07_food_vision_milestone_project_1.ipynb

{
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        "<a href=\"https://colab.research.google.com/github/Pratik94229/Learning-Tensorflow/blob/main/07_food_vision_milestone_project_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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        "id": "4f4813f7-f500-49cc-8896-2e434fc7fb8f"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mrdbourke/tensorflow-deep-learning/blob/main/07_food_vision_milestone_project_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15052ed2-29a5-4685-a7bd-1dd5f135cafc",
      "metadata": {
        "id": "15052ed2-29a5-4685-a7bd-1dd5f135cafc"
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      "source": [
        "# 07 Milestone Project 1: 🍔👁 Food Vision Big™\n",
        "\n",
        "In the previous notebook ([transfer learning part 3: scaling up](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/06_transfer_learning_in_tensorflow_part_3_scaling_up.ipynb)) we built Food Vision mini: a transfer learning model which beat the original results of the [Food101 paper](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) with only 10% of the data.\n",
        "\n",
        "But you might be wondering, what would happen if we used all the data?\n",
        "\n",
        "Well, that's what we're going to find out in this notebook!\n",
        "\n",
        "We're going to be building Food Vision Big™, using all of the data from the Food101 dataset.\n",
        "\n",
        "Yep. All 75,750 training images and 25,250 testing images.\n",
        "\n",
        "And guess what...\n",
        "\n",
        "This time **we've got the goal of beating [DeepFood](https://www.researchgate.net/publication/304163308_DeepFood_Deep_Learning-Based_Food_Image_Recognition_for_Computer-Aided_Dietary_Assessment)**, a 2016 paper which used a Convolutional Neural Network trained for 2-3 days to achieve 77.4% top-1 accuracy.\n",
        "\n",
        "> 🔑 **Note:** **Top-1 accuracy** means \"accuracy for the top softmax activation value output by the model\" (because softmax ouputs a value for every class, but top-1 means only the highest one is evaluated). **Top-5 accuracy** means \"accuracy for the top 5 softmax activation values output by the model\", in other words, did the true label appear in the top 5 activation values? Top-5 accuracy scores are usually noticeably higher than top-1.\n",
        "\n",
        "|  | 🍔👁 Food Vision Big™ | 🍔👁 Food Vision mini |\n",
        "|-----|-----|-----|\n",
        "| Dataset source | TensorFlow Datasets | Preprocessed download from Kaggle |\n",
        "| Train data | 75,750 images | 7,575 images |\n",
        "| Test data | 25,250 images | 25,250 images |\n",
        "| Mixed precision | Yes | No |\n",
        "| Data loading | Performanant tf.data API | TensorFlow pre-built function |\n",
        "| Target results | 77.4% top-1 accuracy (beat [DeepFood paper](https://arxiv.org/abs/1606.05675)) | 50.76% top-1 accuracy (beat [Food101 paper](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf)) |\n",
        "\n",
        "*Table comparing difference between Food Vision Big (this notebook) versus Food Vision mini (previous notebook).*\n",
        "\n",
        "Alongside attempting to beat the DeepFood paper, we're going to learn about two methods to significantly improve the speed of our model training:\n",
        "1. Prefetching\n",
        "2. Mixed precision training\n",
        "\n",
        "But more on these later.\n",
        "\n",
        "## What we're going to cover\n",
        "\n",
        "* Using TensorFlow Datasets to download and explore data\n",
        "* Creating preprocessing function for our data\n",
        "* Batching & preparing datasets for modelling (**making our datasets run fast**)\n",
        "* Creating modelling callbacks\n",
        "* Setting up **mixed precision training**\n",
        "* Building a feature extraction model (see [transfer learning part 1: feature extraction](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/04_transfer_learning_in_tensorflow_part_1_feature_extraction.ipynb))\n",
        "* Fine-tuning the feature extraction model (see [transfer learning part 2: fine-tuning](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/05_transfer_learning_in_tensorflow_part_2_fine_tuning.ipynb))\n",
        "* Viewing training results on TensorBoard\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## How you should approach this notebook\n",
        "\n",
        "You can read through the descriptions and the code (it should all run, except for the cells which error on purpose), but there's a better option.\n",
        "\n",
        "Write all of the code yourself.\n",
        "\n",
        "Yes. I'm serious. Create a new notebook, and rewrite each line by yourself. Investigate it, see if you can break it, why does it break?\n",
        "\n",
        "You don't have to write the text descriptions but writing the code yourself is a great way to get hands-on experience.\n",
        "\n",
        "Don't worry if you make mistakes, we all do. The way to get better and make less mistakes is to write more code.\n",
        "\n",
        "> 📖 **Resources:**\n",
        "> * See the full set of course materials on GitHub: https://github.com/mrdbourke/tensorflow-deep-learning\n",
        "> * See updates to this notebook on GitHub: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/550"
      ],
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      "source": [
        "## Check GPU\n",
        "\n",
        "For this notebook, we're going to be doing something different.\n",
        "\n",
        "We're going to be using mixed precision training.\n",
        "\n",
        "Mixed precision training was introduced in [TensorFlow 2.4.0](https://blog.tensorflow.org/2020/12/whats-new-in-tensorflow-24.html) (a very new feature at the time of writing).\n",
        "\n",
        "What does **mixed precision training** do?\n",
        "\n",
        "Mixed precision training uses a combination of single precision (float32) and half-preicison (float16) data types to speed up model training (up 3x on modern GPUs).\n",
        "\n",
        "We'll talk about this more later on but in the meantime you can read the [TensorFlow documentation on mixed precision](https://www.tensorflow.org/guide/mixed_precision) for more details.\n",
        "\n",
        "For now, before we can move forward if we want to use mixed precision training, we need to make sure the GPU powering our Google Colab instance (if you're using Google Colab) is compataible.\n",
        "\n",
        "For mixed precision training to work, **you need access to a GPU with a compute compability score of 7.0+**.\n",
        "\n",
        "Google Colab offers several kinds of GPU.\n",
        "\n",
        "However, some of them **aren't compatiable with mixed precision training.**\n",
        "\n",
        "Therefore to make sure you have access to mixed precision training in Google Colab, you can check your GPU compute capability score on [Nvidia's developer website](https://developer.nvidia.com/cuda-gpus#compute).\n",
        "\n",
        "As of May 2023, the GPUs available on Google Colab which allow mixed precision training are:\n",
        "* NVIDIA A100 (available with Google Colab Pro)\n",
        "* NVIDIA Tesla T4\n",
        "\n",
        "> 🔑 **Note:** You can run the cell below to check your GPU name and then compare it to [list of GPUs on NVIDIA's developer page](https://developer.nvidia.com/cuda-gpus#compute) to see if it's capable of using mixed precision training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "5daf90cd-d153-4193-8e4c-aab705679323",
      "metadata": {
        "id": "5daf90cd-d153-4193-8e4c-aab705679323",
        "outputId": "a5c0a452-555c-4fcc-ddd1-2d865ce07875",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: Tesla T4 (UUID: GPU-50663067-98d8-9d89-4939-ed4fe04396ea)\n"
          ]
        }
      ],
      "source": [
        "# Get GPU name\n",
        "!nvidia-smi -L"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c25cc91-11e8-442b-9d9f-37ac32e97dbb",
      "metadata": {
        "id": "0c25cc91-11e8-442b-9d9f-37ac32e97dbb"
      },
      "source": [
        "Since mixed precision training was introduced in TensorFlow 2.4.0, make sure you've got at least TensorFlow 2.4.0+."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "2bcbb711-ed02-4616-830b-b13212215eaf",
      "metadata": {
        "id": "2bcbb711-ed02-4616-830b-b13212215eaf",
        "outputId": "c96783b7-eaad-46e1-a4eb-1399770adb2c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m491.2/491.2 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m40.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m70.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m441.0/441.0 kB\u001b[0m \u001b[31m36.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hTensorFlow version: 2.14.0-dev20230615\n",
            "Notebook last run (end-to-end): 2023-06-16 03:58:46.248309\n"
          ]
        }
      ],
      "source": [
        "# Note: As of May 2023, there have been some issues with TensorFlow versions 2.9-2.12\n",
        "# with the following code.\n",
        "# However, these seemed to have been fixed in version 2.13+.\n",
        "# TensorFlow version 2.13 is available in tf-nightly as of May 2023 (will be default in Google Colab soon).\n",
        "# Therefore, to prevent errors we'll install tf-nightly first.\n",
        "# See more here: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/550\n",
        "\n",
        "# Install tf-nightly (required until 2.13.0+ is the default in Google Colab)\n",
        "!pip install -U -q tf-nightly\n",
        "\n",
        "# Check TensorFlow version (should be minimum 2.4.0+ but 2.13.0+ is better)\n",
        "import tensorflow as tf\n",
        "print(f\"TensorFlow version: {tf.__version__}\")\n",
        "\n",
        "# Add timestamp\n",
        "import datetime\n",
        "print(f\"Notebook last run (end-to-end): {datetime.datetime.now()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d80666c6-53e0-433c-996e-9094c6a4cc29",
      "metadata": {
        "id": "d80666c6-53e0-433c-996e-9094c6a4cc29"
      },
      "source": [
        "## Get helper functions\n",
        "\n",
        "We've created a series of helper functions throughout the previous notebooks in the course. Instead of rewriting them (tedious), we'll import the [`helper_functions.py`](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/extras/helper_functions.py) file from the GitHub repo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "5558dbb4-90ca-49a3-856d-6cfe063d3df6",
      "metadata": {
        "id": "5558dbb4-90ca-49a3-856d-6cfe063d3df6",
        "outputId": "309a9869-14ad-48df-b4a6-869aa01c9d05",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-06-16 03:58:46--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10246 (10K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "helper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-06-16 03:58:46 (85.6 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Get helper functions file\n",
        "import os\n",
        "\n",
        "if not os.path.exists(\"helper_functions.py\"):\n",
        "    !wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
        "else:\n",
        "    print(\"[INFO] 'helper_functions.py' already exists, skipping download.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "02363842-bbc3-482d-86c2-90c7d093f7fa",
      "metadata": {
        "id": "02363842-bbc3-482d-86c2-90c7d093f7fa"
      },
      "outputs": [],
      "source": [
        "# Import series of helper functions for the notebook (we've created/used these in previous notebooks)\n",
        "from helper_functions import create_tensorboard_callback, plot_loss_curves, compare_historys"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "87e737e5-e729-4492-8fa2-7f64f376164f",
      "metadata": {
        "id": "87e737e5-e729-4492-8fa2-7f64f376164f"
      },
      "source": [
        "## Use TensorFlow Datasets to Download Data\n",
        "\n",
        "In previous notebooks, we've downloaded our food images (from the [Food101 dataset](https://www.kaggle.com/dansbecker/food-101/home)) from Google Storage.\n",
        "\n",
        "And this is a typical workflow you'd use if you're working on your own datasets.\n",
        "\n",
        "However, there's another way to get datasets ready to use with TensorFlow.\n",
        "\n",
        "For many of the most popular datasets in the machine learning world (often referred to and used as benchmarks), you can access them through [TensorFlow Datasets (TFDS)](https://www.tensorflow.org/datasets/overview).\n",
        "\n",
        "What is **TensorFlow Datasets**?\n",
        "\n",
        "A place for prepared and ready-to-use machine learning datasets.\n",
        "\n",
        "Why use TensorFlow Datasets?\n",
        "\n",
        "* Load data already in Tensors\n",
        "* Practice on well established datasets\n",
        "* Experiment with differet data loading techniques (like we're going to use in this notebook)\n",
        "* Experiment with new TensorFlow features quickly (such as mixed precision training)\n",
        "\n",
        "Why *not* use TensorFlow Datasets?\n",
        "\n",
        "* The datasets are static (they don't change, like your real-world datasets would)\n",
        "* Might not be suited for your particular problem (but great for experimenting)\n",
        "\n",
        "To begin using TensorFlow Datasets we can import it under the alias `tfds`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "ef4f160e-55f6-47e0-8c4e-fba7d1dfd91b",
      "metadata": {
        "id": "ef4f160e-55f6-47e0-8c4e-fba7d1dfd91b"
      },
      "outputs": [],
      "source": [
        "# Get TensorFlow Datasets\n",
        "import tensorflow_datasets as tfds"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6fbb4d29-aeb1-4ed5-928e-334cc3637b13",
      "metadata": {
        "id": "6fbb4d29-aeb1-4ed5-928e-334cc3637b13"
      },
      "source": [
        "To find all of the available datasets in TensorFlow Datasets, you can use the `list_builders()` method.\n",
        "\n",
        "After doing so, we can check to see if the one we're after (`\"food101\"`) is present."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "1aeb0578-b57f-4993-a834-8246aca0e252",
      "metadata": {
        "id": "1aeb0578-b57f-4993-a834-8246aca0e252",
        "outputId": "32b32b20-90a0-46bb-e705-a7973248a9d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'food101' in TensorFlow Datasets: True\n"
          ]
        }
      ],
      "source": [
        "# Get all available datasets in TFDS\n",
        "datasets_list = tfds.list_builders()\n",
        "\n",
        "# Set our target dataset and see if it exists\n",
        "target_dataset = \"food101\"\n",
        "print(f\"'{target_dataset}' in TensorFlow Datasets: {target_dataset in datasets_list}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9f9c2ba-5386-4eca-a26e-1fb564287faa",
      "metadata": {
        "id": "f9f9c2ba-5386-4eca-a26e-1fb564287faa"
      },
      "source": [
        "Beautiful! It looks like the dataset we're after is available (note there are plenty more available but we're on Food101).\n",
        "\n",
        "To get access to the Food101 dataset from the TFDS, we can use the [`tfds.load()`](https://www.tensorflow.org/datasets/api_docs/python/tfds/load) method.\n",
        "\n",
        "In particular, we'll have to pass it a few parameters to let it know what we're after:\n",
        "* `name` (str) : the target dataset (e.g. `\"food101\"`)\n",
        "* `split` (list, optional) : what splits of the dataset we're after (e.g. `[\"train\", \"validation\"]`)\n",
        "  * the `split` parameter is quite tricky. See [the documentation for more](https://github.com/tensorflow/datasets/blob/master/docs/splits.md).\n",
        "* `shuffle_files` (bool) : whether or not to shuffle the files on download, defaults to `False`\n",
        "* `as_supervised` (bool) : `True` to download data samples in tuple format (`(data, label)`) or `False` for dictionary format\n",
        "* `with_info` (bool) : `True` to download dataset metadata (labels, number of samples, etc)\n",
        "\n",
        "> 🔑 **Note:** Calling the `tfds.load()` method will start to download a target dataset to disk if the `download=True` parameter is set (default). This dataset could be 100GB+, so make sure you have space."
