fastai_scratch_with_tpu_mnist_4_experiment.ipynb

{
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  "metadata": {
    "colab": {
      "name": "fastai-scratch-with-tpu-mnist-4-experiment.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1B7tkbphgEvWvgBG5m2JKrOHji7R_Fjl3",
      "authorship_tag": "ABX9TyMM4cremrlCIEsXuuW/VBVO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/butchland/fastai_nb_explorations/blob/master/fastai_scratch_with_tpu_mnist_4_experiment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ws0aMBh7SjXF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "assert os.environ['COLAB_TPU_ADDR'], 'Make sure to select TPU from Edit > Notebook settings > Hardware accelerator'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4jifTAqTJ5z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "d36af709-5c70-49fd-d2e6-125657d836ed"
      },
      "source": [
        "!curl https://course.fast.ai/setup/colab | bash"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   322  100   322    0     0    805      0 --:--:-- --:--:-- --:--:--   805\n",
            "Updating fastai...\n",
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4t2znf8TdDY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 994
        },
        "outputId": "50c80c28-dd5e-44e6-bf36-eabd7a36a0e0"
      },
      "source": [
        "VERSION = \"20200325\"  #@param [\"1.5\" , \"20200325\", \"nightly\"]\n",
        "!curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py\n",
        "!python pytorch-xla-env-setup.py --version $VERSION"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  4264  100  4264    0     0  20901      0 --:--:-- --:--:-- --:--:-- 20901\n",
            "Updating TPU and VM. This may take around 2 minutes.\n",
            "Updating TPU runtime to pytorch-dev20200325 ...\n",
            "Uninstalling torch-1.5.1+cu101:\n",
            "Done updating TPU runtime: <Response [200]>\n",
            "  Successfully uninstalled torch-1.5.1+cu101\n",
            "Uninstalling torchvision-0.6.1+cu101:\n",
            "  Successfully uninstalled torchvision-0.6.1+cu101\n",
            "Copying gs://tpu-pytorch/wheels/torch-nightly+20200325-cp36-cp36m-linux_x86_64.whl...\n",
            "- [1 files][ 83.4 MiB/ 83.4 MiB]                                                \n",
            "Operation completed over 1 objects/83.4 MiB.                                     \n",
            "Copying gs://tpu-pytorch/wheels/torch_xla-nightly+20200325-cp36-cp36m-linux_x86_64.whl...\n",
            "- [1 files][114.5 MiB/114.5 MiB]                                                \n",
            "Operation completed over 1 objects/114.5 MiB.                                    \n",
            "Copying gs://tpu-pytorch/wheels/torchvision-nightly+20200325-cp36-cp36m-linux_x86_64.whl...\n",
            "/ [1 files][  2.5 MiB/  2.5 MiB]                                                \n",
            "Operation completed over 1 objects/2.5 MiB.                                      \n",
            "Processing ./torch-nightly+20200325-cp36-cp36m-linux_x86_64.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==nightly+20200325) (1.18.5)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch==nightly+20200325) (0.16.0)\n",
            "\u001b[31mERROR: fastai 1.0.61 requires torchvision, which is not installed.\u001b[0m\n",
            "Installing collected packages: torch\n",
            "Successfully installed torch-1.5.0a0+d6149a7\n",
            "Processing ./torch_xla-nightly+20200325-cp36-cp36m-linux_x86_64.whl\n",
            "Installing collected packages: torch-xla\n",
            "Successfully installed torch-xla-1.6+e788e5b\n",
            "Processing ./torchvision-nightly+20200325-cp36-cp36m-linux_x86_64.whl\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==nightly+20200325) (7.0.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision==nightly+20200325) (1.18.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision==nightly+20200325) (1.12.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from torchvision==nightly+20200325) (1.5.0a0+d6149a7)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->torchvision==nightly+20200325) (0.16.0)\n",
            "Installing collected packages: torchvision\n",
            "Successfully installed torchvision-0.6.0a0+3c254fb\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-440\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following NEW packages will be installed:\n",
            "  libomp5\n",
            "0 upgraded, 1 newly installed, 0 to remove and 59 not upgraded.\n",
            "Need to get 234 kB of archives.\n",
            "After this operation, 774 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libomp5 amd64 5.0.1-1 [234 kB]\n",
            "Fetched 234 kB in 1s (361 kB/s)\n",
            "Selecting previously unselected package libomp5:amd64.\n",
            "(Reading database ... 144328 files and directories currently installed.)\n",
            "Preparing to unpack .../libomp5_5.0.1-1_amd64.deb ...\n",
            "Unpacking libomp5:amd64 (5.0.1-1) ...\n",
            "Setting up libomp5:amd64 (5.0.1-1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJ-TSJDATxiP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6222f070-97a3-43db-b45e-54e62e5a706d"
      },
      "source": [
        "!pip freeze | grep torchvision"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torchvision==0.6.0a0+3c254fb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7k6LMYgUQmE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "c82a2159-8dd4-44da-aa48-7bf28cdbf594"
