916,    0.081032455],\n",
            "   [    0.09850814,     0.10841299,     0.10259937, ...,   -0.043448877,\n",
            "      -0.031401537,   -0.045771703],\n",
            "   ...,\n",
            "   [   -0.10404759,    0.083645396,    0.092213064, ...,   -0.022437857,\n",
            "         0.0657848,      0.0460873],\n",
            "   [   0.028153842,     0.09961722,     0.07974734, ...,    0.027297363,\n",
            "       -0.06703712,     0.07511437],\n",
            "   [   0.023415076,    0.048259098,    -0.06111549, ...,    0.049718447,\n",
            "        0.08895806,    -0.06604369]],\n",
            "\n",
            "  [[    0.04122965,   -0.058896177,    0.070474446, ...,    0.005129749,\n",
            "       0.055941455,     0.10933356],\n",
            "   [   -0.04349198,    0.052121095,    -0.09994246, ...,   -0.033598203,\n",
            "        0.11019909,     0.06081085],\n",
            "   [   -0.10445293,    -0.01337106,     -0.0857157, ...,     0.10764072,\n",
            "        0.09418163,     0.09781415],\n",
            "   ...,\n",
            "   [   0.018148378,    0.027598936,     0.02965059, ...,    0.005819749,\n",
            "       -0.08179605,     0.07163733],\n",
            "   [    0.08928526,   -0.104894035,   -0.114656314, ...,    -0.09384999,\n",
            "        0.03424322,   -0.083114006],\n",
            "   [    0.05487073,   -0.019615846,    -0.09408652, ...,   -0.098077066,\n",
            "       -0.09726456,    -0.01315774]],\n",
            "\n",
            "  [[   -0.07855605,  -0.0029922014,   -0.077392995, ...,   -0.080872804,\n",
            "       -0.09164414,     0.06977458],\n",
            "   [   -0.07299711,    -0.04503017,     0.09752416, ...,    0.003324712,\n",
            "         0.0762296,     0.05824894],\n",
            "   [   -0.01322703,    -0.10044813,   -0.011155062, ...,   -0.017306423,\n",
            "       0.066649534,     0.03879134],\n",
            "   ...,\n",
            "   [      0.048231,    -0.11010884,     0.06367312, ...,    0.102655284,\n",
            "       0.012318035,    0.060808744],\n",
            "   [-0.00036968407,     0.06718587,   -0.049618136, ...,    -0.07422967,\n",
            "      -0.030013895,    -0.04140043],\n",
            "   [   -0.09325355,     0.03518346,    0.027103795, ...,   -0.051688503,\n",
            "       -0.08171049,    -0.10382401]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
            " 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), conv2: TensorFlow.Conv2D<Swift.Float>(filter: [[[[   0.096648626,   -0.093466364,       0.091259, ...,     0.03638164,\n",
            "         0.0254569,   -0.055180337],\n",
            "   [  -0.067652784,    0.020189354,     0.07010911, ...,   -0.060978305,\n",
            "      0.0042442726,    -0.03621622],\n",
            "   [   -0.06452552,     0.06733463,    -0.09964041, ...,    0.041255545,\n",
            "      -0.044365752,    0.087132186],\n",
            "   ...,\n",
            "   [   -0.06257356,     0.06748374,    0.060213916, ...,    0.083874464,\n",
            "       -0.07980909,    0.030534472],\n",
            "   [  -0.033087764,    0.035654336,    0.026227761, ...,     0.04522594,\n",
            "        0.07339437,    -0.05125773],\n",
            "   [  -0.098736055,   -0.034265213,    0.087799534, ...,   -0.058478303,\n",
            "      -0.086452335,    0.021078696]],\n",
            "\n",
            "  [[    0.08363225,    -0.07260015,   0.0152127175, ...,    -0.01297625,\n",
            "       0.014105275,    -0.09002135],\n",
            "   [   -0.09214369,    0.008671582,    0.017113334, ...,     0.03416199,\n",
            "       0.023106819,     0.02851017],\n",
            "   [    -0.0456646,     0.05892071,     -0.0574134, ...,    -0.08680721,\n",
            "      -0.023232453,    -0.07381099],\n",
            "   ...,\n",
            "   [ -0.0017860542,    0.094748765,     0.09323109, ...,     -0.0874037,\n",
            "       0.026408412,     0.09112319],\n",
            "   [   0.064054884,    0.031412594,   -0.049865585, ...,   -0.097440004,\n",
            "       0.097722195,    0.052851938],\n",
            "   [  -0.024842406,    0.031341273,     0.09753376, ...,    -0.09641632,\n",
            "       0.046996422,    -0.02710087]],\n",
            "\n",
            "  [[   0.076469176,    -0.08726081,    0.060872573, ...,    0.004750604,\n",
            "      -0.097344324,   -0.015275717],\n",
            "   [   0.031397946,     0.07009319,       0.057301, ...,    0.059394803,\n",
            "       0.018221164,     -0.0893313],\n",
            "   [   -0.02666591,    -0.07820031,   -0.069093354, ...,   -0.037498258,\n",
            "      -0.065564826,  0.00042350014],\n",
            "   ...,\n",
            "   [   -0.04437417,    0.053321257,   0.0017054616, ...,    0.005836705,\n",
            "       0.036833636,   -0.028438823],\n",
            "   [   -0.08511346,   -0.024892947,   0.0008181164, ...,   -0.063835494,\n",
            "        0.09934634,    -0.04116276],\n",
            "   [   0.009302573,   -0.005649751,     0.08431558, ...,   -0.057489414,\n",
            "       0.069671616,   -0.018744772]]],\n",
            "\n",
            "\n",
            " [[[    0.05973839,    -0.09375246,    -0.08501907, ...,      0.0867337,\n",
            "       -0.08311344,    0.054445174],\n",
            "   [  -0.027476994,    0.039573904,    -0.06028862, ...,   0.0076377243,\n",
            "        0.05154983,    0.056083962],\n",
            "   [    0.09588655,   -0.016061615,    -0.07910257, ...,   -0.016179973,\n",
            "      -0.027121773,     0.02393591],\n",
            "   ...,\n",
            "   [ 0.00043498556,    0.041806918,     0.09373067, ...,   -0.056937166,\n",
            "      -0.089506574,    -0.08564794],\n",
            "   [ -0.0061874967,   -0.042721927,   -0.061726995, ...,     0.07407457,\n",
            "       -0.09513017,    0.015229483],\n",
            "   [    0.06617888,   -0.052345023,  -0.0037805492, ...,   -0.027635405,\n",
            "        0.08153419,     0.05271584]],\n",
            "\n",
            "  [[    0.02517227,    -0.01413175,    0.014276413, ...,  -0.0074021034,\n",
            "       0.094572395,    0.012870959],\n",
            "   [ -0.0012871203,     0.07411737,    -0.09837521, ...,     0.05733884,\n",
            "      -0.060572006,    -0.08423314],\n",
            "   [   0.007957393,     0.04928978,     0.09577065, ...,     0.09960829,\n",
            "      -0.025690623,    -0.07086199],\n",
            "   ...,\n",
            "   [   0.075512044,    -0.04895644,   -0.008002118, ...,     0.09144189,\n",
            "        0.09873089,    -0.06143195],\n",
            "   [  -0.042859875,   -0.025747905,   -0.007817014, ...,   -0.094442405,\n",
            "         0.0277287,    -0.04278758],\n",
            "   [    0.06612221,    -0.06327059,    -0.08199091, ...,   -0.012844241,\n",
            "       0.085278295,   -0.044733822]],\n",
            "\n",
            "  [[  -0.027309505,    -0.07573745,    0.047038324, ...,   -0.066573136,\n",
            "      -0.051040746,   -0.062048562],\n",
            "   [   -0.06538013,    0.033936076,   -0.049499292, ...,    0.047604226,\n",
            "       -0.09330915,   -0.029677788],\n",
            "   [ -0.0011668397,    0.012895098,    0.054117497, ...,    0.008043899,\n",
            "       0.085993916,     0.08421752],\n",
            "   ...,\n",
            "   [  -0.054564383,     0.09157974,  -0.0022252495, ...,   -0.039815754,\n",
            "      -0.029964047,    -0.08807864],\n",
            "   [   0.055912655,    0.076439805,    0.012380079, ...,    0.012096399,\n",
            "      -0.047115877,    -0.08937272],\n",
            "   [  -0.054815043,   -0.010268808,     0.07814765, ...,    -0.08986189,\n",
            "        0.06509877,     -0.0341086]]],\n",
            "\n",
            "\n",
            " [[[  -0.087607175,   -0.043647792,     0.06700442, ...,     0.06802365,\n",
            "       -0.04083623,    0.045040276],\n",
            "   [  -0.038070314,     0.08073469,     0.05568066, ...,    -0.06594034,\n",
            "      -0.048570897,   -0.065669045],\n",
            "   [   0.028439457,    -0.07092428,     0.07571418, ...,    0.023486324,\n",
            "       -0.04810435,   -0.061422974],\n",
            "   ...,\n",
            "   [  -0.069737166,    -0.04607985,    -0.02162822, ...,     0.07028095,\n",
            "       -0.09494312,   -0.050114907],\n",
            "   [   0.096614905,   -0.025533428,     0.03300026, ...,   0.0039283508,\n",
            "       -0.03385361,   -0.071763806],\n",
            "   [   -0.07219902,   -0.050058622,    -0.10168639, ...,    0.092474826,\n",
            "       0.048632145,    0.010866098]],\n",
            "\n",
            "  [[   0.050576683,   -0.031003453,   -0.058978237, ...,    0.098819174,\n",
            "        0.02075876,    0.015101611],\n",
            "   [    0.06484777,    0.043273058,    0.005793075, ...,   -0.002102633,\n",
            "        0.09423411,   -0.036730755],\n",
            "   [    0.06378094,    0.054605335,     0.07000868, ...,    0.050217863,\n",
            "       0.070272505,    0.096533336],\n",
            "   ...,\n",
            "   [    0.09049016,    0.006049842,   -0.047290005, ...,     0.07229956,\n",
            "        0.06456231,   -0.014837446],\n",
            "   [    0.02082261,   -0.012355284,     0.04660772, ...,    0.016428225,\n",
            "        0.06257402,     0.06054395],\n",
            "   [  -0.015500923,    0.044540785,   -0.022999898, ...,     0.08374236,\n",
            "      -0.079453245,    -0.08637053]],\n",
            "\n",
            "  [[   0.007422081,    -0.06930919,   -0.046247024, ...,    0.066606954,\n",
            "       -0.06099828,    -0.08471353],\n",
            "   [   -0.06717456,  -0.0046230233,   -0.005520662, ...,     0.06943181,\n",
            "       0.037562497,    -0.04508919],\n",
            "   [    0.08345661,     0.10025188,    -0.06281047, ...,     0.08334721,\n",
            "       0.009576423,   -0.023085114],\n",
            "   ...,\n",
            "   [    -0.0923634,    0.017728971,     0.10076737, ...,    0.005351325,\n",
            "       -0.08940087,     0.08828248],\n",
            "   [   -0.09863813,   -0.009059288,  -0.0076600625, ...,   -0.034636248,\n",
            "       -0.09842049,    0.004471596],\n",
            "   [   0.078602955,    -0.05407954,     0.07186827, ...,    0.012957319,\n",
            "       0.062678315,    0.052423377]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
            " 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), latestResult: __lldb_expr_135.LatestResult), down4: __lldb_expr_135.DownBlock(conv1: TensorFlow.Conv2D<Swift.Float>(filter: [[[[   0.028805992,     0.06763949,   -0.013649206, ...,   -0.025631806,\n",
            "      -0.070999645,   -0.010838648],\n",
            "   [   0.009554824,   0.0076780124,    0.038210593, ...,    -0.07817954,\n",
            "      -0.010728955,    -0.07152045],\n",
            "   [  -0.049006246,     0.05510398,   -0.013021469, ...,   -0.079798505,\n",
            "      -0.009146154,    0.005236149],\n",
            "   ...,\n",
            "   [  -0.079499505,    0.008544564,     0.03518667, ...,     0.00688837,\n",
            "        -0.0123353,    -0.07397203],\n",
            "   [  -0.045260094,   -0.068415284,      0.0463626, ...,    0.024588566,\n",
            "      -0.028532287,   -0.067644894],\n",
            "   [   -0.06628847,  -0.0069846315,   -0.015876472, ...,    0.044602774,\n",
            "       -0.07387686,     0.02356404]],\n",
            "\n",
            "  [[   0.006446501,   -0.011342804,    -0.07502475, ...,    0.076567635,\n",
            "       0.028448245,   0.0016197761],\n",
            "   [  -0.018702865,    0.029466968,     -0.0375611, ...,    0.040641487,\n",
            "      -0.033307254,    -0.07057019],\n",
            "   [  -0.033371072,   -0.065367065,   -0.058290523, ...,    -0.05115553,\n",
            "        0.04728351,  -0.0048220754],\n",
            "   ...,\n",
            "   [   0.025539279,    0.075327694,    0.014974356, ...,   0.0025468867,\n",
            "      -0.081712864,    0.055831235],\n",
            "   [  -0.047192633,   0.0028637052,    0.024925968, ...,    0.011351109,\n",
            "       -0.08179085,  -0.0029497943],\n",
            "   [   0.057657182,    0.049029827,  -0.0073194904, ...,     0.05200684,\n",
            "        0.08097043,     0.04085201]],\n",
            "\n",
            "  [[  0.0109811425,     0.06892507,     0.06845965, ...,   -0.059424322,\n",
            "      -0.047508977,     0.04122591],\n",
            "   [  -0.009190957,    -0.05966278,    -0.07407087, ...,    0.016952833,\n",
            "        0.07692339,    0.015511036],\n",
            "   [   0.026038568,  -0.0022621155,    -0.04326461, ...,    0.024497569,\n",
            "        0.07161103,     0.01475203],\n",
            "   ...,\n",
            "   [   0.024549406,    0.027513605,    0.003402273, ...,    0.048965674,\n",
            "       0.075780734,     0.02577138],\n",
            "   [    0.06602075,    -0.07634648,      0.0545548, ...,     0.06906186,\n",
            "       0.016828617,    -0.02795003],\n",
            "   [   0.017392438,   -0.032464843,   -0.058983944, ...,    0.049059056,\n",
            "      -0.021318357,    0.076282546]]],\n",
            "\n",
            "\n",
            " [[[   0.008274834,     0.06784713,    0.042908072, ...,    -0.04182887,\n",
            "        0.07649648,   0.0018001398],\n",
            "   [   0.025140723,   -0.042487286,    0.080512546, ...,   -0.002768457,\n",
            "      -0.016086798,    0.024255356],\n",
            "   [   -0.06923594,    -0.03551986,    -0.06442356, ...,    0.027729433,\n",
            "      -0.038227916,   0.0019253691],\n",
            "   ...,\n",
            "   [ 0.00029569864,     -0.0552335,    -0.05978992, ...,   -0.059727073,\n",
            "     -0.0073452396,   -0.071375236],\n",
            "   [   -0.07014003,   -0.015065472,   -0.046258908, ...,    0.040924095,\n",
            "      0.0074497066,   -0.008944134],\n",
            "   [   -0.06548401,   -0.022365075,     0.03874598, ...,   -0.021627646,\n",
            "         0.0275503,    0.026418328]],\n",
            "\n",
