05_transfer_learning_in_tensorflow_fine_tuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/umar052001/Deep-Learning-with-Tensorflow/blob/master/05_transfer_learning_in_tensorflow_fine_tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6qeEKeHqut6"
      },
      "source": [
        "# Transefer Learning with tensorflow: Fine Tuning\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v83jgtv7sBDO"
      },
      "source": [
        "## Creating helper functions\n",
        "In previous notebook we've created a bunch of helper functions, now we could rewrite them all, however this is tedious.\n",
        "\n",
        "So, we created a script with all of them and now import it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGtGP-vtsah9",
        "outputId": "cbda9c9e-0b65-4b8f-e8b3-f1ff92605885"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-08 20:36:04--  https://raw.githubusercontent.com/umar052001/Deep-Learning-with-Tensorflow/master/helperFunctions.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: 10512 (10K) [text/plain]\n",
            "Saving to: ‘helperFunctions.py’\n",
            "\n",
            "helperFunctions.py  100%[===================>]  10.27K  --.-KB/s    in 0.004s  \n",
            "\n",
            "2022-09-08 20:36:04 (2.37 MB/s) - ‘helperFunctions.py’ saved [10512/10512]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://raw.githubusercontent.com/umar052001/Deep-Learning-with-Tensorflow/master/helperFunctions.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "UWIbqTWYvyFM"
      },
      "outputs": [],
      "source": [
        "from helperFunctions import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjPSvXfDzJEb"
      },
      "source": [
        "## Let's get some data\n",
        "This time we're going to see how we can use the pretrained models within `tf.keras.applications` and apply them to our own problem."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgKoHajM0NDd",
        "outputId": "5f95724c-d5ac-44f2-d388-5ac7f618f9b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-08 20:36:07--  https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.195.128, 142.250.99.128, 142.250.107.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.195.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 168546183 (161M) [application/zip]\n",
            "Saving to: ‘10_food_classes_10_percent.zip’\n",
            "\n",
            "10_food_classes_10_ 100%[===================>] 160.74M   135MB/s    in 1.2s    \n",
            "\n",
            "2022-09-08 20:36:08 (135 MB/s) - ‘10_food_classes_10_percent.zip’ saved [168546183/168546183]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Get 10% of data from food101 dataset\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip\n",
        "unzip_data(\"10_food_classes_10_percent.zip\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6Gg3l6e08HJ",
        "outputId": "4b810361-ae26-494c-ca0d-2f3bf26eb80f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_10_percent'.\n",
            "There are 10 directories and 0 images in '10_food_classes_10_percent/train'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/chicken_wings'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/pizza'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/grilled_salmon'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/sushi'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/ice_cream'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/chicken_curry'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/ramen'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/fried_rice'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/hamburger'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/steak'.\n",
            "There are 10 directories and 0 images in '10_food_classes_10_percent/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/chicken_wings'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/chicken_curry'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/steak'.\n"
          ]
        }
      ],
      "source": [
        "# Check out how many images and subdirectories are in our dataset\n",
        "walk_through_dir(\"10_food_classes_10_percent\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Hqk5ULwQ1GmG"
      },
      "outputs": [],
      "source": [
        "# Create training and test directories paths\n",
        "train_dir = \"10_food_classes_10_percent/train/\"\n",
        "test_dir = \"10_food_classes_10_percent/test/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6AqmzT11ezf",
        "outputId": "aa09a109-7dc0-4df4-a52a-1393df902937"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 750 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "IMG_SIZE = (224,224)\n",
        "train_data_10_percent = tf.keras.preprocessing.image_dataset_from_directory(directory=train_dir,\n",
        "                                                                            image_size=IMG_SIZE,\n",
        "                                                                            label_mode=\"categorical\",\n",
        "                                                                            batch_size=32)\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(directory=test_dir,\n",
        "                                                                image_size=IMG_SIZE,\n",
        "                                                                label_mode=\"categorical\",\n",
        "                                                                batch_size=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DeIpJ25Q2xKx",
        "outputId": "5e2be13b-d23b-4a49-ada8-6706b92d9c2a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BatchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 10), dtype=tf.float32, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train_data_10_percent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "co7AUFiC205z",
        "outputId": "e4156892-3ebe-4e8d-9360-1a6d69563aeb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['chicken_curry',\n",
              " 'chicken_wings',\n",
              " 'fried_rice',\n",
              " 'grilled_salmon',\n",
              " 'hamburger',\n",
              " 'ice_cream',\n",
              " 'pizza',\n",
              " 'ramen',\n",
              " 'steak',\n",
              " 'sushi']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "train_data_10_percent.class_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xt6f04ae3hqd",
        "outputId": "9f5d5cba-0f8c-40c2-b4b8-2250868b4d0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[[[1.34158157e+02 1.34158157e+02 1.44158157e+02]\n",
            "   [1.34142868e+02 1.36928574e+02 1.46000000e+02]\n",
            "   [1.31571426e+02 1.36214279e+02 1.47000000e+02]\n",
            "   ...\n",
            "   [1.63086914e+02 1.30505203e+02 1.43367523e+02]\n",
            "   [1.58025558e+02 1.27311317e+02 1.45382767e+02]\n",
            "   [1.59173447e+02 1.37372513e+02 1.57372467e+02]]\n",
            "\n",
            "  [[1.30734695e+02 1.32525513e+02 1.41928574e+02]\n",
            "   [1.18122444e+02 1.21984688e+02 1.30984695e+02]\n",
            "   [1.23255104e+02 1.30086731e+02 1.40142853e+02]\n",
            "   ...\n",
            "   [1.71168350e+02 1.38556061e+02 1.49112244e+02]\n",
            "   [1.71714279e+02 1.37862244e+02 1.54005127e+02]\n",
            "   [1.66107132e+02 1.36107193e+02 1.54178619e+02]]\n",
            "\n",
            "  [[1.22872452e+02 1.26591835e+02 1.35591827e+02]\n",
            "   [1.21561226e+02 1.28346939e+02 1.36846939e+02]\n",
            "   [1.22433678e+02 1.31051010e+02 1.40816330e+02]\n",
            "   ...\n",
            "   [1.69827332e+02 1.35776215e+02 1.42516037e+02]\n",
            "   [1.82061249e+02 1.39801041e+02 1.51387772e+02]\n",
            "   [2.07766037e+02 1.64480331e+02 1.78051758e+02]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[9.66581650e+01 6.36428566e+01 7.02908554e+01]\n",
            "   [8.17551270e+01 5.40714912e+01 5.89286575e+01]\n",
            "   [3.80714493e+01 1.76683712e+01 2.15204620e+01]\n",
            "   ...\n",
            "   [4.03569031e+01 3.13569031e+01 2.60201931e+01]\n",
            "   [2.26834965e+01 1.31120243e+01 1.08977604e+01]\n",
            "   [1.57091751e+01 7.70917511e+00 5.49491119e+00]]\n",
            "\n",
            "  [[1.03428581e+02 7.20255127e+01 8.12347183e+01]\n",
            "   [8.31479721e+01 5.59336853e+01 6.32806473e+01]\n",
            "   [3.62449265e+01 1.56581917e+01 2.00153332e+01]\n",
            "   ...\n",
            "   [2.78263798e+01 1.88263798e+01 1.33672371e+01]\n",
            "   [1.70051041e+01 7.00510406e+00 5.00510406e+00]\n",
            "   [1.67602806e+01 8.76027966e+00 6.76027966e+00]]\n",
            "\n",
            "  [[9.77293701e+01 6.61885986e+01 7.89589844e+01]\n",
            "   [8.45203629e+01 5.76377449e+01 6.75153580e+01]\n",
            "   [4.21378174e+01 2.12755642e+01 2.58623199e+01]\n",
            "   ...\n",
            "   [2.04233780e+01 1.14233780e+01 5.56632233e+00]\n",
            "   [1.89744835e+01 8.97448254e+00 6.97448254e+00]\n",
            "   [1.72856445e+01 7.28564453e+00 6.28564453e+00]]]\n",
            "\n",
            "\n",
            " [[[9.88571396e+01 8.78571396e+01 6.78571396e+01]\n",
            "   [1.01653061e+02 8.95102081e+01 6.79387741e+01]\n",
            "   [1.01530609e+02 8.95306091e+01 6.75306091e+01]\n",
            "   ...\n",
            "   [5.53265114e+01 6.07550392e+01 5.45407753e+01]\n",
            "   [5.41326523e+01 5.91326523e+01 5.31326523e+01]\n",
            "   [5.17346535e+01 5.67346535e+01 5.07346535e+01]]\n",
            "\n",
            "  [[9.75714264e+01 8.65714264e+01 6.65714264e+01]\n",
            "   [9.91428604e+01 8.61428604e+01 6.71428604e+01]\n",
            "   [9.96632690e+01 8.73265305e+01 6.63367310e+01]\n",
            "   ...\n",
            "   [5.60000000e+01 6.14285278e+01 5.52142639e+01]\n",
            "   [5.53571281e+01 6.03571281e+01 5.43571281e+01]\n",
            "   [5.27142143e+01 5.77142143e+01 5.17142143e+01]]\n",
            "\n",
            "  [[9.72550964e+01 8.62550964e+01 6.62550964e+01]\n",
            "   [9.66224442e+01 8.36224442e+01 6.46224442e+01]\n",
            "   [9.93673477e+01 8.64285736e+01 6.72448959e+01]\n",
            "   ...\n",
            "   [5.60000000e+01 6.12754822e+01 5.66428337e+01]\n",
            "   [5.52653008e+01 6.02653008e+01 5.56938744e+01]\n",
            "   [5.32856445e+01 5.82856445e+01 5.37142143e+01]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[1.09816330e+02 1.10816330e+02 1.12816330e+02]\n",
            "   [1.10663277e+02 1.11663277e+02 1.13663277e+02]\n",
            "   [1.11928581e+02 1.12928581e+02 1.14928581e+02]\n",
            "   ...\n",
            "   [2.29000000e+02 2.48000000e+02 2.54000000e+02]\n",
            "   [2.26714294e+02 2.46714294e+02 2.53714294e+02]\n",
            "   [2.26816345e+02 2.46816345e+02 2.53816345e+02]]\n",
            "\n",
            "  [[1.08581657e+02 1.09581657e+02 1.11581657e+02]\n",
            "   [1.09387772e+02 1.10387772e+02 1.12387772e+02]\n",
            "   [1.10642876e+02 1.11642876e+02 1.13642876e+02]\n",
            "   ...\n",
            "   [2.28551025e+02 2.47551025e+02 2.53551025e+02]\n",
            "   [2.26000000e+02 2.46000000e+02 2.53000000e+02]\n",
            "   [2.26153076e+02 2.46153076e+02 2.53153076e+02]]\n",
            "\n",
            "  [[1.07142853e+02 1.08142853e+02 1.10142853e+02]\n",
            "   [1.07275505e+02 1.08275505e+02 1.10275505e+02]\n",
            "   [1.09357140e+02 1.10357140e+02 1.12357140e+02]\n",
            "   ...\n",
            "   [2.28214264e+02 2.47214264e+02 2.53214264e+02]\n",
            "   [2.26000000e+02 2.46000000e+02 2.53000000e+02]\n",
            "   [2.26000000e+02 2.46000000e+02 2.53000000e+02]]]\n",
            "\n",
            "\n",
            " [[[1.73285721e+02 1.87285721e+02 1.88285721e+02]\n",
            "   [1.74285721e+02 1.88285721e+02 1.89285721e+02]\n",
            "   [1.76005096e+02 1.90005096e+02 1.91005096e+02]\n",
            "   ...\n",
            "   [8.08417969e+01 8.38417969e+01 7.68417969e+01]\n",
            "   [8.63571396e+01 8.72142563e+01 8.22856979e+01]\n",
            "   [8.51989899e+01 8.35101471e+01 8.12806015e+01]]\n",
            "\n",
            "  [[1.74000000e+02 1.88000000e+02 1.89000000e+02]\n",
            "   [1.74000000e+02 1.88000000e+02 1.89000000e+02]\n",
            "   [1.74928574e+02 1.88928574e+02 1.89928574e+02]\n",
            "   ...\n",
            "   [8.57143936e+01 8.71123123e+01 8.08827438e+01]\n",
            "   [8.68571320e+01 8.77244492e+01 8.27244644e+01]\n",
            "   [9.07858887e+01 8.99287491e+01 8.58573151e+01]]\n",
            "\n",
            "  [[1.72428574e+02 1.86428574e+02 1.87428574e+02]\n",
            "   [1.72428574e+02 1.86428574e+02 1.87428574e+02]\n",
            "   [1.72428574e+02 1.86428574e+02 1.87428574e+02]\n",
            "   ...\n",
            "   [7.87194443e+01 7.97194443e+01 7.20561905e+01]\n",
            "   [8.51989746e+01 8.61989746e+01 8.02142868e+01]\n",
            "   [8.94388962e+01 9.04388962e+01 8.54388962e+01]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[1.24280609e+02 1.49280609e+02 1.56280609e+02]\n",
            "   [1.22071426e+02 1.47071426e+02 1.54071426e+02]\n",
            "   [1.21214294e+02 1.46214294e+02 1.53214294e+02]\n",
            "   ...\n",
            "   [1.14214294e+02 1.13214294e+02 1.08214294e+02]\n",
            "   [1.15214294e+02 1.12285736e+02 1.03071411e+02]\n",
            "   [1.16076538e+02 1.14076538e+02 1.00362183e+02]]\n",
            "\n",
            "  [[1.24331635e+02 1.47474472e+02 1.55403061e+02]\n",
            "   [1.23862282e+02 1.47005096e+02 1.54933685e+02]\n",
            "   [1.23857178e+02 1.47000000e+02 1.54928589e+02]\n",
            "   ...\n",
            "   [1.15000000e+02 1.14729630e+02 1.07540749e+02]\n",
            "   [1.16000000e+02 1.13071442e+02 1.03857117e+02]\n",
            "   [1.16954094e+02 1.14954094e+02 1.02500000e+02]]\n",
            "\n",
            "  [[1.25642853e+02 1.48642853e+02 1.56642853e+02]\n",
            "   [1.24045914e+02 1.47045914e+02 1.55045914e+02]\n",
            "   [1.23357147e+02 1.46357147e+02 1.54357147e+02]\n",
            "   ...\n",
            "   [1.13357147e+02 1.13357147e+02 1.03081665e+02]\n",
            "   [1.15403076e+02 1.12403076e+02 1.03494926e+02]\n",
            "   [1.16586761e+02 1.13586761e+02 1.05872467e+02]]]\n",
            "\n",
            "\n",
            " ...\n",
            "\n",
            "\n",
            " [[[1.82759079e+02 1.57759079e+02 1.17759087e+02]\n",
            "   [1.80118622e+02 1.55118622e+02 1.15118622e+02]\n",
            "   [1.79952652e+02 1.54952652e+02 1.14952644e+02]\n",
            "   ...\n",
            "   [1.02494637e+02 8.31262894e+01 5.14209671e+01]\n",
            "   [1.09444069e+02 8.54440689e+01 5.74440651e+01]\n",
            "   [1.11986595e+02 8.79865952e+01 5.99865990e+01]]\n",
            "\n",
            "  [[1.83798950e+02 1.58798950e+02 1.18798950e+02]\n",
            "   [1.84929047e+02 1.59929047e+02 1.19929054e+02]\n",
            "   [1.88498566e+02 1.63498566e+02 1.23498566e+02]\n",
            "   ...\n",
            "   [1.03436752e+02 8.41420746e+01 5.22157478e+01]\n",
            "   [1.09404518e+02 8.64045181e+01 5.54045143e+01]\n",
            "   [1.11629456e+02 8.86294556e+01 5.76294556e+01]]\n",
            "\n",
            "  [[1.85076370e+02 1.60076370e+02 1.19862083e+02]\n",
            "   [1.80729752e+02 1.55729752e+02 1.15515465e+02]\n",
            "   [1.84234695e+02 1.59234695e+02 1.19020409e+02]\n",
            "   ...\n",
            "   [1.03073669e+02 8.39263306e+01 5.20000000e+01]\n",
            "   [1.05586479e+02 8.38884277e+01 5.28884277e+01]\n",
            "   [1.09200882e+02 8.66294556e+01 5.56294556e+01]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[9.07007828e+01 2.33438816e+01 2.89153538e+01]\n",
            "   [9.03020172e+01 2.01660957e+01 2.68491745e+01]\n",
            "   [9.30058441e+01 1.97226048e+01 2.54413986e+01]\n",
            "   ...\n",
            "   [1.70784882e+02 1.32968430e+02 8.10420990e+01]\n",
            "   [1.72571472e+02 1.32571472e+02 8.15714722e+01]\n",
            "   [1.73386200e+02 1.33386200e+02 8.23862000e+01]]\n",
            "\n",
            "  [[9.82857666e+01 3.07856750e+01 4.82143250e+01]\n",
            "   [9.72535706e+01 2.97534828e+01 4.71821327e+01]\n",
            "   [9.53037720e+01 2.87961998e+01 4.34458313e+01]\n",
            "   ...\n",
            "   [1.75504562e+02 1.37285782e+02 9.04189453e+01]\n",
            "   [1.71483917e+02 1.31444214e+02 8.25633316e+01]\n",
            "   [1.72058212e+02 1.32058212e+02 8.30582047e+01]]\n",
            "\n",
            "  [[1.05370537e+02 2.10741711e+01 5.24367104e+01]\n",
            "   [1.05729630e+02 2.30871239e+01 5.26897659e+01]\n",
            "   [1.01273605e+02 2.23329945e+01 4.95044975e+01]\n",
            "   ...\n",
            "   [1.72756439e+02 1.33609100e+02 9.10498276e+01]\n",
            "   [1.73285645e+02 1.32729858e+02 8.53972168e+01]\n",
            "   [1.74100372e+02 1.34100372e+02 8.51003723e+01]]]\n",
            "\n",
            "\n",
            " [[[2.29760208e+02 1.27760201e+02 2.67602043e+01]\n",
            "   [2.34617340e+02 1.31617340e+02 3.06173458e+01]\n",
            "   [2.30290817e+02 1.23862244e+02 2.39234676e+01]\n",
            "   ...\n",
            "   [2.52192936e+01 1.42192926e+01 9.79076481e+00]\n",
            "   [1.74081211e+01 9.76532841e+00 6.62244511e+00]\n",
            "   [1.25152235e+01 9.58675671e+00 6.22957945e+00]]\n",
            "\n",
            "  [[2.11566330e+02 1.11637749e+02 1.34234676e+01]\n",
            "   [2.05066315e+02 1.02137749e+02 6.78060913e+00]\n",
            "   [2.04658157e+02 9.90867310e+01 6.94387579e+00]\n",
            "   ...\n",
            "   [2.51020374e+01 1.41020374e+01 9.67350960e+00]\n",
            "   [1.56478815e+01 8.00508976e+00 4.86220646e+00]\n",
            "   [9.35707283e+00 6.42860603e+00 3.07142830e+00]]\n",
            "\n",
            "  [[1.90484695e+02 9.06989822e+01 3.77040768e+00]\n",
            "   [1.86056122e+02 8.50561142e+01 0.00000000e+00]\n",
            "   [1.87096939e+02 8.23316269e+01 0.00000000e+00]\n",
            "   ...\n",
            "   [2.16632347e+01 1.22806196e+01 6.59187031e+00]\n",
            "   [1.75713882e+01 9.78571415e+00 6.71427250e+00]\n",
            "   [1.26428223e+01 9.69902706e+00 5.98981571e+00]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[2.49642853e+02 1.61071381e+02 0.00000000e+00]\n",
            "   [2.51198959e+02 1.62627487e+02 0.00000000e+00]\n",
            "   [2.50785721e+02 1.61214249e+02 1.66327763e+00]\n",
            "   ...\n",
            "   [2.50290802e+02 2.38785736e+02 1.72954086e+02]\n",
            "   [2.50015305e+02 2.39015305e+02 1.73015305e+02]\n",
            "   [2.50214264e+02 2.39214264e+02 1.73214264e+02]]\n",
            "\n",
            "  [[2.49142883e+02 1.60142883e+02 0.00000000e+00]\n",
            "   [2.51076538e+02 1.59076538e+02 0.00000000e+00]\n",
            "   [2.50071442e+02 1.57857162e+02 1.53089352e-02]\n",
            "   ...\n",
            "   [2.52071442e+02 2.39071442e+02 1.71071442e+02]\n",
            "   [2.49142883e+02 2.39142883e+02 1.70142883e+02]\n",
            "   [2.49142883e+02 2.39142883e+02 1.70142883e+02]]\n",
            "\n",
            "  [[2.52413208e+02 1.60413208e+02 0.00000000e+00]\n",
            "   [2.51311157e+02 1.59311157e+02 0.00000000e+00]\n",
            "   [2.50285645e+02 1.58071365e+02 2.14285851e-01]\n",
            "   ...\n",
            "   [2.52147903e+02 2.39147903e+02 1.71147903e+02]\n",
            "   [2.52714264e+02 2.39714264e+02 1.71714264e+02]\n",
            "   [2.51770401e+02 2.41770401e+02 1.72770401e+02]]]\n",
            "\n",
            "\n",
            " [[[9.76530609e+01 7.90663223e+01 6.50663223e+01]\n",
            "   [1.04392860e+02 8.82500000e+01 7.33214264e+01]\n",
            "   [1.09520409e+02 9.69591827e+01 8.36683655e+01]\n",
            "   ...\n",
            "   [2.44423492e+02 2.37423492e+02 2.19423492e+02]\n",
            "   [2.46760208e+02 2.39760208e+02 2.21760208e+02]\n",
            "   [2.48000000e+02 2.41000000e+02 2.23000000e+02]]\n",
            "\n",
            "  [[1.06326530e+02 8.95204086e+01 7.75867386e+01]\n",
            "   [1.11239799e+02 9.68877640e+01 8.40918427e+01]\n",
            "   [1.50612259e+02 1.41198990e+02 1.26897972e+02]\n",
            "   ...\n",
            "   [2.40857162e+02 2.33857162e+02 2.15857162e+02]\n",
            "   [2.45071442e+02 2.38071442e+02 2.20071442e+02]\n",
            "   [2.47000000e+02 2.40000000e+02 2.22000000e+02]]\n",
            "\n",
            "  [[1.15163269e+02 1.02295921e+02 9.33724518e+01]\n",
            "   [1.54887756e+02 1.45117355e+02 1.34989807e+02]\n",
            "   [2.00071426e+02 1.92836746e+02 1.79790833e+02]\n",
            "   ...\n",
            "   [2.40357193e+02 2.33357193e+02 2.15357193e+02]\n",
            "   [2.43642868e+02 2.36642868e+02 2.18642868e+02]\n",
            "   [2.44571426e+02 2.37571426e+02 2.19571426e+02]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[1.41214325e+02 5.36428566e+01 5.27959023e+01]\n",
            "   [1.37571487e+02 5.00000229e+01 4.82704086e+01]\n",
            "   [1.39000046e+02 5.16428375e+01 4.90000000e+01]\n",
            "   ...\n",
            "   [2.50168350e+02 2.41168350e+02 2.24168350e+02]\n",
            "   [2.50000000e+02 2.41000000e+02 2.24000000e+02]\n",
            "   [2.49785736e+02 2.40785736e+02 2.23785736e+02]]\n",
            "\n",
            "  [[1.41974503e+02 5.39744949e+01 5.19744949e+01]\n",
            "   [1.40357147e+02 5.23571548e+01 5.03571548e+01]\n",
            "   [1.39642853e+02 5.16581650e+01 4.76122398e+01]\n",
            "   ...\n",
            "   [2.51000000e+02 2.42000000e+02 2.25000000e+02]\n",
            "   [2.50933655e+02 2.41933655e+02 2.24933655e+02]\n",
            "   [2.50000000e+02 2.41000000e+02 2.24000000e+02]]\n",
            "\n",
            "  [[1.42571426e+02 5.45714302e+01 5.25714302e+01]\n",
            "   [1.42617371e+02 5.46173782e+01 5.26173782e+01]\n",
            "   [1.39642853e+02 5.18571434e+01 4.72142868e+01]\n",
            "   ...\n",
            "   [2.51494919e+02 2.42494919e+02 2.25494919e+02]\n",
            "   [2.51000000e+02 2.42000000e+02 2.25000000e+02]\n",
            "   [2.51000000e+02 2.42000000e+02 2.25000000e+02]]]], shape=(32, 224, 224, 3), dtype=float32) tf.Tensor(\n",
            "[[0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]], shape=(32, 10), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "# See an example of batch of data\n",
        "for images, labels in train_data_10_percent.take(1):\n",
        "  print(images, labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkphhp3436hS"
      },
      "source": [
        "## Building a transfer learning feature extraction model using the Keras Functional API\n",
        "\n",
        "The sequential API is straight-forward, it runs in sequential order.\n",
        "\n",
        "But the functional api gives us more flexibility with our models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DdJABa-y7Rcp",
        "outputId": "faa748ab-96a7-4964-e1b4-863266d9bf38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16711680/16705208 [==============================] - 0s 0us/step\n",
            "16719872/16705208 [==============================] - 0s 0us/step\n",
            "Shape after passing through base model: (None, 7, 7, 1280)\n",
            "Shape after global average pool 2d: (None, 1280)\n",
            "Saving TensorBoard log files to: transfer_learning/10_percent_feature_extraction/20220908-203618\n",
            "Epoch 1/5\n",
            "24/24 [==============================] - 37s 436ms/step - loss: 1.9561 - accuracy: 0.4000 - val_loss: 1.3828 - val_accuracy: 0.6948\n",
            "Epoch 2/5\n",
            "24/24 [==============================] - 10s 426ms/step - loss: 1.1744 - accuracy: 0.7427 - val_loss: 0.9351 - val_accuracy: 0.7928\n",
            "Epoch 3/5\n",
            "24/24 [==============================] - 13s 523ms/step - loss: 0.8441 - accuracy: 0.8107 - val_loss: 0.7524 - val_accuracy: 0.8168\n",
            "Epoch 4/5\n",
            "24/24 [==============================] - 11s 444ms/step - loss: 0.6878 - accuracy: 0.8413 - val_loss: 0.6626 - val_accuracy: 0.8324\n",
            "Epoch 5/5\n",
            "24/24 [==============================] - 8s 336ms/step - loss: 0.5922 - accuracy: 0.8640 - val_loss: 0.6093 - val_accuracy: 0.8408\n"
          ]
        }
      ],
      "source": [
        "# 1. Create the base model\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "\n",
        "# 2. Freeze the base model (don't want to update the underlying pre-trained patterns)\n",
        "base_model.trainable = False\n",
        "\n",
        "# 3. Create inputs into our model\n",
        "inputs = tf.keras.layers.Input(shape=(224,224,3), name=\"input_layer\")\n",
        "\n",
