07_pytorch_experiment_tracking.ipynb

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    "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/07_pytorch_experiment_tracking.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "[View Source Code](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/07_pytorch_experiment_tracking.ipynb) | [View Slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/07_pytorch_experiment_tracking.pdf) "
   ]
  },
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    "# 07. PyTorch Experiment Tracking\n",
    "\n",
    "> **Note:** This notebook uses `torchvision`'s new [multi-weight support API (available in `torchvision` v0.13+)](https://pytorch.org/blog/introducing-torchvision-new-multi-weight-support-api/). \n",
    "\n",
    "We've trained a fair few models now on the journey to making FoodVision Mini (an image classification model to classify images of pizza, steak or sushi).\n",
    "\n",
    "And so far we've kept track of them via Python dictionaries.\n",
    "\n",
    "Or just comparing them by the metric print outs during training.\n",
    "\n",
    "What if you wanted to run a dozen (or more) different models at once?\n",
    "\n",
    "Surely there's a better way...\n",
    "\n",
    "There is.\n",
    "\n",
    "**Experiment tracking.**\n",
    "\n",
    "And since experiment tracking is so important and integral to machine learning, you can consider this notebook your first milestone project.\n",
    "\n",
    "So welcome to Milestone Project 1: FoodVision Mini Experiment Tracking.\n",
    "\n",
    "We're going to answer the question: **how do I track my machine learning experiments?**"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "## What is experiment tracking?\n",
    "\n",
    "Machine learning and deep learning are very experimental.\n",
    "\n",
    "You have to put on your artist's beret/chef's hat to cook up lots of different models.\n",
    "\n",
    "And you have to put on your scientist's coat to track the results of various combinations of data, model architectures and training regimes.\n",
    "\n",
    "That's where **experiment tracking** comes in.\n",
    "\n",
    "If you're running lots of different experiments, **experiment tracking helps you figure out what works and what doesn't**."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why track experiments?\n",
    "\n",
    "If you're only running a handful of models (like we've done so far), it might be okay just to track their results in print outs and a few dictionaries.\n",
    "\n",
    "However, as the number of experiments you run starts to increase, this naive way of tracking could get out of hand.\n",
    "\n",
    "So if you're following the machine learning practitioner's motto of *experiment, experiment, experiment!*, you'll want a way to track them.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/07-experiment-tracking-can-get-out-of-hand.png\" alt=\"experiment tracking can get out of hand, many different experiments with different names\" width=900/>\n",
    "\n",
    "*After building a few models and tracking their results, you'll start to notice how quickly it can get out of hand.*"
   ]
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    "## Different ways to track machine learning experiments \n",
    "\n",
    "There are as many different ways to track machine learning experiments as there are experiments to run.\n",
    "\n",
    "This table covers a few.\n",
    "\n",
    "| **Method** | **Setup** | **Pros** | **Cons** | **Cost** |\n",
    "| ----- | ----- | ----- | ----- | ----- |\n",
    "| Python dictionaries, CSV files, print outs | None | Easy to setup, runs in pure Python | Hard to keep track of large numbers of experiments | Free |\n",
    "| [TensorBoard](https://www.tensorflow.org/tensorboard/get_started) | Minimal, install [`tensorboard`](https://pypi.org/project/tensorboard/) | Extensions built into PyTorch, widely recognized and used, easily scales. | User-experience not as nice as other options. | Free |\n",
    "| [Weights & Biases Experiment Tracking](https://wandb.ai/site/experiment-tracking) | Minimal, install [`wandb`](https://docs.wandb.ai/quickstart), make an account | Incredible user experience, make experiments public, tracks almost anything. | Requires external resource outside of PyTorch. | Free for personal use | \n",
    "| [MLFlow](https://mlflow.org/) | Minimal, install `mlflow` and start tracking | Fully open-source MLOps lifecycle management, many integrations. | Little bit harder to setup a remote tracking server than other services. | Free | \n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/07-different-places-to-track-experiments.png\" alt=\"various places to track machine learning experiments\" width=900/>\n",
    "\n",
    "*Various places and techniques you can use to track your machine learning experiments. **Note:** There are various other options similar to Weights & Biases and open-source options similar to MLflow but I've left them out for brevity. You can find more by searching \"machine learning experiment tracking\".*"
   ]
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    "## What we're going to cover\n",
    "\n",
    "We're going to be running several different modelling experiments with various levels of data, model size and training time to try and improve on FoodVision Mini.\n",
    "\n",
    "And due to its tight integration with PyTorch and widespread use, this notebook focuses on using TensorBoard to track our experiments.\n",
    "\n",
    "However, the principles we're going to cover are similar across all of the other tools for experiment tracking.\n",
    "\n",
    "| **Topic** | **Contents** |\n",
    "| ----- | ----- |\n",
    "| **0. Getting setup** | We've written a fair bit of useful code over the past few sections, let's download it and make sure we can use it again. |\n",
    "| **1. Get data** | Let's get the pizza, steak and sushi image classification dataset we've been using to try and improve our FoodVision Mini model's results. |\n",
    "| **2. Create Datasets and DataLoaders** | We'll use the `data_setup.py` script we wrote in chapter 05. PyTorch Going Modular to setup our DataLoaders. |\n",
    "| **3. Get and customise a pretrained model** | Just like the last section, 06. PyTorch Transfer Learning we'll download a pretrained model from `torchvision.models` and customise it to our own problem. | \n",
    "| **4. Train model and track results** | Let's see what it's like to train and track the training results of a single model using TensorBoard. |\n",
    "| **5. View our model's results in TensorBoard** | Previously we visualized our model's loss curves with a helper function, now let's see what they look like in TensorBoard. |\n",
    "| **6. Creating a helper function to track experiments** | If we're going to be adhering to the machine learner practitioner's motto of *experiment, experiment, experiment!*, we best create a function that will help us save our modelling experiment results. |\n",
    "| **7. Setting up a series of modelling experiments** | Instead of running experiments one by one, how about we write some code to run several experiments at once, with different models, different amounts of data and different training times. | \n",
    "| **8. View modelling experiments in TensorBoard** | By this stage we'll have run eight modelling experiments in one go, a fair bit to keep track of, let's see what their results look like in TensorBoard. | \n",
    "| **9. Load in the best model and make predictions with it** | The point of experiment tracking is to figure out which model performs the best, let's load in the best performing model and make some predictions with it to *visualize, visualize, visualize!*. |"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Where can you get help?\n",
    "\n",
    "All of the materials for this course [are available on GitHub](https://github.com/mrdbourke/pytorch-deep-learning).\n",
    "\n",
    "If you run into trouble, you can ask a question on the course [GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions).\n",
    "\n",
    "And of course, there's the [PyTorch documentation](https://pytorch.org/docs/stable/index.html) and [PyTorch developer forums](https://discuss.pytorch.org/), a very helpful place for all things PyTorch. "
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "## 0. Getting setup \n",
    "\n",
    "Let's start by downloading all of the modules we'll need for this section.\n",
    "\n",
    "To save us writing extra code, we're going to be leveraging some of the Python scripts (such as `data_setup.py` and `engine.py`) we created in section, [05. PyTorch Going Modular](https://www.learnpytorch.io/05_pytorch_going_modular/).\n",
    "\n",
    "Specifically, we're going to download the [`going_modular`](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/going_modular) directory from the `pytorch-deep-learning` repository (if we don't already have it).\n",
    "\n",
    "We'll also get the [`torchinfo`](https://github.com/TylerYep/torchinfo) package if it's not available. \n",
    "\n",
    "`torchinfo` will help later on to give us visual summaries of our model(s).\n",
    "\n",
    "And since we're using a newer version of the `torchvision` package (v0.13 as of June 2022), we'll make sure we've got the latest versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 1.13.0.dev20220620+cu113\n",
      "torchvision version: 0.14.0.dev20220620+cu113\n"
     ]
    }
   ],
   "source": [
    "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
    "try:\n",
    "    import torch\n",
    "    import torchvision\n",
    "    assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
    "    assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
    "    print(f\"torch version: {torch.__version__}\")\n",
    "    print(f\"torchvision version: {torchvision.__version__}\")\n",
    "except:\n",
    "    print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
    "    !pip3 install -U torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113\n",
    "    import torch\n",
    "    import torchvision\n",
    "    print(f\"torch version: {torch.__version__}\")\n",
    "    print(f\"torchvision version: {torchvision.__version__}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** If you're using Google Colab, you may have to restart your runtime after running the above cell. After restarting, you can run the cell again and verify you've got the right versions of `torch` (0.12+) and `torchvision` (0.13+)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continue with regular imports\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "from torchvision import transforms\n",
    "\n",
    "# Try to get torchinfo, install it if it doesn't work\n",
    "try:\n",
    "    from torchinfo import summary\n",
    "except:\n",
    "    print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
    "    !pip install -q torchinfo\n",
    "    from torchinfo import summary\n",
    "\n",
    "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
    "try:\n",
    "    from going_modular.going_modular import data_setup, engine\n",
    "except:\n",
    "    # Get the going_modular scripts\n",
    "    print(\"[INFO] Couldn't find going_modular scripts... downloading them from GitHub.\")\n",
    "    !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
    "    !mv pytorch-deep-learning/going_modular .\n",
    "    !rm -rf pytorch-deep-learning\n",
    "    from going_modular.going_modular import data_setup, engine"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's setup device agnostic code.\n",
    "\n",
    "> **Note:** If you're using Google Colab, and you don't have a GPU turned on yet, it's now time to turn one on via `Runtime -> Change runtime type -> Hardware accelerator -> GPU`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a helper function to set seeds\n",
    "\n",
    "Since we've been setting random seeds a whole bunch throughout previous sections, how about we functionize it?\n",
    "\n",
    "Let's create a function to \"set the seeds\" called `set_seeds()`.\n",
    "\n",
    "> **Note:** Recalling a [random seed](https://en.wikipedia.org/wiki/Random_seed) is a way of flavouring the randomness generated by a computer. They aren't necessary to always set when running machine learning code, however, they help ensure there's an element of reproducibility (the numbers I get with my code are similar to the numbers you get with your code). Outside of an educational or experimental setting, random seeds generally aren't required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set seeds\n",
    "def set_seeds(seed: int=42):\n",
    "    \"\"\"Sets random sets for torch operations.\n",
    "\n",
    "    Args:\n",
    "        seed (int, optional): Random seed to set. Defaults to 42.\n",
    "    \"\"\"\n",
    "    # Set the seed for general torch operations\n",
    "    torch.manual_seed(seed)\n",
    "    # Set the seed for CUDA torch operations (ones that happen on the GPU)\n",
    "    torch.cuda.manual_seed(seed)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Get data\n",
    "\n",
    "As always, before we can run machine learning experiments, we'll need a dataset.\n",
    "\n",
    "We're going to continue trying to improve upon the results we've been getting on FoodVision Mini.\n",
    "\n",
    "In the previous section, [06. PyTorch Transfer Learning](https://www.learnpytorch.io/06_pytorch_transfer_learning/), we saw how powerful using a pretrained model and transfer learning could be when classifying images of pizza, steak and sushi.\n",