      },
      "source": [
        "!pip install fastcore --upgrade"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fastcore\n",
            "  Downloading https://files.pythonhosted.org/packages/e2/6e/a18c0ff6cdca36915e65cf1690137134241a33d74ceef7882f4a63a6af55/fastcore-0.1.18-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from fastcore) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: dataclasses>='0.7'; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from fastcore) (0.7)\n",
            "Installing collected packages: fastcore\n",
            "Successfully installed fastcore-0.1.18\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SljFZBZXUWJc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "outputId": "3da75c46-eb62-47da-8815-1694dd8e0ac2"
      },
      "source": [
        "!pip install fastai2 --upgrade"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fastai2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/26/4f/0f61bb0d376eb47c20430639bac4946ca0cffcd7e693fb86698656324f2d/fastai2-0.0.17-py3-none-any.whl (190kB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: fastprogress>=0.1.22 in /usr/local/lib/python3.6/dist-packages (from fastai2) (0.2.3)\n",
            "Requirement already satisfied, skipping upgrade: torchvision>=0.5 in /usr/local/lib/python3.6/dist-packages (from fastai2) (0.6.0a0+3c254fb)\n",
            "Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python3.6/dist-packages (from fastai2) (3.13)\n",
            "Requirement already satisfied, skipping upgrade: pillow in /usr/local/lib/python3.6/dist-packages (from fastai2) (7.0.0)\n",
            "Requirement already satisfied, skipping upgrade: scikit-learn in /usr/local/lib/python3.6/dist-packages (from fastai2) (0.22.2.post1)\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from fastai2) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: torch>=1.3.0 in /usr/local/lib/python3.6/dist-packages (from fastai2) (1.5.0a0+d6149a7)\n",
            "Requirement already satisfied, skipping upgrade: pandas in /usr/local/lib/python3.6/dist-packages (from fastai2) (1.0.5)\n",
            "Requirement already satisfied, skipping upgrade: fastcore in /usr/local/lib/python3.6/dist-packages (from fastai2) (0.1.18)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib in /usr/local/lib/python3.6/dist-packages (from fastai2) (3.2.2)\n",
            "Requirement already satisfied, skipping upgrade: spacy in /usr/local/lib/python3.6/dist-packages (from fastai2) (2.2.4)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from fastai2) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from torchvision>=0.5->fastai2) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision>=0.5->fastai2) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->fastai2) (0.15.1)\n",
            "Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.3.0->fastai2) (0.16.0)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->fastai2) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->fastai2) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: dataclasses>='0.7'; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from fastcore->fastai2) (0.7)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai2) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai2) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai2) (1.2.0)\n",
            "Requirement already satisfied, skipping upgrade: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (0.7.0)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (47.3.1)\n",
            "Requirement already satisfied, skipping upgrade: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (2.0.3)\n",
            "Requirement already satisfied, skipping upgrade: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (1.0.2)\n",
            "Requirement already satisfied, skipping upgrade: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (7.4.0)\n",
            "Requirement already satisfied, skipping upgrade: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (1.0.0)\n",
            "Requirement already satisfied, skipping upgrade: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (1.1.3)\n",
            "Requirement already satisfied, skipping upgrade: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (1.0.2)\n",
            "Requirement already satisfied, skipping upgrade: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (0.4.1)\n",
            "Requirement already satisfied, skipping upgrade: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy->fastai2) (3.0.2)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->fastai2) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->fastai2) (2.9)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->fastai2) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->fastai2) (2020.6.20)\n",
            "Requirement already satisfied, skipping upgrade: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy->fastai2) (1.6.1)\n",
            "Requirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy->fastai2) (3.1.0)\n",
            "Installing collected packages: fastai2\n",
            "Successfully installed fastai2-0.0.17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkJcrhthUcFs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "outputId": "b604d735-a90a-40e1-928b-21eb0c62fdeb"
      },
      "source": [
        "pip install fastai --upgrade"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: fastai in /usr/local/lib/python3.6/dist-packages (1.0.61)\n",
            "Requirement already satisfied, skipping upgrade: spacy>=2.0.18; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from fastai) (2.2.4)\n",