        "# 4. If using a model like ResNet50V2 you will need to normalize inputs but you don't have to do that with EfficientNet\n",
        "# x = tf.keras.layers.experimental.preprocessing.Rescaling(1./255)(inputs) \n",
        "\n",
        "# 5. Pass the inputs to the base model\n",
        "x = base_model(inputs)\n",
        "print(f\"Shape after passing through base model: {x.shape}\")\n",
        "\n",
        "# 6. Average pool the outputs of the base model (aggregate all the most important information, reduce the number of computations)\n",
        "x = tf.keras.layers.GlobalAveragePooling2D(name=\"global_average_pooling_layer\")(x)\n",
        "print(f\"Shape after global average pool 2d: {x.shape}\")\n",
        "\n",
        "# 7. Create the activation layer\n",
        "outputs = tf.keras.layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
        "\n",
        "# 8. Combine the inputs with the outputs into a model\n",
        "model_0 = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "# 9. Compile the model\n",
        "model_0.compile(loss = tf.keras.losses.CategoricalCrossentropy(),\n",
        "                optimizer = tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 10. Fit the model and saving history\n",
        "history_0 = model_0.fit(train_data_10_percent,\n",
        "                        epochs=5,\n",
        "                        steps_per_epoch=len(train_data_10_percent),\n",
        "                        validation_data=test_data,\n",
        "                        validation_steps=len(test_data),\n",
        "                        callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                               experiment_name=\"10_percent_feature_extraction\")])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "zU-pFizZ9BTb",
        "outputId": "f056860d-fdb8-47e6-d874-75d907b78ad8"
      },
      "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"
          }
        },
        {
          "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_loss_curves(history_0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LlazhQzFWN7R",
        "outputId": "bd896b92-4ae7-4737-c577-769ab1964a34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 9s 101ms/step - loss: 0.6093 - accuracy: 0.8408\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6092508435249329, 0.8407999873161316]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "model_0.evaluate(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5aSfaDlWkkZ",
        "outputId": "1d34ffe7-92f3-43f2-bbcd-3d2a77a5f9db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 input_1\n",
            "1 rescaling\n",
            "2 normalization\n",
            "3 stem_conv_pad\n",
            "4 stem_conv\n",
            "5 stem_bn\n",
            "6 stem_activation\n",
            "7 block1a_dwconv\n",
            "8 block1a_bn\n",
            "9 block1a_activation\n",
            "10 block1a_se_squeeze\n",
            "11 block1a_se_reshape\n",
            "12 block1a_se_reduce\n",
            "13 block1a_se_expand\n",
            "14 block1a_se_excite\n",
            "15 block1a_project_conv\n",
            "16 block1a_project_bn\n",
            "17 block2a_expand_conv\n",
            "18 block2a_expand_bn\n",
            "19 block2a_expand_activation\n",
            "20 block2a_dwconv_pad\n",
            "21 block2a_dwconv\n",
            "22 block2a_bn\n",
            "23 block2a_activation\n",
            "24 block2a_se_squeeze\n",
            "25 block2a_se_reshape\n",
            "26 block2a_se_reduce\n",
            "27 block2a_se_expand\n",
            "28 block2a_se_excite\n",
            "29 block2a_project_conv\n",
            "30 block2a_project_bn\n",
            "31 block2b_expand_conv\n",
            "32 block2b_expand_bn\n",
            "33 block2b_expand_activation\n",
            "34 block2b_dwconv\n",
            "35 block2b_bn\n",
            "36 block2b_activation\n",
            "37 block2b_se_squeeze\n",
            "38 block2b_se_reshape\n",
            "39 block2b_se_reduce\n",
            "40 block2b_se_expand\n",
            "41 block2b_se_excite\n",
            "42 block2b_project_conv\n",
            "43 block2b_project_bn\n",
            "44 block2b_drop\n",
            "45 block2b_add\n",
            "46 block3a_expand_conv\n",
            "47 block3a_expand_bn\n",
            "48 block3a_expand_activation\n",
            "49 block3a_dwconv_pad\n",
            "50 block3a_dwconv\n",
            "51 block3a_bn\n",
            "52 block3a_activation\n",
            "53 block3a_se_squeeze\n",
            "54 block3a_se_reshape\n",
            "55 block3a_se_reduce\n",
            "56 block3a_se_expand\n",
            "57 block3a_se_excite\n",
            "58 block3a_project_conv\n",
            "59 block3a_project_bn\n",
            "60 block3b_expand_conv\n",
            "61 block3b_expand_bn\n",
            "62 block3b_expand_activation\n",
            "63 block3b_dwconv\n",
            "64 block3b_bn\n",
            "65 block3b_activation\n",
            "66 block3b_se_squeeze\n",
            "67 block3b_se_reshape\n",
            "68 block3b_se_reduce\n",
            "69 block3b_se_expand\n",
            "70 block3b_se_excite\n",
            "71 block3b_project_conv\n",
            "72 block3b_project_bn\n",
            "73 block3b_drop\n",
            "74 block3b_add\n",
            "75 block4a_expand_conv\n",
            "76 block4a_expand_bn\n",
            "77 block4a_expand_activation\n",
            "78 block4a_dwconv_pad\n",
            "79 block4a_dwconv\n",
            "80 block4a_bn\n",
            "81 block4a_activation\n",
            "82 block4a_se_squeeze\n",
            "83 block4a_se_reshape\n",
            "84 block4a_se_reduce\n",
            "85 block4a_se_expand\n",
            "86 block4a_se_excite\n",
            "87 block4a_project_conv\n",
            "88 block4a_project_bn\n",
            "89 block4b_expand_conv\n",
            "90 block4b_expand_bn\n",
            "91 block4b_expand_activation\n",
            "92 block4b_dwconv\n",
            "93 block4b_bn\n",
            "94 block4b_activation\n",
            "95 block4b_se_squeeze\n",
            "96 block4b_se_reshape\n",
            "97 block4b_se_reduce\n",
            "98 block4b_se_expand\n",
            "99 block4b_se_excite\n",
            "100 block4b_project_conv\n",
            "101 block4b_project_bn\n",
            "102 block4b_drop\n",
            "103 block4b_add\n",
            "104 block4c_expand_conv\n",
            "105 block4c_expand_bn\n",
            "106 block4c_expand_activation\n",
            "107 block4c_dwconv\n",
            "108 block4c_bn\n",
            "109 block4c_activation\n",
            "110 block4c_se_squeeze\n",
            "111 block4c_se_reshape\n",
            "112 block4c_se_reduce\n",
            "113 block4c_se_expand\n",
            "114 block4c_se_excite\n",
            "115 block4c_project_conv\n",
            "116 block4c_project_bn\n",
            "117 block4c_drop\n",
            "118 block4c_add\n",
            "119 block5a_expand_conv\n",
            "120 block5a_expand_bn\n",
            "121 block5a_expand_activation\n",
            "122 block5a_dwconv\n",
            "123 block5a_bn\n",
            "124 block5a_activation\n",
            "125 block5a_se_squeeze\n",
            "126 block5a_se_reshape\n",
            "127 block5a_se_reduce\n",
            "128 block5a_se_expand\n",
            "129 block5a_se_excite\n",
            "130 block5a_project_conv\n",
            "131 block5a_project_bn\n",
            "132 block5b_expand_conv\n",
            "133 block5b_expand_bn\n",
            "134 block5b_expand_activation\n",
            "135 block5b_dwconv\n",
            "136 block5b_bn\n",
            "137 block5b_activation\n",
            "138 block5b_se_squeeze\n",
            "139 block5b_se_reshape\n",
            "140 block5b_se_reduce\n",
            "141 block5b_se_expand\n",
            "142 block5b_se_excite\n",
            "143 block5b_project_conv\n",
            "144 block5b_project_bn\n",
            "145 block5b_drop\n",
            "146 block5b_add\n",
            "147 block5c_expand_conv\n",
            "148 block5c_expand_bn\n",
            "149 block5c_expand_activation\n",
            "150 block5c_dwconv\n",
            "151 block5c_bn\n",
            "152 block5c_activation\n",
            "153 block5c_se_squeeze\n",
            "154 block5c_se_reshape\n",
            "155 block5c_se_reduce\n",
            "156 block5c_se_expand\n",
            "157 block5c_se_excite\n",
            "158 block5c_project_conv\n",
            "159 block5c_project_bn\n",
            "160 block5c_drop\n",
            "161 block5c_add\n",
            "162 block6a_expand_conv\n",
            "163 block6a_expand_bn\n",
            "164 block6a_expand_activation\n",
            "165 block6a_dwconv_pad\n",
            "166 block6a_dwconv\n",
            "167 block6a_bn\n",
            "168 block6a_activation\n",
            "169 block6a_se_squeeze\n",
            "170 block6a_se_reshape\n",
            "171 block6a_se_reduce\n",
            "172 block6a_se_expand\n",
            "173 block6a_se_excite\n",
            "174 block6a_project_conv\n",
            "175 block6a_project_bn\n",
            "176 block6b_expand_conv\n",
            "177 block6b_expand_bn\n",
            "178 block6b_expand_activation\n",
            "179 block6b_dwconv\n",
            "180 block6b_bn\n",
            "181 block6b_activation\n",
            "182 block6b_se_squeeze\n",
            "183 block6b_se_reshape\n",
            "184 block6b_se_reduce\n",
            "185 block6b_se_expand\n",
            "186 block6b_se_excite\n",
            "187 block6b_project_conv\n",
            "188 block6b_project_bn\n",
            "189 block6b_drop\n",
            "190 block6b_add\n",
            "191 block6c_expand_conv\n",
            "192 block6c_expand_bn\n",
            "193 block6c_expand_activation\n",
            "194 block6c_dwconv\n",
            "195 block6c_bn\n",
            "196 block6c_activation\n",
            "197 block6c_se_squeeze\n",
            "198 block6c_se_reshape\n",
            "199 block6c_se_reduce\n",
            "200 block6c_se_expand\n",
            "201 block6c_se_excite\n",
            "202 block6c_project_conv\n",
            "203 block6c_project_bn\n",
            "204 block6c_drop\n",
            "205 block6c_add\n",
            "206 block6d_expand_conv\n",
            "207 block6d_expand_bn\n",
            "208 block6d_expand_activation\n",
            "209 block6d_dwconv\n",
            "210 block6d_bn\n",
            "211 block6d_activation\n",
            "212 block6d_se_squeeze\n",
            "213 block6d_se_reshape\n",
            "214 block6d_se_reduce\n",
            "215 block6d_se_expand\n",
            "216 block6d_se_excite\n",
            "217 block6d_project_conv\n",
            "218 block6d_project_bn\n",
            "219 block6d_drop\n",
            "220 block6d_add\n",
            "221 block7a_expand_conv\n",
            "222 block7a_expand_bn\n",
            "223 block7a_expand_activation\n",
            "224 block7a_dwconv\n",
            "225 block7a_bn\n",
            "226 block7a_activation\n",
            "227 block7a_se_squeeze\n",
            "228 block7a_se_reshape\n",
            "229 block7a_se_reduce\n",
            "230 block7a_se_expand\n",
            "231 block7a_se_excite\n",
            "232 block7a_project_conv\n",
            "233 block7a_project_bn\n",
            "234 top_conv\n",
            "235 top_bn\n",
            "236 top_activation\n"
          ]
        }
      ],
      "source": [
        "# Check layers in our base model\n",
        "for layer_number, layer in enumerate(base_model.layers):\n",
        "  print(layer_number, layer.name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JYvD-hm2ErGo",
        "outputId": "cedafadc-569d-4c32-a6bf-a97f6eeb8e7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"efficientnetb0\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, None, None,  0           []                               \n",
            "                                 3)]                                                              \n",
            "                                                                                                  \n",
            " rescaling (Rescaling)          (None, None, None,   0           ['input_1[0][0]']                \n",
            "                                3)                                                                \n",
            "                                                                                                  \n",
            " normalization (Normalization)  (None, None, None,   7           ['rescaling[0][0]']              \n",
            "                                3)                                                                \n",
            "                                                                                                  \n",
            " stem_conv_pad (ZeroPadding2D)  (None, None, None,   0           ['normalization[0][0]']          \n",
            "                                3)                                                                \n",
            "                                                                                                  \n",
            " stem_conv (Conv2D)             (None, None, None,   864         ['stem_conv_pad[0][0]']          \n",
            "                                32)                                                               \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalization)   (None, None, None,   128         ['stem_conv[0][0]']              \n",
            "                                32)                                                               \n",
            "                                                                                                  \n",
            " stem_activation (Activation)   (None, None, None,   0           ['stem_bn[0][0]']                \n",
            "                                32)                                                               \n",
            "                                                                                                  \n",
            " block1a_dwconv (DepthwiseConv2  (None, None, None,   288        ['stem_activation[0][0]']        \n",
            " D)                             32)                                                               \n",
            "                                                                                                  \n",
            " block1a_bn (BatchNormalization  (None, None, None,   128        ['block1a_dwconv[0][0]']         \n",
            " )                              32)                                                               \n",
            "                                                                                                  \n",
            " block1a_activation (Activation  (None, None, None,   0          ['block1a_bn[0][0]']             \n",
            " )                              32)                                                               \n",
            "                                                                                                  \n",
            " block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block1a_se_excite (Multiply)   (None, None, None,   0           ['block1a_activation[0][0]',     \n",
            "                                32)                               'block1a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block1a_project_conv (Conv2D)  (None, None, None,   512         ['block1a_se_excite[0][0]']      \n",
            "                                16)                                                               \n",
            "                                                                                                  \n",
            " block1a_project_bn (BatchNorma  (None, None, None,   64         ['block1a_project_conv[0][0]']   \n",
            " lization)                      16)                                                               \n",
            "                                                                                                  \n",
            " block2a_expand_conv (Conv2D)   (None, None, None,   1536        ['block1a_project_bn[0][0]']     \n",
            "                                96)                                                               \n",
            "                                                                                                  \n",
            " block2a_expand_bn (BatchNormal  (None, None, None,   384        ['block2a_expand_conv[0][0]']    \n",
            " ization)                       96)                                                               \n",
            "                                                                                                  \n",
            " block2a_expand_activation (Act  (None, None, None,   0          ['block2a_expand_bn[0][0]']      \n",
            " ivation)                       96)                                                               \n",
            "                                                                                                  \n",
            " block2a_dwconv_pad (ZeroPaddin  (None, None, None,   0          ['block2a_expand_activation[0][0]\n",
            " g2D)                           96)                              ']                               \n",
            "                                                                                                  \n",
            " block2a_dwconv (DepthwiseConv2  (None, None, None,   864        ['block2a_dwconv_pad[0][0]']     \n",
            " D)                             96)                                                               \n",
            "                                                                                                  \n",
            " block2a_bn (BatchNormalization  (None, None, None,   384        ['block2a_dwconv[0][0]']         \n",
            " )                              96)                                                               \n",
            "                                                                                                  \n",
            " block2a_activation (Activation  (None, None, None,   0          ['block2a_bn[0][0]']             \n",
            " )                              96)                                                               \n",
            "                                                                                                  \n",
            " block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_se_excite (Multiply)   (None, None, None,   0           ['block2a_activation[0][0]',     \n",
            "                                96)                               'block2a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_project_conv (Conv2D)  (None, None, None,   2304        ['block2a_se_excite[0][0]']      \n",
            "                                24)                                                               \n",
            "                                                                                                  \n",
            " block2a_project_bn (BatchNorma  (None, None, None,   96         ['block2a_project_conv[0][0]']   \n",
            " lization)                      24)                                                               \n",
            "                                                                                                  \n",
            " block2b_expand_conv (Conv2D)   (None, None, None,   3456        ['block2a_project_bn[0][0]']     \n",
            "                                144)                                                              \n",
            "                                                                                                  \n",
            " block2b_expand_bn (BatchNormal  (None, None, None,   576        ['block2b_expand_conv[0][0]']    \n",
            " ization)                       144)                                                              \n",
            "                                                                                                  \n",
            " block2b_expand_activation (Act  (None, None, None,   0          ['block2b_expand_bn[0][0]']      \n",
            " ivation)                       144)                                                              \n",
            "                                                                                                  \n",
            " block2b_dwconv (DepthwiseConv2  (None, None, None,   1296       ['block2b_expand_activation[0][0]\n",
            " D)                             144)                             ']                               \n",
            "                                                                                                  \n",
            " block2b_bn (BatchNormalization  (None, None, None,   576        ['block2b_dwconv[0][0]']         \n",
            " )                              144)                                                              \n",
            "                                                                                                  \n",
            " block2b_activation (Activation  (None, None, None,   0          ['block2b_bn[0][0]']             \n",
            " )                              144)                                                              \n",
            "                                                                                                  \n",
            " block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_se_excite (Multiply)   (None, None, None,   0           ['block2b_activation[0][0]',     \n",
            "                                144)                              'block2b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_project_conv (Conv2D)  (None, None, None,   3456        ['block2b_se_excite[0][0]']      \n",
            "                                24)                                                               \n",
            "                                                                                                  \n",
            " block2b_project_bn (BatchNorma  (None, None, None,   96         ['block2b_project_conv[0][0]']   \n",
            " lization)                      24)                                                               \n",
            "                                                                                                  \n",
            " block2b_drop (Dropout)         (None, None, None,   0           ['block2b_project_bn[0][0]']     \n",
            "                                24)                                                               \n",
            "                                                                                                  \n",
            " block2b_add (Add)              (None, None, None,   0           ['block2b_drop[0][0]',           \n",
            "                                24)                               'block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_expand_conv (Conv2D)   (None, None, None,   3456        ['block2b_add[0][0]']            \n",
            "                                144)                                                              \n",
            "                                                                                                  \n",
            " block3a_expand_bn (BatchNormal  (None, None, None,   576        ['block3a_expand_conv[0][0]']    \n",
            " ization)                       144)                                                              \n",
            "                                                                                                  \n",
            " block3a_expand_activation (Act  (None, None, None,   0          ['block3a_expand_bn[0][0]']      \n",
            " ivation)                       144)                                                              \n",
            "                                                                                                  \n",
            " block3a_dwconv_pad (ZeroPaddin  (None, None, None,   0          ['block3a_expand_activation[0][0]\n",
            " g2D)                           144)                             ']                               \n",
            "                                                                                                  \n",
            " block3a_dwconv (DepthwiseConv2  (None, None, None,   3600       ['block3a_dwconv_pad[0][0]']     \n",
            " D)                             144)                                                              \n",
            "                                                                                                  \n",
            " block3a_bn (BatchNormalization  (None, None, None,   576        ['block3a_dwconv[0][0]']         \n",
            " )                              144)                                                              \n",