            "  [[  -0.071551085,    -0.06441216,    0.004733821, ...,     0.06565567,\n",
            "       0.075444505,    -0.06614703],\n",
            "   [   -0.07909912,    -0.06413206,   -0.024508994, ...,    0.062440954,\n",
            "      -0.037410896,   -0.002747814],\n",
            "   [ -0.0029881995,    0.048380338,   -0.003185451, ...,    0.054670036,\n",
            "       0.044153094,  0.00046590966],\n",
            "   ...,\n",
            "   [  -0.024453184,   -0.024321478,     0.04456433, ...,    0.012200098,\n",
            "      -0.012831251,     0.07593542],\n",
            "   [  -0.030649167,     -0.0522696,     0.08283602, ...,    -0.03441491,\n",
            "        0.07390754,    0.038049918],\n",
            "   [    -0.0628193,      0.0211611,   -0.049226545, ...,    0.021776637,\n",
            "       -0.07453011,   -0.019153079]],\n",
            "\n",
            "  [[    0.05601875,    -0.07883483,    0.072678946, ...,    0.002481103,\n",
            "       0.019098481,   -0.029982269],\n",
            "   [  0.0027645628,   -0.079222284,    0.082958125, ...,    -0.03964589,\n",
            "       0.023005646,   -0.029502332],\n",
            "   [  -0.044447403,    -0.01892535,    -0.08227929, ...,    0.009733598,\n",
            "       -0.06378518,    -0.04229782],\n",
            "   ...,\n",
            "   [   -0.03579847,     0.04496344,    0.045372628, ...,    0.008274337,\n",
            "     -0.0002816717,    0.006869535],\n",
            "   [   0.069068894,    -0.07188398,   0.0006560683, ...,   -0.011856715,\n",
            "        0.07267527,    0.065441534],\n",
            "   [  -0.058738552,    0.034675937,   -0.022287687, ...,   0.0020334325,\n",
            "        -0.0447433,    0.060295105]]],\n",
            "\n",
            "\n",
            " [[[   0.042720698,    -0.03006371,    0.016437372, ...,    0.056283575,\n",
            "     -0.0013154944,     0.03605346],\n",
            "   [  -0.056800723,  -0.0009825826,    -0.03975622, ...,    0.053919416,\n",
            "      -0.024790525,   0.0013986429],\n",
            "   [ -0.0015233557,    0.048111998,    -0.07002169, ...,    -0.06979494,\n",
            "      -0.020163437,   -0.011334917],\n",
            "   ...,\n",
            "   [   -0.07620937,   0.0020334523,    0.045807462, ...,   0.0124569535,\n",
            "       -0.02942141,    -0.03431227],\n",
            "   [  -0.018490016,    -0.05840206,    -0.01944836, ...,     0.04563868,\n",
            "        0.07625054,     0.08266469],\n",
            "   [   0.006568293,   -0.012656788,    0.045291685, ...,   -0.051399034,\n",
            "       0.023050984,  -0.0068130693]],\n",
            "\n",
            "  [[    -0.0809356,    0.005812764,    0.014286539, ...,   -0.072178945,\n",
            "      -0.050812304,     0.08064469],\n",
            "   [   0.009999971,   -0.060557626,     0.06888145, ...,   -0.028674781,\n",
            "       0.044974785,    0.055595677],\n",
            "   [  -0.061041713,    -0.07570039,    -0.04674866, ...,      0.0375615,\n",
            "       0.027183235,   -0.069918334],\n",
            "   ...,\n",
            "   [  -0.007580658,    0.043582916,   0.0062289834, ...,    0.074067056,\n",
            "       0.074468896,   -0.033016525],\n",
            "   [   0.052839756,  -0.0015898943,    -0.06305494, ...,    0.063689314,\n",
            "        0.07606121,    0.046406984],\n",
            "   [  -0.042565268,   -0.005443692,     0.05960407, ...,   -0.061208546,\n",
            "       0.054949425,    -0.06479695]],\n",
            "\n",
            "  [[   -0.07290344,     0.08268641,     -0.0744022, ...,    -0.06489891,\n",
            "         0.0622469,    0.005141735],\n",
            "   [  0.0140788555,   -0.023619017,   0.0046598515, ...,    0.000298361,\n",
            "      -0.049152255,   -0.033540867],\n",
            "   [   0.011155347,    -0.06566751,   0.0031637154, ...,    0.007949352,\n",
            "      -0.044509172,    0.032268606],\n",
            "   ...,\n",
            "   [    0.07784049,   0.0135927005,   -0.024921995, ...,  -0.0051608086,\n",
            "        0.04461837,     0.07464938],\n",
            "   [    0.07778945,    0.051431756,   -0.060906135, ...,    0.022410035,\n",
            "      0.0055269003,    0.008819422],\n",
            "   [     0.0705878,   0.0018017888,   -0.061519384, ..., -0.00015316407,\n",
            "       0.018067837,    0.013164422]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
            " 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
            " 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), conv2: TensorFlow.Conv2D<Swift.Float>(filter: [[[[   -0.040897857,    0.0036835414,    0.0029153628, ...,     -0.04438241,\n",
            "       -0.005600969,     0.026597034],\n",
            "   [    0.012949624,    -0.012910255,     7.87536e-05, ...,     0.029331353,\n",
            "         0.06099176,     -0.03708226],\n",
            "   [    -0.06332976,    -0.024154296,   -0.0029826397, ...,     0.017373918,\n",
            "       -0.058963574,    -0.031613227],\n",
            "   ...,\n",
            "   [    0.054799873,     -0.05233616,     -0.06153185, ...,      0.05229082,\n",
            "        0.044911917,     -0.04797116],\n",
            "   [    -0.06282169,     0.045712978,     0.014394925, ...,     0.036971413,\n",
            "        -0.05880827,     0.016865967],\n",
            "   [   -0.004634366,    -0.046730854,      -0.0691136, ...,      0.02997506,\n",
            "        -0.07164614,    0.0034177029]],\n",
            "\n",
            "  [[   -0.014895631,      0.06527123,    -0.054594737, ...,    -0.054867286,\n",
            "         0.04201672,    -0.028711854],\n",
            "   [   -0.058339104,       0.0355395,     0.054680098, ...,    -0.029601958,\n",
            "        0.048306134,     0.044488776],\n",
            "   [   -0.013096893,      0.07033904,    -0.061409876, ...,      -0.0650009,\n",
            "       -0.016407864,    0.0052944887],\n",
            "   ...,\n",
            "   [    -0.06370889,     -0.07028775,    -0.040537607, ...,     -0.03888662,\n",
            "       -0.035695527,      0.01465937],\n",
            "   [   -0.056748822,      0.05773399,     0.003375444, ...,     0.022756193,\n",
            "       -0.018700046,   -0.0007573732],\n",
            "   [   -0.029212043,     0.049450703,     0.051396552, ...,     -0.06000031,\n",
            "        0.070146225,     0.056542654]],\n",
            "\n",
            "  [[    0.050749715,     0.031866163,     0.049456432, ...,     0.025921805,\n",
            "         0.02225263,     -0.03229398],\n",
            "   [    -0.06303997,    -0.064973176,   -0.0035726635, ...,      -0.0704408,\n",
            "         0.02146883,    -0.021359792],\n",
            "   [    0.041963104,   -0.0039854962,     -0.06487864, ...,     0.053075224,\n",
            "        -0.05402037,     -0.06391034],\n",
            "   ...,\n",
            "   [   -0.032979485,    -0.024476692,      0.04270754, ...,     0.050983876,\n",
            "         0.06360789,      0.02786215],\n",
            "   [   -0.033472255,     -0.06625474,    -0.009149836, ...,     -0.01660665,\n",
            "       -0.056431588,     -0.04050903],\n",
            "   [    -0.06646515,      0.06858686,     -0.03852658, ...,    -0.007867221,\n",
            "        -0.06498701,     0.017069604]]],\n",
            "\n",
            "\n",
            " [[[   -0.012716374,     -0.06127577,    -0.057000585, ...,      0.04858448,\n",
            "         0.06351308,    -0.021595778],\n",
            "   [   -0.024595743,      0.01956453,     0.020668423, ...,    -0.051189784,\n",
            "        0.013735422,    -0.060360424],\n",
            "   [   -0.012236918,     -0.03734235,      0.06658968, ...,     -0.04932372,\n",
            "         -0.0636169,     0.047026563],\n",
            "   ...,\n",
            "   [    0.021893224,    -0.043657485,     0.013934396, ...,      0.06491308,\n",
            "       -0.009382415,     0.022160182],\n",
            "   [   -0.039190847,      0.01337617,    -0.060588013, ...,    -0.009824808,\n",
            "       -0.018037584,    -0.008319921],\n",
            "   [   -0.055362727,     0.053374343,    -0.025911016, ...,     0.036310896,\n",
            "        -0.06903123,    -0.047172043]],\n",
            "\n",
            "  [[    0.043101773,     0.010545549,     0.038920175, ...,    -0.039758436,\n",
            "       -0.011100334,     0.050247822],\n",
            "   [    -0.05225386,     0.051973294,     -0.02916631, ...,    -0.061583642,\n",
            "         0.03931926,      0.06941946],\n",
            "   [    0.015029634,    -0.015006887,      0.06840429, ...,    -0.048331168,\n",
            "        0.027894275,    -0.025071723],\n",
            "   ...,\n",
            "   [    0.004892393,     -0.00498018,      0.06630624, ...,     -0.05792118,\n",
            "        0.044228755,    -0.008677728],\n",
            "   [   -0.010231463,     0.037215486,      0.06386564, ...,      0.03318546,\n",
            "        0.071814574,    -0.026495742],\n",
            "   [    -0.01828668,      0.04793468,     0.045968924, ...,      0.05756079,\n",
            "       -0.010359719,     0.036772955]],\n",
            "\n",
            "  [[    0.031173741,     -0.05616489,    -0.046089675, ...,       0.0667077,\n",
            "       -0.067028806,    -0.055373188],\n",
            "   [     0.06634774,    -0.015203453,     -0.04901134, ...,    -0.007206668,\n",
            "       -0.055647563,    -0.036380254],\n",
            "   [    0.045529455,      0.06745267,     -0.06868605, ...,     0.029124033,\n",
            "       -0.027618542,     0.044625137],\n",
            "   ...,\n",
            "   [  -0.0038478968,    0.0117163565,     0.027557908, ...,    -0.026809826,\n",
            "        0.046523087,    0.0032765418],\n",
            "   [    0.043020196,     0.036944933,    -0.056071218, ...,    -0.011406229,\n",
            "       0.0010263949,     0.026246833],\n",
            "   [   -0.069761455,     0.015451018,      0.06425973, ...,    -0.063182645,\n",
            "        0.029911414,    -0.014139789]]],\n",
            "\n",
            "\n",
            " [[[    0.007816273,    -0.042864256,        0.057272, ...,     -0.07026858,\n",
            "      -0.0032022619,     0.054306082],\n",
            "   [   -0.064816944,    -0.061495356,    -0.021419497, ...,    -0.032823578,\n",
            "        0.034608085,    -0.015356641],\n",
            "   [   -0.037616793,     0.028210012,    -0.056680635, ...,     0.028742395,\n",
            "       -0.028374437,     0.039296806],\n",
            "   ...,\n",
            "   [      0.0712587,    -0.049173936,    -0.016912786, ...,    -0.002285248,\n",
            "       -0.033833984,     0.059107043],\n",
            "   [    0.015650699,      0.05209288,     -0.04052928, ...,      0.06825192,\n",
            "       -0.015099836,    -0.025964873],\n",
            "   [     0.06306021,    -0.015285183,     0.067393064, ...,    -0.060699973,\n",
            "        0.011565216,     0.061385028]],\n",
            "\n",
            "  [[    -0.06400167,    -0.013497423,    -0.047870573, ...,     0.030351777,\n",
            "        0.030834725,    0.0040041306],\n",
            "   [  -0.0015926568,    -0.012602123,      0.01750281, ...,     0.005370954,\n",
            "         0.06871443,         0.01696],\n",
            "   [     0.05481417,     0.018480288,   0.00091001095, ...,      0.04367297,\n",
            "      -0.0032901177,     -0.06668504],\n",
            "   ...,\n",
            "   [    0.054333113,    0.0041374112,       -0.052643, ...,     -0.02314122,\n",
            "       -0.009023077,     -0.01194219],\n",
            "   [    0.022400502,     0.011506732,      0.01549006, ...,    -0.049939897,\n",
            "      -0.0032550683,      0.06736285],\n",
            "   [     0.00507466,     0.019536827,     0.052807994, ...,     0.023223862,\n",
            "       -0.051016327,     -0.02668847]],\n",
            "\n",
            "  [[    0.058332995,    -0.030449027,     0.035493955, ...,     -0.03304105,\n",
            "       -0.028136162,    -0.049429107],\n",
            "   [  -0.0014009089,    -0.038560852,    -0.069934085, ...,    -0.030747145,\n",
            "        0.042004537,    -0.045513984],\n",
            "   [       0.048169,     -0.02418871,    0.0018930974, ...,     -0.03140123,\n",
            "       -0.034674156,    -0.059566658],\n",
            "   ...,\n",
            "   [    0.016276391,    -0.026053192,      0.04279352, ...,     -0.05424001,\n",
            "       -0.034459405,     -0.03675076],\n",
            "   [    0.033893123,     0.023214553,    -0.063657954, ...,     0.055142984,\n",
            "       -0.014688999,     0.015350137],\n",
            "   [   -0.046255305,       0.0684717,       0.0258802, ...,    -0.017633148,\n",
            "        -0.03753914,    -0.008899363]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
            " 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
            " 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), latestResult: __lldb_expr_135.LatestResult), pool: TensorFlow.MaxPool2D<Swift.Float>(poolSize: (1, 2, 2, 1), strides: (1, 2, 2, 1), padding: TensorFlow.Padding.valid), trans1: TensorFlow.TransposedConv2D<Swift.Float>(filter: [[[[      0.0208839,     -0.07913562,      0.02792859, ...,   -0.0069508357,\n",
            "       -0.067577325,    -0.077251576],\n",
            "   [   -0.035033405,     -0.08316897,      0.07936488, ...,    -0.063562274,\n",
            "        0.013948202,     0.042808555],\n",
            "   [   -0.022804499,     -0.07824373,     0.011865179, ...,     0.029069126,\n",
            "        0.059833787,     0.004495899],\n",
            "   ...,\n",
            "   [    0.044739686,     0.011083822,      0.06875964, ...,    0.0032690167,\n",
            "         0.05214542,    -0.057156228],\n",
            "   [   -0.032957613,     0.009417018,     0.003389001, ...,    -0.082606435,\n",
            "       -0.063526414,      0.07546072],\n",
            "   [    0.022940874,     0.024700284,    -0.003521959, ...,    -0.060961843,\n",
            "        -0.04385235,      0.02384176]],\n",
            "\n",
            "  [[    0.026459059,      0.05413113,    -0.047215264, ...,    -0.015158594,\n",