            "Requirement already satisfied, skipping upgrade: Pillow in /usr/local/lib/python3.6/dist-packages (from fastai) (7.0.0)\n",
            "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.6/dist-packages (from fastai) (20.4)\n",
            "Requirement already satisfied, skipping upgrade: fastprogress>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from fastai) (0.2.3)\n",
            "Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python3.6/dist-packages (from fastai) (3.13)\n",
            "Requirement already satisfied, skipping upgrade: pandas in /usr/local/lib/python3.6/dist-packages (from fastai) (1.0.5)\n",
            "Requirement already satisfied, skipping upgrade: torch>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from fastai) (1.5.0a0+d6149a7)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from fastai) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from fastai) (0.7)\n",
            "Requirement already satisfied, skipping upgrade: numexpr in /usr/local/lib/python3.6/dist-packages (from fastai) (2.7.1)\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from fastai) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: torchvision in /usr/local/lib/python3.6/dist-packages (from fastai) (0.6.0a0+3c254fb)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib in /usr/local/lib/python3.6/dist-packages (from fastai) (3.2.2)\n",
            "Requirement already satisfied, skipping upgrade: bottleneck in /usr/local/lib/python3.6/dist-packages (from fastai) (1.3.2)\n",
            "Requirement already satisfied, skipping upgrade: beautifulsoup4 in /usr/local/lib/python3.6/dist-packages (from fastai) (4.6.3)\n",
            "Requirement already satisfied, skipping upgrade: nvidia-ml-py3 in /usr/local/lib/python3.6/dist-packages (from fastai) (7.352.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from fastai) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18; python_version < \"3.8\"->fastai) (1.0.2)\n",
            "Requirement already satisfied, skipping upgrade: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18; python_version < \"3.8\"->fastai) (7.4.0)\n",
            "Requirement already satisfied, skipping upgrade: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18; python_version < \"3.8\"->fastai) (1.0.2)\n",
            "Requirement already satisfied, skipping upgrade: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18; python_version < \"3.8\"->fastai) (3.0.2)\n",
            "Requirement already satisfied, skipping upgrade: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18; python_version < \"3.8\"->fastai) (2.0.3)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18; python_version < \"3.8\"->fastai) (47.3.1)\n",
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            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->fastai) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->fastai) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.0.0->fastai) (0.16.0)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (2020.6.20)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (2.9)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (1.2.0)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.0.18; python_version < \"3.8\"->fastai) (1.6.1)\n",
            "Requirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.0.18; python_version < \"3.8\"->fastai) (3.1.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjwXTFa80tdg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIdqQny7UmrL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "ffedd876-630b-4fb6-a4b5-7702ed01abb9"
      },
      "source": [
        "%cd /content/drive/My\\ Drive/course-v4/\n",
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/course-v4\n",
            "/content/drive/My Drive/course-v4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_Io5kRfVmTY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8526afbb-78c6-43d9-8e8a-ee2395d60981"
      },
      "source": [
        "!pip install -r requirements.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: fastai2>=0.0.11 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (0.0.17)\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (3.2.2)\n",
            "Collecting nbdev>=0.2.12\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/19/54/f39f9050f0e1610c4c5f764872812ef72615dac70ea7f1c9bc20948acb04/nbdev-0.2.18-py3-none-any.whl (45kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 2.0MB/s \n",
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            "Collecting azure-cognitiveservices-search-imagesearch\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fe/95/8ab27afeecc8d397eae889e3576b4e8884a60fa9ad0c9e0b43ecec89bf69/azure_cognitiveservices_search_imagesearch-2.0.0-py2.py3-none-any.whl (46kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 4.4MB/s \n",
            "\u001b[?25hCollecting sentencepiece\n",
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            "Collecting fastscript\n",
            "  Downloading https://files.pythonhosted.org/packages/55/0e/ecdc0213646bc82986884121109a38b50bbc2cd2c491bbbfdc7ae39228e3/fastscript-0.1.4-py3-none-any.whl\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 6)) (2018.9)\n",
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            "Collecting azure-common~=1.1\n",
            "  Downloading https://files.pythonhosted.org/packages/e5/4d/d000fc3c5af601d00d55750b71da5c231fcb128f42ac95b208ed1091c2c1/azure_common-1.1.25-py2.py3-none-any.whl\n",