            "                                                                                                  \n",
            " block3a_activation (Activation  (None, None, None,   0          ['block3a_bn[0][0]']             \n",
            " )                              144)                                                              \n",
            "                                                                                                  \n",
            " block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_se_excite (Multiply)   (None, None, None,   0           ['block3a_activation[0][0]',     \n",
            "                                144)                              'block3a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_project_conv (Conv2D)  (None, None, None,   5760        ['block3a_se_excite[0][0]']      \n",
            "                                40)                                                               \n",
            "                                                                                                  \n",
            " block3a_project_bn (BatchNorma  (None, None, None,   160        ['block3a_project_conv[0][0]']   \n",
            " lization)                      40)                                                               \n",
            "                                                                                                  \n",
            " block3b_expand_conv (Conv2D)   (None, None, None,   9600        ['block3a_project_bn[0][0]']     \n",
            "                                240)                                                              \n",
            "                                                                                                  \n",
            " block3b_expand_bn (BatchNormal  (None, None, None,   960        ['block3b_expand_conv[0][0]']    \n",
            " ization)                       240)                                                              \n",
            "                                                                                                  \n",
            " block3b_expand_activation (Act  (None, None, None,   0          ['block3b_expand_bn[0][0]']      \n",
            " ivation)                       240)                                                              \n",
            "                                                                                                  \n",
            " block3b_dwconv (DepthwiseConv2  (None, None, None,   6000       ['block3b_expand_activation[0][0]\n",
            " D)                             240)                             ']                               \n",
            "                                                                                                  \n",
            " block3b_bn (BatchNormalization  (None, None, None,   960        ['block3b_dwconv[0][0]']         \n",
            " )                              240)                                                              \n",
            "                                                                                                  \n",
            " block3b_activation (Activation  (None, None, None,   0          ['block3b_bn[0][0]']             \n",
            " )                              240)                                                              \n",
            "                                                                                                  \n",
            " block3b_se_squeeze (GlobalAver  (None, 240)         0           ['block3b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_se_excite (Multiply)   (None, None, None,   0           ['block3b_activation[0][0]',     \n",
            "                                240)                              'block3b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_project_conv (Conv2D)  (None, None, None,   9600        ['block3b_se_excite[0][0]']      \n",
            "                                40)                                                               \n",
            "                                                                                                  \n",
            " block3b_project_bn (BatchNorma  (None, None, None,   160        ['block3b_project_conv[0][0]']   \n",
            " lization)                      40)                                                               \n",
            "                                                                                                  \n",
            " block3b_drop (Dropout)         (None, None, None,   0           ['block3b_project_bn[0][0]']     \n",
            "                                40)                                                               \n",
            "                                                                                                  \n",
            " block3b_add (Add)              (None, None, None,   0           ['block3b_drop[0][0]',           \n",
            "                                40)                               'block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_expand_conv (Conv2D)   (None, None, None,   9600        ['block3b_add[0][0]']            \n",
            "                                240)                                                              \n",
            "                                                                                                  \n",
            " block4a_expand_bn (BatchNormal  (None, None, None,   960        ['block4a_expand_conv[0][0]']    \n",
            " ization)                       240)                                                              \n",
            "                                                                                                  \n",
            " block4a_expand_activation (Act  (None, None, None,   0          ['block4a_expand_bn[0][0]']      \n",
            " ivation)                       240)                                                              \n",
            "                                                                                                  \n",
            " block4a_dwconv_pad (ZeroPaddin  (None, None, None,   0          ['block4a_expand_activation[0][0]\n",
            " g2D)                           240)                             ']                               \n",
            "                                                                                                  \n",
            " block4a_dwconv (DepthwiseConv2  (None, None, None,   2160       ['block4a_dwconv_pad[0][0]']     \n",
            " D)                             240)                                                              \n",
            "                                                                                                  \n",
            " block4a_bn (BatchNormalization  (None, None, None,   960        ['block4a_dwconv[0][0]']         \n",
            " )                              240)                                                              \n",
            "                                                                                                  \n",
            " block4a_activation (Activation  (None, None, None,   0          ['block4a_bn[0][0]']             \n",
            " )                              240)                                                              \n",
            "                                                                                                  \n",
            " block4a_se_squeeze (GlobalAver  (None, 240)         0           ['block4a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block4a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block4a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block4a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_se_excite (Multiply)   (None, None, None,   0           ['block4a_activation[0][0]',     \n",
            "                                240)                              'block4a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_conv (Conv2D)  (None, None, None,   19200       ['block4a_se_excite[0][0]']      \n",
            "                                80)                                                               \n",
            "                                                                                                  \n",
            " block4a_project_bn (BatchNorma  (None, None, None,   320        ['block4a_project_conv[0][0]']   \n",
            " lization)                      80)                                                               \n",
            "                                                                                                  \n",
            " block4b_expand_conv (Conv2D)   (None, None, None,   38400       ['block4a_project_bn[0][0]']     \n",
            "                                480)                                                              \n",
            "                                                                                                  \n",
            " block4b_expand_bn (BatchNormal  (None, None, None,   1920       ['block4b_expand_conv[0][0]']    \n",
            " ization)                       480)                                                              \n",
            "                                                                                                  \n",
            " block4b_expand_activation (Act  (None, None, None,   0          ['block4b_expand_bn[0][0]']      \n",
            " ivation)                       480)                                                              \n",
            "                                                                                                  \n",
            " block4b_dwconv (DepthwiseConv2  (None, None, None,   4320       ['block4b_expand_activation[0][0]\n",
            " D)                             480)                             ']                               \n",
            "                                                                                                  \n",
            " block4b_bn (BatchNormalization  (None, None, None,   1920       ['block4b_dwconv[0][0]']         \n",
            " )                              480)                                                              \n",
            "                                                                                                  \n",
            " block4b_activation (Activation  (None, None, None,   0          ['block4b_bn[0][0]']             \n",
            " )                              480)                                                              \n",
            "                                                                                                  \n",
            " block4b_se_squeeze (GlobalAver  (None, 480)         0           ['block4b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_se_excite (Multiply)   (None, None, None,   0           ['block4b_activation[0][0]',     \n",
            "                                480)                              'block4b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_conv (Conv2D)  (None, None, None,   38400       ['block4b_se_excite[0][0]']      \n",
            "                                80)                                                               \n",
            "                                                                                                  \n",
            " block4b_project_bn (BatchNorma  (None, None, None,   320        ['block4b_project_conv[0][0]']   \n",
            " lization)                      80)                                                               \n",
            "                                                                                                  \n",
            " block4b_drop (Dropout)         (None, None, None,   0           ['block4b_project_bn[0][0]']     \n",
            "                                80)                                                               \n",
            "                                                                                                  \n",
            " block4b_add (Add)              (None, None, None,   0           ['block4b_drop[0][0]',           \n",
            "                                80)                               'block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_expand_conv (Conv2D)   (None, None, None,   38400       ['block4b_add[0][0]']            \n",
            "                                480)                                                              \n",
            "                                                                                                  \n",
            " block4c_expand_bn (BatchNormal  (None, None, None,   1920       ['block4c_expand_conv[0][0]']    \n",
            " ization)                       480)                                                              \n",
            "                                                                                                  \n",
            " block4c_expand_activation (Act  (None, None, None,   0          ['block4c_expand_bn[0][0]']      \n",
            " ivation)                       480)                                                              \n",
            "                                                                                                  \n",
            " block4c_dwconv (DepthwiseConv2  (None, None, None,   4320       ['block4c_expand_activation[0][0]\n",
            " D)                             480)                             ']                               \n",
            "                                                                                                  \n",
            " block4c_bn (BatchNormalization  (None, None, None,   1920       ['block4c_dwconv[0][0]']         \n",
            " )                              480)                                                              \n",
            "                                                                                                  \n",
            " block4c_activation (Activation  (None, None, None,   0          ['block4c_bn[0][0]']             \n",
            " )                              480)                                                              \n",
            "                                                                                                  \n",
            " block4c_se_squeeze (GlobalAver  (None, 480)         0           ['block4c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_se_excite (Multiply)   (None, None, None,   0           ['block4c_activation[0][0]',     \n",
            "                                480)                              'block4c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_conv (Conv2D)  (None, None, None,   38400       ['block4c_se_excite[0][0]']      \n",
            "                                80)                                                               \n",
            "                                                                                                  \n",
            " block4c_project_bn (BatchNorma  (None, None, None,   320        ['block4c_project_conv[0][0]']   \n",
            " lization)                      80)                                                               \n",
            "                                                                                                  \n",
            " block4c_drop (Dropout)         (None, None, None,   0           ['block4c_project_bn[0][0]']     \n",
            "                                80)                                                               \n",
            "                                                                                                  \n",
            " block4c_add (Add)              (None, None, None,   0           ['block4c_drop[0][0]',           \n",
            "                                80)                               'block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_conv (Conv2D)   (None, None, None,   38400       ['block4c_add[0][0]']            \n",
            "                                480)                                                              \n",
            "                                                                                                  \n",
            " block5a_expand_bn (BatchNormal  (None, None, None,   1920       ['block5a_expand_conv[0][0]']    \n",
            " ization)                       480)                                                              \n",
            "                                                                                                  \n",
            " block5a_expand_activation (Act  (None, None, None,   0          ['block5a_expand_bn[0][0]']      \n",
            " ivation)                       480)                                                              \n",
            "                                                                                                  \n",
            " block5a_dwconv (DepthwiseConv2  (None, None, None,   12000      ['block5a_expand_activation[0][0]\n",
            " D)                             480)                             ']                               \n",
            "                                                                                                  \n",
            " block5a_bn (BatchNormalization  (None, None, None,   1920       ['block5a_dwconv[0][0]']         \n",
            " )                              480)                                                              \n",
            "                                                                                                  \n",
            " block5a_activation (Activation  (None, None, None,   0          ['block5a_bn[0][0]']             \n",
            " )                              480)                                                              \n",
            "                                                                                                  \n",
            " block5a_se_squeeze (GlobalAver  (None, 480)         0           ['block5a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block5a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block5a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block5a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_se_excite (Multiply)   (None, None, None,   0           ['block5a_activation[0][0]',     \n",
            "                                480)                              'block5a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_conv (Conv2D)  (None, None, None,   53760       ['block5a_se_excite[0][0]']      \n",
            "                                112)                                                              \n",
            "                                                                                                  \n",
            " block5a_project_bn (BatchNorma  (None, None, None,   448        ['block5a_project_conv[0][0]']   \n",
            " lization)                      112)                                                              \n",
            "                                                                                                  \n",
            " block5b_expand_conv (Conv2D)   (None, None, None,   75264       ['block5a_project_bn[0][0]']     \n",
            "                                672)                                                              \n",
            "                                                                                                  \n",
            " block5b_expand_bn (BatchNormal  (None, None, None,   2688       ['block5b_expand_conv[0][0]']    \n",
            " ization)                       672)                                                              \n",
            "                                                                                                  \n",
            " block5b_expand_activation (Act  (None, None, None,   0          ['block5b_expand_bn[0][0]']      \n",
            " ivation)                       672)                                                              \n",
            "                                                                                                  \n",
            " block5b_dwconv (DepthwiseConv2  (None, None, None,   16800      ['block5b_expand_activation[0][0]\n",
            " D)                             672)                             ']                               \n",
            "                                                                                                  \n",
            " block5b_bn (BatchNormalization  (None, None, None,   2688       ['block5b_dwconv[0][0]']         \n",
            " )                              672)                                                              \n",
            "                                                                                                  \n",
            " block5b_activation (Activation  (None, None, None,   0          ['block5b_bn[0][0]']             \n",
            " )                              672)                                                              \n",
            "                                                                                                  \n",
            " block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_se_excite (Multiply)   (None, None, None,   0           ['block5b_activation[0][0]',     \n",
            "                                672)                              'block5b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_conv (Conv2D)  (None, None, None,   75264       ['block5b_se_excite[0][0]']      \n",
            "                                112)                                                              \n",
            "                                                                                                  \n",
            " block5b_project_bn (BatchNorma  (None, None, None,   448        ['block5b_project_conv[0][0]']   \n",
            " lization)                      112)                                                              \n",
            "                                                                                                  \n",
            " block5b_drop (Dropout)         (None, None, None,   0           ['block5b_project_bn[0][0]']     \n",
            "                                112)                                                              \n",
            "                                                                                                  \n",
            " block5b_add (Add)              (None, None, None,   0           ['block5b_drop[0][0]',           \n",
            "                                112)                              'block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_expand_conv (Conv2D)   (None, None, None,   75264       ['block5b_add[0][0]']            \n",
            "                                672)                                                              \n",
            "                                                                                                  \n",
            " block5c_expand_bn (BatchNormal  (None, None, None,   2688       ['block5c_expand_conv[0][0]']    \n",
            " ization)                       672)                                                              \n",
            "                                                                                                  \n",
            " block5c_expand_activation (Act  (None, None, None,   0          ['block5c_expand_bn[0][0]']      \n",
            " ivation)                       672)                                                              \n",
            "                                                                                                  \n",
            " block5c_dwconv (DepthwiseConv2  (None, None, None,   16800      ['block5c_expand_activation[0][0]\n",
            " D)                             672)                             ']                               \n",
            "                                                                                                  \n",
            " block5c_bn (BatchNormalization  (None, None, None,   2688       ['block5c_dwconv[0][0]']         \n",
            " )                              672)                                                              \n",
            "                                                                                                  \n",
            " block5c_activation (Activation  (None, None, None,   0          ['block5c_bn[0][0]']             \n",
            " )                              672)                                                              \n",
            "                                                                                                  \n",
            " block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_se_excite (Multiply)   (None, None, None,   0           ['block5c_activation[0][0]',     \n",
            "                                672)                              'block5c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_conv (Conv2D)  (None, None, None,   75264       ['block5c_se_excite[0][0]']      \n",
            "                                112)                                                              \n",
            "                                                                                                  \n",
            " block5c_project_bn (BatchNorma  (None, None, None,   448        ['block5c_project_conv[0][0]']   \n",
            " lization)                      112)                                                              \n",
            "                                                                                                  \n",
            " block5c_drop (Dropout)         (None, None, None,   0           ['block5c_project_bn[0][0]']     \n",
            "                                112)                                                              \n",
            "                                                                                                  \n",
            " block5c_add (Add)              (None, None, None,   0           ['block5c_drop[0][0]',           \n",
            "                                112)                              'block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_conv (Conv2D)   (None, None, None,   75264       ['block5c_add[0][0]']            \n",
            "                                672)                                                              \n",
            "                                                                                                  \n",
            " block6a_expand_bn (BatchNormal  (None, None, None,   2688       ['block6a_expand_conv[0][0]']    \n",
            " ization)                       672)                                                              \n",
            "                                                                                                  \n",
            " block6a_expand_activation (Act  (None, None, None,   0          ['block6a_expand_bn[0][0]']      \n",
            " ivation)                       672)                                                              \n",
            "                                                                                                  \n",
            " block6a_dwconv_pad (ZeroPaddin  (None, None, None,   0          ['block6a_expand_activation[0][0]\n",
            " g2D)                           672)                             ']                               \n",
            "                                                                                                  \n",
            " block6a_dwconv (DepthwiseConv2  (None, None, None,   16800      ['block6a_dwconv_pad[0][0]']     \n",
            " D)                             672)                                                              \n",
            "                                                                                                  \n",
            " block6a_bn (BatchNormalization  (None, None, None,   2688       ['block6a_dwconv[0][0]']         \n",
            " )                              672)                                                              \n",
            "                                                                                                  \n",
            " block6a_activation (Activation  (None, None, None,   0          ['block6a_bn[0][0]']             \n",
            " )                              672)                                                              \n",
            "                                                                                                  \n",
            " block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_se_excite (Multiply)   (None, None, None,   0           ['block6a_activation[0][0]',     \n",
            "                                672)                              'block6a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_conv (Conv2D)  (None, None, None,   129024      ['block6a_se_excite[0][0]']      \n",
            "                                192)                                                              \n",
            "                                                                                                  \n",
            " block6a_project_bn (BatchNorma  (None, None, None,   768        ['block6a_project_conv[0][0]']   \n",
            " lization)                      192)                                                              \n",
            "                                                                                                  \n",
            " block6b_expand_conv (Conv2D)   (None, None, None,   221184      ['block6a_project_bn[0][0]']     \n",
            "                                1152)                                                             \n",
            "                                                                                                  \n",
            " block6b_expand_bn (BatchNormal  (None, None, None,   4608       ['block6b_expand_conv[0][0]']    \n",
            " ization)                       1152)                                                             \n",
            "                                                                                                  \n",
            " block6b_expand_activation (Act  (None, None, None,   0          ['block6b_expand_bn[0][0]']      \n",
            " ivation)                       1152)                                                             \n",
            "                                                                                                  \n",
            " block6b_dwconv (DepthwiseConv2  (None, None, None,   28800      ['block6b_expand_activation[0][0]\n",
            " D)                             1152)                            ']                               \n",
            "                                                                                                  \n",
            " block6b_bn (BatchNormalization  (None, None, None,   4608       ['block6b_dwconv[0][0]']         \n",
            " )                              1152)                                                             \n",
            "                                                                                                  \n",
            " block6b_activation (Activation  (None, None, None,   0          ['block6b_bn[0][0]']             \n",
            " )                              1152)                                                             \n",
            "                                                                                                  \n",
            " block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_se_excite (Multiply)   (None, None, None,   0           ['block6b_activation[0][0]',     \n",
            "                                1152)                             'block6b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_conv (Conv2D)  (None, None, None,   221184      ['block6b_se_excite[0][0]']      \n",
            "                                192)                                                              \n",
            "                                                                                                  \n",
            " block6b_project_bn (BatchNorma  (None, None, None,   768        ['block6b_project_conv[0][0]']   \n",
            " lization)                      192)                                                              \n",
            "                                                                                                  \n",
            " block6b_drop (Dropout)         (None, None, None,   0           ['block6b_project_bn[0][0]']     \n",
            "                                192)                                                              \n",
            "                                                                                                  \n",
            " block6b_add (Add)              (None, None, None,   0           ['block6b_drop[0][0]',           \n",
            "                                192)                              'block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_expand_conv (Conv2D)   (None, None, None,   221184      ['block6b_add[0][0]']            \n",
            "                                1152)                                                             \n",
            "                                                                                                  \n",
            " block6c_expand_bn (BatchNormal  (None, None, None,   4608       ['block6c_expand_conv[0][0]']    \n",
            " ization)                       1152)                                                             \n",
            "                                                                                                  \n",
            " block6c_expand_activation (Act  (None, None, None,   0          ['block6c_expand_bn[0][0]']      \n",
            " ivation)                       1152)                                                             \n",
            "                                                                                                  \n",
            " block6c_dwconv (DepthwiseConv2  (None, None, None,   28800      ['block6c_expand_activation[0][0]\n",
            " D)                             1152)                            ']                               \n",
            "                                                                                                  \n",
            " block6c_bn (BatchNormalization  (None, None, None,   4608       ['block6c_dwconv[0][0]']         \n",
            " )                              1152)                                                             \n",
            "                                                                                                  \n",
            " block6c_activation (Activation  (None, None, None,   0          ['block6c_bn[0][0]']             \n",
            " )                              1152)                                                             \n",
            "                                                                                                  \n",
            " block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_se_excite (Multiply)   (None, None, None,   0           ['block6c_activation[0][0]',     \n",
            "                                1152)                             'block6c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_conv (Conv2D)  (None, None, None,   221184      ['block6c_se_excite[0][0]']      \n",
            "                                192)                                                              \n",
            "                                                                                                  \n",
            " block6c_project_bn (BatchNorma  (None, None, None,   768        ['block6c_project_conv[0][0]']   \n",
            " lization)                      192)                                                              \n",
            "                                                                                                  \n",
            " block6c_drop (Dropout)         (None, None, None,   0           ['block6c_project_bn[0][0]']     \n",
            "                                192)                                                              \n",
            "                                                                                                  \n",
            " block6c_add (Add)              (None, None, None,   0           ['block6c_drop[0][0]',           \n",
            "                                192)                              'block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_conv (Conv2D)   (None, None, None,   221184      ['block6c_add[0][0]']            \n",
            "                                1152)                                                             \n",
            "                                                                                                  \n",
            " block6d_expand_bn (BatchNormal  (None, None, None,   4608       ['block6d_expand_conv[0][0]']    \n",
            " ization)                       1152)                                                             \n",
            "                                                                                                  \n",
            " block6d_expand_activation (Act  (None, None, None,   0          ['block6d_expand_bn[0][0]']      \n",
            " ivation)                       1152)                                                             \n",
            "                                                                                                  \n",
            " block6d_dwconv (DepthwiseConv2  (None, None, None,   28800      ['block6d_expand_activation[0][0]\n",
            " D)                             1152)                            ']                               \n",
            "                                                                                                  \n",
            " block6d_bn (BatchNormalization  (None, None, None,   4608       ['block6d_dwconv[0][0]']         \n",
            " )                              1152)                                                             \n",
            "                                                                                                  \n",
            " block6d_activation (Activation  (None, None, None,   0          ['block6d_bn[0][0]']             \n",
            " )                              1152)                                                             \n",
            "                                                                                                  \n",
            " block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_se_excite (Multiply)   (None, None, None,   0           ['block6d_activation[0][0]',     \n",
            "                                1152)                             'block6d_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_conv (Conv2D)  (None, None, None,   221184      ['block6d_se_excite[0][0]']      \n",
            "                                192)                                                              \n",
            "                                                                                                  \n",
            " block6d_project_bn (BatchNorma  (None, None, None,   768        ['block6d_project_conv[0][0]']   \n",
            " lization)                      192)                                                              \n",
            "                                                                                                  \n",
            " block6d_drop (Dropout)         (None, None, None,   0           ['block6d_project_bn[0][0]']     \n",
            "                                192)                                                              \n",
            "                                                                                                  \n",
            " block6d_add (Add)              (None, None, None,   0           ['block6d_drop[0][0]',           \n",
            "                                192)                              'block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block7a_expand_conv (Conv2D)   (None, None, None,   221184      ['block6d_add[0][0]']            \n",
            "                                1152)                                                             \n",
            "                                                                                                  \n",
            " block7a_expand_bn (BatchNormal  (None, None, None,   4608       ['block7a_expand_conv[0][0]']    \n",
            " ization)                       1152)                                                             \n",
            "                                                                                                  \n",
            " block7a_expand_activation (Act  (None, None, None,   0          ['block7a_expand_bn[0][0]']      \n",
            " ivation)                       1152)                                                             \n",
            "                                                                                                  \n",
            " block7a_dwconv (DepthwiseConv2  (None, None, None,   10368      ['block7a_expand_activation[0][0]\n",
            " D)                             1152)                            ']                               \n",
            "                                                                                                  \n",
            " block7a_bn (BatchNormalization  (None, None, None,   4608       ['block7a_dwconv[0][0]']         \n",
            " )                              1152)                                                             \n",
            "                                                                                                  \n",
            " block7a_activation (Activation  (None, None, None,   0          ['block7a_bn[0][0]']             \n",
            " )                              1152)                                                             \n",
            "                                                                                                  \n",
            " block7a_se_squeeze (GlobalAver  (None, 1152)        0           ['block7a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block7a_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block7a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block7a_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block7a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block7a_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block7a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_se_excite (Multiply)   (None, None, None,   0           ['block7a_activation[0][0]',     \n",
            "                                1152)                             'block7a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_project_conv (Conv2D)  (None, None, None,   368640      ['block7a_se_excite[0][0]']      \n",
            "                                320)                                                              \n",
            "                                                                                                  \n",
            " block7a_project_bn (BatchNorma  (None, None, None,   1280       ['block7a_project_conv[0][0]']   \n",
            " lization)                      320)                                                              \n",
            "                                                                                                  \n",
            " top_conv (Conv2D)              (None, None, None,   409600      ['block7a_project_bn[0][0]']     \n",
            "                                1280)                                                             \n",
            "                                                                                                  \n",
            " top_bn (BatchNormalization)    (None, None, None,   5120        ['top_conv[0][0]']               \n",
            "                                1280)                                                             \n",
            "                                                                                                  \n",
            " top_activation (Activation)    (None, None, None,   0           ['top_bn[0][0]']                 \n",
            "                                1280)                                                             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 4,049,571\n",
            "Trainable params: 0\n",
            "Non-trainable params: 4,049,571\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "base_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ga9sfyvDb3ml",
        "outputId": "b638a1db-43cb-4b5d-ca04-e98a1e0d5aab"
      },
      "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",
            " global_average_pooling_laye  (None, 1280)             0         \n",
            " r (GlobalAveragePooling2D)                                      \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 10)                12810     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,062,381\n",
            "Trainable params: 12,810\n",
            "Non-trainable params: 4,049,571\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model_0.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ja4JxPqEcDv1"
      },
      "source": [
        "## Get a feature vector from trained model\n",
        "Let's demonstrate the global average Pooling 2D layer...\n",
        "\n",
        "We have a tensor after our model goes through `base_model` of shape\n",
        "(None, 7,7,1280)...\n",
        "\n",
        "But when it passes through it turns into (None, 1280).\n",
        "\n",
        "Let's use a similar shape tensor of (1, 4, 4 3) and then pass it through GlobalAveragePooling2D."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HS75BzzCcsO-",
        "outputId": "42901cc7-de0c-47cf-adec-712f2bd4b59b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random input tensor:\n",
            "[[[[ 0.3274685  -0.8426258   0.3194337 ]\n",
            "   [-1.4075519  -2.3880599  -1.0392479 ]\n",
            "   [-0.5573232   0.539707    1.6994323 ]\n",
            "   [ 0.28893656 -1.5066116  -0.2645474 ]]\n",
            "\n",
            "  [[-0.59722406 -1.9171132  -0.62044144]\n",
            "   [ 0.8504023  -0.40604794 -3.0258412 ]\n",
            "   [ 0.9058464   0.29855987 -0.22561555]\n",
            "   [-0.7616443  -1.8917141  -0.93847126]]\n",
            "\n",
            "  [[ 0.77852213 -0.47338897  0.97772694]\n",
            "   [ 0.24694404  0.20573747 -0.5256233 ]\n",
            "   [ 0.32410017  0.02545409 -0.10638497]\n",
            "   [-0.6369475   1.1603122   0.2507359 ]]\n",
            "\n",
            "  [[-0.41728503  0.4012578  -1.4145443 ]\n",
            "   [-0.5931857  -1.6617213   0.33567193]\n",
            "   [ 0.10815629  0.23479682 -0.56668764]\n",
            "   [-0.35819843  0.88698614  0.52744764]]]]\n",
            "\n",
            "2D global average pooled random tensor:\n",
            "[[-0.09368646 -0.45840448 -0.2885598 ]]\n",
            "\n",
            "Shape of 2D global average pooled tensor:\n",
            "(1, 3)\n",
            "\n",
            "Shape of random tensor:\n",
            "(1, 4, 4, 3)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Define input shape\n",
        "input_shape = (1, 4, 4, 3)\n",
        "\n",
        "tf.random.set_seed(42)\n",
        "input_tensor = tf.random.normal(input_shape)\n",
        "print(f\"Random input tensor:\\n{input_tensor}\\n\")\n",
        "\n",
        "# Passing the tensor through global average pooling 2D layer\n",
        "global_average_pooled_tensor = tf.keras.layers.GlobalAveragePooling2D()(input_tensor)\n",
        "print(f\"2D global average pooled random tensor:\\n{global_average_pooled_tensor}\\n\")\n",
        "\n",
        "# Check the shape\n",
        "print(f\"Shape of 2D global average pooled tensor:\\n{global_average_pooled_tensor.shape}\\n\")\n",
        "print(f\"Shape of random tensor:\\n{input_tensor.shape}\\n\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6WDu7wEcsYp",
        "outputId": "9b5a4ef5-9cb9-49e4-e00e-9eb499a20c46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[-0.09368646, -0.45840448, -0.2885598 ]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Let's replicate the golbal pooling layer\n",
        "tf.reduce_mean(input_tensor, axis=[1,2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijN9VnH1csgu"
      },
      "source": [
        "## Running a series of transfer learning experiments\n",
        "\n",
        "We've seen the incredible results transfer learning can get with 10% of our data but how does it go with 1% of our data. How about we set up some experiments.\n",
        "\n",
        "1. `model_1` - use feature extraction transfer learning with 1% of the training data with data augmentation.\n",
        "2. `model_2` - use feature extraction transfer learning with 10% of the training data with data augmentation.\n",
        "3. `model_3` - use fine tuning transfer learning with 10% of the training data with data augmentation.\n",
        "4. `model_4` - use fine tuning transfer learning with 100% of the training data with data augmentation.\n",
        "\n",
        "> Throughout the experiments we'll be using same test dataset. This ensures the consistency across evaluation metrics."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1vCIPMNmkxH"
      },
      "source": [
        "## Getting data with model_1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6yLSu9dcsp3",
        "outputId": "eec91f00-dcf9-4de9-f2b3-5599894338b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-08 20:38:20--  https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_1_percent.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.202.128, 173.194.203.128, 74.125.20.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.202.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 133612354 (127M) [application/zip]\n",