      ]
    },
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        },
        "outputId": "1d890ca7-10a2-4385-d0e2-a884942422e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 4.65 GiB (download: 4.65 GiB, generated: Unknown size, total: 4.65 GiB) to /root/tensorflow_datasets/food101/2.0.0...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e04d7a4ceab4f2098be258e455bea9f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86589c2bbd394843a8254c311cad942c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8085c7d0bae0423fa4792fcd0bd006ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d243b5d95904380b3cc7a72f45ae402"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/75750 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acad93503fa2439288c2c3be244122eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/food101/2.0.0.incomplete2LN8GQ/food101-train.tfrecord*...:   0%|          …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee0cd392877c4067a2da8b28abb4fddf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation examples...:   0%|          | 0/25250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "14742d5a5bd94fc4ae4825c9594a2d89"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/food101/2.0.0.incomplete2LN8GQ/food101-validation.tfrecord*...:   0%|     …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d25632ad7a344a8792fe32df5fb5331c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset food101 downloaded and prepared to /root/tensorflow_datasets/food101/2.0.0. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "# Load in the data (takes about 5-6 minutes in Google Colab)\n",
        "(train_data, test_data), ds_info = tfds.load(name=\"food101\", # target dataset to get from TFDS\n",
        "                                             split=[\"train\", \"validation\"], # what splits of data should we get? note: not all datasets have train, valid, test\n",
        "                                             shuffle_files=True, # shuffle files on download?\n",
        "                                             as_supervised=True, # download data in tuple format (sample, label), e.g. (image, label)\n",
        "                                             with_info=True) # include dataset metadata? if so, tfds.load() returns tuple (data, ds_info)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "51033691-941b-4659-80df-9461689753f0",
      "metadata": {
        "id": "51033691-941b-4659-80df-9461689753f0"
      },
      "source": [
        "Wonderful! After a few minutes of downloading, we've now got access to entire Food101 dataset (in tensor format) ready for modelling.\n",
        "\n",
        "Now let's get a little information from our dataset, starting with the class names.\n",
        "\n",
        "Getting class names from a TensorFlow Datasets dataset requires downloading the \"`dataset_info`\" variable (by using the `as_supervised=True` parameter in the `tfds.load()` method, **note:** this will only work for supervised datasets in TFDS).\n",
        "\n",
        "We can access the class names of a particular dataset using the `dataset_info.features` attribute and accessing `names` attribute of the the `\"label\"` key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "6115265e-469b-4fcd-a54b-7fb4e59b90af",
      "metadata": {
        "id": "6115265e-469b-4fcd-a54b-7fb4e59b90af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7e2e87d-0002-4e81-8ac4-884e41372e6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FeaturesDict({\n",
              "    'image': Image(shape=(None, None, 3), dtype=uint8),\n",
              "    'label': ClassLabel(shape=(), dtype=int64, num_classes=101),\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Features of Food101 TFDS\n",
        "ds_info.features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "51532ab7-9ffa-4d19-9726-389984f39a04",
      "metadata": {
        "id": "51532ab7-9ffa-4d19-9726-389984f39a04",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9437bfc-0d0f-4068-8d36-2c6a0c9791a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apple_pie',\n",
              " 'baby_back_ribs',\n",
              " 'baklava',\n",
              " 'beef_carpaccio',\n",
              " 'beef_tartare',\n",
              " 'beet_salad',\n",
              " 'beignets',\n",
              " 'bibimbap',\n",
              " 'bread_pudding',\n",
              " 'breakfast_burrito']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Get class names\n",
        "class_names = ds_info.features[\"label\"].names\n",
        "class_names[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "896c2153-9420-432d-a424-704424789ba8",
      "metadata": {
        "id": "896c2153-9420-432d-a424-704424789ba8"
      },
      "source": [
        "### Exploring the Food101 data from TensorFlow Datasets\n",
        "\n",
        "Now we've downloaded the Food101 dataset from TensorFlow Datasets, how about we do what any good data explorer should?\n",
        "\n",
        "In other words, \"visualize, visualize, visualize\".\n",
        "\n",
        "Let's find out a few details about our dataset:\n",
        "* The shape of our input data (image tensors)\n",
        "* The datatype of our input data\n",
        "* What the labels of our input data look like (e.g. one-hot encoded versus label-encoded)\n",
        "* Do the labels match up with the class names?\n",
        "\n",
        "To do, let's take one sample off the training data (using the [`.take()` method](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#take)) and explore it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "60b89a61-50e3-4058-9dae-bb0cefacca16",
      "metadata": {
        "id": "60b89a61-50e3-4058-9dae-bb0cefacca16"
      },
      "outputs": [],
      "source": [
        "# Take one sample off the training data\n",
        "train_one_sample = train_data.take(1) # samples are in format (image_tensor, label)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42bb1dfd-7c88-49a1-a174-1f6ba973ad61",
      "metadata": {
        "id": "42bb1dfd-7c88-49a1-a174-1f6ba973ad61"
      },
      "source": [
        "Because we used the `as_supervised=True` parameter in our `tfds.load()` method above, data samples come in the tuple format structure `(data, label)` or in our case `(image_tensor, label)`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "8a13d601-0911-4a36-be2c-a59ca6b809a4",
      "metadata": {
        "id": "8a13d601-0911-4a36-be2c-a59ca6b809a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "104c9045-a2f8-4f54-e522-1a2cabc24122"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_TakeDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# What does one sample of our training data look like?\n",
        "train_one_sample"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a83d5488-6cd0-4dd2-8d83-ae80eaf06c73",
      "metadata": {
        "id": "a83d5488-6cd0-4dd2-8d83-ae80eaf06c73"
      },
      "source": [
        "Let's loop through our single training sample and get some info from the `image_tensor` and `label`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "123d5880-776f-4002-8eca-5f1fae500b7a",
      "metadata": {
        "id": "123d5880-776f-4002-8eca-5f1fae500b7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2201a6e-2975-4416-d225-dd7690cf8bc9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  Image shape: (512, 512, 3)\n",
            "  Image dtype: <dtype: 'uint8'>\n",
            "  Target class from Food101 (tensor form): 64\n",
            "  Class name (str form): miso_soup\n",
            "        \n"
          ]
        }
      ],
      "source": [
        "# Output info about our training sample\n",
        "for image, label in train_one_sample:\n",
        "  print(f\"\"\"\n",
        "  Image shape: {image.shape}\n",
        "  Image dtype: {image.dtype}\n",
        "  Target class from Food101 (tensor form): {label}\n",
        "  Class name (str form): {class_names[label.numpy()]}\n",
        "        \"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ab240eb4-fedf-42d5-aaba-8906d11af3e2",
      "metadata": {
        "id": "ab240eb4-fedf-42d5-aaba-8906d11af3e2"
      },
      "source": [
        "Because we set the `shuffle_files=True` parameter in our `tfds.load()` method above, running the cell above a few times will give a different result each time.\n",
        "\n",
        "Checking these you might notice some of the images have different shapes, for example `(512, 342, 3)` and `(512, 512, 3)` (height, width, color_channels).\n",
        "\n",
        "Let's see what one of the image tensors from TFDS's Food101 dataset looks like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "5bc3ed60-3003-4239-ad3b-9a77b4e750f0",
      "metadata": {
        "id": "5bc3ed60-3003-4239-ad3b-9a77b4e750f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5712a7ff-bbbd-4045-f0e8-740ba6f5dc88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(512, 512, 3), dtype=uint8, numpy=\n",
              "array([[[ 43,  89, 125],\n",
              "        [ 52,  96, 131],\n",
              "        [ 85, 128, 162],\n",
              "        ...,\n",
              "        [251, 254, 223],\n",
              "        [250, 253, 222],\n",
              "        [250, 253, 222]],\n",
              "\n",
              "       [[ 42,  88, 124],\n",
              "        [ 53,  97, 132],\n",
              "        [ 92, 135, 169],\n",
              "        ...,\n",
              "        [251, 254, 223],\n",
              "        [250, 253, 222],\n",
              "        [250, 253, 222]],\n",
              "\n",
              "       [[ 45,  89, 124],\n",
              "        [ 52,  96, 131],\n",
              "        [ 92, 135, 169],\n",
              "        ...,\n",
              "        [251, 254, 223],\n",
              "        [250, 253, 222],\n",
              "        [250, 253, 222]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[ 91,  99,  86],\n",
              "        [ 89,  97,  84],\n",
              "        [ 88,  94,  82],\n",
              "        ...,\n",
              "        [ 37,  44,  50],\n",
              "        [ 34,  41,  47],\n",
              "        [ 31,  38,  44]],\n",
              "\n",
              "       [[ 91,  99,  86],\n",
              "        [ 90,  98,  85],\n",
              "        [ 88,  96,  83],\n",
              "        ...,\n",
              "        [ 38,  43,  47],\n",
              "        [ 35,  40,  44],\n",
              "        [ 33,  38,  42]],\n",
              "\n",
              "       [[ 93, 101,  88],\n",
              "        [ 93, 101,  88],\n",
              "        [ 89,  97,  84],\n",
              "        ...,\n",
              "        [ 37,  42,  46],\n",
              "        [ 35,  40,  44],\n",
              "        [ 35,  40,  44]]], dtype=uint8)>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# What does an image tensor from TFDS's Food101 look like?\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "808e21e5-6951-4eac-bb3c-00fc1ae3fa0f",
      "metadata": {
        "id": "808e21e5-6951-4eac-bb3c-00fc1ae3fa0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a2ef80e-8b90-453b-a7c6-11acbc388aed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=uint8, numpy=0>,\n",
              " <tf.Tensor: shape=(), dtype=uint8, numpy=255>)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# What are the min and max values?\n",
        "tf.reduce_min(image), tf.reduce_max(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ff7dad2-0972-4905-b14c-549609401d98",
      "metadata": {
        "id": "3ff7dad2-0972-4905-b14c-549609401d98"
      },
      "source": [
        "Alright looks like our image tensors have values of between 0 & 255 (standard red, green, blue colour values) and the values are of data type `unit8`.\n",
        "\n",
        "We might have to preprocess these before passing them to a neural network. But we'll handle this later.\n",
        "\n",
        "In the meantime, let's see if we can plot an image sample."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "630fb994-b61f-4a03-8831-83fe50ab6ca1",
      "metadata": {
        "id": "630fb994-b61f-4a03-8831-83fe50ab6ca1"
      },
      "source": [
        "### Plot an image from TensorFlow Datasets\n",
        "\n",
        "We've seen our image tensors in tensor format, now let's really adhere to our motto.\n",
        "\n",
        "\"Visualize, visualize, visualize!\"\n",
        "\n",
        "Let's plot one of the image samples using [`matplotlib.pyplot.imshow()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) and set the title to target class name."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "3f19d6a3-b2ac-47e9-a670-c08461897d6c",
      "metadata": {
        "id": "3f19d6a3-b2ac-47e9-a670-c08461897d6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "9dbd9aeb-2938-4339-fa54-89b6bb2f9b3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot an image tensor\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(image)\n",
        "plt.title(class_names[label.numpy()]) # add title to image by indexing on class_names list\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04fa7e9a-9918-4bc0-8e0f-c87893922157",
      "metadata": {
        "id": "04fa7e9a-9918-4bc0-8e0f-c87893922157"
      },
      "source": [
        "Delicious!\n",
        "\n",
        "Okay, looks like the Food101 data we've got from TFDS is similar to the datasets we've been using in previous notebooks.\n",
        "\n",
        "Now let's preprocess it and get it ready for use with a neural network."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e1e2538-2340-4394-8700-6a126f1b2faa",
      "metadata": {
        "id": "8e1e2538-2340-4394-8700-6a126f1b2faa"
      },
      "source": [
        "## Create preprocessing functions for our data\n",
        "\n",
        "In previous notebooks, when our images were in folder format we used the method [`tf.keras.utils.image_dataset_from_directory()`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory) to load them in.\n",
        "\n",
        "Doing this meant our data was loaded into a format ready to be used with our models.\n",
        "\n",
        "However, since we've downloaded the data from TensorFlow Datasets, there are a couple of preprocessing steps we have to take before it's ready to model.\n",
        "\n",
        "More specifically, our data is currently:\n",
        "\n",
        "* In `uint8` data type\n",
        "* Comprised of all differnet sized tensors (different sized images)\n",
        "* Not scaled (the pixel values are between 0 & 255)\n",
        "\n",
        "Whereas, models like data to be:\n",
        "\n",
        "* In `float32` data type\n",
        "* Have all of the same size tensors (batches require all tensors have the same shape, e.g. `(224, 224, 3)`)\n",
        "* Scaled (values between 0 & 1), also called normalized\n",
        "\n",
        "To take care of these, we'll create a `preprocess_img()` function which:\n",
        "\n",
        "* Resizes an input image tensor to a specified size using [`tf.image.resize()`](https://www.tensorflow.org/api_docs/python/tf/image/resize)\n",
        "* Converts an input image tensor's current datatype to `tf.float32` using [`tf.cast()`](https://www.tensorflow.org/api_docs/python/tf/cast)\n",
        "\n",
        "> 🔑 **Note:** Pretrained EfficientNetBX models in [`tf.keras.applications.efficientnet`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet) (what we're going to be using) have rescaling built-in. But for many other model architectures you'll want to rescale your data (e.g. get its values between 0 & 1). This could be incorporated inside your \"`preprocess_img()`\" function (like the one below) or within your model as a [`tf.keras.layers.Rescaling`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Rescaling) layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "51b7307e-165a-4509-8e99-ce484bfc39f8",
      "metadata": {
        "id": "51b7307e-165a-4509-8e99-ce484bfc39f8"
      },
      "outputs": [],
      "source": [
        "# Make a function for preprocessing images\n",
        "def preprocess_img(image, label, img_shape=224):\n",
        "    \"\"\"\n",
        "    Converts image datatype from 'uint8' -> 'float32' and reshapes image to\n",
        "    [img_shape, img_shape, color_channels]\n",
        "    \"\"\"\n",
        "    image = tf.image.resize(image, [img_shape, img_shape]) # reshape to img_shape\n",
        "    return tf.cast(image, tf.float32), label # return (float32_image, label) tuple"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "253aaffa-6722-46cc-9b4e-2b625cca84fc",
      "metadata": {
        "id": "253aaffa-6722-46cc-9b4e-2b625cca84fc"
      },
      "source": [
        "Our `preprocess_img()` function above takes image and label as input (even though it does nothing to the label) because our dataset is currently in the tuple structure `(image, label)`.\n",
        "\n",
        "Let's try our function out on a target image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "9cdb051f-6e67-4189-a69a-44748d4a7bd5",
      "metadata": {
        "id": "9cdb051f-6e67-4189-a69a-44748d4a7bd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10c6b3a5-b293-48af-bfdf-dafa6b92379e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image before preprocessing:\n",
