03_Deep_Learning_Development_with_PyTorch.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/danielsoy/ALOCC-CVPR2018/blob/master/03_Deep_Learning_Development_with_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2fkP_np3Zpl"
      },
      "source": [
        "<!--BOOK_INFORMATION-->\n",
        "<img align=\"left\" style=\"width:80px;height:98px;padding-right:20px;\" src=\"https://github.com/raw/joe-papa/pytorch-book/main/files/pytorch-book-cover.jpg\">\n",
        "\n",
        "This notebook contains an excerpt from the [PyTorch Pocket Reference](http://pytorchbook.com) book by [Joe Papa](http://joepapa.ai); content is available [on GitHub](https://github.com/joe-papa/pytorch-book)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnkbZaV2F43d"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/joe-papa/pytorch-book/blob/main/03_Deep_Learning_Development_with_PyTorch.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aXkszegKMMMn"
      },
      "source": [
        "# Chapter 3 - Deep Learning Development with PyTorch "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gmdHVgDurviK",
        "outputId": "410850dc-7f8d-4862-8cdf-19e7fe02d590"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "\n",
        "print(torch.__version__)\n",
        "# out: 1.7.0+cu101\n",
        "\n",
        "print(torchvision.__version__)\n",
        "# out: 0.8.1+cu101\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.10.0+cu111\n",
            "0.11.1+cu111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbzMwCbOLF4T"
      },
      "source": [
        "## Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "8a58e02499804254a277d758da784821",
            "0f7df4e318a7428b907a669fabd5be9f",
            "c90f3bd3046e48649fbd86c3d0f1b6cb",
            "fd4eaddd6e36470594be8b134e573650",
            "f3057334e798431d9c6656f383acb2ec",
            "ccf5ce52e33143ba8ad2d71d687e01d7",
            "1c75f69d88af43f8a9ea08f4b3f24270",
            "74ed76a3e015440fbcb5acc34bab7c49",
            "75c136a9aaa3493dad4551d23bdcbe50",
            "fde0ea86264b49db8bc2389c90a55440",
            "ee90d954e2a4484891ba4b1eae1b73ab"
          ]
        },
        "id": "G5sT2wsuq0pL",
        "outputId": "97aa1d9a-f496-4486-f5fb-7dd0df8297dd"
      },
      "source": [
        "from torchvision.datasets import CIFAR10\n",
        "\n",
        "train_data = CIFAR10(root=\"./train/\",\n",
        "                    train=True, \n",
        "                    download=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./train/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a58e02499804254a277d758da784821",
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              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./train/cifar-10-python.tar.gz to ./train/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXOxZR4cq8Gk",
        "outputId": "59ce36f3-7d0a-448d-f36b-4024e7b369c5"
      },
      "source": [
        "# Use tab complete to view attributes and methods\n",
        "\n",
        "print(train_data)\n",
        "# out:\n",
        "# Dataset CIFAR10                       Printing the object returns its general information.\n",
        "#     Number of datapoints: 50000\n",
        "#     Root location: ./train/\n",
        "#     Split: Train\n",
        "\n",
        "print(len(train_data))\n",
        "# out: 50000                            Check the number of data samples with len().\n",
        "\n",
        "print(train_data.data.shape) # ndarray  The data is a NumPy array of 50,000 32 × 32-pixel color images.\n",
        "# out: (50000, 32, 32, 3)\n",
        "\n",
        "print(train_data.targets) # list.The targets are a list of 50,000 data labels\n",
        "# out: [6, 9, ...,  1, 1]\n",
        "\n",
        "print(train_data.classes)   # You can map numeric labels to class names using classes.\n",
        "# out: ['airplane', 'automobile', 'bird', \n",
        "#       'cat', 'deer', 'dog', 'frog', \n",
        "#       'horse', 'ship', 'truck']\n",
        "\n",
        "print(train_data.class_to_idx) # You can map class names to index values using class_to_idx.\n",
        "# out: \n",
        "# {'airplane': 0, 'automobile': 1, 'bird': 2, \n",
        "#  'cat': 3, 'deer': 4, 'dog': 5, 'frog': 6, \n",
        "#  'horse': 7, 'ship': 8, 'truck': 9}\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset CIFAR10\n",
            "    Number of datapoints: 50000\n",
            "    Root location: ./train/\n",
            "    Split: Train\n",
            "50000\n",
            "(50000, 32, 32, 3)\n",