            "Saving to: ‘10_food_classes_1_percent.zip’\n",
            "\n",
            "10_food_classes_1_p 100%[===================>] 127.42M   176MB/s    in 0.7s    \n",
            "\n",
            "2022-09-08 20:38:21 (176 MB/s) - ‘10_food_classes_1_percent.zip’ saved [133612354/133612354]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download and unzip - Preprocessed from food101\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_1_percent.zip\n",
        "unzip_data(\"10_food_classes_1_percent.zip\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "dzGkGrO9csyf"
      },
      "outputs": [],
      "source": [
        "# Create training and test dir\n",
        "train_dir_1_percent = \"10_food_classes_1_percent/train/\"\n",
        "test_dir = \"10_food_classes_1_percent/test/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8WIq6vJcs5c",
        "outputId": "b0c47076-9566-4418-9aa0-20775d9c5b73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_1_percent'.\n",
            "There are 10 directories and 0 images in '10_food_classes_1_percent/train'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/chicken_wings'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/pizza'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/grilled_salmon'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/sushi'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/ice_cream'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/chicken_curry'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/ramen'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/fried_rice'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/hamburger'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/steak'.\n",
            "There are 10 directories and 0 images in '10_food_classes_1_percent/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/chicken_wings'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/chicken_curry'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/steak'.\n"
          ]
        }
      ],
      "source": [
        "walk_through_dir(\"10_food_classes_1_percent\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2MddbIsctA9",
        "outputId": "0d324e53-f063-4c86-eb4d-85876515c712"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 70 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ]
        }
      ],
      "source": [
        "# Setup data loaders\n",
        "IMG_SIZE = (224, 224)\n",
        "train_data_1_percent = tf.keras.preprocessing.image_dataset_from_directory(train_dir_1_percent,\n",
        "                                                                           label_mode=\"categorical\",\n",
        "                                                                           image_size=IMG_SIZE,\n",
        "                                                                           batch_size=32)\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,\n",
        "                                                                label_mode=\"categorical\",\n",
        "                                                                image_size=IMG_SIZE,\n",
        "                                                                batch_size=32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DbDh8Q6tctHg"
      },
      "source": [
        "## Adding data augmentation\n",
        "\n",
        "To add data augmentation right into our models we can use the layers inside:\n",
        "* `tf.keras.layers.experimental.preprocessing()` "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "TXo4iL9CctOJ"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "\n",
        "# Create data augmentation stage with horizontal flipping ,rotations, zoom etc\n",
        "data_augmentation = keras.Sequential([\n",
        "    preprocessing.RandomFlip(\"horizontal\"),\n",
        "    preprocessing.RandomRotation(0.2),\n",
        "    preprocessing.RandomZoom(0.2),\n",
        "    preprocessing.RandomHeight(0.2),\n",
        "    preprocessing.RandomWidth(0.2)\n",
        "    # preprocessing.Rescale(1./255) for ResNet\n",
        "], name=\"data_augmentation\") "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ifXWRK6jolj-"
      },
      "source": [
        "## Visualize our data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "pL7_8A2wolo3",
        "outputId": "1d92bd03-2d6e-4e24-f093-10fb90b8f100"
      },
      "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"
          }
        },
        {
          "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": [
        "# View a random image and compare it to it's augmented version\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import os\n",
        "import random\n",
        "target_class = random.choice(train_data_1_percent.class_names)\n",
        "target_dir = \"10_food_classes_1_percent/train/\" + target_class\n",
        "random_image = random.choice(os.listdir(target_dir))\n",
        "random_image_path = target_dir + \"/\" + random_image\n",
        "\n",
        "# Read in random image\n",
        "img = mpimg.imread(random_image_path)\n",
        "plt.imshow(img)\n",
        "plt.title(f\"Original image from class {target_class}\")\n",
        "plt.axis(False)\n",
        "\n",
        "# Now let's plot augmented random image\n",
        "plt.figure()\n",
        "augmented_img = data_augmentation(tf.expand_dims(img, axis=0), training=True)\n",
        "plt.imshow(tf.squeeze(augmented_img)/255.)\n",
        "plt.title(f\"Augmented image from class {target_class}\")\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDxQuWVjolt6"
      },
      "source": [
        "## Model_1: Feature extractioin transfer learning with 1% of data with augmentation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0n_Lj1xYolzi",
        "outputId": "1c8a5f7d-f6dc-4d1c-bc39-edba66db5201"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: transfer learning/1_percent_data_aug/20220908-203826\n",
            "Epoch 1/5\n",
            "3/3 [==============================] - 12s 2s/step - loss: 2.4062 - accuracy: 0.1000 - val_loss: 2.2519 - val_accuracy: 0.1464\n",
            "Epoch 2/5\n",
            "3/3 [==============================] - 4s 2s/step - loss: 2.1357 - accuracy: 0.2857 - val_loss: 2.1265 - val_accuracy: 0.2319\n",
            "Epoch 3/5\n",
            "3/3 [==============================] - 3s 1s/step - loss: 2.0402 - accuracy: 0.3571 - val_loss: 2.0126 - val_accuracy: 0.3010\n",
            "Epoch 4/5\n",
            "3/3 [==============================] - 4s 2s/step - loss: 1.8056 - accuracy: 0.4714 - val_loss: 1.9359 - val_accuracy: 0.3668\n",
            "Epoch 5/5\n",
            "3/3 [==============================] - 4s 2s/step - loss: 1.6468 - accuracy: 0.6000 - val_loss: 1.8628 - val_accuracy: 0.4145\n"
          ]
        }
      ],
      "source": [
        "# Setup our input_shapes and base model, freezing the base model layers\n",
        "input_shape=(224, 224, 3)\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create the input layers\n",
        "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "\n",
        "# Add in data augmentation Sequential model as layer\n",
        "x = data_augmentation(inputs)\n",
        "\n",
        "# Give base_model the inputs after augmentation and don't train it\n",
        "x = base_model(x, training=False)\n",
        "\n",
        "# Pool the output features of the model\n",
        "x =  layers.GlobalAveragePooling2D(name=\"global_average_pooling_layer\")(x)\n",
        "\n",
        "# Put a dense layer on as the output\n",
        "outputs = layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
        "\n",
        "# Make a model using inputs and outputs\n",
        "model_1 = keras.Model(inputs, outputs)\n",
        "\n",
        "# Compile the model\n",
        "model_1.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history_1_percent = model_1.fit(train_data_1_percent,\n",
        "                                epochs=5,\n",
        "                                steps_per_epoch=len(train_data_1_percent),\n",
        "                                validation_data=test_data,\n",
        "                                validation_steps=int(0.25 * len(test_data)),\n",
        "                                callbacks=[create_tensorboard_callback(dir_name=\"transfer learning\",\n",
        "                                                                       experiment_name=\"1_percent_data_aug\")])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YY6DPHLpol50",
        "outputId": "ad0aac0d-1585-458c-a8a2-7f2277672215"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " data_augmentation (Sequenti  (None, None, None, 3)    0         \n",
            " al)                                                             \n",
            "                                                                 \n",
            " efficientnetb0 (Functional)  (None, None, None, 1280)  4049571  \n",
            "                                                                 \n",
            " global_average_pooling_laye  (None, 1280)             0         \n",
            " r (GlobalAveragePooling2D)                                      \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 10)                12810     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,062,381\n",
            "Trainable params: 12,810\n",
            "Non-trainable params: 4,049,571\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model_1.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIxfRk_GomBC",
        "outputId": "af524f82-2577-41d1-d6c6-4db70d17826a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 7s 76ms/step - loss: 1.8407 - accuracy: 0.4256\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.8407230377197266, 0.42559999227523804]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Evaluate on full test data\n",
        "results_1_percent_data_aug = model_1.evaluate(test_data)\n",
        "results_1_percent_data_aug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "65QqDNbsomGK",
        "outputId": "d3bbe74b-a14e-4bf5-a8cd-9a614870d266"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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tg0reY1CpqsoYU6Wm7UZFRVWJ9crL4lK2By319A0RqQ18jdUfv2hFK2PMt8aYCGAwVj+9sAKnGWO6GmO6NmzYsMzFOlNMs3rMubcXBcZw+/uriN933NklKVUhfH19OXLkyCUFinIMYwxHjhzB19e3TI8r1SbRIuIN/AgsMMa8XorxSUB3Y0x6UWOqwpuihdl75BQjPlxNelYO00Z2pU/rBs4uSSmHysvLIyUlxWHzvNWl8fX1pUmTJnh7nz8Zo7g3RUszy0WAT4GjxpiHixjTCthljDEi0gX4AWhiijl4VQ10gMOZ2Yz6aA1JaSeZcmdnBkaFOLskpVQ1Ud5ZLr2BkUB/EYm3fw0SkYkiMtE+5jZgk4jEA+8AQ4sL86quUR1f5kzoRVSTutw/O44v1ux1dklKKVW6lktFqMpX6Gecys1n0mdxLE1M48mBEUzs29LZJSml3Fx5r9BVEWrW8GL6qK7c2Kkx/5q/jZfnb9U3kpRSTqMf/S+nGl4e/HdoZ+r6efH+0iQyTuXx0i1ReHpUnSlfSin3oIHuAJ4ewgs3RxJYswZvLd7J8VN5TBnWGR8vXf9FKVV5tOXiICLCY9e25e83tOfnzQcZ+8lasnLynV2WUqoa0UB3sHv6NOc/t3fij6SjDP9gNcdO5jq7JKVUNVH1Aj07A+I/h3zXXSjrtpgmTB0Rw9bUTG5/fxWpGaedXZJSqhqoeoG+5Tv430R4owMsfgkyU51dUaGuaR/EjLHdOZiRzZD3VpGUllXyg5RSqhyqXqBHj4SR/4PQrrDsVfhvJHw1FvatARebMtizRX2+mNCT7LwCbp+6ik37q8ZGs0qpqqlqf7DoaBKs+QDWfwY5GdA4GnpMhA63gJePYwp1gF1pWYz6cA2Zp/P44O6u9GhR39klKaWqqHKt5VJRHPpJ0Zws2PgFrH4f0hOhVkPoOtb68i/9ruAV6cDx04z8cDUpx07zzl1duLp9kLNLUkpVQe4f6GcYA0lLYPU0SPwZPDyh/WDrqr1JV3Dy+s5HT+Yy+uM1bD6QyWu3d+SW6CZOrUcpVfVUn0A/15/tmJmQkwmNu9jbMYOd2o7JyslnwoxYVu46wjM3tmdM7+ZOq0UpVfVUz0A/IycLNnwOa6a5TDsmO6+Ah75Yz4LNh3jwqtY8cnXrKrU7jFLKeap3oJ9hs1ntmDXTIHGB1Y7pcMvZdkwlyy+w8dS3CcyNTWFUr2Y8e2MHPHT9F6VUCYoL9OqzlouHB7S6yvo6sgvW2mfHJHwJoTHQ/d5Kbcd4eXrw79s6ElCzBtOWJXH8VB7/uaMT3p5VbyapUso1VJ8r9MLknIAN9tkxR3ZArUb2dsyYSm3HvPfbLv798zaubNuQd4fH4FdDF/VSShVOWy4lOdOOWf0+7FgAHt7ntGNiKqWE2av38rf/JdC1WSAf3N2Nun7eJT9IKVXtlGuDCxFpKiJLRGSLiGwWkYcKGTNcRDaKSIKIrBSRTo4ovNKcaccMnwuT46DbOGva4wf9YfpVsHEu5FfsIlt39Qjj7WFdiN93nKHvr+LwCd2gVylVNqXZJDoECDHGxImIP7AOGGyM2XLOmMuArcaYYyIyEHjWGNOjuOO61BV6Yf5sx0yFIzuhdpDVjokZA/4V96GgZYlp3DtzHY3q+PDZPT1oWq9mhZ1LKVX1OLTlIiLfAW8bYxYWcX8gsMkYE1rccVw+0M+w2SBpsb0d84vVjom8FXrca72ZWgHi9h5jzMdr8fHyYOY9PWgb7F8h51FKVT0OC3QRCQeWAZHGmMwixvwFiDDGjCvkvgnABICwsLCY5OTkUp/bJRzZZU17XD8Lck9YC4T1mAjtbwavGg49VeKhE4z8cDXZeTY+HtONLmGBDj2+Uqpqckigi0htYCnwkjHmmyLGXAm8C/Qxxhwp7nhV5gq9MDknrDXZ17x/TjvmHmt2TO1GDjvNvqOnGPnhag5l5vD+yBiuaNPQYcdWSlVN5Q50EfEGfgQWGGNeL2JMR+BbYKAxJrGkY1bpQD/DZoNdi60++86F9nbMbdBjgsPaMWknchj10Rp2Hj7BG0M7c0PHxg45rlKqaipXoIv1mfRPgaPGmIeLGBMGLAZGGWNWlqYotwj0c6XvhLXTz7ZjmnSz2jHtbip3OybjdB7jPl1LbPIxXhocxV09whxUtFKqqilvoPcBlgMJgM1+81NAGIAxZqqIfADcBpxpiucXdcIz3C7Qz8jOtNaOWf0+HN0FtYOh2z0QM7pc7ZjTuQXcPzuOxdsO8/h1bbmvX0td/0Wpakg/WOQMNhvs+tXejlkEnjWgw5nZMV0u6ZB5BTb+8uUGvos/wPjLm/PUoHYa6kpVM7qWizN4eEDra6yv9B2wZjrEz7I24mjS3Qr29jeDZ+k/Eert6cEbd3QmwM+b6ct3c/xUHi/fGoWXrv+ilEKv0CtXdibEz7ZmxxxNAv8Q+4eVRpepHWOMYcqvO/jvoh1c2z6IN4dF4+ut678oVR1oy8XVFNaOibzNumpvHF3qw3zy+26e/WELvVrUZ/rdXanto39wKeXuNNBdWfoO68NK8bMhNwua9rCCvd1NpWrH/G/9fh77cgMdGtfh49HdqF/bdTbHVko5ngZ6VZCdYW/HTDunHXNmdkzxHyj6desh7psVR5NAP2be04PGAX6VU7NSqtJpoFclNpvVhlk91WrLeNaAyCH2dkznIh+2OukI4z6Nxd/Xi5njetCyYe1KLFopVVk00KuqtMSz7Zi8k9C0p70dc2Oh7ZjNBzK4+6M12Ax8OqY7UU3qOqFopVRF0kCv6rIzrE+grpkGx3aDf2PoZl/Kt1aD84buTj/JiA9Wk3E6j+mjutKrZX0nFa2Uqgga6O7CZrPWjFk91VpDxtMHouztmJCze4oczMhm5IerST56ireHRXNth8rbTk8pVbE00N1R2nZ7O+Zzqx0T1gu6T/izHXPsZC6jP1nLpv0Z/Pu2jgyJaeLsipVSDqCB7s5OHz/7YaVje+ztGGt2zEmvAO6duY4VO9OJDK3DwMgQBkYG00LfMFWqytJArw5sBbDD3o5JWmJvx9xObtdxzNhdl58SUlm/9zgAEcH+DIgMZlBUCK0b1db1YJSqQjTQq5vC2jExYzgQeg0LtmcwP+Ega5OPYgy0bFjLunKPCqZ9SB0Nd6VcnAZ6dXX6uLUg2Jrp1uwYn7rQ8Q7oMpLDtduyYPMh5iek8kfSEWwGmtWvaV25R4bQsUldDXelXJAGenVns0Hy7xA3A7Z+D/nZ1qyYLqMgcghHCvxYuOUQ8zYdZOXOdPJthtAAP3tbJpjopoF4eGi4K+UKNNDVWaePQcJXsO5TOJQAXn7WMr5dRkGzy8g4nc/CrdaV+/Id6eQW2Aiq48OADsEMiAyhe/N6eGq4K+U0GujqYsZAajzEzYSELyEnE+q3guiR0GkY+AdxIjuPxdsOMz/hIEu2HyYn30aD2jW4tkMwAyOD6dmiPt66FrtSlaq8W9A1BWYAQYABphljplwwJgL4GOgC/M0Y81pJRWmgu5DcU7DlO6sls3cliCe0HWhdtbe8Cjy9OJmTz2/b05i/KZXF2w5zKreAgJreXNs+iIGRIfRu1YAaXhruSlW08gZ6CBBijIkTEX9gHTDYGLPlnDGNgGbAYOCYBnoVlr4D1s+05rafTLPmtXe+C6JHQL3mAGTnFbAsMY35mw6yaMshTuTk4+/rxdXtghgYGcwVbRrqhhtKVRCHtlxE5DvgbWPMwkLuexbI0kB3AwV5kPizddW+cxEYGzTva121R9wA3r4A5OQXsHLnEeYlpPLLlkNknM6jVg1ProxoxKCoEPq1bUjNGrrxhlKO4rBAF5FwYBkQaYzJLOT+Zykm0EVkAjABICwsLCY5ObnU51ZOlLHfumJfPwOO7wW/QOg41Ar3oA5/DssrsPFH0hHmJRzkl80HOXIyF19vD/q1acTAqGD6RzTC37f0e6gqpS7mkEAXkdrAUuAlY8w3RYx5Fr1Cd182G+xeal21b/sRCnIhNMY+/fE28PH/c2iBzbBm91Hmb0rl500HOXwihxqeHlzRpgEDI0O4ul0QdWtquCtVVuUOdBHxBn4EFhhjXi9m3LNooFcPp47CxjlWuB/eAt61oMMtVrg37Q7nfCjJZjPE7T3GvISD/LwplQMZ2Xh5CL1bNWBQVDDXtA+mXq0aTnwySlUd5X1TVIBPgaPGmIdLGPssGujVizGwf50V7Ju+tvZFbdDWCvZOd160Xrsxhg0pGcxPSGX+poPsPXoKTw+hZ4t6DIgM4boOQTTy93XSk1HK9ZU30PsAy4EEwGa/+SkgDMAYM1VEgoFYoI59TBbQvrA++xka6G4oJws2f2uFe8oa8PCGiEFWuLe4EjzOn/lijGHzgUx+3nSQeZtSSUo7iQh0C6/HwMhgBkQGE1JX90dV6lz6wSJV+Q5vOzv98fRRqNsUOg+H6OEQEHbRcGMMOw5nMS8hlfkJB9l+6AQAXcICGBgZwoDIYJrWq1nZz0Ipl6OBrpwnPwe2z7Ou2nctsW5r2d+6am87CLwK753vSsuyrtwTUtl8wPpDr2OTugyIDGZgZAjNG9SqrGeglEvRQFeu4fhea2/U9Z9BZgrUrG8tMxA9EhpFFPmwvUdOMX9TKvM2HWTDvrNrug+KsjbsaB3kX+RjlXI3GujKtdgKrKv19TNg2zyw5UHTHlawd7gFfIreUWn/8dP8vMmaLRObfAxjoFWj2gyKDGZgVAgRwf667K9yaxroynVlpcHGL6yWTHoi1KhtzWnvcjeEdjlv+uOFDmVms2DzQeYnHGT1bmtN9/D6NRlov3KPCtU13ZX70UBXrs8Y2LfGCvbN30DeKWjU3uq1dxwKNesV+/D0rBx+2XyI+ZtSWbnrCAX2Nd0HRVnL/kY3DdA13ZVb0EBXVUt2pjWnPW4GHIgDzxrQ7kYr3MOvAI/iV3U8fiqXhVsOMX/TQZbvSCOvwBBcx9f+hmowXcN1TXdVdWmgq6rr4CZr+uOGLyD7OAQ0s3rtne+CuqElPjwzO4/FWw8zLyGVpYlp9jXdfbiuQxCDokLo0bweXrqmu6pCNNBV1ZeXba0fE/cp7F4G4gGtroEuI6HNAPAseV2Ykzn5LNlubdixeNthTucVEFjTm2vbBzMwKpjLWuqa7sr1aaAr93J0tzX1MX4WnEiFWo2g8zCIHgUNWpXqEKdzC1iaaG3Y8evWw2Tl5FPH14ur2wcxKDKEKyMaaVtGuSQNdOWeCvKttdrXz4Tt88EUQNhlVq+9/c1Qo3SfLM3JL2DFjnTmJRxk4ZaDZGbn07dNQ968M1pXhFQuRwNdub8Th2DDbOuN1KNJ4FMHom63wr1x51IfJjffxhdr9/LCj1toHODH9FFdaaMfXFIuRANdVR/GQPLv1ubXW/4H+dkQHGXNa48aYm3OUQqxe44y8bM4TuXm8/odnRgQGVLBhStVOhroqno6fRwSvrSu2g9uBC9fqxUTPRLC+xT7oSWAgxnZ3PvZOjbsO84DV7bi0Wva6Fx25XQa6EodiLd67Ru/hJwMqNfi7PRH/+AiH5adV8A/vtvE3NgU+kc04o2hnanrp3115Twa6EqdkXsKtn5vXbUn/w7iCW2us3rtra4Bz4s3tDbGMPOPZJ7/YQtN69Vk+qgYWjXSvrpyDg10pQqTvvPsmu0nD0PtYGu99ugR1hX8BVYnHeH+2XFk59l4/Y5OXNuh6Ct7pSqKBrpSxSnIg8QFVrjv+AWMDRq2gxZ9oXlfCO8NvnUBOHD8NBM/W8fGlAweuqo1D13VWvvqqlKVdwu6psAMIAgwwDRjzJQLxggwBRgEnAJGG2PiijuuBrpySZkHIOErSFoCyasg/7TVlmkcDS36QYu+ZAfH8LcfdvJ1XApXtwvijaGd8PfVvrqqHOUN9BAgxBgTJyL+wDpgsDFmyzljBgGTsQK9BzDFGNOjuONqoCuXl58DKWsh6TdIWmpthm0KwMsXE9aTOI+OvLC1EScD2zP17h60bFj0Ou5KOYpDWy4i8h3wtjFm4Tm3vQ/8Zoz53P7zdqCfMSa1qONooKsqJzsTklfC7qVWwB/eDEAmtVhr2hPaZQARl90IDdqUOCVSqUtVXKBf/JZ+8QcKB6KB1RfcFQrsO6S9Yh0AABbqSURBVOfnFPtt5wW6iEwAJgCEhV28UbBSLs23DrQdYH0BZB2G3cvw3L6YDlsWEbz+BVj/AsY/BGne92wPvhSrQirlCKUOdBGpDXwNPGyMybyUkxljpgHTwLpCv5RjKOUyajeCqCHUihqCZ14BL36xgKxtv3KbbScxOxfhsfELa1z9Vlawt+gL4ZeXuFmHUpeqVIEuIt5YYT7LGPNNIUP2A03P+bmJ/TalqgVfb0/+NmIgH/3ejjvnbaVlfT8+urM2TY6tsdozG+dA7IeAQEhH6w3W5n0hrFepFxFTqiSleVNUgE+Bo8aYh4sYcz3wAGffFH3TGNO9uONqD125q5U707l/dhz5NsObd0ZzZUQja2rk/nVWuO9eam23Z8uzdmNq0v1seya0S6nWdlfVV3lnufQBlgMJgM1+81NAGIAxZqo99N8GBmBNWxxjjCk2rTXQlTvbd/QU985cx9aDmfzl2rbc16/l+RtW556EvavOBnzqRsBYm2Q363024IM66Bus6jz6wSKlnOB0bgFPfL2R7zccYGBkMK/d3olaPkV0OU8dtXZiOjOD5ugu6/ZaDaH5FWd78IHhlVa/ck0a6Eo5iTGGD5bv5uX5W2ndyJ9po2JoVr9WyQ/MSDl79Z60FLIOWrcHNDt79d68L9RuWLFPQLkcDXSlnGz5jjQmf74em83w1l1d6NumDEFsDKQnWsGe9BvsWWGtGAnQqIMV8C36QbPLwEcXDXN3GuhKuYC9R04xYWYsiYdO8Ph1EUzs2+L8vnppFeRD6gbY/ZsV8nv/gIIc8PCC0Jiz7Zkm3cDLx+HPQzmXBrpSLuJUbj6Pf7WRnzamcn3HEF4d0pGaNcr0+b6L5WXDvtVn2zMH4qwFxrz8oFkve8D3g+CO4OHhiKehnEgDXSkXYoxh6tIkXlmwjbZB/kwb2ZWw+g6ci376uLXW+5kefNo263a/QOuDTS36QvN+UL+lzqCpgjTQlXJBSxPTmDw7Dg8P4e1hXejTukHFnOjEQWsGzZkefGaKdXud0LPtmeZ9oY7um1oVaKAr5aL2pJ9kwsxYdh7O4v8GtmPc5c0vra9eWsbA0SQr2Hcvhd3L4fRR674Gbc62Z8L7gF9AxdWhLpkGulIu7GROPn/5cgPzNx3k5s6N+detHfGr4Vk5J7fZ4FDC2fZM8krIOwXiASGdz169h/UEb7/KqUkVSwNdKRdnjOHd33bx2i/baRdch2mjYmgS6IQ1XvJzrTXgz7zBuj8WbPng6QNNu5/tvzeOLnT/VVXxNNCVqiKWbDvMg1+sx9vTg7fviuaylhXUVy+tnBPWzk1nAv5QgnW7Tx37EgX9rJBvGKFvsFYSDXSlqpDd6ScZPyOW3ekneWpQO8b2Dq/YvnpZnEy3v8H6mxXyx/ZYt9dqaPXdw/tYM2l0k48Ko4GuVBWTlZPPo3Pi+WXLIW6NDuWft0bh611JffWyOJZsBfue32HPcsi0r5qtAV9hNNCVqoJsNsPbS3by+sJEokLrMnVkDKEBLvzGpDFwbLe1NMGeFdYMmhMHrPs04B1GA12pKmzRlkM8MieeGl4evDO8Cz1b1Hd2SaWjAV8hNNCVquJ2pWUxfkYse4+c4u83tGdUr2au01cvLQ14h9BAV8oNZGbn8eiceBZtPcyQmCa8ODjSNfvqpVVswDe6IOBba8DbaaAr5SZsNsOUX3cw5dcddGpi9dVD6rpwX70sNOBLpbxb0H0E3AAcNsZEFnJ/IPAR0BLIBsYaYzaVVJQGulKXbsHmgzw6Jx6/Gp68NyKGbuH1nF2S42nAF6q8gX4FkAXMKCLQXwWyjDHPiUgE8I4x5qqSitJAV6p8dhw6wYSZ69h39BTP3NSBET3Cql5fvSzOrENzJuD3rKiWAV/ulouIhAM/FhHoPwH/MsYst/+8C7jMGHOouGNqoCtVfhmn83hkTjyLtx1maNemPD+4Az5eVbivXhbVNOArOtD/CfgZYx4Rke7ASqCHMWZdIWMnABMAwsLCYpKTk8vyPJRShbDZDG8sSuStxTuJDgtg6ogYgur4OrusyndRwC+HE6nWfbWDzg/4+q2qbMBXdKDXAaYA0UACEAGMN8bEF3dMvUJXyrF+3pTKo3M3UMvHi6kjuhDTzA376mXhpgFfoYF+wTgBdgMdjTGZxY3VQFfK8bYfPMGEmbEcOH6a526K5K4eYc4uyXW4ScBX9BV6AHDKGJMrIuOBy40xo0o6pga6UhUj41QeD36xnqWJadzVI4xnb+xADS/dS/QiVTTgyzvL5XOgH9AAOAQ8A3gDGGOmikgv4FPAAJuBe4wxx0oqSgNdqYpTYDO89st23vttFzHNAnlveBcaVce+eln8GfDLz3mT1fUCXj9YpFQ19dPGVP7y5Qbq+Hnx3ogYuoQFOrukqqPYgA++IOArb8NtDXSlqrGtqZlMmBnLoYwcXhjcgaHdtK9+SS4M+N3LIeugdV8lBrwGulLV3PFTuUz+fD3Ld6Qzsmcz/n5De+2rl5eTAl4DXSlFfoGNVxds5/1lSXQLD+Td4TE09Pdxdlnuo5ICXgNdKfWn7zcc4K9fbSDArwbvj4yhU9MAZ5fknooL+J73w4B/XtJhNdCVUufZfCCDe2eu4/CJHF4aHMntXZs6uyT3Zwwc2WUFfKN2ENbzkg5TXKBrE02paqhD47p8/0AfujYL5PGvNvLs95vJK7A5uyz3JgINWkHXMZcc5iXRQFeqmqpXqwYzxnZnXJ/mfLJyDyM+WE16Vo6zy1LloIGuVDXm5enB0ze0579DOxO/7zg3vbWChJQMZ5elLpEGulKKwdGhfD3pMkSEIVNX8k1cirNLUpdAA10pBUBkaF2+f6A30WEBPDp3A8//sIV87atXKRroSqk/1a/tw8x7ejCmdzgf/b6bUR+t4ejJXGeXpUpJA10pdR5vTw+eubED/7m9E7HJx7jxrRVs2q999apAA10pVajbYprw1cRe2IxhyNSVfBe/39klqRJooCulitSxSQDfP9CHjqEBPPRFPC/9pH11V6aBrpQqVkN/H2aN78HdvZoxffluRn+8lmPaV3dJGuhKqRJ5e3rw3M2RvDKkI2t2H+Wmd1aw5UCxu0wqJ9BAV0qV2h1dmzLn3p7k5tu47b2V/LjxgLNLUucoMdBF5CMROSwim4q4v66I/CAiG0Rks4iMcXyZSilXER0WyA+T+9ChcR0emL2el+dvpcDmnEX+1PlKc4X+CTCgmPvvB7YYYzph7T36HxGpUf7SlFKuqpG/L7PH92R4jzDeX5rEoCnLWbLtMM5avVVZSgx0Y8wy4GhxQwB/ERGgtn1svmPKU0q5qhpeHrx0SxTvDe9CTn4BYz5Zy7Dpf7Bh33Fnl1ZtOaKH/jbQDjgAJAAPGWMKndckIhNEJFZEYtPS0hxwaqWUsw2MCuGXR/ry3E0d2HEoi5vf+Z37Z8exJ/2ks0urdkq1wYWIhAM/GmMiC7lvCNAbeBRoCSwEOhljin0LXDe4UMr9nMjOY/qyJKYv301egY3hPcKYfFVrGtTWre4cpaI3uBgDfGMsO4HdQIQDjquUqmL8fb159Nq2LH28H3d0a8pnq/fS95UlvPnrDk7laie2ojki0PcCVwGISBDQFkhywHGVUlVUozq+/POWKBY8fAV9Wjfg9YWJ9H31N2atTtZPmlagElsuIvI51uyVBsAh4BnAG8AYM1VEGmPNhAkBBPiXMeazkk6sLRelqo91yUd5ed42YpOP0aJhLf56XQTXdQjCmkuhykI3iVZKOZ0xhoVbDvHvn7exK+0kMc0C+b+BEXQNr+fs0qoU3SRaKeV0IsK1HYJZ8PAVvHxrFPuOnmLI1FVMmBHLzsNZzi7PLegVulLKKU7l5vPRit1MXZrE6bwC7ujalEeubk2jOr7OLs2lactFKeWyjmTl8NbincxanYyXhwfjLm/OhCta4O/r7ezSXJIGulLK5SUfOcmrC7bz48ZU6tWqwYP9W3FXj2bU8NLO8Lm0h66UcnnN6tfi7bu68N39vWkb5M+zP2zhmjeW8sOGA7pGTClpoCulXEqnpgHMHt+Dj8d0w8/bk8mfr+fmd35n5a50Z5fm8jTQlVIuR0S4sm0jfnrwcl67vRPpJ3K4a/pqRn+8hm0HdWONomgPXSnl8rLzCvh05R7eWbKTEzn53BrdhMeubUPjAD9nl1bp9E1RpZRbOH4ql3d/28UnK/cAMKZ3OPf1bUXdmtVnRowGulLKraQcO8XrCxP5dv1+6vh688CVrRjZqxm+3p7OLq3C6SwXpZRbaRJYk9fv6MxPky+nU9MAXpq3lav+s5Rv4lKwVePt8DTQlVJVVvvGdZgxtjuzxvUgsJY3j87dwPVvrWBpYlq1nOqoga6UqvJ6t2rA9/f3YcqdncnKyePuj9Yw4sPVbNqf4ezSKpUGulLKLXh4CDd3DmXRo335xw3t2XIgkxveWsGDn69n39FTzi6vUuibokopt5SZncf7S3fx4YrdFNgMI3o2Y3L/1tSrVcPZpZWLznJRSlVbBzOy+e+iRObG7qNWDS8m9mvJ2N7N8atRNWfEaKArpaq9HYdO8O+ft7No6yGC6vjw6DVtuK1LE7w8q1bnuVzTFkXkIxE5LCKbirj/cRGJt39tEpECEdEtSJRSLqV1kD8f3N2Vuff2onGAH098ncDAKctZtOWQ28yIKc2eolcAWcAMY0xkCWNvBB4xxvQv6cR6ha6UchZjDD9vOsgrC7azO/0k3cPr8eSgCLqEBTq7tBKV6wrdGLMMOFrKcw0DPi9DbUopVelEhIFRIfzyyBW8MDiSpPST3PruSiZ9to6ktKq7HV6peugiEg78WNwVuojUBFKAVsaYQv8BEJEJwASAsLCwmOTk5EsoWSmlHOtkTj7TlycxbVkSOfk2hnVvykNXtaGhv4+zS7tIud8ULWWgDwVGGGNuLE1R2nJRSrmatBM5vPnrDj5fs5caXh6Mv7wF469oQW0fL2eX9qfKWsvlTrTdopSqwhr6+/DC4EgWPtqXfm0bMuXXHfR7dQkzV+0hr8Dm7PJK5JBAF5G6QF/gO0ccTymlnKl5g1q8OzyGb++7jBYNa/P37zZz7RvLmJeQ6tIzYkozbfFzYBXQVkRSROQeEZkoIhPPGXYL8Isx5mRFFaqUUpUtOiyQORN68uHdXfH2FO6bFcct765kddIRZ5dWKP1gkVJKlUKBzfD1uhReX5jIwcxsropoxBMDI2gT5F+pdegnRZVSykFO5xbw8crdvLdkFydz87k9pimPXNOG4Lq+lXJ+DXSllHKwYydzeXvJTmauSsbDA8b2bs7Efi2p41ux2+FpoCulVAXZd/QUr/2yne/iDxBY05sH+rdmRM8wfLwqZvEv3YJOKaUqSNN6NZlyZzQ/Tu5Dh8Z1eeHHLVz1n6V8F7+/0rfD00BXSikHiAyty2fjejBjbHfq+Hrz0Bfx3PTOClbsSK+0GjTQlVLKga5o05AfJ/fhjaGdOHYyjxEfrmbkh6vZfKDit8PTQFdKKQfz8BBuiW7Cr4/15enr27ExJYMb3lrBI3PiSTlWcdvhaaArpVQF8fX2ZNzlLVj21yu594qWzEtIpf9rS/lgeVKFnE8DXSmlKlhdP2+eHBjBkr/04+bOjWlar2aFnMd1lhBTSik31zjAj1dv71Rhx9crdKWUchMa6Eop5SY00JVSyk1ooCullJvQQFdKKTehga6UUm5CA10ppdyEBrpSSrkJp62HLiJpQPIlPrwBUHlLmJWeq9YFrlub1lU2WlfZuGNdzYwxDQu7w2mBXh4iElvUAu/O5Kp1gevWpnWVjdZVNtWtLm25KKWUm9BAV0opN1FVA32aswsogqvWBa5bm9ZVNlpX2VSruqpkD10ppdTFquoVulJKqQtooCullJtw6UAXkQEisl1EdorIk4Xc7yMic+z3rxaRcBepa7SIpIlIvP1rXCXV9ZGIHBaRTUXcLyLypr3ujSLSxUXq6iciGee8Xv+ohJqaisgSEdkiIptF5KFCxlT661XKuir99bKf11dE1ojIBnttzxUyptJ/J0tZl7N+Jz1FZL2I/FjIfY5/rYwxLvkFeAK7gBZADWAD0P6CMfcBU+3f3wnMcZG6RgNvO+E1uwLoAmwq4v5BwHxAgJ7Aahepqx/wYyW/ViFAF/v3/kBiIf8dK/31KmVdlf562c8rQG37997AaqDnBWOc8TtZmrqc9Tv5KDC7sP9eFfFaufIVendgpzEmyRiTC3wB3HzBmJuBT+3ffwVcJSLiAnU5hTFmGXC0mCE3AzOM5Q8gQERCXKCuSmeMSTXGxNm/PwFsBUIvGFbpr1cp63IK++uQZf/R2/514ayKSv+dLGVdlU5EmgDXAx8UMcThr5UrB3oosO+cn1O4+P/Yf44xxuQDGUB9F6gL4Db7n+lfiUjTCq6ptEpbuzP0sv/JPF9EOlTmie1/6kZjXdmdy6mvVzF1gZNeL3sLIR44DCw0xhT5mlXi72Rp6oLK/538L/BXwFbE/Q5/rVw50KuyH4BwY0xHYCFn/xVWhYvDWp+iE/AW8L/KOrGI1Aa+Bh42xmRW1nlLUkJdTnu9jDEFxpjOQBOgu4hEVta5i1OKuir1d1JEbgAOG2PWVeR5LuTKgb4fOPdf0Sb22wodIyJeQF3giLPrMsYcMcbk2H/8AIip4JpKqzSvaaUzxmSe+ZPZGDMP8BaRBhV9XhHxxgrNWcaYbwoZ4pTXq6S6nPV6XVDDcWAJMOCCu5zxO1liXU74newN3CQie7Dasv1F5LMLxjj8tXLlQF8LtBaR5iJSA+tNg+8vGPM9cLf9+yHAYmN/h8GZdV3QZ70Jqw/qCr4HRtlnb/QEMowxqc4uSkSCz/QORaQ71v8vKzQE7Of7ENhqjHm9iGGV/nqVpi5nvF72czUUkQD7937ANcC2C4ZV+u9kaeqq7N9JY8z/GWOaGGPCsTJisTFmxAXDHP5aeZXnwRXJGJMvIg8AC7BmlnxkjNksIs8DscaY77H+jz9TRHZivel2p4vU9aCI3ATk2+saXdF1AYjI51gzIBqISArwDNYbRBhjpgLzsGZu7AROAWNcpK4hwCQRyQdOA3dWwj/MvYGRQIK99wrwFBB2Tl3OeL1KU5czXi+wZuB8KiKeWP+IzDXG/Ojs38lS1uWU38kLVfRrpR/9V0opN+HKLRellFJloIGulFJuQgNdKaXchAa6Ukq5CQ10pZRyExroyu2ISME5q+rFSyErYpbj2OFSxKqRSjmby85DV6ocTts/Bq5UtaJX6KraEJE9IvKKiCTY189uZb89XEQW2xdu+lVEwuy3B4nIt/ZFsDaIyGX2Q3mKyHSx1t7+xf7pRETkQbHWMd8oIl846WmqakwDXbkjvwtaLkPPuS/DGBMFvI21Gh5YC1x9al+4aRbwpv32N4Gl9kWwugCb7be3Bt4xxnQAjgO32W9/Eoi2H2diRT05pYqinxRVbkdEsowxtQu5fQ/Q3xiTZF8A66Axpr6IpAMhxpg8++2pxpgGIpIGNDlnUaczS9ouNMa0tv/8BOBtjHlRRH4GsrBWP/zfOWt0K1Up9ApdVTemiO/LIuec7ws4+17U9cA7WFfza+0r6ClVaTTQVXUz9Jz/XWX/fiVnF0YaDiy3f/8rMAn+3EChblEHFREPoKkxZgnwBNZSqBf9laBURdIrCOWO/M5ZqRDgZ2PMmamLgSKyEesqe5j9tsnAxyLyOJDG2VUVHwKmicg9WFfik4Cils/1BD6zh74Ab9rX5laq0mgPXVUb9h56V2NMurNrUaoiaMtFKaXchF6hK6WUm9ArdKWUchMa6Eop5SY00JVSyk1ooCullJvQQFdKKTfx//u2h2OBCn6mAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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gAX/9619ZvXo1Pj4+JCcnc/ToURo0aHDR1zLG8PTTT1/wvJ9//pnhw4efXYXoTP/wn3/++WzPcF9fXwIDAy8Z6Gcab4G1GMXIkSM5fPgweXl5Z/uhl9QHvXfv3ixZsoTWrVuTn59P+/btL/NsKeVZcgtsvLsigQ9+2UOd6pWYNa4bt7Ty/FF5Ye4b6C4yfPhw5s2bx5EjRxg5ciSzZ88mNTWV6Oho/P39CQ8Pd6rv+JU+rzA/Pz/sdvvZny/Wr/yRRx7h8ccfZ/DgwaxatYopU6Zc9LUnTJjAyy+/TKtWrbS9rfJ6McmZ/G2ONSof1iWMZwe2IbCqd4zKC9P2uUWMHDmSr7/+mnnz5jF8+HAyMzOpV68e/v7+rFy5kgMHDjj1OiU9r3fv3sydO5e0NGuFvjNTLrfeeisffPABADabjczMTOrXr09KSgppaWnk5uayZMmSix7vTL/yTz/99OzjJfVB7969O4mJiXz55ZeMGjXK2dOjlEfJK7Dzxk+7GDL1VzJO5/HxuAjeGNHRK8McNNAv0LZtW06cOEFoaCghISGMGTOGqKgo2rdvz2effUarVq2cep2Snte2bVueeeYZbr75Zjp27Mjjj1ur9r399tusXLmS9u3b07VrV+Li4vD39+fZZ58lMjKSPn36XPTYU6ZMYfjw4XTt2vW8RaVL6oMOMGLECHr27OnUcnRKeZqY5EwGv7eWd39O4I5Oofz0l5vp3aq+q8sqU9oPvQIbOHAgf/3rX4vt5lhe9O9clba8Ajvv/RzP1FV7CK5Wif8Mbc+trb0nyLUfujpPRkYGkZGRdOzY0aVhrlRpi0m2rmDZeeQEQzuH8tygtl47vVIcDfSrtH37dsaOHXveY5UrV2bDhg0uqujSatWqxe7du11dhlKlJq/AznsrE3h/ZQJB1Sox854IbmvjPaNyZ7ldoBtjLnmNtztp37