    "\n",
    "So how about we run some experiments and try to further improve our results?\n",
    "\n",
    "To do so, we'll use similar code to the previous section to download the [`pizza_steak_sushi.zip`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/data/pizza_steak_sushi.zip) (if the data doesn't already exist) except this time it's been functionalised.\n",
    "\n",
    "This will allow us to use it again later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('data/pizza_steak_sushi')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import zipfile\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import requests\n",
    "\n",
    "def download_data(source: str, \n",
    "                  destination: str,\n",
    "                  remove_source: bool = True) -> Path:\n",
    "    \"\"\"Downloads a zipped dataset from source and unzips to destination.\n",
    "\n",
    "    Args:\n",
    "        source (str): A link to a zipped file containing data.\n",
    "        destination (str): A target directory to unzip data to.\n",
    "        remove_source (bool): Whether to remove the source after downloading and extracting.\n",
    "    \n",
    "    Returns:\n",
    "        pathlib.Path to downloaded data.\n",
    "    \n",
    "    Example usage:\n",
    "        download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
    "                      destination=\"pizza_steak_sushi\")\n",
    "    \"\"\"\n",
    "    # Setup path to data folder\n",
    "    data_path = Path(\"data/\")\n",
    "    image_path = data_path / destination\n",
    "\n",
    "    # If the image folder doesn't exist, download it and prepare it... \n",
    "    if image_path.is_dir():\n",
    "        print(f\"[INFO] {image_path} directory exists, skipping download.\")\n",
    "    else:\n",
    "        print(f\"[INFO] Did not find {image_path} directory, creating one...\")\n",
    "        image_path.mkdir(parents=True, exist_ok=True)\n",
    "        \n",
    "        # Download pizza, steak, sushi data\n",
    "        target_file = Path(source).name\n",
    "        with open(data_path / target_file, \"wb\") as f:\n",
    "            request = requests.get(source)\n",
    "            print(f\"[INFO] Downloading {target_file} from {source}...\")\n",
    "            f.write(request.content)\n",
    "\n",
    "        # Unzip pizza, steak, sushi data\n",
    "        with zipfile.ZipFile(data_path / target_file, \"r\") as zip_ref:\n",
    "            print(f\"[INFO] Unzipping {target_file} data...\") \n",
    "            zip_ref.extractall(image_path)\n",
    "\n",
    "        # Remove .zip file\n",
    "        if remove_source:\n",
    "            os.remove(data_path / target_file)\n",
    "    \n",
    "    return image_path\n",
    "\n",
    "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
    "                           destination=\"pizza_steak_sushi\")\n",
    "image_path"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! Looks like we've got our pizza, steak and sushi images in standard image classification format ready to go."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create Datasets and DataLoaders\n",
    "\n",
    "Now we've got some data, let's turn it into PyTorch DataLoaders.\n",
    "\n",
    "We can do so using the `create_dataloaders()` function we created in [05. PyTorch Going Modular part 2](https://www.learnpytorch.io/05_pytorch_going_modular/#2-create-datasets-and-dataloaders-data_setuppy).\n",
    "\n",
    "And since we'll be using transfer learning and specifically pretrained models from [`torchvision.models`](https://pytorch.org/vision/stable/models.html), we'll create a transform to prepare our images correctly.\n",
    "\n",
    "To transform our images into tensors, we can use:\n",
    "1. Manually created transforms using `torchvision.transforms`.\n",
    "2. Automatically created transforms using `torchvision.models.MODEL_NAME.MODEL_WEIGHTS.DEFAULT.transforms()`.\n",
    "    * Where `MODEL_NAME` is a specific `torchvision.models` architecture, `MODEL_WEIGHTS` is a specific set of pretrained weights and `DEFAULT` means the \"best available weights\".\n",
    "    \n",
    "We saw an example of each of these in [06. PyTorch Transfer Learning section 2](https://www.learnpytorch.io/06_pytorch_transfer_learning/#2-create-datasets-and-dataloaders).\n",
    "\n",
    "Let's see first an example of manually creating a `torchvision.transforms` pipeline (creating a transforms pipeline this way gives the most customization but can potentially result in performance degradation if the transforms don't match the pretrained model).\n",
    "\n",
    "The main manual transformation we need to be sure of is that all of our images are normalized in ImageNet format (this is because pretrained `torchvision.models` are all pretrained on [ImageNet](https://www.image-net.org/)).\n",
    "\n",
    "We can do this with:\n",
    "\n",
    "```python\n",
    "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Create DataLoaders using manually created transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manually created transforms: Compose(\n",
      "    Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
      "    ToTensor()\n",
      "    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<torch.utils.data.dataloader.DataLoader at 0x7febf1d218e0>,\n",
       " <torch.utils.data.dataloader.DataLoader at 0x7febf1d216a0>,\n",
       " ['pizza', 'steak', 'sushi'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup directories\n",
    "train_dir = image_path / \"train\"\n",
    "test_dir = image_path / \"test\"\n",
    "\n",
    "# Setup ImageNet normalization levels (turns all images into similar distribution as ImageNet)\n",
    "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])\n",
    "\n",
    "# Create transform pipeline manually\n",
    "manual_transforms = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    normalize\n",
    "])           \n",
    "print(f\"Manually created transforms: {manual_transforms}\")\n",
    "\n",
    "# Create data loaders\n",
    "train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(\n",
    "    train_dir=train_dir,\n",
    "    test_dir=test_dir,\n",
    "    transform=manual_transforms, # use manually created transforms\n",
    "    batch_size=32\n",
    ")\n",
    "\n",
    "train_dataloader, test_dataloader, class_names"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Create DataLoaders using automatically created transforms\n",
    "\n",
    "Data transformed and DataLoaders created!\n",
    "\n",
    "Let's now see what the same transformation pipeline looks like but this time by using automatic transforms.\n",
    "\n",
    "We can do this by first instantiating a set of pretrained weights (for example `weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT`)  we'd like to use and calling the `transforms()` method on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created transforms: ImageClassification(\n",
      "    crop_size=[224]\n",
      "    resize_size=[256]\n",
      "    mean=[0.485, 0.456, 0.406]\n",
      "    std=[0.229, 0.224, 0.225]\n",
      "    interpolation=InterpolationMode.BICUBIC\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<torch.utils.data.dataloader.DataLoader at 0x7febf1d213a0>,\n",
       " <torch.utils.data.dataloader.DataLoader at 0x7febf1d21490>,\n",
       " ['pizza', 'steak', 'sushi'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup dirs\n",
    "train_dir = image_path / \"train\"\n",
    "test_dir = image_path / \"test\"\n",
    "\n",
    "# Setup pretrained weights (plenty of these available in torchvision.models)\n",
    "weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT\n",
    "\n",
    "# Get transforms from weights (these are the transforms that were used to obtain the weights)\n",
    "automatic_transforms = weights.transforms() \n",
    "print(f\"Automatically created transforms: {automatic_transforms}\")\n",
    "\n",
    "# Create data loaders\n",
    "train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(\n",
    "    train_dir=train_dir,\n",
    "    test_dir=test_dir,\n",
    "    transform=automatic_transforms, # use automatic created transforms\n",
    "    batch_size=32\n",
    ")\n",
    "\n",
    "train_dataloader, test_dataloader, class_names"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Getting a pretrained model, freezing the base layers and changing the classifier head\n",
    "\n",
    "Before we run and track multiple modelling experiments, let's see what it's like to run and track a single one.\n",
    "\n",
    "And since our data is ready, the next thing we'll need is a model.\n",
    "\n",
    "Let's download the pretrained weights for a `torchvision.models.efficientnet_b0()` model and prepare it for use with our own data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: This is how a pretrained model would be created in torchvision > 0.13, it will be deprecated in future versions.\n",
    "# model = torchvision.models.efficientnet_b0(pretrained=True).to(device) # OLD \n",
    "\n",
    "# Download the pretrained weights for EfficientNet_B0\n",
    "weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT # NEW in torchvision 0.13, \"DEFAULT\" means \"best weights available\"\n",
    "\n",
    "# Setup the model with the pretrained weights and send it to the target device\n",
    "model = torchvision.models.efficientnet_b0(weights=weights).to(device)\n",
    "\n",
    "# View the output of the model\n",
    "# model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful!\n",
    "\n",
    "Now we've got a pretrained model let's turn it into a feature extractor model.\n",
    "\n",
    "In essence, we'll freeze the base layers of the model (we'll use these to extract features from our input images) and we'll change the classifier head (output layer) to suit the number of classes we're working with (we've got 3 classes: pizza, steak, sushi).\n",
    "\n",
    "> **Note:** The idea of creating a feature extractor model (what we're doing here) was covered in more depth in [06. PyTorch Transfer Learning section 3.2: Setting up a pretrained model](https://www.learnpytorch.io/06_pytorch_transfer_learning/#32-setting-up-a-pretrained-model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Freeze all base layers by setting requires_grad attribute to False\n",
    "for param in model.features.parameters():\n",
    "    param.requires_grad = False\n",
    "    \n",
    "# Since we're creating a new layer with random weights (torch.nn.Linear), \n",
    "# let's set the seeds\n",
    "set_seeds() \n",
    "\n",
    "# Update the classifier head to suit our problem\n",
    "model.classifier = torch.nn.Sequential(\n",
    "    nn.Dropout(p=0.2, inplace=True),\n",
    "    nn.Linear(in_features=1280, \n",
    "              out_features=len(class_names),\n",
    "              bias=True).to(device))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base layers frozen, classifier head changed, let's get a summary of our model with `torchinfo.summary()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "# # Get a summary of the model (uncomment for full output)\n",
    "# summary(model, \n",
    "#         input_size=(32, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\" (batch_size, color_channels, height, width)\n",
    "#         verbose=0,\n",
    "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "#         col_width=20,\n",
    "#         row_settings=[\"var_names\"]\n",
    "# )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/07-output-of-torchinfo-summary.png\" alt=\"output of torchinfo.summary() when passed our model when base layers are frozen and classifier head is updated\" width=900/>\n",
    "\n",
    "*Output of `torchinfo.summary()` with our feature extractor EffNetB0 model, notice how the base layers are frozen (not trainable) and the output layers are customized to our own problem.*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Train model and track results\n",
    "\n",
    "Model ready to go!\n",
    "\n",
    "Let's get ready to train it by creating a loss function and an optimizer.\n",
    "\n",
    "Since we're working with multiple classes, we'll use [`torch.nn.CrossEntropyLoss()`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) as the loss function.\n",
    "\n",
    "And we'll stick with [`torch.optim.Adam()`](https://pytorch.org/docs/stable/optim.html) with learning rate of `0.001` for the optimizer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define loss and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjust `train()` function to track results with `SummaryWriter()`\n",
    "\n",
    "Beautiful!\n",
    "\n",
    "All of the pieces of our training code are starting to come together.\n",
    "\n",
    "Let's now add the final piece to track our experiments.\n",
    "\n",
    "Previously, we've tracked our modelling experiments using multiple Python dictionaries (one for each model).\n",
    "\n",
    "But you can imagine this could get out of hand if we were running anything more than a few experiments.\n",
    "\n",
    "Not to worry, there's a better option!\n",
    "\n",
    "We can use PyTorch's [`torch.utils.tensorboard.SummaryWriter()`](https://pytorch.org/docs/stable/tensorboard.html) class to save various parts of our model's training progress to file.\n",
    "\n",
    "By default, the `SummaryWriter()` class saves various information about our model to a file set by the `log_dir` parameter. \n",
    "\n",
