End_to_end_Food_Vision_Project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0oBJvRIRkBs"
      },
      "source": [
        "# Milestone Project 1: Food Vision Big\n",
        "In this notebook we're going to be building a Food vision model that can beat the original results of the [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 CNN trained for 2-3 days to achieve 77.4% top-1 accuracy.\n",
        "\n",
        "Alongside attempting to beat the DeepFood paper, we're going to implement  two methods to significantly improve the speed of our model training:\n",
        "\n",
        "  1. Prefetching\n",
        "  2. Mixed precision training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TodJIPNkCVWc"
      },
      "source": [
        "## Get Helper Functions\n",
        "We'll import some `helper_functions.py`, that can help us across the experimentations.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKe8ss_uD82-",
        "outputId": "1ef7ec4c-3fc2-44c4-c25b-b3ba8446b0a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-02-24 21:30:16--  https://raw.githubusercontent.com/Charliecr94/Tensor_flow_projects/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: 10234 (10.0K) [text/plain]\n",
            "Saving to: ‘helper_functions.py.1’\n",
            "\n",
            "\rhelper_functions.py   0%[                    ]       0  --.-KB/s               \rhelper_functions.py 100%[===================>]   9.99K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-02-24 21:30:16 (60.8 MB/s) - ‘helper_functions.py.1’ saved [10234/10234]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Dowloead helper functions scripts\n",
        "!wget https://raw.githubusercontent.com/Charliecr94/Tensor_flow_projects/main/Extras/helper_functions.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "i29dOZOMEQ90"
      },
      "outputs": [],
      "source": [
        "# Import series of helper functions for the notebook\n",
        "from helper_functions import create_tensorboard_callback, plot_loss_curves, compare_historys"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRYTJoAHEl2h"
      },
      "source": [
        "## Use TensorFlow Dataset to Download Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2sjwwGtErrs",
        "outputId": "fb8e958b-f7c3-4d28-decf-d9da773d79c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "# Get TensorFlow Datasets\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "datasets_list = tfds.list_builders()\n",
        "print(\"food101\" in datasets_list)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.__version__, np.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLjZKhGL5Yhe",
        "outputId": "4e0b5fbf-a877-4209-9969-23693a3c7658"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('2.11.0', '1.22.4')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "hphxKG39HrkN"
      },
      "outputs": [],
      "source": [
        "# Load in the data.\n",
        "(train_data, test_data), ds_info = tfds.load(name=\"food101\",\n",
        "                                            split=[\"train\",\"validation\"],\n",
        "                                            shuffle_files= True,\n",
        "                                            as_supervised= True,\n",
        "                                            with_info = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_uRtYN3kTme",
        "outputId": "4424bdbb-4a48-4fb9-e23c-104dc0fa22d7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<PrefetchDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>,\n",
              " <PrefetchDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ot5tv7uQH4et"
      },
      "source": [
        "We've now got access to entire Food101 dataset (in tensor format) ready for modeling.\n",
        "\n",
        "Now let's get a little information from our datasets, starting with the class names."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "MpwF-mlkMnFs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87283260-86c0-4f54-df0f-3ffe32de4aec"
      },
      "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": 7
        }
      ],
      "source": [
        "# Get class names\n",
        "class_names = ds_info.features[\"label\"].names\n",
        "class_names[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhyMHaHMLhlP"
      },
      "source": [
        "## Exploring the Food101 data from TensorFlow Datasets\n",
        "\n",
        "To become one with our data, we want to find:\n",
        "  * The shape of our input data (image tensors)\n",
        "  * The datatype of our input data\n",
        "  * What the labels look like\n",
        "  * Do the labels match up with the class_names\n",
        "\n",
        "To do so, let's take one sample off the training data (using the `.take()` method) and explore it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Zg7lQLd-MB-3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2968d184-4b9a-4b9d-c5db-c86391b416e3"