            " [[[ 43  89 125]\n",
            "  [ 52  96 131]\n",
            "  [ 85 128 162]\n",
            "  ...\n",
            "  [251 254 223]\n",
            "  [250 253 222]\n",
            "  [250 253 222]]\n",
            "\n",
            " [[ 42  88 124]\n",
            "  [ 53  97 132]\n",
            "  [ 92 135 169]\n",
            "  ...\n",
            "  [251 254 223]\n",
            "  [250 253 222]\n",
            "  [250 253 222]]]...,\n",
            "Shape: (512, 512, 3),\n",
            "Datatype: <dtype: 'uint8'>\n",
            "\n",
            "Image after preprocessing:\n",
            " [[[ 48.969387  93.68367  129.04082 ]\n",
            "  [124.78572  164.07144  195.28572 ]\n",
            "  [125.37245  158.94388  183.5153  ]\n",
            "  ...\n",
            "  [251.78574  254.78574  223.78574 ]\n",
            "  [251.       254.       223.      ]\n",
            "  [250.       253.       222.      ]]\n",
            "\n",
            " [[ 65.28572  108.688774 143.09183 ]\n",
            "  [129.93878  169.09184  200.17348 ]\n",
            "  [ 79.61224  115.04081  140.88266 ]\n",
            "  ...\n",
            "  [251.78574  254.78574  223.78574 ]\n",
            "  [251.       254.       223.      ]\n",
            "  [250.       253.       222.      ]]]...,\n",
            "Shape: (224, 224, 3),\n",
            "Datatype: <dtype: 'float32'>\n"
          ]
        }
      ],
      "source": [
        "# Preprocess a single sample image and check the outputs\n",
        "preprocessed_img = preprocess_img(image, label)[0]\n",
        "print(f\"Image before preprocessing:\\n {image[:2]}...,\\nShape: {image.shape},\\nDatatype: {image.dtype}\\n\")\n",
        "print(f\"Image after preprocessing:\\n {preprocessed_img[:2]}...,\\nShape: {preprocessed_img.shape},\\nDatatype: {preprocessed_img.dtype}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2f56a9f-6d66-4c7a-9321-5831391a1e3e",
      "metadata": {
        "id": "b2f56a9f-6d66-4c7a-9321-5831391a1e3e"
      },
      "source": [
        "Excellent! Looks like our `preprocess_img()` function is working as expected.\n",
        "\n",
        "The input image gets converted from `uint8` to `float32` and gets reshaped from its current shape to `(224, 224, 3)`.\n",
        "\n",
        "How does it look?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "6046678b-0e38-40bf-b4c5-e0d4b0d98f97",
      "metadata": {
        "id": "6046678b-0e38-40bf-b4c5-e0d4b0d98f97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "15d43ff7-011f-49b8-ba18-55efdc346883"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# We can still plot our preprocessed image as long as we\n",
        "# divide by 255 (for matplotlib capatibility)\n",
        "plt.imshow(preprocessed_img/255.)\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3324aa01-f28e-4960-b5e5-0e245310f2d9",
      "metadata": {
        "id": "3324aa01-f28e-4960-b5e5-0e245310f2d9"
      },
      "source": [
        "All this food visualization is making me hungry. How about we start preparing to model it?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24a4982c-dc50-4aae-b823-9ac13c8d95b6",
      "metadata": {
        "id": "24a4982c-dc50-4aae-b823-9ac13c8d95b6"
      },
      "source": [
        "## Batch & prepare datasets\n",
        "\n",
        "Before we can model our data, we have to turn it into batches.\n",
        "\n",
        "Why?\n",
        "\n",
        "Because computing on batches is memory efficient.\n",
        "\n",
        "We turn our data from 101,000 image tensors and labels (train and test combined) into batches of 32 image and label pairs, thus enabling it to fit into the memory of our GPU.\n",
        "\n",
        "To do this in effective way, we're going to be leveraging a number of methods from the [`tf.data` API](https://www.tensorflow.org/api_docs/python/tf/data).\n",
        "\n",
        "> 📖 **Resource:** For loading data in the most performant way possible, see the TensorFlow docuemntation on [Better performance with the tf.data API](https://www.tensorflow.org/guide/data_performance).\n",
        "\n",
        "Specifically, we're going to be using:\n",
        "\n",
        "* [`map()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map) - maps a predefined function to a target dataset (e.g. `preprocess_img()` to our image tensors)\n",
        "* [`shuffle()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) - randomly shuffles the elements of a target dataset up `buffer_size` (ideally, the `buffer_size` is equal to the size of the dataset, however, this may have implications on memory)\n",
        "* [`batch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch) - turns elements of a target dataset into batches (size defined by parameter `batch_size`)\n",
        "* [`prefetch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch) - prepares subsequent batches of data whilst other batches of data are being computed on (improves data loading speed but costs memory)\n",
        "* Extra: [`cache()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache) - caches (saves them for later) elements in a target dataset, saving loading time (will only work if your dataset is small enough to fit in memory, standard Colab instances only have 12GB of memory)\n",
        "\n",
        "Things to note:\n",
        "- Can't batch tensors of different shapes (e.g. different image sizes, need to reshape images first, hence our `preprocess_img()` function)\n",
        "- `shuffle()` keeps a buffer of the number you pass it images shuffled, ideally this number would be all of the samples in your training set, however, if your training set is large, this buffer might not fit in memory (a fairly large number like 1000 or 10000 is usually suffice for shuffling)\n",
        "- For methods with the `num_parallel_calls` parameter available (such as `map()`), setting it to`num_parallel_calls=tf.data.AUTOTUNE` will parallelize preprocessing and significantly improve speed\n",
        "- Can't use `cache()` unless your dataset can fit in memory\n",
        "\n",
        "Woah, the above is alot. But once we've coded below, it'll start to make sense.\n",
        "\n",
        "We're going to through things in the following order:\n",
        "\n",
        "```\n",
        "Original dataset (e.g. train_data) -> map() -> shuffle() -> batch() -> prefetch() -> PrefetchDataset\n",
        "```\n",
        "\n",
        "This is like saying,\n",
        "\n",
        "> \"Hey, map this preprocessing function across our training dataset, then shuffle a number of elements before batching them together and make sure you prepare new batches (prefetch) whilst the model is looking through the current batch\".\n",
        "\n",
        "![](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/07-prefetching-from-hands-on-ml.png)\n",
        "\n",
        "*What happens when you use prefetching (faster) versus what happens when you don't use prefetching (slower). **Source:** Page 422 of [Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow Book by Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/).*\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "15991d7f-b649-4a3b-b443-e61b56ce653c",
      "metadata": {
        "id": "15991d7f-b649-4a3b-b443-e61b56ce653c"
      },
      "outputs": [],
      "source": [
        "# Map preprocessing function to training data (and paralellize)\n",
        "train_data = train_data.map(map_func=preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "# Shuffle train_data and turn it into batches and prefetch it (load it faster)\n",
        "train_data = train_data.shuffle(buffer_size=1000).batch(batch_size=32).prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "\n",
        "# Map prepreprocessing function to test data\n",
        "test_data = test_data.map(preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "# Turn test data into batches (don't need to shuffle)\n",
        "test_data = test_data.batch(32).prefetch(tf.data.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f504e9c7-6bf8-4fdb-acd4-c67abffbf091",
      "metadata": {
        "id": "f504e9c7-6bf8-4fdb-acd4-c67abffbf091"
      },
      "source": [
        "And now let's check out what our prepared datasets look like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "9b2f3c75-739c-4eb0-8156-a21dea3de937",
      "metadata": {
        "id": "9b2f3c75-739c-4eb0-8156-a21dea3de937",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcea1a54-ee0f-4992-bf1c-e9281c8a09b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>,\n",
              " <_PrefetchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "train_data, test_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5781af7a-7faa-4fc6-b1e9-8b5db0d233b0",
      "metadata": {
        "id": "5781af7a-7faa-4fc6-b1e9-8b5db0d233b0"
      },
      "source": [
        "Excellent! Looks like our data is now in tutples of `(image, label)` with datatypes of `(tf.float32, tf.int64)`, just what our model is after.\n",
        "\n",
        "> 🔑 **Note:** You can get away without calling the `prefetch()` method on the end of your datasets, however, you'd probably see significantly slower data loading speeds when building a model. So most of your dataset input pipelines should end with a call to [`prefecth()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch).\n",
        "\n",
        "Onward."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "df30b415-ee22-4694-b7d2-dfe968e7bd10",
      "metadata": {
        "id": "df30b415-ee22-4694-b7d2-dfe968e7bd10"
      },
      "source": [
        "## Create modelling callbacks\n",
        "\n",
        "Since we're going to be training on a large amount of data and training could take a long time, it's a good idea to set up some modelling callbacks so we be sure of things like our model's training logs being tracked and our model being checkpointed (saved) after various training milestones.\n",
        "\n",
        "To do each of these we'll use the following callbacks:\n",
        "* [`tf.keras.callbacks.TensorBoard()`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/TensorBoard) - allows us to keep track of our model's training history so we can inspect it later (**note:** we've created this callback before have imported it from `helper_functions.py` as `create_tensorboard_callback()`)\n",
        "* [`tf.keras.callbacks.ModelCheckpoint()`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint) - saves our model's progress at various intervals so we can load it and resuse it later without having to retrain it\n",
        "  * Checkpointing is also helpful so we can start fine-tuning our model at a particular epoch and revert back to a previous state if fine-tuning offers no benefits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "2f54a576-3892-4043-a500-9a6b95199dce",
      "metadata": {
        "id": "2f54a576-3892-4043-a500-9a6b95199dce"
      },
      "outputs": [],
      "source": [
        "# Create TensorBoard callback (already have \"create_tensorboard_callback()\" from a previous notebook)\n",
        "from helper_functions import create_tensorboard_callback\n",
        "\n",
        "# Create ModelCheckpoint callback to save model's progress\n",
        "checkpoint_path = \"model_checkpoints/cp.ckpt\" # saving weights requires \".ckpt\" extension\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n",
        "                                                      monitor=\"val_accuracy\", # save the model weights with best validation accuracy\n",
        "                                                      save_best_only=True, # only save the best weights\n",
        "                                                      save_weights_only=True, # only save model weights (not whole model)\n",
        "                                                      verbose=0) # don't print out whether or not model is being saved"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04981d24-169e-4148-8b83-de19983a93ee",
      "metadata": {
        "id": "04981d24-169e-4148-8b83-de19983a93ee"
      },
      "source": [
        "## Setup mixed precision training\n",
        "\n",
        "We touched on mixed precision training above.\n",
        "\n",
        "However, we didn't quite explain it.\n",
        "\n",
        "Normally, tensors in TensorFlow default to the float32 datatype (unless otherwise specified).\n",
        "\n",
        "In computer science, float32 is also known as [single-precision floating-point format](https://en.wikipedia.org/wiki/Single-precision_floating-point_format). The 32 means it usually occupies 32 bits in computer memory.\n",
        "\n",
        "Your GPU has a limited memory, therefore it can only handle a number of float32 tensors at the same time.\n",
        "\n",
        "This is where mixed precision training comes in.\n",
        "\n",
        "Mixed precision training involves using a mix of float16 and float32 tensors to make better use of your GPU's memory.\n",
        "\n",
        "Can you guess what float16 means?\n",
        "\n",
        "Well, if you thought since float32 meant single-precision floating-point, you might've guessed float16 means [half-precision floating-point format](https://en.wikipedia.org/wiki/Half-precision_floating-point_format). And if you did, you're right! And if not, no trouble, now you know.\n",
        "\n",
        "For tensors in float16 format, each element occupies 16 bits in computer memory.\n",
        "\n",
        "So, where does this leave us?\n",
        "\n",
        "As mentioned before, when using mixed precision training, your model will make use of float32 and float16 data types to use less memory where possible and in turn run faster (using less memory per tensor means more tensors can be computed on simultaneously).\n",
        "\n",
        "As a result, using mixed precision training can improve your performance on modern GPUs (those with a compute capability score of 7.0+) by up to 3x.\n",
        "\n",
        "For a more detailed explanation, I encourage you to read through the [TensorFlow mixed precision guide](https://www.tensorflow.org/guide/mixed_precision) (I'd highly recommend at least checking out the summary).\n",
        "\n",
        "![](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/07-mixed-precision-speedup-equals-3x-gpu.png)\n",
        "*Because mixed precision training uses a combination of float32 and float16 data types, you may see up to a 3x speedup on modern GPUs.*\n",
        "\n",
        "> 🔑 **Note:** If your GPU doesn't have a score of over 7.0+ (e.g. P100 in Google Colab), mixed precision won't work (see: [\"Supported Hardware\"](https://www.tensorflow.org/guide/mixed_precision#supported_hardware) in the mixed precision guide for more).\n",
        "\n",
        "> 📖 **Resource:** If you'd like to learn more about precision in computer science (the detail to which a numerical quantity is expressed by a computer), see the [Wikipedia page](https://en.wikipedia.org/wiki/Precision_(computer_science)) (and accompanying resources).\n",
        "\n",
        "Okay, enough talk, let's see how we can turn on mixed precision training in TensorFlow.\n",
        "\n",
        "The beautiful thing is, the [`tensorflow.keras.mixed_precision`](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/) API has made it very easy for us to get started.\n",
        "\n",
        "First, we'll import the API and then use the [`set_global_policy()`](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/set_global_policy) method to set the *dtype policy* to `\"mixed_float16\"`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "4a06d5fd-54e6-4220-a2f3-67445225e033",
      "metadata": {
        "id": "4a06d5fd-54e6-4220-a2f3-67445225e033"
      },
      "outputs": [],
      "source": [
        "# Turn on mixed precision training\n",
        "from tensorflow.keras import mixed_precision\n",
        "mixed_precision.set_global_policy(policy=\"mixed_float16\") # set global policy to mixed precision"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c5e49ae-4276-49f3-b736-f2479866e156",
      "metadata": {
        "id": "1c5e49ae-4276-49f3-b736-f2479866e156"
      },
      "source": [
        "Nice! As long as the GPU you're using has a compute capability of 7.0+ the cell above should run without error.\n",
        "\n",
        "Now we can check the global dtype policy (the policy which will be used by layers in our model) using the [`mixed_precision.global_policy()`](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/global_policy) method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "d45bd7e1-b5f1-4ffb-8bec-1f8281023510",
      "metadata": {
        "id": "d45bd7e1-b5f1-4ffb-8bec-1f8281023510",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fee90f15-e91a-47f3-dbca-10459b4628c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Policy \"mixed_float16\">"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "mixed_precision.global_policy() # should output \"mixed_float16\" (if your GPU is compatible with mixed precision)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e66df4cf-9daa-4ee2-8df0-cf29e667b5e4",
      "metadata": {
        "id": "e66df4cf-9daa-4ee2-8df0-cf29e667b5e4"
      },
      "source": [