    "The default location for `log_dir` is under `runs/CURRENT_DATETIME_HOSTNAME`, where the `HOSTNAME` is the name of your computer.\n",
    "\n",
    "But of course, you can change where your experiments are tracked (the filename is as customisable as you'd like).\n",
    "\n",
    "The outputs of the `SummaryWriter()` are saved in [TensorBoard format](https://www.tensorflow.org/tensorboard/).\n",
    "\n",
    "TensorBoard is a part of the TensorFlow deep learning library and is an excellent way to visualize different parts of your model.\n",
    "\n",
    "To start tracking our modelling experiments, let's create a default `SummaryWriter()` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from torch.utils.tensorboard import SummaryWriter\n",
    "except:\n",
    "    print(\"[INFO] Couldn't find tensorboard... installing it.\")\n",
    "    !pip install -q tensorboard\n",
    "    from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "\n",
    "# Create a writer with all default settings\n",
    "writer = SummaryWriter()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to use the writer, we could write a new training loop or we could adjust the existing `train()` function we created in [05. PyTorch Going Modular section 4](https://www.learnpytorch.io/05_pytorch_going_modular/#4-creating-train_step-and-test_step-functions-and-train-to-combine-them).\n",
    "\n",
    "Let's take the latter option.\n",
    "\n",
    "We'll get the `train()` function from [`engine.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/engine.py) and adjust it to use `writer`.\n",
    "\n",
    "Specifically, we'll add the ability for our `train()` function to log our model's training and test loss and accuracy values.\n",
    "\n",
    "We can do this with [`writer.add_scalars(main_tag, tag_scalar_dict)`](https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_scalars), where:\n",
    "* `main_tag` (string) - the name for the scalars being tracked (e.g. \"Accuracy\")\n",
    "* `tag_scalar_dict` (dict) - a dictionary of the values being tracked (e.g. `{\"train_loss\": 0.3454}`)\n",
    "    * > **Note:** The method is called `add_scalars()` because our loss and accuracy values are generally scalars (single values).\n",
    "\n",
    "Once we've finished tracking values, we'll call `writer.close()` to tell the `writer` to stop looking for values to track.\n",
    "\n",
    "To start modifying `train()` we'll also import `train_step()` and `test_step()` from [`engine.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/engine.py).\n",
    "\n",
    "> **Note:** You can track information about your model almost anywhere in your code. But quite often experiments will be tracked *while* a model is training (inside a training/testing loop).\n",
    ">\n",
    "> The `torch.utils.tensorboard.SummaryWriter()` class also has many different methods to track different things about your model/data, such as [`add_graph()`](https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_graph) which tracks the computation graph of your model. For more options, [check the `SummaryWriter()` documentation](https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, List\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from going_modular.going_modular.engine import train_step, test_step\n",
    "\n",
    "# Import train() function from: \n",
    "# https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/engine.py\n",
    "def train(model: torch.nn.Module, \n",
    "          train_dataloader: torch.utils.data.DataLoader, \n",
    "          test_dataloader: torch.utils.data.DataLoader, \n",
    "          optimizer: torch.optim.Optimizer,\n",
    "          loss_fn: torch.nn.Module,\n",
    "          epochs: int,\n",
    "          device: torch.device) -> Dict[str, List]:\n",
    "    \"\"\"Trains and tests a PyTorch model.\n",
    "\n",
    "    Passes a target PyTorch models through train_step() and test_step()\n",
    "    functions for a number of epochs, training and testing the model\n",
    "    in the same epoch loop.\n",
    "\n",
    "    Calculates, prints and stores evaluation metrics throughout.\n",
    "\n",
    "    Args:\n",
    "      model: A PyTorch model to be trained and tested.\n",
    "      train_dataloader: A DataLoader instance for the model to be trained on.\n",
    "      test_dataloader: A DataLoader instance for the model to be tested on.\n",
    "      optimizer: A PyTorch optimizer to help minimize the loss function.\n",
    "      loss_fn: A PyTorch loss function to calculate loss on both datasets.\n",
    "      epochs: An integer indicating how many epochs to train for.\n",
    "      device: A target device to compute on (e.g. \"cuda\" or \"cpu\").\n",
    "      \n",
    "    Returns:\n",
    "      A dictionary of training and testing loss as well as training and\n",
    "      testing accuracy metrics. Each metric has a value in a list for \n",
    "      each epoch.\n",
    "      In the form: {train_loss: [...],\n",
    "                train_acc: [...],\n",
    "                test_loss: [...],\n",
    "                test_acc: [...]} \n",
    "      For example if training for epochs=2: \n",
    "              {train_loss: [2.0616, 1.0537],\n",
    "                train_acc: [0.3945, 0.3945],\n",
    "                test_loss: [1.2641, 1.5706],\n",
    "                test_acc: [0.3400, 0.2973]} \n",
    "    \"\"\"\n",
    "    # Create empty results dictionary\n",
    "    results = {\"train_loss\": [],\n",
    "               \"train_acc\": [],\n",
    "               \"test_loss\": [],\n",
    "               \"test_acc\": []\n",
    "    }\n",
    "\n",
    "    # Loop through training and testing steps for a number of epochs\n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        train_loss, train_acc = train_step(model=model,\n",
    "                                           dataloader=train_dataloader,\n",
    "                                           loss_fn=loss_fn,\n",
    "                                           optimizer=optimizer,\n",
    "                                           device=device)\n",
    "        test_loss, test_acc = test_step(model=model,\n",
    "                                        dataloader=test_dataloader,\n",
    "                                        loss_fn=loss_fn,\n",
    "                                        device=device)\n",
    "\n",
    "        # Print out what's happening\n",
    "        print(\n",
    "          f\"Epoch: {epoch+1} | \"\n",
    "          f\"train_loss: {train_loss:.4f} | \"\n",
    "          f\"train_acc: {train_acc:.4f} | \"\n",
    "          f\"test_loss: {test_loss:.4f} | \"\n",
    "          f\"test_acc: {test_acc:.4f}\"\n",
    "        )\n",
    "\n",
    "        # Update results dictionary\n",
    "        results[\"train_loss\"].append(train_loss)\n",
    "        results[\"train_acc\"].append(train_acc)\n",
    "        results[\"test_loss\"].append(test_loss)\n",
    "        results[\"test_acc\"].append(test_acc)\n",
    "\n",
    "        ### New: Experiment tracking ###\n",
    "        # Add loss results to SummaryWriter\n",
    "        writer.add_scalars(main_tag=\"Loss\", \n",
    "                           tag_scalar_dict={\"train_loss\": train_loss,\n",
    "                                            \"test_loss\": test_loss},\n",
    "                           global_step=epoch)\n",
    "\n",
    "        # Add accuracy results to SummaryWriter\n",
    "        writer.add_scalars(main_tag=\"Accuracy\", \n",
    "                           tag_scalar_dict={\"train_acc\": train_acc,\n",
    "                                            \"test_acc\": test_acc}, \n",
    "                           global_step=epoch)\n",
    "        \n",
    "        # Track the PyTorch model architecture\n",
    "        writer.add_graph(model=model, \n",
    "                         # Pass in an example input\n",
    "                         input_to_model=torch.randn(32, 3, 224, 224).to(device))\n",
    "    \n",
    "    # Close the writer\n",
    "    writer.close()\n",
    "    \n",
    "    ### End new ###\n",
    "\n",
    "    # Return the filled results at the end of the epochs\n",
    "    return results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Woohoo!\n",
    "\n",
    "Our `train()` function is now updated to use a `SummaryWriter()` instance to track our model's results.\n",
    "\n",
    "How about we try it out for 5 epochs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf70c256625142c283475bdf9af948a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 1.0924 | train_acc: 0.3984 | test_loss: 0.9133 | test_acc: 0.5398\n",
      "Epoch: 2 | train_loss: 0.8975 | train_acc: 0.6562 | test_loss: 0.7838 | test_acc: 0.8561\n",
      "Epoch: 3 | train_loss: 0.8037 | train_acc: 0.7461 | test_loss: 0.6723 | test_acc: 0.8864\n",
      "Epoch: 4 | train_loss: 0.6769 | train_acc: 0.8516 | test_loss: 0.6698 | test_acc: 0.8049\n",
      "Epoch: 5 | train_loss: 0.7065 | train_acc: 0.7188 | test_loss: 0.6746 | test_acc: 0.7737\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "# Note: Not using engine.train() since the original script isn't updated to use writer\n",
    "set_seeds()\n",
    "results = train(model=model,\n",
    "                train_dataloader=train_dataloader,\n",
    "                test_dataloader=test_dataloader,\n",
    "                optimizer=optimizer,\n",
    "                loss_fn=loss_fn,\n",
    "                epochs=5,\n",
    "                device=device)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** You might notice the results here are slightly different to what our model got in 06. PyTorch Transfer Learning. The difference comes from using the `engine.train()` and our modified `train()` function. Can you guess why? The [PyTorch documentation on randomness](https://pytorch.org/docs/stable/notes/randomness.html) may help more.\n",
    "\n",
    "Running the cell above we get similar outputs we got in [06. PyTorch Transfer Learning section 4: Train model](https://www.learnpytorch.io/06_pytorch_transfer_learning/#4-train-model) but the difference is that behind the scenes our `writer` instance has created a `runs/` directory storing our model's results.\n",
    "\n",
    "For example, the save location might look like:\n",
    "\n",
    "```\n",
    "runs/Jun21_00-46-03_daniels_macbook_pro\n",
    "```\n",
    "\n",
    "Where the [default format](https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter) is `runs/CURRENT_DATETIME_HOSTNAME`. \n",
    "\n",
    "We'll check these out in a second but just as a reminder, we were previously tracking our model's results in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_loss': [1.0923754647374153,\n",
       "  0.8974628075957298,\n",
       "  0.803724929690361,\n",
       "  0.6769256368279457,\n",
       "  0.7064960040152073],\n",
       " 'train_acc': [0.3984375, 0.65625, 0.74609375, 0.8515625, 0.71875],\n",
       " 'test_loss': [0.9132757981618246,\n",
       "  0.7837507526079813,\n",
       "  0.6722926497459412,\n",
       "  0.6698453426361084,\n",
       "  0.6746167540550232],\n",
       " 'test_acc': [0.5397727272727273,\n",
       "  0.8560606060606061,\n",
       "  0.8863636363636364,\n",
       "  0.8049242424242425,\n",
       "  0.7736742424242425]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the model results\n",
    "results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm, we could format this to be a nice plot but could you imagine keeping track of a bunch of these dictionaries?\n",
    "\n",
    "There has to be a better way..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. View our model's results in TensorBoard\n",
    "\n",
    "The `SummaryWriter()` class stores our model's results in a directory called `runs/` in TensorBoard format by default.\n",
    "\n",
    "TensorBoard is a visualization program created by the TensorFlow team to view and inspect information about models and data.\n",
    "\n",
    "You know what that means?\n",
    "\n",
    "It's time to follow the data visualizer's motto and *visualize, visualize, visualize!* \n",
    "\n",
    "You can view TensorBoard in a number of ways:\n",
    "\n",
    "| Code environment | How to view TensorBoard | Resource |\n",
    "| ----- | ----- | ----- |\n",
    "| VS Code (notebooks or Python scripts) | Press `SHIFT + CMD + P` to open the Command Palette and search for the command \"Python: Launch TensorBoard\". | [VS Code Guide on TensorBoard and PyTorch](https://code.visualstudio.com/docs/datascience/pytorch-support#_tensorboard-integration) |\n",
    "| Jupyter and Colab Notebooks | Make sure [TensorBoard is installed](https://pypi.org/project/tensorboard/), load it with `%load_ext tensorboard` and then view your results with `%tensorboard --logdir DIR_WITH_LOGS`. | [`torch.utils.tensorboard`](https://pytorch.org/docs/stable/tensorboard.html) and [Get started with TensorBoard](https://www.tensorflow.org/tensorboard/get_started) |\n",
    "\n",
    "Running the following code in a Google Colab or Jupyter Notebook will start an interactive TensorBoard session to view TensorBoard files in the `runs/` directory.\n",
    "\n",
    "```python\n",
    "%load_ext tensorboard # line magic to load TensorBoard\n",
    "%tensorboard --logdir runs # run TensorBoard session with the \"runs/\" directory\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example code to run in Jupyter or Google Colab Notebook (uncomment to try it out)\n",
    "# %load_ext tensorboard\n",