      },
      "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": 8
        }
      ],
      "source": [
        "# Take one sample of the train data\n",
        "train_one_sample = train_data.take(1)\n",
        "train_one_sample"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Output info abour our training sample\n",
        "for image, label in train_one_sample:\n",
        "  print(f\"\"\"\n",
        "   Image shape: {image.shape}\n",
        "   Image datatype: {image.dtype}\n",
        "   Target Class From Food101: {label}\n",
        "   Class name (str form): {class_names[label.numpy()]}\n",
        "  \"\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rBZBwz_nX8P",
        "outputId": "7efa0ee1-5b76-46e0-d9e1-7671ad868124"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "   Image shape: (512, 512, 3)\n",
            "   Image datatype: <dtype: 'uint8'>\n",
            "   Target Class From Food101: 1\n",
            "   Class name (str form): baby_back_ribs\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGe4rL1AQEM7"
      },
      "source": [
        "## Plot an  image from TensorFlow Datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "f9c9QedCQsv0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "1d50c140-0726-4ad1-da86-ac18465826b0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot an image tensor\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(image)\n",
        "plt.title(class_names[label.numpy()])\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ck-xFYbWRD-_"
      },
      "source": [
        "## Create preprocessing functions for our data\n",
        "\n",
        "In order to get the data ready for a neural network, we'll had to write preprocessing functions and map it to the data.\n",
        "\n",
        "What we know about our data:\n",
        "\n",
        "  * In `uint8` datatype\n",
        "  * Comprised of all different size tensors (different sized images)\n",
        "  * Not scaled (the pixel values are between 0 & 255)\n",
        "\n",
        "What we know models like:\n",
        "  \n",
        "  * Data in float32 dtype.\n",
        "  * For Batches, tensorflow likes all of the tensoers within a batch to be of the same size.\n",
        "  * Scaled (vlues between 0 & 1) also called normalized tensors generally perform better.\n",
        "\n",
        "With these points in mind, we've got a few things we can tackle with a preprocessing function. This functions needs to:\n",
        "  1. Reshape our images to all the same size\n",
        "  2. Convert the dtype of our image tensors from `unit8` to `float32`\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "KhCTy_daS84o"
      },
      "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",
      "source": [
        "Let's try our function out on a target image."
      ],
      "metadata": {
        "id": "6LxmN3owlte_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ZqEirvWSYKiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfc97240-a4e1-4758-cd53-6f4154b494b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image before preprocessing:\n",
            " [[[ 95  95  25]\n",
            "  [ 87  87  17]\n",
            "  [ 96  93  26]\n",
            "  ...\n",
            "  [178 189 191]\n",
            "  [180 191 193]\n",
            "  [178 189 191]]\n",
            "\n",
            " [[ 92  92  22]\n",
            "  [ 86  86  16]\n",
            "  [ 91  88  21]\n",
            "  ...\n",
            "  [183 194 196]\n",
            "  [184 195 197]\n",
            "  [180 191 193]]]...,\n",
            "Shape: (512, 512, 3),\n",
            "Datatype: <dtype: 'uint8'>\n",
            "\n",
            "Image after preprocessing:\n",
            " [[[ 88.755104  88.755104  18.755102]\n",
            "  [ 88.341835  85.341835  18.341835]\n",
            "  [ 86.377556  81.16327   17.591837]\n",
            "  ...\n",
            "  [182.5153   193.5153   195.5153  ]\n",
            "  [181.47958  192.47958  194.47958 ]\n",
            "  [181.39784  192.39784  194.39784 ]]\n",
            "\n",
            " [[ 96.57143   93.57143   24.571428]\n",
            "  [ 92.38265   89.38265   22.382656]\n",
            "  [ 93.72959   88.515305  24.94388 ]\n",
            "  ...\n",
            "  [189.42863  200.42863  202.42863 ]\n",
            "  [189.78568  200.78568  202.78568 ]\n",
            "  [183.90295  197.90295  198.90295 ]]]...,\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",
      "metadata": {
        "id": "D6_T3CClYoeN"
      },
      "source": [
        "Excellent! Looks like our `preprocess_img()` function is working as expected. Becuase the input image gets converted from `uint8` to `float32` and also get reshaped from its current shape to `(224,224,3)`"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can still plot our preprocessed image as long as we divide by 255 (for matplotlib capatibility))\n",
