computer_vision_lecture_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "computer-vision-lecture-1.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "FAhKo1EEN3Zv",
        "3FmF6Mi_6qWj",
        "-mhx2OUeCFFy",
        "PQXy87ZQYrvi",
        "8E7fxhfAZgmn",
        "0qwpp7DsgRLH",
        "mYX45leMDSqd",
        "HkO2Ygiocd0X",
        "m1lzOzMCDSCo",
        "I_Dk5S5OH1eM",
        "mT0knu-YKWqC",
        "AZ_bOisXDRc5",
        "nm-V3qykfQLK",
        "FDKF3-AtOvI5",
        "pvcq8U9VSsXX",
        "LpQ25u7y6wkU",
        "0ExLFWf165Vl",
        "qNRF0IP78A54",
        "RlCe2zGx1hCa",
        "2AjmMkHtDrLC",
        "i4Kjrs7Nb09F",
        "F-DcJbU177Vj",
        "dRxWhH0g3tKF",
        "1ilOvhjA3xS1",
        "WguU1NgEsCL6",
        "ItXfxkxvosLH",
        "hPWO_zyRopXN",
        "3eBtMqwD2I4f",
        "13WPkJ5P2ud1"
      ],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/noahgift/edge-computer-vision/blob/master/computer_vision_lecture_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAhKo1EEN3Zv",
        "colab_type": "text"
      },
      "source": [
        "# Computer Vision Overview\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3FmF6Mi_6qWj",
        "colab_type": "text"
      },
      "source": [
        "## Intro"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0afTf7Ad6dtd",
        "colab_type": "text"
      },
      "source": [
        "![hero](https://user-images.githubusercontent.com/58792/45260894-08e67a00-b3a8-11e8-941f-e539cb10f8e1.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mhx2OUeCFFy",
        "colab_type": "text"
      },
      "source": [
        "## Computing Trends supporting applied Computer Vision\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_nVTYlRzB3Z7",
        "colab_type": "text"
      },
      "source": [
        "### Introductory Concepts in Hardware AI and IoT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mb_rCjqF2U14",
        "colab_type": "text"
      },
      "source": [
        "#### Golden Era\n",
        "\n",
        "![Golden Era of Computer Architecture](https://user-images.githubusercontent.com/58792/44999482-688cf180-af72-11e8-80ee-7c15be551bc3.png)\n",
        "\n",
        "Source: https://riscv.org/wp-content/uploads/2017/05/Mon0915-RISC-V-50-Years-Computer-Arch.pdf\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0Rurw6B48JV",
        "colab_type": "text"
      },
      "source": [
        "#### Amhdal's Law\n",
        "\n",
        "![Amhdal's Law](https://upload.wikimedia.org/wikipedia/commons/e/ea/AmdahlsLaw.svg)\n",
        "\n",
        "\n",
        "Source:  https://upload.wikimedia.org/wikipedia/commons/e/ea/AmdahlsLaw.svg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0uKhloSMEDx",
        "colab_type": "text"
      },
      "source": [
        "#### ASIC vs CPU vs GPU\n",
        "\n",
        "**TPU in Production**\n",
        "![TPU Production](https://user-images.githubusercontent.com/58792/45001200-fd4a1c00-af7f-11e8-8fef-530cf2d70ada.png)\n",
        "\n",
        "**CPU**\n",
        "![How CPUs work](https://user-images.githubusercontent.com/58792/45001241-5d40c280-af80-11e8-9a82-81f900995721.png)\n",
        "\n",
        "**GPU**\n",
        "![GPUs work](https://user-images.githubusercontent.com/58792/45001287-b90b4b80-af80-11e8-9cc7-cc6d2fb2b19c.png)\n",
        "\n",
        "**TPU**\n",
        "![TPUs work](https://user-images.githubusercontent.com/58792/45001315-ef48cb00-af80-11e8-8880-aa59a272e095.png)\n",
        "\n",
        "Sources: https://storage.googleapis.com/nexttpu/index.html\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajVQgVU4M3I_",
        "colab_type": "text"
      },
      "source": [
        "#### CPUs\n",
        "\n",
        "\n",
        "\n",
        "*   Stuck in 3% Yearly Performance Growth\n",
        "*   Part of the Machine Learning Solution, not **THE** solution of the future\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XIwh_IOwa4cS",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4DC5AUdvZwdK",
        "colab_type": "text"
      },
      "source": [
        "#### Amazon ARM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vn-mAOhqZzGX",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "AWS Graviton processors are **custom-built by Amazon Web Services** using 64-bit Arm Neoverse cores to deliver the best price performance for your cloud workloads running in Amazon EC2\n",
        "\n",
        "https://aws.amazon.com/ec2/graviton/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kV6wqRvSDSa8",
        "colab_type": "text"
      },
      "source": [
        "### GPUs and other ASICS\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQXy87ZQYrvi",
        "colab_type": "text"
      },
      "source": [
        "#### Nvidia GPU\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9AunxhpnfHz",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "![NVidia](https://user-images.githubusercontent.com/58792/45267130-a333d580-b41b-11e8-8548-5a773b381b8f.png)\n",
        "\n",
        "[Machine Learning with NVidia](https://www.nvidia.com/object/machine-learning.html)\n",
        "\n",
        "*  [Deep Learning Institute](https://www.nvidia.com/en-us/deep-learning-ai/education/)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8E7fxhfAZgmn",
        "colab_type": "text"
      },
      "source": [
        "#### AMD GPU\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yYvP1DGfWaK",
        "colab_type": "text"
      },
      "source": [
        "![Radeon Instinct](https://user-images.githubusercontent.com/58792/45267237-a334d500-b41d-11e8-9097-5968c53f034c.png)\n",
        "\n",
        "![Human Brain?](https://user-images.githubusercontent.com/58792/45267301-d6c42f00-b41e-11e8-8349-ca15e5f93392.png)\n",
        "\n",
        "[AMD Deep Learning](https://www.amd.com/en/graphics/servers-radeon-instinct-deep-learning)\n",
        "\n",
        "\n",
        "\n",
        "*   [Machine Learning vs Deep Learning](https://www.amd.com/en/technologies/deep-machine-learning)\n",
        "*   [ROCm](https://rocm.github.io/index.html)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3VIl1ePeBK9",
        "colab_type": "code",
        "outputId": "deb6a355-2eaa-4d9c-9c80-30e769f045b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "from IPython.display import HTML\n",
        "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ZSMaMKbw1y0?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ZSMaMKbw1y0?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0qwpp7DsgRLH",
        "colab_type": "text"
      },
      "source": [
        "#### Intel"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_cbPYdPgVYI",
        "colab_type": "text"
      },
      "source": [
        "![Intel AI Academy](https://user-images.githubusercontent.com/58792/45267399-746c2e00-b420-11e8-84de-0657e9593f78.png)\n",
        "\n",
        "![Intel Dev Cloud](https://user-images.githubusercontent.com/58792/45267467-ac27a580-b421-11e8-969f-bcca059f6536.png)\n",
        "\n",
        "[Intel AI Academy](https://software.intel.com/en-us/ai-academy)\n",
        "\n",
        "\n",
        "*   [Intel AI Academy Tensorflow Tutorial](https://software.intel.com/en-us/articles/using-tensorflow-for-deep-learning-training-and-testing)\n",
        "*   [Intel AI DevCloud](https://software.intel.com/en-us/ai-academy/devcloud)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYX45leMDSqd",
        "colab_type": "text"
      },
      "source": [
        "#### FPGA \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLRLoXN2jgV4",
        "colab_type": "text"
      },
      "source": [
        "##### Amazon F1 Instances\n",
        "\n",
        "![Amazon FGPA](https://user-images.githubusercontent.com/58792/45267490-3839cd00-b422-11e8-9cb9-de0c1a559280.png)\n",
        "\n",
        "![FPGA](https://user-images.githubusercontent.com/58792/45267584-06c20100-b424-11e8-892e-9fe55a84f338.png)\n",
        "\n",
        "[Amazon EC2 F1 Instances](https://aws.amazon.com/ec2/instance-types/f1/)\n",
        "\n",
        "\n",
        "\n",
        "*   [Amazon EC2 F1 Webinar](https://youtu.be/5sDVprory3c)\n",
        "*   [Reconfigure IO](https://reconfigure.io/) A platform for software defined chips\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KaksDKBDSyD",
        "colab_type": "code",
        "outputId": "998f79bc-2149-467c-acda-96842ccc340d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "from IPython.display import HTML\n",
        "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5sDVprory3c?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5sDVprory3c?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HkO2Ygiocd0X",
        "colab_type": "text"
      },
      "source": [
        "#### A11/12/13 Chip\n",
        "\n",
        "Apple Designed Chip for iOS that includes integrated GPU and dedicated neural network hardware.\n",
        "\n",
        "* [A13](https://en.wikipedia.org/wiki/Apple_A13)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PgbVUbVKtz1A",
        "colab_type": "text"
      },
      "source": [
        "[A11 \"Neural Engine\" Chip from Apple](https://en.wikipedia.org/wiki/Apple_A11)\n",
        "![A11](https://user-images.githubusercontent.com/58792/45267788-6e7a4b00-b428-11e8-99c2-e80827961480.jpg)\n",
        "![CreateML](https://user-images.githubusercontent.com/58792/45268030-03cb0e80-b42c-11e8-9a47-b8eef57e74a6.png)\n",
        "\n",
        "\n",
        "*  [600 billion operations per second](https://www.apple.com/newsroom/2017/09/the-future-is-here-iphone-x/)\n",
        "*  [Metal for Accelerating Machine Learning](https://developer.apple.com/videos/play/wwdc2018/609/)\n",
        "*  [Introducing Create ML](https://developer.apple.com/videos/play/wwdc2018/703/)\n",
        "*  [What's New in Core ML](https://developer.apple.com/videos/play/wwdc2018/708/)\n",
        "*  [AWS Lambda + Swift](https://medium.com/capital-one-developers/serverless-computing-with-swift-f515ff052919) Tangentially related...running Swift code in AWS Lambda\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oaz3fzwvcd0Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1lzOzMCDSCo",
        "colab_type": "text"
      },
      "source": [
        "#### TPUs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_DA-TyN09Pr",
        "colab_type": "text"
      },
      "source": [
        "TPU MNIST training code snippit\n",
        "\n",
        "```python\n",
        "python /usr/share/models/official/mnist/mnist_tpu.py \\\n",
        "  --tpu=$TPU_NAME \\\n",
        "  --data_dir=${STORAGE_BUCKET}/data \\\n",
        "  --model_dir=${STORAGE_BUCKET}/output \\\n",
        "  --use_tpu=True \\\n",
        "  --iterations=500 \\\n",
        "  --train_steps=2000\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "**Resources:**\n",
        "\n",
        "\n",
        "\n",
        "*   [Minst](https://cloud.google.com/tpu/docs/tutorials/mnist)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_Dk5S5OH1eM",
        "colab_type": "text"
      },
      "source": [
        "#### AWS Inferentia"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgLPHfFhICoT",
        "colab_type": "text"
      },
      "source": [
        "[AWS Inferentia](https://aws.amazon.com/machine-learning/inferentia/)\n",
        "\n",
        "\n",
        "\n",
        "*   machine learning inference chip \n",
        "*   will support the TensorFlow, Apache MXNet, and PyTorch deep learning frameworks, as well as models that use the ONNX format\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mT0knu-YKWqC",
        "colab_type": "text"
      },
      "source": [
        "#### Amazon Elastic Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Beiajj5FK1Ol",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "[Elastic Inference](https://aws.amazon.com/machine-learning/elastic-inference/)\n",
        "\n",
        "*  to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances \n",
        "*  inference—can drive as much as 90% of the compute costs of the application\n",
        "*  supports TensorFlow and MXnet\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZ_bOisXDRc5",
        "colab_type": "text"
      },
      "source": [