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8, 2, 7, 9, 3, 8, 2, 2, 0, 2, 0, 9, 1, 1, 0, 3, 8, 6, 8, 2, 5, 5, 5, 7, 4, 3, 7, 9, 4, 1, 8, 1, 9, 2, 3, 8, 0, 0, 5, 8, 2, 1, 2, 0, 6, 3, 0, 5, 9, 5, 3, 9, 8, 4, 8, 8, 6, 4, 9, 9, 5, 8, 7, 2, 9, 3, 0, 2, 7, 0, 4, 6, 9, 2, 0, 5, 7, 1, 0, 4, 6, 5, 0, 2, 2, 2, 4, 3, 6, 2, 9, 8, 6, 8, 1, 3, 3, 0, 8, 8, 5, 7, 5, 2, 9, 7, 9, 2, 0, 3, 2, 2, 8, 4, 5, 6, 5, 5, 4, 6, 6, 3, 5, 2, 9, 6, 4, 9, 3, 3, 5, 5, 4, 2, 0, 0, 2, 5, 0, 7, 5, 5, 7, 0, 4, 3, 2, 0, 4, 0, 9, 8, 4, 9, 9, 7, 5, 7, 9, 8, 1, 9, 3, 5, 8, 6, 4, 6, 9, 9, 9, 3, 7, 5, 9, 7, 8, 0, 8, 6, 9, 8, 6, 7, 1, 7, 7, 9, 8, 0, 9, 2, 6, 5, 8, 0, 5, 4, 3, 2, 8, 2, 6, 4, 0, 7, 7, 4, 0, 7, 8, 0, 2, 3, 5, 1, 6, 2, 2, 8, 2, 4, 9, 2, 4, 3, 3, 0, 9, 5, 6, 6, 4, 0, 1, 0, 3, 2, 6, 0, 4, 2, 7, 3, 6, 6, 7, 1, 4, 2, 2, 6, 4, 8, 5, 3, 5, 4, 1, 3, 8, 6, 3, 9, 2, 0, 2, 7, 2, 8, 7, 8, 2, 0, 5, 0, 0, 8, 9, 0, 3, 8, 4, 1, 6, 5, 7, 9, 8, 1, 5, 3, 5, 1, 6, 7, 9, 7, 7, 0, 7, 6, 8, 1, 3, 7, 0, 4, 2, 9, 4, 3, 6, 2, 1, 7, 8, 2, 3, 6, 3, 8, 1, 8, 4, 9, 6, 2, 3, 1, 8, 1, 4, 3, 9, 4, 1, 0, 6, 7, 4, 9, 9, 5, 5, 9, 6, 2, 3, 5, 1, 5, 9, 4, 0, 1, 4, 3, 1, 1, 8, 7, 0, 1, 5, 9, 8, 9, 7, 6, 2, 9, 0, 9, 1, 1, 3, 6, 6, 5, 7, 9, 0, 6, 6, 5, 1, 8, 1, 8, 0, 9, 5, 5, 1, 2, 0, 8, 7, 9, 1, 9, 5, 7, 5, 3, 0, 7, 4, 8, 0, 2, 5, 2, 7, 9, 7, 8, 4, 8, 5, 2, 2, 5, 9, 1, 4, 5, 1, 6, 5, 2, 9, 7, 5, 4, 7, 9, 3, 7, 2, 3, 6, 5, 8, 8, 7, 1, 0, 5, 4, 7, 2, 9, 2, 5, 8, 4, 2, 1, 7, 3, 9, 8, 2, 1, 6, 0, 1, 5, 9, 8, 6, 5, 8, 8, 8, 3, 7, 4, 3, 3, 3, 2, 6, 9, 7, 3, 9, 6, 6, 3, 7, 8, 5, 3, 0, 0, 2, 4, 4, 0, 2, 2, 5, 5, 4, 0, 6, 2, 1, 0, 8, 8, 6, 4, 5, 3, 1, 2, 4, 6, 7, 6, 8, 1, 4, 3, 9, 9, 7, 4, 7, 3, 7, 9, 5, 1, 6, 1, 8, 5, 7, 4, 4, 7, 9, 5, 7, 3, 2, 5, 2, 3, 2, 8, 7, 8, 9, 1, 6, 0, 5, 0, 1, 3, 0, 7, 3, 0, 0, 0, 7, 6, 6, 0, 7, 7, 0, 4, 0, 4, 9, 8, 7, 6, 3, 9, 0, 2, 8, 8, 2, 8, 4, 4, 8, 4, 4, 3, 4, 5, 5, 4, 2, 6, 9, 6, 4, 5, 0, 4, 2, 2, 6, 4, 6, 1, 1, 5, 1, 4, 7, 6, 8, 2, 3, 0, 3, 5, 8, 3, 6, 3, 1, 9, 1, 7, 9, 8, 9, 2, 7, 0, 6, 0, 7, 3, 6, 0, 3, 6, 3, 5, 5, 8, 4, 7, 0, 6, 6, 6, 8, 2, 4, 3, 6, 7, 9, 4, 2, 3, 8, 1, 4, 6, 3, 0, 8, 7, 4, 1, 9, 8, 8, 3, 5, 0, 4, 1, 0, 1, 2, 6, 1, 3, 6, 0, 0, 5, 6, 9, 6, 8, 4, 0, 9, 2, 2, 8, 5, 5, 3, 2, 1, 9, 4, 9, 3, 8, 9, 4, 9, 0, 5, 0, 8, 2, 3, 7, 8, 9, 4, 5, 3, 2, 5, 0, 7, 2, 1, 1, 4, 4, 8, 9, 4, 6, 9, 4, 9, 3, 5, 6, 9, 9, 7, 7, 6, 1, 6, 8, 8, 3, 1, 6, 7, 7, 2, 0, 7, 5, 2, 3, 4, 6, 9, 7, 6, 1, 6, 6, 7, 9, 9, 1, 0, 2, 2, 5, 7, 9, 9, 9, 1, 6, 4, 3, 3, 1, 3, 6, 3, 1, 1, 9, 9, 6, 3, 8, 0, 0, 2, 6, 6, 1, 4, 4, 2, 0, 2, 8, 7, 2, 5, 2, 0, 8, 7, 6, 5, 9, 5, 4, 8, 7, 3, 5, 6, 5, 1, 1, 9, 9, 9, 7, 8, 0, 6, 0, 3, 7, 7, 8, 6, 1, 1, 4, 7, 3, 7, 3, 3, 8, 6, 3, 9, 1, 0, 1, 1, 8, 2, 4, 5, 2, 3, 9, 2, 8, 8, 7, 1, 0, 8, 8, 9, 0, 0, 3, 6, 5, 1, 0, 7, 0, 8, 3, 2, 8, 9, 8, 7, 8, 5, 1, 4, 5, 0, 3, 8, 6, 9, 9, 8, 1, 3, 7, 4, 6, 8, 7, 7, 4, 0, 9, 4, 9, 6, 6, 9, 6, 4, 6, 5, 8, 9, 3, 0, 8, 1, 3, 3, 2, 1, 7, 3, 9, 7, 1, 7, 1, 3, 1, 8, 0, 4, 9, 1, 3, 7, 5, 4, 9, 0, 7, 8, 2, 9, 8, 4, 4, 8, 5, 0, 2, 8, 8, 5, 9, 6, 7, 0, 7, 6, 9, 5, 2, 1, 7, 5, 7, 3, 2, 2, 0, 7, 9, 6, 3, 7, 2, 1, 9, 6, 1, 9, 5, 6, 2, 8, 4, 7, 5, 9, 6, 2, 3, 3, 9, 5, 6, 0, 5, 6, 3, 6, 0, 7, 7, 6, 6, 1, 1, 5, 5, 4, 4, 3, 6, 8, 3, 7, 5, 3, 1, 5, 2, 2, 8, 2, 7, 5, 5, 7, 3, 1, 9, 0, 0, 7, 2, 6, 0, 1, 0, 8, 1, 0, 7, 6, 4, 6, 1, 4, 1, 2, 8, 0, 9, 6, 1, 7, 6, 4, 3, 3, 3, 9, 9, 0, 8, 0, 8, 0, 1, 7, 8, 8, 5, 1, 6, 1, 3, 9, 3, 0, 8, 8, 4, 7, 1, 8, 6, 2, 7, 2, 9, 5, 8, 1, 4, 9, 6, 9, 6, 7, 3, 1, 0, 1, 6, 7, 4, 8, 1, 9, 6, 2, 0, 4, 5, 0, 3, 9, 1, 9, 5, 8, 3, 5, 2, 6, 8, 1, 1, 5, 1, 0, 6, 0, 9, 3, 3, 8, 8, 1, 3, 2, 2, 7, 5, 9, 7, 5, 7, 2, 2, 6, 9, 9, 6, 3, 6, 8, 3, 5, 3, 1, 7, 2, 5, 8, 5, 4, 7, 9, 3, 8, 4, 3, 5, 1, 5, 7, 1, 1, 4, 1, 0, 1, 3, 5, 1, 5, 6, 0, 0, 3, 3, 8, 9, 1, 4, 1, 4, 0, 5, 1, 0, 0, 6, 0, 5, 4, 6, 0, 4, 5, 9, 8, 5, 5, 7, 0, 1, 2, 0, 7, 0, 5, 8, 6, 9, 0, 2, 6, 2, 1, 9, 5, 8, 8, 3, 3, 9, 5, 5, 2, 6, 8, 5, 8, 1, 0, 0, 0, 2, 0, 2, 1, 1, 3, 4, 5, 1, 8, 8, 1, 8, 8, 1, 2, 3, 4, 3, 5, 7, 5, 9, 6, 3, 9, 6, 1, 4, 1, 1, 1, 1, 8, 7, 1, 1, 4, 9, 4, 4, 3, 3, 9, 3, 6, 1, 4, 7, 6, 4, 3, 7, 9, 5, 1, 3, 0, 1, 2, 3, 3, 7, 6, 4, 6, 6, 8, 3, 7, 0, 1, 7, 8, 3, 0, 9, 0, 6, 9, 5, 6, 7, 0, 9, 1, 5, 4, 3, 5, 7, 5, 7, 0, 1, 6, 8, 6, 0, 8, 1, 7, 0, 5, 9, 1, 9, 5, 2, 6, 3, 4, 9, 0, 0, 7, 1, 5, 1, 8, 2, 8, 5, 8, 5, 2, 2, 4, 2, 4, 9, 7, 4, 3, 3, 2, 9, 7, 8, 8, 6, 4, 2, 3, 1, 5, 6, 1, 7, 5, 6, 5, 2, 0, 8, 3, 5, 5, 1, 0, 4, 4, 5, 2, 5, 0, 2, 2, 3, 4, 7, 5, 2, 7, 9, 1, 0, 0, 2, 7, 7, 0, 4, 3, 3, 4, 8, 7, 8, 6, 1, 4, 9, 4, 8, 5, 6, 2, 2, 8, 4, 1, 7, 0, 3, 7, 8, 7, 9, 8, 1, 1, 3, 6, 7, 3, 7, 3, 6, 1, 8, 9, 9, 2, 8, 9, 2, 9, 3, 1, 8, 8, 3, 3, 3, 9, 7, 1, 7, 5, 6, 0, 2, 7, 2, 5, 6, 4, 4, 3, 6, 7, 5, 7, 4, 6, 7, 7, 9, 2, 6, 3, 1, 8, 3, 9, 4, 3, 5, 8, 8, 6, 8, 9, 3, 9, 6, 3, 6, 0, 8, 3, 3, 7, 9, 7, 9, 5, 7, 1, 5, 1, 7, 8, 8, 6, 6, 3, 0, 8, 2, 5, 9, 8, 6, 0, 9, 8, 5, 5, 0, 0, 2, 1, 3, 1, 6, 1, 8, 2, 9, 9, 9, 2, 5, 5, 5, 7, 1, 8, 2, 9, 5, 0, 6, 7, 1, 4, 3, 8, 4, 8, 3, 3, 4, 7, 9, 8, 3, 7, 9, 6, 8, 9, 7, 1, 2, 8, 8, 9, 3, 1, 5, 0, 3, 6, 8, 7, 9, 5, 6, 6, 0, 9, 1, 1, 3, 7, 8, 1, 5, 7, 3, 0, 7, 7, 3, 9, 4, 4, 6, 6, 4, 4, 6, 8, 4, 8, 2, 5, 3, 1, 8, 7, 6, 9, 5, 7, 6, 8, 8, 2, 3, 9, 2, 1, 4, 3, 8, 1, 7, 3, 5, 4, 3, 3, 4, 8, 7, 2, 5, 1, 4, 2, 0, 1, 0, 2, 6, 9, 1, 1]\n",