            "          0.0758729,     0.019467872],\n",
            "   [  -0.0032329957,     -0.03171931,    -0.017614445, ...,     0.058744967,\n",
            "          0.0364186,     -0.01775539],\n",
            "   [     0.00058345,    -0.025378367,     0.019948464, ...,   -0.0032605927,\n",
            "        0.043758295,      -0.0249695],\n",
            "   ...,\n",
            "   [  0.00053743523,     0.062025867,      0.06561401, ...,     -0.03630992,\n",
            "       -0.030276895,    -0.017133158],\n",
            "   [    0.009585917,    -0.030435383,    -0.060203515, ...,     -0.07602857,\n",
            "        0.028949063,     -0.04682104],\n",
            "   [   -0.041141212,      0.06914787,    -0.008880695, ...,     -0.06786512,\n",
            "        0.042703193,    -0.050152324]],\n",
            "\n",
            "  [[   -0.032001477,      0.07816305,       0.0787767, ...,     0.046281558,\n",
            "       0.0007209381,     0.048458856],\n",
            "   [     0.08309134,     0.029604277,    -0.014700254, ...,     -0.07735755,\n",
            "        0.044894617,     -0.06843923],\n",
            "   [    -0.03440839,      0.05471202,    -0.022637626, ...,    0.0035297673,\n",
            "        0.035759866,     -0.07214701],\n",
            "   ...,\n",
            "   [   -0.015589058,     -0.04804784,     0.045426093, ...,    -0.036848508,\n",
            "       -0.077838704,     0.009300411],\n",
            "   [    -0.05366121,     -0.03741771,    -0.022577941, ...,      0.05771943,\n",
            "        0.019756615,      0.07359084],\n",
            "   [    0.076666914,      0.07197299,     -0.05631288, ...,    -0.021893363,\n",
            "       -0.029083451,     0.037218593]]],\n",
            "\n",
            "\n",
            " [[[    0.037011825,    -0.016777119,      0.05583465, ...,    -0.055841964,\n",
            "        0.003673792,     0.030273935],\n",
            "   [   -0.011191547,     0.013604621,     0.055941105, ...,      -0.0159839,\n",
            "        0.045269273,     0.005313059],\n",
            "   [   -0.019957703,      0.03576579,    -0.008705914, ...,    -0.035547756,\n",
            "       -0.013516149,     0.012386402],\n",
            "   ...,\n",
            "   [    0.061855517,      0.05026108,    -0.025231283, ...,      0.00889959,\n",
            "        0.025600117,     0.017568987],\n",
            "   [    -0.06724211,      0.05009681,    -0.081569396, ...,   -0.0087375445,\n",
            "        0.037122272,    -0.020387512],\n",
            "   [   -0.008868953,     0.064796135,     0.047236465, ...,     0.030862372,\n",
            "        -0.00824815,    -0.058681846]],\n",
            "\n",
            "  [[     0.02828908,    -0.063705646,     0.033998154, ...,     0.035123073,\n",
            "        0.040249508,      0.05991455],\n",
            "   [    0.017181259,      0.01610454,   -0.0041865706, ...,     -0.02323246,\n",
            "        -0.04140697,    -0.012625119],\n",
            "   [   0.0014808774,    -0.036648415,     0.029275417, ...,     0.017776867,\n",
            "        0.035094064,      0.00832057],\n",
            "   ...,\n",
            "   [   -0.018341642,    -0.043386757,    -0.042900763, ...,    -0.014625013,\n",
            "        0.075607836,    -0.026176613],\n",
            "   [     0.06603579,     0.045276225,     -0.06683048, ...,     -0.06151869,\n",
            "        -0.06196835,     0.041265905],\n",
            "   [    -0.08204325,     0.047714632,       0.0628893, ...,     0.011045377,\n",
            "         0.07176791,      0.07350241]],\n",
            "\n",
            "  [[   0.0131364465,     0.027803838,     -0.03999909, ...,     -0.06488733,\n",
            "         0.05142524,     0.038944446],\n",
            "   [   -0.017584026,      0.03298533,      0.01972143, ...,    -0.038526695,\n",
            "       -0.011327228,      0.08252792],\n",
            "   [    -0.04390804,      0.06306519,    -0.048203647, ...,    -0.016700983,\n",
            "      -0.0052244067,      0.02046603],\n",
            "   ...,\n",
            "   [   -0.063777685,     0.035448372,      0.03924489, ...,    -0.067165434,\n",
            "      -0.0035243235,      0.07242908],\n",
            "   [   0.0034270883,     0.037392758,     0.016563892, ...,     0.050393205,\n",
            "       -0.062680826,    -0.056506675],\n",
            "   [    0.021735549,     -0.02888324,    -0.039903007, ...,     0.059046447,\n",
            "     -0.00062050426,      0.06339403]]],\n",
            "\n",
            "\n",
            " [[[    0.032543086,      0.07625743,      -0.0498273, ...,    -0.061997533,\n",
            "      -0.0045965714,     0.003041486],\n",
            "   [   -0.017968219,    -0.037894785,     -0.06671004, ...,      0.07530811,\n",
            "        0.008623223,     -0.06725852],\n",
            "   [    0.017712316,    -0.058243673,     0.044694982, ...,     -0.07970824,\n",
            "         0.05229318,     0.077181324],\n",
            "   ...,\n",
            "   [    0.007924239,     -0.07354798,    -0.028543692, ...,    -0.007544677,\n",
            "     -0.00043735903,     0.068962395],\n",
            "   [     0.06270766,      -0.0461758,    -0.050033174, ...,     0.049120367,\n",
            "        -0.00970004,     -0.07239628],\n",
            "   [    0.047184765,    -0.024219513,     -0.00923725, ...,     -0.03462593,\n",
            "       -0.026569208,     0.062060893]],\n",
            "\n",
            "  [[  -0.0064711175,     -0.07322278,     0.034275495, ...,   -0.0035728812,\n",
            "       -0.052374285,     0.017511528],\n",
            "   [   -0.013449848,     0.048088532,   -0.0006791949, ...,     0.058831096,\n",
            "       -0.050308168,    -0.036997616],\n",
            "   [   -0.042669516,   -0.0036010942,     0.020914078, ...,     0.062110722,\n",
            "       -0.013735235,      0.06944116],\n",
            "   ...,\n",
            "   [   -0.064964496,     -0.07903095,    -0.002565086, ...,      0.03359165,\n",
            "        0.024930915,      0.03413419],\n",
            "   [   -0.031520985,     -0.07555988,     0.022791725, ...,      0.08223896,\n",
            "        0.057412546,    -0.054091275],\n",
            "   [    0.045109473,     -0.06309386,    -0.018230718, ...,     -0.07338544,\n",
            "       -0.048529487,     0.035336714]],\n",
            "\n",
            "  [[      0.0529419,      0.07567108,     0.060331307, ...,      0.03589952,\n",
            "        0.014777025,    -0.062358916],\n",
            "   [   -0.014621636,    -0.064573094,     0.036548357, ...,    -0.059058808,\n",
            "       -0.043750763,     0.043638747],\n",
            "   [   -0.054581128,     0.039039336,     -0.07955563, ...,    -0.024207732,\n",
            "        -0.07351147,    -0.004761398],\n",
            "   ...,\n",
            "   [     0.03432274,     0.017930668,     0.019260626, ...,    -0.017424148,\n",
            "       0.0052107377,     0.030221265],\n",
            "   [   -0.033138257,    -0.035614114,     0.038706265, ...,    -0.027543565,\n",
            "        0.031091034,      0.02132678],\n",
            "   [   -0.044423163,      0.08273505,     -0.07063379, ...,     0.025425335,\n",
            "        -0.07393666,   -0.0069767637]]]], bias: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
            " 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], activation: (Function), strides: (2, 2), padding: TensorFlow.Padding.valid, paddingIndex: 1), trans2: TensorFlow.TransposedConv2D<Swift.Float>(filter: [[[[   -0.09755521,   -0.024202388,     0.05238347, ...,    0.073530756,\n",
            "        0.10185203,    0.114689626],\n",
            "   [    0.03136282,  -0.0002950935,   -0.090100005, ...,     0.10060785,\n",
            "        0.06787863,     0.06255039],\n",
            "   [   -0.07909772,   -0.014340797,    0.045163807, ...,   -0.036348686,\n",
            "        0.07122814,   -0.015923359],\n",
            "   ...,\n",
            "   [   -0.09935064,    0.084456176,    -0.11623545, ...,     0.13545474,\n",
            "      -0.061884385,   -0.035606865],\n",
            "   [   0.057510015,     0.07923761,   -0.110814124, ...,     0.08435053,\n",
            "        0.06681932,    -0.03908298],\n",
            "   [   0.071368545,    -0.06575321,   -0.070823126, ...,    0.038239073,\n",
            "        0.01726077,     -0.0691621]],\n",
            "\n",
            "  [[    0.07811335,      0.0748784,    0.095686264, ...,    0.078134865,\n",
            "       0.048761327,    -0.05580359],\n",
            "   [   0.044849224,     0.09837336,     0.06675739, ...,   -0.080494806,\n",
            "        0.07796371,    -0.07152821],\n",
            "   [    0.12575875,    -0.05673214,     0.05954199, ...,   -0.023852931,\n",
            "        0.09149369,   -0.009342565],\n",
            "   ...,\n",
            "   [     0.1256118,    0.034514926,    -0.11198787, ...,     0.09430929,\n",
            "       -0.07939877,    0.074942306],\n",
            "   [   -0.10876565,     0.10556931,    -0.03690768, ...,    0.068812534,\n",
            "        0.07397529,    -0.08191247],\n",
            "   [   -0.10554851,   -0.030135784,    0.036810696, ...,     0.08043607,\n",
            "        0.04098895,    -0.10408022]],\n",
            "\n",
            "  [[   -0.07024349,    0.023491459,     0.07564549, ...,  -0.0078083724,\n",
            "       0.010601738,      0.1034735],\n",
            "   [  -0.014719169,     0.10849263,   -0.024268163, ...,    0.105723806,\n",
            "     -0.0052466486,    0.019572664],\n",
            "   [   0.085386515,    0.009485442,    -0.08535963, ...,    -0.05826983,\n",
            "         0.1668266,     0.10970016],\n",
            "   ...,\n",
            "   [    0.07348067,    -0.04917733,    0.025036097, ...,     0.13903585,\n",
            "        0.15761097,    0.055676673],\n",
            "   [    -0.0734242,     -0.0662774,     0.08353119, ...,   0.0027586042,\n",
            "      -0.065288015,   -0.044769727],\n",
            "   [   0.022110969,   -0.014804184,    -0.06194856, ...,   -0.058083143,\n",
            "       0.044642285,    0.114456855]]],\n",
            "\n",
            "\n",
            " [[[    0.00635016,    -0.09340356,     0.09163342, ...,    -0.07420197,\n",
            "      -0.040824205,   -0.058123637],\n",
            "   [   -0.09189294,     0.05831979,     0.08694698, ...,   -0.099796645,\n",
            "        0.09213218,     0.08477083],\n",
            "   [   -0.04833615,   -0.007892431,   -0.052325413, ...,    0.027969984,\n",
            "        0.13365401,    0.027966266],\n",
            "   ...,\n",
            "   [  -0.037328817,     0.07056353,   -0.012974456, ...,    0.044400677,\n",
            "        0.05169716,   -0.021181485],\n",
            "   [   0.025243383,    0.050322294,    0.027760968, ...,    -0.11408385,\n",
            "      -0.018617371,    0.010917548],\n",
            "   [    0.07494757,   -0.070229165,   -0.089161225, ...,   -0.099492356,\n",
            "       0.058257174,    0.080151185]],\n",
            "\n",
            "  [[   0.042891707,      0.0389087,    -0.10970633, ...,   0.0038832442,\n",
            "       0.018454282,  -0.0030651495],\n",
            "   [   -0.08574234,     0.11947926,   -0.034835022, ...,    0.067338765,\n",
            "        0.07534821,    -0.09868637],\n",
            "   [   0.063754156,   -0.024498733,    0.034540717, ...,    0.029008504,\n",
            "       0.031913634,    -0.06591341],\n",
            "   ...,\n",
            "   [  -0.084697746,   -0.018227093,    0.009691054, ...,     0.07840484,\n",
            "       0.023749622,     0.04857803],\n",
            "   [  -0.011366828,   -0.010839214,    0.041340824, ...,    -0.12668085,\n",
            "       -0.13775924,   0.0017155594],\n",
            "   [   -0.11943625,    -0.08620069,   -0.059322055, ...,    -0.04372416,\n",
            "       0.074990384,   -0.105245665]],\n",
            "\n",
            "  [[    0.08255172,     0.08772833,    -0.06961431, ...,    -0.05937363,\n",
            "       0.029374052,     0.03265729],\n",
            "   [   0.036973324,    0.088054284,    0.042675864, ...,     0.11292211,\n",
            "        -0.0997204,    -0.07890127],\n",
            "   [   0.013383311,     0.09152074,     0.10827341, ...,    0.116517335,\n",
            "      -0.016322646,     0.08303419],\n",
            "   ...,\n",
            "   [    -0.0655988,     0.10836634,     0.03264141, ...,     0.06689655,\n",
            "        0.04516155,   -0.016276592],\n",
            "   [    -0.0684126,    0.029253839,    0.004036676, ...,    0.021509342,\n",
            "        0.09055795,    0.002505709],\n",
            "   [   -0.06109097,    -0.03856898,   -0.079978384, ...,   -0.013981431,\n",
            "        -0.1366999,    0.066841766]]],\n",
            "\n",
            "\n",
            " [[[    0.09296042,    0.028904475,   -0.101765245, ...,    -0.11751487,\n",
            "     -0.0064791096,     0.07573219],\n",
            "   [    0.06977827,     0.07355491,    0.087007694, ...,   -0.055904243,\n",
            "       -0.04891944,    0.041595656],\n",
            "   [   -0.05660159,    0.060705602,     -0.0747938, ...,   -0.042859137,\n",
            "       0.112297945,    -0.03598066],\n",
            "   ...,\n",
            "   [   -0.07805382,    -0.03537303,     0.09122381, ...,    0.063332774,\n",
            "      -0.046224516,    0.107413195],\n",
            "   [    0.09975159,   -0.094291024,    0.026448166, ...,    0.010442176,\n",
            "      -0.040166847,   -0.020562725],\n",
            "   [   0.038640775,     0.07369395,    0.021411024, ...,      0.0812641,\n",
            "       -0.07561306,   -0.110267825]],\n",
            "\n",
            "  [[    0.06637173,     0.10314276,    0.094711326, ...,    0.077837005,\n",
            "      -0.046702426,    -0.09650179],\n",
            "   [    0.08912564,   -0.098831855,    0.089888126, ...,     0.06587947,\n",
            "      -0.004037915,  -0.0010509251],\n",
            "   [   -0.06484245,    0.037441295,     0.10241452, ...,    -0.06265332,\n",
            "        0.11333405,     0.04727687],\n",
            "   ...,\n",
            "   [  -0.030133171,   0.0057213996,    0.092961766, ...,  -0.0025312963,\n",
            "       -0.03486794,    0.066532046],\n",