        "## Emerging Hardware and AI and Managed ML Platforms\n",
        "\n",
        "Dedicated AI Hardware + Better ML Framework Cloud-Native Abstractions\n",
        "\n",
        "**Examples:**\n",
        "\n",
        "* [AWS Sagemaker](https://aws.amazon.com/sagemaker/) \n",
        "* [AWS DeepLense](https://aws.amazon.com/deeplens/)\n",
        "* [Google AI Platform](https://cloud.google.com/ai-platform/)\n",
        "* [Google BigQueryML](https://cloud.google.com/bigquery/)\n",
        "* [Google AutoML](https://cloud.google.com/automl/)\n",
        "* [Azure ML Designer](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-designer)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JRSfoXScqc-",
        "colab_type": "text"
      },
      "source": [
        "###### AWS IoT Ecosystem\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ct4ISjzyLxWQ",
        "colab_type": "text"
      },
      "source": [
        "Many New Services from ***Reinvent 2018 & 2019***\n",
        "\n",
        "* [IoT Graph](https://aws.amazon.com/about-aws/whats-new/2018/11/iot-things-graph-now-in-preview/)\n",
        "* [IoT Events](https://aws.amazon.com/about-aws/whats-new/2018/11/introducing-aws-iot-events-now-available-in-preview/)\n",
        "* [IoT SiteWise](https://aws.amazon.com/about-aws/whats-new/2018/11/announcing-aws-iot-sitewise-now-available-in-limited-preview/)\n",
        "* [IoT Device Tester](https://aws.amazon.com/about-aws/whats-new/2018/11/bluetooth-low-energy-ble-support-in-amazon-freertos-now-available-in-beta/)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QD75VVPNyGLe",
        "colab_type": "text"
      },
      "source": [
        "![DeepLense ](https://user-images.githubusercontent.com/58792/45337586-a6bf7d80-b53d-11e8-9796-ecef716335a8.png)\n",
        "\n",
        "Greengrass part of Embedded Machine Intelligence Platform\n",
        "\n",
        "[AWS Greengrass](https://aws.amazon.com/greengrass/)\n",
        "\n",
        "**Dev Kits**\n",
        "\n",
        "![aws iot dev kit](https://user-images.githubusercontent.com/58792/46483952-a6697980-c7ad-11e8-84e5-90beb17a460f.jpg)\n",
        "\n",
        "![AWS IoT](https://user-images.githubusercontent.com/58792/46484897-8935aa80-c7af-11e8-9f73-940d1dca9ad1.jpg)\n",
        "\n",
        "**Resource Examples:**\n",
        "\n",
        "* [DeepLens AWS Blog](https://aws.amazon.com/blogs/aws/deeplens/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nm-V3qykfQLK",
        "colab_type": "text"
      },
      "source": [
        "### Azure IoT Ecosystem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRbbApUMn9b6",
        "colab_type": "text"
      },
      "source": [
        "[Azure IoT kits](https://microsoft.github.io/azure-iot-developer-kit/)\n",
        "\n",
        "Integrated into Visual Studio via Azure IoT Workbench\n",
        "\n",
        "![mxchip](https://user-images.githubusercontent.com/58792/46483506-a9179f00-c7ac-11e8-8c48-3a87f49c12f9.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dI9IwAyfcqc_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDKF3-AtOvI5",
        "colab_type": "text"
      },
      "source": [
        "### Edge Computing Devices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPZbWOdb8QWe",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Low Latency (Role of 5G?)\n",
        "*   Available Offline\n",
        "*   Privacy\n",
        "*   ASIC (Application Specific Integrated Circuits) play key role\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pvcq8U9VSsXX",
        "colab_type": "text"
      },
      "source": [
        "Key Industry Players"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfcSnJYhSycx",
        "colab_type": "text"
      },
      "source": [
        "All have \"inference\" based solutions\n",
        "\n",
        "*   AWS\n",
        "*   Google\n",
        "*   Apple\n",
        "*   Intel\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yylxGK6_3c7u",
        "colab_type": "text"
      },
      "source": [
        "**Devices**\n",
        "\n",
        "These will include:\n",
        "\n",
        "* [AWS DeepLense](https://aws.amazon.com/deeplens/)\n",
        "* [Intel® NCS2](https://software.intel.com/en-us/neural-compute-stick)\n",
        "* [Raspberry Pi](https://www.raspberrypi.org/)\n",
        "* [NVidia Jetson Nano](https://developer.nvidia.com/buy-jetson)\n",
        "* [iOS A13 & Core ML](https://en.wikipedia.org/wiki/Apple_A13)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpQ25u7y6wkU",
        "colab_type": "text"
      },
      "source": [
        "#### Intel Movidius"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ExLFWf165Vl",
        "colab_type": "text"
      },
      "source": [
        "##### Overview of Movidius\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tM7aVgGn78to",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "[Intel Movidius Neural Compute Stick](https://developer.movidius.com/)\n",
        "\n",
        "\"tiny fanless deep learning device for...AI programming at the edge\"\n",
        "\n",
        "\n",
        "![movidius workflow](https://user-images.githubusercontent.com/58792/45309910-b73e0c80-b4d9-11e8-8c72-e3aa17d61ab9.png)\n",
        "\n",
        "\n",
        "**How To**\n",
        "\n",
        "*  Using an Ubuntu VM is most straightforward path to install SDK\n",
        "\n",
        "![alt text](https://user-images.githubusercontent.com/58792/45322711-f5e5be00-b4fd-11e8-9a19-894d73450f70.png)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "**Resources:**\n",
        "\n",
        "*   [Neural Network Prototyping Video](https://www.youtube.com/watch?v=FDguqBh3V6g)\n",
        "*   [Getting Started Guide](https://developer.movidius.com/start)\n",
        "* [NCSDK](https://github.com/movidius/ncsdk)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNRF0IP78A54",
        "colab_type": "text"
      },
      "source": [
        "##### Movidius Hello World"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j7ZKmDlyPH91",
        "colab_type": "text"
      },
      "source": [
        "Checkout sample applications and run hello world (hello_ncs_py):  https://github.com/movidius/ncappzoo/blob/master/README.md\n",
        "\n",
        "\n",
        "```shell\n",
        "\n",
        "parallels@parallels-vm:~/workspace/ncappzoo/apps/hello_ncs_py$ make run\n",
        "\n",
        "making run\n",
        "python3 hello_ncs.py;\n",
        "Error - no NCS devices detected, verify an NCS device is connected.\n",
        "parallels@parallels-vm:~/workspace/ncappzoo/apps/hello_ncs_py$ make run\n",
        "\n",
        "making run\n",
        "python3 hello_ncs.py;\n",
        "Hello NCS! Device opened normally.\n",
        "Goodbye NCS! Device closed normally.\n",
        "NCS device working.\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlCe2zGx1hCa",
        "colab_type": "text"
      },
      "source": [
        "###### Movidius Real-time Image Classification\n",
        "\n",
        "Example using Caffe model from [ncappzoo Github repo](https://github.com/movidius/ncappzoo)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkwHx8cS1kwm",
        "colab_type": "text"
      },
      "source": [
        "![Movidius](https://user-images.githubusercontent.com/58792/45392092-a1fcd700-b5da-11e8-85d3-35e792fd216f.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2AjmMkHtDrLC",
        "colab_type": "text"
      },
      "source": [
        "#### iOS CoreML"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_r2CsFMDrLE",
        "colab_type": "text"
      },
      "source": [
        "##### Overview of CoreML\n",
        "\n",
        "\n",
        "![CoreML](https://docs-assets.developer.apple.com/published/7e05fb5a2e/4b0ecf58-a51a-4bfa-a361-eb77e59ed76e.png)\n",
        "\n",
        "[CoreML Docs](https://developer.apple.com/documentation/coreml)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQorH943DrLF",
        "colab_type": "text"
      },
      "source": [
        "##### Key Concepts\n",
        "\n",
        "*  Optimized for on-device performance\n",
        "*  Minimal memory footprint\n",
        "*  Can convert trained models from third-party machine learning framework\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4Kjrs7Nb09F",
        "colab_type": "text"
      },
      "source": [
        "##### Converting Trained Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-DcJbU177Vj",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "Example of conversion process:\n",
        "\n",
        "*Install python package:*\n",
        "\n",
        "```python\n",
        "pip install -U coremltools\n",
        "```\n",
        "\n",
        "```python\n",
        "\n",
        "#import coremltools\n",
        "import coremltools\n",
        "\n",
        "#convert model\n",
        "coreml_model = coremltools.converters.caffe.convert('my_caffe_model.caffemodel')\n",
        "\n",
        "#save model to Core ML model format\n",
        "coremltools.utils.save_spec(coreml_model, 'my_model.mlmodel')\n",
        "```\n",
        "\n",
        "\n",
        "**Convertors**\n",
        "\n",
        "\n",
        "\n",
        "*   [MXNet Convertor](https://github.com/apache/incubator-mxnet/tree/master/tools/coreml)\n",
        "*   [TensorFlow Convertor](https://github.com/tf-coreml/tf-coreml)\n",
        "\n",
        "\n",
        "**Resources:**\n",
        "\n",
        "\n",
        "*   [Convert trained Models docs](https://developer.apple.com/documentation/coreml/converting_trained_models_to_core_ml)\n",
        "*   [coremltools](https://pypi.org/project/coremltools/)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2sO5hiqzTNN",
        "colab_type": "code",
        "outputId": "2e4651d4-9c5d-48f9-89c2-2aa4132f5dbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "!pip -q install -U coremltools"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mgoogle-colab 0.0.1a1 has requirement six~=1.11.0, but you'll have six 1.10.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mcufflinks 0.14.6 has requirement plotly>=3.0.0, but you'll have plotly 1.12.12 which is incompatible.\u001b[0m\n",
            "\u001b[31mpymc3 3.6 has requirement joblib<0.13.0, but you'll have joblib 0.13.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mfeaturetools 0.4.1 has requirement pandas>=0.23.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_mtYudXzc29",
        "colab_type": "code",
        "outputId": "aaffeff7-ad22-4af6-c617-a7f02b3b3b30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import coremltools"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Keras version 2.2.4 detected. Last version known to be fully compatible of Keras is 2.1.6 .\n",
            "WARNING:root:TensorFlow version 1.12.0 detected. Last version known to be fully compatible is 1.5.0 .\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XN4O6Q7Izj_y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "coremltools.converters.sklearn.convert?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9oZIawL9q-3",
        "colab_type": "text"
      },
      "source": [
        "###### CoreML Image Classification Demo\n",
        "\n",
        "\n",
        "\n",
        "*   [Classifying Images with Vision and Core ML](https://developer.apple.com/documentation/vision/classifying_images_with_vision_and_core_ml)\n",
        "\n",
        "\n",
        "*Mobilenet.mlmodel (could have been converted)*\n",
        "\n",
        "![Mobilenet.mlmodel](https://user-images.githubusercontent.com/58792/45366919-e1083980-b594-11e8-9b4e-9900da6c452f.png)\n",
        "\n",
        "iOS Simulator Doing Image Classification\n",
        "\n",
        "![iOS Simulator](https://user-images.githubusercontent.com/58792/45366912-dea5df80-b594-11e8-8b67-81571f0f8b44.png)\n",
        "\n",
        "To use MXNet models:\n",
        "\n",
        "https://github.com/apache/incubator-mxnet/tree/master/tools/coreml\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6QgQhawBSU2",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRxWhH0g3tKF",
        "colab_type": "text"
      },
      "source": [
        "# TensorFlow Russian Dolls\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EggdJbEp6Pak",