    "# %tensorboard --logdir runs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If all went correctly, you should see something like the following:\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/07-tensorboard-single-experiment.png\" alt=\"output of viewing a single experiment in tensorboard\" width=900/>\n",
    "\n",
    "*Viewing a single modelling experiment's results for accuracy and loss in TensorBoard.*\n",
    "\n",
    "> **Note:** For more information on running TensorBoard in notebooks or in other locations, see the following:\n",
    "> * [Using TensorBoard in Notebooks guide by TensorFlow](https://www.tensorflow.org/tensorboard/tensorboard_in_notebooks)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Create a helper function to build `SummaryWriter()` instances\n",
    "\n",
    "The `SummaryWriter()` class logs various information to a directory specified by the `log_dir` parameter.\n",
    "\n",
    "How about we make a helper function to create a custom directory per experiment?\n",
    "\n",
    "In essence, each experiment gets its own logs directory.\n",
    "\n",
    "For example, say we'd like to track things like:\n",
    "* **Experiment date/timestamp** - when did the experiment take place?\n",
    "* **Experiment name** - is there something we'd like to call the experiment?\n",
    "* **Model name** - what model was used?\n",
    "* **Extra** - should anything else be tracked?\n",
    "\n",
    "You could track almost anything here and be as creative as you want but these should be enough to start.\n",
    "\n",
    "Let's create a helper function called `create_writer()` that produces a `SummaryWriter()` instance tracking to a custom `log_dir`.\n",
    "\n",
    "Ideally, we'd like the `log_dir` to be something like: \n",
    "\n",
    "`runs/YYYY-MM-DD/experiment_name/model_name/extra` \n",
    "\n",
    "Where `YYYY-MM-DD` is the date the experiment was run (you could add the time if you wanted to as well)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_writer(experiment_name: str, \n",
    "                  model_name: str, \n",
    "                  extra: str=None) -> torch.utils.tensorboard.writer.SummaryWriter():\n",
    "    \"\"\"Creates a torch.utils.tensorboard.writer.SummaryWriter() instance saving to a specific log_dir.\n",
    "\n",
    "    log_dir is a combination of runs/timestamp/experiment_name/model_name/extra.\n",
    "\n",
    "    Where timestamp is the current date in YYYY-MM-DD format.\n",
    "\n",
    "    Args:\n",
    "        experiment_name (str): Name of experiment.\n",
    "        model_name (str): Name of model.\n",
    "        extra (str, optional): Anything extra to add to the directory. Defaults to None.\n",
    "\n",
    "    Returns:\n",
    "        torch.utils.tensorboard.writer.SummaryWriter(): Instance of a writer saving to log_dir.\n",
    "\n",
    "    Example usage:\n",
    "        # Create a writer saving to \"runs/2022-06-04/data_10_percent/effnetb2/5_epochs/\"\n",
    "        writer = create_writer(experiment_name=\"data_10_percent\",\n",
    "                               model_name=\"effnetb2\",\n",
    "                               extra=\"5_epochs\")\n",
    "        # The above is the same as:\n",
    "        writer = SummaryWriter(log_dir=\"runs/2022-06-04/data_10_percent/effnetb2/5_epochs/\")\n",
    "    \"\"\"\n",
    "    from datetime import datetime\n",
    "    import os\n",
    "\n",
    "    # Get timestamp of current date (all experiments on certain day live in same folder)\n",
    "    timestamp = datetime.now().strftime(\"%Y-%m-%d\") # returns current date in YYYY-MM-DD format\n",
    "\n",
    "    if extra:\n",
    "        # Create log directory path\n",
    "        log_dir = os.path.join(\"runs\", timestamp, experiment_name, model_name, extra)\n",
    "    else:\n",
    "        log_dir = os.path.join(\"runs\", timestamp, experiment_name, model_name)\n",
    "        \n",
    "    print(f\"[INFO] Created SummaryWriter, saving to: {log_dir}...\")\n",
    "    return SummaryWriter(log_dir=log_dir)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beautiful!\n",
    "\n",
    "Now we've got a `create_writer()` function, let's try it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_10_percent/effnetb0/5_epochs...\n"
     ]
    }
   ],
   "source": [
    "# Create an example writer\n",
    "example_writer = create_writer(experiment_name=\"data_10_percent\",\n",
    "                               model_name=\"effnetb0\",\n",
    "                               extra=\"5_epochs\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking good, now we've got a way to log and trace back our various experiments."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Update the `train()` function to include a `writer` parameter\n",
    "\n",
    "Our `create_writer()` function works fantastic.\n",
    "\n",
    "How about we give our `train()` function the ability to take in a `writer` parameter so we actively update the `SummaryWriter()` instance we're using each time we call `train()`.\n",
    "\n",
    "For example, say we're running a series of experiments, calling `train()` multiple times for multiple different models, it would be good if each experiment used a different `writer`.\n",
    "\n",
    "One `writer` per experiment = one logs directory per experiment.\n",
    "\n",
    "To adjust the `train()` function we'll add a `writer` parameter to the function and then we'll add some code to see if there's a `writer` and if so, we'll track our information there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, List\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "# Add writer parameter to train()\n",
    "def train(model: torch.nn.Module, \n",
    "          train_dataloader: torch.utils.data.DataLoader, \n",
    "          test_dataloader: torch.utils.data.DataLoader, \n",
    "          optimizer: torch.optim.Optimizer,\n",
    "          loss_fn: torch.nn.Module,\n",
    "          epochs: int,\n",
    "          device: torch.device, \n",
    "          writer: torch.utils.tensorboard.writer.SummaryWriter # new parameter to take in a writer\n",
    "          ) -> Dict[str, List]:\n",
    "    \"\"\"Trains and tests a PyTorch model.\n",
    "\n",
    "    Passes a target PyTorch models through train_step() and test_step()\n",
    "    functions for a number of epochs, training and testing the model\n",
    "    in the same epoch loop.\n",
    "\n",
    "    Calculates, prints and stores evaluation metrics throughout.\n",
    "\n",
    "    Stores metrics to specified writer log_dir if present.\n",
    "\n",
    "    Args:\n",
    "      model: A PyTorch model to be trained and tested.\n",
    "      train_dataloader: A DataLoader instance for the model to be trained on.\n",
    "      test_dataloader: A DataLoader instance for the model to be tested on.\n",
    "      optimizer: A PyTorch optimizer to help minimize the loss function.\n",
    "      loss_fn: A PyTorch loss function to calculate loss on both datasets.\n",
    "      epochs: An integer indicating how many epochs to train for.\n",
    "      device: A target device to compute on (e.g. \"cuda\" or \"cpu\").\n",
    "      writer: A SummaryWriter() instance to log model results to.\n",
    "\n",
    "    Returns:\n",
    "      A dictionary of training and testing loss as well as training and\n",
    "      testing accuracy metrics. Each metric has a value in a list for \n",
    "      each epoch.\n",
    "      In the form: {train_loss: [...],\n",
    "                train_acc: [...],\n",
    "                test_loss: [...],\n",
    "                test_acc: [...]} \n",
    "      For example if training for epochs=2: \n",
    "              {train_loss: [2.0616, 1.0537],\n",
    "                train_acc: [0.3945, 0.3945],\n",
    "                test_loss: [1.2641, 1.5706],\n",
    "                test_acc: [0.3400, 0.2973]} \n",
    "    \"\"\"\n",
    "    # Create empty results dictionary\n",
    "    results = {\"train_loss\": [],\n",
    "               \"train_acc\": [],\n",
    "               \"test_loss\": [],\n",
    "               \"test_acc\": []\n",
    "    }\n",
    "\n",
    "    # Loop through training and testing steps for a number of epochs\n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        train_loss, train_acc = train_step(model=model,\n",
    "                                          dataloader=train_dataloader,\n",
    "                                          loss_fn=loss_fn,\n",
    "                                          optimizer=optimizer,\n",
    "                                          device=device)\n",
    "        test_loss, test_acc = test_step(model=model,\n",
    "          dataloader=test_dataloader,\n",
    "          loss_fn=loss_fn,\n",
    "          device=device)\n",
    "\n",
    "        # Print out what's happening\n",
    "        print(\n",
    "          f\"Epoch: {epoch+1} | \"\n",
    "          f\"train_loss: {train_loss:.4f} | \"\n",
    "          f\"train_acc: {train_acc:.4f} | \"\n",
    "          f\"test_loss: {test_loss:.4f} | \"\n",
    "          f\"test_acc: {test_acc:.4f}\"\n",
    "        )\n",
    "\n",
    "        # Update results dictionary\n",
    "        results[\"train_loss\"].append(train_loss)\n",
    "        results[\"train_acc\"].append(train_acc)\n",
    "        results[\"test_loss\"].append(test_loss)\n",
    "        results[\"test_acc\"].append(test_acc)\n",
    "\n",
    "\n",
    "        ### New: Use the writer parameter to track experiments ###\n",
    "        # See if there's a writer, if so, log to it\n",
    "        if writer:\n",
    "            # Add results to SummaryWriter\n",
    "            writer.add_scalars(main_tag=\"Loss\", \n",
    "                               tag_scalar_dict={\"train_loss\": train_loss,\n",
    "                                                \"test_loss\": test_loss},\n",
    "                               global_step=epoch)\n",
    "            writer.add_scalars(main_tag=\"Accuracy\", \n",
    "                               tag_scalar_dict={\"train_acc\": train_acc,\n",
    "                                                \"test_acc\": test_acc}, \n",
    "                               global_step=epoch)\n",
    "\n",
    "            # Close the writer\n",
    "            writer.close()\n",
    "        else:\n",
    "            pass\n",
    "    ### End new ###\n",
    "\n",
    "    # Return the filled results at the end of the epochs\n",
    "    return results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Setting up a series of modelling experiments\n",
    "\n",
    "It's to step things up a notch.\n",
    "\n",
    "Previously we've been running various experiments and inspecting the results one by one.\n",
    "\n",
    "But what if we could run multiple experiments and then inspect the results all together?\n",
    "\n",
    "You in?\n",
    "\n",
    "C'mon, let's go."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1 What kind of experiments should you run?\n",
    "\n",
    "That's the million dollar question in machine learning.\n",
    "\n",
    "Because there's really no limit to the experiments you can run.\n",
    "\n",
    "Such a freedom is why machine learning is so exciting and terrifying at the same time.\n",
    "\n",
    "This is where you'll have to put on your scientist coat and remember the machine learning practitioner's motto: *experiment, experiment, experiment!*\n",
    "\n",
    "Every hyperparameter stands as a starting point for a different experiment: \n",
    "* Change the number of **epochs**.\n",
    "* Change the number of **layers/hidden units**.\n",
    "* Change the amount of **data**.\n",
    "* Change the **learning rate**.\n",
    "* Try different kinds of **data augmentation**.\n",
    "* Choose a different **model architecture**. \n",
    "\n",
    "With practice and running many different experiments, you'll start to build an intuition of what *might* help your model.\n",
    "\n",
    "I say *might* on purpose because there's no guarantee.\n",
    "\n",
    "But generally, in light of [*The Bitter Lesson*](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) (I've mentioned this twice now because it's an important essay in the world of AI), generally the bigger your model (more learnable parameters) and the more data you have (more opportunities to learn), the better the performance.\n",
    "\n",
    "However, when you're first approaching a machine learning problem: start small and if something works, scale it up.\n",
    "\n",
    "Your first batch of experiments should take no longer than a few seconds to a few minutes to run.\n",
    "\n",
    "The quicker you can experiment, the faster you can work out what *doesn't* work, in turn, the faster you can work out what *does* work.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 7.2 What experiments are we going to run?\n",
    "\n",
    "Our goal is to improve the model powering FoodVision Mini without it getting too big.\n",
    "\n",
    "In essence, our ideal model achieves a high level of test set accuracy (90%+) but doesn't take too long to train/perform inference (make predictions).\n",
    "\n",
    "We've got plenty of options but how about we keep things simple?\n",
    "\n",
    "Let's try a combination of:\n",
    "1. A different amount of data (10% of Pizza, Steak, Sushi vs. 20%)\n",
    "2. A different model ([`torchvision.models.efficientnet_b0`](https://pytorch.org/vision/stable/generated/torchvision.models.efficientnet_b0.html#torchvision.models.efficientnet_b0) vs. [`torchvision.models.efficientnet_b2`](https://pytorch.org/vision/stable/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2))\n",