        "Great, since the global dtype policy is now `\"mixed_float16\"` our model will automatically take advantage of float16 variables where possible and in turn speed up training."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a64ec4e-8774-4bdc-a5d2-f0a44f85d50e",
      "metadata": {
        "id": "1a64ec4e-8774-4bdc-a5d2-f0a44f85d50e"
      },
      "source": [
        "## Build feature extraction model\n",
        "\n",
        "Callbacks: ready to roll.\n",
        "\n",
        "Mixed precision: turned on.\n",
        "\n",
        "Let's build a model.\n",
        "\n",
        "Because our dataset is quite large, we're going to move towards fine-tuning an existing pretrained model (EfficienetNetB0).\n",
        "\n",
        "But before we get into fine-tuning, let's set up a feature-extraction model.\n",
        "\n",
        "Recall, the typical order for using transfer learning is:\n",
        "\n",
        "1. Build a feature extraction model (replace the top few layers of a pretrained model)\n",
        "2. Train for a few epochs with lower layers frozen\n",
        "3. Fine-tune if necessary with multiple layers unfrozen\n",
        "\n",
        "![](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/07-feature-extraction-then-fine-tune.png)\n",
        "*Before fine-tuning, it's best practice to train a feature extraction model with custom top layers.*\n",
        "\n",
        "To build the feature extraction model (covered in [Transfer Learning in TensorFlow Part 1: Feature extraction](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/04_transfer_learning_in_tensorflow_part_1_feature_extraction.ipynb)), we'll:\n",
        "* Use `EfficientNetB0` from [`tf.keras.applications`](https://www.tensorflow.org/api_docs/python/tf/keras/applications) pre-trained on ImageNet as our base model\n",
        "  * We'll download this without the top layers using `include_top=False` parameter so we can create our own output layers\n",
        "* Freeze the base model layers so we can use the pre-learned patterns the base model has found on ImageNet\n",
        "* Put together the input, base model, pooling and output layers in a [Functional model](https://keras.io/guides/functional_api/)\n",
        "* Compile the Functional model using the Adam optimizer and [sparse categorical crossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy) as the loss function (since our labels **aren't** one-hot encoded)\n",
        "* Fit the model for 3 epochs using the TensorBoard and ModelCheckpoint callbacks\n",
        "\n",
        "> 🔑 **Note:** Since we're using mixed precision training, our model needs a separate output layer with a hard-coded `dtype=float32`, for example, `layers.Activation(\"softmax\", dtype=tf.float32)`. This ensures the outputs of our model are returned back to the float32 data type which is more numerically stable than the float16 datatype (important for loss calculations). See the [\"Building the model\"](https://www.tensorflow.org/guide/mixed_precision#building_the_model) section in the TensorFlow mixed precision guide for more.\n",
        "\n",
        "![](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/07-mixed-precision-code-before-and-after.png)\n",
        "*Turning mixed precision on in TensorFlow with 3 lines of code.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "7573b3d3-28bd-469b-a1c4-f19e780fce38",
      "metadata": {
        "id": "7573b3d3-28bd-469b-a1c4-f19e780fce38",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d0f3916-cf50-49c6-fa1d-ff47de2c05f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16705208/16705208 [==============================] - 2s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras import layers\n",
        "\n",
        "# Create base model\n",
        "input_shape = (224, 224, 3)\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "base_model.trainable = False # freeze base model layers\n",
        "\n",
        "# Create Functional model\n",
        "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "# Note: EfficientNetBX models have rescaling built-in but if your model didn't you could have a layer like below\n",
        "# x = layers.Rescaling(1./255)(x)\n",
        "x = base_model(inputs, training=False) # set base_model to inference mode only\n",
        "x = layers.GlobalAveragePooling2D(name=\"pooling_layer\")(x)\n",
        "x = layers.Dense(len(class_names))(x) # want one output neuron per class\n",
        "# Separate activation of output layer so we can output float32 activations\n",
        "outputs = layers.Activation(\"softmax\", dtype=tf.float32, name=\"softmax_float32\")(x)\n",
        "model = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", # Use sparse_categorical_crossentropy when labels are *not* one-hot\n",
        "              optimizer=tf.keras.optimizers.Adam(),\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "79cdfae0-7229-4923-bf55-9529c2d09134",
      "metadata": {
        "id": "79cdfae0-7229-4923-bf55-9529c2d09134",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea7f3fb5-2167-48d7-c724-0b336c25ac79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " efficientnetb0 (Functional  (None, None, None, 1280   4049571   \n",
            " )                           )                                   \n",
            "                                                                 \n",
            " pooling_layer (GlobalAvera  (None, 1280)              0         \n",
            " gePooling2D)                                                    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 101)               129381    \n",
            "                                                                 \n",
            " softmax_float32 (Activatio  (None, 101)               0         \n",
            " n)                                                              \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4178952 (15.94 MB)\n",
            "Trainable params: 129381 (505.39 KB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Check out our model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26787fb7-3762-41de-b61e-f01b54412d00",
      "metadata": {
        "id": "26787fb7-3762-41de-b61e-f01b54412d00"
      },
      "source": [
        "## Checking layer dtype policies (are we using mixed precision?)\n",
        "\n",
        "Model ready to go!\n",
        "\n",
        "Before we said the mixed precision API will automatically change our layers' dtype policy's to whatever the global dtype policy is (in our case it's `\"mixed_float16\"`).\n",
        "\n",
        "We can check this by iterating through our model's layers and printing layer attributes such as `dtype` and `dtype_policy`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "2a2df5ac-c297-4049-b721-9967ea6be88f",
      "metadata": {
        "id": "2a2df5ac-c297-4049-b721-9967ea6be88f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8503854c-52b9-4e49-e503-2c9f9dbb8453"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_layer True float32 <Policy \"float32\">\n",
            "efficientnetb0 False float32 <Policy \"mixed_float16\">\n",
            "pooling_layer True float32 <Policy \"mixed_float16\">\n",
            "dense True float32 <Policy \"mixed_float16\">\n",
            "softmax_float32 True float32 <Policy \"float32\">\n"
          ]
        }
      ],
      "source": [
        "# Check the dtype_policy attributes of layers in our model\n",
        "for layer in model.layers:\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy) # Check the dtype policy of layers"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "48db7fea-fb9d-4cc1-8b78-072e1dbc93e8",
      "metadata": {
        "id": "48db7fea-fb9d-4cc1-8b78-072e1dbc93e8"
      },
      "source": [
        "Going through the above we see:\n",
        "* `layer.name` (str) : a layer's human-readable name, can be defined by the `name` parameter on construction\n",
        "* `layer.trainable` (bool) : whether or not a layer is trainable (all of our layers are trainable except the efficientnetb0 layer since we set it's `trainable` attribute to `False`\n",
        "* `layer.dtype` : the data type a layer stores its variables in\n",
        "* `layer.dtype_policy` : the data type a layer computes in\n",
        "\n",
        "> 🔑 **Note:** A layer can have a dtype of `float32` and a dtype policy of `\"mixed_float16\"` because it stores its variables (weights & biases) in `float32` (more numerically stable), however it computes in `float16` (faster).\n",
        "\n",
        "We can also check the same details for our model's base model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "a1d186ff-d01d-46d3-a88e-6fc7a849865f",
      "metadata": {
        "id": "a1d186ff-d01d-46d3-a88e-6fc7a849865f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f371d88-2fad-4c77-bbd6-6b71aed41882"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_1 False float32 <Policy \"float32\">\n",
            "rescaling False float32 <Policy \"mixed_float16\">\n",
            "normalization False float32 <Policy \"mixed_float16\">\n",
            "rescaling_1 False float32 <Policy \"mixed_float16\">\n",
            "stem_conv_pad False float32 <Policy \"mixed_float16\">\n",
            "stem_conv False float32 <Policy \"mixed_float16\">\n",
            "stem_bn False float32 <Policy \"mixed_float16\">\n",
            "stem_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv False float32 <Policy \"mixed_float16\">\n",
            "block1a_bn False float32 <Policy \"mixed_float16\">\n",
            "block1a_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn False float32 <Policy \"mixed_float16\">\n"
          ]
        }
      ],
      "source": [
        "# Check the layers in the base model and see what dtype policy they're using\n",
        "for layer in model.layers[1].layers[:20]: # only check the first 20 layers to save output space\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "616b4059-5d78-4466-afe4-9d49df3646ff",
      "metadata": {
        "id": "616b4059-5d78-4466-afe4-9d49df3646ff"
      },
      "source": [
        "> 🔑 **Note:** The mixed precision API automatically causes layers which can benefit from using the `\"mixed_float16\"` dtype policy to use it. It also prevents layers which shouldn't use it from using it (e.g. the normalization layer at the start of the base model)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17ebfec1-b7ad-4d38-b42d-b266d09650a2",
      "metadata": {
        "id": "17ebfec1-b7ad-4d38-b42d-b266d09650a2"
      },
      "source": [
        "## Fit the feature extraction model\n",
        "\n",
        "Now that's one good looking model. Let's fit it to our data shall we?\n",
        "\n",
        "Three epochs should be enough for our top layers to adjust their weights enough to our food image data.\n",
        "\n",
        "To save time per epoch, we'll also only validate on 15% of the test data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "c1b8f418-391f-4232-af76-102eac0b9d82",
      "metadata": {
        "id": "c1b8f418-391f-4232-af76-102eac0b9d82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7126f6ba-05db-4202-9b7e-31e4ee9cb06d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: training_logs/efficientnetb0_101_classes_all_data_feature_extract/20230616-051542\n",
            "Epoch 1/3\n",
            "2368/2368 [==============================] - 202s 78ms/step - loss: 1.7188 - accuracy: 0.5825 - val_loss: 1.1294 - val_accuracy: 0.7015\n",
            "Epoch 2/3\n",
            "2368/2368 [==============================] - 178s 73ms/step - loss: 1.1986 - accuracy: 0.6890 - val_loss: 1.0230 - val_accuracy: 0.7161\n",
            "Epoch 3/3\n",
            "2368/2368 [==============================] - 178s 74ms/step - loss: 1.0542 - accuracy: 0.7226 - val_loss: 0.9884 - val_accuracy: 0.7301\n"
          ]
        }
      ],
      "source": [
        "# Turn off all warnings except for errors\n",
        "tf.get_logger().setLevel('ERROR')\n",
        "\n",
        "# Fit the model with callbacks\n",
        "history_101_food_classes_feature_extract = model.fit(train_data,\n",
        "                                                     epochs=3,\n",
        "                                                     steps_per_epoch=len(train_data),\n",
        "                                                     validation_data=test_data,\n",
        "                                                     validation_steps=int(0.15 * len(test_data)),\n",
        "                                                     callbacks=[create_tensorboard_callback(\"training_logs\",\n",
        "                                                                                            \"efficientnetb0_101_classes_all_data_feature_extract\"),\n",
        "                                                                model_checkpoint])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29280c9a-c828-4cf5-8753-8c1bfdced3c0",
      "metadata": {
        "id": "29280c9a-c828-4cf5-8753-8c1bfdced3c0"
      },
      "source": [
        "Nice, looks like our feature extraction model is performing pretty well. How about we evaluate it on the whole test dataset?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "f30fb985-9735-4cc6-8d61-63451fc73bce",
      "metadata": {
        "id": "f30fb985-9735-4cc6-8d61-63451fc73bce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fd3fe69-1bee-447a-e490-76cb1842124a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 51s 64ms/step - loss: 0.9963 - accuracy: 0.7297\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9963232278823853, 0.7297029495239258]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "# Evaluate model (unsaved version) on whole test dataset\n",
        "results_feature_extract_model = model.evaluate(test_data)\n",
        "results_feature_extract_model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4a7f263b-5ae0-4d43-a216-97d868003590",
      "metadata": {
        "id": "4a7f263b-5ae0-4d43-a216-97d868003590"
      },
      "source": [
        "And since we used the `ModelCheckpoint` callback, we've got a saved version of our model in the `model_checkpoints` directory.\n",
        "\n",
        "Let's load it in and make sure it performs just as well."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "693c7376-892d-4525-83ce-8a82ec12df76",
      "metadata": {
        "id": "693c7376-892d-4525-83ce-8a82ec12df76"
      },
      "source": [
        "## Load and evaluate checkpoint weights\n",
        "\n",
        "We can load in and evaluate our model's checkpoints by:\n",
        "\n",
        "1. Recreating a new instance of our model called `created_model` by turning our original model creation code into a function called `create_model()`.\n",
        "2. Compiling our `created_model` with the same loss, optimizer and metrics as the original model (every time you create a new model, you must compile it).\n",
        "3. Calling the `load_weights()` method on our `created_model` and passing it the path to where our checkpointed weights are stored.\n",
        "4. Calling `evaluate()` on `created_model` with loaded weights and saving the results.\n",
        "5. Comparing the `created_model` results to our previous `model` results (these should be the exact same, if not very close).\n",
        "\n",
        "A reminder, checkpoints are helpful for when you perform an experiment such as fine-tuning your model. In the case you fine-tune your feature extraction model and find it doesn't offer any improvements, you can always revert back to the checkpointed version of your model.\n",
        "\n",
        "> **Note:** This section originally used the [`tf.keras.clone_model` method](https://www.tensorflow.org/api_docs/python/tf/keras/models/clone_model), however, due to several potential errors with that method, it changed to create a new model (rather than cloning) via a `create_model()` function. See the [discussion on the course GitHub](https://github.com/mrdbourke/tensorflow-deep-learning/discussions/550) for more."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Create a function to recreate the original model\n",
        "def create_model():\n",
        "  # Create base model\n",
        "  input_shape = (224, 224, 3)\n",
        "  base_model = tf.keras.applications.efficientnet.EfficientNetB0(include_top=False)\n",
        "  base_model.trainable = False # freeze base model layers\n",
        "\n",
        "  # Create Functional model\n",
        "  inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "  # Note: EfficientNetBX models have rescaling built-in but if your model didn't you could have a layer like below\n",
        "  # x = layers.Rescaling(1./255)(x)\n",
        "  x = base_model(inputs, training=False) # set base_model to inference mode only\n",
        "  x = layers.GlobalAveragePooling2D(name=\"pooling_layer\")(x)\n",
        "  x = layers.Dense(len(class_names))(x) # want one output neuron per class\n",
        "  # Separate activation of output layer so we can output float32 activations\n",
        "  outputs = layers.Activation(\"softmax\", dtype=tf.float32, name=\"softmax_float32\")(x)\n",
        "  model = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "  return model\n",
        "\n",
        "# 2. Create and compile a new version of the original model (new weights)\n",
        "created_model = create_model()\n",