            "['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
            "{'airplane': 0, 'automobile': 1, 'bird': 2, 'cat': 3, 'deer': 4, 'dog': 5, 'frog': 6, 'horse': 7, 'ship': 8, 'truck': 9}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQw-u9pDis8J",
        "outputId": "5e773ed3-aac7-4f0e-d2c3-3aabb634a2fd"
      },
      "source": [
        "print(type(train_data[0])) #We can access a data sample using an index, as shown in the following code:\n",
        "# out: <class 'tuple'>\n",
        "\n",
        "print(len(train_data[0]))\n",
        "# out: 2\n",
        "\n",
        "data, label = train_data[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'tuple'>\n",
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhzUTLE-51Jq",
        "outputId": "5d170a9d-f8f0-4122-efc6-058bb3a4dc8f"
      },
      "source": [
        "print(type(data))\n",
        "# out: <class 'PIL.Image.Image'>\n",
        "\n",
        "print(data)\n",
        "# out: \n",
        "# <PIL.Image.Image image mode=RGB \n",
        "#       size=32x32 at 0x7FA61D6F1748>"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'PIL.Image.Image'>\n",
            "<PIL.Image.Image image mode=RGB size=32x32 at 0x7F5EFE5AB790>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "vAhQrllQLA-j",
        "outputId": "56ea2781-f33b-42b8-9f75-8e2b5f8589c9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5f4d064410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idrPrE7r51eN",
        "outputId": "2105cb2b-b8b4-4a9d-f8c3-e43223fb7db9"
      },
      "source": [
        "print(type(label))\n",
        "# out: <class 'int'>\n",
        "\n",
        "print(label)\n",
        "# out: 6\n",
        "\n",
        "print(train_data.classes[label])\n",
        "# out: frog"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'int'>\n",
            "6\n",
            "frog\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187,
          "referenced_widgets": [
            "c35a5390aae647b8b6a6a57642257417",
            "d7c72262592d45bfa5ed31e630ca8a42",
            "816896dd6c9b490db10edeb04edfaa8f",
            "3643244f66014d98a9c1f2b377ef28c7",
            "730050982b27418394fc819c91db79d2",
            "3fbc8d76caf34f029a2ab899f884f21a",
            "2e8e1a3fc2604b499ed1ab5b593d6044",
            "b1706660b23e45dc92710036e0e324b5"
          ]
        },
        "id": "awDbzyQN81Iv",
        "outputId": "09eab0be-5d5e-422b-bec1-42a8a4712957"
      },
      "source": [
        "test_data = CIFAR10(root=\"./test/\", \n",
        "                    train=False, \n",
        "                    download=True)\n",
        "\n",
        "print(test_data)\n",
        "# out:\n",
        "# Dataset CIFAR10\n",
        "#     Number of datapoints: 10000\n",
        "#     Root location: ./test/\n",
        "#     Split: Test\n",
        "\n",
        "print(len(test_data))\n",
        "# out: 10000\n",
        "\n",
        "print(test_data.data.shape) # ndarray\n",
        "# out: (10000, 32, 32, 3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./test/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c35a5390aae647b8b6a6a57642257417",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./test/cifar-10-python.tar.gz to ./test/\n",
            "Dataset CIFAR10\n",
            "    Number of datapoints: 10000\n",
            "    Root location: ./test/\n",
            "    Split: Test\n",
            "10000\n",
            "(10000, 32, 32, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-BCIj1oG_sA"
      },
      "source": [
        "## Data Transforms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgMYPb-DLP3i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08c74024-6775-4d17-837b-041f8145438b"
      },
      "source": [
        "from torchvision import transforms\n",
        "\n",
        "train_transforms = transforms.Compose([\n",
        "  transforms.RandomCrop(32, padding=4),\n",
        "  transforms.RandomHorizontalFlip(),\n",
        "  transforms.ToTensor(),\n",
        "  transforms.Normalize(\n",
        "      (0.4914, 0.4822, 0.4465),\n",
        "      (0.2023, 0.1994, 0.2010))])\n",
        "\n",
        "train_data = CIFAR10(root=\"./train/\",\n",
        "                    train=True, \n",
        "                    download=True,\n",
        "                    transform=train_transforms)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QhcgHjQlv5Pr",