            "   [  -0.066307396,    -0.08165183,   -0.026096212, ...,   -0.052258313,\n",
            "        0.08165777,     0.10909147],\n",
            "   [     0.0548783,   -0.114513166,     -0.0233333, ...,     -0.0382938,\n",
            "      -0.054638486,    -0.03650081]],\n",
            "\n",
            "  [[   0.048587427,   -0.010065718, -0.00057715044, ...,    0.044963185,\n",
            "      -0.018623274,     0.05458905],\n",
            "   [    0.10931011,   -0.018860701,    -0.09813879, ...,    0.041267898,\n",
            "        0.08599529,    -0.10169399],\n",
            "   [  -0.028929532,     0.09988826,    0.020927114, ...,  -0.0017266955,\n",
            "        0.16358116,    0.052188713],\n",
            "   ...,\n",
            "   [    0.06445227,   -0.067605704,     0.11711805, ...,    0.063296385,\n",
            "         0.1032356,    0.086828835],\n",
            "   [   0.010875676,     0.10399106,   0.0098671485, ...,     0.05236981,\n",
            "       0.067406155,    -0.06849926],\n",
            "   [     -0.068773,     -0.0643704,     0.05254902, ...,    0.018601175,\n",
            "       0.094034426,    -0.11260195]]]], bias: [ -0.4580873,   0.3927371,   0.5811719,  -0.5065535,   0.5812149, -0.54824126,   0.5616767,\n",
            " -0.37630805,  0.47030967,   0.5732538, -0.38329282, -0.44637883, -0.57159716,  0.56369025,\n",
            " -0.46913034,  -0.5300394], activation: (Function), strides: (2, 2), padding: TensorFlow.Padding.same, paddingIndex: 0), trans3: TensorFlow.TransposedConv2D<Swift.Float>(filter: [[[[    0.23627783,     0.34178513,     0.37522694, ...,     0.14641625,\n",
            "      -0.074488975,    0.034433678],\n",
            "   [   -0.36910447,     -0.4364149,    -0.38957933, ...,   -0.062323295,\n",
            "        -0.1307675,  -0.0012665689],\n",
            "   [   -0.55063426,    -0.40691632,      -0.535825, ...,    -0.14610432,\n",
            "       0.050683614,     0.08865876],\n",
            "   ...,\n",
            "   [     0.4067482,     0.40856856,      0.6299471, ...,   -0.121213384,\n",
            "        0.15814257,    -0.10991219],\n",
            "   [    0.52379847,     0.41692138,      0.5343817, ...,   -0.017985743,\n",
            "       0.079716645,     0.20173234],\n",
            "   [  0.0079466505,     -0.4147689,    -0.48256418, ...,     0.11770922,\n",
            "       0.080931865,    0.081009425]],\n",
            "\n",
            "  [[     0.2929619,     0.32319564,      0.3842308, ...,    -0.15593815,\n",
            "        0.09314267,      0.0794121],\n",
            "   [   -0.05788109,    0.076055214,   -0.054225005, ...,    -0.14358142,\n",
            "      -0.059424043,     0.10473993],\n",
            "   [    -0.6645505,     -0.5690783,     -0.5341278, ...,     -0.1247272,\n",
            "      -0.022502303,   -0.025460975],\n",
            "   ...,\n",
            "   [     0.5836441,     0.68328655,     0.72062737, ...,  -0.0038676204,\n",
            "        -0.0987939,    0.032851335],\n",
            "   [    0.31540397,     0.66728956,     0.44378927, ...,    -0.07837989,\n",
            "      -0.100879915,     0.19177625],\n",
            "   [     0.2931042,     0.30024284,       0.442728, ...,    0.046296142,\n",
            "       -0.08946804,   -0.063091055]],\n",
            "\n",
            "  [[    0.21708381,      0.2831456,      0.6067156, ...,    -0.15739329,\n",
            "        0.07277932,     -0.0472656],\n",
            "   [    -0.5787753,    -0.36403236,    -0.53148806, ...,    -0.08486551,\n",
            "      0.0042726994,   -0.111438684],\n",
            "   [   -0.54740804,    -0.42681858,    -0.72980446, ...,   -0.008296228,\n",
            "       -0.12995388,    0.039904494],\n",
            "   ...,\n",
            "   [    0.39227206,      0.5466901,     0.54631114, ...,     0.07508943,\n",
            "         0.1298098,     0.20727408],\n",
            "   [    0.44191444,     0.67965335,      0.7230612, ...,    0.025712807,\n",
            "       -0.06296858,     0.08787729],\n",
            "   [  0.0045238067,    -0.42466858,     -0.5045872, ...,     0.14790362,\n",
            "        0.09614208,   0.0034788533]]],\n",
            "\n",
            "\n",
            " [[[     0.3206958,      0.2589938,     0.52091044, ...,   -0.022768697,\n",
            "       0.009226769,    -0.10428967],\n",
            "   [   -0.28728166,    -0.37781435,    -0.54053795, ...,   0.0097200675,\n",
            "       0.020496832,     0.09022403],\n",
            "   [   -0.32506734,    -0.42035857,     -0.3911943, ...,    0.094725825,\n",
            "        0.06951785,     0.11431361],\n",
            "   ...,\n",
            "   [     0.3126553,     0.61223286,      0.4454377, ...,    -0.15497988,\n",
            "      0.0018486718,     0.16330175],\n",
            "   [    0.43330783,     0.66861534,     0.44654283, ...,   -0.002882927,\n",
            "       -0.07510312,    0.052223645],\n",
            "   [     -0.418045,     -0.5124831,    -0.60729325, ...,   0.0021268006,\n",
            "        0.15135461,     0.10081733]],\n",
            "\n",
            "  [[   -0.10453231,    -0.26824042,    -0.48575166, ...,    -0.15435183,\n",
            "         0.1028681,    0.033951093],\n",
            "   [   -0.05082702,     0.17214933,     0.29895517, ...,     0.07987497,\n",
            "       -0.08656159,    0.029525131],\n",
            "   [     -0.601175,    -0.51201826,     -0.6925178, ...,    -0.14216341,\n",
            "       0.089057095,    -0.09342367],\n",
            "   ...,\n",
            "   [    0.29629692,      0.4268767,      0.5263252, ...,    -0.02702206,\n",
            "      -0.105476685,      0.0704176],\n",
            "   [    0.58518416,     0.39750925,     0.50187474, ...,     -0.0765304,\n",
            "        0.15132862,     0.03687963],\n",
            "   [     0.3394674,     0.35045046,      0.5645955, ...,    0.056160416,\n",
            "       -0.04143659,     0.06471602]],\n",
            "\n",
            "  [[   0.060550783,     0.24915971,     0.27818203, ...,    -0.04909406,\n",
            "        0.07003495,    -0.14114834],\n",
            "   [    -0.1167388,     -0.3096321,     -0.5331995, ...,    0.110871084,\n",
            "      -0.041577876,     0.06159766],\n",
            "   [   -0.32046384,     -0.5883906,    -0.60371155, ...,    -0.08316456,\n",
            "       0.114241526,    -0.06498395],\n",
            "   ...,\n",
            "   [     0.5508882,     0.59038675,       0.549854, ...,   -0.054040488,\n",
            "        0.11941518,    0.041786663],\n",
            "   [      0.610963,     0.62295204,     0.48929268, ...,   -0.072013386,\n",
            "        0.01594851,    0.025674904],\n",
            "   [   -0.30919868,     -0.6019887,    -0.46545842, ...,    0.102796204,\n",
            "       0.024938354,     0.08806045]]],\n",
            "\n",
            "\n",
            " [[[    0.25983736,      0.5461323,     0.37870747, ...,    -0.15207788,\n",
            "        0.07847736,    -0.11431234],\n",
            "   [   -0.45863417,     -0.4141946,    -0.45377424, ...,    -0.09386037,\n",
            "        0.13046429,     0.08852953],\n",
            "   [   -0.40746972,     -0.6874838,     -0.6473819, ...,    0.012675282,\n",
            "       0.024031593,   -0.005093515],\n",
            "   ...,\n",
            "   [    0.26371148,     0.42291763,     0.68976295, ...,    -0.15793982,\n",
            "       -0.12307058,      0.1997059],\n",
            "   [     0.5376627,      0.6054529,     0.58224785, ...,     0.17203853,\n",
            "      -0.047623172,   0.0033300335],\n",
            "   [   -0.17959772,    -0.25110668,    -0.39896387, ...,    0.024908576,\n",
            "       0.006577422,    -0.12754264]],\n",
            "\n",
            "  [[     0.2584814,     0.33607066,     0.56321824, ...,    -0.10664622,\n",
            "       -0.10321533,    -0.10782644],\n",
            "   [   0.031310383,  -0.0129314475,      -0.098233, ...,    0.080456376,\n",
            "       -0.09781087,    -0.13441968],\n",
            "   [    -0.3858949,    -0.45754158,     -0.5428729, ...,    0.031037826,\n",
            "        0.07344725,    -0.20999356],\n",
            "   ...,\n",
            "   [    0.35659197,     0.59620273,      0.5303674, ...,    -0.04127117,\n",
            "      -0.079684995,     0.12555493],\n",
            "   [    0.43709254,     0.59691805,     0.70954376, ...,   -0.058537524,\n",
            "        0.05947698,    -0.03141007],\n",
            "   [    0.20965935,     0.46961448,     0.45039013, ...,    0.017158907,\n",
            "       0.010842717,    0.050567724]],\n",
            "\n",
            "  [[     0.1490001,      0.3475279,      0.5861114, ...,      0.1052481,\n",
            "       -0.01820414,     0.13216576],\n",
            "   [   -0.38456595,    -0.55790234,    -0.40778497, ...,    -0.08307806,\n",
            "       0.069140285,    -0.06839774],\n",
            "   [    -0.3961103,    -0.51443744,     -0.7319709, ...,     0.14060095,\n",
            "        0.07422354,   -0.102683954],\n",
            "   ...,\n",
            "   [    0.48669794,     0.43843517,     0.65683997, ...,    -0.08251948,\n",
            "       -0.09496949,     0.07197076],\n",
            "   [     0.5039439,     0.39251173,     0.41137525, ...,     0.14031729,\n",
            "       0.104167275,    0.047630176],\n",
            "   [   -0.16704115,    -0.24137461,    -0.20899276, ...,   -0.015464236,\n",
            "       -0.11061201,   -0.055740003]]]], bias: [ 0.59422165, -0.59617907,  -0.5992654,     0.59857,  -0.5983839,   0.5986751,   0.5988724,\n",
            "  -0.5840756], activation: (Function), strides: (2, 2), padding: TensorFlow.Padding.same, paddingIndex: 0), up1: __lldb_expr_135.DownBlock(conv1: TensorFlow.Conv2D<Swift.Float>(filter: [[[[     0.07013556,     -0.03213879,    -0.004657778, ...,    -0.081672765,\n",
            "         0.03729628,     -0.06600694],\n",
            "   [   -0.012686387,     0.050654765,     0.038362216, ...,    -0.019970749,\n",
            "        0.059640225,    -0.039715573],\n",
            "   [      0.0366929,    -0.023763262,     0.081701525, ...,     -0.03761475,\n",
            "        -0.05849135,   -0.0026948268],\n",
            "   ...,\n",
            "   [   -0.032936502,     -0.06831846,     0.039521668, ...,     0.060535993,\n",
            "        0.064043105,     -0.07098782],\n",
            "   [     0.03957636,    -0.024468271,    -0.022562156, ...,      -0.0268948,\n",
            "       -0.005183887,     -0.03216737],\n",
            "   [     0.06599573,      0.05125013,    0.0071597355, ...,    -0.013247531,\n",
            "         0.03618702,    -0.070958234]],\n",
            "\n",
            "  [[     0.03306977,     0.083042994,    -0.007815938, ...,       0.0813401,\n",
            "         -0.0620268,     -0.01570279],\n",
            "   [   0.0053496375,      0.05231525,     0.023253925, ...,        0.069782,\n",
            "        0.029990764,     0.049923226],\n",
            "   [    0.009139007,    0.0039754147,     0.034915682, ...,    -0.075564936,\n",
            "         0.07296263,     0.052883785],\n",
            "   ...,\n",
            "   [    0.003241614,       -0.073757,    -0.060510516, ...,    -0.035417672,\n",
            "       -0.019471008,     0.010226828],\n",
            "   [   -0.012752953,     -0.07266875,     0.022893224, ...,    -0.014096696,\n",
            "        -0.05178346,    -0.026062245],\n",
            "   [    0.012416816,      0.07818086,       0.0809201, ...,     0.069482684,\n",
            "        -0.01691436,     -0.02782504]],\n",
            "\n",
            "  [[  -0.0015170336,      0.06934806,      0.05548651, ...,    -0.050870985,\n",
            "         0.07234094,     -0.07803515],\n",
            "   [    -0.05128266,     -0.03805603,     -0.03429369, ...,     -0.07970175,\n",
            "        -0.06269672,      0.02388052],\n",
            "   [   -0.018695533,    -0.041389186,    -0.003735098, ...,     -0.04008899,\n",
            "        0.053540543,     0.015555136],\n",
            "   ...,\n",
            "   [    0.015754363,   -0.0072261943,    -0.034851443, ...,     0.053742312,\n",
            "       -0.077156186,     -0.04635828],\n",
            "   [     0.07218124,    -0.037716314,      0.03401698, ...,     0.042996474,\n",
            "       -0.016366934,    -0.022219973],\n",
            "   [    0.077347316,      0.08013095,     -0.07836616, ...,      0.04970407,\n",
            "       -0.058035254,     0.021462955]]],\n",
            "\n",
            "\n",
            " [[[    0.074670635,   -0.0071829064,     -0.04525592, ...,     -0.04019012,\n",
            "       -0.009620755,    -0.043611288],\n",
            "   [   -0.015728962,      0.05712536,     -0.07210262, ...,    -0.058961622,\n",
            "         0.06547395,    -0.053502373],\n",
            "   [     0.01847525,     -0.06917439,     -0.07502031, ...,   -0.0060329014,\n",
            "         0.07946569,      0.07390825],\n",
            "   ...,\n",
            "   [    -0.07915623,     -0.03696481,    -0.073873885, ...,    -0.017464545,\n",
            "        0.023336329,     0.047080673],\n",
            "   [   -0.038747597,     0.073065005,    -0.035797387, ...,    0.0071502123,\n",
            "       -0.011601206,       -0.068025],\n",
            "   [    -0.05212621,      0.07417406,     -0.03521839, ...,      0.02036098,\n",
            "        0.078210525,    -0.058617495]],\n",
            "\n",
            "  [[    0.038703613,     0.020814957,     0.011539596, ...,     0.055705808,\n",
            "       -0.054943707,   -0.0037386038],\n",
            "   [   -0.024697948,     -0.08314335,     0.044062097, ...,    -0.004250329,\n",
            "       -0.016571164,    -0.071514085],\n",
            "   [    -0.05712897,       0.0794303,     -0.06568772, ...,     0.069202974,\n",
            "       -0.055156793,     0.018348306],\n",
            "   ...,\n",
            "   [    -0.06734531,      0.07583708,   -0.0123815155, ...,    -0.032315668,\n",
            "       -0.042703923,     0.051011834],\n",
            "   [   -0.027039554,    -0.010214616,     0.038389873, ...,     0.057606846,\n",
            "      0.00066106406,     0.016016347],\n",
            "   [    0.016314756,     0.029364504,    -0.025801383, ...,   -0.0015203304,\n",