    "3. A different training time (5 epochs vs. 10 epochs)\n",
    "\n",
    "Breaking these down we get: \n",
    "\n",
    "| Experiment number | Training Dataset | Model (pretrained on ImageNet) | Number of epochs |\n",
    "| ----- | ----- | ----- | ----- |\n",
    "| 1 | Pizza, Steak, Sushi 10% percent | EfficientNetB0 | 5 |\n",
    "| 2 | Pizza, Steak, Sushi 10% percent | EfficientNetB2 | 5 | \n",
    "| 3 | Pizza, Steak, Sushi 10% percent | EfficientNetB0 | 10 | \n",
    "| 4 | Pizza, Steak, Sushi 10% percent | EfficientNetB2 | 10 |\n",
    "| 5 | Pizza, Steak, Sushi 20% percent | EfficientNetB0 | 5 |\n",
    "| 6 | Pizza, Steak, Sushi 20% percent | EfficientNetB2 | 5 |\n",
    "| 7 | Pizza, Steak, Sushi 20% percent | EfficientNetB0 | 10 |\n",
    "| 8 | Pizza, Steak, Sushi 20% percent | EfficientNetB2 | 10 |\n",
    "\n",
    "Notice how we're slowly scaling things up. \n",
    "\n",
    "With each experiment we slowly increase the amount of data, the model size and the length of training.\n",
    "\n",
    "By the end, experiment 8 will be using double the data, double the model size and double the length of training compared to experiment 1.\n",
    "\n",
    "> **Note:** I want to be clear that there truly is no limit to amount of experiments you can run. What we've designed here is only a very small subset of options. However, you can't test *everything* so best to try a few things to begin with and then follow the ones which work the best.\n",
    ">\n",
    "> And as a reminder, the datasets we're using are a subset of the [Food101 dataset](https://pytorch.org/vision/stable/generated/torchvision.datasets.Food101.html#torchvision.datasets.Food101) (3 classes, pizza, steak, suhsi, instead of 101) and 10% and 20% of the images rather than 100%. If our experiments work, we could start to run more on more data (though this will take longer to compute). You can see how the datasets were created via the [`04_custom_data_creation.ipynb` notebook](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/04_custom_data_creation.ipynb). \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3 Download different datasets\n",
    "\n",
    "Before we start running our series of experiments, we need to make sure our datasets are ready.\n",
    "\n",
    "We'll need two forms of a training set:\n",
    "1. A training set with **10% of the data** of Food101 pizza, steak, sushi images (we've already created this above but we'll do it again for completeness).\n",
    "2. A training set with **20% of the data** of Food101 pizza, steak, sushi images.\n",
    "\n",
    "For consistency, all experiments will use the same testing dataset (the one from the 10% data split).\n",
    "\n",
    "We'll start by downloading the various datasets we need using the `download_data()` function we created earlier.\n",
    "\n",
    "Both datasets are available from the course GitHub:\n",
    "1. [Pizza, steak, sushi 10% training data](https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip).\n",
    "2. [Pizza, steak, sushi 20% training data](https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n",
      "[INFO] data/pizza_steak_sushi_20_percent directory exists, skipping download.\n"
     ]
    }
   ],
   "source": [
    "# Download 10 percent and 20 percent training data (if necessary)\n",
    "data_10_percent_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
    "                                     destination=\"pizza_steak_sushi\")\n",
    "\n",
    "data_20_percent_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip\",\n",
    "                                     destination=\"pizza_steak_sushi_20_percent\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data downloaded!\n",
    "\n",
    "Now let's setup the filepaths to data we'll be using for the different experiments.\n",
    "\n",
    "We'll create different training directory paths but we'll only need one testing directory path since all experiments will be using the same test dataset (the test dataset from pizza, steak, sushi 10%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training directory 10%: data/pizza_steak_sushi/train\n",
      "Training directory 20%: data/pizza_steak_sushi_20_percent/train\n",
      "Testing directory: data/pizza_steak_sushi/test\n"
     ]
    }
   ],
   "source": [
    "# Setup training directory paths\n",
    "train_dir_10_percent = data_10_percent_path / \"train\"\n",
    "train_dir_20_percent = data_20_percent_path / \"train\"\n",
    "\n",
    "# Setup testing directory paths (note: use the same test dataset for both to compare the results)\n",
    "test_dir = data_10_percent_path / \"test\"\n",
    "\n",
    "# Check the directories\n",
    "print(f\"Training directory 10%: {train_dir_10_percent}\")\n",
    "print(f\"Training directory 20%: {train_dir_20_percent}\")\n",
    "print(f\"Testing directory: {test_dir}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.4 Transform Datasets and create DataLoaders\n",
    "\n",
    "Next we'll create a series of transforms to prepare our images for our model(s).\n",
    "\n",
    "To keep things consistent, we'll manually create a transform (just like we did above) and use the same transform across all of the datasets.\n",
    "\n",
    "The transform will: \n",
    "1. Resize all the images (we'll start with 224, 224 but this could be changed).\n",
    "2. Turn them into tensors with values between 0 & 1. \n",
    "3. Normalize them in way so their distributions are inline with the ImageNet dataset (we do this because our models from [`torchvision.models`](https://pytorch.org/vision/stable/models.html) have been pretrained on ImageNet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "\n",
    "# Create a transform to normalize data distribution to be inline with ImageNet\n",
    "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], # values per colour channel [red, green, blue]\n",
    "                                 std=[0.229, 0.224, 0.225]) # values per colour channel [red, green, blue]\n",
    "\n",
    "# Compose transforms into a pipeline\n",
    "simple_transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)), # 1. Resize the images\n",
    "    transforms.ToTensor(), # 2. Turn the images into tensors with values between 0 & 1\n",
    "    normalize # 3. Normalize the images so their distributions match the ImageNet dataset \n",
    "])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform ready!\n",
    "\n",
    "Now let's create our DataLoaders using the `create_dataloaders()` function from `data_setup.py` we created in [05. PyTorch Going Modular section 2](https://www.learnpytorch.io/05_pytorch_going_modular/#2-create-datasets-and-dataloaders-data_setuppy). \n",
    "\n",
    "We'll create the DataLoaders with a batch size of 32.\n",
    "\n",
    "For all of our experiments we'll be using the same `test_dataloader` (to keep comparisons consistent)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches of size 32 in 10 percent training data: 8\n",
      "Number of batches of size 32 in 20 percent training data: 15\n",
      "Number of batches of size 32 in testing data: 8 (all experiments will use the same test set)\n",
      "Number of classes: 3, class names: ['pizza', 'steak', 'sushi']\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 32\n",
    "\n",
    "# Create 10% training and test DataLoaders\n",
    "train_dataloader_10_percent, test_dataloader, class_names = data_setup.create_dataloaders(train_dir=train_dir_10_percent,\n",
    "    test_dir=test_dir, \n",
    "    transform=simple_transform,\n",
    "    batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "# Create 20% training and test data DataLoders\n",
    "train_dataloader_20_percent, test_dataloader, class_names = data_setup.create_dataloaders(train_dir=train_dir_20_percent,\n",
    "    test_dir=test_dir,\n",
    "    transform=simple_transform,\n",
    "    batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "# Find the number of samples/batches per dataloader (using the same test_dataloader for both experiments)\n",
    "print(f\"Number of batches of size {BATCH_SIZE} in 10 percent training data: {len(train_dataloader_10_percent)}\")\n",
    "print(f\"Number of batches of size {BATCH_SIZE} in 20 percent training data: {len(train_dataloader_20_percent)}\")\n",
    "print(f\"Number of batches of size {BATCH_SIZE} in testing data: {len(test_dataloader)} (all experiments will use the same test set)\")\n",
    "print(f\"Number of classes: {len(class_names)}, class names: {class_names}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.5 Create feature extractor models\n",
    "\n",
    "Time to start building our models.\n",
    "\n",
    "We're going to create two feature extractor models: \n",
    "\n",
    "1. [`torchvision.models.efficientnet_b0()`](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b0.html) pretrained backbone + custom classifier head (EffNetB0 for short).\n",
    "2. [`torchvision.models.efficientnet_b2()`](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html) pretrained backbone + custom classifier head (EffNetB2 for short).\n",
    "\n",
    "To do this, we'll freeze the base layers (the feature layers) and update the model's classifier heads (output layers) to suit our problem just like we did in [06. PyTorch Transfer Learning section 3.4](https://www.learnpytorch.io/06_pytorch_transfer_learning/#34-freezing-the-base-model-and-changing-the-output-layer-to-suit-our-needs).\n",
    "\n",
    "We saw in the previous chapter the `in_features` parameter to the classifier head of EffNetB0 is `1280` (the backbone turns the input image into a feature vector of size `1280`).\n",
    "\n",
    "Since EffNetB2 has a different number of layers and parameters, we'll need to adapt it accordingly.\n",
    "\n",
    "> **Note:** Whenever you use a different model, one of the first things you should inspect is the input and output shapes. That way you'll know how you'll have to prepare your input data/update the model to have the correct output shape.\n",
    "\n",
    "We can find the input and output shapes of EffNetB2 using [`torchinfo.summary()`](https://github.com/TylerYep/torchinfo) and passing in the `input_size=(32, 3, 224, 224)` parameter (`(32, 3, 224, 224)` is equivalent to `(batch_size, color_channels, height, width)`, i.e we pass in an example of what a single batch of data would be to our model).\n",
    "\n",
    "> **Note:** Many modern models can handle input images of varying sizes thanks to [`torch.nn.AdaptiveAvgPool2d()`](https://pytorch.org/docs/stable/generated/torch.nn.AdaptiveAvgPool2d.html) layer, this layer adaptively adjusts the `output_size` of a given input as required. You can try this out by passing different size input images to `torchinfo.summary()` or to your own models using the layer.\n",
    "\n",
    "To find the required input shape to the final layer of EffNetB2, let's:\n",
    "1. Create an instance of `torchvision.models.efficientnet_b2(pretrained=True)`.\n",
    "2. See the various input and output shapes by running `torchinfo.summary()`.\n",
    "3. Print out the number of `in_features` by inspecting `state_dict()` of the classifier portion of EffNetB2 and printing the length of the weight matrix.\n",
    "    * **Note:** You could also just inspect the output of `effnetb2.classifier`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of in_features to final layer of EfficientNetB2: 1408\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "from torchinfo import summary\n",
    "\n",
    "# 1. Create an instance of EffNetB2 with pretrained weights\n",
    "effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT # \"DEFAULT\" means best available weights\n",
    "effnetb2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights)\n",
    "\n",
    "# # 2. Get a summary of standard EffNetB2 from torchvision.models (uncomment for full output)\n",
    "# summary(model=effnetb2, \n",
    "#         input_size=(32, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
    "#         # col_names=[\"input_size\"], # uncomment for smaller output\n",
    "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "#         col_width=20,\n",
    "#         row_settings=[\"var_names\"]\n",
    "# ) \n",
    "\n",
    "# 3. Get the number of in_features of the EfficientNetB2 classifier layer\n",
    "print(f\"Number of in_features to final layer of EfficientNetB2: {len(effnetb2.classifier.state_dict()['1.weight'][0])}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/07-effnetb2-unfrozen-summary-output.png\" alt=\"output of torchinfo.summary() when passed our effnetb2 model with all layers trainable and default classifier head\" width=900/>\n",
    "\n",
    "*Model summary of EffNetB2 feature extractor model with all layers unfrozen (trainable) and default classifier head from ImageNet pretraining.*\n",
    "\n",
    "Now we know the required number of `in_features` for the EffNetB2 model, let's create a couple of helper functions to setup our EffNetB0 and EffNetB2 feature extractor models.\n",
    "\n",
    "We want these functions to:\n",
    "1. Get the base model from `torchvision.models`\n",
    "2. Freeze the base layers in the model (set `requires_grad=False`)\n",
    "3. Set the random seeds (we don't *need* to do this but since we're running a series of experiments and initalizing a new layer with random weights, we want the randomness to be similar for each experiment)\n",
