A_Gentle_Introduction_to_PyTorch_1_2.ipynb

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        "<a href=\"https://colab.research.google.com/github/omarsar/pytorch_notebooks/blob/master/A_Gentle_Introduction_to_PyTorch_1_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "# A Gentle Introduction to PyTorch 1.2\n",
        "\n",
        "In our previous PyTorch [notebook](https://medium.com/dair-ai/pytorch-1-2-quickstart-with-google-colab-6690a30c38d), we learned about how to get started quickly with PyTorch 1.2 using Google Colab. In this tutorial, we are going to take a step back and review some of the basic components of building a deep learning model using PyTorch. \n",
        "\n",
        "This will be a brief tutorial and will avoid using jargon and overcomplicated code. That said, this is perhaps the most basic of models you can build with PyTorch. \n",
        "\n",
        "If fact, it is so basic that it's ideal for those starting to learn about PyTorch and deep learning. So if you have a friend or colleague that wants to jump in, I highly encourage you to refer them to this tutorial as a starting point. Let's get started!\n",
        "\n",
        "\n",
        "**Author:** [Elvis Saravia](https://twitter.com/omarsar0)\n",
        "\n",
        "**Complete Code Walkthrough:** [Blog post](https://medium.com/dair-ai/pytorch-1-2-introduction-guide-f6fa9bb7597c)\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cA0Ke48uRkIM",
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      "source": [
        "## Getting Started\n",
        "Before you get started with coding, you need to install the latest version of PyTorch. We are using Google Colab for coding our notebook, so we will use the following command to install PyTorch. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "source": [
        "## install torch\n",
        "!pip3 install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Collecting torch==1.2.0+cu92\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/cu92/torch-1.2.0%2Bcu92-cp36-cp36m-manylinux1_x86_64.whl (663.1MB)\n",
            "\u001b[K     |████████████████████████████████| 663.1MB 39kB/s \n",
            "\u001b[?25hCollecting torchvision==0.4.0+cu92\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/cu92/torchvision-0.4.0%2Bcu92-cp36-cp36m-manylinux1_x86_64.whl (8.8MB)\n",
            "\u001b[K     |████████████████████████████████| 8.8MB 26.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.2.0+cu92) (1.16.4)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.4.0+cu92) (4.3.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision==0.4.0+cu92) (1.12.0)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.1.1->torchvision==0.4.0+cu92) (0.46)\n",
            "Installing collected packages: torch, torchvision\n",
            "  Found existing installation: torch 1.1.0\n",
            "    Uninstalling torch-1.1.0:\n",
            "      Successfully uninstalled torch-1.1.0\n",
            "  Found existing installation: torchvision 0.3.0\n",
            "    Uninstalling torchvision-0.3.0:\n",
            "      Successfully uninstalled torchvision-0.3.0\n",
            "Successfully installed torch-1.2.0+cu92 torchvision-0.4.0+cu92\n"
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    {
      "cell_type": "markdown",
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      "source": [
        "Now we need to import a few modules which will be useful to obtain the necessary functions that will help us to build our deep learning model. The main ones are `torch` and `torchvision`. They contain the majority of the functions that you need to get started with PyTorch. However, as this is a deep learning tutorial we will need `torch.nn`, `torch.nn.functional` and `torchvision.transforms` which all contain utility functions to build our model. We probably won't use all the modules listed below but they are the typical modules you will be importing when starting your deep learning projects.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuhJIaeXO2W9",
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      "source": [
        "## The usual imports\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "## for printing image\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSTcn13qS7EC",
        "colab_type": "text"
      },
      "source": [
        "Below we check for the PyTorch version just to make sure that you are using the proper version. At the time of this tutorial, we are working with PyTorch 1.2."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eVhGiUJO42I",
        "colab_type": "code",
        "outputId": "28d35e2c-f247-4f1f-d52c-4d767fc1529c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "## print out the pytorch version used\n",
        "print(torch.__version__)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.2.0+cu92\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a2C_nneO_wp",
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      "source": [
        "## Loading the Data\n",
        "Let's get right into it! As with any machine learning project, you need to load your dataset. We are using the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), which is the Hello World of datasets in the machine learning world. \n",