            "Collecting msrest>=0.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b6/b4/a95380d5199c4785b318038db6d199a203f6970188876b473c00533bc17f/msrest-0.6.17-py2.py3-none-any.whl (84kB)\n",
            "\u001b[K     |████████████████████████████████| 92kB 10.0MB/s \n",
            "\u001b[?25hCollecting msrestazure<2.0.0,>=0.4.32\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/01/70/4abd575d876428e3892ca6b7acafb59b53cb9923fa6aec2cbbf173495ce1/msrestazure-0.6.3-py2.py3-none-any.whl (40kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/9f/b36f7774ff5ea8e428fdcfc4bb332c39ee5b9362ddd3d40d9516a55221b2/isodate-0.6.0-py2.py3-none-any.whl (45kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/46/58/a19e0eb0c388fb7aced40f940c09069343862613d83095b592a8d3961ba1/adal-1.2.4-py2.py3-none-any.whl (55kB)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert>=5.6.1->nbdev>=0.2.12->-r requirements.txt (line 5)) (0.5.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.5.0->msrest>=0.5.0->azure-cognitiveservices-search-imagesearch->-r requirements.txt (line 8)) (3.1.0)\n",
            "Collecting PyJWT>=1.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/87/8b/6a9f14b5f781697e51259d81657e6048fd31a113229cf346880bb7545565/PyJWT-1.7.1-py2.py3-none-any.whl\n",
            "Collecting cryptography>=1.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3c/04/686efee2dcdd25aecf357992e7d9362f443eb182ecd623f882bc9f7a6bba/cryptography-2.9.2-cp35-abi3-manylinux2010_x86_64.whl (2.7MB)\n",
            "\u001b[K     |████████████████████████████████| 2.7MB 32.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy->fastai2>=0.0.11->-r requirements.txt (line 1)) (3.1.0)\n",
            "Requirement already satisfied: cffi!=1.11.3,>=1.8 in /usr/local/lib/python3.6/dist-packages (from cryptography>=1.1.0->adal<2.0.0,>=0.6.0->msrestazure<2.0.0,>=0.4.32->azure-cognitiveservices-search-imagesearch->-r requirements.txt (line 8)) (1.14.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi!=1.11.3,>=1.8->cryptography>=1.1.0->adal<2.0.0,>=0.6.0->msrestazure<2.0.0,>=0.4.32->azure-cognitiveservices-search-imagesearch->-r requirements.txt (line 8)) (2.20)\n",
            "Installing collected packages: fastscript, nbdev, azure-common, isodate, msrest, PyJWT, cryptography, adal, msrestazure, azure-cognitiveservices-search-imagesearch, sentencepiece\n",
            "Successfully installed PyJWT-1.7.1 adal-1.2.4 azure-cognitiveservices-search-imagesearch-2.0.0 azure-common-1.1.25 cryptography-2.9.2 fastscript-0.1.4 isodate-0.6.0 msrest-0.6.17 msrestazure-0.6.3 nbdev-0.2.18 sentencepiece-0.1.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZJWqOmdVzWY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d30fef53-f471-4bbc-a12e-89a5d858ad79"
      },
      "source": [
        "%cd nbs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/course-v4/nbs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yNVBZ74koKV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "885532a1-8c41-4450-f8fe-8db8b3159195"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/course-v4/nbs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-180nJSB1KkJ",
        "colab_type": "text"
      },
      "source": [
        "### Start of import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCYsUhyUV7e2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai2.vision.all import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-WEKRGrVf-p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from utils import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9cf9kAmWLa1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1e56e242-a349-4b2d-9185-bb01973efaff"
      },
      "source": [
        "path = untar_data(URLs.MNIST_SAMPLE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZww5QnlVcqA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Path.BASE_PATH = path"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDG9B6sNWf18",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d4d4484b-e03a-4ed1-b7cd-cdbc5aa199ea"
      },
      "source": [
        "path.ls()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#3) [Path('labels.csv'),Path('valid'),Path('train')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zRWl_ZPk1cpK",
        "colab_type": "text"
      },
      "source": [
        "### Import torch xla libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGtKpUptYL0h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClQR3UOuYYw7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch_xla"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "athAR2e3Ycu7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch_xla.core.xla_model as xm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcThbatP7nNC",
        "colab_type": "text"
      },
      "source": [
        "### define load tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLpMZuwfWk_D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_tensors(dpath):\n",
        "    return torch.stack([tensor(Image.open(o)) \n",
        "                        for o in dpath.ls().sorted()]\n",
        "                      ).float()/255.\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSLjIjq4a-Ox",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def count_images(dpath):\n",
        "    return len(dpath.ls())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5JNSJUKbKGI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_x = torch.cat([load_tensors(path/'train'/'3'), \n",
        "                    load_tensors(path/'train'/'7')]).view(-1,28*28)\n",
        "valid_x = torch.cat([load_tensors(path/'valid'/'3'), \n",