        "colab_type": "text"
      },
      "source": [
        "![wikipedia_commons](https://upload.wikimedia.org/wikipedia/commons/thumb/0/0a/Russian_Dolls.jpg/640px-Russian_Dolls.jpg)\n",
        "\n",
        "[*source:  wikipedia*](https://upload.wikimedia.org/wikipedia/commons/thumb/0/0a/Russian_Dolls.jpg/640px-Russian_Dolls.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5uaujBRJ7Z9",
        "colab_type": "text"
      },
      "source": [
        "A *crash course* involving several layers of abstraction with [TensorFlow](https://www.tensorflow.org/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ilOvhjA3xS1",
        "colab_type": "text"
      },
      "source": [
        "## TensorFlow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WguU1NgEsCL6"
      },
      "source": [
        "### TensorFlow Hello World"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDpT_ks65v4J",
        "colab_type": "text"
      },
      "source": [
        "References\n",
        "\n",
        "*  [Official Hello World Example]( https://colab.research.google.com/notebooks/welcome.ipynb#scrollTo=oYZkU7ZN3CL0)\n",
        "*  Adds two matrices\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5iX0wEVI6t49",
        "colab_type": "code",
        "outputId": "d7714f55-e491-468d-ac46-15075c7f1a84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "input1 = tf.ones((2, 3))\n",
        "input2 = tf.reshape(tf.range(1, 7, dtype=tf.float32), (2, 3))\n",
        "\n",
        "print(\"Two Tensor Flow Matrices with shape:\")\n",
        "print(input1.shape)\n",
        "print(input2.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Two Tensor Flow Matrices with shape:\n",
            "(2, 3)\n",
            "(2, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afyOLD6p5l3_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output = input1 + input2\n",
        "with tf.Session():\n",
        "  result = output.eval()\n",
        "\n",
        "print(f\"Type of result:  {type(result)}\\n\")\n",
        "print(\"Result of addition of two Matrics:\")\n",
        "result\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ng1Yv1pXUhWk",
        "colab_type": "text"
      },
      "source": [
        "### Tensorflow Linear Regression (Project)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZdpALUHUw6c",
        "colab_type": "text"
      },
      "source": [
        "Based *on official Google Tutorial [here](https://developers.google.com/machine-learning/crash-course/first-steps-with-tensorflow/programming-exercises)*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bd2Zkk1LE2Zr",
        "colab_type": "text"
      },
      "source": [
        "**Learning Objectives:**\n",
        "  * Learn fundamental TensorFlow concepts\n",
        "  * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n",
        "  * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n",
        "  * Improve the accuracy of a model by tuning its hyperparameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxiIKhP4E2Zr",
        "colab_type": "text"
      },
      "source": [
        "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TjLjL9IU80G",
        "colab_type": "text"
      },
      "source": [
        "#### Setup\n",
        "In this first cell, we'll load the necessary libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVFf5asKE2Zt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.data import Dataset\n",
        "\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ipRyUHjhU80Q",
        "colab_type": "text"
      },
      "source": [
        "Next, we'll load our data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ivCDWnwE2Zx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVk_qlG6U80j",
        "colab_type": "text"
      },
      "source": [
        "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0eVyguIU80m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
        "    np.random.permutation(california_housing_dataframe.index))\n",
        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
        "california_housing_dataframe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzzlSs3PtTmt",
        "colab_type": "text"
      },
      "source": [
        "#### Examine the Data\n",
        "\n",
        "It's a good idea to get to know your data a little bit before you work with it.\n",
        "\n",
        "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzb10yoVrydW",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "california_housing_dataframe.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr6wYl2bt2Ep",
        "colab_type": "text"
      },
      "source": [
        "#### Build the First Model\n",
        "\n",
        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
        "\n",
        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
        "\n",
        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0cpcsieFhsNI",
        "colab_type": "text"
      },
      "source": [
        "##### Step 1: Define Features and Configure Feature Columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EL8-9d4ZJNR7",
        "colab_type": "text"
      },
      "source": [
        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
        "\n",
        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
        "\n",
        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
        "\n",
        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
        "\n",
        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhEbFCZ86cDZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the input feature: total_rooms.\n",
        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
        "\n",
        "# Configure a numeric feature column for total_rooms.\n",
        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_3S8teX7Rd2",
        "colab_type": "text"
      },
      "source": [
        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMl3qrU5MGV6",
        "colab_type": "text"
      },
      "source": [
        "##### Step 2: Define the Target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw4nrfcB7kyk",
        "colab_type": "text"
      },
      "source": [
        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1NvvNkH8Kbt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the label.\n",
        "targets = california_housing_dataframe[\"median_house_value\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4M-rTFHL2UkA",
        "colab_type": "text"
      },
      "source": [
        "##### Step 3: Configure the LinearRegressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUfGQUNp7jdL",
        "colab_type": "text"
      },
      "source": [
        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
        "\n",
        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubhtW-NGU802",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use gradient descent as the optimizer for training the model.\n",
        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "\n",
        "# Configure the linear regression model with our feature columns and optimizer.\n",
        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
        "linear_regressor = tf.estimator.LinearRegressor(\n",
        "    feature_columns=feature_columns,\n",
        "    optimizer=my_optimizer\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0IztwdK2f3F",
        "colab_type": "text"
      },
      "source": [
        "##### Step 4: Define the Input Function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5M5j6xSCHxx",
        "colab_type": "text"
      },
      "source": [
        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
        "\n",
        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
        "\n",
        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
        "\n",
        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
        "the size of the dataset from which `shuffle` will randomly sample.\n",
        "\n",
        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKZ9zNcHJtwc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
        "    \"\"\"Trains a linear regression model of one feature.\n",
        "  \n",
        "    Args:\n",
        "      features: pandas DataFrame of features\n",
        "      targets: pandas DataFrame of targets\n",
        "      batch_size: Size of batches to be passed to the model\n",
        "      shuffle: True or False. Whether to shuffle the data.\n",
        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
        "    Returns:\n",
        "      Tuple of (features, labels) for next data batch\n",
        "    \"\"\"\n",
        "  \n",
        "    # Convert pandas data into a dict of np arrays.\n",
        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
        " \n",
        "    # Construct a dataset, and configure batching/repeating.\n",
        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "    \n",
        "    # Shuffle the data, if specified.\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(buffer_size=10000)\n",
        "    \n",
        "    # Return the next batch of data.\n",
        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwa6UeA1V5F_",
        "colab_type": "text"
      },
      "source": [
        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YS50CQb2ooO",
        "colab_type": "text"
      },
      "source": [
        "##### Step 5: Train the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yP92XkzhU803",
        "colab_type": "text"
      },
      "source": [
        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
        "train for 100 steps."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5M-Kt6w8U803",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "_ = linear_regressor.train(\n",
        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
        "    steps=100\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Nwxqxlx2sOv",
        "colab_type": "text"
      },
      "source": [
        "##### Step 6: Evaluate the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoDaF2dlJQG5",
        "colab_type": "text"
      },
      "source": [
        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
        "\n",
        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDIxp6vcU809",
        "colab_type": "code",
        "outputId": "ae607f00-6779-42a5-c958-7352ad390c32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Create an input function for predictions.\n",
        "# Note: Since we're making just one prediction for each example, we don't \n",
        "# need to repeat or shuffle the data here.\n",
        "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
        "\n",
        "# Call predict() on the linear_regressor to make predictions.\n",
        "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
        "\n",
        "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
        "predictions = np.array([item['predictions'][0] for item in predictions])\n",
        "\n",
        "# Print Mean Squared Error and Root Mean Squared Error.\n",
        "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
        "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
        "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
        "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error (on training data): 56367.025\n",
            "Root Mean Squared Error (on training data): 237.417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKWstXXPzOVz",
        "colab_type": "text"
      },
      "source": [
        "Is this a good model? How would you judge how large this error is?\n",
        "\n",
        "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
        "instead.  A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
        "\n",
        "Let's compare the RMSE to the difference of the min and max of our targets:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UwqGbbxP53O",
        "colab_type": "code",
        "outputId": "cdf7b39d-7de3-41d1-9c9f-c297e9e04616",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
        "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
        "min_max_difference = max_house_value - min_house_value\n",