        "\n",
        "The data consists of number images that are of size `28 X 28`. We will discuss the images shortly, but our plan is to load data into batches of size `32`, similar to the figure below.\n",
        "\n",
        "\n",
        "![alt text](https://drive.google.com/uc?export=view&id=19AC_WpscyXkrK_o4PaFFGpt_jG0aJm_f)\n",
        "\n",
        "\n",
        "Here are the complete steps we are performing when importing our data:\n",
        "- We will import and tranform the data into tensors using the `transforms` module\n",
        "- We will use `DataLoader` to build convenient data loaders, which makes it easy to efficiently feed data in batches to deep learning models. We will get to the topic of batches in a bit but for now just think of them as subsets of your data. \n",
        "- As hinted above, we will also create batches of the data by setting the `batch` parameter inside the data loader. Notice we use batches of `32` in this tutorial but you can change it to `64` if you like. "
      ]
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        "outputId": "a691535f-482d-4290-dd5c-08e46b7a3fea",
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      "source": [
        "## parameter denoting the batch size\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "## transformations\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor()])\n",
        "\n",
        "## download and load training dataset\n",
        "trainset = torchvision.datasets.MNIST(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,\n",
        "                                          shuffle=True, num_workers=2)\n",
        "\n",
        "## download and load testing dataset\n",
        "testset = torchvision.datasets.MNIST(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,\n",
        "                                         shuffle=False, num_workers=2)"
      ],
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            "\r0it [00:00, ?it/s]"
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            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
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            "9920512it [00:01, 7888836.26it/s]                             \n"
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            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n"
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            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
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            "1654784it [00:00, 2128406.36it/s]                           \n",
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        {
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            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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            "8192it [00:00, 48073.80it/s]            "
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          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r26nONGt5EoQ",
        "colab_type": "text"
      },
      "source": [
        "Let's inspect what the trainset and testset objects contain. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWmZhRJ8fRx4",
        "colab_type": "code",
        "outputId": "08ef128f-600f-472a-b57e-e9a2acbc8d47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "## print the trainset and testset\n",
        "print(trainset)\n",
        "print(testset)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset MNIST\n",
            "    Number of datapoints: 60000\n",
            "    Root location: ./data\n",
            "    Split: Train\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "           )\n",
            "Dataset MNIST\n",
            "    Number of datapoints: 10000\n",
            "    Root location: ./data\n",
            "    Split: Test\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "           )\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zM19_49wbkfr",
        "colab_type": "text"
      },
      "source": [
        "This is a beginner's tutorial so I will break down things a bit here:\n",
        "- `BATCH_SIZE` is a parameter that denotes the batch size we will use for our model\n",
        "- `transform` holds code for whatever transformations you will apply to your data. I will show you an example below to demonstrate exactly what it does to shed more light into its use\n",
        "- `trainset` and `testset` contain the actual dataset object. Notice I use `train=True` to specify that this corresponds to the training dataset, and I use `train=False` to specify that this is the remainder of the dataset which we call the testset. From the portion I printed above you can see that the split of the data was 85% (60000) / 15% (10000), corresponding to the portions of samples for training set and testing set, respectively. \n",
        "- `trainloader` is what holds the data loader object which takes care of shuffling the data and constructing the batches.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vWk66EEgjHW",
        "colab_type": "text"
      },
      "source": [