        "                    load_tensors(path/'valid'/'7')]).view(-1,28*28)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8vjZF-J2hoA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4d425a76-fca4-4e2a-ec47-4e4e888875c8"
      },
      "source": [
        "(train_x.device, valid_x.device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(device(type='cpu'), device(type='cpu'))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phgSCiko8Cop",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0b37e96d-5405-46fb-9974-54576dcec96f"
      },
      "source": [
        "(train_x.shape, valid_x.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([12396, 784]), torch.Size([2038, 784]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErweZ0C38SUh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "9fc2f439-deb9-4b15-c557-70f3b50d7988"
      },
      "source": [
        "train_y = tensor([1]*count_images(path/'train'/'3') + [0]*count_images(path/'train'/'7')).unsqueeze(1)\n",
        "valid_y = tensor([1]*count_images(path/'valid'/'3') + [0]*count_images(path/'valid'/'7')).unsqueeze(1)\n",
        "(train_y.shape, valid_y.shape, train_y.device, valid_y.device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([12396, 1]),\n",
              " torch.Size([2038, 1]),\n",
              " device(type='cpu'),\n",
              " device(type='cpu'))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqKOPrYB80w0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dl = DataLoader(list(zip(train_x, train_y)),batch_size=64)\n",
        "valid_dl = DataLoader(list(zip(valid_x, valid_y)), batch_size=64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wD5DwTqbw9x",
        "colab_type": "text"
      },
      "source": [
        "### Get TPU Device"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9_bVKZ_YmKi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tpu_dev = xm.xla_device()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6lttXjaZo95",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5496a9fe-050c-42a4-f774-0c7db9d48b16"
      },
      "source": [
        "tpu_dev"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='xla', index=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOcVq-xt-kCA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x1c, y1c = train_dl.one_batch()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Otd_5ZhHaIbp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x1 = x1c.to(tpu_dev)\n",
        "y1 = y1c.to(tpu_dev)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkVDBJpA-q1X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dbb518ea-fdc5-457e-b07b-d32ae8a3b74e"
      },
      "source": [
        "(x1.shape, y1.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([64, 784]), torch.Size([64, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yhRIgfg-tRe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c806fe36-0195-4574-dc6d-5160680f9e80"
      },
      "source": [
        "(x1.device, y1.device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(device(type='xla', index=1), device(type='xla', index=1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IuloHqMz9QaZ",
        "colab_type": "text"
      },
      "source": [
        "## Fix Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ke44BN1I9WMV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "torch.manual_seed(42)\n",
        "np.random.seed(42)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0pmKnDRx9XO7",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Loss function\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBhmPMWs9YQs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# define loss function using sigmoid to return a val between 0.0 and 1\n",
        "def mnist_loss_sigmoid(qpreds, qtargs):\n",
        "    qqpreds = qpreds.sigmoid()\n",
        "    return torch.where(qtargs==1, 1.-qqpreds, qqpreds).mean()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6hbzUlB9ZoU",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Forward Pass + Back Propagation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1Wh7JrE9aYC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# forward prop + back prop\n",
        "def calc_grad(xb,yb,m):\n",
        "    qpreds = m(xb)\n",
        "    qloss = mnist_loss_sigmoid(qpreds,yb)\n",
        "    qloss.backward()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PTJ61kKDRU_",
        "colab_type": "text"
      },
      "source": [
        "### Basic Optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtVMfHn1DGln",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class BasicOptimizer:\n",
        "    def __init__(self, params,lr): self.lr, self.params = lr,list(params)\n",
        "    def step(self, *args, **kwargs):\n",
        "        for p in self.params: p.data -= p.grad.data * self.lr\n",
        "    def zero_grad(self, *args, **kwargs):\n",
        "        for p in self.params: p.grad = None\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RngLEQzG9b1y",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Train Epoch\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWK9orsj9jxT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def train_epoch(qdl,qmodel,qopt):\n",
        "    for xb,yb in qdl:\n",
        "        calc_grad(xb.to(tpu_dev),yb.to(tpu_dev),qmodel)\n",
        "        # qopt.step()\n",
        "        # replace optimizer step with xla device step computation\n",
        "        xm.optimizer_step(qopt, barrier=True)\n",