        "created_model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "                      optimizer=tf.keras.optimizers.Adam(),\n",
        "                      metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Load the saved weights\n",
        "created_model.load_weights(checkpoint_path)\n",
        "\n",
        "# 4. Evaluate the model with loaded weights\n",
        "results_created_model_with_loaded_weights = created_model.evaluate(test_data)"
      ],
      "metadata": {
        "id": "95f3PBdfWwoX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7cdc10c-1492-4fcf-f844-5678c625287a"
      },
      "id": "95f3PBdfWwoX",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 52s 62ms/step - loss: 0.9963 - accuracy: 0.7297\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4132ed85-f9c8-4380-8d26-c1aedec44fe4",
      "metadata": {
        "id": "4132ed85-f9c8-4380-8d26-c1aedec44fe4"
      },
      "source": [
        "Our `created_model` with loaded weight's results should be very close to the feature extraction model's results (if the cell below errors, something went wrong)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "8f5e3324-ff9f-4ef3-a645-30001378df9b",
      "metadata": {
        "id": "8f5e3324-ff9f-4ef3-a645-30001378df9b"
      },
      "outputs": [],
      "source": [
        "# 5. Loaded checkpoint weights should return very similar results to checkpoint weights prior to saving\n",
        "import numpy as np\n",
        "assert np.isclose(results_feature_extract_model, results_created_model_with_loaded_weights).all(), \"Loaded weights results are not close to original model.\"  # check if all elements in array are close"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "003e3c86-b5b2-4b0c-b505-4c7595e02a7e",
      "metadata": {
        "id": "003e3c86-b5b2-4b0c-b505-4c7595e02a7e"
      },
      "source": [
        "Cloning the model preserves `dtype_policy`'s of layers (but doesn't preserve weights) so if we wanted to continue fine-tuning our `created_model`, we could and it would still use the mixed precision dtype policy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "b67712a5-53ad-44ce-be8b-efda79b67726",
      "metadata": {
        "id": "b67712a5-53ad-44ce-be8b-efda79b67726",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b987ba50-40e4-4b6d-a941-d6f3b477b693"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_2 False float32 <Policy \"float32\">\n",
            "rescaling_2 False float32 <Policy \"mixed_float16\">\n",
            "normalization_1 False float32 <Policy \"mixed_float16\">\n",
            "rescaling_3 False float32 <Policy \"mixed_float16\">\n",
            "stem_conv_pad False float32 <Policy \"mixed_float16\">\n",
            "stem_conv False float32 <Policy \"mixed_float16\">\n",
            "stem_bn False float32 <Policy \"mixed_float16\">\n",
            "stem_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv False float32 <Policy \"mixed_float16\">\n",
            "block1a_bn False float32 <Policy \"mixed_float16\">\n",
            "block1a_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn False float32 <Policy \"mixed_float16\">\n"
          ]
        }
      ],
      "source": [
        "# Check the layers in the base model and see what dtype policy they're using\n",
        "for layer in created_model.layers[1].layers[:20]: # check only the first 20 layers to save printing space\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3476a48-9518-4519-967c-3007580e4768",
      "metadata": {
        "id": "a3476a48-9518-4519-967c-3007580e4768"
      },
      "source": [
        "## Save the whole model to file\n",
        "\n",
        "We can also save the whole model using the [`save()`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#save) method.\n",
        "\n",
        "Since our model is quite large, you might want to save it to Google Drive (if you're using Google Colab) so you can load it in for use later.\n",
        "\n",
        "> 🔑 **Note:** Saving to Google Drive requires mounting Google Drive (go to Files -> Mount Drive)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "78b47170-a22c-4293-878b-3d360462bd59",
      "metadata": {
        "id": "78b47170-a22c-4293-878b-3d360462bd59"
      },
      "outputs": [],
      "source": [
        "# ## Saving model to Google Drive (optional)\n",
        "\n",
        "# # Create save path to drive\n",
        "# save_dir = \"drive/MyDrive/tensorflow_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision/\"\n",
        "# # os.makedirs(save_dir) # Make directory if it doesn't exist\n",
        "\n",
        "# # Save model\n",
        "# model.save(save_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7a028463-2218-4379-9608-d4a4078ce7cf",
      "metadata": {
        "id": "7a028463-2218-4379-9608-d4a4078ce7cf"
      },
      "source": [
        "We can also save it directly to our Google Colab instance.\n",
        "\n",
        "> 🔑 **Note:** Google Colab storage is ephemeral and your model will delete itself (along with any other saved files) when the Colab session expires."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "c65f5b3f-27a8-44d4-a3fa-52f1055f37f2",
      "metadata": {
        "id": "c65f5b3f-27a8-44d4-a3fa-52f1055f37f2"
      },
      "outputs": [],
      "source": [
        "# Save model locally (if you're using Google Colab, your saved model will Colab instance terminates)\n",
        "save_dir = \"07_efficientnetb0_feature_extract_model_mixed_precision\"\n",
        "model.save(save_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "006eef4e-b9e6-415c-b69e-1583ffc1d1d0",
      "metadata": {
        "id": "006eef4e-b9e6-415c-b69e-1583ffc1d1d0"
      },
      "source": [
        "And again, we can check whether or not our model saved correctly by loading it in and evaluating it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "927b1e9d-ebd9-47c4-b167-83950a2bd58d",
      "metadata": {
        "id": "927b1e9d-ebd9-47c4-b167-83950a2bd58d"
      },
      "outputs": [],
      "source": [
        "# Load model previously saved above\n",
        "loaded_saved_model = tf.keras.models.load_model(save_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1579c008-0fb0-48a2-85b0-a5ee0e97a697",
      "metadata": {
        "id": "1579c008-0fb0-48a2-85b0-a5ee0e97a697"
      },
      "source": [
        "Loading a `SavedModel` also retains all of the underlying layers `dtype_policy` (we want them to be `\"mixed_float16\"`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "0afb18ec-2fdf-470f-b853-63104b0271c3",
      "metadata": {
        "id": "0afb18ec-2fdf-470f-b853-63104b0271c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a2e32c6-8dfa-48ce-c091-6f3502601482"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_1 True float32 <Policy \"float32\">\n",
            "rescaling False float32 <Policy \"mixed_float16\">\n",
            "normalization False float32 <Policy \"mixed_float16\">\n",
            "rescaling_1 False float32 <Policy \"mixed_float16\">\n",
            "stem_conv_pad False float32 <Policy \"mixed_float16\">\n",
            "stem_conv False float32 <Policy \"mixed_float16\">\n",
            "stem_bn False float32 <Policy \"mixed_float16\">\n",
            "stem_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv False float32 <Policy \"mixed_float16\">\n",
            "block1a_bn False float32 <Policy \"mixed_float16\">\n",
            "block1a_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn False float32 <Policy \"mixed_float16\">\n"
          ]
        }
      ],
      "source": [
        "# Check the layers in the base model and see what dtype policy they're using\n",
        "for layer in loaded_saved_model.layers[1].layers[:20]: # check only the first 20 layers to save output space\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "fd05f8f5-3470-40ae-a754-e644e2c8e5e4",
      "metadata": {
        "id": "fd05f8f5-3470-40ae-a754-e644e2c8e5e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a3ec40f-47b2-4a3b-8470-184cc01986b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 52s 64ms/step - loss: 0.9963 - accuracy: 0.7297\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9963226318359375, 0.7297029495239258]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# Check loaded model performance (this should be the same as results_feature_extract_model)\n",
        "results_loaded_saved_model = loaded_saved_model.evaluate(test_data)\n",
        "results_loaded_saved_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "8ebde33b-380a-4656-94bb-a1c7cb1db452",
      "metadata": {
        "id": "8ebde33b-380a-4656-94bb-a1c7cb1db452"
      },
      "outputs": [],
      "source": [
        "# The loaded model's results should equal (or at least be very close) to the model's results prior to saving\n",
        "# Note: this will only work if you've instatiated results variables\n",
        "import numpy as np\n",
        "assert np.isclose(results_feature_extract_model, results_loaded_saved_model).all()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "916caf08-193e-43b3-ad80-b783b33ecb5a",
      "metadata": {
        "id": "916caf08-193e-43b3-ad80-b783b33ecb5a"
      },
      "source": [
        "That's what we want! Our loaded model performing as it should.\n",
        "\n",
        "> 🔑 **Note:** We spent a fair bit of time making sure our model saved correctly because training on a lot of data can be time-consuming, so we want to make sure we don't have to continaully train from scratch."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "359b51f5-2f97-42d8-835c-d84cef3c6966",
      "metadata": {
        "id": "359b51f5-2f97-42d8-835c-d84cef3c6966"
      },
      "source": [
        "## Preparing our model's layers for fine-tuning\n",
        "\n",
        "Our feature-extraction model is showing some great promise after three epochs. But since we've got so much data, it's probably worthwhile that we see what results we can get with fine-tuning (fine-tuning usually works best when you've got quite a large amount of data).\n",
        "\n",
        "Remember our goal of beating the [DeepFood paper](https://arxiv.org/pdf/1606.05675.pdf)?\n",
        "\n",
        "They were able to achieve 77.4% top-1 accuracy on Food101 over 2-3 days of training.\n",
        "\n",
        "Do you think fine-tuning will get us there?\n",
        "\n",
        "Let's find out.\n",
        "\n",
        "To start, let's load in our saved model.\n",
        "\n",
        "> 🔑 **Note:** It's worth remembering a traditional workflow for fine-tuning is to freeze a pre-trained base model and then train only the output layers for a few iterations so their weights can be updated inline with your custom data (feature extraction). And then unfreeze a number or all of the layers in the base model and continue training until the model stops improving.\n",
        "\n",
        "Like all good cooking shows, I've saved a model I prepared earlier (the feature extraction model from above) to Google Storage.\n",
        "\n",
        "We can download it to make sure we're using the same model going forward."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "6d135faa-3ad7-4502-80b8-ea0f931f0c5c",
      "metadata": {
        "id": "6d135faa-3ad7-4502-80b8-ea0f931f0c5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9744d23f-0cf4-4089-82f9-35a0fa6c04e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-06-16 05:28:52--  https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.4.128, 142.251.10.128, 142.251.12.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.4.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16976857 (16M) [application/zip]\n",
            "Saving to: ‘07_efficientnetb0_feature_extract_model_mixed_precision.zip’\n",
            "\n",
            "07_efficientnetb0_f 100%[===================>]  16.19M  8.03MB/s    in 2.0s    \n",
            "\n",
            "2023-06-16 05:28:55 (8.03 MB/s) - ‘07_efficientnetb0_feature_extract_model_mixed_precision.zip’ saved [16976857/16976857]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download the saved model from Google Storage\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "4b09f43f-b0cd-4300-8605-67ecde8e5181",
      "metadata": {
        "id": "4b09f43f-b0cd-4300-8605-67ecde8e5181",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c56ed23-e420-4d5c-c1b8-6c8824610d65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  07_efficientnetb0_feature_extract_model_mixed_precision.zip\n",
            "   creating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/\n",
            "   creating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/variables/\n",
            "  inflating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/variables/variables.data-00000-of-00001  \n",
            "  inflating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/variables/variables.index  \n",
            "  inflating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/saved_model.pb  \n",
            "   creating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/assets/\n"
          ]
        }
      ],
      "source": [
        "# Unzip the SavedModel downloaded from Google Stroage\n",
        "!mkdir downloaded_gs_model # create new dir to store downloaded feature extraction model\n",
        "!unzip 07_efficientnetb0_feature_extract_model_mixed_precision.zip -d downloaded_gs_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "402dc7fb-2ea5-4b5e-8c13-08e5589f8940",
      "metadata": {
        "id": "402dc7fb-2ea5-4b5e-8c13-08e5589f8940",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58c630cc-2210-433c-be7b-4dabf74ceb6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Importing a function (__inference_block1a_activation_layer_call_and_return_conditional_losses_158253) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_activation_layer_call_and_return_conditional_losses_191539) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_expand_activation_layer_call_and_return_conditional_losses_196076) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_activation_layer_call_and_return_conditional_losses_195780) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_activation_layer_call_and_return_conditional_losses_196153) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_model_layer_call_and_return_conditional_losses_180010) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_stem_activation_layer_call_and_return_conditional_losses_191136) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_expand_activation_layer_call_and_return_conditional_losses_160354) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_expand_activation_layer_call_and_return_conditional_losses_195703) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_expand_activation_layer_call_and_return_conditional_losses_159392) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block1a_activation_layer_call_and_return_conditional_losses_191213) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_se_reduce_layer_call_and_return_conditional_losses_193678) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5a_se_reduce_layer_call_and_return_conditional_losses_194051) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_expand_activation_layer_call_and_return_conditional_losses_158768) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_se_reduce_layer_call_and_return_conditional_losses_191907) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_se_reduce_layer_call_and_return_conditional_losses_162720) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_activation_layer_call_and_return_conditional_losses_194708) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_se_reduce_layer_call_and_return_conditional_losses_196195) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_expand_activation_layer_call_and_return_conditional_losses_194258) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_188022) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6b_activation_layer_call_and_return_conditional_losses_161995) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_183149) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_activation_layer_call_and_return_conditional_losses_158824) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_activation_layer_call_and_return_conditional_losses_159787) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_expand_activation_layer_call_and_return_conditional_losses_158482) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_se_reduce_layer_call_and_return_conditional_losses_158588) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6b_se_reduce_layer_call_and_return_conditional_losses_195449) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_se_reduce_layer_call_and_return_conditional_losses_194377) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_expand_activation_layer_call_and_return_conditional_losses_162615) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_activation_layer_call_and_return_conditional_losses_192238) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_se_reduce_layer_call_and_return_conditional_losses_160121) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_expand_activation_layer_call_and_return_conditional_losses_192860) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_activation_layer_call_and_return_conditional_losses_191865) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_expand_activation_layer_call_and_return_conditional_losses_160016) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_se_reduce_layer_call_and_return_conditional_losses_194750) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_169029) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_170771) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_activation_layer_call_and_return_conditional_losses_159448) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_expand_activation_layer_call_and_return_conditional_losses_194631) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_se_reduce_layer_call_and_return_conditional_losses_192979) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_activation_layer_call_and_return_conditional_losses_193263) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_expand_activation_layer_call_and_return_conditional_losses_160977) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_expand_activation_layer_call_and_return_conditional_losses_162953) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_se_reduce_layer_call_and_return_conditional_losses_159836) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_se_reduce_layer_call_and_return_conditional_losses_191581) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_activation_layer_call_and_return_conditional_losses_158539) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_se_reduce_layer_call_and_return_conditional_losses_162382) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_expand_activation_layer_call_and_return_conditional_losses_196449) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_top_activation_layer_call_and_return_conditional_losses_163238) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_expand_activation_layer_call_and_return_conditional_losses_162277) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_expand_activation_layer_call_and_return_conditional_losses_192487) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block1a_se_reduce_layer_call_and_return_conditional_losses_191255) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_activation_layer_call_and_return_conditional_losses_163009) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_activation_layer_call_and_return_conditional_losses_194335) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_expand_activation_layer_call_and_return_conditional_losses_193559) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_expand_activation_layer_call_and_return_conditional_losses_159730) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_se_reduce_layer_call_and_return_conditional_losses_161759) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_expand_activation_layer_call_and_return_conditional_losses_192161) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_se_reduce_layer_call_and_return_conditional_losses_193305) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5a_activation_layer_call_and_return_conditional_losses_160748) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_activation_layer_call_and_return_conditional_losses_161371) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_activation_layer_call_and_return_conditional_losses_192937) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_se_reduce_layer_call_and_return_conditional_losses_196568) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_expand_activation_layer_call_and_return_conditional_losses_191788) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_expand_activation_layer_call_and_return_conditional_losses_159106) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_se_reduce_layer_call_and_return_conditional_losses_159497) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_expand_activation_layer_call_and_return_conditional_losses_161315) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_184891) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_model_layer_call_and_return_conditional_losses_178256) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_activation_layer_call_and_return_conditional_losses_161710) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_expand_activation_layer_call_and_return_conditional_losses_161653) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_se_reduce_layer_call_and_return_conditional_losses_159212) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_stem_activation_layer_call_and_return_conditional_losses_158197) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_189764) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_se_reduce_layer_call_and_return_conditional_losses_192606) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_activation_layer_call_and_return_conditional_losses_195081) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_activation_layer_call_and_return_conditional_losses_162333) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5a_se_reduce_layer_call_and_return_conditional_losses_160797) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5a_activation_layer_call_and_return_conditional_losses_194009) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_se_reduce_layer_call_and_return_conditional_losses_195822) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_activation_layer_call_and_return_conditional_losses_161033) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6b_expand_activation_layer_call_and_return_conditional_losses_195330) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_activation_layer_call_and_return_conditional_losses_159163) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_se_reduce_layer_call_and_return_conditional_losses_160459) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6b_activation_layer_call_and_return_conditional_losses_195407) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_se_reduce_layer_call_and_return_conditional_losses_163058) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_se_reduce_layer_call_and_return_conditional_losses_192280) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_activation_layer_call_and_return_conditional_losses_162671) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference__wrapped_model_152628) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6b_se_reduce_layer_call_and_return_conditional_losses_162044) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_se_reduce_layer_call_and_return_conditional_losses_158873) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_activation_layer_call_and_return_conditional_losses_160410) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_expand_activation_layer_call_and_return_conditional_losses_195004) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_activation_layer_call_and_return_conditional_losses_192564) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_se_reduce_layer_call_and_return_conditional_losses_161082) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_se_reduce_layer_call_and_return_conditional_losses_161420) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_activation_layer_call_and_return_conditional_losses_193636) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_top_activation_layer_call_and_return_conditional_losses_196775) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_activation_layer_call_and_return_conditional_losses_160072) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6b_expand_activation_layer_call_and_return_conditional_losses_161939) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5a_expand_activation_layer_call_and_return_conditional_losses_193932) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_expand_activation_layer_call_and_return_conditional_losses_193186) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block1a_se_reduce_layer_call_and_return_conditional_losses_158302) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_se_reduce_layer_call_and_return_conditional_losses_195123) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_expand_activation_layer_call_and_return_conditional_losses_191462) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_activation_layer_call_and_return_conditional_losses_196526) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5a_expand_activation_layer_call_and_return_conditional_losses_160692) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n"
          ]
        }
      ],
      "source": [
        "# Load and evaluate downloaded GS model\n",
        "loaded_gs_model = tf.keras.models.load_model(\"downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "1978476b-9d4f-4b87-8448-4cebad39c51a",
      "metadata": {
        "id": "1978476b-9d4f-4b87-8448-4cebad39c51a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fe05a98-4290-41de-b314-b36d53e31fd7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " efficientnetb0 (Functional  (None, None, None, 1280   4049571   \n",
            " )                           )                                   \n",
            "                                                                 \n",
            " pooling_layer (GlobalAvera  (None, 1280)              0         \n",
            " gePooling2D)                                                    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 101)               129381    \n",
            "                                                                 \n",
            " softmax_float32 (Activatio  (None, 101)               0         \n",
            " n)                                                              \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4178952 (15.94 MB)\n",
            "Trainable params: 129381 (505.39 KB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Get a summary of our downloaded model\n",
        "loaded_gs_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59acbfcd-8bb0-4528-8265-71400cf488af",
      "metadata": {
        "id": "59acbfcd-8bb0-4528-8265-71400cf488af"
      },
      "source": [
        "And now let's make sure our loaded model is performing as expected."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "cb87cb50-36d1-408d-b606-83ebab34fa9c",
      "metadata": {
        "id": "cb87cb50-36d1-408d-b606-83ebab34fa9c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef09305c-f948-4ddf-eddc-9d4dbacb3fa4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 57s 71ms/step - loss: 1.0881 - accuracy: 0.7066\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0880998373031616, 0.7066138386726379]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "# How does the loaded model perform?\n",
        "results_loaded_gs_model = loaded_gs_model.evaluate(test_data)\n",
        "results_loaded_gs_model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27177391-7647-46e9-bfa6-f5ebd934d524",
      "metadata": {
        "id": "27177391-7647-46e9-bfa6-f5ebd934d524"
      },
      "source": [
        "Great, our loaded model is performing as expected.\n",
        "\n",
        "When we first created our model, we froze all of the layers in the base model by setting `base_model.trainable=False` but since we've loaded in our model from file, let's check whether or not the layers are trainable or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "9cf5be86-4539-4e19-aef6-1ae66d50f2cb",
      "metadata": {
        "id": "9cf5be86-4539-4e19-aef6-1ae66d50f2cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e46257e2-0349-4785-ff2e-df6102cfd96c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_layer True float32 <Policy \"float32\">\n",
            "efficientnetb0 True float32 <Policy \"mixed_float16\">\n",
            "pooling_layer True float32 <Policy \"mixed_float16\">\n",
            "dense True float32 <Policy \"mixed_float16\">\n",
            "softmax_float32 True float32 <Policy \"float32\">\n"
          ]
        }
      ],
      "source": [
        "# Are any of the layers in our model frozen?\n",
        "for layer in loaded_gs_model.layers:\n",
        "    layer.trainable = True # set all layers to trainable\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy) # make sure loaded model is using mixed precision dtype_policy (\"mixed_float16\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "af719483-1dbc-44ba-91a1-9cf0b69c52ca",
      "metadata": {
        "id": "af719483-1dbc-44ba-91a1-9cf0b69c52ca"
      },
      "source": [
        "Alright, it seems like each layer in our loaded model is trainable. But what if we got a little deeper and inspected each of the layers in our base model?\n",
        "\n",
        "> 🤔 **Question:** *Which layer in the loaded model is our base model?*\n",
        "\n",
        "Before saving the Functional model to file, we created it with five layers (layers below are 0-indexed):\n",
        "0. The input layer\n",
        "1. The pre-trained base model layer ([`tf.keras.applications.efficientnet.EfficientNetB0`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet/EfficientNetB0))\n",
        "2. The pooling layer\n",
        "3. The fully-connected (dense) layer\n",
        "4. The output softmax activation (with float32 dtype)\n",
        "\n",
        "Therefore to inspect our base model layer, we can access the `layers` attribute of the layer at index 1 in our model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "c767b7fb-36da-401d-afcb-c701b748e16c",
      "metadata": {
        "id": "c767b7fb-36da-401d-afcb-c701b748e16c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df802251-d94d-4d37-d9a2-6323f103dbea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_1 True float32 <Policy \"float32\">\n",
            "rescaling True float32 <Policy \"mixed_float16\">\n",
            "normalization True float32 <Policy \"float32\">\n",
            "stem_conv_pad True float32 <Policy \"mixed_float16\">\n",
            "stem_conv True float32 <Policy \"mixed_float16\">\n",
            "stem_bn True float32 <Policy \"mixed_float16\">\n",
            "stem_activation True float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv True float32 <Policy \"mixed_float16\">\n",
            "block1a_bn True float32 <Policy \"mixed_float16\">\n",
            "block1a_activation True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite True float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv True float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn True float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv True float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn True float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_activation True float32 <Policy \"mixed_float16\">\n"
          ]
        }
      ],
      "source": [
        "# Check the layers in the base model and see what dtype policy they're using\n",
        "for layer in loaded_gs_model.layers[1].layers[:20]:\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a41092a-c1fc-4aad-aa95-5f4ec45346c2",
      "metadata": {
        "id": "9a41092a-c1fc-4aad-aa95-5f4ec45346c2"
      },
      "source": [
        "Wonderful, it looks like each layer in our base model is trainable (unfrozen) and every layer which should be using the dtype policy `\"mixed_policy16\"` is using it.\n",
        "\n",
        "Since we've got so much data (750 images x 101 training classes = 75750 training images), let's keep all of our base model's layers unfrozen.\n",
        "\n",
        "> 🔑 **Note:** If you've got a small amount of data (less than 100 images per class), you may want to only unfreeze and fine-tune a small number of layers in the base model at a time. Otherwise, you risk overfitting."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "caef02fc-bd6a-4dd5-85b9-b28a3b20aede",
      "metadata": {
        "id": "caef02fc-bd6a-4dd5-85b9-b28a3b20aede"
      },
      "source": [
        "## A couple more callbacks\n",
        "\n",
        "We're about to start fine-tuning a deep learning model with over 200 layers using over 100,000 (75k+ training, 25K+ testing) images, which means our model's training time is probably going to be much longer than before.\n",
        "\n",
        "> 🤔 **Question:** *How long does training take?*\n",
        "\n",
        "It could be a couple of hours or in the case of the [DeepFood paper](https://arxiv.org/pdf/1606.05675.pdf) (the baseline we're trying to beat), their best performing model took 2-3 days of training time.\n",
        "\n",
        "You will really only know how long it'll take once you start training.\n",
        "\n",
        "> 🤔 **Question:** *When do you stop training?*\n",
        "\n",
        "Ideally, when your model stops improving. But again, due to the nature of deep learning, it can be hard to know when exactly a model will stop improving.\n",
        "\n",
        "Luckily, there's a solution: the [`EarlyStopping` callback](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping).\n",
        "\n",
        "The `EarlyStopping` callback monitors a specified model performance metric (e.g. `val_loss`) and when it stops improving for a specified number of epochs, automatically stops training.\n",
        "\n",
        "Using the `EarlyStopping` callback combined with the `ModelCheckpoint` callback saving the best performing model automatically, we could keep our model training for an unlimited number of epochs until it stops improving.\n",
        "\n",
        "Let's set both of these up to monitor our model's `val_loss`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "bc2668c2-2406-46a5-a325-331234c64335",
      "metadata": {
        "id": "bc2668c2-2406-46a5-a325-331234c64335"
      },
      "outputs": [],