        "Now let's look at that `transforms.Compose(...)` function and see what it does. We will use a randomized image to demonstrate its use. Let's generate an image. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjGeZC4vj6VT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image = transforms.ToPILImage(mode='L')(torch.randn(1, 96, 96))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKpof5dPj-SA",
        "colab_type": "text"
      },
      "source": [
        "And let's render it:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQzB0tXIjhRG",
        "colab_type": "code",
        "outputId": "3bc06b95-2eb9-4fbd-ceab-b649b29ff904",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.imshow(image)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2a00da7898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mh+Z+px97ncvPvljAlKXLXc5xUkmpICzSKwIAkDWJQcbw9jxm5S9Bxpz7JAAV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8b+Fmv8DFtEFwKmw6r4kIdko8NjmitjDB4IK1fgES9XcWOQ1y1hgS1JBIrf1ATPbuIYxn7KzYQF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\npfgRBaX4EQV9A94D65YnPuOlAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsszgyIChv71",
        "colab_type": "text"
      },
      "source": [
        "Okay, we have our image sample. And now let's apply some dummy transformation to it. We are going to rotate the image by `45` degrees. The transformation below takes care of that:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afjxe4WVgtNR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## dummy transformation\n",
        "dummy_transform = transforms.Compose(\n",
        "    [transforms.RandomRotation(45)])\n",
        "\n",
        "dummy_result = dummy_transform(image)\n",
        "\n",
        "plt.imshow(dummy_result)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFd7BFYnkLy1",
        "colab_type": "code",
        "outputId": "ab9809c2-87f5-49b1-a8ac-399969e73c8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f29fed3a438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rJldCAQDrxjcEAKR3FVch8SKzCv12ktCUZU+w73G7bGWuVdVm6UnTuFkRltu+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CGqqhYEEAqgjzbXkQD2qaoaDGCf9v+MhgEALpn9fyqAGaqqBoEkwC7vZVZvxywAu1RVzQ+gKDj3\nDL3OiqLkAtAfQClVVQsDsAbQChl/rdNCVdW//R+AsgB+Nvv/KACj0uPc/4O5bwZQHUAEAB9tmw+A\niPc9t9fm6Qv+UKoC2Aamn8UBsHnTPXjf/wBkA3Admp/JbHtGX+dcAG4BcAcp79sA1MzIa/2mf+ml\n6psWy4Tb2rYMDUVR/AEUB3AMgLeqqne1XTEAvN/TtN6GmQCGQ6jmHgAeqqpqKlaf0dY8AEAsgCWa\nebJQURRnZPB1VlX1DoDpAG4CuAvgEYBTyNhrbQHDufcWKIqSBcAGAANVVX1svk/laz3DhEMURakH\n4L6qqqf+cnDGgQ2AEgB+UFW1OEjlTqPWZ7R1BgDN59AQfHHlBOAMoNa//FIGRHr98O8A8DP7v6+2\nLUNCURRb8Ee/SlXVjdrme4qi+Gj7fQDcf1/zewPKA2igKEoUgNWguj8LgKuiKKYMzIy25rcB3FZV\n9Zj2//XgiyAjrzMAVANwXVXVWFVVkwFsBNc/I6+1BdLrh38CQLDm+bQDnSFb0unc/xYURVEALAJw\nSVVV8x7PWyCV3zuAtn+GgKqqo1RV9VVV1R9c2/2qqn4C4ACAZtqwjDbnGAC3FEUx9Zv+GMBFZOB1\n1nATQKiiKE7as2Kad4Zd6zciHZ0idQBEArgKYPT7dm78i3lWANXLcwDOav/qgDbzPgCXAewF4P6+\n5/qW+VcBsE37nBfAcQBXAKwDYP++5/faXIsBOKmt9U8A3P4J6wxgAoBwAH8CWAHAPqOv9ev/DOae\nAQOZEIZzz4CBTAjjh2/AQCaE8cM3YCATwvjhGzCQCWH88A0YyIQwfvgGDGRCGD98AwYyIYwfvgED\nmRD/B2PuXu6We0TnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUu-djsMkw9r",
        "colab_type": "text"
      },
      "source": [
        "Notice you can put the transformations within `transforms.Compose(...)`. You can use the built in transformations offered by PyTorch or you can build your own and compose as you wish. In fact, you can place as many transformation as you wish in there. Let's try another composition of transformations: rotate + vertical flip. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48fDFFjhk9lO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## dummy transform \n",
        "dummy2_transform = transforms.Compose(\n",
        "    [transforms.RandomRotation(45), transforms.RandomVerticalFlip()])\n",
        "\n",
        "dummy2_result = dummy2_transform(image)\n",
        "\n",
        "plt.imshow(dummy2_result)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQx_axfzlTlU",
        "colab_type": "code",
        "outputId": "f022f8f1-4f50-40fe-fc6f-a036d1534d49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        ""
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f29fec91d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wsOrvDPEodXmUVP4jcq8Ie4YqD5ZhiArfyz063Qv64J5O17nFB66ScJ5JEjdr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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkMitRallkW4",
        "colab_type": "text"
      },
      "source": [
        "That's pretty cool right! Keep trying other transform methods. On the topic of exploring our data further, let's take a look at our images dataset. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMUTV8ooxAbB",
        "colab_type": "text"
      },
      "source": [
        "## Exploring the Data\n",
        "As a practioner and researcher, I am always spend a bit of time and effort exploring and understanding my datasets. It's fun and this is a good practise to ensure that everything is in order.\n",
        "\n",
        "Let's check what the train and test dataset contain. I will use matplotlib to print out some of the images from our dataset. With a bit of numpy I can convert images into numpy and print them out. Below I print out an entire batch. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fNCwuSTHljxM",
        "colab_type": "code",
        "outputId": "6fcd8d79-6dc5-4963-b3f2-912415f206f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        }