        "outputId": "44ccf3a2-5eaf-4751-b2db-2d200de7bc8b"
      },
      "source": [
        "print(train_data)\n",
        "# out: \n",
        "# Dataset CIFAR10\n",
        "#     Number of datapoints: 50000\n",
        "#     Root location: ./train/\n",
        "#     Split: Train\n",
        "#     StandardTransform\n",
        "# Transform: Compose(\n",
        "#                RandomCrop(size=(32, 32), \n",
        "#                  padding=4)\n",
        "#                RandomHorizontalFlip(p=0.5)\n",
        "#                ToTensor()\n",
        "#                Normalize(\n",
        "#                  mean=(0.4914, 0.4822, 0.4465), \n",
        "#                  std=(0.2023, 0.1994, 0.201))\n",
        "#            )\n",
        "\n",
        "print(train_data.transforms)\n",
        "# out:\n",
        "# StandardTransform\n",
        "# Transform: Compose(\n",
        "#                RandomCrop(size=(32, 32), \n",
        "#                  padding=4)\n",
        "#                RandomHorizontalFlip(p=0.5)\n",
        "#                ToTensor()\n",
        "#                Normalize(\n",
        "#                  mean=(0.4914, 0.4822, 0.4465), \n",
        "#                  std=(0.2023, 0.1994, 0.201))\n",
        "#            )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset CIFAR10\n",
            "    Number of datapoints: 50000\n",
            "    Root location: ./train/\n",
            "    Split: Train\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               RandomCrop(size=(32, 32), padding=4)\n",
            "               RandomHorizontalFlip(p=0.5)\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.201))\n",
            "           )\n",
            "StandardTransform\n",
            "Transform: Compose(\n",
            "               RandomCrop(size=(32, 32), padding=4)\n",
            "               RandomHorizontalFlip(p=0.5)\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.201))\n",
            "           )\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAAlSyQok5Im",
        "outputId": "97c78c7a-7c04-4166-d5a5-6a6e44113b05"
      },
      "source": [
        "data, label = train_data[0]\n",
        "\n",
        "print(type(data))\n",
        "# out: <class 'torch.Tensor'>\n",
        "\n",
        "print(data.size())\n",
        "# out: torch.Size([3, 32, 32])\n",
        "\n",
        "print(data)\n",
        "# out: \n",
        "# tensor([[[-0.1416,  ..., -2.4291],\n",
        "#          [-0.0060,  ..., -2.4291],\n",
        "#          [-0.7426,  ..., -2.4291],\n",
        "#          ...,\n",
        "#          [ 0.5100, ..., -2.2214],\n",
        "#          [-2.2214, ..., -2.2214],\n",
        "#          [-2.2214, ..., -2.2214]]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'torch.Tensor'>\n",
            "torch.Size([3, 32, 32])\n",
            "tensor([[[-2.4291, -2.4291, -2.4291,  ..., -2.4291, -2.4291, -2.4291],\n",
            "         [-2.4291, -2.4291, -2.4291,  ..., -2.4291, -2.4291, -2.4291],\n",
            "         [-2.4291, -2.4291, -2.4291,  ..., -2.4291, -2.4291, -2.4291],\n",
            "         ...,\n",
            "         [-2.4291, -2.4291, -2.4291,  ...,  0.0910,  0.1297,  0.8858],\n",
            "         [-2.4291, -2.4291, -2.4291,  ...,  0.2267,  0.3430,  1.5061],\n",
            "         [-2.4291, -2.4291, -2.4291,  ...,  0.0328, -0.2192,  1.1765]],\n",
            "\n",
            "        [[-2.4183, -2.4183, -2.4183,  ..., -2.4183, -2.4183, -2.4183],\n",
            "         [-2.4183, -2.4183, -2.4183,  ..., -2.4183, -2.4183, -2.4183],\n",
            "         [-2.4183, -2.4183, -2.4183,  ..., -2.4183, -2.4183, -2.4183],\n",
            "         ...,\n",
            "         [-2.4183, -2.4183, -2.4183,  ..., -0.5892, -0.6482,  0.1581],\n",
            "         [-2.4183, -2.4183, -2.4183,  ..., -0.4319, -0.3139,  0.8661],\n",
            "         [-2.4183, -2.4183, -2.4183,  ..., -0.4319, -0.7072,  0.6301]],\n",
            "\n",
            "        [[-2.2214, -2.2214, -2.2214,  ..., -2.2214, -2.2214, -2.2214],\n",
            "         [-2.2214, -2.2214, -2.2214,  ..., -2.2214, -2.2214, -2.2214],\n",
            "         [-2.2214, -2.2214, -2.2214,  ..., -2.2214, -2.2214, -2.2214],\n",
            "         ...,\n",
            "         [-2.2214, -2.2214, -2.2214,  ..., -1.3629, -1.3239, -0.8557],\n",
            "         [-2.2214, -2.2214, -2.2214,  ..., -1.1678, -1.1093, -0.2313],\n",
            "         [-2.2214, -2.2214, -2.2214,  ..., -1.0703, -1.3239, -0.3094]]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "MiJ_cguSHnsF",
        "outputId": "14647a10-7677-4cc5-dd3a-66f89bfb7bd6"
      },
      "source": [