    "4. Change the classifier head (to suit our problem)\n",
    "5. Give the model a name (e.g. \"effnetb0\" for EffNetB0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "from torch import nn\n",
    "\n",
    "# Get num out features (one for each class pizza, steak, sushi)\n",
    "OUT_FEATURES = len(class_names)\n",
    "\n",
    "# Create an EffNetB0 feature extractor\n",
    "def create_effnetb0():\n",
    "    # 1. Get the base model with pretrained weights and send to target device\n",
    "    weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT\n",
    "    model = torchvision.models.efficientnet_b0(weights=weights).to(device)\n",
    "\n",
    "    # 2. Freeze the base model layers\n",
    "    for param in model.features.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "    # 3. Set the seeds\n",
    "    set_seeds()\n",
    "\n",
    "    # 4. Change the classifier head\n",
    "    model.classifier = nn.Sequential(\n",
    "        nn.Dropout(p=0.2),\n",
    "        nn.Linear(in_features=1280, out_features=OUT_FEATURES)\n",
    "    ).to(device)\n",
    "\n",
    "    # 5. Give the model a name\n",
    "    model.name = \"effnetb0\"\n",
    "    print(f\"[INFO] Created new {model.name} model.\")\n",
    "    return model\n",
    "\n",
    "# Create an EffNetB2 feature extractor\n",
    "def create_effnetb2():\n",
    "    # 1. Get the base model with pretrained weights and send to target device\n",
    "    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
    "    model = torchvision.models.efficientnet_b2(weights=weights).to(device)\n",
    "\n",
    "    # 2. Freeze the base model layers\n",
    "    for param in model.features.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "    # 3. Set the seeds\n",
    "    set_seeds()\n",
    "\n",
    "    # 4. Change the classifier head\n",
    "    model.classifier = nn.Sequential(\n",
    "        nn.Dropout(p=0.3),\n",
    "        nn.Linear(in_features=1408, out_features=OUT_FEATURES)\n",
    "    ).to(device)\n",
    "\n",
    "    # 5. Give the model a name\n",
    "    model.name = \"effnetb2\"\n",
    "    print(f\"[INFO] Created new {model.name} model.\")\n",
    "    return model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those are some nice looking functions!\n",
    "\n",
    "Let's test them out by creating an instance of EffNetB0 and EffNetB2 and checking out their `summary()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Created new effnetb0 model.\n"
     ]
    }
   ],
   "source": [
    "effnetb0 = create_effnetb0() \n",
    "\n",
    "# Get an output summary of the layers in our EffNetB0 feature extractor model (uncomment to view full output)\n",
    "# summary(model=effnetb0, \n",
    "#         input_size=(32, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
    "#         # col_names=[\"input_size\"], # uncomment for smaller output\n",
    "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "#         col_width=20,\n",
    "#         row_settings=[\"var_names\"]\n",
    "# ) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/07-effnetb0-frozen-summary-output.png\" alt=\"output of torchinfo.summary() when passed our effnetb0 model with base layers are frozen and classifier head is updated\" width=900/>\n",
    "\n",
    "*Model summary of EffNetB0 model with base layers frozen (untrainable) and updated classifier head (suited for pizza, steak, sushi image classification).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Created new effnetb2 model.\n"
     ]
    }
   ],
   "source": [
    "effnetb2 = create_effnetb2()\n",
    "\n",
    "# Get an output summary of the layers in our EffNetB2 feature extractor model (uncomment to view full output)\n",
    "# summary(model=effnetb2, \n",
    "#         input_size=(32, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
    "#         # col_names=[\"input_size\"], # uncomment for smaller output\n",
    "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "#         col_width=20,\n",
    "#         row_settings=[\"var_names\"]\n",
    "# ) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/07-effnetb2-frozen-summary-output.png\" alt=\"output of torchinfo.summary() when passed our effnetb2 model with base layers are frozen and classifier head is updated\" width=900/>\n",
    "\n",
    "*Model summary of EffNetB2 model with base layers frozen (untrainable) and updated classifier head (suited for pizza, steak, sushi image classification).*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the outputs of the summaries, it seems the EffNetB2 backbone has nearly double the amount of parameters as EffNetB0.\n",
    "\n",
    "| Model | Total parameters (before freezing/changing head) | Total parameters (after freezing/changing head) | Total trainable parameters (after freezing/changing head) |\n",
    "| ----- | ----- | ----- | ----- |\n",
    "| EfficientNetB0 | 5,288,548 | 4,011,391 | 3,843 |  \n",
    "| EfficientNetB2 | 9,109,994 | 7,705,221 | 4,227 |\n",
    "\n",
    "This gives the backbone of the EffNetB2 model more opportunities to form a representation of our pizza, steak and sushi data.\n",
    "\n",
    "However, the trainable parameters for each model (the classifier heads) aren't very different.\n",
    "\n",
    "Will these extra parameters lead to better results?\n",
    "\n",
    "We'll have to wait and see... \n",
    "\n",
    "> **Note:** In the spirit of experimenting, you really could try almost any model from `torchvision.models` in a similar fashion to what we're doing here. I've only chosen EffNetB0 and EffNetB2 as examples. Perhaps you might want to throw something like `torchvision.models.convnext_tiny()` or `torchvision.models.convnext_small()` into the mix."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.6 Create experiments and set up training code\n",
    "\n",
    "We've prepared our data and prepared our models, the time has come to setup some experiments!\n",
    "\n",
    "We'll start by creating two lists and a dictionary:\n",
    "1. A list of the number of epochs we'd like to test (`[5, 10]`)\n",
    "2. A list of the models we'd like to test (`[\"effnetb0\", \"effnetb2\"]`)\n",
    "3. A dictionary of the different training DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Create epochs list\n",
    "num_epochs = [5, 10]\n",
    "\n",
    "# 2. Create models list (need to create a new model for each experiment)\n",
    "models = [\"effnetb0\", \"effnetb2\"]\n",
    "\n",
    "# 3. Create dataloaders dictionary for various dataloaders\n",
    "train_dataloaders = {\"data_10_percent\": train_dataloader_10_percent,\n",
    "                     \"data_20_percent\": train_dataloader_20_percent}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists and dictionary created!\n",
    "\n",
    "Now we can write code to iterate through each of the different options and try out each of the different combinations.\n",
    "\n",
    "We'll also save the model at the end of each experiment so later on we can load back in the best model and use it for making predictions.\n",
    "\n",
    "Specifically, let's go through the following steps: \n",
    "1. Set the random seeds (so our experiment results are reproducible, in practice, you might run the same experiment across ~3 different seeds and average the results).\n",
    "2. Keep track of different experiment numbers (this is mostly for pretty print outs).\n",
    "3. Loop through the `train_dataloaders` dictionary items for each of the different training DataLoaders.\n",
    "4. Loop through the list of epoch numbers.\n",
    "5. Loop through the list of different model names.\n",
    "6. Create information print outs for the current running experiment (so we know what's happening).\n",
    "7. Check which model is the target model and create a new EffNetB0 or EffNetB2 instance (we create a new model instance each experiment so all models start from the same standpoint).\n",
    "8. Create a new loss function (`torch.nn.CrossEntropyLoss()`) and optimizer (`torch.optim.Adam(params=model.parameters(), lr=0.001)`) for each new experiment.\n",
    "9. Train the model with the modified `train()` function passing the appropriate details to the `writer` parameter.\n",
    "10. Save the trained model with an appropriate file name to file with `save_model()` from [`utils.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/utils.py). \n",
    "\n",
    "We can also use the `%%time` magic to see how long all of our experiments take together in a single Jupyter/Google Colab cell.\n",
    "\n",
    "Let's do it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Experiment number: 1\n",
      "[INFO] Model: effnetb0\n",
      "[INFO] DataLoader: data_10_percent\n",
      "[INFO] Number of epochs: 5\n",
      "[INFO] Created new effnetb0 model.\n",
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_10_percent/effnetb0/5_epochs...\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 1.0528 | train_acc: 0.4961 | test_loss: 0.9217 | test_acc: 0.4678\n",
      "Epoch: 2 | train_loss: 0.8747 | train_acc: 0.6992 | test_loss: 0.8138 | test_acc: 0.6203\n",
      "Epoch: 3 | train_loss: 0.8099 | train_acc: 0.6445 | test_loss: 0.7175 | test_acc: 0.8258\n",
      "Epoch: 4 | train_loss: 0.7097 | train_acc: 0.7578 | test_loss: 0.5897 | test_acc: 0.8864\n",
      "Epoch: 5 | train_loss: 0.5980 | train_acc: 0.9141 | test_loss: 0.5676 | test_acc: 0.8864\n",
      "[INFO] Saving model to: models/07_effnetb0_data_10_percent_5_epochs.pth\n",
      "--------------------------------------------------\n",
      "\n",
      "[INFO] Experiment number: 2\n",
      "[INFO] Model: effnetb2\n",
      "[INFO] DataLoader: data_10_percent\n",
      "[INFO] Number of epochs: 5\n",
      "[INFO] Created new effnetb2 model.\n",
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_10_percent/effnetb2/5_epochs...\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 1.0928 | train_acc: 0.3711 | test_loss: 0.9557 | test_acc: 0.6610\n",
      "Epoch: 2 | train_loss: 0.9247 | train_acc: 0.6445 | test_loss: 0.8711 | test_acc: 0.8144\n",
      "Epoch: 3 | train_loss: 0.8086 | train_acc: 0.7656 | test_loss: 0.7511 | test_acc: 0.9176\n",
      "Epoch: 4 | train_loss: 0.7191 | train_acc: 0.8867 | test_loss: 0.7150 | test_acc: 0.9081\n",
      "Epoch: 5 | train_loss: 0.6851 | train_acc: 0.7695 | test_loss: 0.7076 | test_acc: 0.8873\n",
      "[INFO] Saving model to: models/07_effnetb2_data_10_percent_5_epochs.pth\n",
      "--------------------------------------------------\n",
      "\n",
      "[INFO] Experiment number: 3\n",
      "[INFO] Model: effnetb0\n",
      "[INFO] DataLoader: data_10_percent\n",
      "[INFO] Number of epochs: 10\n",
      "[INFO] Created new effnetb0 model.\n",
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_10_percent/effnetb0/10_epochs...\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 1.0528 | train_acc: 0.4961 | test_loss: 0.9217 | test_acc: 0.4678\n",
      "Epoch: 2 | train_loss: 0.8747 | train_acc: 0.6992 | test_loss: 0.8138 | test_acc: 0.6203\n",
      "Epoch: 3 | train_loss: 0.8099 | train_acc: 0.6445 | test_loss: 0.7175 | test_acc: 0.8258\n",
      "Epoch: 4 | train_loss: 0.7097 | train_acc: 0.7578 | test_loss: 0.5897 | test_acc: 0.8864\n",
      "Epoch: 5 | train_loss: 0.5980 | train_acc: 0.9141 | test_loss: 0.5676 | test_acc: 0.8864\n",
      "Epoch: 6 | train_loss: 0.5611 | train_acc: 0.8984 | test_loss: 0.5949 | test_acc: 0.8864\n",
      "Epoch: 7 | train_loss: 0.5573 | train_acc: 0.7930 | test_loss: 0.5566 | test_acc: 0.8864\n",
      "Epoch: 8 | train_loss: 0.4702 | train_acc: 0.9492 | test_loss: 0.5176 | test_acc: 0.8759\n",
      "Epoch: 9 | train_loss: 0.5728 | train_acc: 0.7773 | test_loss: 0.5095 | test_acc: 0.8873\n",
      "Epoch: 10 | train_loss: 0.4794 | train_acc: 0.8242 | test_loss: 0.4640 | test_acc: 0.9072\n",
      "[INFO] Saving model to: models/07_effnetb0_data_10_percent_10_epochs.pth\n",
      "--------------------------------------------------\n",
      "\n",
      "[INFO] Experiment number: 4\n",
      "[INFO] Model: effnetb2\n",
      "[INFO] DataLoader: data_10_percent\n",
      "[INFO] Number of epochs: 10\n",
      "[INFO] Created new effnetb2 model.\n",
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_10_percent/effnetb2/10_epochs...\n"
     ]
    },
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     "text": [
      "Epoch: 1 | train_loss: 1.0928 | train_acc: 0.3711 | test_loss: 0.9557 | test_acc: 0.6610\n",
      "Epoch: 2 | train_loss: 0.9247 | train_acc: 0.6445 | test_loss: 0.8711 | test_acc: 0.8144\n",
      "Epoch: 3 | train_loss: 0.8086 | train_acc: 0.7656 | test_loss: 0.7511 | test_acc: 0.9176\n",
      "Epoch: 4 | train_loss: 0.7191 | train_acc: 0.8867 | test_loss: 0.7150 | test_acc: 0.9081\n",
      "Epoch: 5 | train_loss: 0.6851 | train_acc: 0.7695 | test_loss: 0.7076 | test_acc: 0.8873\n",
      "Epoch: 6 | train_loss: 0.6111 | train_acc: 0.7812 | test_loss: 0.6325 | test_acc: 0.9280\n",
      "Epoch: 7 | train_loss: 0.6127 | train_acc: 0.8008 | test_loss: 0.6404 | test_acc: 0.8769\n",
      "Epoch: 8 | train_loss: 0.5202 | train_acc: 0.9336 | test_loss: 0.6200 | test_acc: 0.8977\n",
      "Epoch: 9 | train_loss: 0.5425 | train_acc: 0.8008 | test_loss: 0.6227 | test_acc: 0.8466\n",
      "Epoch: 10 | train_loss: 0.4908 | train_acc: 0.8125 | test_loss: 0.5870 | test_acc: 0.8873\n",
      "[INFO] Saving model to: models/07_effnetb2_data_10_percent_10_epochs.pth\n",
      "--------------------------------------------------\n",
      "\n",
      "[INFO] Experiment number: 5\n",
      "[INFO] Model: effnetb0\n",
      "[INFO] DataLoader: data_20_percent\n",