        "\n",
        "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
        "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
        "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n",
        "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min. Median House Value: 14.999\n",
            "Max. Median House Value: 500.001\n",
            "Difference between Min. and Max.: 485.002\n",
            "Root Mean Squared Error: 237.417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JigJr0C7Pzit",
        "colab_type": "text"
      },
      "source": [
        "Our error spans nearly half the range of the target values. Can we do better?\n",
        "\n",
        "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n",
        "\n",
        "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "941nclxbzqGH",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "6fc86590-a989-43f6-ad69-90af4278f66b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "calibration_data = pd.DataFrame()\n",
        "calibration_data[\"predictions\"] = pd.Series(predictions)\n",
        "calibration_data[\"targets\"] = pd.Series(targets)\n",
        "calibration_data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.1</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.1</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.2</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.9</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean           0.1    207.3\n",
              "std            0.1    116.0\n",
              "min            0.0     15.0\n",
              "25%            0.1    119.4\n",
              "50%            0.1    180.4\n",
              "75%            0.2    265.0\n",
              "max            1.9    500.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2-bf8Hq36y8",
        "colab_type": "text"
      },
      "source": [
        "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n",
        "\n",
        "We can also visualize the data and the line we've learned.  Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n",
        "\n",
        "First, we'll get a uniform random sample of the data so we can make a readable scatter plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGRIi3mAU81H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample = california_housing_dataframe.sample(n=300)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-JwuJBKU81J",
        "colab_type": "text"
      },
      "source": [
        "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7G12E76-339G",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "ae421130-30a1-455d-bb5c-4afa0294d306",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Get the min and max total_rooms values.\n",
        "x_0 = sample[\"total_rooms\"].min()\n",
        "x_1 = sample[\"total_rooms\"].max()\n",
        "\n",
        "# Retrieve the final weight and bias generated during training.\n",
        "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
        "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "\n",
        "# Get the predicted median_house_values for the min and max total_rooms values.\n",
        "y_0 = weight * x_0 + bias \n",
        "y_1 = weight * x_1 + bias\n",
        "\n",
        "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n",
        "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
        "\n",
        "# Label the graph axes.\n",
        "plt.ylabel(\"median_house_value\")\n",
        "plt.xlabel(\"total_rooms\")\n",
        "\n",
        "# Plot a scatter plot from our data sample.\n",
        "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
        "\n",
        "# Display graph.\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlPj/+nVhLZhPo7QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0lRt4USU81L",
        "colab_type": "text"
      },
      "source": [
        "This initial line looks way off.  See if you can look back at the summary stats and see the same information encoded there.\n",
        "\n",
        "Together, these initial sanity checks suggest we may be able to find a much better line."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZWF67uv0HTG",
        "colab_type": "text"
      },
      "source": [
        "#### Tweak the Model Hyperparameters\n",
        "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n",
        "\n",
        "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n",
        "\n",
        "For each period, we'll compute and graph training loss.  This may help you judge when a model is converged, or if it needs more iterations.\n",
        "\n",
        "We'll also plot the feature weight and bias term values learned by the model over time.  This is another way to see how things converge."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgSMeD5UU81N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
        "  \"\"\"Trains a linear regression model of one feature.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: A `float`, the learning rate.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
        "      to use as input feature.\n",
        "  \"\"\"\n",
        "  \n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "\n",
        "  my_feature = input_feature\n",
        "  my_feature_data = california_housing_dataframe[[my_feature]]\n",
        "  my_label = \"median_house_value\"\n",
        "  targets = california_housing_dataframe[my_label]\n",
        "\n",
        "  # Create feature columns.\n",
        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
        "  \n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
        "  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
        "  \n",
        "  # Create a linear regressor object.\n",
        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  linear_regressor = tf.estimator.LinearRegressor(\n",
        "      feature_columns=feature_columns,\n",
        "      optimizer=my_optimizer\n",
        "  )\n",
        "\n",
        "  # Set up to plot the state of our model's line each period.\n",
        "  plt.figure(figsize=(15, 6))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.title(\"Learned Line by Period\")\n",
        "  plt.ylabel(my_label)\n",
        "  plt.xlabel(my_feature)\n",
        "  sample = california_housing_dataframe.sample(n=300)\n",
        "  plt.scatter(sample[my_feature], sample[my_label])\n",
        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training model...\")\n",
        "  print(\"RMSE (on training data):\")\n",
        "  root_mean_squared_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    linear_regressor.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "    # Take a break and compute predictions.\n",
        "    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
        "    \n",
        "    # Compute loss.\n",
        "    root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(predictions, targets))\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
        "    # Finally, track the weights and biases over time.\n",
        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
        "    y_extents = np.array([0, sample[my_label].max()])\n",
        "    \n",
        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "\n",
        "    x_extents = (y_extents - bias) / weight\n",
        "    x_extents = np.maximum(np.minimum(x_extents,\n",
        "                                      sample[my_feature].max()),\n",
        "                           sample[my_feature].min())\n",
        "    y_extents = weight * x_extents + bias\n",
        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
        "  print(\"Model training finished.\")\n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.ylabel('RMSE')\n",
        "  plt.xlabel('Periods')\n",
        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(root_mean_squared_errors)\n",
        "\n",
        "  # Output a table with calibration data.\n",
        "  calibration_data = pd.DataFrame()\n",
        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
        "  display.display(calibration_data.describe())\n",
        "\n",
        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kg8A4ArBU81Q",
        "colab_type": "text"
      },
      "source": [
        "#### Task 1:  Achieve an RMSE of 180 or Below\n",
        "\n",
        "Tweak the model hyperparameters to improve loss and better match the target distribution.\n",
        "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzoZUSdLIolF",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "230a0203-0f1f-4baf-c311-69de1e0ceb73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        }
      },
      "source": [
        "train_model(\n",
        "    learning_rate=0.00001,\n",
        "    steps=100,\n",
        "    batch_size=1\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 236.32\n",
            "  period 01 : 235.11\n",
            "  period 02 : 233.90\n",
            "  period 03 : 232.70\n",
            "  period 04 : 231.50\n",
            "  period 05 : 230.31\n",
            "  period 06 : 229.13\n",
            "  period 07 : 227.96\n",
            "  period 08 : 226.79\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-a555d2465b4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.00001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m )\n",
            "\u001b[0;32m<ipython-input-17-08b005cc5bf7>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(learning_rate, steps, batch_size, input_feature)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;31m# Take a break and compute predictions.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_regressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprediction_input_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'predictions'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;31m# Compute loss.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-17-08b005cc5bf7>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;31m# Take a break and compute predictions.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_regressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprediction_input_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'predictions'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;31m# Compute loss.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, input_fn, predict_keys, hooks, checkpoint_path, yield_single_examples)\u001b[0m\n\u001b[1;32m    638\u001b[0m             hooks=all_hooks) as mon_sess:\n\u001b[1;32m    639\u001b[0m           \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmon_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 640\u001b[0;31m             \u001b[0mpreds_evaluated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmon_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    641\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0myield_single_examples\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m               \u001b[0;32myield\u001b[0m \u001b[0mpreds_evaluated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    752\u001b[0m         \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m         run_metadata=run_metadata)\n\u001b[0m\u001b[1;32m    755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun_step_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1257\u001b[0m             \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1258\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1259\u001b[0;31m             run_metadata=run_metadata)\n\u001b[0m\u001b[1;32m   1260\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0m_PREEMPTION_ERRORS\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1261\u001b[0m         logging.info(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1343\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1344\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1345\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1346\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_PREEMPTION_ERRORS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m       \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1416\u001b[0m         \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m         \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m         run_metadata=run_metadata)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1178\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun_step_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_with_hooks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    954\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    955\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 956\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    957\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1180\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1181\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1357\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1359\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1360\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1363\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1365\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1366\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m       return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n\u001b[0;32m-> 1350\u001b[0;31m                                       target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1441\u001b[0m     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n\u001b[1;32m   1442\u001b[0m                                             \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m                                             run_metadata)\n\u001b[0m\u001b[1;32m   1444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1445\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbMAAAGECAYAAAC8ks6SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajVM7rkoYXeL",