699iu/Qq6a7lPeJfZQJk/M3caOw1kM7RzKs4PaUKtqJVeX5RJuFegBAQGkpaURHBzsUaGuLp8xhrS0NAICAlxdivJQeQV2pq5MYKpjVD7jngj6VMBReWFuFehhYWEkJSWRmprq6lJUOQgICCAsLMzVZSgPFHcoi7/N3cqOw1nc0akhUwa3rbCj8sLcKtD9/f3P3uGolFJF5dusUfl7PydQq2olpo/tyu1tL37XdkXiVoGulFIliTuUxRNztxJ3OIshnRoyZVBbgqrpqLwwDXSllFvLt9l5f+Ue3v05nlpVKzFtbFf+oKPyYmmgK6Xc1o7D1qg89lAWgzs25PnBOiq/GA10pZTbybfZ+WCVNSoPrOLPh3d3pW87HZVfiga6Usqt7DxijcpjkrMY5BiV19ZRuVOcas4lIn1FZJeIJIjI5GK2jxORVBHZ4viaUPqlKqW8Wb7Nzrsr4hn07lqOZObw4d1deHdUZw3zy3DJEbqI+AJTgT5AErBRRBYZY+KK7PqNMebhMqhRKeXldh05wd/mbiEmOYuBHUJ4YUg7DfIr4MyUSySQYIzZCyAiXwNDgKKBrpRSl6XAZufDX/bw9op4agb488GYLvRrH+LqsjyWM4EeCiQW+jkJ6F7MfsNE5CZgN/BXY0xi0R1EZBIwCaBx48aXX61SymvsOnKCJ+ZuZXtyJgM6hPDC4LYEV6/s6rI8WmktcLEYCDfGdACWAZ8Wt5MxZroxJsIYE1G3bt1SOrRSypMUOO72HPTuWg5lnOb9MV2YOrqLhnkpcGaEngw0KvRzmOOxs4wxaYV+nAm8evWlKaW8ze6j1qh8W1ImA9qH8MIQHZWXJmcCfSPQXESaYgX5XcDowjuISIgx5rDjx8HAjlKtUinl0Qpsdqat3svby+OpHuDH1NFdGNBB58pL2yUD3RhTICIPA0sBX+BjY0ysiLwARBljFgGPishgoAA4Dowrw5qVUh4k3jEq35qUSf/2DXhhSDvq6Ki8TLjVmqJKKe9RYLMzfc1e3loWT7XKvrx4RzsGdmjo6rI8nq4pqpQqV/FHT/DEvG1sTcygX7sGvHiHjsrLgwa6UqrUFNjszFizjzeX76ZaJV/eHdWZgR1CdAWycqKBrpQqFQkpJ3hi7ja2JGbQt601Kq9bQ0fl5UkDXSl1VWx2w4w1e/nvst1UreTLO6M6M0hH5S6hga6UumKxhzJ5ZmEMWxIz+EPb+rx0R3sdlbuQBrpS6rJlnsrnjWW7+GL9AWpVrcTbd3VicMeGOip3MQ10pZTT7HbDnKhEXl26i4xTeYzt0YTH+7QksKq/q0tTaKArpZy0JTGD576LYWtSJt3Cg3h+cHfaNKzp6rJUIRroSqmLSsvO5dUfd/FNVCJ1a1TmrZGdGNJJp1fckQa6UqpYBTY7X/5+kNeX7uJUno1JN13DI72bUSNAp1fclQa6UuoCG/cf59nvYtlxOIuezYJ5fnBbmtWr4eqy1CVooCulzjqalcN/vt/Bt1sO0TAwgA/GdKFvuwY6veIhNNCVUuQV2Pnkt328vTyefJvhkd7NeLDXtVStpBHhSfRvS6kKbm38MZ5bFMOe1JP0blWPZwe2IbxONVeXpa6ABrpSFVRyxmleWhLHDzFHaBJclY/ujeDW1vVdXZa6ChroSlUwOfk2Zq7Zy3srEwB44vYWTLjxGgL8fV1cmbpaGuhKVSA/7zzK84vjOJB2iv7tG/DMgDaE1qri6rJUKdFAV6oCOJB2khcWx7FiZwrX1q3GF/d354bmdVxdliplGuhKebHTeTbeX5XAtF/24u8rPNO/NfdeH04lPx9Xl6bKgAa6Ul7IGMOPMUd46X87SM44zZ2dQ5ncrxX1awa4ujRVhjTQlfIyCSknmLIojrUJx2jVoAZz/nQdkU1ru7osVQ400JXyEtm5BbyzIp6P1+6jaiVfnh/cljHdG+Pnq9MrFYUGulIezhjDd1sO8fL3O0g5kcvIiEY82bcldarrykEVjQa6Uh4s7lAWUxbF8vv+43QIC2T6PRF0alTL1WUpF9FAV8oDZZ7K57/LdvH5+gMEVvHnlaHtGRHRCB8fbaJVkWmgK+VB7HbD3OhE/u9Hawm4u3s04fE+LahVtZKrS1NuQANdKQ+xNTGDZx1LwEU0CeL5IZG0bRjo6rLU5TAGju+FKkFQtfSvPNJAV8rNpWXn8tpSawm4OtUr8+bIjtzRKVR7lHsCYyAtAfavgf2/wv61kH0EBvwXut1f6ofTQFfKTRVdAm7CDU159NbmugScOzMGUnfBgbVWeO//FU6mWNtqhED4DdZXs9vK5PAa6Eq5oaJLwE0Z1Jbm9XUJOLdjt0PqTiu8DzgC/NQxa1vNULim17kQr30NlPFvVU4Fuoj0Bd4GfIGZxphXSthvGDAP6GaMiSq1KpWqIFKycvjPDztZuDmZhoEBvD+mC/10CTj3YbdDStz5AX76uLUtsBE07wNNeloBHhRe5gFe1CUDXUR8galAHyAJ2Cgii4wxcUX2qwE8Bmwoi0KV8mb5Njuf/Lqft1fEk1dg5+FbmvHQLboEnMvZbXA0xgruA46v0+nWtlqNoWW/QgHexLW14twIPRJIMMbsBRCRr4EhQFyR/V4E/g94slQrVMrL/ZpwjOcWxZKQks0tLevy3KC2ugScq9htcGTbuQ8wD/4GOZnWtqCm0GoAhN9ohXitRq6ttRjOBHookFjo5ySge+EdRKQL0MgY8z8RKTHQRWQSMAmgcePGl1+tUl4kOeM0L/9vB//bfpjGtXUJOJewFcCRrec+wDy4DnKzrG21r4U2Q84FeGCoa2t1wlX/PiciPsB/gXGX2tcYMx2YDhAREWGu9thKeaLcAhsz1+zjvZ8TMBj+1qcFE2/SJeDKhS0fDm89dxnhwfWQd8LaFtwc2g2zpk+a9ISaIa6t9Qo4E+jJQOHfLcIcj51RA2gHrHJ8cNMAWCQig/WDUaXOt3JnCs8vjmV/2in6tWvAMwNaExZU1dVlea+CPDi0+dwHmAfXQ/5Ja1udltBhBIT3hCY3QA3P/+3ImUDfCDQXkaZYQX4XMPrMRmNMJnB2LSsRWQU8oWGu1DkH0k7y4pI4lu9I4Zq61fj8/khubF7X1WV5n4JcSN507jrwxN8h/5S1rV4b6DTaEeA9oXo919ZaBi4Z6MaYAhF5GFiKddnix8aYWBF5AYgyxiwq6yKV8lSn82x8sCqBD1fvxd9HeKpfK8b3bKpLwJWWglxIirKuPtm/BhI3QsFpa1v9dtB5rGMK5Xqo5v1rqDo1h26M+R74vshjz5awb6+rL0spz2aMYWnsEV5cYi0Bd0enhjzVv7UuAXe18nMgaaPjOvBfrRG4LRcQaNAOuo47F+Bl0CvF3elFrkqVsoSUbJ5fHMuaeGsJuG8m9aD7NcGuLssz5Z06P8CTNoItD8QHGrSHyInW9EmT66yGVxWcBrpSpSQ7t4B3V8Tz0dp9VNEl4K5M3klI3HDuOvDkaLDnWwEe0hG6/8n6ALNxD6iiC3kUpYGu1FUyxrBo6yH+/T9dAu6y5WZD4vpz14Ef2gT2AhBfaNgJrnvoXIAH1HR1tW5PA12pq7DjcBbPfXduCbhpY7vSubH+6l+inCzHCNxxHfihzWBs4OMHDbvA9Y9Yc+CNukNlbUZ2uTTQlboCmafzeXPZbj5bt5/AKv78Z2h7RuoScBfKyYQD685dRnh4Kxg7+PhDaFe44S/nAryStju4WhroSl0Gu90wLzqJ//txJ+mn8hjTvQl/u12XgDvrdLojwB2XER7ZbgW4byUIjYAbn7CuAw+LhEp6Q1Vp00BXyknbkjL413exbE3MoGuTID4dHEm70Aq+BFz+adiz0jGFsgaOxAAGfCtDWDe46e+OAO8G/lVcXa3X00BX6hKOn8zjtaU7+XpjIsHVKvPfER25s3MFXgKuIBf2/AwxC2DX95CXDX4B0CgSej1lTaGEdgV/vea+vGmgK1UCm93w5YYDvP7TbrJzC7i/Z1Meu62CLgFnK4B9v1ghvnOxNTdeJchqZtX2TutGHj+9qsfVNNCVKkaUYwm4uMNZXH9tMFMGt6VFRVsCzm6DA79B7AKI+w5OpUHlmlZP8HbDrOXVfCvgP25uTANdqULiDmUxc81eFmxOJiQwgKmju9C/fQVaAs4Y627MmPkQ+621Qr1/VWtlnrZDrcWNdSrFbWmgqwov9UQu321JZv6mZHYczqKSrw9/vuVa/nxLs4qxBJwxcHiLNZ0SuxAyE60PNZv3sUbiLf6glxR6iArwf6tSF8otsLFiRwrzo5NYtTsVm93QMSyQF4a0ZVCHhgRVqwCXIR6Nc4zEF8DxvdbNPdfeCr3/CS37652ZHkgDXVUYxhi2JGYwf1MSi7ceJvN0PvVrVmbijdcwrEsozSvCHPmxhHMhnrrT6pHS9Cbo+RdoPahCdij0JhroyusdzjzNgk3JzN+UxN7UkwT4+/CHtg0Y1iWMns3q4Ovtd3emH7ACPGaBtQAyAo2vg/6vW2tmeuFCDxWVBrrySqfyClgae4T50cn8uucYxkBkeG3+dNM19G8f4v2XHmYdsj7UjJkPyY7Fw0Ij4A8vQ5s7PGLBY3X5NNCV17DbDb/vP8786CS+336Yk3k2GtWuwqO9mzOsSxiNg738VvPsVIj71vpg88BvgLF6ht82xbpWPCjctfWpMqeBrjzegbSTzN+UzIJNSSSln6Z6ZT8GdAhhWJcwuoXX9u6GWaeOw84l1kh832qrb0qdltYdm+2GQp3mrq5QlSMNdOWRsnLy+X7bYeZvSmLj/nRE4IZmdXji9pb8oW0DqlTydXWJZScny7rlPmaBdQu+PR+CmsINj1shXq8NVJTr5tV5NNCVx7DZDWsTjjE/OomlsUfILbBzbd1q/L1vS+7sHEpIoBc3f8o7CbuXWiPx+GXWOpqBjaDHA9a14iGdNMSVBrpyf/FHTzBvUxLfbk7maFYugVX8GRHRiGFdw+gYFui9d3Hm50DCcusKlV0/QP4pqF4fIsZbd22GdQMfXd5OnaOBrtxS+sk8Fm09xPxNSWxLysTXR7ilZV2mDAqjd+t6VPbz0ikVWz7sXWWNxHf+D3KzoGowdBhpjcSbXA8+XvpnV1dNA125jXybnZU7U5i/KYmfd6aQbzO0CanJvwa2YUinht67RqfdZvUSj1kAOxZZi4HqZQgAABe0SURBVERUDoTWg6HdndD0Zm2CpZyiga5cyhhD7KEs5kUnsWjrIY6fzKNO9crce104w7qG0TrES28/t9uttTVj5ludDE+mQKXq1i337YbCtb21Ha26bBroyiVSsnL4dksy86OT2XX0BJV8fejTpj7DuoZyU/O6+Pl64dywMZC8yZoTj10IWcnWwhAt/mBNpzS/XVf1UVdFA12Vm5x8G8vijjJ/UxKrd6diN9C5cS1euqMdgzo0JLCqF04rGANHY6yReMwCyDhgLZDcvA/c9jy07Kur26tSo4GuypQxhk0H05kXncySbYc4kVNASGAAD/a6lqFdwri2bnVXl1g2Unc52tEugGO7QXytBSFu/oe1QESVWq6uUHkhDXRVJpLST7FwUzILNiez79hJqvj70q9dA4Z2CeO6a4O9syHW8X3nmmAdjQHEWl+zx4PWB5zV6ri6QuXlNNBVqTmZW8APMUeYH53Eur1pAPS4pjYP9bqWfu1DqF7ZC/93y0yy5sNjFsChTdZjjbpDv1etToY1Gri2PlWheOE7TJUnu92wfm8a8zYl8WPMEU7l2WgSXJXH+7Tgzs6hNKrthQ2xThy1rkyJmQ+J663HGnaGPi9aTbBqNXJtfarC0kBXV2TfsZPMj05i4eZkkjNOU6OyH0M6NWRYlzC6Ngnyvrs3T6ZZ14jHLoD9a60mWPXaQu9/WSEefK2rK1TKuUAXkb7A24AvMNMY80qR7Q8AfwZsQDYwyRgTV8q1KhfLPJ3Pkm2HmB+dxKaDGfgI3Ni8Lv/o14rb29QnwN/L7mDMybTu1oyZb929aS+A4GZw05PWrff1Wrm6QqXOc8lAFxFfYCrQB0gCNorIoiKB/aUx5kPH/oOB/wJ9y6BeVc4KbHbWxB9j3qYklsUdJa/ATvN61ZncrxV3dg6lfk0vWwE+76TVNyVmASQsA1se1GoM1z9ihXiD9toES7ktZ0bokUCCMWYvgIh8DQwBzga6MSar0P7VAFOaRaryt/NIFvOjk/h2yyFST+QSVNWfUd2shljtQ72sIZatAPb9AtvmwI7FkH8SajSEbhOtG35Cu2iIK4/gTKCHAomFfk4CuhfdSUT+DDwOVAJ6F/dCIjIJmATQuHHjy61VlbG07Fy+22I1xIo9lIWfj9C7VT2Gdgmjd6t6VPLzors3jYHDW60Qj5kH2UchIBDa/xE6jIDG12snQ+VxSu1DUWPMVGCqiIwG/gncW8w+04HpABERETqKdwN5BXZ+3nmUedHJrNqVQoHd0D40kCmD2jC4Uyi1q1VydYmlK30/bJ9rBfmx3eBbybr1vsNI69Z77Z+iPJgzgZ4MFL4OK8zxWEm+Bj64mqJU2TLGsC0pk/mbrIZYGafyqVujMvfd0JRhXcJo2cDLbkU/ddxaa3PbHDi4znqsSU+47s/WteJVglxbn1KlxJlA3wg0F5GmWEF+FzC68A4i0twYE+/4cQAQj3I7RzJzWLg5mfmbkkhIyaaSnw+3t6nPsK5h3Nisjnc1xMrPgfilVojvXmot01anJdz6LLQfbn3QqZSXuWSgG2MKRORhYCnWZYsfG2NiReQFIMoYswh4WERuA/KBdIqZblGuUWCz87/th5kXncSvCcewG+jaJIiX72zPgA4hBFbxooZYdjsc+BW2fQNxiyA3E6o3gO5/subFG3TQDzeVVxNjXDOVHRERYaKiolxy7Ioit8DGo19tZmnsUUJrVWFol1CGdgmjaZ1qri6tdB2Ns0J8+zzISrL6ircebIV405t0hR/lVUQk2hgTUdw2vVPUS+Xk23jwi2hW7krlnwNac1/Ppvh4U0OszGTr6pRtc+HodqubYbPboM/z1iIRlbyw5YBSl6CB7oVO5RUw4dMo1u1N4+U72zO6u5fMF+dkWbffb/sG9q0BDIRGQL/XrFV+tJuhquA00L3MiZx87vtkI9EH0nljeEeGdglzdUlXpyAP9qywQnzXD1CQA7WvgV6TrQ83tYeKUmdpoHuRzFP53DPrd2KTM3lnVGcGdmjo6pKujDGQ+LsV4rEL4fRxa+X7LvdY14uHdtUPN5Uqhga6l0jLzmXsR7+TkJLN+2O6cHtbD+zDfSzeusxw+xzrBiC/KtbqPh1GWIsm68r3Sl2UBroXSDmRw5gZGzh4/BQz7o3g5hZ1XV2S87JTrEZY276xFogQH2h6M9w8GVoP1PU2lboMGuge7nDmacbM2MCRrBxmje/G9dd6wAeDeSettrTb5sCen8HYrGvEb/+31QyrZoirK1TKI2mge7DE46cYPXM9GSfz+ey+SCLCa7u6pJLZCmDfKkdHwyVWR8PARtDzMWtKpV5rV1eolMfTQPdQe1OzGTNzA6fybMye2J0OYW64irwxcHiLY158HpxMsToadhgO7UdA4+u0o6FSpUgD3QPFHz3B6JkbsNsNX03sQZuGNV1d0vnS91s3/GzXjoZKlScNdA8TeyiTsR/9jp+P8PWkHjSv7yYfGp46bl1iuG3OuYWTtaOhUuVKA92DbEnM4J6PNlC9sh9fTuxBuKt7suTnwO4frRCP/8nqaFi3lXY0VMpFNNA9xMb9xxk/ayO1q1Vi9oTuNKrtol4l53U0/A5yswp1NBypa24q5UIa6B7gt4Rj3P9pFCG1AvhyQg8aBLpgYeajsYU6GiZrR0Ol3JAGuptbuSuFBz6PJjy4Gl9M6E7dGuX4geLZjoZz4GgM+Pg5Ohq+oB0NlXJDGuhubGnsER7+chMt6tfg8/u7l8/6njmZsGPx+R0Nw7pB/9eh7Z3a0VApN6aB7qYWbz3EX77ZQoewQD4ZH1m2KwsV5EHC8nMdDW252tFQKQ+kge6G5kUn8fd5W4kIr83H47pRvXIZ/DWd19FwAZxOtzoadr1XOxoq5aE00N3M7A0HeGZhDDc0q8OMeyKoUqmUP2w8Fm+F+LY5kHFAOxoq5UU00N3Ix2v38cKSOHq3qsf7Y7oQ4F9KYZ6dAjHzHR0NN5/raNjrKe1oqJQX0UB3E++vSuDVH3fRr10D3r6rM5X8SqHHSeJGWPee9SGndjRUyutpoLuYMYY3l8fzzop4hnRqyBvDO+LnexVhbiuAnYth3fuQ9DtUDoTrHoJOY7SjoVJeTgPdhYwxvPLDTqat3suIiDD+M7QDvj5X+EFkThZs/hzWfwiZByGoKfR71QryytVLt3CllFvSQHcRu93w/OJYPl13gLE9mvD84Lb4XEmYpx+A36dD9KeQdwIaXw99/wMt++ndm0pVMBroLmCzG55ZuJ2vNyYy8camPN2/NXK5lwienR9fBAi0Gwo9HoLQLmVSs1LK/Wmgl7MCm50n521j4eZkHundjMf7tHA+zIubH7/+EYicBIFhZVu4UsrtaaCXo3ybnb98vYX/bT/ME7e34OHezZ17os6PK6WcoIFeTnLybTz85SaW70jhnwNaM+HGay79pPQDsGEabPpM58eVUpekgV4OTufZmPR5FGvij/HiHe0Y26PJxZ+Q+Dusm6rz40qpy6KBXsZO5hZw/6cb2bDvOK/+sQMjIhoVv+PZ+fGpkLTRWkxZ58eVUpfBqUAXkb7A24AvMNMY80qR7Y8DE4ACIBW4zxhzoJRr9ThZOfmM+/h3tiZl8tbITgzpFHrhTsXOj78GnUbr/LhS6rJcMtBFxBeYCvQBkoCNIrLIGBNXaLfNQIQx5pSIPAi8Cowsi4I9RfrJPO75+Hd2Hsli6ujO9G1X5Fb7ovPjTXrq/LhS6qo4M0KPBBKMMXsBRORrYAhwNtCNMSsL7b8euLs0i/Q0x7JzuXvmBvYeO8m0sV3p3ar+uY2F58fFx1o0QufHlVKlwJlADwUSC/2cBHS/yP73Az8Ut0FEJgGTABo39s4V4Y9m5TB6xnqSM07z8b3duKF5HZ0fV0qVi1L9UFRE7gYigJuL226MmQ5MB4iIiDCleWx3kJR+ijEzN3DsRC6f3dedyBA/K8R1flwpVQ6cCfRkoPClGWGOx84jIrcBzwA3G2NyS6c8z3Eg7SSjZ2zgRE4+c0Y2pO2u1+ErnR9XSpUfZwJ9I9BcRJpiBfldwOjCO4hIZ2Aa0NcYk1LqVbq5hJRsxsxcT6v8HbzTdB2B837Q+XGlVLm7ZKAbYwpE5GFgKdZlix8bY2JF5AUgyhizCHgNqA7MdfQlOWiMGVyGdbuNnYeOM2vmu8ywL6EDu+Gwzo8rpVxDjHHNVHZERISJiopyybFLRU4mh1fOwGz4kIakkl8zHP+ef9b5caVUmRKRaGNMRHHb9E7Ry+W4ftwW/Skh+dls9mmDf9//o27EHTo/rpRyKQ10ZyX+fnZ9Tjs+/GDvzuIqd/LcA3dTt1YVV1enlFIa6BdVzPXjB1tPYOz2jlQKasTsCd2pVzPA1VUqpRSggV68nEzY9Ll1a36h68dXVbmNSd/s4tp61fni/kiCq1d2daVKKXWWBnphxfVX6fcKtOjL97EpPPrVZto2rMmn90VSq2olV1erlFLn0UCH8+bHi7t+/NvNyTw+ZwudGwcxa3w3agb4u7hgpZS6UMUN9GL7qzzquH78XJvbbzYeZPKC7fRoGszMeyOoVrninjKllHureOlUwvx4cdePf7ZuP89+F8vNLeoybWxXAvz1skSllPuqOIF+kfnx4q4fn756Dy9/v5M+berz3ujOVPbTMFdKuTfvD/RLzI8X590V8byxbDcDOoTw1shO+Pv6lGPBSil1Zbwz0J2cHy/KGMPrP+1i6so9DO0Symt/7Iivj5Rj4UopdeW8K9CLzo/Xvsbp/uPGGF763w4+WruPUZGN+fcd7fDRMFdKeRDvCPT0/Y758c+dmh8vym43/Ou7GGZvOMi468N5blAbHF0jlVLKY3h2oF8wPz4UrnsIGnZ2+iVsdsM/5m9jXnQSD/a6lr//oaWGuVLKI3leoNsKrAWW102F5Cin58eLk2+z8/icrSzeeoi/3taCR29tpmGulPJYnhfov7wCq1+z5sf7vw4dR11R//HcAhuPfrWZpbFHmdyvFQ/cfG0ZFKuUUuXH8wK9yz3WlIqT8+PFycm38eAX0azclcqUQW0Y17NpKReplFLlz/MCvVZj6+sKncorYOJnUfy2J43/DG3PqMgrfy2llHInnhfoV+FETj73fbKR6APpvDG8I0O76JqfSinvUWECPfNUPvfM+p3Y5EzeHdWFAR1CXF2SUkqVqgoR6MdP5nH3zA0kpGTzwd1d6dOmvqtLUkqpUuf1gZ5yIoe7Z27gQNopZtwbwc0t6rq6JKWUKhNeHeiHM08zZsYGjmTl8Mn4SK67NtjVJSmlVJnx2kBPPH6K0TPXk3Eyn8/vj6Rrk9quLkkppcqUVwb6vmMnGT1jPafybMye2J0OYbVcXZJSSpU5rwv0+KMnGD1zA3a74etJPWgdUtPVJSmlVLnwqkCPPZTJ2I9+x89H+OZPPWhWr4arS1JKqXLjNUvxbEnMYNT09QT4+TDnT9dpmCulKhyvGKFv3H+c8bM2UrtaJWZP6E6j2lVdXZJSSpU7jw/03xKOcf+nUYTUCuDLCT1oEBjg6pKUUsolPHrKZeWuFMZ/spHGtavyzaTrNMyVUhWaU4EuIn1FZJeIJIjI5GK23yQim0SkQET+WPplXmhp7BEmfRZF8/rV+WpSD+rWqFweh1VKKbd1yUAXEV9gKtAPaAOMEpE2RXY7CIwDviztAouzeOshHpq9iXahgcye0IPa1SqVx2GVUsqtOTOHHgkkGGP2AojI18AQIO7MDsaY/Y5t9jKo8TwLNyfxtzlbiQivzcfjulG9ssd/DKCUUqXCmSmXUCCx0M9Jjscum4hMEpEoEYlKTU29kpcgLKgqt7Wuz6fjIzXMlVKqkHJNRGPMdGA6QEREhLmS1+gWXptu4dqXRSmlinJmhJ4MNCr0c5jjMaWUUm7EmUDfCDQXkaYiUgm4C1hUtmUppZS6XJcMdGNMAfAwsBTYAcwxxsSKyAsiMhhARLqJSBIwHJgmIrFlWbRSSqkLOTWHboz5Hvi+yGPPFvp+I9ZUjFJKKRfx6DtFlVJKnaOBrpRSXkIDXSmlvIQGulJKeQkx5oru77n6A4ukAgeu8Ol1gGOlWE5p0bouj9Z1+dy1Nq3r8lxNXU2MMXWL2+CyQL8aIhJljIlwdR1FaV2XR+u6fO5am9Z1ecqqLp1yUUopL6GBrpRSXsJTA326qwsogdZ1ebSuy+eutWldl6dM6vLIOXSllFIX8tQRulJKqSI00JVSyku4daA7sTh1ZRH5xrF9g4iEu0ld40QkVUS2OL4mlFNdH4tIiojElLBdROQdR93bRKSLm9TVS0QyC52vZ4vbr5RraiQiK0UkTkRiReSxYvYp9/PlZF2uOF8BIvK7iGx11PV8MfuU+/vRybpc8n50HNtXRDaLyJJitpX++TLGuOUX4AvsAa4BKgFbgTZF9nkI+NDx/V3AN25S1zjgPRecs5uALkBMCdv7Az8AAvQANrhJXb2AJeV8rkKALo7vawC7i/l7LPfz5WRdrjhfAlR3fO8PbAB6FNnHFe9HZ+pyyfvRcezHgS+L+/sqi/PlziP0s4tTG2PygDOLUxc2BPjU8f084FYRETeoyyWMMauB4xfZZQjwmbGsB2qJSIgb1FXujDGHjTGbHN+fwOr1X3St3HI/X07WVe4c5yDb8aO/46voFRXl/n50si6XEJEwYAAws4RdSv18uXOgO7M49dl9jLUQRyYQ7AZ1AQxz/Jo+T0QaFbPdFUptwe8ycJ3j1+YfRKRteR7Y8atuZ6zRXWEuPV8XqQtccL4c0wdbgBRgmTGmxPNVju9HZ+oC17wf3wL+DthL2F7q58udA92TLQbCjTEdgGWc+1dYFW8TVn+KjsC7wLfldWARqQ7MB/5ijMkqr+NeyiXqcsn5MsbYjDGdsBaziRSRduVx3Etxoq5yfz+KyEAgxRgTXdbHKsydA92ZxanP7iMifkAgkObquowxacaYXMePM4GuZVyTs9xywW9jTNaZX5uNtTqWv4jUKevjiog/VmjONsYsKGYXl5yvS9XlqvNV6PgZwEqgb5FNrng/XrIuF70fewKDRWQ/1rRsbxH5osg+pX6+3DnQnVmcehFwr+P7PwI/G8cnDK6sq8g862CseVB3sAi4x3H1Rg8g0xhz2NVFiUiDM3OHIhKJ9f9lmQaB43gfATuMMf8tYbdyP1/O1OWi81VXRGo5vq8C9AF2Ftmt3N+PztTlivejMeYpY0yYMSYcKyN+NsbcXWS3Uj9fTq0p6grGmAIRObM4tS/wsXEsTg1EGWMWYf2P/7mIJGB96HaXm9T1qFgLaBc46hpX1nUBiMhXWFdA1BFr0e7nsD4kwhjzIda6sP2BBOAUMN5N6voj8KCIFACngbvK4R/mnsBYYLtj/hXgaaBxobpccb6cqcsV5ysE+FREfLH+AZljjFni6vejk3W55P1YnLI+X3rrv1JKeQl3nnJRSil1GTTQlVLKS2igK6WUl9BAV0opL6GBrpRSXkIDXXkdEbEV6qy3RYrpiHkVrx0uJXSNVMrV3PY6dKWuwmnHreBKVSg6QlcVhojsF5FXRWS7o4d2M8fj4SLys6N50woRaex4vL6ILHQ0wdoqItc7XspXRGaI1X/7J8cdiojIo2L1Md8mIl+76I+pKjANdOWNqhSZchlZaFumMaY98B5WNzywGlx96mjeNBt4x/H4O8AvjiZYXYBYx+PNganGmLZABjDM8fhkoLPjdR4oqz+cUiXRO0WV1xGRbGNM9WIe3w/0NsbsdTTAOmKMCRaRY0CIMSbf8fhhY0wdEUkFwgo1djrT0naZMaa54+d/AP7GmJdE5EcgG6v74beF+nQrVS50hK4qGlPC95cjt9D3Ns59FjUAmIo1mt/o6KCnVLnRQFcVzchC/13n+P43zjVGGgOscXy/AngQzi6iEFjSi4qID9DIGLMS+AdWK9QLfktQqizpCEJ5oyqFOhUC/GiMOXPpYpCIbMMaZY9yPPYIMEtEngRSOddV8TFguojcjzUSfxAoqX2uL/CFI/QFeMfRn1upcqNz6KrCcMyhRxhjjrm6FqXKgk65KKWUl9ARulJKeQkdoSullJfQQFdKKS+hga6UUl5CA10ppbyEBrpSSnmJ/wdrto3zfM1b9gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_loss_curves(history_1_percent)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHgAdLExomLG"
      },
      "source": [
        "## Model_2: feature extraction transfer learning model with 10% of data and data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "54C19m0XctVK"
      },
      "outputs": [],
      "source": [
        "# Get 10% of data...\n",
        "# !wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip\n",
        "# unzip_data(\"10_food_classes_10_percent.zip\")\n",
        "\n",
        "train_dir_10_percent = \"10_food_classes_10_percent/train/\"\n",
        "test_dir = \"10_food_classes_10_percent/test/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BW_Y5DNtctmy",
        "outputId": "4332f365-e452-40c6-c084-50c1230f4d40"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 750 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ]
        }
      ],
      "source": [
        "# Setup data loaders \n",
        "import tensorflow as tf\n",
        "train_data_10_percent = tf.keras.preprocessing.image_dataset_from_directory(train_dir_10_percent,\n",
        "                                                                            label_mode=\"categorical\",\n",
        "                                                                            image_size=IMG_SIZE)\n",
        "\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,\n",
        "                                                                label_mode=\"categorical\",\n",
        "                                                                image_size=IMG_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zftK8JhActvG",
        "outputId": "01e5180f-ee87-4c43-ad06-cfd93324d737"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_10_percent'.\n",
            "There are 10 directories and 0 images in '10_food_classes_10_percent/train'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/chicken_wings'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/pizza'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/grilled_salmon'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/sushi'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/ice_cream'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/chicken_curry'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/ramen'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/fried_rice'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/hamburger'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/steak'.\n",
            "There are 10 directories and 0 images in '10_food_classes_10_percent/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/chicken_wings'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/chicken_curry'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/steak'.\n"
          ]
        }
      ],
      "source": [
        "walk_through_dir(\"10_food_classes_10_percent\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "UKjZ5Pf9AwhM"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "from tensorflow.keras import Sequential\n",
        "\n",
        "# Build data augmentation layer\n",
        "data_augmentation = Sequential([\n",
        "    preprocessing.RandomFlip(\"horizontal\"),\n",
        "    preprocessing.RandomHeight(0.2),\n",
        "    preprocessing.RandomWidth(0.2),\n",
        "    preprocessing.RandomZoom(0.2),\n",
        "    preprocessing.RandomRotation(0.2),\n",
        "    # preprocessing.Rescaling(1./255) for models like Resnet\n",
        "], name=\"data_augmentation\")\n",
        "\n",
        "# Setup the input shape\n",
        "input_shape=(224,224,3)\n",
        "\n",
        "# Create a frozen base model\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create inputs and outputs\n",
        "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "x = data_augmentation(inputs, training=True)\n",
        "x = base_model(x, training=False)\n",
        "x = layers.GlobalAveragePooling2D(name=\"global_average_pooling_2D\")(x)\n",
        "output = layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
        "model_2 = tf.keras.Model(inputs, output)\n",
        "\n",
        "# Compile\n",
        "model_2.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T75tdG9UAwrR",
        "outputId": "e9fc8e25-054f-4a12-aeb0-598602b1edd7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " data_augmentation (Sequenti  (None, None, None, 3)    0         \n",
            " al)                                                             \n",
            "                                                                 \n",
            " efficientnetb0 (Functional)  (None, None, None, 1280)  4049571  \n",
            "                                                                 \n",
            " global_average_pooling_2D (  (None, 1280)             0         \n",
            " GlobalAveragePooling2D)                                         \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 10)                12810     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,062,381\n",
            "Trainable params: 12,810\n",
            "Non-trainable params: 4,049,571\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model_2.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a model checkpoint callback\n",
        "The ModelCheckPoint callback intermediately saves our model (the full model or just the weights) during training. This is useful so that we can start where we left off"
      ],
      "metadata": {
        "id": "wmlWgX4iAwye"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "RYG244_RAw5V"
      },
      "outputs": [],
      "source": [
        "# Set checkpoint path\n",
        "checkpointPath = \"ten_percent_model_checkpoint_weights/checkpoint.ckpt\"\n",
        "\n",
        "# Create a model checkoint callback that saves the model's weights only\n",
        "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath = checkpointPath,\n",
        "                                                         save_weights_only = True,\n",
        "                                                         save_best_only=False,\n",
        "                                                         save_freq=\"epoch\",\n",
        "                                                         verbose=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fitting the model 2 and passing it ModelCheckPointCallback"
      ],
      "metadata": {
        "id": "p_J_xesk8d6Z"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Azrej0ZfAw_K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6614bc34-2184-40cc-e679-262e2c6b9789"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: transfer_learning/10_percent_data_aug/20220908-203913\n",
            "Epoch 1/5\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.9991 - accuracy: 0.3440\n",
            "Epoch 1: saving model to ten_percent_model_checkpoint_weights/checkpoint.ckpt\n",
            "24/24 [==============================] - 25s 765ms/step - loss: 1.9991 - accuracy: 0.3440 - val_loss: 1.6290 - val_accuracy: 0.5609\n",
            "Epoch 2/5\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.3497 - accuracy: 0.6760\n",
            "Epoch 2: saving model to ten_percent_model_checkpoint_weights/checkpoint.ckpt\n",
            "24/24 [==============================] - 18s 768ms/step - loss: 1.3497 - accuracy: 0.6760 - val_loss: 1.1881 - val_accuracy: 0.7220\n",
            "Epoch 3/5\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.0665 - accuracy: 0.7240\n",
            "Epoch 3: saving model to ten_percent_model_checkpoint_weights/checkpoint.ckpt\n",
            "24/24 [==============================] - 15s 616ms/step - loss: 1.0665 - accuracy: 0.7240 - val_loss: 1.0425 - val_accuracy: 0.7303\n",
            "Epoch 4/5\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.9129 - accuracy: 0.7600\n",
            "Epoch 4: saving model to ten_percent_model_checkpoint_weights/checkpoint.ckpt\n",
            "24/24 [==============================] - 16s 634ms/step - loss: 0.9129 - accuracy: 0.7600 - val_loss: 0.9593 - val_accuracy: 0.7253\n",
            "Epoch 5/5\n",
            "24/24 [==============================] - ETA: 0s - loss: 0.7995 - accuracy: 0.8027\n",
            "Epoch 5: saving model to ten_percent_model_checkpoint_weights/checkpoint.ckpt\n",
            "24/24 [==============================] - 12s 487ms/step - loss: 0.7995 - accuracy: 0.8027 - val_loss: 0.8593 - val_accuracy: 0.7648\n"
          ]
        }
      ],
      "source": [
        "initial_epochs = 5\n",
        "history_10_percet_data_aug = model_2.fit(train_data_10_percent,\n",
        "                                          epochs=initial_epochs,\n",
        "                                         validation_data=test_data,\n",
        "                                         validation_steps=int(0.25 * len(test_data)),\n",
        "                                         callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                                                experiment_name=\"10_percent_data_aug\",),\n",
        "                                                    checkpoint_callback])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "Y13MhdUrAxEx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31d4ab57-b0ac-4b63-fc9c-ea2057e17528"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 8s 91ms/step - loss: 0.6093 - accuracy: 0.8408\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6092506051063538, 0.8407999873161316]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# Comparing model_0 and model_2 result on whole test data\n",
        "model_0.evaluate(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "-q9PrGh7AxKE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cbb601c-8d4a-4cd2-8622-e78b700e29fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 20s 252ms/step - loss: 0.8388 - accuracy: 0.7660\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8387944102287292, 0.765999972820282]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "results_10_percent_data_aug = model_2.evaluate(test_data)\n",
        "results_10_percent_data_aug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "cZP2wdZDAxPv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "5c2259b6-146f-4a18-84aa-e2bb13e95985"
      },
      "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"
          }
        },
        {
          "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": [
        "# Loss curves\n",
        "plot_loss_curves(history_10_percet_data_aug)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading in checkpointed weights"
      ],
      "metadata": {
        "id": "zLFqyEaUAxUp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "H4-HNJBbAxZ3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08a67d27-e007-4cfc-ba40-3ea743f6bbc1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f7056fbd510>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "# Load in saved model weights and evaluate\n",
        "model_2.load_weights(checkpointPath)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "TtUkjbBOAxer",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c40f7ed-5ce5-4fda-9f5c-b8b6b6bb72ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 20s 246ms/step - loss: 0.8640 - accuracy: 0.7516\n"
          ]
        }
      ],
      "source": [
        "loaded_weights_model_results = model_2.evaluate(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "6vDvs7RPAxji",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45cfe12b-ef6f-4cce-9653-4b4744ec25e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# Check to see both loaded and previous model results are close to each other\n",
        "import numpy as np\n",
        "np.isclose(np.array(results_10_percent_data_aug), np.array(loaded_weights_model_results))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model_3 : Fine tuning an existing model on 10% of the data\n",
        "\n",
        "> Fine tuning usually works best after training a feature extraction model for a few epochs with large amount of custom data."