        "plt.imshow(preprocessed_img/255.)\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False);"
      ],
      "metadata": {
        "id": "5wdAHyIvKsIr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "4004a47b-de06-40c4-951a-866f8deb57ef"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvhfk9JCZSDb"
      },
      "source": [
        "# Batch & prepare datasets.\n",
        "\n",
        "Before we can model our data, we have to turn it into batches, that way our computing memory will be efficient, so 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",
        "More specifically, we're going to be using:\n",
        "  * `map() `- maps a predefined function to a target dataset.\n",
        "  *` shuffle()` - randomly shuffles the elements of a target dataset up \n",
        "`buffer_size`. \n",
        "  * `batch()` - Turns elemenents of a target dataset into batches\n",
        "  * `prefetch()` - Prepares subsequent batches of data whilist other batches of data are being computed on.\n",
        "  * `cache()` - Caches elements in a target dataset, saving loading time.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIT4XyfKl8uZ",
        "outputId": "00bd2e39-e5a4-4deb-e336-c9d8e5b23e2f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<PrefetchDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>,\n",
              " <PrefetchDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Ei0GW4icaYhu"
      },
      "outputs": [],
      "source": [
        "# Map preprocessing function to training data (and paralellize)\n",
        "train_data = train_data.map(preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "# Shuffle train_data and turn it into batches and prefetch it (Load i faster).\n",
        "train_data = train_data.shuffle(buffer_size=1000).batch(batch_size=32).prefetch(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",
      "source": [
        "And now let's check out what our prepared datasets look like."
      ],
      "metadata": {
        "id": "U_TKPd0-lVC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hCurdO43O5S",
        "outputId": "ecbaa807-4806-473c-b77e-1dad2601201a"
      },
      "execution_count": 16,
      "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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create modelling callbacks\n",
        "\n",
        "Since we're going the be training on a large amount of data and training could take a long time, is a good idea to set up some modelling callbacks.\n",
        "\n",
        "To do each we'll use the following callbacks:\n",
        "  * ` tf.keras.callbacks.TensorBoard()` - allows us to kkep tack our model's training history so we can inspect it later.\n",
        "  * ` tf.keras.callbacks.ModelCheckpoint()` saves our model's progress at various intervals so we can load it and reuses it later withour having to retrain it."
      ],
      "metadata": {
        "id": "XeKsa05nrkXs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create TensorBoard callback\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\",\n",
        "                                                      save_best_only= True,\n",
        "                                                      save_weights_only= True,\n",
        "                                                      verbose=0)\n"
      ],
      "metadata": {
        "id": "ksDwYkjtk5k7"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup Mixed precision training.\n",
        "For speed up the performance of our model, let's set up Mixed precision, which is a method that utilized a combination of float32 and float16 data types to speed up model performance. \n",
        "\n",
        "According to his documentation, this API can improve perfomance by more thean 3 times on modern GPUS and 60% on TPUS.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ehbDIM6noSZy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Turn on mixed precision training\n",
        "from tensorflow.keras import mixed_precision\n",
        "mixed_precision.set_global_policy(\"mixed_float16\") # set global data policy to mixed precision"
      ],
      "metadata": {
        "id": "TluADlhEp45H"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mixed_precision.global_policy()"
      ],
      "metadata": {
        "id": "VwofpRGdt7ku",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cf37d47-25f4-4a2c-eb09-4434f4c528b0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Policy \"mixed_float16\">"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build Feature extraction model\n",
        "\n",
        "Now that or Callbacks are ready to roll and the mixed precision is ON, we are ready to build our firts model."