        "        qopt.zero_grad()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UL3GMyZn9kyI",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Compute Metrics\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79P8ut2P9lg2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def batch_accuracy(qpreds, qtargets):\n",
        "    qqpreds = qpreds.sigmoid()\n",
        "    correct = (qqpreds > 0.5) == qtargets\n",
        "    return correct.float().mean()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGD0W4Aa9mi2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def validate_epoch(qmodel, qdl):\n",
        "    accs = [batch_accuracy(qmodel(xb.to(tpu_dev)), yb.to(tpu_dev)) for xb,yb in qdl]\n",
        "    return round(torch.stack(accs).mean().item(),4)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKj00e7w9nz4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def train_model(qtrain_dl, qvalid_dl, qmodel, qopt, epochs):\n",
        "    for i in range(epochs):\n",
        "        train_epoch(qtrain_dl, qmodel, qopt)\n",
        "        print(validate_epoch(qmodel, qvalid_dl), end=' ')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzjH0mlQ9o0Y",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Build and Train Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhl1-UNf9plg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "model = nn.Linear(28*28,1).to(tpu_dev)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFjf0bo4MQRp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optim = BasicOptimizer(model.parameters(),0.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcYJr5vqMhWG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# calc_grad(x1,y1,m)\n",
        "qpreds = model(x1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0zKszt2LNvZW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "00d14025-edc5-477f-b5e2-f96f3208c6cf"
      },
      "source": [
        "(qpreds.device,qpreds.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(device(type='xla', index=1), torch.Size([64, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFALrO81NznA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3a5b17b8-aaf6-40a8-ba31-c894e0ca70d0"
      },
      "source": [
        "(y1.device, y1.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(device(type='xla', index=1), torch.Size([64, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmlviMMEODPH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "qqpreds = qpreds.sigmoid()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3yH0saJOZCd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "qq1 = torch.where(y1==1, 1.-qqpreds, qqpreds)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ao1PjHtOO7T0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bc3392d5-a0e2-40f2-ea23-3420a8ec0853"
      },
      "source": [
        "(qq1.device, qq1.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(device(type='xla', index=1), torch.Size([64, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Js9NearfO-6r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bf7fae6e-d35f-4294-d9cd-5e81346c18fe"
      },
      "source": [
        "mqq1 = qq1.mean(); (mqq1.shape,mqq1.device) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([]), device(type='xla', index=1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fsjpSwESpRe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c4b2ea5d-267c-4e3b-95a9-c80d81acc614"
      },
      "source": [
        "mqq1_cpu = mqq1.cpu(); mqq1_cpu"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.5760, grad_fn=<CopyBackwards>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdAuiIrXNIpH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "qloss = mnist_loss_sigmoid(qpreds,y1)\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkIDWETYNo9T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "138933e8-4acb-4dd3-ea71-3ca2f9cefbf3"
      },
      "source": [
        "qloss.device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='xla', index=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPcQ1Vc5NZJE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8aaea0ca-f733-4077-c396-1b160ae0d177"
      },
      "source": [
        "qloss.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbraOw18NRpu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "qloss.backward()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jh0fNcDH9r1Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "5c3ae582-5b08-4a30-fe5b-f14826d4547d"
      },
      "source": [
        "\n",
        "# use basic Optimizer\n",
        "train_model(train_dl, valid_dl, model, BasicOptimizer(model.parameters(),0.5), 50)                     \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7222 0.7754 0.8599 0.9048 0.9238 0.937 0.9438 0.9512 0.9546 0.9604 0.9614 0.9624 0.9663 0.9683 0.9702 0.9707 0.9727 0.9741 0.9761 0.9761 0.9761 0.9766 0.9771 0.9771 0.9771 0.978 0.9785 0.979 0.9795 0.9795 0.98 0.9805 0.9805 0.98 0.9805 0.98 0.98 0.98 0.9805 0.9805 0.9805 0.9805 0.981 0.981 0.981 0.981 0.9814 0.9819 0.9819 0.9819 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ev_9sfE-C70",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "4c3ce338-4507-4d75-b6de-b104e00075b7"
      },
      "source": [
        "\n",
        "train_model(train_dl, valid_dl, model, SGD(model.parameters(),0.1), 50)                     "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9829 0.9834 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9839 0.9844 0.9844 0.9844 0.9844 0.9844 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 0.9849 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Nhnxiz6fRKR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}