      "[INFO] Number of epochs: 5\n",
      "[INFO] Created new effnetb0 model.\n",
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_20_percent/effnetb0/5_epochs...\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.9577 | train_acc: 0.6167 | test_loss: 0.6545 | test_acc: 0.8655\n",
      "Epoch: 2 | train_loss: 0.6881 | train_acc: 0.8438 | test_loss: 0.5798 | test_acc: 0.9176\n",
      "Epoch: 3 | train_loss: 0.5798 | train_acc: 0.8604 | test_loss: 0.4575 | test_acc: 0.9176\n",
      "Epoch: 4 | train_loss: 0.4930 | train_acc: 0.8646 | test_loss: 0.4458 | test_acc: 0.9176\n",
      "Epoch: 5 | train_loss: 0.4886 | train_acc: 0.8500 | test_loss: 0.3909 | test_acc: 0.9176\n",
      "[INFO] Saving model to: models/07_effnetb0_data_20_percent_5_epochs.pth\n",
      "--------------------------------------------------\n",
      "\n",
      "[INFO] Experiment number: 6\n",
      "[INFO] Model: effnetb2\n",
      "[INFO] DataLoader: data_20_percent\n",
      "[INFO] Number of epochs: 5\n",
      "[INFO] Created new effnetb2 model.\n",
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_20_percent/effnetb2/5_epochs...\n"
     ]
    },
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.9830 | train_acc: 0.5521 | test_loss: 0.7767 | test_acc: 0.8153\n",
      "Epoch: 2 | train_loss: 0.7298 | train_acc: 0.7604 | test_loss: 0.6673 | test_acc: 0.8873\n",
      "Epoch: 3 | train_loss: 0.6022 | train_acc: 0.8458 | test_loss: 0.5622 | test_acc: 0.9280\n",
      "Epoch: 4 | train_loss: 0.5435 | train_acc: 0.8354 | test_loss: 0.5679 | test_acc: 0.9186\n",
      "Epoch: 5 | train_loss: 0.4404 | train_acc: 0.9042 | test_loss: 0.4462 | test_acc: 0.9489\n",
      "[INFO] Saving model to: models/07_effnetb2_data_20_percent_5_epochs.pth\n",
      "--------------------------------------------------\n",
      "\n",
      "[INFO] Experiment number: 7\n",
      "[INFO] Model: effnetb0\n",
      "[INFO] DataLoader: data_20_percent\n",
      "[INFO] Number of epochs: 10\n",
      "[INFO] Created new effnetb0 model.\n",
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_20_percent/effnetb0/10_epochs...\n"
     ]
    },
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.9577 | train_acc: 0.6167 | test_loss: 0.6545 | test_acc: 0.8655\n",
      "Epoch: 2 | train_loss: 0.6881 | train_acc: 0.8438 | test_loss: 0.5798 | test_acc: 0.9176\n",
      "Epoch: 3 | train_loss: 0.5798 | train_acc: 0.8604 | test_loss: 0.4575 | test_acc: 0.9176\n",
      "Epoch: 4 | train_loss: 0.4930 | train_acc: 0.8646 | test_loss: 0.4458 | test_acc: 0.9176\n",
      "Epoch: 5 | train_loss: 0.4886 | train_acc: 0.8500 | test_loss: 0.3909 | test_acc: 0.9176\n",
      "Epoch: 6 | train_loss: 0.3705 | train_acc: 0.8854 | test_loss: 0.3568 | test_acc: 0.9072\n",
      "Epoch: 7 | train_loss: 0.3551 | train_acc: 0.9250 | test_loss: 0.3187 | test_acc: 0.9072\n",
      "Epoch: 8 | train_loss: 0.3745 | train_acc: 0.8938 | test_loss: 0.3349 | test_acc: 0.8873\n",
      "Epoch: 9 | train_loss: 0.2972 | train_acc: 0.9396 | test_loss: 0.3092 | test_acc: 0.9280\n",
      "Epoch: 10 | train_loss: 0.3620 | train_acc: 0.8479 | test_loss: 0.2780 | test_acc: 0.9072\n",
      "[INFO] Saving model to: models/07_effnetb0_data_20_percent_10_epochs.pth\n",
      "--------------------------------------------------\n",
      "\n",
      "[INFO] Experiment number: 8\n",
      "[INFO] Model: effnetb2\n",
      "[INFO] DataLoader: data_20_percent\n",
      "[INFO] Number of epochs: 10\n",
      "[INFO] Created new effnetb2 model.\n",
      "[INFO] Created SummaryWriter, saving to: runs/2022-06-23/data_20_percent/effnetb2/10_epochs...\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 0.9830 | train_acc: 0.5521 | test_loss: 0.7767 | test_acc: 0.8153\n",
      "Epoch: 2 | train_loss: 0.7298 | train_acc: 0.7604 | test_loss: 0.6673 | test_acc: 0.8873\n",
      "Epoch: 3 | train_loss: 0.6022 | train_acc: 0.8458 | test_loss: 0.5622 | test_acc: 0.9280\n",
      "Epoch: 4 | train_loss: 0.5435 | train_acc: 0.8354 | test_loss: 0.5679 | test_acc: 0.9186\n",
      "Epoch: 5 | train_loss: 0.4404 | train_acc: 0.9042 | test_loss: 0.4462 | test_acc: 0.9489\n",
      "Epoch: 6 | train_loss: 0.3889 | train_acc: 0.9104 | test_loss: 0.4555 | test_acc: 0.8977\n",
      "Epoch: 7 | train_loss: 0.3483 | train_acc: 0.9271 | test_loss: 0.4227 | test_acc: 0.9384\n",
      "Epoch: 8 | train_loss: 0.3862 | train_acc: 0.8771 | test_loss: 0.4344 | test_acc: 0.9280\n",
      "Epoch: 9 | train_loss: 0.3308 | train_acc: 0.8979 | test_loss: 0.4242 | test_acc: 0.9384\n",
      "Epoch: 10 | train_loss: 0.3383 | train_acc: 0.8896 | test_loss: 0.3906 | test_acc: 0.9384\n",
      "[INFO] Saving model to: models/07_effnetb2_data_20_percent_10_epochs.pth\n",
      "--------------------------------------------------\n",
      "\n",
      "CPU times: user 29.5 s, sys: 1min 28s, total: 1min 58s\n",
      "Wall time: 2min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from going_modular.going_modular.utils import save_model\n",
    "\n",
    "# 1. Set the random seeds\n",
    "set_seeds(seed=42)\n",
    "\n",
    "# 2. Keep track of experiment numbers\n",
    "experiment_number = 0\n",
    "\n",
    "# 3. Loop through each DataLoader\n",
    "for dataloader_name, train_dataloader in train_dataloaders.items():\n",
    "\n",
    "    # 4. Loop through each number of epochs\n",
    "    for epochs in num_epochs: \n",
    "\n",
    "        # 5. Loop through each model name and create a new model based on the name\n",
    "        for model_name in models:\n",
    "\n",
    "            # 6. Create information print outs\n",
    "            experiment_number += 1\n",
    "            print(f\"[INFO] Experiment number: {experiment_number}\")\n",
    "            print(f\"[INFO] Model: {model_name}\")\n",
    "            print(f\"[INFO] DataLoader: {dataloader_name}\")\n",
    "            print(f\"[INFO] Number of epochs: {epochs}\")  \n",
    "\n",
    "            # 7. Select the model\n",
    "            if model_name == \"effnetb0\":\n",
    "                model = create_effnetb0() # creates a new model each time (important because we want each experiment to start from scratch)\n",
    "            else:\n",
    "                model = create_effnetb2() # creates a new model each time (important because we want each experiment to start from scratch)\n",
    "            \n",
    "            # 8. Create a new loss and optimizer for every model\n",
    "            loss_fn = nn.CrossEntropyLoss()\n",
    "            optimizer = torch.optim.Adam(params=model.parameters(), lr=0.001)\n",
    "\n",
    "            # 9. Train target model with target dataloaders and track experiments\n",
    "            train(model=model,\n",
    "                  train_dataloader=train_dataloader,\n",
    "                  test_dataloader=test_dataloader, \n",
    "                  optimizer=optimizer,\n",
    "                  loss_fn=loss_fn,\n",
    "                  epochs=epochs,\n",
    "                  device=device,\n",
    "                  writer=create_writer(experiment_name=dataloader_name,\n",
    "                                       model_name=model_name,\n",
    "                                       extra=f\"{epochs}_epochs\"))\n",
    "            \n",
    "            # 10. Save the model to file so we can get back the best model\n",
    "            save_filepath = f\"07_{model_name}_{dataloader_name}_{epochs}_epochs.pth\"\n",
    "            save_model(model=model,\n",
    "                       target_dir=\"models\",\n",
    "                       model_name=save_filepath)\n",
    "            print(\"-\"*50 + \"\\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. View experiments in TensorBoard\n",
    "\n",
    "Ho, ho!\n",
    "\n",
    "Look at us go!\n",
    "\n",
    "Training eight models in one go?\n",
    "\n",
    "Now that's living up to the motto!\n",
    "\n",
    "*Experiment, experiment, experiment!*\n",
    "\n",
    "How about we check out the results in TensorBoard?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Viewing TensorBoard in Jupyter and Google Colab Notebooks (uncomment to view full TensorBoard instance)\n",
    "# %load_ext tensorboard\n",
    "# %tensorboard --logdir runs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the cell above we should get an output similar to the following.\n",
    "\n",
    "> **Note:** Depending on the random seeds you used/hardware you used there's a chance your numbers aren't exactly the same as what's here. This is okay. It's due to the inherent randomness of deep learning. What matters most is the trend. Where your numbers are heading. If they're off by a large amount, perhaps there's something wrong and best to go back and check the code. But if they're off by a small amount (say a couple of decimal places or so), that's okay. \n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/07-tensorboard-lowest-test-loss.png\" alt=\"various modelling experiments visualized on tensorboard with model that has the lowest test loss highlighted\" width=900/>\n",
    "\n",
    "*Visualizing the test loss values for the different modelling experiments in TensorBoard, you can see that the EffNetB0 model trained for 10 epochs and with 20% of the data achieves the lowest loss. This sticks with the overall trend of the experiments that: more data, larger model and longer training time is generally better.*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Load in the best model and make predictions with it\n",
    "\n",
    "Looking at the TensorBoard logs for our eight experiments, it seems experiment number eight achieved the best overall results (highest test accuracy, second lowest test loss).\n",
    "\n",
    "This is the experiment that used:\n",
    "* EffNetB2 (double the parameters of EffNetB0)\n",
    "* 20% pizza, steak, sushi training data (double the original training data)\n",
    "* 10 epochs (double the original training time)\n",
    "\n",
    "In essence, our biggest model achieved the best results.\n",
    "\n",
    "Though it wasn't as if these results were far better than the other models.\n",
    "\n",
    "The same model on the same data achieved similar results in half the training time (experiment number 6).\n",
    "\n",
    "This suggests that potentially the most influential parts of our experiments were the number of parameters and the amount of data.\n",
    "\n",
    "Inspecting the results further it seems that generally a model with more parameters (EffNetB2) and more data (20% pizza, steak, sushi training data) performs better (lower test loss and higher test accuracy).\n",
    "\n",
    "More experiments could be done to further test this but for now, let's import our best performing model from experiment eight (saved to: `models/07_effnetb2_data_20_percent_10_epochs.pth`, you can [download this model from the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/models/07_effnetb2_data_20_percent_10_epochs.pth)) and perform some qualitative evaluations.\n",
    "\n",
    "In other words, let's *visualize, visualize, visualize!*\n",
    "\n",
    "We can import the best saved model by creating a new instance of EffNetB2 using the `create_effnetb2()` function and then load in the saved `state_dict()` with `torch.load()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Created new effnetb2 model.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup the best model filepath\n",
    "best_model_path = \"models/07_effnetb2_data_20_percent_10_epochs.pth\"\n",
    "\n",
    "# Instantiate a new instance of EffNetB2 (to load the saved state_dict() to)\n",
    "best_model = create_effnetb2()\n",
    "\n",
    "# Load the saved best model state_dict()\n",
    "best_model.load_state_dict(torch.load(best_model_path))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best model loaded!\n",
    "\n",
    "While we're here, let's check its filesize.\n",
    "\n",
    "This is an important consideration later on when deploying the model (incorporating it in an app).\n",
    "\n",
    "If the model is too large, it can be hard to deploy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EfficientNetB2 feature extractor model size: 29 MB\n"
     ]
    }
   ],
   "source": [
    "# Check the model file size\n",
    "from pathlib import Path\n",
    "\n",
    "# Get the model size in bytes then convert to megabytes\n",
    "effnetb2_model_size = Path(best_model_path).stat().st_size // (1024*1024)\n",
    "print(f\"EfficientNetB2 feature extractor model size: {effnetb2_model_size} MB\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like our best model so far is 29 MB in size. We'll keep this in mind if we want to deploy it later on.\n",
    "\n",
    "Time to make and visualize some predictions.\n",
    "\n",
    "We created a `pred_and_plot_image()` function to use a trained model to make predictions on an image in [06. PyTorch Transfer Learning section 6](https://www.learnpytorch.io/06_pytorch_transfer_learning/#6-make-predictions-on-images-from-the-test-set).\n",
    "\n",
    "And we can reuse this function by importing it from [`going_modular.going_modular.predictions.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/predictions.py) (I put the `pred_and_plot_image()` function in a script so we could reuse it).\n",
    "\n",
    "So to make predictions on various images the model hasn't seen before, we'll first get a list of all the image filepaths from the 20% pizza, steak, sushi testing dataset and then we'll randomly select a subset of these filepaths to pass to our `pred_and_plot_image()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import function to make predictions on images and plot them \n",