      "source": [
        "# Setup EarlyStopping callback to stop training if model's val_loss doesn't improve for 3 epochs\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", # watch the val loss metric\n",
        "                                                  patience=3) # if val loss decreases for 3 epochs in a row, stop training\n",
        "\n",
        "# Create ModelCheckpoint callback to save best model during fine-tuning\n",
        "checkpoint_path = \"fine_tune_checkpoints/\"\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n",
        "                                                      save_best_only=True,\n",
        "                                                      monitor=\"val_loss\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f0d3270a-393d-450d-9d8c-90abf2667738",
      "metadata": {
        "id": "f0d3270a-393d-450d-9d8c-90abf2667738"
      },
      "source": [
        "Woohoo! Fine-tuning callbacks ready.\n",
        "\n",
        "If you're planning on training large models, the `ModelCheckpoint` and `EarlyStopping` are two callbacks you'll want to become very familiar with.\n",
        "\n",
        "We're almost ready to start fine-tuning our model but there's one more callback we're going to implement: [`ReduceLROnPlateau`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ReduceLROnPlateau).\n",
        "\n",
        "Remember how the learning rate is the most important model hyperparameter you can tune? (if not, treat this as a reminder).\n",
        "\n",
        "Well, the `ReduceLROnPlateau` callback helps to tune the learning rate for you.\n",
        "\n",
        "Like the `ModelCheckpoint` and `EarlyStopping` callbacks, the `ReduceLROnPlateau` callback montiors a specified metric and when that metric stops improving, it reduces the learning rate by a specified factor (e.g. divides the learning rate by 10).\n",
        "\n",
        "> 🤔 **Question:** *Why lower the learning rate?*\n",
        "\n",
        "Imagine having a coin at the back of the couch and you're trying to grab with your fingers.\n",
        "\n",
        "Now think of the learning rate as the size of the movements your hand makes towards the coin.\n",
        "\n",
        "The closer you get, the smaller you want your hand movements to be, otherwise the coin will be lost.\n",
        "\n",
        "Our model's ideal performance is the equivalent of grabbing the coin. So as training goes on and our model gets closer and closer to it's ideal performance (also called **convergence**), we want the amount it learns to be less and less.\n",
        "\n",
        "To do this we'll create an instance of the `ReduceLROnPlateau` callback to monitor the validation loss just like the `EarlyStopping` callback.\n",
        "\n",
        "Once the validation loss stops improving for two or more epochs, we'll reduce the learning rate by a factor of 5 (e.g. `0.001` to `0.0002`).\n",
        "\n",
        "And to make sure the learning rate doesn't get too low (and potentially result in our model learning nothing), we'll set the minimum learning rate to `1e-7`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "cb2d24c1-7909-43da-bb27-5e6035806bb4",
      "metadata": {
        "id": "cb2d24c1-7909-43da-bb27-5e6035806bb4"
      },
      "outputs": [],
      "source": [
        "# Creating learning rate reduction callback\n",
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",\n",
        "                                                 factor=0.2, # multiply the learning rate by 0.2 (reduce by 5x)\n",
        "                                                 patience=2,\n",
        "                                                 verbose=1, # print out when learning rate goes down\n",
        "                                                 min_lr=1e-7)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "53f03fb9-b8b2-403b-b4e8-2a7934631a1e",
      "metadata": {
        "id": "53f03fb9-b8b2-403b-b4e8-2a7934631a1e"
      },
      "source": [
        "Learning rate reduction ready to go!\n",
        "\n",
        "Now before we start training, we've got to recompile our model.\n",
        "\n",
        "We'll use sparse categorical crossentropy as the loss and since we're fine-tuning, we'll use a 10x lower learning rate than the Adam optimizers default (`1e-4` instead of `1e-3`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "e8fffadf-49f9-447c-ac44-ad59f5e07e25",
      "metadata": {
        "id": "e8fffadf-49f9-447c-ac44-ad59f5e07e25"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "loaded_gs_model.compile(loss=\"sparse_categorical_crossentropy\", # sparse_categorical_crossentropy for labels that are *not* one-hot\n",
        "                        optimizer=tf.keras.optimizers.Adam(0.0001), # 10x lower learning rate than the default\n",
        "                        metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7fd557de-314b-4627-a0fd-f50e0bdb7ee7",
      "metadata": {
        "id": "7fd557de-314b-4627-a0fd-f50e0bdb7ee7"
      },
      "source": [
        "Okay, model compiled.\n",
        "\n",
        "Now let's fit it on all of the data.\n",
        "\n",
        "We'll set it up to run for up to 100 epochs.\n",
        "\n",
        "Since we're going to be using the `EarlyStopping` callback, it might stop before reaching 100 epochs.\n",
        "\n",
        "> 🔑 **Note:** Running the cell below will set the model up to fine-tune all of the pre-trained weights in the base model on all of the Food101 data. Doing so with **unoptimized** data pipelines and **without** mixed precision training will take a fairly long time per epoch depending on what type of GPU you're using (about 15-20 minutes on Colab GPUs). But don't worry, **the code we've written above will ensure it runs much faster** (more like 4-5 minutes per epoch)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "514dc1e0-0899-4831-9442-d007a8f4782b",
      "metadata": {
        "id": "514dc1e0-0899-4831-9442-d007a8f4782b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "318ea20b-9bbe-4bd1-d432-50a56b1b2193"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: training_logs/efficientb0_101_classes_all_data_fine_tuning/20230616-053033\n",
            "Epoch 1/100\n",
            "2368/2368 [==============================] - 477s 179ms/step - loss: 0.9209 - accuracy: 0.7531 - val_loss: 0.7959 - val_accuracy: 0.7736 - lr: 1.0000e-04\n",
            "Epoch 2/100\n",
            "2368/2368 [==============================] - 419s 175ms/step - loss: 0.5794 - accuracy: 0.8404 - val_loss: 0.7580 - val_accuracy: 0.7932 - lr: 1.0000e-04\n",
            "Epoch 3/100\n",
            "2368/2368 [==============================] - 381s 160ms/step - loss: 0.3315 - accuracy: 0.9045 - val_loss: 0.8302 - val_accuracy: 0.7826 - lr: 1.0000e-04\n",
            "Epoch 4/100\n",
            "2368/2368 [==============================] - ETA: 0s - loss: 0.1723 - accuracy: 0.9491\n",
            "Epoch 4: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n",
            "2368/2368 [==============================] - 381s 160ms/step - loss: 0.1723 - accuracy: 0.9491 - val_loss: 0.9751 - val_accuracy: 0.7847 - lr: 1.0000e-04\n",
            "Epoch 5/100\n",
            "2368/2368 [==============================] - 386s 162ms/step - loss: 0.0346 - accuracy: 0.9921 - val_loss: 1.0535 - val_accuracy: 0.8046 - lr: 2.0000e-05\n"
          ]
        }
      ],
      "source": [
        "# Start to fine-tune (all layers)\n",
        "history_101_food_classes_all_data_fine_tune = loaded_gs_model.fit(train_data,\n",
        "                                                        epochs=100, # fine-tune for a maximum of 100 epochs\n",
        "                                                        steps_per_epoch=len(train_data),\n",
        "                                                        validation_data=test_data,\n",
        "                                                        validation_steps=int(0.15 * len(test_data)), # validation during training on 15% of test data\n",
        "                                                        callbacks=[create_tensorboard_callback(\"training_logs\", \"efficientb0_101_classes_all_data_fine_tuning\"), # track the model training logs\n",
        "                                                                   model_checkpoint, # save only the best model during training\n",
        "                                                                   early_stopping, # stop model after X epochs of no improvements\n",
        "                                                                   reduce_lr]) # reduce the learning rate after X epochs of no improvements"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d58b2ebd-1955-416c-a76d-ec0999a25736",
      "metadata": {
        "id": "d58b2ebd-1955-416c-a76d-ec0999a25736"
      },
      "source": [
        "> 🔑 **Note:** If you didn't use mixed precision or use techniques such as [`prefetch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch) in the *Batch & prepare datasets* section, your model fine-tuning probably takes up to 2.5-3x longer per epoch (see the output below for an example).\n",
        "\n",
        "| | Prefetch and mixed precision | No prefetch and no mixed precision |\n",
        "|-----|-----|-----|\n",
        "| Time per epoch | ~280-300s | ~1127-1397s |\n",
        "\n",
        "*Results from fine-tuning 🍔👁 Food Vision Big™ on Food101 dataset using an EfficienetNetB0 backbone using a Google Colab Tesla T4 GPU.*\n",
        "\n",
        "```\n",
        "Saving TensorBoard log files to: training_logs/efficientB0_101_classes_all_data_fine_tuning/20200928-013008\n",
        "Epoch 1/100\n",
        "2368/2368 [==============================] - 1397s 590ms/step - loss: 1.2068 - accuracy: 0.6820 - val_loss: 1.1623 - val_accuracy: 0.6894\n",
        "Epoch 2/100\n",
        "2368/2368 [==============================] - 1193s 504ms/step - loss: 0.9459 - accuracy: 0.7444 - val_loss: 1.1549 - val_accuracy: 0.6872\n",
        "Epoch 3/100\n",
        "2368/2368 [==============================] - 1143s 482ms/step - loss: 0.7848 - accuracy: 0.7838 - val_loss: 1.0402 - val_accuracy: 0.7142\n",
        "Epoch 4/100\n",
        "2368/2368 [==============================] - 1127s 476ms/step - loss: 0.6599 - accuracy: 0.8149 - val_loss: 0.9599 - val_accuracy: 0.7373\n",
        "```\n",
        "*Example fine-tuning time for non-prefetched data as well as non-mixed precision training (~2.5-3x longer per epoch).*\n",
        "\n",
        "Let's make sure we save our model before we start evaluating it."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00ce480d-7f0a-40fc-b42e-3d28cfadd462",
      "metadata": {
        "id": "00ce480d-7f0a-40fc-b42e-3d28cfadd462"
      },
      "source": [
        "From the above, does it look like our model is overfitting or underfitting?\n",
        "\n",
        "Remember, if the training loss is significantly lower than the validation loss, it's a hint that the model has overfit the training data and not learned generalizable patterns to unseen data.\n",
        "\n",
        "But it does look like our model has gained a few performance points from fine-tuning, let's evaluate on the whole test dataset and see if managed to beat the [DeepFood paper's](https://arxiv.org/abs/1606.05675) result of 77.4% accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "483515ad-3ae8-4bf9-b484-fef56b87ed4c",
      "metadata": {
        "id": "483515ad-3ae8-4bf9-b484-fef56b87ed4c"
      },
      "outputs": [],
      "source": [
        "# # Save model to Google Drive (optional)\n",
        "# loaded_gs_model.save(\"/content/drive/MyDrive/tensorflow_course/food_vision/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "578aea68-5351-4fea-a4b4-eeedb511c71b",
      "metadata": {
        "id": "578aea68-5351-4fea-a4b4-eeedb511c71b"
      },
      "outputs": [],
      "source": [
        "# Save model locally (note: if you're using Google Colab and you save your model locally, it will be deleted when your Google Colab session ends)\n",
        "loaded_gs_model.save(\"07_efficientnetb0_fine_tuned_101_classes_mixed_precision\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "312cb4c9-e399-4efb-8258-828a3f9291f9",
      "metadata": {
        "id": "312cb4c9-e399-4efb-8258-828a3f9291f9"
      },
      "source": [
        "## Download fine-tuned model from Google Storage\n",
        "\n",
        "As mentioned before, training models can take a significant amount of time.\n",
        "\n",
        "And again, like any good cooking show, here's something we prepared earlier...\n",
        "\n",
        "It's a fine-tuned model exactly like the one we trained above but it's saved to Google Storage so it can be accessed, imported and evaluated."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "325e55b2-dee4-4e3f-9113-82c9e749efbb",
      "metadata": {
        "id": "325e55b2-dee4-4e3f-9113-82c9e749efbb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a546e9c-d967-4183-ffd1-dbed1ac4b124"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-06-16 06:07:23--  https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.12.128, 172.217.194.128, 172.253.118.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.12.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 46790356 (45M) [application/zip]\n",
            "Saving to: ‘07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip’\n",
            "\n",
            "07_efficientnetb0_f 100%[===================>]  44.62M  13.0MB/s    in 3.4s    \n",
            "\n",
            "2023-06-16 06:07:27 (13.0 MB/s) - ‘07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip’ saved [46790356/46790356]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download and evaluate fine-tuned model from Google Storage\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c5f856e-e58f-4575-b725-ca7b5004af10",
      "metadata": {
        "id": "1c5f856e-e58f-4575-b725-ca7b5004af10"
      },
      "source": [
        "The downloaded model comes in zip format (`.zip`) so we'll unzip it into the Google Colab instance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "29b9f6ba-1d0b-4043-a55d-40bef03d816b",
      "metadata": {
        "id": "29b9f6ba-1d0b-4043-a55d-40bef03d816b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e281198a-5350-4dd8-9137-ba66bdb2c254"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip\n",
            "   creating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/\n",
            "   creating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/variables/\n",
            "  inflating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/variables/variables.data-00000-of-00001  \n",
            "  inflating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/variables/variables.index  \n",
            "  inflating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/saved_model.pb  \n",
            "   creating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/assets/\n"
          ]
        }
      ],
      "source": [
        "# Unzip fine-tuned model\n",
        "!mkdir downloaded_fine_tuned_gs_model # create separate directory for fine-tuned model downloaded from Google Storage\n",
        "!unzip 07_efficientnetb0_fine_tuned_101_classes_mixed_precision -d downloaded_fine_tuned_gs_model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "584eb626-33ea-489e-be8a-ba8517035688",
      "metadata": {
        "id": "584eb626-33ea-489e-be8a-ba8517035688"
      },
      "source": [
        "Now we can load it using the [`tf.keras.models.load_model()`](https://www.tensorflow.org/tutorials/keras/save_and_load) method and get a summary (it should be the exact same as the model we created above)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "1c0f85d6-3613-48bf-8ce1-fb6b58865545",
      "metadata": {