            "        0.022854988,     -0.03445089]],\n",
            "\n",
            "  [[     0.07961155,     0.058159728,     0.028426738, ...,    -0.022905227,\n",
            "       -0.028697407,     -0.05085401],\n",
            "   [     0.05273095,     0.040426396,    -0.026071291, ...,      0.05455128,\n",
            "        0.008186963,      0.07125919],\n",
            "   [    0.036732197,     0.068837106,      0.03197441, ...,      0.08064285,\n",
            "       -0.021013035,    -0.028169474],\n",
            "   ...,\n",
            "   [    -0.06870771,    -0.004146902,    -0.039135106, ...,      0.06409058,\n",
            "        0.026117455,     0.033799615],\n",
            "   [    -0.07663125,    0.0057755834,    -0.048688702, ...,    0.0045883525,\n",
            "       0.0035511204,     0.023996009],\n",
            "   [    0.016459523,   -0.0053776028,   -0.0042349254, ...,     0.046624016,\n",
            "         -0.0429998,     0.042898796]]],\n",
            "\n",
            "\n",
            " [[[    0.033119366,    -0.073858626,     0.012285418, ...,    -0.044233873,\n",
            "        0.011127402,    -0.067803405],\n",
            "   [     -0.0430285,   -0.0062348605,    -0.046543315, ...,     -0.05724075,\n",
            "       -0.010722728,     0.041263826],\n",
            "   [   -0.071490064,     0.059893444,     0.082654886, ...,    -0.062482744,\n",
            "        0.013542648,     -0.06889132],\n",
            "   ...,\n",
            "   [     0.06118715,   -0.0022977898,     0.025275044, ...,    -0.050813664,\n",
            "        0.040021814,   -0.0035126237],\n",
            "   [  -0.0012374778,     -0.07051738,      0.08027311, ...,   -0.0018129763,\n",
            "        0.004078299,    -0.054667667],\n",
            "   [  0.00082829746,    -0.042630732,     0.013708805, ...,     -0.06758041,\n",
            "        0.011801129,     0.021770408]],\n",
            "\n",
            "  [[    0.031135557,     0.076328464,     0.028162466, ...,     0.032712918,\n",
            "         0.04050707,   -0.0040970263],\n",
            "   [     0.02635072,    -0.049148057,      0.06171435, ...,     0.031943914,\n",
            "        -0.02968312,    -0.072763905],\n",
            "   [    0.014138631,     -0.05362388,    -0.056637447, ...,     0.062384326,\n",
            "       0.0039594527,    0.0077662775],\n",
            "   ...,\n",
            "   [   -0.007818812,     0.005837612,     0.008245702, ...,      0.07626951,\n",
            "         0.08153843,     0.061360326],\n",
            "   [   -0.040895827,     0.018068898,    -0.053584315, ...,    0.0002251341,\n",
            "       -0.036674865,     -0.08006127],\n",
            "   [   0.0032681588,     0.041519683,     0.033737604, ...,    0.0018144598,\n",
            "       -0.079717845,      0.06530349]],\n",
            "\n",
            "  [[    -0.05122165,      0.07213914,    -0.036554918, ...,    0.0020684686,\n",
            "          0.0116982,       -0.071662],\n",
            "   [   -0.056980547,    -0.006161372,      0.07629036, ...,        0.054469,\n",
            "       -0.081703134,      0.05256089],\n",
            "   [     0.07027436,    -0.065917656,    -0.016993698, ...,      0.06355419,\n",
            "        -0.02664912,    -0.013656938],\n",
            "   ...,\n",
            "   [     -0.0680572,    0.0051664924,     -0.00800727, ...,     0.050930493,\n",
            "        0.021253174,     -0.04734349],\n",
            "   [     0.07292855,    -0.039666004,    0.0028605724, ...,    -0.033460725,\n",
            "       -0.056495126,     0.016597934],\n",
            "   [    0.024956262,    -0.020885855,    -0.010254087, ...,   -0.0124565195,\n",
            "        0.008725066,       0.0747988]]]], bias: [ -0.008623928,  -0.025428189,  -0.007827018,   0.032110356,    -0.3219018,    0.23448408,\n",
            "  -0.015841622,  -0.023822231,  -0.039131187,    0.46092293,    -0.1421141,   -0.15678437,\n",
            "   0.005438767,  -0.036631934,    0.30294743,   0.011917808, 8.0668986e-05,   -0.13683222,\n",
            "    -0.4970633,    0.18546996,  -0.021550786, 0.00047850882,    0.46591008,  -0.112362854,\n",
            "    -0.4341972,    -0.5140915,    -0.3396232,  0.0010713019,  -0.034426186,   -0.08968466,\n",
            "  -0.003656635, -0.0066174953], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), conv2: TensorFlow.Conv2D<Swift.Float>(filter: [[[[    0.09791258,   -0.052772067,   -0.040460043, ...,     0.08259367,\n",
            "       0.042245533,    -0.05154043],\n",
            "   [   -0.05737956,    0.035801474,    -0.03883233, ...,    0.013137358,\n",
            "        -0.0818923,   -0.029822636],\n",
            "   [  -0.082051344,    0.068661936,       0.071508, ...,    0.039197046,\n",
            "        0.06597637,   -0.099182665],\n",
            "   ...,\n",
            "   [    0.05198637,    -0.03598419,   -0.013121472, ...,    0.003102881,\n",
            "      -0.019288799,   -0.059773125],\n",
            "   [  -0.022715256,     0.05300002,     0.07245537, ...,    -0.00660006,\n",
            "       -0.08761688,      0.0285385],\n",
            "   [   -0.06912069,   -0.074265175,     0.07174016, ...,    0.015087196,\n",
            "           0.04001,    -0.09065383]],\n",
            "\n",
            "  [[    0.08409429,    0.011806107,   -0.057546705, ...,    -0.07144162,\n",
            "       -0.06050903,    -0.09788776],\n",
            "   [   0.008168284,   -0.098602556,     0.01673126, ...,    -0.07899382,\n",
            "      -0.022606676,   -0.094472244],\n",
            "   [    0.03803763,    0.018108342,   -0.034943487, ...,    0.019909251,\n",
            "       0.045974966,    -0.03907272],\n",
            "   ...,\n",
            "   [  0.0025116932,    0.060963415,   -0.026322521, ...,   -0.031971294,\n",
            "      -0.086735025,   -0.022060279],\n",
            "   [  0.0022489573,    0.001876926,    0.060325194, ...,    -0.07890768,\n",
            "      -0.033355605,  -0.0046437704],\n",
            "   [   0.026438482,   -0.062139574,     0.09474498, ...,   -0.056615695,\n",
            "       -0.07886624,    0.019515274]],\n",
            "\n",
            "  [[   0.011165211,    0.060080383,  -0.0033391342, ...,    -0.08841992,\n",
            "      -0.022092575,    -0.08625193],\n",
            "   [    0.04676118,   -0.024700986,    -0.10204306, ...,     0.08809254,\n",
            "       -0.09478269,    -0.02705574],\n",
            "   [   0.081815034,   -0.095753536,    0.036111828, ...,     0.09795921,\n",
            "       0.024481596,    0.017769763],\n",
            "   ...,\n",
            "   [    0.06882793,    -0.08031209,    -0.04029591, ...,    0.025870036,\n",
            "      -0.036637615,    -0.07678011],\n",
            "   [  -0.025510862,    0.011212574,     0.06929992, ...,    0.019792588,\n",
            "        0.07981093,     0.02489969],\n",
            "   [  -0.010198569,    -0.06625665,    -0.04294954, ...,     0.08054648,\n",
            "        0.07697243,   -0.073409334]]],\n",
            "\n",
            "\n",
            " [[[  -0.066811815,     0.07504789,   -0.053261984, ...,   -0.057482608,\n",
            "       -0.09967862,     -0.0639915],\n",
            "   [  -0.004402495,   -0.045216735,   -0.024773225, ...,     0.02784491,\n",
            "       -0.05492426,    0.023159243],\n",
            "   [    0.05595851,   -0.058878656,   -0.099980965, ...,      0.0862399,\n",
            "       -0.01394691,    0.028650684],\n",
            "   ...,\n",
            "   [   -0.09768397,     0.06852244,    -0.05320396, ...,    -0.09602436,\n",
            "        0.05324015,     0.07039547],\n",
            "   [   -0.10183519,   0.0028122547,      0.0385382, ...,    0.049593173,\n",
            "        0.05941835,     0.09321346],\n",
            "   [    0.09914078,      0.0593509,     0.09464508, ...,   0.0037791685,\n",
            "      -0.018836599,   0.0011615005]],\n",
            "\n",
            "  [[  -0.025317572,     0.03972971,   -0.002751928, ...,   -0.039899305,\n",
            "      -0.056581814,    0.032388594],\n",
            "   [    0.05887277,    0.064348996,     0.08291596, ...,  0.00061001285,\n",
            "       -0.08353224,    -0.10177642],\n",
            "   [   -0.07323522,  0.00036965648,   -0.059816476, ...,    0.042604983,\n",
            "        0.06211981,     0.08396423],\n",
            "   ...,\n",
            "   [    0.03506517,    -0.05203246,      0.1015657, ...,    -0.06819945,\n",
            "       0.021948265,   -0.046720054],\n",
            "   [    0.08863942,   -0.030220589,  -0.0003557008, ...,    0.098877855,\n",
            "       -0.03423449,    -0.09523525],\n",
            "   [    0.10090197,   0.0022126061,     0.03584393, ...,    0.023453088,\n",
            "       -0.06427737,    -0.09073686]],\n",
            "\n",
            "  [[  -0.010658932,     0.06547614,    0.049966265, ...,   -0.009712694,\n",
            "       -0.06212383,     0.09924315],\n",
            "   [    0.07329381,   0.0076103345,      0.0871823, ...,   -0.052907135,\n",
            "       0.013187118,   -0.036855508],\n",
            "   [   -0.08116104,    -0.07086289,    -0.08300694, ...,     0.04799688,\n",
            "        0.06765074,  -0.0036866188],\n",
            "   ...,\n",
            "   [    0.07205528,    -0.08466887,   0.0047202986, ...,   -0.010717797,\n",
            "       0.015488021,  -0.0052113165],\n",
            "   [    0.05510438,    -0.09939348,   -0.038588293, ...,     0.01323319,\n",
            "        0.08044413,     0.08345557],\n",
            "   [   0.055802476,    0.022790069,  -0.0076559125, ...,    0.092153735,\n",
            "      -0.012247928,    0.080904834]]],\n",
            "\n",
            "\n",
            " [[[ -0.0075997873,    0.036392085,   -0.021613512, ...,   -0.095767185,\n",
            "       0.070905976,     0.07977558],\n",
            "   [    0.03236115,    0.015180257,   -0.055220503, ...,    0.076504014,\n",
            "      -0.012925294,     -0.0926773],\n",
            "   [    0.08096433,   -0.052879732,  -0.0055160434, ...,    -0.09610528,\n",
            "       -0.01930173,    -0.09424787],\n",
            "   ...,\n",
            "   [  -0.025018813,  -0.0041011376,   -0.072821036, ...,     0.04800654,\n",
            "       0.047320075,   -0.009741413],\n",
            "   [   0.051283006,    -0.03166412,    0.009400386, ...,    0.098605365,\n",
            "      -0.021272687,     0.05212029],\n",
            "   [   -0.08094821,   0.0011651529,    -0.05162621, ...,    -0.08221919,\n",
            "        0.09136616,   -0.044881403]],\n",
            "\n",
            "  [[  -0.057177182,   -0.022543265,     0.07522284, ...,    -0.08432483,\n",
            "       0.037636332,    -0.00950684],\n",
            "   [  -0.050242662,    0.024520218,    -0.08355193, ...,   -0.035970602,\n",
            "       0.008780832,     0.08632035],\n",
            "   [   0.068230286,    0.044917896,   -0.033173956, ...,    -0.08574556,\n",
            "       0.030460894,    -0.10101002],\n",
            "   ...,\n",
            "   [  -0.068806976,   -0.027759785,    0.038607046, ...,    -0.09060543,\n",
            "       0.020329773,    0.035945177],\n",
            "   [  -0.093155935,    -0.08913377,    0.090353586, ...,     0.08989089,\n",
            "        0.05387269,    -0.06835859],\n",
            "   [  0.0030377957,    0.054264765,     0.09054388, ...,    0.054897998,\n",
            "      -0.049777128,    -0.03707731]],\n",
            "\n",
            "  [[   0.083697066,    -0.01751503,    0.046470523, ...,    0.058347527,\n",
            "       -0.01309286,    0.099721275],\n",
            "   [  -0.015801556,    -0.06642852,     0.05422459, ...,   -0.081402384,\n",
            "        0.05578808,   -0.044754002],\n",
            "   [  -0.054381225,    0.053547364,    0.015037109, ...,    -0.06577842,\n",
            "        0.05137022,     -0.0689766],\n",
            "   ...,\n",
            "   [  -0.051020257,      -0.078633,    0.065521754, ...,     0.06585311,\n",
            "       -0.06573246,    0.045867123],\n",
            "   [  -0.007821884,   0.0028559226,     0.06107179, ...,   -0.009273368,\n",
            "       -0.04824983,    -0.05269892],\n",
            "   [     0.0702503,    -0.04471223,   -0.051270016, ...,   -0.047570594,\n",
            "       0.061582588,    -0.03250471]]]], bias: [  -0.51170194,    -0.5147447,   0.021338722,    0.39208293,  0.0070306007,   -0.44645134,\n",
            "  0.0010774093,   -0.37313432,   0.004633158,   -0.41073337,   0.105475634,   0.105316125,\n",
            "  -0.023672076,  0.0039629624,    -0.5212489,    -0.2479841,   0.002274726, -0.0003018842,\n",
            "   -0.48145232, -0.0031293947,  -0.046066083, -0.0008267593,  0.0063218884,   -0.42787978,\n",
            "     0.2438646,   -0.48036838,     0.1470286,    0.48952192,  0.0028888942,    -0.4580909,\n",
            "    0.12944746,    0.44393468], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), latestResult: __lldb_expr_135.LatestResult), up2: __lldb_expr_135.DownBlock(conv1: TensorFlow.Conv2D<Swift.Float>(filter: [[[[   0.008369192,   -0.032951564,   -0.029715048, ...,   -0.001668952,\n",
            "       0.087360986,    -0.15299031],\n",
            "   [   0.015626345,     0.08305436,   -0.007669445, ...,     0.08114818,\n",
            "       0.115461335,       0.271097],\n",
            "   [  -0.017220711,   -0.019276984,   -0.026591353, ...,   -0.100278035,\n",
            "        0.14704251,    0.040503442],\n",
            "   ...,\n",
            "   [  -0.079396605,     0.05496402,      0.0847301, ...,    0.013727362,\n",
            "      -0.008380363,    0.031089853],\n",
            "   [    0.07105249,   -0.091006495,   -0.033277627, ...,    -0.08419246,\n",
            "        -0.3858273,    -0.42698827],\n",
            "   [   0.014947576,   -0.059605114,   -0.033390075, ...,    -0.10725521,\n",
            "       -0.41542533,    -0.59406084]],\n",
            "\n",
            "  [[    0.04175199,  -0.0002397569,    0.050891247, ...,   -0.040611856,\n",
            "        -0.1310993,   -0.104447015],\n",
            "   [   0.015520519,    0.006670397,     0.06609448, ...,   -0.023059843,\n",
            "        0.20470218,     0.18164572],\n",