        "colab_type": "text"
      },
      "source": [
        "##### Solution\n",
        "\n",
        "Click below for one possible solution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3zmldDwYy5c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=500,\n",
        "    batch_size=5\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8H0_D4vYa49",
        "colab_type": "text"
      },
      "source": [
        "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoD41nAmVUxM",
        "colab_type": "text"
      },
      "source": [
        "##### Is There a Standard Heuristic for Model Tuning?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QU5sLyYTqzqL",
        "colab_type": "text"
      },
      "source": [
        "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
        "\n",
        "That said, here are a few rules of thumb that may help guide you:\n",
        "\n",
        " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
        " * If the training has not converged, try running it for longer.\n",
        " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
        "   * But sometimes the exact opposite may happen if the learning rate is too high.\n",
        " * If the training error varies wildly, try decreasing the learning rate.\n",
        "   * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
        " * Very small batch sizes can also cause instability.  First try larger values like 100 or 1000, and decrease until you see degradation.\n",
        "\n",
        "Again, never go strictly by these rules of thumb, because the effects are data dependent.  Always experiment and verify."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GpV-uF_cBCBU",
        "colab_type": "text"
      },
      "source": [
        "#### Task 2: Try a Different Feature\n",
        "\n",
        "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
        "\n",
        "Don't take more than 5 minutes on this portion."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMyOxzb0ZlAH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# YOUR CODE HERE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ci1ISxxrZ7v0",
        "colab_type": "text"
      },
      "source": [
        "##### Solution\n",
        "\n",
        "Click below for one possible solution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjdQQCduZ7BV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%time\n",
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=1000,\n",
        "    batch_size=5,\n",
        "    input_feature=\"population\"\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DskmJEsu8xdX",
        "colab_type": "text"
      },
      "source": [
        "### Enable TPU Support in Colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrWQ83Cd81dN",
        "colab_type": "text"
      },
      "source": [
        "![TPU](https://user-images.githubusercontent.com/58792/53053297-d14f2400-3455-11e9-836d-3857de1f4337.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QDDm6TsK34PC",
        "colab_type": "text"
      },
      "source": [
        "## Keras (TensorFlow2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ItXfxkxvosLH",
        "colab_type": "text"
      },
      "source": [
        "### Text classification with movie reviews (Project)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKY4XMc9o8iB",
        "colab_type": "text"
      },
      "source": [
        "*Modified based on this tutorial: * https://research.google.com/seedbank/seed/classify_movie_reviews_using_tfkeras\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/basic_text_classification\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_text_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_text_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eg62Pmz3o83v",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "This notebook classifies movie reviews as *positive* or *negative* using the text of the review. This is an example of *binary*—or two-class—classification, an important and widely applicable kind of machine learning problem. \n",
        "\n",
        "We'll use the [IMDB dataset](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb) that contains the text of 50,000 movie reviews from the [Internet Movie Database](https://www.imdb.com/). These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are *balanced*, meaning they contain an equal number of positive and negative reviews. \n",
        "\n",
        "This notebook uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow. For a more advanced text classification tutorial using `tf.keras`, see the [MLCC Text Classification Guide](https://developers.google.com/machine-learning/guides/text-classification/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ew7HTbPpCJH",
        "colab_type": "code",
        "outputId": "5b471aec-f8d5-434a-d1c1-7d722b022e6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1.15.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZ8c25ug_FTf",
        "colab_type": "text"
      },
      "source": [
        "#### Ingest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iAsKG535pHep",
        "colab_type": "text"
      },
      "source": [
        "##### Download the IMDB dataset\n",
        "\n",
        "The IMDB dataset comes packaged with TensorFlow. It has already been preprocessed such that the reviews (sequences of words) have been converted to sequences of integers, where each integer represents a specific word in a dictionary.\n",
        "\n",
        "The following code downloads the IMDB dataset to your machine (or uses a cached copy if you've already downloaded it):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXXx5Oc3pOmN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "imdb = keras.datasets.imdb\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odr-KlzO-lkL",
        "colab_type": "text"
      },
      "source": [
        "The argument `num_words=10000` keeps the top 10,000 most frequently occurring words in the training data. The rare words are discarded to keep the size of the data manageable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l50X3GfjpU4r",
        "colab_type": "text"
      },
      "source": [
        "#### Explore the data (EDA)\n",
        "\n",
        "Let's take a moment to understand the format of the data. The dataset comes preprocessed: each example is an array of integers representing the words of the movie review. Each label is an integer value of either 0 or 1, where 0 is a negative review, and 1 is a positive review."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8qCnve_-lkO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(\"Training entries: {}, labels: {}\".format(len(train_data), len(train_labels)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnKvHWW4-lkW",
        "colab_type": "text"
      },
      "source": [
        "The text of reviews have been converted to integers, where each integer represents a specific word in a dictionary. Here's what the first review looks like:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtTS4kpEpjbi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(train_data[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIE4l_72x7DP",
        "colab_type": "text"
      },
      "source": [
        "Movie reviews may be different lengths. The below code shows the number of words in the first and second reviews. Since inputs to a neural network must be the same length, we'll need to resolve this later."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-6Ii9Pfx6Nr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "len(train_data[0]), len(train_data[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4wJg2FiYpuoX",
        "colab_type": "text"
      },
      "source": [
        "##### Convert the integers back to words\n",
        "\n",
        "It may be useful to know how to convert integers back to text. Here, we'll create a helper function to query a dictionary object that contains the integer to string mapping:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tr5s_1alpzop",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# A dictionary mapping words to an integer index\n",
        "word_index = imdb.get_word_index()\n",
        "\n",
        "# The first indices are reserved\n",
        "word_index = {k:(v+3) for k,v in word_index.items()} \n",
        "word_index[\"<PAD>\"] = 0\n",
        "word_index[\"<START>\"] = 1\n",
        "word_index[\"<UNK>\"] = 2  # unknown\n",
        "word_index[\"<UNUSED>\"] = 3\n",
        "\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "\n",
        "def decode_review(text):\n",
        "    return ' '.join([reverse_word_index.get(i, '?') for i in text])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3CNRvEZVppl",
        "colab_type": "text"
      },
      "source": [
        "Now we can use the `decode_review` function to display the text for the first review:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_OqxmH6-lkn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decode_review(train_data[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFP_XKVRp4_S",
        "colab_type": "text"
      },
      "source": [
        "##### Prepare the data\n",
        "\n",
        "The reviews—the arrays of integers—must be converted to tensors before fed into the neural network. This conversion can be done a couple of ways:\n",
        "\n",
        "* Convert the arrays into vectors of 0s and 1s indicating word occurrence, similar to a one-hot encoding. For example, the sequence  [3, 5] would become a 10,000-dimensional vector that is all zeros except for indices 3 and 5, which are ones. Then, make this the first layer in our network—a Dense layer—that can handle floating point vector data. This approach is memory intensive, though, requiring a `num_words * num_reviews` size matrix.\n",
        "\n",
        "* Alternatively, we can pad the arrays so they all have the same length, then create an integer tensor of shape `max_length * num_reviews`. We can use an embedding layer capable of handling this shape as the first layer in our network.\n",
        "\n",
        "In this tutorial, we will use the second approach. \n",
        "\n",
        "Since the movie reviews must be the same length, we will use the [pad_sequences](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence/pad_sequences) function to standardize the lengths:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jQv-omsHurp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = keras.preprocessing.sequence.pad_sequences(train_data,\n",