        "id": "1c0f85d6-3613-48bf-8ce1-fb6b58865545",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04e28702-cd3d-4aae-b913-e5e3de5eec90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Importing a function (__inference_block3b_expand_activation_layer_call_and_return_conditional_losses_443625) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_activation_layer_call_and_return_conditional_losses_412189) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block1a_se_reduce_layer_call_and_return_conditional_losses_409120) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_expand_activation_layer_call_and_return_conditional_losses_446895) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block1a_activation_layer_call_and_return_conditional_losses_442329) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_421687) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_activation_layer_call_and_return_conditional_losses_411851) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6b_se_reduce_layer_call_and_return_conditional_losses_446637) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_activation_layer_call_and_return_conditional_losses_447353) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_expand_activation_layer_call_and_return_conditional_losses_445426) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block1a_se_reduce_layer_call_and_return_conditional_losses_442371) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_419945) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_se_reduce_layer_call_and_return_conditional_losses_442703) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5a_activation_layer_call_and_return_conditional_losses_445173) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_440876) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_se_reduce_layer_call_and_return_conditional_losses_447774) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_expand_activation_layer_call_and_return_conditional_losses_413771) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_se_reduce_layer_call_and_return_conditional_losses_413200) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_expand_activation_layer_call_and_return_conditional_losses_410834) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_se_reduce_layer_call_and_return_conditional_losses_411277) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_expand_activation_layer_call_and_return_conditional_losses_410210) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5a_se_reduce_layer_call_and_return_conditional_losses_445215) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_activation_layer_call_and_return_conditional_losses_446974) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_se_reduce_layer_call_and_return_conditional_losses_443414) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_expand_activation_layer_call_and_return_conditional_losses_413433) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_stem_activation_layer_call_and_return_conditional_losses_442250) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_activation_layer_call_and_return_conditional_losses_410890) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_se_reduce_layer_call_and_return_conditional_losses_413876) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_top_activation_layer_call_and_return_conditional_losses_414056) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_activation_layer_call_and_return_conditional_losses_409981) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_435905) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_activation_layer_call_and_return_conditional_losses_410266) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_expand_activation_layer_call_and_return_conditional_losses_409924) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_block5a_se_reduce_layer_call_and_return_conditional_losses_411615) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_block4b_expand_activation_layer_call_and_return_conditional_losses_444336) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_model_layer_call_and_return_conditional_losses_429172) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_434163) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_top_activation_layer_call_and_return_conditional_losses_447985) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_block6a_expand_activation_layer_call_and_return_conditional_losses_446184) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_block4b_activation_layer_call_and_return_conditional_losses_444415) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_block2a_expand_activation_layer_call_and_return_conditional_losses_409300) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_activation_layer_call_and_return_conditional_losses_443372) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_se_reduce_layer_call_and_return_conditional_losses_447395) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_block4c_se_reduce_layer_call_and_return_conditional_losses_444836) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_block5b_expand_activation_layer_call_and_return_conditional_losses_411795) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
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            "WARNING:absl:Importing a function (__inference_block4c_expand_activation_layer_call_and_return_conditional_losses_444715) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_se_reduce_layer_call_and_return_conditional_losses_443035) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_activation_layer_call_and_return_conditional_losses_442661) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_model_layer_call_and_return_conditional_losses_430926) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_expand_activation_layer_call_and_return_conditional_losses_444004) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_expand_activation_layer_call_and_return_conditional_losses_413095) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_se_reduce_layer_call_and_return_conditional_losses_411900) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_se_reduce_layer_call_and_return_conditional_losses_447016) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_activation_layer_call_and_return_conditional_losses_412528) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_se_reduce_layer_call_and_return_conditional_losses_443746) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_activation_layer_call_and_return_conditional_losses_442993) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_expand_activation_layer_call_and_return_conditional_losses_443293) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_se_reduce_layer_call_and_return_conditional_losses_446305) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6b_activation_layer_call_and_return_conditional_losses_446595) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_activation_layer_call_and_return_conditional_losses_413827) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3b_activation_layer_call_and_return_conditional_losses_443704) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_activation_layer_call_and_return_conditional_losses_409357) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6c_activation_layer_call_and_return_conditional_losses_413151) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block3a_se_reduce_layer_call_and_return_conditional_losses_410030) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4b_se_reduce_layer_call_and_return_conditional_losses_444457) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block1a_activation_layer_call_and_return_conditional_losses_409071) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_expand_activation_layer_call_and_return_conditional_losses_445805) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_activation_layer_call_and_return_conditional_losses_411228) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2a_expand_activation_layer_call_and_return_conditional_losses_442582) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_se_reduce_layer_call_and_return_conditional_losses_410654) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block7a_expand_activation_layer_call_and_return_conditional_losses_447653) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6a_activation_layer_call_and_return_conditional_losses_446263) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block6d_se_reduce_layer_call_and_return_conditional_losses_413538) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5c_expand_activation_layer_call_and_return_conditional_losses_412133) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_se_reduce_layer_call_and_return_conditional_losses_409691) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_expand_activation_layer_call_and_return_conditional_losses_409586) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block5b_activation_layer_call_and_return_conditional_losses_445505) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4a_activation_layer_call_and_return_conditional_losses_410605) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_stem_activation_layer_call_and_return_conditional_losses_409015) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block2b_activation_layer_call_and_return_conditional_losses_409642) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_block4c_expand_activation_layer_call_and_return_conditional_losses_411172) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Importing a function (__inference_efficientnetb0_layer_call_and_return_conditional_losses_439134) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n"
          ]
        }
      ],
      "source": [
        "# Load in fine-tuned model from Google Storage and evaluate\n",
        "loaded_fine_tuned_gs_model = tf.keras.models.load_model(\"downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "861798eb-0a34-4d91-ae7a-0b0b78561fdf",
      "metadata": {
        "id": "861798eb-0a34-4d91-ae7a-0b0b78561fdf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5596f582-85e2-4f63-8d5a-d5bc2cf783a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " efficientnetb0 (Functional  (None, None, None, 1280   4049571   \n",
            " )                           )                                   \n",
            "                                                                 \n",
            " pooling_layer (GlobalAvera  (None, 1280)              0         \n",
            " gePooling2D)                                                    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 101)               129381    \n",
            "                                                                 \n",
            " softmax_float32 (Activatio  (None, 101)               0         \n",
            " n)                                                              \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4178952 (15.94 MB)\n",
            "Trainable params: 4136929 (15.78 MB)\n",
            "Non-trainable params: 42023 (164.16 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Get a model summary (same model architecture as above)\n",
        "loaded_fine_tuned_gs_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "235fa9a8-ba2e-484e-aad2-dc2c00855934",
      "metadata": {
        "id": "235fa9a8-ba2e-484e-aad2-dc2c00855934"
      },
      "source": [
        "Finally, we can evaluate our model on the test data (this requires the `test_data` variable to be loaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "2276cadc-c4b2-4e9a-b7b6-15746ea6eb20",
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        "id": "2276cadc-c4b2-4e9a-b7b6-15746ea6eb20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cb10616-39af-4170-bf05-36a21902c612"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 58s 71ms/step - loss: 0.9072 - accuracy: 0.8015\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9072167873382568, 0.8014653325080872]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "# Note: Even if you're loading in the model from Google Storage, you will still need to load the test_data variable for this cell to work\n",
        "results_downloaded_fine_tuned_gs_model = loaded_fine_tuned_gs_model.evaluate(test_data)\n",
        "results_downloaded_fine_tuned_gs_model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "92419d08-3803-43bd-b588-9359e4a720d9",
      "metadata": {
        "id": "92419d08-3803-43bd-b588-9359e4a720d9"
      },
      "source": [
        "Excellent! Our saved model is performing as expected (better results than the DeepFood paper!).\n",
        "\n",
        "Congrautlations! You should be excited! You just trained a computer vision model with competitive performance to a research paper and in far less time (our model took ~20 minutes to train versus DeepFood's quoted 2-3 days).\n",
        "\n",
        "In other words, you brought Food Vision life!\n",
        "\n",
        "If you really wanted to step things up, you could try using the [`EfficientNetB4`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/EfficientNetB4) model (a larger version of `EfficientNetB0`). At at the time of writing, the EfficientNet family has the [state of the art classification results](https://paperswithcode.com/sota/fine-grained-image-classification-on-food-101) on the Food101 dataset.\n",
        "\n",
        "> 📖 **Resource:** To see which models are currently performing the best on a given dataset or problem type as well as the latest trending machine learning research, be sure to check out [paperswithcode.com](http://paperswithcode.com/) and [sotabench.com](https://sotabench.com/)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24b6846d-f542-47b8-979b-65215bd12a74",
      "metadata": {
        "id": "24b6846d-f542-47b8-979b-65215bd12a74"
      },
      "source": [
        "## View training results on TensorBoard\n",
        "\n",
        "Since we tracked our model's fine-tuning training logs using the `TensorBoard` callback, let's upload them and inspect them on TensorBoard.dev."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "8bf8e3fb-1169-4160-8486-9f4a50a1ab4e",
      "metadata": {
        "id": "8bf8e3fb-1169-4160-8486-9f4a50a1ab4e"
      },
      "outputs": [],
      "source": [
        "# Upload experiment results to TensorBoard (uncomment to run)\n",
        "# !tensorboard dev upload --logdir ./training_logs \\\n",
        "#   --name \"Fine-tuning EfficientNetB0 on all Food101 Data\" \\\n",
        "#   --description \"Training results for fine-tuning EfficientNetB0 on Food101 Data with learning rate 0.0001\" \\\n",
        "#   --one_shot"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c9e3985-0626-456c-9d45-e5b3134eb5e5",
      "metadata": {
        "id": "3c9e3985-0626-456c-9d45-e5b3134eb5e5"
      },
      "source": [
        "Viewing at our [model's training curves on TensorBoard.dev](https://tensorboard.dev/experiment/2KINdYxgSgW2bUg7dIvevw/), it looks like our fine-tuning model gains boost in performance but starts to overfit as training goes on.\n",
        "\n",
        "See the training curves on TensorBoard.dev here: https://tensorboard.dev/experiment/2KINdYxgSgW2bUg7dIvevw/\n",
        "\n",
        "To fix this, in future experiments, we might try things like:\n",
        "* A different iteration of `EfficientNet` (e.g. `EfficientNetB4` instead of `EfficientNetB0`).\n",
        "* Unfreezing less layers of the base model and training them rather than unfreezing the whole base model in one go."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "d5052bf8-29d4-48c0-8314-3c9ab6cb6aef",
      "metadata": {
        "id": "d5052bf8-29d4-48c0-8314-3c9ab6cb6aef"
      },
      "outputs": [],
      "source": [
        "# View past TensorBoard experiments\n",
        "# !tensorboard dev list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "f132dd1e-653f-46b6-9adb-e084bac84a84",
      "metadata": {
        "id": "f132dd1e-653f-46b6-9adb-e084bac84a84"
      },
      "outputs": [],
      "source": [
        "# Delete past TensorBoard experiments\n",
        "# !tensorboard dev delete --experiment_id YOUR_EXPERIMENT_ID\n",
        "\n",
        "# Example\n",
        "# !tensorboard dev delete --experiment_id OAE6KXizQZKQxDiqI3cnUQ"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d86fa758-b1bf-4113-88f7-c3d2996b93f5",
      "metadata": {
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      "source": [
        "## 🛠 Exercises\n",
        "\n",
        "1. Use the same evaluation techniques on the large-scale Food Vision model as you did in the previous notebook ([Transfer Learning Part 3: Scaling up](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/06_transfer_learning_in_tensorflow_part_3_scaling_up.ipynb)). More specifically, it would be good to see:\n",
        "  * A confusion matrix between all of the model's predictions and true labels.\n",
        "  * A graph showing the f1-scores of each class.\n",
        "  * A visualization of the model making predictions on various images and comparing the predictions to the ground truth.\n",
        "    * For example, plot a sample image from the test dataset and have the title of the plot show the prediction, the prediction probability and the ground truth label.\n",
        "2. Take 3 of your own photos of food and use the Food Vision model to make predictions on them. How does it go? Share your images/predictions with the other students.\n",
        "3. Retrain the model (feature extraction and fine-tuning) we trained in this notebook, except this time use [`EfficientNetB4`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/EfficientNetB4) as the base model instead of `EfficientNetB0`. Do you notice an improvement in performance? Does it take longer to train? Are there any tradeoffs to consider?\n",
        "4. Name one important benefit of mixed precision training, how does this benefit take place?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f78edd4a-9363-4816-a828-36d9c6ff23d4",
      "metadata": {
        "id": "f78edd4a-9363-4816-a828-36d9c6ff23d4"
      },
      "source": [
        "## 📖 Extra-curriculum\n",
        "\n",
        "* Read up on learning rate scheduling and the [learning rate scheduler callback](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler). What is it? And how might it be helpful to this project?\n",
        "* Read up on TensorFlow data loaders ([improving TensorFlow data loading performance](https://www.tensorflow.org/guide/data_performance)). Is there anything we've missed? What methods you keep in mind whenever loading data in TensorFlow? Hint: check the summary at the bottom of the page for a gret round up of ideas.\n",
        "* Read up on the documentation for [TensorFlow mixed precision training](https://www.tensorflow.org/guide/mixed_precision). What are the important things to keep in mind when using mixed precision training?"
      ]
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