      },
      "source": [
        "## functions to show an image\n",
        "def imshow(img):\n",
        "    #img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "## get some random training images\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "## show images\n",
        "imshow(torchvision.utils.make_grid(images))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KlChhZ64xv9vp06dLtmzZXC5PwlK2bFkpW7asDB8+XIYPHy6BgYESGBgo77//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\nUcUMi34AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eXfF53MCxf7M",
        "colab_type": "text"
      },
      "source": [
        "The dimensions of our batches are as follow:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onfWHzacxkmi",
        "colab_type": "code",
        "outputId": "4ad87c23-8627-459a-ba98-d7d83e4c0d9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "for images, labels in trainloader:\n",
        "    print(\"Image batch dimensions:\", images.shape)\n",
        "    print(\"Image label dimensions:\", labels.shape)\n",
        "    break"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image batch dimensions: torch.Size([32, 1, 28, 28])\n",
            "Image label dimensions: torch.Size([32])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sko2xLi6x8ti",
        "colab_type": "text"
      },
      "source": [
        "## The Model\n",
        "Now it's time to build the deep learning model that will be used to perform the image classification. We will keeps things simple and stack a few dense layers and a dropout layer to train our model.\n",
        "\n",
        "Let's discuss a bit about the model:\n",
        "\n",
        "- First of all the following structure involving a `class` is standard code that's used to build the neural network model in PyTorch:\n",
        "\n",
        "```python\n",
        "class MyModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MyModel, self).__init__()\n",
        "        \n",
        "        # layers go here\n",
        "    \n",
        "    def forward(self, x):\n",
        "        \n",
        "        # computations go here\n",
        "```\n",
        "- The layers are defined inside `def __init__()`. `super(...).__init__()` is just there to stick things together. For our model, we stack a hidden layer (`self.d1`) followed by a dropout layer (`self.dropout`), which is then followed by an output layer (`self.d2`). \n",
        "- `nn.Linear(...)` defines the dense layer and it requires the `in` and `out` dimensions, which corresponds to the size of the input feature and output feature of that layer, respectively. \n",
        "- `nn.Dropout(...)` is used to define a dropout layer. Dropout is an approach in deep learning that helps a model to avoid overfitting. This means that dropout acts as a regularization technique that helps the model to not overfit on the images it has seen while training. We want this because we need a model that generalizes well to unseen examples -- in our case, the testing dataset. Dropout randomly zeroes some of the units of the neural network layer with probability of `p=0.2`. Read more about the dropout layer [here](https://pytorch.org/docs/stable/nn.html#dropout). \n",
        "- The entry point of the model, i.e. where the data enters, is placed under the `forward(...)` function. Typically, we also place other transformations we perform on the data while training inside this function. \n",
        "- In the `forward()` function we are performing a series of computations on the input data\n",
        "    - we flatten the images first, converting it from 2D (`28 X 28`) to 1D (`1 X 784`).\n",
        "    - then we feed the batches of those 1D images into the first hidden layer\n",
        "    - the output of that hidden layer is then applied a [non-linear activate function](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) called `ReLU`. It's not so important to know what `F.relu()` does at the moment, but the effect that it achieves is that it allows faster and more effective training of neural architectures on large datasets\n",
        "    - as explained above, the dropout also helps the model to train more efficiently by avoiding overfitting on the training data\n",
        "    - we then feed the output of that dropout layer into the output layer (`d2`)\n",
        "    - the result of that is then fed to the [softmax function](https://en.wikipedia.org/wiki/Softmax_function), which converts or normalized the output into a probability distribution which helps with outputting proper predictions values that are used to calculate the accuracy of the model; this will the final output of the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FhO-24HxseW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## the model\n",
        "class MyModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MyModel, self).__init__()\n",
        "        self.d1 = nn.Linear(28 * 28, 128)\n",
        "        self.dropout = nn.Dropout(p=0.2)\n",
        "        self.d2 = nn.Linear(128, 10)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        x = x.flatten(start_dim = 1)\n",
        "        x = self.d1(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.dropout(x)\n",