            "   [   0.045598056,     0.11971251,    -0.10214249, ...,     -0.0358733,\n",
            "      -0.007251822,    0.044744883],\n",
            "   ...,\n",
            "   [     0.0758979,   -0.089482576,     -0.0925174, ...,   -0.090306506,\n",
            "       0.017135391,    -0.06833658],\n",
            "   [  -0.032281607,     0.11248533, -0.00023188828, ...,   -0.082155086,\n",
            "       -0.34827217,     -0.3548624],\n",
            "   [  -0.055294205,   -0.103767134,   -0.043513514, ...,     0.03768848,\n",
            "         -0.478346,    -0.46387956]],\n",
            "\n",
            "  [[   0.038928527,    0.046519384,   -0.021109695, ...,  -0.0131994095,\n",
            "        0.03305533,    -0.12215267],\n",
            "   [   -0.07488716,     0.09678524,    -0.10890654, ...,    0.017970813,\n",
            "       0.116827026,    0.124847256],\n",
            "   [  -0.034838554,    0.058826998,     0.06578517, ...,     0.07376819,\n",
            "      -0.014739388,     0.05856833],\n",
            "   ...,\n",
            "   [    0.09745868,    -0.09448688,   0.0071551907, ...,     0.03943104,\n",
            "        0.04842426,     -0.0115385],\n",
            "   [    0.02796003,     0.06349177,     0.10542029, ...,    -0.06947245,\n",
            "        -0.3150691,    -0.28544235],\n",
            "   [  -0.037032723,    -0.06861265,    -0.11345476, ...,    -0.11736531,\n",
            "        -0.5851062,    -0.45080775]]],\n",
            "\n",
            "\n",
            " [[[   -0.07108222,   -0.045365695,     0.07941821, ...,   -0.098857366,\n",
            "       0.023014842,     0.06374415],\n",
            "   [  -0.048672494,    -0.03832373,    -0.07390097, ...,     0.04136042,\n",
            "        0.19080079,     0.26238364],\n",
            "   [   -0.06766617,    -0.00610788,   -0.059078258, ...,     0.06181495,\n",
            "        0.19617282,     0.16877967],\n",
            "   ...,\n",
            "   [   0.113079354,    0.002419187,   -0.017876199, ...,   -0.031711582,\n",
            "        0.10781182,    -0.09720447],\n",
            "   [   0.045667738,   -0.013229914,    -0.05335904, ...,    -0.07253771,\n",
            "       -0.30051216,    -0.32432324],\n",
            "   [    0.12518382,   -0.009114516,    -0.03919524, ...,   0.0027495332,\n",
            "        -0.5278875,    -0.48973972]],\n",
            "\n",
            "  [[    0.10035588,     0.10047272,     0.10350334, ...,     0.03153847,\n",
            "       0.010934086,    -0.13861357],\n",
            "   [   0.057643685,    0.029710758,    0.052537534, ...,    0.090981625,\n",
            "        0.14047325,     0.13904935],\n",
            "   [  -0.008020663,   -0.057976156,   -0.052823313, ...,    -0.01211795,\n",
            "       0.037017956,       0.232941],\n",
            "   ...,\n",
            "   [  -0.014151178,   -0.086755596,     0.10094051, ...,     -0.1149383,\n",
            "        0.02945408,    0.047024824],\n",
            "   [     0.1454136,   -0.020136908,    -0.06969599, ...,     0.08194789,\n",
            "       -0.38650638,    -0.44650528],\n",
            "   [    0.21181424,     0.09544126,      -0.025888, ...,    -0.10039988,\n",
            "       -0.49003646,    -0.48946717]],\n",
            "\n",
            "  [[    0.10745437,     0.10879285,    0.047896713, ...,     -0.0606529,\n",
            "      -0.013365333,     -0.0359344],\n",
            "   [    0.07118874,    0.059493694,   -0.037400994, ...,     0.08615346,\n",
            "        0.10476921,     0.14833315],\n",
            "   [  -0.018125098,     0.11862783,    -0.10814157, ...,     0.08633038,\n",
            "        0.14630902,    0.060078207],\n",
            "   ...,\n",
            "   [  -0.046805866,   -0.087458625,    0.041821517, ...,    -0.09266006,\n",
            "       -0.10756749,    -0.06694062],\n",
            "   [    0.06675985,    -0.05376485,    0.079739116, ...,     0.03800552,\n",
            "       -0.26684064,    -0.39339006],\n",
            "   [    0.11531486,   -0.038913503,    0.022626512, ...,   -0.018068103,\n",
            "       -0.61668277,     -0.4815786]]],\n",
            "\n",
            "\n",
            " [[[  -0.067689076,     0.07991235,  -0.0020394712, ...,     -0.0988116,\n",
            "       -0.13459991,    -0.12405517],\n",
            "   [  -0.034580495,   -0.096363336,     0.02469648, ...,    -0.05420053,\n",
            "        0.24130054,     0.05425285],\n",
            "   [    0.01003706,    -0.04346365,    0.027846748, ...,    -0.03228524,\n",
            "       0.036187522,    0.070196815],\n",
            "   ...,\n",
            "   [    0.09383312,   -0.044039775,   -0.020034188, ...,    0.035840172,\n",
            "       0.046912048,    -0.10769627],\n",
            "   [    0.08432622,    -0.06713332,   -0.031674147, ...,     0.09807065,\n",
            "       -0.20095973,    -0.34275666],\n",
            "   [    0.02232673,     0.00839159,   -0.020243479, ...,   -0.033441395,\n",
            "        -0.5768269,      -0.435543]],\n",
            "\n",
            "  [[   -0.09904662,    0.005143065,    0.003805093, ...,   -0.077523455,\n",
            "       0.029677354,    0.044963416],\n",
            "   [   0.058521673,   -0.048717357,     0.10168718, ...,    -0.09366182,\n",
            "         0.1551587,     0.20193192],\n",
            "   [   0.105710596,    0.047898665,   -0.012610523, ...,     -0.0285049,\n",
            "       0.019467797,     0.11011135],\n",
            "   ...,\n",
            "   [    0.11577626,    -0.10474024,     -0.0819827, ...,     0.06862568,\n",
            "        0.07353445,    0.015269669],\n",
            "   [  -0.008572724,   -0.096629016,     0.03505293, ...,   -0.025521586,\n",
            "        -0.2515462,    -0.24754265],\n",
            "   [    0.15672185,     0.06085466,   -0.028878035, ...,    -0.07429177,\n",
            "        -0.5776868,    -0.43619463]],\n",
            "\n",
            "  [[    0.11929856,   -0.061718743,    0.044839017, ...,     0.04622267,\n",
            "       0.018413577,   -0.072367474],\n",
            "   [     0.1030197,    0.044582374,     -0.1129905, ...,     0.10289942,\n",
            "        0.16168165,      0.1037114],\n",
            "   [  -0.078223184,    -0.05280371,   -0.036312304, ...,    -0.03638797,\n",
            "        0.04666597,     0.07971707],\n",
            "   ...,\n",
            "   [  -0.017281428,   -0.026815413,    -0.03879251, ...,    0.061464813,\n",
            "       -0.08794937,    0.012746931],\n",
            "   [    0.16337116,    0.059015732,    -0.11158072, ...,   -0.059204962,\n",
            "       -0.28943014,     -0.4809544],\n",
            "   [   0.092870496,   -0.014939524,    -0.01931195, ...,    -0.11037772,\n",
            "        -0.5991892,      -0.515501]]]], bias: [  0.35422316, -0.004748756, -0.010507552,  -0.56191564, -0.024111286,   0.47524202,\n",
            "   -0.5257827,   0.31552175,   -0.0922236,   -0.3261122,   -0.5576769,   -0.4923184,\n",
            "   -0.5432772, 0.0061703003,   -0.5883855,  -0.59260166], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), conv2: TensorFlow.Conv2D<Swift.Float>(filter: [[[[   0.002521807,    -0.17366332,   -0.025981352, ...,     0.06646399,\n",
            "       0.012082948,      0.1173076],\n",
            "   [    0.03838359,    -0.10538955,    0.031831097, ...,   -0.075567596,\n",
            "        -0.1141349,    -0.09971008],\n",
            "   [    0.10858104,     0.14363377,     0.13684583, ...,    -0.05639812,\n",
            "       -0.07252273,   -0.056191772],\n",
            "   ...,\n",
            "   [    0.10758601,   -0.092314884,    0.038469255, ...,    -0.03401151,\n",
            "       -0.12273365,     0.08788641],\n",
            "   [   0.057258483,    -0.51036304,    -0.40191117, ...,     0.08562447,\n",
            "       -0.13648678,   -0.013379573],\n",
            "   [    0.27530494,    -0.48527905,    -0.48862004, ...,    -0.12811933,\n",
            "       -0.08062273,    -0.45994318]],\n",
            "\n",
            "  [[   0.047623307,     0.09693254,     0.10230857, ...,     0.02192949,\n",
            "       -0.06546076,    0.034352563],\n",
            "   [   -0.08680769,     0.03981954,     0.09512523, ...,     0.03681263,\n",
            "       0.056021556,   -0.038588945],\n",
            "   [   0.020333724,    0.021421414,    0.010549909, ...,   -0.007881408,\n",
            "       -0.14060645,    -0.13168986],\n",
            "   ...,\n",
            "   [  -0.117594264,    -0.11793409,    0.119344145, ...,    -0.11416939,\n",
            "    -0.00097219227,    0.007999442],\n",
            "   [   0.085154265,     -0.5523341,    -0.30305827, ...,    -0.05800937,\n",
            "        0.10275002,    -0.19482061],\n",
            "   [    0.32795888,     -0.5810526,     -0.5611321, ...,   -0.054742463,\n",
            "       -0.20526907,    -0.30200195]],\n",
            "\n",
            "  [[  -0.091012135,    0.024237406,   -0.101988256, ...,     0.14265181,\n",
            "        0.10055076,   -0.016038802],\n",
            "   [  -0.014807771,    0.038647886,     0.10131209, ...,   0.0012638917,\n",
            "       0.042022247,   -0.045309775],\n",
            "   [   -0.06374101,    -0.02299176,     0.13681915, ...,    0.073049344,\n",
            "      -0.045060974,    -0.06777056],\n",
            "   ...,\n",
            "   [   0.050502002,  -0.0029000812,   0.0052814665, ...,     0.14109273,\n",
            "        0.08972805,     0.13693435],\n",
            "   [   0.119319744,    -0.43851498,    -0.27064282, ...,   -0.033545963,\n",
            "        0.09454624,   -0.028125025],\n",
            "   [    0.30857825,    -0.43406755,    -0.45214844, ...,   -0.009036138,\n",
            "       -0.12610206,     -0.4460226]]],\n",
            "\n",
            "\n",
            " [[[   0.039579686,    -0.00951864,     0.08429265, ...,     0.13801101,\n",
            "       -0.11467697,    0.119460255],\n",
            "   [   0.061848763,    0.058038957,     0.04082332, ...,     0.09416525,\n",
            "        0.07371402,    0.047839966],\n",
            "   [   0.118091345,     0.12556815,   -0.089420795, ...,   -0.111321874,\n",
            "       0.072017044,    0.047229886],\n",
            "   ...,\n",
            "   [     -0.113058,     0.08609696,    0.025287824, ...,    0.024647363,\n",
            "       -0.12465791,    -0.12160642],\n",
            "   [   0.122567885,     -0.4305401,     -0.4565638, ...,    0.053448476,\n",
            "        0.03136018,    -0.19071129],\n",
            "   [    0.52226955,    -0.61350447,    -0.54253626, ...,   0.0053574964,\n",
            "       -0.10800096,     -0.4543829]],\n",
            "\n",
            "  [[   0.045461845,    -0.18969251,     0.07058577, ...,   -0.064094424,\n",
            "      -0.103925414,     0.08921299],\n",
            "   [   -0.15014187,    0.067698695,    0.033842888, ...,    -0.10230434,\n",
            "      -0.045535706,   -0.011280601],\n",
            "   [    0.07725908,     0.13236746,   -0.024327619, ...,    0.071833365,\n",
            "       0.061258916,   -0.019292552],\n",
            "   ...,\n",
            "   [   -0.09139672,   -0.117358714,     0.06809475, ...,     0.11605162,\n",
            "       -0.11544574,     0.06926771],\n",
            "   [     0.3071028,     -0.4854009,    -0.39058116, ...,    0.036000483,\n",
            "       -0.12950101,   -0.025320228],\n",
            "   [      0.548157,     -0.7060801,    -0.61860436, ...,    -0.07768104,\n",
            "       0.036612116,     -0.2813078]],\n",
            "\n",
            "  [[   0.022074947,    -0.14210215,    -0.14632343, ...,     -0.1177961,\n",
            "       0.056388974,    0.028264942],\n",
            "   [   0.012867837,     0.11273806,     0.13250288, ...,   -0.113429725,\n",
            "       -0.12333079,    -0.10786918],\n",
            "   [  -0.090187564,    0.008422318,     0.12931444, ...,    0.027027017,\n",
            "       -0.13469517,     0.07060233],\n",
            "   ...,\n",
            "   [   0.123889856,    0.049040455,   -0.084993966, ...,   -0.005217306,\n",
            "      -0.016020263,    0.027544715],\n",
            "   [    0.22739443,     -0.4153067,     -0.4109011, ...,     0.06942751,\n",
            "       -0.11717623,    -0.18040027],\n",
            "   [    0.30076042,    -0.45588443,    -0.57724017, ...,    -0.08042815,\n",
            "       -0.19919315,    -0.24270181]]],\n",
            "\n",
            "\n",
            " [[[    0.15065627,   -0.096665226,    -0.13778622, ...,   -0.063832544,\n",
            "        0.09305198,    -0.14240879],\n",
            "   [    0.07378309,    0.047162477,     0.02947048, ...,     0.06577941,\n",
            "     -0.0018824299,    -0.05896851],\n",
            "   [  -0.048137557,     -0.1377153,    0.040131472, ...,    -0.07175062,\n",
            "      -0.114086725,     0.08418477],\n",
            "   ...,\n",
            "   [   0.005795939,   -0.114594266,  0.00060884916, ...,    -0.09656549,\n",
            "      -0.046788227,     0.03332268],\n",
            "   [    0.24701144,     -0.4013772,     -0.4183092, ...,    -0.06018882,\n",
            "       -0.10918714,    -0.16199304],\n",
            "   [      0.534073,     -0.6288928,    -0.40861848, ...,   -0.013527041,\n",
            "      -0.062025353,    -0.23809478]],\n",
            "\n",
            "  [[  -0.101147965,    -0.07091044,   -0.034272872, ...,     0.10898704,\n",
            "       -0.14651714,     0.06389886],\n",
            "   [  0.0018490363,    0.086649634,     0.08782622, ...,    -0.05468467,\n",
            "      -0.023702443,     0.09594077],\n",
            "   [    0.07968317,    0.009489384,   -0.041147508, ...,    -0.10988323,\n",
            "        0.12107876,      0.1438361],\n",
            "   ...,\n",
            "   [   -0.08927442,    0.065071285,    -0.10382171, ...,    0.015044938,\n",
            "      -0.016954195,   -0.041653465],\n",
            "   [    0.28594422,    -0.43565384,    -0.30192786, ...,    0.027648978,\n",
            "      -0.036085613,   -0.033914432],\n",
            "   [    0.53464365,    -0.53269583,    -0.42312664, ...,   -0.090128854,\n",
            "       -0.11829483,    -0.36398593]],\n",
            "\n",
            "  [[   -0.08201566,    0.049535103,     0.05323555, ...,    0.043504424,\n",