        "                                                        value=word_index[\"<PAD>\"],\n",
        "                                                        padding='post',\n",
        "                                                        maxlen=256)\n",
        "\n",
        "test_data = keras.preprocessing.sequence.pad_sequences(test_data,\n",
        "                                                       value=word_index[\"<PAD>\"],\n",
        "                                                       padding='post',\n",
        "                                                       maxlen=256)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VO5MBpyQdipD",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the length of the examples now:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USSSBnkE-lky",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "len(train_data[0]), len(train_data[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJoxZGyfjT5V",
        "colab_type": "text"
      },
      "source": [
        "And inspect the (now padded) first review:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TG8X9cqi-lk9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(train_data[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLC02j2g-llC",
        "colab_type": "text"
      },
      "source": [
        "#### Modeling\n",
        "\n",
        "The neural network is created by stacking layers—this requires two main architectural decisions:\n",
        "\n",
        "* How many layers to use in the model?\n",
        "* How many *hidden units* to use for each layer?\n",
        "\n",
        "In this example, the input data consists of an array of word-indices. The labels to predict are either 0 or 1. Let's build a model for this problem:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpKOoWgu-llD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# input shape is the vocabulary count used for the movie reviews (10,000 words)\n",
        "vocab_size = 10000\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Embedding(vocab_size, 16))\n",
        "model.add(keras.layers.GlobalAveragePooling1D())\n",
        "model.add(keras.layers.Dense(16, activation=tf.nn.relu))\n",
        "model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6PbKQ6mucuKL",
        "colab_type": "text"
      },
      "source": [
        "The layers are stacked sequentially to build the classifier:\n",
        "\n",
        "1. The first layer is an `Embedding` layer. This layer takes the integer-encoded vocabulary and looks up the embedding vector for each word-index. These vectors are learned as the model trains. The vectors add a dimension to the output array. The resulting dimensions are: `(batch, sequence, embedding)`.\n",
        "2. Next, a `GlobalAveragePooling1D` layer returns a fixed-length output vector for each example by averaging over the sequence dimension. This allows the model to handle input of variable length, in the simplest way possible.\n",
        "3. This fixed-length output vector is piped through a fully-connected (`Dense`) layer with 16 hidden units.\n",
        "4. The last layer is densely connected with a single output node. Using the `sigmoid` activation function, this value is a float between 0 and 1, representing a probability, or confidence level."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bn9ukmAb_pKE",
        "colab_type": "text"
      },
      "source": [
        "##### Hidden units"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XMwnDOp-llH",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "The above model has two intermediate or \"hidden\" layers, between the input and output. The number of outputs (units, nodes, or neurons) is the dimension of the representational space for the layer. In other words, the amount of freedom the network is allowed when learning an internal representation.\n",
        "\n",
        "If a model has more hidden units (a higher-dimensional representation space), and/or more layers, then the network can learn more complex representations. However, it makes the network more computationally expensive and may lead to learning unwanted patterns—patterns that improve performance on training data but not on the test data. This is called *overfitting*, and we'll explore it later."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4EqVWg4-llM",
        "colab_type": "text"
      },
      "source": [
        "##### Loss function and optimizer\n",
        "\n",
        "A model needs a loss function and an optimizer for training. Since this is a binary classification problem and the model outputs a probability (a single-unit layer with a sigmoid activation), we'll use the `binary_crossentropy` loss function. \n",
        "\n",
        "This isn't the only choice for a loss function, you could, for instance, choose `mean_squared_error`. But, generally, `binary_crossentropy` is better for dealing with probabilities—it measures the \"distance\" between probability distributions, or in our case, between the ground-truth distribution and the predictions.\n",
        "\n",
        "Later, when we are exploring regression problems (say, to predict the price of a house), we will see how to use another loss function called mean squared error.\n",
        "\n",
        "Now, configure the model to use an optimizer and a loss function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mr0GP-cQ-llN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCWYwkug-llQ",
        "colab_type": "text"
      },
      "source": [
        "##### Create a validation set\n",
        "\n",
        "When training, we want to check the accuracy of the model on data it hasn't seen before. Create a *validation set* by setting apart 10,000 examples from the original training data. (Why not use the testing set now? Our goal is to develop and tune our model using only the training data, then use the test data just once to evaluate our accuracy)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NpcXY9--llS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_val = train_data[:10000]\n",
        "partial_x_train = train_data[10000:]\n",
        "\n",
        "y_val = train_labels[:10000]\n",
        "partial_y_train = train_labels[10000:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35jv_fzP-llU",
        "colab_type": "text"
      },
      "source": [
        "##### Train the model\n",
        "\n",
        "Train the model for 40 epochs in mini-batches of 512 samples. This is 40 iterations over all samples in the `x_train` and `y_train` tensors. While training, monitor the model's loss and accuracy on the 10,000 samples from the validation set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXSGrjWZ-llW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=40,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val),\n",
        "                    verbose=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EEGuDVuzb5r",
        "colab_type": "text"
      },
      "source": [
        "##### Evaluate the model\n",
        "\n",
        "And let's see how the model performs. Two values will be returned. Loss (a number which represents our error, lower values are better), and accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOMKywn4zReN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "results = model.evaluate(test_data, test_labels)\n",
        "\n",
        "print(results)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1iEXVTR0Z2t",
        "colab_type": "text"
      },
      "source": [
        "This fairly naive approach achieves an accuracy of about 87%. With more advanced approaches, the model should get closer to 95%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KggXVeL-llZ",
        "colab_type": "text"
      },
      "source": [
        "##### Create a graph of accuracy and loss over time\n",
        "\n",
        "`model.fit()` returns a `History` object that contains a dictionary with everything that happened during training:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcvSXvhp-llb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRKsqL40-lle",
        "colab_type": "text"
      },
      "source": [
        "There are four entries: one for each monitored metric during training and validation. We can use these to plot the training and validation loss for comparison, as well as the training and validation accuracy:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGoYf2Js-lle",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history_dict['acc']\n",
        "val_acc = history_dict['val_acc']\n",
        "loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hXx-xOv-llh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.clf()   # clear figure\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFEmZ5zq-llk",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "In this plot, the dots represent the training loss and accuracy, and the solid lines are the validation loss and accuracy.\n",
        "\n",
        "Notice the training loss *decreases* with each epoch and the training accuracy *increases* with each epoch. This is expected when using a gradient descent optimization—it should minimize the desired quantity on every iteration.\n",
        "\n",
        "This isn't the case for the validation loss and accuracy—they seem to peak after about twenty epochs. This is an example of overfitting: the model performs better on the training data than it does on data it has never seen before. After this point, the model over-optimizes and learns representations *specific* to the training data that do not *generalize* to test data.\n",
        "\n",
        "For this particular case, we could prevent overfitting by simply stopping the training after twenty or so epochs. Later, you'll see how to do this automatically with a callback."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvoiEwiAWrWy",
        "colab_type": "text"
      },
      "source": [
        "## Ludwig (Open Source AutoML)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbnbFKcTXNOn",
        "colab_type": "text"
      },
      "source": [
        "**Github Project URL**:  https://uber.github.io/ludwig/\n",
        "\n",
        "![alt text](https://user-images.githubusercontent.com/58792/52920000-f78d8c00-32bc-11e9-8e5e-adf53f1b8a37.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aa-KnZKcfvkV",
        "colab_type": "text"
      },
      "source": [
        "### Install Ludwig"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3FrtesdfyV9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install --upgrade numpy #must restart colab runtime\n",
        "!pip install --upgrade scikit-image\n",
        "!pip install -q ludwig\n",
        "!python -m spacy download en "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVLSSAUViyiX",
        "colab_type": "text"
      },
      "source": [
        "### Basic Ideas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHaRqAN5i1iV",
        "colab_type": "text"
      },
      "source": [
        "* **Training Models**\n",
        "* **Prediction (Inference)**\n",
        "* **Datatypes**\n",
        " - binary\n",
        " - numerical\n",
        " - category\n",
        " - set\n",
        " - bag\n",
        " - sequence\n",
        " - text\n",
        " - timeseries\n",
        " - image\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3G5sZ3yo-yK",
        "colab_type": "text"
      },
      "source": [
        "### Classification of Book Title Categories (Project)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIbXYrxU8ySd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "e42f8f26-92ad-4423-9028-05d57134f004"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/uchidalab/book-dataset/master/Task1/book30-listing-train.csv\n",
        "!wget https://raw.githubusercontent.com/noahgift/recommendations/master/model_definition.yaml"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-09 14:14:11--  https://raw.githubusercontent.com/uchidalab/book-dataset/master/Task1/book30-listing-train.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 9728786 (9.3M) [text/plain]\n",
            "Saving to: ‘book30-listing-train.csv’\n",