        "        logits = self.d2(x)\n",
        "        out = F.softmax(logits, dim=1)\n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ZqgEqSRBoKL",
        "colab_type": "text"
      },
      "source": [
        "Visually, the following is a diagram of the model we have built. Just keep in mind that the hidden layer is much bigger as shown in the diagram but due to space constraint, the diagram is just an approximation to the actual model. \n",
        "\n",
        "![alt text](https://drive.google.com/uc?export=view&id=1NuFflDPOW_hIAHTH2pXZAEhSINygPlnB)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xi_c1Fos_4no",
        "colab_type": "text"
      },
      "source": [
        "As I have done in my previous tutorials, I always encourage to test the model with 1 batch to ensure that the output dimensions are what we expect. Notice how we are iterating over the dataloader which conveniently stores the `images` and `labels` pairs. `out` contains the output of the model, which are the logits applied a `softmax` layer which helps with prediction. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJuiB4J74N84",
        "colab_type": "code",
        "outputId": "5d3dad41-eb8f-4b9b-cd25-2a4869a1dfed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "## test the model with 1 batch\n",
        "model = MyModel()\n",
        "for images, labels in trainloader:\n",
        "    print(\"batch size:\", images.shape)\n",
        "    out = model(images)\n",
        "    print(out.shape)\n",
        "    break"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "batch size: torch.Size([32, 1, 28, 28])\n",
            "torch.Size([32, 10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxSu3yIbAn6B",
        "colab_type": "text"
      },
      "source": [
        "We can clearly see that we get back the batches with 10 output values associate with it. These are used to compute the performance of the model. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNQbf3WPBBne",
        "colab_type": "text"
      },
      "source": [
        "## Training the Model\n",
        "Now we are ready to train the model but before that we are going to setup a loss function, an optimizer and a function to compute accuracy of the model. \n",
        "\n",
        "- The `learning_rate` is the rate at which the model will try to optimize its weights, which is just another parameter for the model. \n",
        "- `num_epochs` is the number of training steps. \n",
        "- `device` determines what hardware we will use to train the model. If a `gpu` is present, then that will be used, otherwise it defaults to the `cpu`.\n",
        "- `model` is just the model instance.\n",
        "- `model.to(device)` is in charge of setting the actaull device that will be used for training the model\n",
        "- `criterion` is just the metric that's used to compute the loss of the model while it forward and backward trains to optimize its weights. \n",
        "- `optimizer` is the optimization technique used to modify the weights in the backward propagation. Notice that it requires the `learning_rate` and the model parameters which are part of the calculation to optimize weights.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvFAg3D4A_oz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learning_rate = 0.001\n",
        "num_epochs = 5\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = MyModel()\n",
        "model = model.to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjhAkfrLZfvr",
        "colab_type": "text"
      },
      "source": [
        "The utility function below helps to calculate the accuracy of the model. For now, it's not important to understand how it's calculated but basically it compares the outputs of the model (predictions) with the actual target values (i.e., the labels of the dataset), and tries to compute the average of correct predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMXFCP24BNGS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## utility function to compute accuracy\n",
        "def get_accuracy(output, target, batch_size):\n",
        "    ''' Obtain accuracy for training round '''\n",
        "    corrects = (torch.max(output, 1)[1].view(target.size()).data == target.data).sum()\n",
        "    accuracy = 100.0 * corrects/batch_size\n",
        "    return accuracy.item()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czTadqw2Bapb",
        "colab_type": "text"
      },
      "source": [
        "## Training the Model\n",
        "Now it's time to train the model. The code portion that follows can be descrive in the following steps:\n",
        "\n",
        "- The first thing in training a neural network model is defining the training loop, which is achieved by:\n",
        "\n",
        "```python\n",
        "for epoch in range(num_epochs):\n",
        "    ...\n",
        "```\n",
        "\n",
        "- We define two variables, `training_running_loss` and `train_acc` that will help us to monitor the running accuracy and loss of the modes while it trains over the different batches.\n",
        "- `model.train()` explicitly indicates that we are ready to start training. \n",
        "- Notice how we are iterating over the dataloader, which conveniently gives us the batches in image-label pairs. \n",