      ],
      "metadata": {
        "id": "yPwQntWhAxoe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "_TJaFpPCAxtM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9e4f610-6e3b-4451-c4db-2158759a8b12"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.engine.input_layer.InputLayer at 0x7f716dad3f10>,\n",
              " <keras.engine.sequential.Sequential at 0x7f716dad3a10>,\n",
              " <keras.engine.functional.Functional at 0x7f716d664210>,\n",
              " <keras.layers.pooling.GlobalAveragePooling2D at 0x7f716dad3110>,\n",
              " <keras.layers.core.dense.Dense at 0x7f716d59ba50>]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# Layers in our loaded model\n",
        "model_2.layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "sb4MdjMsAxyX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f50c48d6-f92f-40b3-8127-01f3a950ce66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<keras.engine.input_layer.InputLayer object at 0x7f716dad3f10> True\n",
            "<keras.engine.sequential.Sequential object at 0x7f716dad3a10> True\n",
            "<keras.engine.functional.Functional object at 0x7f716d664210> False\n",
            "<keras.layers.pooling.GlobalAveragePooling2D object at 0x7f716dad3110> True\n",
            "<keras.layers.core.dense.Dense object at 0x7f716d59ba50> True\n"
          ]
        }
      ],
      "source": [
        "# Are model_2 layers trainable?\n",
        "for layer in model_2.layers:\n",
        "  print(layer, layer.trainable)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "PSyt17ZXAx2_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbb1c7d7-c10d-47fb-a448-2b047f7df975"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 False input_3\n",
            "1 False rescaling_2\n",
            "2 False normalization_2\n",
            "3 False stem_conv_pad\n",
            "4 False stem_conv\n",
            "5 False stem_bn\n",
            "6 False stem_activation\n",
            "7 False block1a_dwconv\n",
            "8 False block1a_bn\n",
            "9 False block1a_activation\n",
            "10 False block1a_se_squeeze\n",
            "11 False block1a_se_reshape\n",
            "12 False block1a_se_reduce\n",
            "13 False block1a_se_expand\n",
            "14 False block1a_se_excite\n",
            "15 False block1a_project_conv\n",
            "16 False block1a_project_bn\n",
            "17 False block2a_expand_conv\n",
            "18 False block2a_expand_bn\n",
            "19 False block2a_expand_activation\n",
            "20 False block2a_dwconv_pad\n",
            "21 False block2a_dwconv\n",
            "22 False block2a_bn\n",
            "23 False block2a_activation\n",
            "24 False block2a_se_squeeze\n",
            "25 False block2a_se_reshape\n",
            "26 False block2a_se_reduce\n",
            "27 False block2a_se_expand\n",
            "28 False block2a_se_excite\n",
            "29 False block2a_project_conv\n",
            "30 False block2a_project_bn\n",
            "31 False block2b_expand_conv\n",
            "32 False block2b_expand_bn\n",
            "33 False block2b_expand_activation\n",
            "34 False block2b_dwconv\n",
            "35 False block2b_bn\n",
            "36 False block2b_activation\n",
            "37 False block2b_se_squeeze\n",
            "38 False block2b_se_reshape\n",
            "39 False block2b_se_reduce\n",
            "40 False block2b_se_expand\n",
            "41 False block2b_se_excite\n",
            "42 False block2b_project_conv\n",
            "43 False block2b_project_bn\n",
            "44 False block2b_drop\n",
            "45 False block2b_add\n",
            "46 False block3a_expand_conv\n",
            "47 False block3a_expand_bn\n",
            "48 False block3a_expand_activation\n",
            "49 False block3a_dwconv_pad\n",
            "50 False block3a_dwconv\n",
            "51 False block3a_bn\n",
            "52 False block3a_activation\n",
            "53 False block3a_se_squeeze\n",
            "54 False block3a_se_reshape\n",
            "55 False block3a_se_reduce\n",
            "56 False block3a_se_expand\n",
            "57 False block3a_se_excite\n",
            "58 False block3a_project_conv\n",
            "59 False block3a_project_bn\n",
            "60 False block3b_expand_conv\n",
            "61 False block3b_expand_bn\n",
            "62 False block3b_expand_activation\n",
            "63 False block3b_dwconv\n",
            "64 False block3b_bn\n",
            "65 False block3b_activation\n",
            "66 False block3b_se_squeeze\n",
            "67 False block3b_se_reshape\n",
            "68 False block3b_se_reduce\n",
            "69 False block3b_se_expand\n",
            "70 False block3b_se_excite\n",
            "71 False block3b_project_conv\n",
            "72 False block3b_project_bn\n",
            "73 False block3b_drop\n",
            "74 False block3b_add\n",
            "75 False block4a_expand_conv\n",
            "76 False block4a_expand_bn\n",
            "77 False block4a_expand_activation\n",
            "78 False block4a_dwconv_pad\n",
            "79 False block4a_dwconv\n",
            "80 False block4a_bn\n",
            "81 False block4a_activation\n",
            "82 False block4a_se_squeeze\n",
            "83 False block4a_se_reshape\n",
            "84 False block4a_se_reduce\n",
            "85 False block4a_se_expand\n",
            "86 False block4a_se_excite\n",
            "87 False block4a_project_conv\n",
            "88 False block4a_project_bn\n",
            "89 False block4b_expand_conv\n",
            "90 False block4b_expand_bn\n",
            "91 False block4b_expand_activation\n",
            "92 False block4b_dwconv\n",
            "93 False block4b_bn\n",
            "94 False block4b_activation\n",
            "95 False block4b_se_squeeze\n",
            "96 False block4b_se_reshape\n",
            "97 False block4b_se_reduce\n",
            "98 False block4b_se_expand\n",
            "99 False block4b_se_excite\n",
            "100 False block4b_project_conv\n",
            "101 False block4b_project_bn\n",
            "102 False block4b_drop\n",
            "103 False block4b_add\n",
            "104 False block4c_expand_conv\n",
            "105 False block4c_expand_bn\n",
            "106 False block4c_expand_activation\n",
            "107 False block4c_dwconv\n",
            "108 False block4c_bn\n",
            "109 False block4c_activation\n",
            "110 False block4c_se_squeeze\n",
            "111 False block4c_se_reshape\n",
            "112 False block4c_se_reduce\n",
            "113 False block4c_se_expand\n",
            "114 False block4c_se_excite\n",
            "115 False block4c_project_conv\n",
            "116 False block4c_project_bn\n",
            "117 False block4c_drop\n",
            "118 False block4c_add\n",
            "119 False block5a_expand_conv\n",
            "120 False block5a_expand_bn\n",
            "121 False block5a_expand_activation\n",
            "122 False block5a_dwconv\n",
            "123 False block5a_bn\n",
            "124 False block5a_activation\n",
            "125 False block5a_se_squeeze\n",
            "126 False block5a_se_reshape\n",
            "127 False block5a_se_reduce\n",
            "128 False block5a_se_expand\n",
            "129 False block5a_se_excite\n",
            "130 False block5a_project_conv\n",
            "131 False block5a_project_bn\n",
            "132 False block5b_expand_conv\n",
            "133 False block5b_expand_bn\n",
            "134 False block5b_expand_activation\n",
            "135 False block5b_dwconv\n",
            "136 False block5b_bn\n",
            "137 False block5b_activation\n",
            "138 False block5b_se_squeeze\n",
            "139 False block5b_se_reshape\n",
            "140 False block5b_se_reduce\n",
            "141 False block5b_se_expand\n",
            "142 False block5b_se_excite\n",
            "143 False block5b_project_conv\n",
            "144 False block5b_project_bn\n",
            "145 False block5b_drop\n",
            "146 False block5b_add\n",
            "147 False block5c_expand_conv\n",
            "148 False block5c_expand_bn\n",
            "149 False block5c_expand_activation\n",
            "150 False block5c_dwconv\n",
            "151 False block5c_bn\n",
            "152 False block5c_activation\n",
            "153 False block5c_se_squeeze\n",
            "154 False block5c_se_reshape\n",
            "155 False block5c_se_reduce\n",
            "156 False block5c_se_expand\n",
            "157 False block5c_se_excite\n",
            "158 False block5c_project_conv\n",
            "159 False block5c_project_bn\n",
            "160 False block5c_drop\n",
            "161 False block5c_add\n",
            "162 False block6a_expand_conv\n",
            "163 False block6a_expand_bn\n",
            "164 False block6a_expand_activation\n",
            "165 False block6a_dwconv_pad\n",
            "166 False block6a_dwconv\n",
            "167 False block6a_bn\n",
            "168 False block6a_activation\n",
            "169 False block6a_se_squeeze\n",
            "170 False block6a_se_reshape\n",
            "171 False block6a_se_reduce\n",
            "172 False block6a_se_expand\n",
            "173 False block6a_se_excite\n",
            "174 False block6a_project_conv\n",
            "175 False block6a_project_bn\n",
            "176 False block6b_expand_conv\n",
            "177 False block6b_expand_bn\n",
            "178 False block6b_expand_activation\n",
            "179 False block6b_dwconv\n",
            "180 False block6b_bn\n",
            "181 False block6b_activation\n",
            "182 False block6b_se_squeeze\n",
            "183 False block6b_se_reshape\n",
            "184 False block6b_se_reduce\n",
            "185 False block6b_se_expand\n",
            "186 False block6b_se_excite\n",
            "187 False block6b_project_conv\n",
            "188 False block6b_project_bn\n",
            "189 False block6b_drop\n",
            "190 False block6b_add\n",
            "191 False block6c_expand_conv\n",
            "192 False block6c_expand_bn\n",
            "193 False block6c_expand_activation\n",
            "194 False block6c_dwconv\n",
            "195 False block6c_bn\n",
            "196 False block6c_activation\n",
            "197 False block6c_se_squeeze\n",
            "198 False block6c_se_reshape\n",
            "199 False block6c_se_reduce\n",
            "200 False block6c_se_expand\n",
            "201 False block6c_se_excite\n",
            "202 False block6c_project_conv\n",
            "203 False block6c_project_bn\n",
            "204 False block6c_drop\n",
            "205 False block6c_add\n",
            "206 False block6d_expand_conv\n",
            "207 False block6d_expand_bn\n",
            "208 False block6d_expand_activation\n",
            "209 False block6d_dwconv\n",
            "210 False block6d_bn\n",
            "211 False block6d_activation\n",
            "212 False block6d_se_squeeze\n",
            "213 False block6d_se_reshape\n",
            "214 False block6d_se_reduce\n",
            "215 False block6d_se_expand\n",
            "216 False block6d_se_excite\n",
            "217 False block6d_project_conv\n",
            "218 False block6d_project_bn\n",
            "219 False block6d_drop\n",
            "220 False block6d_add\n",
            "221 False block7a_expand_conv\n",
            "222 False block7a_expand_bn\n",
            "223 False block7a_expand_activation\n",
            "224 False block7a_dwconv\n",
            "225 False block7a_bn\n",
            "226 False block7a_activation\n",
            "227 False block7a_se_squeeze\n",
            "228 False block7a_se_reshape\n",
            "229 False block7a_se_reduce\n",
            "230 False block7a_se_expand\n",
            "231 False block7a_se_excite\n",
            "232 False block7a_project_conv\n",
            "233 False block7a_project_bn\n",
            "234 False top_conv\n",
            "235 False top_bn\n",
            "236 False top_activation\n"
          ]
        }
      ],
      "source": [
        "# What layers were frozen\n",
        "for i,  layer in enumerate(model_2.layers[2].layers):\n",
        "  print(i, layer.trainable, layer.name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "3UrK0Dp9AyLs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a4fac43-865b-4bf7-b119-3426e06f2bf9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ],
      "source": [
        "# How many trainable variables are there in our base model\n",
        "print(len(model_2.layers[2].trainable_variables))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "I4EujSPaAyQJ"
      },
      "outputs": [],
      "source": [
        "# To begin fine tuning lets start with setting last 10 layers of our model.trainable = True\n",
        "base_model.trainable = True\n",
        "# Freeze all layers except last 10\n",
        "for layer in base_model.layers[:-10]:\n",
        "  layer.trainable = False\n",
        "\n",
        "# Recompile our model every time we make changes in our model\n",
        "model_2.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), # When fine tunining we typically want to lower the learning rate by 10x\n",
        "                metrics = [\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, layer in enumerate(model_2.layers[2].layers):\n",
        "  print(i, layer.trainable, layer.name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ce2Oy17JOwO1",
        "outputId": "fec10291-a246-48fb-a1d0-df8965cff354"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 False input_3\n",
            "1 False rescaling_2\n",
            "2 False normalization_2\n",
            "3 False stem_conv_pad\n",
            "4 False stem_conv\n",
            "5 False stem_bn\n",
            "6 False stem_activation\n",
            "7 False block1a_dwconv\n",
            "8 False block1a_bn\n",
            "9 False block1a_activation\n",
            "10 False block1a_se_squeeze\n",
            "11 False block1a_se_reshape\n",
            "12 False block1a_se_reduce\n",
            "13 False block1a_se_expand\n",
            "14 False block1a_se_excite\n",
            "15 False block1a_project_conv\n",
            "16 False block1a_project_bn\n",
            "17 False block2a_expand_conv\n",
            "18 False block2a_expand_bn\n",
            "19 False block2a_expand_activation\n",
            "20 False block2a_dwconv_pad\n",
            "21 False block2a_dwconv\n",
            "22 False block2a_bn\n",
            "23 False block2a_activation\n",
            "24 False block2a_se_squeeze\n",
            "25 False block2a_se_reshape\n",
            "26 False block2a_se_reduce\n",
            "27 False block2a_se_expand\n",
            "28 False block2a_se_excite\n",
            "29 False block2a_project_conv\n",
            "30 False block2a_project_bn\n",
            "31 False block2b_expand_conv\n",
            "32 False block2b_expand_bn\n",
            "33 False block2b_expand_activation\n",
            "34 False block2b_dwconv\n",
            "35 False block2b_bn\n",
            "36 False block2b_activation\n",
            "37 False block2b_se_squeeze\n",
            "38 False block2b_se_reshape\n",
            "39 False block2b_se_reduce\n",
            "40 False block2b_se_expand\n",
            "41 False block2b_se_excite\n",
            "42 False block2b_project_conv\n",
            "43 False block2b_project_bn\n",
            "44 False block2b_drop\n",
            "45 False block2b_add\n",
            "46 False block3a_expand_conv\n",
            "47 False block3a_expand_bn\n",
            "48 False block3a_expand_activation\n",
            "49 False block3a_dwconv_pad\n",
            "50 False block3a_dwconv\n",
            "51 False block3a_bn\n",
            "52 False block3a_activation\n",
            "53 False block3a_se_squeeze\n",
            "54 False block3a_se_reshape\n",
            "55 False block3a_se_reduce\n",
            "56 False block3a_se_expand\n",
            "57 False block3a_se_excite\n",
            "58 False block3a_project_conv\n",
            "59 False block3a_project_bn\n",
            "60 False block3b_expand_conv\n",
            "61 False block3b_expand_bn\n",
            "62 False block3b_expand_activation\n",
            "63 False block3b_dwconv\n",
            "64 False block3b_bn\n",
            "65 False block3b_activation\n",
            "66 False block3b_se_squeeze\n",
            "67 False block3b_se_reshape\n",
            "68 False block3b_se_reduce\n",
            "69 False block3b_se_expand\n",
            "70 False block3b_se_excite\n",
            "71 False block3b_project_conv\n",
            "72 False block3b_project_bn\n",
            "73 False block3b_drop\n",
            "74 False block3b_add\n",
            "75 False block4a_expand_conv\n",
            "76 False block4a_expand_bn\n",
            "77 False block4a_expand_activation\n",
            "78 False block4a_dwconv_pad\n",
            "79 False block4a_dwconv\n",
            "80 False block4a_bn\n",
            "81 False block4a_activation\n",
            "82 False block4a_se_squeeze\n",
            "83 False block4a_se_reshape\n",
            "84 False block4a_se_reduce\n",
            "85 False block4a_se_expand\n",
            "86 False block4a_se_excite\n",
            "87 False block4a_project_conv\n",
            "88 False block4a_project_bn\n",
            "89 False block4b_expand_conv\n",
            "90 False block4b_expand_bn\n",
            "91 False block4b_expand_activation\n",
            "92 False block4b_dwconv\n",
            "93 False block4b_bn\n",
            "94 False block4b_activation\n",
            "95 False block4b_se_squeeze\n",
            "96 False block4b_se_reshape\n",
            "97 False block4b_se_reduce\n",
            "98 False block4b_se_expand\n",
            "99 False block4b_se_excite\n",
            "100 False block4b_project_conv\n",
            "101 False block4b_project_bn\n",
            "102 False block4b_drop\n",
            "103 False block4b_add\n",
            "104 False block4c_expand_conv\n",
            "105 False block4c_expand_bn\n",
            "106 False block4c_expand_activation\n",
            "107 False block4c_dwconv\n",
            "108 False block4c_bn\n",
            "109 False block4c_activation\n",
            "110 False block4c_se_squeeze\n",
            "111 False block4c_se_reshape\n",
            "112 False block4c_se_reduce\n",
            "113 False block4c_se_expand\n",
            "114 False block4c_se_excite\n",
            "115 False block4c_project_conv\n",
            "116 False block4c_project_bn\n",
            "117 False block4c_drop\n",
            "118 False block4c_add\n",
            "119 False block5a_expand_conv\n",
            "120 False block5a_expand_bn\n",
            "121 False block5a_expand_activation\n",
            "122 False block5a_dwconv\n",
            "123 False block5a_bn\n",
            "124 False block5a_activation\n",
            "125 False block5a_se_squeeze\n",
            "126 False block5a_se_reshape\n",
            "127 False block5a_se_reduce\n",
            "128 False block5a_se_expand\n",
            "129 False block5a_se_excite\n",
            "130 False block5a_project_conv\n",
            "131 False block5a_project_bn\n",
            "132 False block5b_expand_conv\n",
            "133 False block5b_expand_bn\n",
            "134 False block5b_expand_activation\n",
            "135 False block5b_dwconv\n",
            "136 False block5b_bn\n",
            "137 False block5b_activation\n",
            "138 False block5b_se_squeeze\n",
            "139 False block5b_se_reshape\n",
            "140 False block5b_se_reduce\n",
            "141 False block5b_se_expand\n",
            "142 False block5b_se_excite\n",
            "143 False block5b_project_conv\n",
            "144 False block5b_project_bn\n",
            "145 False block5b_drop\n",
            "146 False block5b_add\n",
            "147 False block5c_expand_conv\n",
            "148 False block5c_expand_bn\n",
            "149 False block5c_expand_activation\n",
            "150 False block5c_dwconv\n",
            "151 False block5c_bn\n",
            "152 False block5c_activation\n",
            "153 False block5c_se_squeeze\n",
            "154 False block5c_se_reshape\n",
            "155 False block5c_se_reduce\n",
            "156 False block5c_se_expand\n",
            "157 False block5c_se_excite\n",
            "158 False block5c_project_conv\n",
            "159 False block5c_project_bn\n",
            "160 False block5c_drop\n",
            "161 False block5c_add\n",
            "162 False block6a_expand_conv\n",
            "163 False block6a_expand_bn\n",
            "164 False block6a_expand_activation\n",
            "165 False block6a_dwconv_pad\n",
            "166 False block6a_dwconv\n",
            "167 False block6a_bn\n",
            "168 False block6a_activation\n",
            "169 False block6a_se_squeeze\n",
            "170 False block6a_se_reshape\n",
            "171 False block6a_se_reduce\n",
            "172 False block6a_se_expand\n",
            "173 False block6a_se_excite\n",
            "174 False block6a_project_conv\n",
            "175 False block6a_project_bn\n",
            "176 False block6b_expand_conv\n",
            "177 False block6b_expand_bn\n",
            "178 False block6b_expand_activation\n",
            "179 False block6b_dwconv\n",
            "180 False block6b_bn\n",
            "181 False block6b_activation\n",
            "182 False block6b_se_squeeze\n",
            "183 False block6b_se_reshape\n",
            "184 False block6b_se_reduce\n",
            "185 False block6b_se_expand\n",
            "186 False block6b_se_excite\n",
            "187 False block6b_project_conv\n",
            "188 False block6b_project_bn\n",
            "189 False block6b_drop\n",
            "190 False block6b_add\n",
            "191 False block6c_expand_conv\n",
            "192 False block6c_expand_bn\n",
            "193 False block6c_expand_activation\n",
            "194 False block6c_dwconv\n",
            "195 False block6c_bn\n",
            "196 False block6c_activation\n",
            "197 False block6c_se_squeeze\n",
            "198 False block6c_se_reshape\n",
            "199 False block6c_se_reduce\n",
            "200 False block6c_se_expand\n",
            "201 False block6c_se_excite\n",
            "202 False block6c_project_conv\n",
            "203 False block6c_project_bn\n",
            "204 False block6c_drop\n",
            "205 False block6c_add\n",
            "206 False block6d_expand_conv\n",
            "207 False block6d_expand_bn\n",
            "208 False block6d_expand_activation\n",
            "209 False block6d_dwconv\n",
            "210 False block6d_bn\n",
            "211 False block6d_activation\n",
            "212 False block6d_se_squeeze\n",
            "213 False block6d_se_reshape\n",
            "214 False block6d_se_reduce\n",
            "215 False block6d_se_expand\n",
            "216 False block6d_se_excite\n",
            "217 False block6d_project_conv\n",
            "218 False block6d_project_bn\n",
            "219 False block6d_drop\n",
            "220 False block6d_add\n",
            "221 False block7a_expand_conv\n",
            "222 False block7a_expand_bn\n",
            "223 False block7a_expand_activation\n",
            "224 False block7a_dwconv\n",
            "225 False block7a_bn\n",
            "226 False block7a_activation\n",
            "227 True block7a_se_squeeze\n",
            "228 True block7a_se_reshape\n",
            "229 True block7a_se_reduce\n",
            "230 True block7a_se_expand\n",
            "231 True block7a_se_excite\n",
            "232 True block7a_project_conv\n",
            "233 True block7a_project_bn\n",
            "234 True top_conv\n",
            "235 True top_bn\n",
            "236 True top_activation\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(model_2.trainable_variables))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0iPm1VPOwLZ",
        "outputId": "59beb583-c6ab-49eb-9668-db438dafd5df"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine tune for another 5 epochs\n",
        "fine_tune_epochs = initial_epochs + 5\n",
        "\n",
        "history_fine_10_percent_data_aug = model_2.fit(train_data_10_percent,\n",
        "                                               epochs=fine_tune_epochs,\n",
        "                                               validation_data=test_data,\n",
        "                                               validation_steps=int(0.25 * len(test_data)),\n",
        "                                               initial_epoch=history_10_percet_data_aug.epoch[-1],# start training from last epoch\n",
        "                                               callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                                                      experiment_name=\"10_percent_fine_tune_last_10\")])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61PuU_pOOwIW",
        "outputId": "a495665e-a8d2-4cd0-c8dc-251f66240436"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: transfer_learning/10_percent_fine_tune_last_10/20220908-204142\n",
            "Epoch 5/10\n",
            "24/24 [==============================] - 26s 590ms/step - loss: 0.6796 - accuracy: 0.8040 - val_loss: 0.7367 - val_accuracy: 0.7533\n",
            "Epoch 6/10\n",
            "24/24 [==============================] - 11s 432ms/step - loss: 0.5808 - accuracy: 0.8240 - val_loss: 0.7270 - val_accuracy: 0.7763\n",
            "Epoch 7/10\n",
            "24/24 [==============================] - 9s 361ms/step - loss: 0.4805 - accuracy: 0.8493 - val_loss: 0.6826 - val_accuracy: 0.7878\n",
            "Epoch 8/10\n",
            "24/24 [==============================] - 11s 439ms/step - loss: 0.4765 - accuracy: 0.8640 - val_loss: 0.6840 - val_accuracy: 0.7812\n",
            "Epoch 9/10\n",
            "24/24 [==============================] - 11s 433ms/step - loss: 0.4278 - accuracy: 0.8667 - val_loss: 0.6580 - val_accuracy: 0.7681\n",
            "Epoch 10/10\n",
            "24/24 [==============================] - 10s 392ms/step - loss: 0.3883 - accuracy: 0.8947 - val_loss: 0.6715 - val_accuracy: 0.7862\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the fine tuned model \n",
        "results_fine_tune_10_percent = model_2.evaluate(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIGRQqIDOwFc",