            "\n",
            "book30-listing-trai 100%[===================>]   9.28M  22.8MB/s    in 0.4s    \n",
            "\n",
            "2020-05-09 14:14:12 (22.8 MB/s) - ‘book30-listing-train.csv’ saved [9728786/9728786]\n",
            "\n",
            "--2020-05-09 14:14:13--  https://raw.githubusercontent.com/noahgift/recommendations/master/model_definition.yaml\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 180 [text/plain]\n",
            "Saving to: ‘model_definition.yaml’\n",
            "\n",
            "model_definition.ya 100%[===================>]     180  --.-KB/s    in 0s      \n",
            "\n",
            "2020-05-09 14:14:13 (35.7 MB/s) - ‘model_definition.yaml’ saved [180/180]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-w5Zkzcumoi",
        "colab_type": "text"
      },
      "source": [
        "#### Ingest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ef8dbaV4tHrz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://media.githubusercontent.com/media/noahgift/recommendations/master/data/book30-listing-train-with-headers.csv\")\n",
        "df = df.drop(\"Unnamed: 0\", axis=1)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efw0icqJ_0Bq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(\"book30-listing-train-with-headers.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wR_L2OPkuqH4",
        "colab_type": "text"
      },
      "source": [
        "#### EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUGz5U5duj-D",
        "colab_type": "text"
      },
      "source": [
        "**Columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVYJIiwHuhiT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ir0Viy2zuyr1",
        "colab_type": "text"
      },
      "source": [
        "**Shape**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kaJsKyruyAl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJNSA3n0u3Zf",
        "colab_type": "text"
      },
      "source": [
        "#### Training w/Ludwig"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bldBWuL2Nwmh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!head book30-listing-train-with-headers.csv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ous6EqC8Nocg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "19633136-7b68-46f8-d541-9422e879da28"
      },
      "source": [
        "!cat model_definition.yaml"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_features:\n",
            "    -\n",
            "        name: TITLE\n",
            "        type: text\n",
            "        encoder: parallel_cnn\n",
            "        level: word\n",
            "\n",
            "output_features:\n",
            "    -\n",
            "        name: CATEGORY\n",
            "        type: category"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WpVA2fyXLRoK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ludwig experiment --data_csv book30-listing-train-with-headers.csv --model_definition_file model_definition.yaml\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21q0EGuO5dpd",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ExRA9v55cr6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggjuE4es7SDH",
        "colab_type": "text"
      },
      "source": [
        "## GCP AutoML\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ydR9SXDKAXvk",
        "colab_type": "text"
      },
      "source": [
        "![GCP AutoML](https://user-images.githubusercontent.com/58792/45260264-134c4800-b397-11e8-9832-fd56a8eeaa3c.png)\n",
        "\n",
        "\n",
        "**[GCP AutoML Products](https://cloud.google.com/automl/)**\n",
        "\n",
        "*  [AutoML Vision](https://cloud.google.com/vision/automl/docs/)\n",
        "*  [AutoML Natural Language](https://cloud.google.com/natural-language/automl/docs/)\n",
        "*  [AutoML Translation](https://cloud.google.com/translate/automl/docs/)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYU7DysIifKH",
        "colab_type": "text"
      },
      "source": [
        "### GCP AutoML Demo\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAtZVZgRin7e",
        "colab_type": "text"
      },
      "source": [
        "Training to recognize flowers\n",
        "\n",
        "![upload_data](https://user-images.githubusercontent.com/58792/45438280-94dbf880-b66b-11e8-850d-e7d2a32c45c8.png)\n",
        "\n",
        "![train](https://user-images.githubusercontent.com/58792/45438281-94dbf880-b66b-11e8-9951-80202c581b0c.png)\n",
        "\n",
        "![predict](https://user-images.githubusercontent.com/58792/45439236-1fbdf280-b66e-11e8-9f51-d92d26a63c64.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tho4WI7ikZb0",
        "colab_type": "text"
      },
      "source": [
        "## GCP Vision API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "desIuhDTkogY",
        "colab_type": "text"
      },
      "source": [
        "https://cloud.google.com/vision/\n",
        "\n",
        "![Vision API](https://cloud.google.com/images/products/vision/image-search.svg)\n",
        "\n",
        "* [Step 1:  Enable API](https://cloud.google.com/vision/docs/before-you-begin)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "emW2WhQYn5e7",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPWO_zyRopXN",
        "colab_type": "text"
      },
      "source": [
        "### Load Google Credentials with GDrive\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XI73HZNLobp4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNyzZwgmoxwm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os;os.listdir(\"/content/gdrive/My Drive/awsml\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7LLMTLNuxgI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ls -la /content/gdrive/My\\ Drive/awsml"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w2f1xHUMwCJT",
        "colab_type": "text"
      },
      "source": [
        "Install cloud vision Api"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRlg-4kPk1Wl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!export GOOGLE_APPLICATION_CREDENTIALS=\"/content/gdrive/My\\ Drive/awsml/cloudai-7ab42a4d8a43.json\"\n",
        "!gcloud auth activate-service-account --key-file /content/gdrive/My\\ Drive/awsml/cloudai-7ab42a4d8a43.json\n",
        "!pip install --upgrade -q google-cloud-vision"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oIJgeisov5kb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import io\n",
        "import os\n",
        "\n",
        "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"/content/gdrive/My Drive/awsml/cloudai-7ab42a4d8a43.json\"\n",
        "\n",
        "# Imports the Google Cloud client library\n",
        "from google.cloud import vision\n",
        "from google.cloud.vision import types\n",
        "\n",
        "# Instantiates a client\n",
        "client = vision.ImageAnnotatorClient()\n",
        "\n",
        "# The name of the image file to annotate\n",
        "file_name = 'wakeupcat.jpg'\n",
        "\n",
        "# Loads the image into memory\n",
        "with io.open(file_name, 'rb') as image_file:\n",
        "    content = image_file.read()\n",
        "\n",
        "image = types.Image(content=content)\n",
        "\n",
        "# Performs label detection on the image file\n",
        "response = client.label_detection(image=image)\n",
        "labels = response.label_annotations\n",
        "\n",
        "print('Labels:')\n",
        "for label in labels:\n",
        "    print(label.description)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBbAdZspTRGW",
        "colab_type": "text"
      },
      "source": [
        "## References\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjOc_DzTTU8v",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ju6xqhsJw-MR",
        "colab_type": "text"
      },
      "source": [
        "## GPU Demos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4wdW5u_vyOQP",
        "colab_type": "text"
      },
      "source": [
        "#### Numba Install"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THWVfCh9yfw_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!apt-get install nvidia-cuda-toolkit"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kTpVEwi0hDc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!pip3 install numba"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Re9jdU5nxBYx",
        "colab_type": "text"
      },
      "source": [
        "#### Mandelbrot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9iISjADi2D_Y",
        "colab_type": "text"
      },
      "source": [
        "##### Non-GPU Mandelbrot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APrhGfCC1xlG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from pylab import imshow, show\n",
        "from timeit import default_timer as timer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6KviMLo10I9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mandel(x, y, max_iters):\n",
        "  \"\"\"\n",
        "    Given the real and imaginary parts of a complex number,\n",
        "    determine if it is a candidate for membership in the Mandelbrot\n",
        "    set given a fixed number of iterations.\n",
        "  \"\"\"\n",
        "  c = complex(x, y)\n",
        "  z = 0.0j\n",
        "  for i in range(max_iters):\n",
        "    z = z*z + c\n",
        "    if (z.real*z.real + z.imag*z.imag) >= 4:\n",
        "      return i\n",
        "\n",
        "  return max_iters"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2K9mG5P12b1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_fractal(min_x, max_x, min_y, max_y, image, iters):\n",
        "  height = image.shape[0]\n",
        "  width = image.shape[1]\n",
        "\n",
        "  pixel_size_x = (max_x - min_x) / width\n",
        "  pixel_size_y = (max_y - min_y) / height\n",
        "    \n",
        "  for x in range(width):\n",
        "    real = min_x + x * pixel_size_x\n",
        "    for y in range(height):\n",
        "      imag = min_y + y * pixel_size_y\n",
        "      color = mandel(real, imag, iters)\n",
        "      image[y, x] = color"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SdUxzp-19Jc",
        "colab_type": "code",
        "outputId": "3d0ebcee-8c3e-4990-806f-54e87cb0dec2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "image = np.zeros((1024, 1536), dtype = np.uint8)\n",
        "start = timer()\n",
        "create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20) \n",
        "dt = timer() - start\n",
        "\n",
        "print(\"Mandelbrot created in %f s\" % dt)\n",
        "imshow(image)\n",
        "show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mandelbrot created in 4.584855 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eBtMqwD2I4f",
        "colab_type": "text"
      },
      "source": [
        "##### Numba Mandelbrot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jjzp4kAj7yN5",
        "colab_type": "text"
      },
      "source": [
        "Numba is [LLVM JIT](http://numba.pydata.org/) support for Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1M03OI67s08",
        "colab_type": "text"
      },
      "source": [
        "###### Run Numba Mandelbrot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8IlJnNu2e1W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from numba import jit\n",
        "\n",
        "@jit(nopython=True)\n",
        "def mandel(x, y, max_iters):\n",
        "  \"\"\"\n",
        "    Given the real and imaginary parts of a complex number,\n",
        "    determine if it is a candidate for membership in the Mandelbrot\n",
        "    set given a fixed number of iterations.\n",
        "  \"\"\"\n",
        "  c = complex(x, y)\n",
        "  z = 0.0j\n",
        "  for i in range(max_iters):\n",
        "    z = z*z + c\n",
        "    if (z.real*z.real + z.imag*z.imag) >= 4:\n",
        "      return i\n",
        "\n",
        "  return max_iters\n",
        "\n",
        "@jit(nopython=True)\n",
        "def create_fractal(min_x, max_x, min_y, max_y, image, iters):\n",
        "  height = image.shape[0]\n",
        "  width = image.shape[1]\n",
        "\n",
        "  pixel_size_x = (max_x - min_x) / width\n",
        "  pixel_size_y = (max_y - min_y) / height\n",