      ],
      "metadata": {
        "id": "m698tP1rpvVG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers.experimental import preprocessing\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",
        "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\"])"
      ],
      "metadata": {
        "id": "QUzBBZWmvezq"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out our model\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "JtDGPD2p_jho",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d6ec2ed-dcd4-4948-f49e-50e84af47ddb"
      },
      "execution_count": 21,
      "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",
            " pooling_layer (GlobalAverag  (None, 1280)             0         \n",
            " ePooling2D)                                                     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 101)               129381    \n",
            "                                                                 \n",
            " softmax_float32 (Activation  (None, 101)              0         \n",
            " )                                                               \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,178,952\n",
            "Trainable params: 129,381\n",
            "Non-trainable params: 4,049,571\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking layer dtype policies (are we using mixed precision?)"
      ],
      "metadata": {
        "id": "I6a8Z4lvABTs"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "2VLUG0XjAG_r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adabf674-7976-4d30-f342-d62540cca4b1"
      },
      "execution_count": 22,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the dtype_policy attributes of layers in the base model\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)"
      ],
      "metadata": {
        "id": "bDbMEcfgBd0t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40cdec44-4ff8-418d-a224-a3381d84d98c"
      },
      "execution_count": 23,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Alrigth! sees like the mixed precision API automatically change our layers `dtype policy's` to whatever the global dtype policy is."
      ],
      "metadata": {
        "id": "Y3Eqe5phDAVU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Fit the feature extraction model.\n",
        "\n",
        "Due to our final goal is to  fine-tune a pretrained model, the general order of doing things is:\n",
        "  1. Buila a feauture extracion model (Just training a couple of output layers with base layers frozen)\n",
        "  2. Fine-tune some of the frozen layers."
      ],
      "metadata": {
        "id": "0wCz6SQrBcZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn off all warnings except for errors\n",
        "tf.get_logger().setLevel('ERROR')\n",
        "\n",
        "# Fith the feature extraction 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])"
      ],
      "metadata": {
        "id": "Kuur2HUGFCRv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb7ac589-b9a5-4872-a051-92d3de195937"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: training_logs/efficientnetb0_101_classes_all_data_feature_extract/20230224-213042\n",
            "Epoch 1/3\n",
            "2368/2368 [==============================] - 201s 78ms/step - loss: 1.7166 - accuracy: 0.5812 - val_loss: 1.1246 - val_accuracy: 0.6994\n",
            "Epoch 2/3\n",
            "2368/2368 [==============================] - 181s 75ms/step - loss: 1.1990 - accuracy: 0.6893 - val_loss: 1.0161 - val_accuracy: 0.7264\n",
            "Epoch 3/3\n",
            "2368/2368 [==============================] - 180s 75ms/step - loss: 1.0538 - accuracy: 0.7246 - val_loss: 0.9962 - val_accuracy: 0.7259\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_feature_extract_model = model.evaluate(test_data)"
      ],
      "metadata": {
        "id": "_B7z_76tWiqk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67980dde-42dd-4685-ede7-0d2bd9f0b320"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 53s 67ms/step - loss: 1.0042 - accuracy: 0.7257\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save the whole model to file (In google drive)"
      ],
      "metadata": {
        "id": "JYpezUeHX7kS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Save path to drive\n",
        "save_dir = \"/content/drive/MyDrive/Food_vision_model/efficientnetb0_feature_extract_model_mixed_precision/\"\n",
        "\n",
        "# Save model\n",
        "model.save(save_dir)"
      ],
      "metadata": {
        "id": "9c16iuEEX9ns"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model previously saved above\n",
        "loaded_saved_model = tf.keras.models.load_model(save_dir)"
      ],
      "metadata": {
        "id": "IRQfPVNvyyna"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "cRhYZXU_zB-d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if the loaded model's results are equals to the saved models results.\n",
        "import numpy as np\n",
        "assert np.isclose(results_feature_extract_model, results_loaded_saved_model).all()"
      ],
      "metadata": {
        "id": "7XVD78C0-4SL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Preparing our model's layers for fine-tuning."