            "       -0.04460363,   -0.014380538],\n",
            "   [   -0.02554477,     0.11797345,    0.120458394, ...,   -0.052152622,\n",
            "      -0.030474598,    0.026565172],\n",
            "   [  -0.048089087,    -0.13189544,    0.033379182, ...,    0.007043509,\n",
            "       0.037601035,     0.11292821],\n",
            "   ...,\n",
            "   [   0.055346817,    -0.08341919,    0.011419729, ...,     -0.0861946,\n",
            "        -0.0994308,    0.059076156],\n",
            "   [   0.084994264,    -0.59952694,     -0.3292279, ...,    0.020260366,\n",
            "      -0.046189737,    -0.24457219],\n",
            "   [    0.47250387,     -0.4847717,     -0.4803702, ...,    -0.16934891,\n",
            "       -0.01288892,    -0.39816523]]]], bias: [    0.5229238,   -0.59504867,   -0.58629227,     0.2768706,   0.011765374,    0.11363231,\n",
            "   -0.40805697, -0.0023725284,   -0.07547882,   -0.06940013,   -0.59407586,    -0.5922613,\n",
            "     0.5310184,   -0.24512391,    -0.3791667,    -0.5070855], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), latestResult: __lldb_expr_135.LatestResult), up3: __lldb_expr_135.DownBlock(conv1: TensorFlow.Conv2D<Swift.Float>(filter: [[[[    0.1789938,    -0.6621623,  -0.032682497, ...,     0.6180409,   -0.67595446,\n",
            "        0.6419512],\n",
            "   [  -0.16635291,    0.35013852,    0.18794993, ...,   -0.40789023,    0.54044247,\n",
            "      -0.41691253],\n",
            "   [   0.20108312,   -0.47336707,    0.15107547, ...,    0.60532135,    -0.2982416,\n",
            "        0.6543139],\n",
            "   ...,\n",
            "   [  -0.24850412,    0.13869095,   -0.14948028, ...,   -0.16506939,    0.21458665,\n",
            "      -0.62733227],\n",
            "   [  0.061654136,    0.15443006,      0.141265, ...,   0.099176936,  -0.023868108,\n",
            "      0.028586457],\n",
            "   [  -0.47564256,     0.7610211,    0.34772587, ...,    -0.6582227,     0.7465157,\n",
            "      -0.54046315]],\n",
            "\n",
            "  [[   0.32053152,    -0.4327373,   -0.22790727, ...,     0.6404213,   -0.59325224,\n",
            "       0.47048092],\n",
            "   [  -0.29032752,    0.33823392,    0.21282502, ...,    -0.4311247,    0.17833585,\n",
            "        -0.502629],\n",
            "   [   0.01152353,    -0.0605207,     0.1388402, ...,     0.3238041,    -0.5015741,\n",
            "        0.3598744],\n",
            "   ...,\n",
            "   [ -0.026680613,    0.09005023,   0.031488396, ...,   -0.21318874,    0.37161267,\n",
            "       -0.4681261],\n",
            "   [ -0.036899514,   -0.13172649,    0.11963926, ...,    0.13977812,   -0.10716259,\n",
            "      -0.01675196],\n",
            "   [   -0.5526201,    0.44182938,    0.22494487, ...,   -0.72436553,    0.65930456,\n",
            "       -0.4515477]],\n",
            "\n",
            "  [[   0.10888205,    -0.6928004,   0.015432225, ...,     0.5343088,   -0.39896837,\n",
            "       0.43025652],\n",
            "   [  -0.08741982,    0.62192845,    0.06399592, ...,   -0.46350092,    0.45186296,\n",
            "        -0.637294],\n",
            "   [  -0.03865639,   -0.45397604,    0.03937399, ...,     0.5071068,   -0.32028866,\n",
            "        0.6893196],\n",
            "   ...,\n",
            "   [  -0.10785863,    0.29920876,    -0.1360522, ...,   -0.27894735,     0.2313913,\n",
            "       -0.3648559],\n",
            "   [   0.10809425,   0.043467235,   0.095526665, ...,   -0.16081652,   0.077952914,\n",
            "       0.19889663],\n",
            "   [  -0.57314277,    0.46728826,    0.15474752, ...,    -0.7130825,    0.69408876,\n",
            "      -0.69564706]]],\n",
            "\n",
            "\n",
            " [[[    0.3036388,   -0.41004634,    0.06097774, ...,    0.63150954,    -0.4863057,\n",
            "        0.6624655],\n",
            "   [  -0.09224402,    0.46612567,       0.04641, ...,     0.3058021,    0.57130367,\n",
            "      -0.56897205],\n",
            "   [  0.050797608,   -0.08295059,   0.018665925, ...,   -0.06879631,   -0.42926198,\n",
            "       0.32285634],\n",
            "   ...,\n",
            "   [   -0.1910982,  0.0010764165,    0.08600197, ...,   -0.25404736,    0.38321158,\n",
            "       -0.5866758],\n",
            "   [  -0.09677737,    -0.0845189,   0.072127886, ...,   -0.02648523,   0.038742974,\n",
            "      0.042370647],\n",
            "   [  -0.59714645,    0.68058914,   0.013646363, ...,   -0.68794245,    0.65822357,\n",
            "       -0.5752859]],\n",
            "\n",
            "  [[  0.109736204,    -0.6518228,    0.05426873, ...,    0.65032136,    -0.4587369,\n",
            "         0.701766],\n",
            "   [  -0.08610986,     0.5666803,  -0.079351015, ...,    -0.4228514,     0.4262313,\n",
            "      -0.45107844],\n",
            "   [   0.14821184,   -0.38346016,  -0.060080938, ...,    0.39951232,     -0.683224,\n",
            "       0.59423196],\n",
            "   ...,\n",
            "   [   -0.2000047,   -0.09638171,   -0.14557715, ...,    -0.3860538,     0.5532208,\n",
            "       -0.5795289],\n",
            "   [   -0.1403857,   -0.04057865,     0.1406087, ...,    0.14876446,    0.11889331,\n",
            "      -0.03983496],\n",
            "   [  -0.54234314,    0.66705424,   -0.14285979, ...,   -0.65184784,     0.5936452,\n",
            "        -0.524251]],\n",
            "\n",
            "  [[   0.09299182,   -0.42194358,  -0.022207577, ...,    0.45948416,    -0.6845487,\n",
            "       0.46421725],\n",
            "   [   0.04076201,    0.38157955,    0.10663705, ...,    0.33170366,    0.59005183,\n",
            "       -0.6222465],\n",
            "   [  -0.13162003,  -0.010187824,   0.032548897, ...,  -0.052013647,   -0.43291363,\n",
            "       0.42296115],\n",
            "   ...,\n",
            "   [  -0.06851875,   0.046362467,   0.047259413, ...,   -0.23644626,    0.34743878,\n",
            "      -0.54373294],\n",
            "   [   0.07944806,   0.022416463,  -0.094025545, ...,   0.060867846,    0.15178002,\n",
            "     -0.041622512],\n",
            "   [  -0.48272696,     0.6403966,   0.007912655, ...,   -0.48940474,     0.7625947,\n",
            "       -0.6356684]]],\n",
            "\n",
            "\n",
            " [[[    0.2566004,   -0.42075932,   -0.16454251, ...,     0.6262456,    -0.6357755,\n",
            "       0.67191994],\n",
            "   [   -0.1311169,     0.3614704,   0.017364873, ...,   -0.40366203,    0.49912938,\n",
            "      -0.69334567],\n",
            "   [  0.019942634,    -0.5954254,   0.027304992, ...,     0.6379842,   -0.39025143,\n",
            "        0.4024971],\n",
            "   ...,\n",
            "   [  -0.14268343,     0.2632292,  -0.121917404, ...,   -0.36057144,    0.44322383,\n",
            "        -0.395438],\n",
            "   [ -0.120995715,   -0.14023544,    0.13240851, ...,  -0.047151197,  -0.048426528,\n",
            "      0.011929663],\n",
            "   [   -0.6929905,    0.56536454,   -0.29381886, ...,    -0.5286796,    0.49805632,\n",
            "       -0.5857327]],\n",
            "\n",
            "  [[    0.2148204,    -0.5096493,     0.1387883, ...,    0.44158715,    -0.6090987,\n",
            "       0.72998303],\n",
            "   [  0.020661322,    0.39752322,   0.052075922, ...,    -0.6957541,    0.17827284,\n",
            "       -0.6608191],\n",
            "   [ -0.016381761,   -0.22244182,    0.12771265, ...,    0.38782328,   -0.39544025,\n",
            "       0.52195716],\n",
            "   ...,\n",
            "   [   -0.0284425,     0.1617271, 0.00045999588, ...,   -0.31821728,     0.5453292,\n",
            "       -0.3129792],\n",
            "   [  -0.12103545,    0.14212771,    0.16078855, ...,   0.063430496,  -0.045518257,\n",
            "       0.14013113],\n",
            "   [   -0.4729381,     0.6378878,   -0.40512222, ...,   -0.48052824,    0.44676423,\n",
            "       -0.5218458]],\n",
            "\n",
            "  [[   0.10904745,   -0.38310444,  -0.028788643, ...,    0.46136567,   -0.47212678,\n",
            "        0.6066384],\n",
            "   [  -0.22360039,    0.66805434,  -0.061275795, ...,   -0.45052072,    0.31041032,\n",
            "      -0.56691724],\n",
            "   [   0.18578534,   -0.46169457, -0.0019270655, ...,    0.38077214,     -0.402695,\n",
            "       0.44438386],\n",
            "   ...,\n",
            "   [ -0.094212055,   0.005864614,    0.02921795, ...,   -0.42501792,     0.2923354,\n",
            "      -0.62225187],\n",
            "   [   0.11182639,   0.036753077,    0.06126939, ...,  -0.028429667,   0.026084088,\n",
            "       0.13385601],\n",
            "   [   -0.6297255,     0.6970994,   -0.14650424, ...,    -0.5471705,     0.6337528,\n",
            "       -0.6085714]]]], bias: [ -0.5668264,  0.59750575, 0.028587682,  0.59966356,  -0.5999335,   -0.598446,   0.5998608,\n",
            "  -0.5995955], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), conv2: TensorFlow.Conv2D<Swift.Float>(filter: [[[[  -0.17046301,   -0.18876344, -0.0076256082,    -0.3543252,  -0.015940405,   -0.14482643,\n",
            "      -0.07373113,  -0.095183566],\n",
            "   [  -0.68185836,    -0.7696608,   -0.09782457,    -0.6209786,   -0.66427207,  -0.044097673,\n",
            "      -0.45775175,    -0.5546013],\n",
            "   [  -0.07340529,   0.056357067,   0.042206064,   -0.12134898,   -0.17089626,    0.16546166,\n",
            "      -0.12375308,   -0.13043058],\n",
            "   [  -0.54286575,    -0.6937073,   -0.24584082,   -0.67520356,    -0.5168184,  -0.029081987,\n",
            "      -0.50140923,   -0.40737447],\n",
            "   [  -0.41458464,   -0.42680907,   -0.16629359,    -0.5747659,    -0.4461672,   -0.18505351,\n",
            "       -0.4140036,   -0.48022756],\n",
            "   [   -0.6649515,    -0.5326211,     0.0455219,   -0.48752943,    -0.4436567,   -0.27535322,\n",
            "       -0.4864423,   -0.59217465],\n",
            "   [   -0.7236942,   -0.43587148,   -0.14960471,     -0.772923,   -0.47578192,  -0.039654803,\n",
            "       -0.4686004,    -0.7679014],\n",
            "   [  -0.53300405,    -0.6177244,   -0.41327316,   -0.44108394,   -0.68512905,   -0.10023497,\n",
            "       -0.4781599,   -0.56985873]],\n",
            "\n",
            "  [[  -0.09089954,   -0.13145158,   -0.18383634,   -0.36734858,   -0.26336798,    0.11286912,\n",
            "      0.034862835,   -0.08714067],\n",
            "   [   -0.7064186,    -0.6079504,   -0.26720834,   -0.78408885,    -0.5199521,  -0.123535514,\n",
            "      -0.27875605,   -0.38745925],\n",
            "   [  -0.06972531,   -0.12701334,    0.03458223,   -0.14882554,   -0.07050487,    0.10681294,\n",
            "       -0.2369306,  -0.092976764],\n",
            "   [  -0.59914356,    -0.6848709,   -0.18408658,   -0.52489483,   -0.78862906,   -0.16133864,\n",
            "      -0.43199608,   -0.46511483],\n",
            "   [    -0.790703,    -0.6123959,   -0.39249733,   -0.44604504,   -0.47019207,    0.06301466,\n",
            "       -0.5537203,    -0.4559603],\n",
            "   [  -0.41576356,    -0.6679108,   -0.12386176,   -0.39424112,   -0.41929668,   -0.17598666,\n",
            "      -0.72731686,   -0.41382256],\n",
            "   [  -0.71062154,   -0.50466996,    -0.3360404,    -0.7294684,    -0.4475428,    0.22915643,\n",
            "       0.22960971,   -0.60552764],\n",
            "   [   -0.6830921,    -0.7668391,  -0.077076524,    -0.6209917,    -0.4557694,   -0.18336233,\n",
            "      -0.29366904,   -0.66270715]],\n",
            "\n",
            "  [[  -0.11699393,   -0.08307035,  -0.080533415,    -0.2794237,    -0.3049614,    0.03762212,\n",
            "       0.15523833,    0.07632481],\n",
            "   [  -0.48976436,   -0.41800264,  -0.029503163,    -0.5372366,    -0.7187026,   -0.17838182,\n",
            "       0.20617376,    -0.7485159],\n",
            "   [   -0.0206318,   -0.26724023,   0.043158337,   0.024165547,   -0.15988947,   0.049054053,\n",
            "      -0.07926506,   -0.18228954],\n",
            "   [  -0.46406713,    -0.7312873,   -0.26213005,    -0.7042949,   -0.67665464,   -0.17798077,\n",
            "     -0.123516954,    -0.5649946],\n",
            "   [  -0.50755095,   -0.69872874,   -0.18405077,    -0.5673648,   -0.45813942,    -0.2506667,\n",
            "       0.18670934,   -0.49076816],\n",
            "   [  -0.59360594,   -0.46496996,    -0.2419995,    -0.5460177,    -0.6184303,  -0.008221995,\n",
            "      -0.40424567,    -0.4726766],\n",
            "   [  -0.75452936,    -0.5697428,   -0.23757516,   -0.77031636,    -0.5164937,   -0.05419482,\n",
            "       -0.3606374,    -0.4506207],\n",
            "   [  -0.44662923,   -0.41016105,  -0.024506768,    -0.6264842,   -0.64450425,   -0.10283681,\n",
            "      -0.45122072,   -0.54792166]]],\n",
            "\n",
            "\n",
            " [[[  -0.12297015,   -0.14563796,   -0.20250006,    -0.3277132,   -0.29618108,   -0.08988044,\n",
            "       0.13051464,   -0.17571086],\n",
            "   [   -0.5657715,     -0.776054,   -0.21494438,    -0.6165936,   -0.49360502,   -0.24274601,\n",
            "      -0.41254264,   -0.47773895],\n",
            "   [  -0.14000645,    0.10405997,   -0.12680347,    0.11529057,   -0.24994373,   -0.07566923,\n",
            "      -0.16246782,  -0.104888454],\n",
            "   [  -0.60123056,    -0.5664698,  -0.042740222,   -0.71158504,   -0.52698755,   -0.17608307,\n",
            "       -0.6335903,   -0.59726197],\n",
            "   [      -0.5744,   -0.58130485,   -0.26489407,   -0.45020437,    -0.5264913,   -0.20523797,\n",
            "       -0.5445568,   -0.60966253],\n",
            "   [  -0.49800184,   -0.75032943,   -0.19730726,   -0.50995183,    -0.5352423,   -0.16712661,\n",
            "      -0.55921394,    -0.7144859],\n",
            "   [   -0.7726401,   -0.42478138,    -0.1240583,     -0.680594,   -0.54232883,   -0.18579696,\n",
            "      -0.44528615,   -0.66757756],\n",
            "   [  -0.67879856,    -0.5913078,  -0.007584338,    -0.6024812,    -0.4858805,    0.08177036,\n",
            "      -0.67512727,    -0.5944422]],\n",
            "\n",