        "plt.imshow(data.permute(1, 2, 0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5f4d064890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIMW68MlET2J",
        "outputId": "859506f4-c5c8-4c1c-ac87-34d9c4c28f89"
      },
      "source": [
        "test_transforms = transforms.Compose([\n",
        "  transforms.ToTensor(),\n",
        "  transforms.Normalize(\n",
        "      (0.4914, 0.4822, 0.4465),\n",
        "      (0.2023, 0.1994, 0.2010))])\n",
        "\n",
        "test_data = torchvision.datasets.CIFAR10(\n",
        "      root=\"./test/\", \n",
        "      train=False, \n",
        "      transform=test_transforms)\n",
        "\n",
        "print(test_data)\n",
        "# out:\n",
        "# Dataset CIFAR10\n",
        "#     Number of datapoints: 10000\n",
        "#     Root location: ./test/\n",
        "#     Split: Test\n",
        "#     StandardTransform\n",
        "# Transform: Compose(\n",
        "#     ToTensor()\n",
        "#     Normalize(\n",
        "#       mean=(0.4914, 0.4822, 0.4465),\n",
        "#       std=(0.2023, 0.1994, 0.201)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset CIFAR10\n",
            "    Number of datapoints: 10000\n",
            "    Root location: ./test/\n",
            "    Split: Test\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.201))\n",
            "           )\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6W21V3InIG50"
      },
      "source": [
        "## Data Batching"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXYBiTpjHr0N"
      },
      "source": [
        "trainloader = torch.utils.data.DataLoader(\n",
        "                    train_data, \n",
        "                    batch_size=16, \n",
        "                    shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PZ-qrHmg4xG",
        "outputId": "112556a5-368a-4560-f827-d8c088d95879"
      },
      "source": [
        "data_batch, labels_batch = next(iter(trainloader))\n",
        "print(data_batch.size())\n",
        "# out: torch.Size([16, 3, 32, 32])\n",
        "\n",
        "print(labels_batch.size())\n",
        "# out: torch.Size([16])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([16, 3, 32, 32])\n",
            "torch.Size([16])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwY6m10IThoG"
      },
      "source": [
        "testloader = torch.utils.data.DataLoader(\n",
        "                    test_data, \n",
        "                    batch_size=16, \n",
        "                    shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKrUoF3MIp2i"
      },
      "source": [
        "## Model Design\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_1AMyp5phja"
      },
      "source": [
        "### Using Existing & Pre-trained models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "1e3ac1e7ed9f4e259135ab348fff5b80",
            "ab25a08ae2ac4d1f94ea946926a73ac4",
            "173c84071d6e4c10826d449493811468",
            "2634472c0b9f4cf1b06fff7911be1978",
            "b1ae174706d344978ced2df4995b52fa",
            "f033eae71da045ea97603919371aaab0",
            "d03cf801ec48411c8be10b55b2a84657",
            "b1c80cb38cd94512ba5b459f3b62ace0"
          ]
        },
        "id": "eLqiKEcf2d7R",
        "outputId": "952c5231-6aea-4cf8-a973-3cfb40a5c879"
      },
      "source": [
        "from torchvision import models\n",
        "\n",
        "vgg16 = models.vgg16(pretrained=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1e3ac1e7ed9f4e259135ab348fff5b80",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=553433881.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dBa8c5E8bmt",
        "outputId": "222e1375-706c-4b71-a693-5ae2191c20a0"
      },
      "source": [
        "print(vgg16.classifier)\n",
        "\n",
        "# out:\n",
        "# Sequential(\n",
        "#   (0): Linear(in_features=25088, \n",
        "#               out_features=4096, bias=True)\n",
        "#   (1): ReLU(inplace=True)\n",
        "#   (2): Dropout(p=0.5, inplace=False)\n",
        "#   (3): Linear(in_features=4096, \n",
        "#               out_features=4096, bias=True)\n",
        "#   (4): ReLU(inplace=True)\n",
        "#   (5): Dropout(p=0.5, inplace=False)\n",
        "#   (6): Linear(in_features=4096, \n",
        "#               out_features=1000, bias=True)\n",
        "# )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sequential(\n",
            "  (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
            "  (1): ReLU(inplace=True)\n",
            "  (2): Dropout(p=0.5, inplace=False)\n",
            "  (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
            "  (4): ReLU(inplace=True)\n",