      ],
      "metadata": {
        "id": "a-zrrxrQABFs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOn1HIUuloj6",
        "outputId": "5a10f8df-c5f7-44af-d797-c886f37fad47"
      },
      "execution_count": 27,
      "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",
            " pooling_layer (GlobalAverag  (None, 1280)             0         \n",
            " ePooling2D)                                                     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 101)               129381    \n",
            "                                                                 \n",
            " softmax_float32 (Activation  (None, 101)              0         \n",
            " )                                                               \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,178,952\n",
            "Trainable params: 129,381\n",
            "Non-trainable params: 4,049,571\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Unfreeze all of the layers in the base model\n",
        "base_model.trainable = True\n",
        "\n",
        "# Check which layers are trainable\n",
        "for layer_number, layer in enumerate(base_model.layers):\n",
        "  print(layer_number, layer.name, layer.trainable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8Q4lcsGBWkt",
        "outputId": "cb42285f-2e57-4e50-fb23-adce4de2b9d7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 input_1 True\n",
            "1 rescaling True\n",
            "2 normalization True\n",
            "3 rescaling_1 True\n",
            "4 stem_conv_pad True\n",
            "5 stem_conv True\n",
            "6 stem_bn True\n",
            "7 stem_activation True\n",
            "8 block1a_dwconv True\n",
            "9 block1a_bn True\n",
            "10 block1a_activation True\n",
            "11 block1a_se_squeeze True\n",
            "12 block1a_se_reshape True\n",
            "13 block1a_se_reduce True\n",
            "14 block1a_se_expand True\n",
            "15 block1a_se_excite True\n",
            "16 block1a_project_conv True\n",
            "17 block1a_project_bn True\n",
            "18 block2a_expand_conv True\n",
            "19 block2a_expand_bn True\n",
            "20 block2a_expand_activation True\n",
            "21 block2a_dwconv_pad True\n",
            "22 block2a_dwconv True\n",
            "23 block2a_bn True\n",
            "24 block2a_activation True\n",
            "25 block2a_se_squeeze True\n",
            "26 block2a_se_reshape True\n",
            "27 block2a_se_reduce True\n",
            "28 block2a_se_expand True\n",
            "29 block2a_se_excite True\n",
            "30 block2a_project_conv True\n",
            "31 block2a_project_bn True\n",
            "32 block2b_expand_conv True\n",
            "33 block2b_expand_bn True\n",
            "34 block2b_expand_activation True\n",
            "35 block2b_dwconv True\n",
            "36 block2b_bn True\n",
            "37 block2b_activation True\n",
            "38 block2b_se_squeeze True\n",
            "39 block2b_se_reshape True\n",
            "40 block2b_se_reduce True\n",
            "41 block2b_se_expand True\n",
            "42 block2b_se_excite True\n",
            "43 block2b_project_conv True\n",
            "44 block2b_project_bn True\n",
            "45 block2b_drop True\n",
            "46 block2b_add True\n",
            "47 block3a_expand_conv True\n",
            "48 block3a_expand_bn True\n",
            "49 block3a_expand_activation True\n",
            "50 block3a_dwconv_pad True\n",
            "51 block3a_dwconv True\n",
            "52 block3a_bn True\n",
            "53 block3a_activation True\n",
            "54 block3a_se_squeeze True\n",
            "55 block3a_se_reshape True\n",
            "56 block3a_se_reduce True\n",
            "57 block3a_se_expand True\n",
            "58 block3a_se_excite True\n",
            "59 block3a_project_conv True\n",
            "60 block3a_project_bn True\n",