    "# See the function previously created in section: https://www.learnpytorch.io/06_pytorch_transfer_learning/#6-make-predictions-on-images-from-the-test-set\n",
    "from going_modular.going_modular.predictions import pred_and_plot_image\n",
    "\n",
    "# Get a random list of 3 images from 20% test set\n",
    "import random\n",
    "num_images_to_plot = 3\n",
    "test_image_path_list = list(Path(data_20_percent_path / \"test\").glob(\"*/*.jpg\")) # get all test image paths from 20% dataset\n",
    "test_image_path_sample = random.sample(population=test_image_path_list,\n",
    "                                       k=num_images_to_plot) # randomly select k number of images\n",
    "\n",
    "# Iterate through random test image paths, make predictions on them and plot them\n",
    "for image_path in test_image_path_sample:\n",
    "    pred_and_plot_image(model=best_model,\n",
    "                        image_path=image_path,\n",
    "                        class_names=class_names,\n",
    "                        image_size=(224, 224))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice!\n",
    "\n",
    "Running the cell above a few times we can see our model performs quite well and often has higher prediction probabilities than previous models we've built.\n",
    "\n",
    "This suggests the model is more confident in the decisions it's making. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1 Predict on a custom image with the best model\n",
    "\n",
    "Making predictions on the test dataset is cool but the real magic of machine learning is making predictions on custom images of your own.\n",
    "\n",
    "So let's import the trusty [pizza dad image](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/images/04-pizza-dad.jpeg) (a photo of my dad in front of a pizza) we've been using for the past couple of sections and see how our model performs on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/04-pizza-dad.jpeg already exists, skipping download.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download custom image\n",
    "import requests\n",
    "\n",
    "# Setup custom image path\n",
    "custom_image_path = Path(\"data/04-pizza-dad.jpeg\")\n",
    "\n",
    "# Download the image if it doesn't already exist\n",
    "if not custom_image_path.is_file():\n",
    "    with open(custom_image_path, \"wb\") as f:\n",
    "        # When downloading from GitHub, need to use the \"raw\" file link\n",
    "        request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/04-pizza-dad.jpeg\")\n",
    "        print(f\"Downloading {custom_image_path}...\")\n",
    "        f.write(request.content)\n",
    "else:\n",
    "    print(f\"{custom_image_path} already exists, skipping download.\")\n",
    "\n",
    "# Predict on custom image\n",
    "pred_and_plot_image(model=model,\n",
    "                    image_path=custom_image_path,\n",
    "                    class_names=class_names)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Woah!\n",
    "\n",
    "Two thumbs again!\n",
    "\n",
    "Our best model predicts \"pizza\" correctly and this time with an even higher prediction probability (0.978) than the first feature extraction model we trained and used in [06. PyTorch Transfer Learning section 6.1](https://www.learnpytorch.io/06_pytorch_transfer_learning/#61-making-predictions-on-a-custom-image).\n",
    "\n",
    "This again suggests our current best model (EffNetB2 feature extractor trained on 20% of the pizza, steak, sushi training data and for 10 epochs) has learned patterns to make it more confident of its decision to predict pizza.\n",
    "\n",
    "I wonder what could improve our model's performance even further? \n",
    "\n",
    "I'll leave that as a challenge for you to investigate."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main takeaways\n",
    "\n",
    "We've now gone full circle on the PyTorch workflow introduced in [01. PyTorch Workflow Fundamentals](https://www.learnpytorch.io/01_pytorch_workflow/), we've gotten data ready, we've built and picked a pretrained model, we've used our various helper functions to train and evaluate the model and in this notebook we've improved our FoodVision Mini model by running and tracking a series of experiments.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/01_a_pytorch_workflow.png\" width=900 alt=\"a pytorch workflow flowchat\"/>\n",
    "\n",
    "You should be proud of yourself, this is no small feat!\n",
    "\n",
    "The main ideas you should take away from this Milestone Project 1 are:\n",
    "\n",
    "* The machine learning practitioner's motto: *experiment, experiment, experiment!* (though we've been doing plenty of this already).\n",
    "* In the beginning, keep your experiments small so you can work fast, your first few experiments shouldn't take more than a few seconds to a few minutes to run.\n",
    "* The more experiments you do, the quicker you can figure out what *doesn't* work.\n",
    "* Scale up when you find something that works. For example, since we've found a pretty good performing model with EffNetB2 as a feature extractor, perhaps you'd now like to see what happens when you scale it up to the whole [Food101 dataset](https://pytorch.org/vision/main/generated/torchvision.datasets.Food101.html) from `torchvision.datasets`.\n",
    "* Programmatically tracking your experiments takes a few steps to set up but it's worth it in the long run so you can figure out what works and what doesn't.\n",
    "    * There are many different machine learning experiment trackers out there so explore a few and try them out."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "> **Note:** These exercises expect the use of `torchvision` v0.13+ (released July 2022), previous versions may work but will likely have errors.\n",
    "\n",
    "All of the exercises are focused on practicing the code above.\n",
    "\n",
    "You should be able to complete them by referencing each section or by following the resource(s) linked.\n",
    "\n",
    "All exercises should be completed using [device-agnostic code](https://pytorch.org/docs/stable/notes/cuda.html#device-agnostic-code).\n",
    "\n",
    "**Resources:**\n",
    "* [Exercise template notebook for 07](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/exercises/07_pytorch_experiment_tracking_exercise_template.ipynb)\n",
    "* [Example solutions notebook for 07](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/solutions/07_pytorch_experiment_tracking_exercise_solutions.ipynb) (try the exercises *before* looking at this)\n",
    "    * See a live [video walkthrough of the solutions on YouTube](https://youtu.be/cO_r2FYcAjU) (errors and all)\n",
    "\n",
    "\n",
    "1. Pick a larger model from [`torchvision.models`](https://pytorch.org/vision/main/models.html) to add to the list of experiments (for example, EffNetB3 or higher). \n",
    "    * How does it perform compared to our existing models?\n",
    "2. Introduce data augmentation to the list of experiments using the 20% pizza, steak, sushi training and test datasets, does this change anything?\n",
    "    * For example, you could have one training DataLoader that uses data augmentation (e.g. `train_dataloader_20_percent_aug` and `train_dataloader_20_percent_no_aug`) and then compare the results of two of the same model types training on these two DataLoaders.\n",
    "    * **Note:** You may need to alter the `create_dataloaders()` function to be able to take a transform for the training data and the testing data (because you don't need to perform data augmentation on the test data). See [04. PyTorch Custom Datasets section 6](https://www.learnpytorch.io/04_pytorch_custom_datasets/#6-other-forms-of-transforms-data-augmentation) for examples of using data augmentation or the script below for an example:\n",
    "\n",
    "```python\n",
    "# Note: Data augmentation transform like this should only be performed on training data\n",
    "train_transform_data_aug = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.TrivialAugmentWide(),\n",
    "    transforms.ToTensor(),\n",
    "    normalize\n",
    "])\n",
    "\n",
    "# Helper function to view images in a DataLoader (works with data augmentation transforms or not) \n",
    "def view_dataloader_images(dataloader, n=10):\n",
    "    if n > 10:\n",
    "        print(f\"Having n higher than 10 will create messy plots, lowering to 10.\")\n",
    "        n = 10\n",
    "    imgs, labels = next(iter(dataloader))\n",
    "    plt.figure(figsize=(16, 8))\n",
    "    for i in range(n):\n",
    "        # Min max scale the image for display purposes\n",
    "        targ_image = imgs[i]\n",
    "        sample_min, sample_max = targ_image.min(), targ_image.max()\n",
    "        sample_scaled = (targ_image - sample_min)/(sample_max - sample_min)\n",
    "\n",
    "        # Plot images with appropriate axes information\n",
    "        plt.subplot(1, 10, i+1)\n",
    "        plt.imshow(sample_scaled.permute(1, 2, 0)) # resize for Matplotlib requirements\n",
    "        plt.title(class_names[labels[i]])\n",
    "        plt.axis(False)\n",
    "\n",
    "# Have to update `create_dataloaders()` to handle different augmentations\n",
    "import os\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets\n",
    "\n",
    "NUM_WORKERS = os.cpu_count() # use maximum number of CPUs for workers to load data \n",
    "\n",
    "# Note: this is an update version of data_setup.create_dataloaders to handle\n",
    "# different train and test transforms.\n",
    "def create_dataloaders(\n",
    "    train_dir, \n",
    "    test_dir, \n",
    "    train_transform, # add parameter for train transform (transforms on train dataset)\n",
    "    test_transform,  # add parameter for test transform (transforms on test dataset)\n",
    "    batch_size=32, num_workers=NUM_WORKERS\n",
    "):\n",
    "    # Use ImageFolder to create dataset(s)\n",
    "    train_data = datasets.ImageFolder(train_dir, transform=train_transform)\n",
    "    test_data = datasets.ImageFolder(test_dir, transform=test_transform)\n",
    "\n",
    "    # Get class names\n",
    "    class_names = train_data.classes\n",
    "\n",
    "    # Turn images into data loaders\n",
    "    train_dataloader = DataLoader(\n",
    "        train_data,\n",
    "        batch_size=batch_size,\n",
    "        shuffle=True,\n",
    "        num_workers=num_workers,\n",
    "        pin_memory=True,\n",
    "    )\n",
    "    test_dataloader = DataLoader(\n",
    "        test_data,\n",
    "        batch_size=batch_size,\n",
    "        shuffle=True,\n",
    "        num_workers=num_workers,\n",
    "        pin_memory=True,\n",
    "    )\n",
    "\n",
    "    return train_dataloader, test_dataloader, class_names\n",
    "```\n",
    "\n",
    "3. Scale up the dataset to turn FoodVision Mini into FoodVision Big using the entire [Food101 dataset from `torchvision.models`](https://pytorch.org/vision/stable/generated/torchvision.datasets.Food101.html#torchvision.datasets.Food101)\n",
    "    * You could take the best performing model from your various experiments or even the EffNetB2 feature extractor we created in this notebook and see how it goes fitting for 5 epochs on all of Food101.\n",
    "    * If you try more than one model, it would be good to have the model's results tracked.\n",
    "    * If you load the Food101 dataset from `torchvision.models`, you'll have to create PyTorch DataLoaders to use it in training.\n",
    "    * **Note:** Due to the larger amount of data in Food101 compared to our pizza, steak, sushi dataset, this model will take longer to train."
   ]
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    "## Extra-curriculum\n",
    "\n",
    "* Read [The Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) blog post by Richard Sutton to get an idea of how many of the latest advancements in AI have come from increased scale (bigger datasets and bigger models) and more general (less meticulously crafted) methods.\n",
    "* Go through the [PyTorch YouTube/code tutorial](https://pytorch.org/tutorials/beginner/introyt/tensorboardyt_tutorial.html) for TensorBoard for 20-minutes and see how it compares to the code we've written in this notebook.\n",
    "* Perhaps you may want to view and rearrange your model's TensorBoard logs with a DataFrame (so you can sort the results by lowest loss or highest accuracy), there's a guide for this [in the TensorBoard documentation](https://www.tensorflow.org/tensorboard/dataframe_api). \n",
    "* If you like to use VSCode for development using scripts or notebooks (VSCode can now use Jupyter Notebooks natively), you can setup TensorBoard right within VSCode using the  [PyTorch Development in VSCode guide](https://code.visualstudio.com/docs/datascience/pytorch-support).\n",
    "* To go further with experiment tracking and see how your PyTorch model is performing from a speed perspective (are there any bottlenecks that could be improved to speed up training?), see the [PyTorch documentation for the PyTorch profiler](https://pytorch.org/blog/introducing-pytorch-profiler-the-new-and-improved-performance-tool/).\n",
    "* Made With ML is an outstanding resource for all things machine learning by Goku Mohandas and their [guide on experiment tracking](https://madewithml.com/courses/mlops/experiment-tracking/) contains a fantastic introduction to tracking machine learning experiments with MLflow."
   ]
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