        "- That second `for` loop means that for every training step we will iterate over all the batches and train the model over them.\n",
        "- We feed the model the images via `model(images)` and the output are the predictions of the model. \n",
        "- The predictions together with the target labels are used to compute the loss using the loss function we defined earlier.\n",
        "- Before we update our weights for the next round of training, we perform the following steps:\n",
        "    - we use the optimizer object to reset all the gradients for the variables it will update. This is a safe step and it doesn't overwrites the gradients the model accumulates while training (those are stored in a buffer [link text](https://pytorch.org/tutorials/beginner/pytorch_with_examples.html#pytorch-optim) via the `loss.backward() call)\n",
        "    - `loss.backward()` simply computes the gradient of the loss w.r.t to the model parameters\n",
        "    - `optimizer.step()` then ensures that the model parameters are updated\n",
        "\n",
        "- Then we gather and accumulate the loss and accuracy, which is what we will use to tell us if the model is learning properly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-WMQXIWBZ3U",
        "colab_type": "code",
        "outputId": "ade721d0-3423-4871-9044-36d68a782ce6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "## train the model\n",
        "for epoch in range(num_epochs):\n",
        "    train_running_loss = 0.0\n",
        "    train_acc = 0.0\n",
        "\n",
        "    ## commence training\n",
        "    model = model.train()\n",
        "\n",
        "    ## training step\n",
        "    for i, (images, labels) in enumerate(trainloader):\n",
        "        \n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        ## forward + backprop + loss\n",
        "        predictions = model(images)\n",
        "        loss = criterion(predictions, labels)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "\n",
        "        ## update model params\n",
        "        optimizer.step()\n",
        "\n",
        "        train_running_loss += loss.detach().item()\n",
        "        train_acc += get_accuracy(predictions, labels, BATCH_SIZE)\n",
        "    \n",
        "    model.eval()\n",
        "    print('Epoch: %d | Loss: %.4f | Train Accuracy: %.2f' \\\n",
        "          %(epoch, train_running_loss / i, train_acc/i)) "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0 | Loss: 1.6167 | Train Accuracy: 86.02\n",
            "Epoch: 1 | Loss: 1.5299 | Train Accuracy: 93.26\n",
            "Epoch: 2 | Loss: 1.5143 | Train Accuracy: 94.69\n",
            "Epoch: 3 | Loss: 1.5059 | Train Accuracy: 95.46\n",
            "Epoch: 4 | Loss: 1.5003 | Train Accuracy: 95.98\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxSFFoeDjQj_",
        "colab_type": "text"
      },
      "source": [
        "After all the training steps are over, we can clearly see that the loss keeps decreasing while the training accuracy of the model keeps rising, which is a good sign that the model is effectively learning to classify images.\n",
        "\n",
        "We can verify that by computing the accuracy on the testing dataset to see how well the model performs on the image classificaiton task. As you can see below, our basic CNN model is performing very well on the MNIST classification task."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWU90wx9jpDS",
        "colab_type": "code",
        "outputId": "d180ed33-92b2-4823-b74f-1639470abbfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_acc = 0.0\n",
        "for i, (images, labels) in enumerate(testloader, 0):\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "    outputs = model(images)\n",
        "    test_acc += get_accuracy(outputs, labels, BATCH_SIZE)\n",
        "        \n",
        "print('Test Accuracy: %.2f'%( test_acc/i))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy: 96.32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozX4V1GhPLyr",
        "colab_type": "text"
      },
      "source": [
        "## Final Words\n",
        "\n",
        "Congratulation! You have made it to the end of this tutorial. This is a really long tutorial that aims to give an very basic introduction to the fundamentals of image classification using neural networks and PyTorch.\n",
        "\n",
        "*This tutorial was heavily inspired by this [TensorFlow tutorial.](https://www.tensorflow.org/beta/tutorials/quickstart/beginner) We thank the authors of the corresponding reference for their valuable work.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-o4w9vpPHZz",
        "colab_type": "text"
      },
      "source": [
        "## References\n",
        "- [PyTorch 1.2 Quickstart with Google Colab](https://medium.com/dair-ai/pytorch-1-2-quickstart-with-google-colab-6690a30c38d)\n",
        "- [Get started with TensorFlow 2.0 for beginners](https://www.tensorflow.org/beta/tutorials/quickstart/beginner)\n",
        "- [PyTorch Data Loading Tutorial](https://pytorch.org/tutorials/beginner/data_loading_tutorial.html)\n",
        "-[ Neural Networks with PyTorch](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py)"
      ]
    }
  ]
}