            "  (5): Dropout(p=0.5, inplace=False)\n",
            "  (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WoLvxm2S-TGi",
        "outputId": "855b6d01-5e11-4db3-811b-00d2c9e4a559"
      },
      "source": [
        "waveglow = torch.hub.load(\n",
        "    'nvidia/DeepLearningExamples:torchhub', \n",
        "    'nvidia_waveglow')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://github.com/nvidia/DeepLearningExamples/archive/torchhub.zip\" to /root/.cache/torch/hub/torchhub.zip\n",
            "Downloading checkpoint from https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ckpt_fp32/versions/19.09.0/files/nvidia_waveglowpyt_fp32_20190427\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnJuo0yoAZxn",
        "outputId": "e5489396-5942-426b-c101-3eb432adc3f2"
      },
      "source": [
        "torch.hub.list('nvidia/DeepLearningExamples:torchhub')\n",
        "\n",
        "# out:\n",
        "# ['checkpoint_from_distributed',\n",
        "#  'nvidia_ncf',\n",
        "#  'nvidia_ssd',\n",
        "#  'nvidia_ssd_processing_utils',\n",
        "#  'nvidia_tacotron2',\n",
        "#  'nvidia_waveglow',\n",
        "#  'unwrap_distributed']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://github.com/nvidia/DeepLearningExamples/archive/torchhub.zip\" to /root/.cache/torch/hub/torchhub.zip\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['checkpoint_from_distributed',\n",
              " 'nvidia_ncf',\n",
              " 'nvidia_ssd',\n",
              " 'nvidia_ssd_processing_utils',\n",
              " 'nvidia_tacotron2',\n",
              " 'nvidia_waveglow',\n",
              " 'unwrap_distributed']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pl7yIw-uY-RM"
      },
      "source": [
        "## The PyTorch NN Module (torch.nn)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWKdV4PMnL06"
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class SimpleNet(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(SimpleNet, self).__init__()\n",
        "        self.fc1 = nn.Linear(2048, 256)\n",
        "        self.fc2 = nn.Linear(256, 64)\n",
        "        self.fc3 = nn.Linear(64,2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 2048)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = F.softmax(self.fc3(x),dim=1)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkAy9PmTpFkj",
        "outputId": "3c4c27d1-6cba-4101-d600-aaafa70eb5b7"
      },
      "source": [
        "simplenet = SimpleNet()\n",
        "print(simplenet)\n",
        "# out:\n",
        "# SimpleNet(\n",
        "#   (fc1): Linear(in_features=2048, \n",
        "#                 out_features=256, bias=True)\n",
        "#   (fc2): Linear(in_features=256, \n",
        "#                 out_features=64, bias=True)\n",
        "#   (fc3): Linear(in_features=64, \n",
        "#                 out_features=2, bias=True)\n",
        "# )\n",
        "\n",
        "input = torch.rand(2048)\n",
        "output = simplenet(input)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SimpleNet(\n",
            "  (fc1): Linear(in_features=2048, out_features=256, bias=True)\n",
            "  (fc2): Linear(in_features=256, out_features=64, bias=True)\n",
            "  (fc3): Linear(in_features=64, out_features=2, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LurpNmIpnpLo"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trFZ1GftyRqj"
      },
      "source": [
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class LeNet5(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(LeNet5, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5) # <1>\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
        "        x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
        "        x = x.view(-1, int(x.nelement() / x.shape[0]))\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model = LeNet5().to(device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d2FZ-sJqI4F"
      },
      "source": [
        "### Fundamental Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEOGrjvNeNAN"
      },
      "source": [
        "from torch import optim\n",
        "from torch import nn\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), # <1>\n",
        "                      lr=0.001, \n",
        "                      momentum=0.9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbnVGZAqyx4Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "920014b6-7298-4a1b-c394-6c793fbd086d"
      },
      "source": [
        "N_EPOCHS = 10 \n",
        "for epoch in range(N_EPOCHS): # <1>\n",
        "\n",
        "    epoch_loss = 0.0\n",
        "    for inputs, labels in trainloader:\n",
        "        inputs = inputs.to(device) # <2>\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad() # <3>\n",