            "61 block3b_expand_conv True\n",
            "62 block3b_expand_bn True\n",
            "63 block3b_expand_activation True\n",
            "64 block3b_dwconv True\n",
            "65 block3b_bn True\n",
            "66 block3b_activation True\n",
            "67 block3b_se_squeeze True\n",
            "68 block3b_se_reshape True\n",
            "69 block3b_se_reduce True\n",
            "70 block3b_se_expand True\n",
            "71 block3b_se_excite True\n",
            "72 block3b_project_conv True\n",
            "73 block3b_project_bn True\n",
            "74 block3b_drop True\n",
            "75 block3b_add True\n",
            "76 block4a_expand_conv True\n",
            "77 block4a_expand_bn True\n",
            "78 block4a_expand_activation True\n",
            "79 block4a_dwconv_pad True\n",
            "80 block4a_dwconv True\n",
            "81 block4a_bn True\n",
            "82 block4a_activation True\n",
            "83 block4a_se_squeeze True\n",
            "84 block4a_se_reshape True\n",
            "85 block4a_se_reduce True\n",
            "86 block4a_se_expand True\n",
            "87 block4a_se_excite True\n",
            "88 block4a_project_conv True\n",
            "89 block4a_project_bn True\n",
            "90 block4b_expand_conv True\n",
            "91 block4b_expand_bn True\n",
            "92 block4b_expand_activation True\n",
            "93 block4b_dwconv True\n",
            "94 block4b_bn True\n",
            "95 block4b_activation True\n",
            "96 block4b_se_squeeze True\n",
            "97 block4b_se_reshape True\n",
            "98 block4b_se_reduce True\n",
            "99 block4b_se_expand True\n",
            "100 block4b_se_excite True\n",
            "101 block4b_project_conv True\n",
            "102 block4b_project_bn True\n",
            "103 block4b_drop True\n",
            "104 block4b_add True\n",
            "105 block4c_expand_conv True\n",
            "106 block4c_expand_bn True\n",
            "107 block4c_expand_activation True\n",
            "108 block4c_dwconv True\n",
            "109 block4c_bn True\n",
            "110 block4c_activation True\n",
            "111 block4c_se_squeeze True\n",
            "112 block4c_se_reshape True\n",
            "113 block4c_se_reduce True\n",
            "114 block4c_se_expand True\n",
            "115 block4c_se_excite True\n",
            "116 block4c_project_conv True\n",
            "117 block4c_project_bn True\n",
            "118 block4c_drop True\n",
            "119 block4c_add True\n",
            "120 block5a_expand_conv True\n",
            "121 block5a_expand_bn True\n",
            "122 block5a_expand_activation True\n",
            "123 block5a_dwconv True\n",
            "124 block5a_bn True\n",
            "125 block5a_activation True\n",
            "126 block5a_se_squeeze True\n",
            "127 block5a_se_reshape True\n",
            "128 block5a_se_reduce True\n",
            "129 block5a_se_expand True\n",
            "130 block5a_se_excite True\n",
            "131 block5a_project_conv True\n",
            "132 block5a_project_bn True\n",
            "133 block5b_expand_conv True\n",
            "134 block5b_expand_bn True\n",
            "135 block5b_expand_activation True\n",
            "136 block5b_dwconv True\n",
            "137 block5b_bn True\n",
            "138 block5b_activation True\n",
            "139 block5b_se_squeeze True\n",
            "140 block5b_se_reshape True\n",
            "141 block5b_se_reduce True\n",
            "142 block5b_se_expand True\n",
            "143 block5b_se_excite True\n",
            "144 block5b_project_conv True\n",
            "145 block5b_project_bn True\n",
            "146 block5b_drop True\n",
            "147 block5b_add True\n",
            "148 block5c_expand_conv True\n",
            "149 block5c_expand_bn True\n",
            "150 block5c_expand_activation True\n",
            "151 block5c_dwconv True\n",