        "    \n",
        "  for x in range(width):\n",
        "    real = min_x + x * pixel_size_x\n",
        "    for y in range(height):\n",
        "      imag = min_y + y * pixel_size_y\n",
        "      color = mandel(real, imag, iters)\n",
        "      image[y, x] = color"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0ovBdjl2rsn",
        "colab_type": "code",
        "outputId": "95b5fe33-3485-45cd-9573-47369bc34d92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "image = np.zeros((1024, 1536), dtype = np.uint8)\n",
        "start = timer()\n",
        "create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20) \n",
        "dt = timer() - start\n",
        "\n",
        "print(\"Mandelbrot created in %f s\" % dt)\n",
        "imshow(image)\n",
        "show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mandelbrot created in 0.357561 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13WPkJ5P2ud1",
        "colab_type": "text"
      },
      "source": [
        "##### CUDA GPU Mandelbrot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bg00L6HA21Oe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from numba import cuda\n",
        "from numba import *\n",
        "\n",
        "mandel_gpu = cuda.jit(restype=uint32, argtypes=[f8, f8, uint32], device=True)(mandel)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M31bUz5Y22sx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@cuda.jit(argtypes=[f8, f8, f8, f8, uint8[:,:], uint32])\n",
        "def mandel_kernel(min_x, max_x, min_y, max_y, image, iters):\n",
        "  height = image.shape[0]\n",
        "  width = image.shape[1]\n",
        "\n",
        "  pixel_size_x = (max_x - min_x) / width\n",
        "  pixel_size_y = (max_y - min_y) / height\n",
        "\n",
        "  startX, startY = cuda.grid(2)\n",
        "  gridX = cuda.gridDim.x * cuda.blockDim.x;\n",
        "  gridY = cuda.gridDim.y * cuda.blockDim.y;\n",
        "\n",
        "  for x in range(startX, width, gridX):\n",
        "    real = min_x + x * pixel_size_x\n",
        "    for y in range(startY, height, gridY):\n",
        "      imag = min_y + y * pixel_size_y \n",
        "      image[y, x] = mandel_gpu(real, imag, iters)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfH-xwR_7WX0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "b8b02332-dc78-4883-f3a0-51f2daf32bf9"
      },
      "source": [
        "image = np.zeros((1024, 1536), dtype = np.uint8)\n",
        "start = timer()\n",
        "create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20) \n",
        "dt = timer() - start\n",
        "\n",
        "print(\"Mandelbrot created in %f s\" % dt)\n",
        "imshow(image)\n",
        "show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mandelbrot created in 0.064233 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yeFvfri9qC6",
        "colab_type": "text"
      },
      "source": [
        "##### Cuda Vectorize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apd8ic7m-BZB",
        "colab_type": "code",
        "outputId": "6d96e42c-3ac2-4738-8497-5b23eb1dcacf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        }
      },
      "source": [
        "from numba import (cuda, vectorize)\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "from functools import wraps\n",
        "from time import time\n",
        "\n",
        "def real_estate_df():\n",
        "    \"\"\"30 Years of Housing Prices\"\"\"\n",
        "\n",
        "    df = pd.read_csv(\"https://raw.githubusercontent.com/noahgift/real_estate_ml/master/data/Zip_Zhvi_SingleFamilyResidence.csv\")\n",
        "    df.rename(columns={\"RegionName\":\"ZipCode\"}, inplace=True)\n",
        "    df[\"ZipCode\"]=df[\"ZipCode\"].map(lambda x: \"{:.0f}\".format(x))\n",
        "    df[\"RegionID\"]=df[\"RegionID\"].map(lambda x: \"{:.0f}\".format(x))\n",
        "    return df\n",
        "\n",
        "def numerical_real_estate_array(df):\n",
        "    \"\"\"Converts df to numpy numerical array\"\"\"\n",
        "\n",
        "    columns_to_drop = ['RegionID', 'ZipCode', 'City', 'State', 'Metro', 'CountyName']\n",
        "    df_numerical = df.dropna()\n",
        "    df_numerical = df_numerical.drop(columns_to_drop, axis=1)\n",
        "    return df_numerical.values\n",
        "\n",
        "def real_estate_array():\n",
        "    \"\"\"Returns Real Estate Array\"\"\"\n",
        "\n",
        "    df = real_estate_df()\n",
        "    rea = numerical_real_estate_array(df)\n",
        "    return np.float32(rea)\n",
        "\n",
        "\n",
        "@vectorize(['float32(float32, float32)'], target='cuda')\n",
        "def add_ufunc(x, y):\n",
        "    return x + y\n",
        "\n",
        "def cuda_operation():\n",
        "    \"\"\"Performs Vectorized Operations on GPU\"\"\"\n",
        "\n",
        "    x = real_estate_array()\n",
        "    y = real_estate_array()\n",
        "\n",
        "    print(\"Moving calculations to GPU memory\")\n",
        "    x_device = cuda.to_device(x)\n",
        "    y_device = cuda.to_device(y)\n",
        "    out_device = cuda.device_array(\n",
        "        shape=(x_device.shape[0],x_device.shape[1]), dtype=np.float32)\n",
        "    print(x_device)\n",
        "    print(x_device.shape)\n",
        "    print(x_device.dtype)\n",
        "\n",
        "    print(\"Calculating on GPU\")\n",
        "    add_ufunc(x_device,y_device, out=out_device)\n",
        "\n",
        "    out_host = out_device.copy_to_host()\n",
        "    print(f\"Calculations from GPU {out_host}\")\n",
        "\n",
        "cuda_operation()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Moving calculations to GPU memory\n",
            "<numba.cuda.cudadrv.devicearray.DeviceNDArray object at 0x7f2af44c1358>\n",
            "(10015, 259)\n",
            "float32\n",
            "Calculating on GPU\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NvvmSupportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/nvvm.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m    110\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m                     \u001b[0minst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_cudalib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nvvm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mccc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/libs.py\u001b[0m in \u001b[0;36mopen_cudalib\u001b[0;34m(lib, ccc)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'library %s not found'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mccc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: library nvvm not found",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mNvvmSupportError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-03a9523bfdbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Calculations from GPU {out_host}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0mcuda_operation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-11-03a9523bfdbf>\u001b[0m in \u001b[0;36mcuda_operation\u001b[0;34m()\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Calculating on GPU\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0madd_ufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_device\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mout_host\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout_device\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_to_host\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m     86\u001b[0m                       \u001b[0mthe\u001b[0m \u001b[0minput\u001b[0m \u001b[0marguments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \"\"\"\n\u001b[0;32m---> 88\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mCUDAUFuncMechanism\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/npyufunc/deviceufunc.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(cls, typemap, args, kws)\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0mdevout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m             \u001b[0mdevarys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdevout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m             \u001b[0mcr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevarys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    322\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdevout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36mlaunch\u001b[0;34m(self, func, count, stream, args)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mis_device_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAutoJitCUDAKernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mspecialize\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    762\u001b[0m         argtypes = tuple(\n\u001b[1;32m    763\u001b[0m             [self.typingctx.resolve_argument_type(a) for a in args])\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(self, sig)\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefinitions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m                 \u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mbind\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    498\u001b[0m         \u001b[0mForce\u001b[0m \u001b[0mbinding\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mCUDA\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m         \"\"\"\n\u001b[0;32m--> 500\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[0mcufunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcufunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m             \u001b[0mptx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mptx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0;31m# Link\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0march\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnvvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_arch_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m             ptx = nvvm.llvm_to_ptx(self.llvmir, opt=3, arch=arch,\n\u001b[0;32m--> 350\u001b[0;31m                                    **self._extra_options)\n\u001b[0m\u001b[1;32m    351\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mptx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDUMP_ASSEMBLY\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/nvvm.py\u001b[0m in \u001b[0;36mllvm_to_ptx\u001b[0;34m(llvmir, **opts)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mllvm_to_ptx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mllvmir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m     \u001b[0mcu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCompilationUnit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m     \u001b[0mlibdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLibDevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0march\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'compute_20'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m     \u001b[0;31m# New LLVM generate a shorthand for datalayout that NVVM does not know\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/nvvm.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mCompilationUnit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNVVM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnvvm_program\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnvvmCreateProgram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbyref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numba/cuda/cudadrv/nvvm.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m    114\u001b[0m                     errmsg = (\"libNVVM cannot be found. Do `conda install \"\n\u001b[1;32m    115\u001b[0m                               \"cudatoolkit`:\\n%s\")\n\u001b[0;32m--> 116\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mNvvmSupportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m                 \u001b[0;31m# Find & populate functions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNvvmSupportError\u001b[0m: libNVVM cannot be found. Do `conda install cudatoolkit`:\nlibrary nvvm not found",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_SAHKhgd_AJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRcaqlgS5Zhu",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}