        "\n",
        "        outputs = model(inputs) # <4>\n",
        "        loss = criterion(outputs, labels) # <5>\n",
        "        loss.backward() # <6>\n",
        "        optimizer.step() # <7>\n",
        "\n",
        "        epoch_loss += loss.item() # <8>\n",
        "    print(\"Epoch: {} Loss: {}\".format(epoch, \n",
        "                  epoch_loss/len(trainloader)))\n",
        "\n",
        "# out: (results will vary and make take minutes)\n",
        "# Epoch: 0 Loss: 1.8982970092773437\n",
        "# Epoch: 1 Loss: 1.6062103009033204\n",
        "# Epoch: 2 Loss: 1.484384165763855\n",
        "# Epoch: 3 Loss: 1.3944422281837463\n",
        "# Epoch: 4 Loss: 1.334191104450226\n",
        "# Epoch: 5 Loss: 1.2834235876464843\n",
        "# Epoch: 6 Loss: 1.2407222446250916\n",
        "# Epoch: 7 Loss: 1.2081411465930938\n",
        "# Epoch: 8 Loss: 1.1832368299865723\n",
        "# Epoch: 9 Loss: 1.1534993273162841"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0 Loss: 1.9328176640319825\n",
            "Epoch: 1 Loss: 1.5952847471237184\n",
            "Epoch: 2 Loss: 1.4674046397018432\n",
            "Epoch: 3 Loss: 1.3846740203285217\n",
            "Epoch: 4 Loss: 1.325514745130539\n",
            "Epoch: 5 Loss: 1.2827296934509278\n",
            "Epoch: 6 Loss: 1.251210474948883\n",
            "Epoch: 7 Loss: 1.226371928281784\n",
            "Epoch: 8 Loss: 1.1959582293987274\n",
            "Epoch: 9 Loss: 1.179223330335617\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6yMHCA4sRUB"
      },
      "source": [
        "Code Annotations:\n",
        "\n",
        "<1> Our training loop\n",
        "\n",
        "<2> Need to move inputs and labels to GPU is avail.\n",
        "\n",
        "<3> Zero out gradients before each backprop or they'll accumulate\n",
        "\n",
        "<4> Forward pass\n",
        "\n",
        "<5> Compute loss\n",
        "\n",
        "<6> Backpropagation, compute gradients\n",
        "\n",
        "<7> Adjust parameters based on gradients\n",
        "\n",
        "<8> accumulate batch loss so we can average over epoch\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YB9ASgpbntGf"
      },
      "source": [
        "## Validation & Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PR4ORHO1qXrk"
      },
      "source": [
        "### Splitting Training Dataset into Training & Validation Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blijECmJg9aF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ef38677-d854-4a4e-8979-a1a507735a06"
      },
      "source": [
        "from torch.utils.data import random_split\n",
        "\n",
        "train_set, val_set = random_split(\n",
        "                      train_data, \n",
        "                      [40000, 10000])\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(\n",
        "                    train_set, \n",
        "                    batch_size=16, \n",
        "                    shuffle=True)\n",
        "\n",
        "valloader = torch.utils.data.DataLoader(\n",
        "                    val_set, \n",
        "                    batch_size=16, \n",
        "                    shuffle=True)\n",
        "\n",
        "print(len(trainloader))\n",
        "# out: 2500\n",
        "print(len(valloader))\n",
        "# out: 625"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2500\n",
            "625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4rR9gFCxn6yK"
      },
      "source": [
        "### Training Loop with Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nzIXSsbiARZ"
      },
      "source": [
        "from torch import optim\n",
        "from torch import nn\n",
        "\n",
        "model = LeNet5().to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), \n",
        "                      lr=0.001, \n",
        "                      momentum=0.9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfaZf6Zlugzm",
        "outputId": "fd7c6b8c-8812-4aca-8563-3c8f68c62120"
      },
      "source": [
        "N_EPOCHS = 10\n",
        "for epoch in range(N_EPOCHS):\n",
        "\n",
        "    # Training \n",
        "    train_loss = 0.0\n",
        "    model.train() # <1>\n",
        "    for inputs, labels in trainloader:\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        train_loss += loss.item()\n",
        "\n",
        "    # Validation\n",
        "    val_loss = 0.0\n",
        "    model.eval() # <2>\n",
        "    for inputs, labels in valloader:\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        val_loss += loss.item()\n",
        "\n",
        "    print(\"Epoch: {} Train Loss: {} Val Loss: {}\".format(\n",