            "152 block5c_bn True\n",
            "153 block5c_activation True\n",
            "154 block5c_se_squeeze True\n",
            "155 block5c_se_reshape True\n",
            "156 block5c_se_reduce True\n",
            "157 block5c_se_expand True\n",
            "158 block5c_se_excite True\n",
            "159 block5c_project_conv True\n",
            "160 block5c_project_bn True\n",
            "161 block5c_drop True\n",
            "162 block5c_add True\n",
            "163 block6a_expand_conv True\n",
            "164 block6a_expand_bn True\n",
            "165 block6a_expand_activation True\n",
            "166 block6a_dwconv_pad True\n",
            "167 block6a_dwconv True\n",
            "168 block6a_bn True\n",
            "169 block6a_activation True\n",
            "170 block6a_se_squeeze True\n",
            "171 block6a_se_reshape True\n",
            "172 block6a_se_reduce True\n",
            "173 block6a_se_expand True\n",
            "174 block6a_se_excite True\n",
            "175 block6a_project_conv True\n",
            "176 block6a_project_bn True\n",
            "177 block6b_expand_conv True\n",
            "178 block6b_expand_bn True\n",
            "179 block6b_expand_activation True\n",
            "180 block6b_dwconv True\n",
            "181 block6b_bn True\n",
            "182 block6b_activation True\n",
            "183 block6b_se_squeeze True\n",
            "184 block6b_se_reshape True\n",
            "185 block6b_se_reduce True\n",
            "186 block6b_se_expand True\n",
            "187 block6b_se_excite True\n",
            "188 block6b_project_conv True\n",
            "189 block6b_project_bn True\n",
            "190 block6b_drop True\n",
            "191 block6b_add True\n",
            "192 block6c_expand_conv True\n",
            "193 block6c_expand_bn True\n",
            "194 block6c_expand_activation True\n",
            "195 block6c_dwconv True\n",
            "196 block6c_bn True\n",
            "197 block6c_activation True\n",
            "198 block6c_se_squeeze True\n",
            "199 block6c_se_reshape True\n",
            "200 block6c_se_reduce True\n",
            "201 block6c_se_expand True\n",
            "202 block6c_se_excite True\n",
            "203 block6c_project_conv True\n",
            "204 block6c_project_bn True\n",
            "205 block6c_drop True\n",
            "206 block6c_add True\n",
            "207 block6d_expand_conv True\n",
            "208 block6d_expand_bn True\n",
            "209 block6d_expand_activation True\n",
            "210 block6d_dwconv True\n",
            "211 block6d_bn True\n",
            "212 block6d_activation True\n",
            "213 block6d_se_squeeze True\n",
            "214 block6d_se_reshape True\n",
            "215 block6d_se_reduce True\n",
            "216 block6d_se_expand True\n",
            "217 block6d_se_excite True\n",
            "218 block6d_project_conv True\n",
            "219 block6d_project_bn True\n",
            "220 block6d_drop True\n",
            "221 block6d_add True\n",
            "222 block7a_expand_conv True\n",
            "223 block7a_expand_bn True\n",
            "224 block7a_expand_activation True\n",
            "225 block7a_dwconv True\n",
            "226 block7a_bn True\n",
            "227 block7a_activation True\n",
            "228 block7a_se_squeeze True\n",
            "229 block7a_se_reshape True\n",
            "230 block7a_se_reduce True\n",
            "231 block7a_se_expand True\n",
            "232 block7a_se_excite True\n",
            "233 block7a_project_conv True\n",
            "234 block7a_project_bn True\n",
            "235 top_conv True\n",
            "236 top_bn True\n",
            "237 top_activation True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wonderful, it looks like each layer in our base model is trainable, now let's check if every layer which should be using the dtaype policy `\"mixed_policy\"` is using it."