            "  [[  -0.18029548,   -0.28370634,    0.06393636, -0.0059243254,   -0.11018541,  -0.053567387,\n",
            "       0.25759792,   -0.18375789],\n",
            "   [   -0.4508114,   -0.58778435,   -0.18833129,   -0.68084484,    -0.6424148,   -0.19194211,\n",
            "      -0.35083413,    -0.4686908],\n",
            "   [    -0.235856,    0.09201011,   -0.13344745,  -0.046582133,   -0.15288301,  -0.090753354,\n",
            "      -0.01614032,   -0.18773964],\n",
            "   [  -0.41183513,    -0.6109236,   -0.16202456,    -0.6650224,    -0.7922677,   -0.18564212,\n",
            "      -0.32831404,   -0.48747286],\n",
            "   [   -0.7426074,    -0.5122465,   -0.26905084,    -0.4986594,    -0.7159099,   -0.17038396,\n",
            "      -0.45723674,   -0.53559124],\n",
            "   [   -0.4543521,   -0.62886333,   -0.17813008,    -0.7950695,   -0.57665104,   -0.13463189,\n",
            "      -0.46091357,    -0.4898351],\n",
            "   [  -0.50050974,   -0.48984078,   -0.21781152,    -0.5946755,    -0.6007654,  -0.041757595,\n",
            "       -0.5435956,   -0.50931096],\n",
            "   [  -0.74324405,   -0.51125324,   -0.17043185,   -0.70842075,    -0.5687638,    0.08399256,\n",
            "      -0.35926196,    -0.3976069]],\n",
            "\n",
            "  [[  -0.08533643,  -0.035308495,   -0.13305824,   0.030066801,   0.023494355,   0.054758154,\n",
            "       0.10741617,    -0.0631886],\n",
            "   [  -0.55162233,   -0.45618254,  0.0014829698,    -0.5340403,    -0.7736075,   -0.31584728,\n",
            "       -0.3914356,   -0.57785606],\n",
            "   [  -0.18064603,   -0.19205865,    -0.0889363,   -0.12733337,   -0.16815282,   -0.12670703,\n",
            "      -0.04431794, 0.00014751237],\n",
            "   [   -0.6349731,   -0.75657505,    -0.2557604,   -0.39463723,    -0.7529545,   -0.15205468,\n",
            "      -0.33150855,    -0.6195047],\n",
            "   [    -0.666036,   -0.64683133,   -0.32044557,   -0.45874006,   -0.44828102,   -0.36506453,\n",
            "      -0.45829016,    -0.7889233],\n",
            "   [  -0.46203148,   -0.79843116,    0.08356414,    -0.7878387,     -0.634863,   -0.12766843,\n",
            "       -0.5224175,   -0.48227566],\n",
            "   [   -0.7971969,     -0.678844,   -0.07402197,    -0.7959708,    -0.7087081,    0.10414882,\n",
            "       0.05446521,    -0.7244024],\n",
            "   [   -0.5131992,    -0.5077553,    -0.3280149,    -0.4212619,    -0.6387491,   -0.16022262,\n",
            "       -0.4879147,   -0.41753548]]],\n",
            "\n",
            "\n",
            " [[[  -0.30456063,   -0.36755997,    0.13418159,   -0.25225842,   -0.27334303,    0.13581684,\n",
            "      0.073850766,  0.0022856595],\n",
            "   [   -0.6219541,    -0.5974321,  -0.010517532,   -0.48015383,   -0.41297916,   0.012815103,\n",
            "       -0.6007998,   -0.64367443],\n",
            "   [-0.0003212021,   -0.27573502,      0.179737,    -0.0700561,   -0.23634347,   -0.05470987,\n",
            "       0.06189437,    0.07570446],\n",
            "   [   -0.6881062,   -0.56507295,   -0.23894127,    -0.5797854,   -0.68626195,   -0.22388203,\n",
            "       -0.6135959,    -0.7168366],\n",
            "   [  -0.43931565,    -0.5538603,  -0.024503404,   -0.49437433,    -0.5689756,    -0.2789971,\n",
            "      -0.51172113,   -0.46952042],\n",
            "   [   -0.5253312,   -0.79042846,   0.007155722,   -0.74669886,    -0.6916707,   -0.13397212,\n",
            "      -0.72225326,    -0.4668633],\n",
            "   [   -0.5475396,   -0.49798796,   -0.09128864,    -0.6707562,     -0.610614,   -0.10279428,\n",
            "       -0.6329242,   -0.67128384],\n",
            "   [   -0.7640535,   -0.50337005,    -0.0534847,     -0.669452,   -0.76086295,  -0.026092198,\n",
            "      -0.34829864,    -0.6340991]],\n",
            "\n",
            "  [[  -0.20023137,   -0.35657415,   -0.04294841,     -0.131105,   -0.33475885,   -0.07680999,\n",
            "     -0.042484183,    -0.1099753],\n",
            "   [   -0.4709226,    -0.6937076,   -0.21524599,    -0.4495691,    -0.7133674,   -0.26579893,\n",
            "      -0.35257018,     -0.366364],\n",
            "   [   0.07009686,    -0.2978177,    0.08947355,   -0.20420489,   -0.24823228,   0.081377454,\n",
            "      -0.22252469,   -0.23158605],\n",
            "   [  -0.47785422,   -0.46636403,   -0.08342407,    -0.7159822,    -0.5334141,    -0.2619021,\n",
            "      -0.38442147,   -0.40043247],\n",
            "   [  -0.41740543,    -0.5541153,   -0.25864485,    -0.7762816,   -0.68582296,   -0.33181122,\n",
            "      -0.40656447,    -0.6169701],\n",
            "   [   -0.7318358,    -0.7751427,    0.11056274,    -0.3978861,   -0.48275056,   -0.14438193,\n",
            "       -0.5905399,   -0.38556057],\n",
            "   [    -0.632263,   -0.46922764,    -0.2032818,   -0.54051113,     -0.564175,   0.087790154,\n",
            "       -0.5691885,    -0.6418563],\n",
            "   [   -0.6022803,   -0.53433925,   -0.18125172,    -0.4900095,   -0.68508446,    -0.1822735,\n",
            "      -0.65996504,     -0.752446]],\n",
            "\n",
            "  [[  -0.16749851,   -0.11678862,   -0.18991248,   -0.29319906,   -0.28403366,     0.1010756,\n",
            "      -0.05077022,    0.04997378],\n",
            "   [   -0.6232713,    -0.7490872,   -0.12378739,   -0.73972183,    -0.5159671,    -0.2407978,\n",
            "      -0.30859652,    -0.7339645],\n",
            "   [  -0.14668319,   -0.12102992,   -0.09745442,   -0.11916974, -0.0070561506,  -0.122066006,\n",
            "      -0.21638699, -0.0024621617],\n",
            "   [  -0.50064075,    -0.7860907,   -0.06429163,    -0.7740401,    -0.6614576,   0.021570047,\n",
            "      -0.53156555,   -0.37473887],\n",
            "   [    -0.607312,    -0.5424697,   -0.16334198,   -0.53581303,   -0.68601584,   -0.11943822,\n",
            "       -0.6178398,   -0.76444435],\n",
            "   [  -0.40162098,    -0.5817203,   0.026613992,   -0.50475097,   -0.75907147,    -0.1641216,\n",
            "       -0.6178206,     -0.560537],\n",
            "   [    -0.685132,   -0.58656687,   -0.21825604,   -0.43348637,    -0.6693542,    -0.1917548,\n",
            "      -0.40290675,   -0.74058324],\n",
            "   [  -0.40191644,   -0.41102844,   -0.20008798,    -0.4407936,    -0.6987691,   -0.17222436,\n",
            "       -0.3119998,    -0.5634476]]]], bias: [ -0.5999163,  -0.5998808, -0.43834186,  -0.5997967, -0.59952277, -0.37925735,   -0.608477,\n",
            "  -0.5957449], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)), latestResult: __lldb_expr_135.LatestResult), output: TensorFlow.Conv2D<Swift.Float>(filter: [[[[   0.54350626,    0.36912644,    -0.6387129,    -0.5631491],\n",
            "   [   0.66139305,    0.44911283,   -0.53021055,   -0.76226085],\n",
            "   [   -0.2501363,   -0.08318057,   0.021244489,   -0.19799529],\n",
            "   [   0.37758243,     0.4451282,   -0.48947576,    -0.5127232],\n",
            "   [    0.7845043,    0.48424825,   -0.44672057,   -0.76299304],\n",
            "   [ -0.011697962,    0.33718976,    0.07779811,    -0.2217781],\n",
            "   [    0.4538745,     0.5951113,      -0.77038,   -0.58726126],\n",
            "   [   0.68863076,    0.68405795,    -0.4927777,    -0.5103857]],\n",
            "\n",
            "  [[    0.6771741,     0.4177961,   -0.38307053,     -0.503904],\n",
            "   [     0.691365,    0.73282415,   -0.38954356,   -0.73210907],\n",
            "   [   0.08455588,    0.24361292,    0.07988151,   -0.06919021],\n",
            "   [    0.5721985,    0.53853905,    -0.3777241,   -0.46437103],\n",
            "   [   0.40141928,     0.4837508,   -0.65778947,    -0.5114651],\n",
            "   [  -0.16479668,    -0.0942526,    0.11963467,    0.02091566],\n",
            "   [   0.78107864,     0.7334399,    -0.3930217,    -0.7909532],\n",
            "   [    0.5728891,     0.5998243,    -0.7130681,   -0.55498105]],\n",
            "\n",
            "  [[   0.46837303,     0.5079451,    -0.6017683,    -0.6873601],\n",
            "   [   0.47831333,    0.47704187,   -0.77050364,   -0.53056616],\n",
            "   [ 0.0022420038,     0.2211996,   -0.26627856,  -0.058284115],\n",
            "   [   0.68229866,    0.43813175,   -0.42957944,    -0.7715595],\n",
            "   [    0.6962746,     0.7863049,   -0.51530814,    -0.4079552],\n",
            "   [  0.109203026,  -0.033205368,   0.026082976,   -0.08189419],\n",
            "   [    0.6171406,    0.36945978,    -0.3867584,   -0.73041993],\n",
            "   [   0.73056966,     0.7131593,    -0.7246948,    -0.7576386]]],\n",
            "\n",
            "\n",
            " [[[    0.5189882,    0.47748834,   -0.38496318,   -0.47614747],\n",
            "   [   0.35417625,    0.47263256,    -0.6405072,    -0.4251601],\n",
            "   [  -0.24306138,  -0.039193217,   0.036665678,   0.080214255],\n",
            "   [    0.7102015,    0.53253174,    -0.6385763,    -0.6721286],\n",
            "   [    0.7894302,    0.44022915,    -0.8246856,    -0.4934092],\n",
            "   [ -0.055057004,    0.04119336,   -0.05024686,    0.08659095],\n",
            "   [    0.7209338,     0.5298335,    -0.7829244,    -0.7282564],\n",
            "   [    0.4557156,    0.47210628,   -0.50076157,    -0.4354677]],\n",
            "\n",
            "  [[   0.47510478,     0.6929867,   -0.66835916,     -0.541769],\n",
            "   [    0.6657957,     0.3804939,   -0.73857844,    -0.5002131],\n",
            "   [   0.09884149,    0.33964127,  -0.099822186,    0.09939251],\n",
            "   [    0.7042677,     0.7117351,   -0.82771486,   -0.37682566],\n",
            "   [   0.79062307,     0.6132387,   -0.73981214,   -0.81839657],\n",
            "   [   0.14940467,    0.16878009,    0.18441717,   -0.29895493],\n",
            "   [   0.79524565,    0.55483806,   -0.76385325,   -0.39774883],\n",
            "   [     0.559042,    0.43773994,   -0.59146357,    -0.6482264]],\n",
            "\n",
            "  [[     0.810372,     0.6433294,   -0.36230907,   -0.50711143],\n",
            "   [    0.3729471,     0.5412034,    -0.4352192,    -0.5392546],\n",
            "   [  -0.12581989,   0.040613934,  -0.065969355,   0.097153455],\n",
            "   [    0.5725759,     0.8238927,   -0.45702362,   -0.58162904],\n",
            "   [   0.67387867,    0.78583187,   -0.36339238,   -0.45392656],\n",
            "   [  -0.08631225,  -0.109342225,    -0.1724692,   -0.30817217],\n",
            "   [    0.3759356,    0.39811447,    -0.6437498,   -0.37875476],\n",
            "   [    0.4985876,    0.66777813,   -0.52234745,   -0.79127383]]],\n",
            "\n",
            "\n",
            " [[[    0.5423673,     0.4527935,    -0.6905824,     -0.674515],\n",
            "   [    0.3612162,    0.58419883,    -0.6508376,   -0.75919443],\n",
            "   [  -0.10413934,    0.21704614, -0.0031718016,    0.21902002],\n",
            "   [   0.59899753,    0.37461725,    -0.4116813,   -0.57418936],\n",
            "   [   0.48832682,      0.540494,    -0.3928253,    -0.3557282],\n",
            "   [   -0.1318777,     0.0899538,    -0.1654059,   -0.19719338],\n",
            "   [    0.7211881,    0.54378605,    -0.6283414,   -0.74319625],\n",
            "   [   0.67787623,    0.60121775,    -0.7937138,   -0.59888846]],\n",
            "\n",
            "  [[    0.7852921,     0.8083031,    -0.7177438,   -0.40098268],\n",
            "   [    0.5667264,    0.48001924,   -0.56729954,    -0.4519939],\n",
            "   [   -0.0090753,    0.14827995,   -0.35091615,   -0.14106083],\n",
            "   [    0.4500678,    0.60572124,    -0.7677216,    -0.6996991],\n",
            "   [    0.7440587,     0.6639964,    -0.4947259,    -0.8189637],\n",
            "   [  -0.08285226,    0.10302072,   -0.19435684,    0.21681444],\n",
            "   [   0.38063002,     0.6474018,    -0.8184031,     -0.754548],\n",
            "   [      0.57392,    0.73711634,    -0.5449789,    -0.5596254]],\n",
            "\n",
            "  [[   0.45058614,    0.68562025,   -0.42984316,   -0.65833396],\n",
            "   [    0.6315661,     0.6060795,   -0.59290326,     -0.705428],\n",
            "   [   0.04561249,    0.35577047,  -0.052293967,    0.11709543],\n",
            "   [   0.39523727,    0.53980243,    -0.7594655,    -0.5063309],\n",
            "   [   0.54705566,     0.4264751,   -0.55677474,    -0.5807183],\n",
            "   [  -0.16957296,    0.14155506,    0.15511025,    -0.1713847],\n",
            "   [   0.38752383,     0.3972916,    -0.7803603,   -0.59685004],\n",
            "   [   0.65207857,    0.38148814,   -0.36647528,   -0.53217363]]]], bias: [0.0022283846, 0.0013209125, 0.0017073716, 0.0007471752], activation: (Function), strides: (1, 1), padding: TensorFlow.Padding.same, dilations: (1, 1)))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2TOOg-ATLFx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6a4990df-bfe1-464b-95cb-d8b77e05880f"
      },
      "source": [
        "let (loss, grad) = model.valueWithGradient { (model: UNet) -> Tensor<Float> in\n",
        "  let logits = model(firstTrainFeatures)\n",
        "  print(logits)\n",
        "  return softmaxCrossEntropy(logits: \n",
        "    logits.reshaped(to: [batchSize, 324 * 324 * 4]), \n",
        "    probabilities: firstTrainLabels.reshaped(to: [batchSize, 324 * 324 * 4])\n",
        "  )\n",
        "}"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  ...,\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  ...,\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  ...,\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]]],\r\n",
            "\r\n",
            "\r\n",
            " ...,\r\n",
            "\r\n",
            "\r\n",
            " [[[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  ...,\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  ...,\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]]],\r\n",
            "\r\n",
            "\r\n",
            " [[[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  ...,\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]],\r\n",
            "\r\n",
            "  [[0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   ...,\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157],\r\n",
            "   [0.25018188, 0.24995495, 0.25005156, 0.24981157]]]]\r\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}