        "                  epoch, \n",
        "                  train_loss/len(trainloader), \n",
        "                  val_loss/len(valloader)))\n",
        "\n",
        "# out: (results may vary and take a few minutes)\n",
        "# Epoch: 0 Train Loss: 1.9876076080799103 Val Loss: 1.7407869798660278\n",
        "# Epoch: 1 Train Loss: 1.6497538920879364 Val Loss: 1.5870195521354675\n",
        "# Epoch: 2 Train Loss: 1.5117236899614335 Val Loss: 1.4355393668174743\n",
        "# Epoch: 3 Train Loss: 1.408525426363945 Val Loss: 1.3614536597251892\n",
        "# Epoch: 4 Train Loss: 1.3395055189609528 Val Loss: 1.2934591544151306\n",
        "# Epoch: 5 Train Loss: 1.290560259628296 Val Loss: 1.245048282814026\n",
        "# Epoch: 6 Train Loss: 1.2592685657382012 Val Loss: 1.2859896109580993\n",
        "# Epoch: 7 Train Loss: 1.235161985707283 Val Loss: 1.2538409409046174\n",
        "# Epoch: 8 Train Loss: 1.2070518508672714 Val Loss: 1.2157000193595886\n",
        "# Epoch: 9 Train Loss: 1.189215132522583 Val Loss: 1.1833322570323943"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0 Train Loss: 1.9950691596984864 Val Loss: 1.7358538597106934\n",
            "Epoch: 1 Train Loss: 1.6696419578790664 Val Loss: 1.5826319568634033\n",
            "Epoch: 2 Train Loss: 1.5508703550815581 Val Loss: 1.4990441875457763\n",
            "Epoch: 3 Train Loss: 1.4702660526752471 Val Loss: 1.420825472354889\n",
            "Epoch: 4 Train Loss: 1.4136667536258698 Val Loss: 1.362186644935608\n",
            "Epoch: 5 Train Loss: 1.3608775995016098 Val Loss: 1.359514545917511\n",
            "Epoch: 6 Train Loss: 1.3161776222586632 Val Loss: 1.3110978411197662\n",
            "Epoch: 7 Train Loss: 1.2865340428471566 Val Loss: 1.3035572074890136\n",
            "Epoch: 8 Train Loss: 1.2575521076202392 Val Loss: 1.2465155982017517\n",
            "Epoch: 9 Train Loss: 1.2253268226146699 Val Loss: 1.2103867791175842\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pv8KL1rwoBqR"
      },
      "source": [
        "### Testing Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrXSQmnBKZew",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23b570fc-6195-4df9-e856-db433dbc68ae"
      },
      "source": [
        "num_correct = 0.0\n",
        "for x_test_batch, y_test_batch in testloader:\n",
        "  model.eval()\n",
        "  y_test_batch = y_test_batch.to(device)\n",
        "  x_test_batch = x_test_batch.to(device)\n",
        "  y_pred_batch = model(x_test_batch)\n",
        "  _, predicted = torch.max(y_pred_batch, 1)\n",
        "  num_correct += (predicted == y_test_batch).float().sum()\n",
        "  \n",
        "accuracy = num_correct/(len(testloader)*testloader.batch_size) \n",
        "\n",
        "print(len(testloader), testloader.batch_size)\n",
        "# out: 625 16\n",
        "\n",
        "print(\"Test Accuracy: {}\".format(accuracy))\n",
        "# out: Test Accuracy: 0.6322000026702881"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "625 16\n",
            "Test Accuracy: 0.5938000082969666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgGlh3G6q03-"
      },
      "source": [
        "## Model Deployment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CcjcrSfmCj5"
      },
      "source": [
        "### Saving Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoiKrYfse6IK"
      },
      "source": [
        "torch.save(model.state_dict(), \"./lenet5_model.pt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ry_4_HXae5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d8457ae-cf64-41ed-c1f3-15e15a5ad557"
      },
      "source": [
        "model = LeNet5().to(device)\n",
        "model.load_state_dict(torch.load(\"./lenet5_model.pt\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H10Sf46Mq5m6"
      },
      "source": [
        "### Deploy to PyTorch Hub"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QO0OdXx5R0C9"
      },
      "source": [
        "dependencies = ['torch']\n",
        "from torchvision.models.vgg import vgg16"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PNZIDSvSfX6"
      },
      "source": [
        "dependencies = ['torch']\n",
        "from torchvision.models.vgg import vgg16 as _vgg16\n",
        "\n",
        "# vgg16 is the name of entrypoint\n",
        "def vgg16(pretrained=False, **kwargs):\n",
        " \"\"\" # This docstring shows up in hub.help()\n",
        " VGG16 model\n",
        " pretrained (bool): kwargs, load pretrained\n",
        "weights into the model\n",
        " \"\"\"\n",
        " # Call the model, load pretrained weights\n",
        " model = _vgg16(pretrained=pretrained, **kwargs)\n",
        " return model"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}