      ],
      "metadata": {
        "id": "NiuTDnFHD72s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the dtype_policy of the layers in our model.\n",
        "for layer in model.layers:\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GgbovPRwBlEH",
        "outputId": "3778c856-ab72-4a17-ae6d-46eabc3eb21d"
      },
      "execution_count": 29,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See's like everything is ready for compile our fine-tune model, but firts let's set a `EarlyStopping callback`, a `ModelCheckpoint` and a `learning rate` reduction callback.\n",
        "\n"
      ],
      "metadata": {
        "id": "3dmrhF9gqRwT"
      }
    },
    {
      "cell_type": "code",
      "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',\n",
        "                                                  patience=3)\n",
        "\n",
        "# Create Model Checkpoint callback to save best model during fine-tuning\n",
        "checkpoint_path = \"model_fine_tuning_checkpoints/cp.ckpt\"\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n",
        "                                                      monitor= \"val_accuracy\",\n",
        "                                                      save_weights_only= True,\n",
        "                                                      save_best_only= True)\n",
        "\n",
        "# Creating learning rate reduction callbacks.\n",
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor= \"val_loss\",\n",
        "                                                 factor = 0.2,\n",
        "                                                 patience=2,\n",
        "                                                 verbose=1,\n",
        "                                                 min_lr=1e-7)"
      ],
      "metadata": {
        "id": "5-pmkBcEJvhl"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we've got to recompile our model"
      ],
      "metadata": {
        "id": "fngGRARguqis"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer= tf.keras.optimizers.Adam(0.0001),\n",
        "              metrics=[\"accuracy\"])\n"
      ],
      "metadata": {
        "id": "8RV_vXoZxp5L"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine-tuning\n",
        "Let's fit the model on all  our data, we'll set it up to run for up to 100 epochs, but since we're going to be using the EarlyStopping callback, it might stop before reaching 100 epochs.\n",
        "\n"
      ],
      "metadata": {
        "id": "1GN7LCcOuWB6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn off all warnings except for errors\n",
        "tf.get_logger().setLevel('ERROR')\n",
        "\n",
        "# Strat to fine-tune\n",
        "history_101_food_class_all_data_fine_tuning = model.fit(train_data,\n",
        "                                                        epochs= 100,\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,\n",
        "                                                                   early_stopping,\n",
        "                                                                   reduce_lr])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FuqjYUjureOc",
        "outputId": "9cf02076-c1e7-4a87-fac4-527864f2a33a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: training_logs/efficientnetb0_101_classes_all_data_feature_extract/20230224-215020\n",
            "Epoch 1/100\n",
            "2368/2368 [==============================] - 414s 158ms/step - loss: 0.8169 - accuracy: 0.7787 - val_loss: 0.7373 - val_accuracy: 0.7942 - lr: 1.0000e-04\n",
            "Epoch 2/100\n",
            "2368/2368 [==============================] - 370s 155ms/step - loss: 0.5053 - accuracy: 0.8582 - val_loss: 0.7780 - val_accuracy: 0.7860 - lr: 1.0000e-04\n",
            "Epoch 3/100\n",
            "2368/2368 [==============================] - ETA: 0s - loss: 0.2795 - accuracy: 0.9197\n",
            "Epoch 3: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n",
            "2368/2368 [==============================] - 372s 156ms/step - loss: 0.2795 - accuracy: 0.9197 - val_loss: 0.8934 - val_accuracy: 0.7842 - lr: 1.0000e-04\n",
            "Epoch 4/100\n",
            "2368/2368 [==============================] - 369s 155ms/step - loss: 0.0664 - accuracy: 0.9840 - val_loss: 0.9148 - val_accuracy: 0.8059 - lr: 2.0000e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate mixed precision trained fine-tuned model.\n",
        "results_fine_tuning_model = model.evaluate(test_data)\n",
        "results_fine_tuning_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OALje-3m23DQ",
        "outputId": "9c28f171-a1c6-4671-d255-a35386139c54"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 52s 65ms/step - loss: 0.9516 - accuracy: 0.8015\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9515812397003174, 0.8015049695968628]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Excellent, the main goal is complete! Our model performance accuracy is more than **5.00%** above of the \"DeepFood paper\". 🎉🎉\n",
        "\n",
        "Also the training time is less, our model took ~20 minutes to train versus DeepFood's quoted 2-3 days.\n",
        "\n"
      ],
      "metadata": {
        "id": "Yt4VkIFB9NcR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## View training results on TensorBoard."
      ],
      "metadata": {
        "id": "DPH02bGj5vsI"
      }
    },
    {
      "cell_type": "code",
      "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_sh"
      ],
      "metadata": {
        "id": "degZQ71o7lpT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Viewing our monen at TensorBoard, it looks like our fine-tuning model gains boost in performance but starts to overfit as training goes on.\n"
      ],
      "metadata": {
        "id": "LHZOziWL8ET-"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}