        "outputId": "240f45be-d72a-4006-e381-d5fb9c7b8fc9"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 19s 236ms/step - loss: 0.6332 - accuracy: 0.7876\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_10_percent_data_aug"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOauk0EzOwCn",
        "outputId": "86bb1f7c-a2b3-4c6b-f070-e8a2e1e2a53d"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8387944102287292, 0.765999972820282]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out the loss curves of our fined tuned model\n",
        "plot_loss_curves(history_fine_10_percent_data_aug)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "iSYolvXBOv_d",
        "outputId": "da37d950-21b7-40ed-f87d-8f4ea0bf0fce"
      },
      "execution_count": 51,
      "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"
          }
        },
        {
          "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": "code",
      "source": [
        "# Let's create a function for plotting loss curves to compare before and after fine tuning or comparing histories\n",
        "def compare_histories(original_history, new_history, initial_epochs=5):\n",
        "  \"\"\"\n",
        "  Compares tensorflow 2 history objects\n",
        "  \"\"\"\n",
        "  # Get original history measurements\n",
        "  acc = original_history.history[\"accuracy\"]\n",
        "  loss = original_history.history[\"loss\"]\n",
        "\n",
        "  val_acc = original_history.history[\"val_accuracy\"]\n",
        "  val_loss = original_history.history[\"val_loss\"]\n",
        "\n",
        "  # Combine original history with new history\n",
        "  total_acc = acc + new_history.history[\"accuracy\"]\n",
        "  total_loss = loss + new_history.history[\"loss\"]\n",
        "\n",
        "  total_val_acc = val_acc + new_history.history[\"val_accuracy\"]\n",
        "  total_val_loss = val_loss + new_history.history[\"val_loss\"]\n",
        "\n",
        "  # Make plots accuracy\n",
        "  plt.figure(figsize=(8,8))\n",
        "  plt.subplot(2, 1, 1)\n",
        "  plt.plot(total_acc, label=\"Training Accuracy\")\n",
        "  plt.plot(total_val_acc, label=\"Validation Accuracy\")\n",
        "\n",
        "  plt.plot([initial_epochs-1, initial_epochs-1], plt.ylim(), label=\"Start Fine Tuning\")\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.title(\"Training and Validation Accuracy\")\n",
        "\n",
        "  # Make plot for loss\n",
        "  plt.figure(figsize=(8,8))\n",
        "  plt.subplot(2, 1, 1)\n",
        "  plt.plot(total_loss, label=\"Training Loss\")\n",
        "  plt.plot(total_val_loss, label=\"Validation Loss\")\n",
        "\n",
        "  plt.plot([initial_epochs-1, initial_epochs-1], plt.ylim(), label=\"Start Fine Tuning\")\n",
        "  plt.legend(loc=\"upper right\")\n",
        "  plt.title(\"Training and Validation Loss\")"
      ],
      "metadata": {
        "id": "0ZZUf4HSOv8Z"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "compare_histories(history_10_percet_data_aug,\n",
        "                  history_fine_10_percent_data_aug,\n",
        "                  initial_epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "pbPHTovzOv5w",
        "outputId": "d6824ac8-289b-499a-e7fa-106cf0f492b1"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model_4: Fine tune existing model on all of the data"
      ],
      "metadata": {
        "id": "BA0PF1AhOv2z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download and unzip 10 classes food 101 datset\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip\n",
        "unzip_data(\"10_food_classes_all_data.zip\")"
      ],
      "metadata": {
        "id": "q4k-E-3oOv0A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10bc0432-14c4-4861-e399-1cfc3788c008"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-08 20:44:11--  https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.99.128, 142.250.107.128, 74.125.20.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.99.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 519183241 (495M) [application/zip]\n",
            "Saving to: ‘10_food_classes_all_data.zip.1’\n",
            "\n",
            "10_food_classes_all 100%[===================>] 495.13M  50.8MB/s    in 6.3s    \n",
            "\n",
            "2022-09-08 20:44:17 (79.2 MB/s) - ‘10_food_classes_all_data.zip.1’ saved [519183241/519183241]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# setup train and test dirs\n",
        "train_dir_all_data = \"10_food_classes_all_data/train\"\n",
        "test_dir = \"10_food_classes_all_data/test\""
      ],
      "metadata": {
        "id": "2ywF8qCQOvxE"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "walk_through_dir(\"10_food_classes_all_data\")"
      ],
      "metadata": {
        "id": "yzApqd-0OvuH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb93c223-5876-464c-e946-23d0f3effbcf"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_all_data'.\n",
            "There are 10 directories and 0 images in '10_food_classes_all_data/train'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/chicken_wings'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/pizza'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/grilled_salmon'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/sushi'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/ice_cream'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/chicken_curry'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/ramen'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/fried_rice'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/hamburger'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/steak'.\n",
            "There are 10 directories and 0 images in '10_food_classes_all_data/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/chicken_wings'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/chicken_curry'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/steak'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup data inputs\n",
        "import tensorflow as tf\n",
        "IMG_SIZE = (224,224)\n",
        "train_data_10_classes_full = tf.keras.preprocessing.image_dataset_from_directory(train_dir_all_data,\n",
        "                                                                                 label_mode=\"categorical\",\n",
        "                                                                                 image_size=IMG_SIZE)\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,\n",
        "                                                                label_mode=\"categorical\",\n",
        "                                                                image_size=IMG_SIZE)"
      ],
      "metadata": {
        "id": "FrJcnY-2OvrF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2b4988a-7466-42d0-c10d-b1583a24fd0f"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 7500 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_10_classes_full.class_names"
      ],
      "metadata": {
        "id": "xkJne6AeOvom",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4261e2dd-8fb9-49d0-cb87-efd544700407"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['chicken_curry',\n",
              " 'chicken_wings',\n",
              " 'fried_rice',\n",
              " 'grilled_salmon',\n",
              " 'hamburger',\n",
              " 'ice_cream',\n",
              " 'pizza',\n",
              " 'ramen',\n",
              " 'steak',\n",
              " 'sushi']"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To train a fine-tuning model we need to revert model_2 back to it's feature extraction weights."
      ],
      "metadata": {
        "id": "XYarbgVeOvmA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load weights from checkpoints so we can fine tune\n",
        "# from same stage 10 percent model was fine tuned\n",
        "model_2.load_weights(checkpointPath)"
      ],
      "metadata": {
        "id": "39szEmDpOvjY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e433e9b-f165-41cf-fa04-7a04baeaac25"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f7057759f10>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.evaluate(test_data)"
      ],
      "metadata": {
        "id": "0zLM-H-5Ovg2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a12aa54c-2312-48e7-c7fa-0e20c9fbd207"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 17s 209ms/step - loss: 0.8425 - accuracy: 0.7716\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8425078392028809, 0.7716000080108643]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_fine_tune_10_percent"
      ],
      "metadata": {
        "id": "U25P_d8uOveZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f246106c-f9a8-4849-aa43-27acca722b71"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.633224606513977, 0.7875999808311462]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_10_percent_data_aug"
      ],
      "metadata": {
        "id": "Vi0zWvRzOvb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e27302e8-c5d5-4eeb-944e-8eeecd0995c9"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8387944102287292, 0.765999972820282]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All we've done is:\n",
        "1. Trained a feature extraction transfer learning model for 5 epochson 10 % of data with data_augmentation (model_2) and we saved the model's weight using model checkpoint callback\n",
        "2. Fine tuned the same model on same 10% of the data for a further 5 epochs with the last 10 layers unfrozen(model_3).\n",
        "3. Saved the results and training logs each time.\n",
        "4. Reloaded the model from step 1 to do the same steps as model_2 except this time we're using all of the data."
      ],
      "metadata": {
        "id": "fhLrzalbOvZf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check which layers are tunable\n",
        "for layer_number, layer in enumerate(model_2.layers):\n",
        "  print(layer_number, layer.name, layer.trainable)"
      ],
      "metadata": {
        "id": "iUqwGLF6OvW8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c771531d-af6c-4dbe-ae9c-6af9636c7018"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 input_layer True\n",
            "1 data_augmentation True\n",
            "2 efficientnetb0 True\n",
            "3 global_average_pooling_2D True\n",
            "4 output_layer True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, layer in enumerate(model_2.layers[2].layers):\n",
        "  print(i, layer.name, layer.trainable)"
      ],
      "metadata": {
        "id": "7p-FUxhAOvIX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "960e949e-5002-41d7-e1fd-11a232876252"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 input_3 False\n",
            "1 rescaling_2 False\n",
            "2 normalization_2 False\n",
            "3 stem_conv_pad False\n",
            "4 stem_conv False\n",
            "5 stem_bn False\n",
            "6 stem_activation False\n",
            "7 block1a_dwconv False\n",
            "8 block1a_bn False\n",
            "9 block1a_activation False\n",
            "10 block1a_se_squeeze False\n",
            "11 block1a_se_reshape False\n",
            "12 block1a_se_reduce False\n",
            "13 block1a_se_expand False\n",
            "14 block1a_se_excite False\n",
            "15 block1a_project_conv False\n",
            "16 block1a_project_bn False\n",
            "17 block2a_expand_conv False\n",
            "18 block2a_expand_bn False\n",
            "19 block2a_expand_activation False\n",
            "20 block2a_dwconv_pad False\n",
            "21 block2a_dwconv False\n",
            "22 block2a_bn False\n",
            "23 block2a_activation False\n",
            "24 block2a_se_squeeze False\n",
            "25 block2a_se_reshape False\n",
            "26 block2a_se_reduce False\n",
            "27 block2a_se_expand False\n",
            "28 block2a_se_excite False\n",
            "29 block2a_project_conv False\n",
            "30 block2a_project_bn False\n",
            "31 block2b_expand_conv False\n",
            "32 block2b_expand_bn False\n",
            "33 block2b_expand_activation False\n",
            "34 block2b_dwconv False\n",
            "35 block2b_bn False\n",
            "36 block2b_activation False\n",
            "37 block2b_se_squeeze False\n",
            "38 block2b_se_reshape False\n",
            "39 block2b_se_reduce False\n",
            "40 block2b_se_expand False\n",
            "41 block2b_se_excite False\n",
            "42 block2b_project_conv False\n",
            "43 block2b_project_bn False\n",
            "44 block2b_drop False\n",
            "45 block2b_add False\n",
            "46 block3a_expand_conv False\n",
            "47 block3a_expand_bn False\n",
            "48 block3a_expand_activation False\n",
            "49 block3a_dwconv_pad False\n",
            "50 block3a_dwconv False\n",
            "51 block3a_bn False\n",
            "52 block3a_activation False\n",
            "53 block3a_se_squeeze False\n",
            "54 block3a_se_reshape False\n",
            "55 block3a_se_reduce False\n",
            "56 block3a_se_expand False\n",
            "57 block3a_se_excite False\n",
            "58 block3a_project_conv False\n",
            "59 block3a_project_bn False\n",
            "60 block3b_expand_conv False\n",
            "61 block3b_expand_bn False\n",
            "62 block3b_expand_activation False\n",
            "63 block3b_dwconv False\n",
            "64 block3b_bn False\n",
            "65 block3b_activation False\n",
            "66 block3b_se_squeeze False\n",
            "67 block3b_se_reshape False\n",
            "68 block3b_se_reduce False\n",
            "69 block3b_se_expand False\n",
            "70 block3b_se_excite False\n",
            "71 block3b_project_conv False\n",
            "72 block3b_project_bn False\n",
            "73 block3b_drop False\n",
            "74 block3b_add False\n",
            "75 block4a_expand_conv False\n",
            "76 block4a_expand_bn False\n",
            "77 block4a_expand_activation False\n",
            "78 block4a_dwconv_pad False\n",
            "79 block4a_dwconv False\n",
            "80 block4a_bn False\n",
            "81 block4a_activation False\n",
            "82 block4a_se_squeeze False\n",
            "83 block4a_se_reshape False\n",
            "84 block4a_se_reduce False\n",
            "85 block4a_se_expand False\n",
            "86 block4a_se_excite False\n",
            "87 block4a_project_conv False\n",
            "88 block4a_project_bn False\n",
            "89 block4b_expand_conv False\n",
            "90 block4b_expand_bn False\n",
            "91 block4b_expand_activation False\n",
            "92 block4b_dwconv False\n",
            "93 block4b_bn False\n",
            "94 block4b_activation False\n",
            "95 block4b_se_squeeze False\n",
            "96 block4b_se_reshape False\n",
            "97 block4b_se_reduce False\n",
            "98 block4b_se_expand False\n",
            "99 block4b_se_excite False\n",
            "100 block4b_project_conv False\n",
            "101 block4b_project_bn False\n",
            "102 block4b_drop False\n",
            "103 block4b_add False\n",
            "104 block4c_expand_conv False\n",
            "105 block4c_expand_bn False\n",
            "106 block4c_expand_activation False\n",
            "107 block4c_dwconv False\n",
            "108 block4c_bn False\n",
            "109 block4c_activation False\n",
            "110 block4c_se_squeeze False\n",
            "111 block4c_se_reshape False\n",
            "112 block4c_se_reduce False\n",
            "113 block4c_se_expand False\n",
            "114 block4c_se_excite False\n",
            "115 block4c_project_conv False\n",
            "116 block4c_project_bn False\n",
            "117 block4c_drop False\n",
            "118 block4c_add False\n",
            "119 block5a_expand_conv False\n",
            "120 block5a_expand_bn False\n",
            "121 block5a_expand_activation False\n",
            "122 block5a_dwconv False\n",
            "123 block5a_bn False\n",
            "124 block5a_activation False\n",
            "125 block5a_se_squeeze False\n",
            "126 block5a_se_reshape False\n",
            "127 block5a_se_reduce False\n",
            "128 block5a_se_expand False\n",
            "129 block5a_se_excite False\n",
            "130 block5a_project_conv False\n",
            "131 block5a_project_bn False\n",
            "132 block5b_expand_conv False\n",
            "133 block5b_expand_bn False\n",
            "134 block5b_expand_activation False\n",
            "135 block5b_dwconv False\n",
            "136 block5b_bn False\n",
            "137 block5b_activation False\n",
            "138 block5b_se_squeeze False\n",
            "139 block5b_se_reshape False\n",
            "140 block5b_se_reduce False\n",
            "141 block5b_se_expand False\n",
            "142 block5b_se_excite False\n",
            "143 block5b_project_conv False\n",
            "144 block5b_project_bn False\n",
            "145 block5b_drop False\n",
            "146 block5b_add False\n",
            "147 block5c_expand_conv False\n",
            "148 block5c_expand_bn False\n",
            "149 block5c_expand_activation False\n",
            "150 block5c_dwconv False\n",
            "151 block5c_bn False\n",
            "152 block5c_activation False\n",
            "153 block5c_se_squeeze False\n",
            "154 block5c_se_reshape False\n",
            "155 block5c_se_reduce False\n",
            "156 block5c_se_expand False\n",
            "157 block5c_se_excite False\n",
            "158 block5c_project_conv False\n",
            "159 block5c_project_bn False\n",
            "160 block5c_drop False\n",
            "161 block5c_add False\n",
            "162 block6a_expand_conv False\n",
            "163 block6a_expand_bn False\n",
            "164 block6a_expand_activation False\n",
            "165 block6a_dwconv_pad False\n",
            "166 block6a_dwconv False\n",
            "167 block6a_bn False\n",
            "168 block6a_activation False\n",
            "169 block6a_se_squeeze False\n",
            "170 block6a_se_reshape False\n",
            "171 block6a_se_reduce False\n",
            "172 block6a_se_expand False\n",
            "173 block6a_se_excite False\n",
            "174 block6a_project_conv False\n",
            "175 block6a_project_bn False\n",
            "176 block6b_expand_conv False\n",
            "177 block6b_expand_bn False\n",
            "178 block6b_expand_activation False\n",
            "179 block6b_dwconv False\n",
            "180 block6b_bn False\n",
            "181 block6b_activation False\n",
            "182 block6b_se_squeeze False\n",
            "183 block6b_se_reshape False\n",
            "184 block6b_se_reduce False\n",
            "185 block6b_se_expand False\n",
            "186 block6b_se_excite False\n",
            "187 block6b_project_conv False\n",
            "188 block6b_project_bn False\n",
            "189 block6b_drop False\n",
            "190 block6b_add False\n",
            "191 block6c_expand_conv False\n",
            "192 block6c_expand_bn False\n",
            "193 block6c_expand_activation False\n",
            "194 block6c_dwconv False\n",
            "195 block6c_bn False\n",
            "196 block6c_activation False\n",
            "197 block6c_se_squeeze False\n",
            "198 block6c_se_reshape False\n",
            "199 block6c_se_reduce False\n",
            "200 block6c_se_expand False\n",
            "201 block6c_se_excite False\n",
            "202 block6c_project_conv False\n",
            "203 block6c_project_bn False\n",
            "204 block6c_drop False\n",
            "205 block6c_add False\n",
            "206 block6d_expand_conv False\n",
            "207 block6d_expand_bn False\n",
            "208 block6d_expand_activation False\n",
            "209 block6d_dwconv False\n",
            "210 block6d_bn False\n",
            "211 block6d_activation False\n",
            "212 block6d_se_squeeze False\n",
            "213 block6d_se_reshape False\n",
            "214 block6d_se_reduce False\n",
            "215 block6d_se_expand False\n",
            "216 block6d_se_excite False\n",
            "217 block6d_project_conv False\n",
            "218 block6d_project_bn False\n",
            "219 block6d_drop False\n",
            "220 block6d_add False\n",
            "221 block7a_expand_conv False\n",
            "222 block7a_expand_bn False\n",
            "223 block7a_expand_activation False\n",
            "224 block7a_dwconv False\n",
            "225 block7a_bn False\n",
            "226 block7a_activation False\n",
            "227 block7a_se_squeeze True\n",
            "228 block7a_se_reshape True\n",
            "229 block7a_se_reduce True\n",
            "230 block7a_se_expand True\n",
            "231 block7a_se_excite True\n",
            "232 block7a_project_conv True\n",
            "233 block7a_project_bn True\n",
            "234 top_conv True\n",
            "235 top_bn True\n",
            "236 top_activation True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile\n",
        "model_2.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "uXxk772YOu6z"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "rqFeELfQAyVX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28786a7f-3cda-4d45-aa29-cd3325eeced6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: transfer_learning/fine_tuned_10_classes/20220908-212721\n",
            "Epoch 5/10\n",
            "235/235 [==============================] - 50s 204ms/step - loss: 0.5998 - accuracy: 0.8045 - val_loss: 0.5028 - val_accuracy: 0.8240\n",
            "Epoch 6/10\n",
            "235/235 [==============================] - 48s 201ms/step - loss: 0.5326 - accuracy: 0.8288 - val_loss: 0.4164 - val_accuracy: 0.8635\n",
            "Epoch 7/10\n",
            "235/235 [==============================] - 43s 183ms/step - loss: 0.4871 - accuracy: 0.8453 - val_loss: 0.3861 - val_accuracy: 0.8766\n",
            "Epoch 8/10\n",
            "235/235 [==============================] - 35s 148ms/step - loss: 0.4512 - accuracy: 0.8539 - val_loss: 0.4299 - val_accuracy: 0.8553\n",
            "Epoch 9/10\n",
            "235/235 [==============================] - 38s 160ms/step - loss: 0.4232 - accuracy: 0.8648 - val_loss: 0.4176 - val_accuracy: 0.8503\n",
            "Epoch 10/10\n",
            "235/235 [==============================] - 37s 155ms/step - loss: 0.3935 - accuracy: 0.8739 - val_loss: 0.4083 - val_accuracy: 0.8651\n"
          ]
        }
      ],
      "source": [
        "fine_tune_epochs = initial_epochs + 5\n",
        "\n",
        "history_fine_10_classes_full = model_2.fit(train_data_10_classes_full,\n",
        "                                           epochs=fine_tune_epochs,\n",
        "                                           validation_data=test_data,\n",
        "                                           validation_steps=int(0.25 * len(test_data)),\n",
        "                                           initial_epoch=history_10_percet_data_aug.epoch[-1],\n",
        "                                           callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                                                  experiment_name=\"fine_tuned_10_classes\")])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_fine_tune_full_data = model_2.evaluate(test_data)\n",
        "results_fine_tune_full_data"
      ],
      "metadata": {
        "id": "JWAfA6qfOtMc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7b4df32-da9d-4073-a5e2-c236402fe599"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 12s 141ms/step - loss: 0.4062 - accuracy: 0.8696\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4062058627605438, 0.8695999979972839]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "Vr5S4ZhEAyaA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "9f4092a3-abc2-4204-d90f-9264deb88356"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "compare_histories(history_10_percet_data_aug,\n",
        "                  history_fine_10_classes_full,\n",
        "                  initial_epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Viewing our experiment data on Tensorboard"
      ],
      "metadata": {
        "id": "xN8hv2YxAyep"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Il56w6b3Ayka"
      },
      "outputs": [],
      "source": [
        "!tensorboard dev upload --logdir ./transfer_learning \\\n",
        "  --name \"Transfer Learning experiments with 10 food101 classes\" \\\n",
        "  --description \"A series of different transfer learning experiments with varying amount of data\"\\\n",
        "  --one_shot # exits the uploader"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "My tensorboard experiments are available at https://tensorboard.dev/experiment/TVsLTnnuQ1OE58gyU5P9mw/"
      ],
      "metadata": {
        "id": "h7nXRwlHAypN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "iKWbZfpIAyuK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce92e81f-dbc5-4842-d98f-58eff601c237"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://tensorboard.dev/experiment/TVsLTnnuQ1OE58gyU5P9mw/\n",
            "\tName                 Transfer Learning experiments with 10 food101 classes\n",
            "\tDescription          A series of different transfer learning experiments with varying amount of data\n",
            "\tId                   TVsLTnnuQ1OE58gyU5P9mw\n",
            "\tCreated              2022-09-08 21:41:40 (7 minutes ago)\n",
            "\tUpdated              2022-09-08 21:41:45 (7 minutes ago)\n",
            "\tRuns                 10\n",
            "\tTags                 5\n",
            "\tScalars              138\n",
            "\tTensor bytes         0\n",
            "\tBinary object bytes  4323660\n",
            "https://tensorboard.dev/experiment/FWgOPYwYT96QBHONzC92sw/\n",
            "\tName                 EfficientNetB0 vs. ResNet50V2\n",
            "\tDescription          Comparing two different tf hub feature extraction model architechtures using 10% of the training data\n",
            "\tId                   FWgOPYwYT96QBHONzC92sw\n",
            "\tCreated              2022-09-07 00:46:37\n",
            "\tUpdated              2022-09-07 00:46:46\n",
            "\tRuns                 10\n",
            "\tTags                 5\n",
            "\tScalars              120\n",
            "\tTensor bytes         0\n",
            "\tBinary object bytes  12681087\n",
            "Total: 2 experiment(s)\n"
          ]
        }
      ],
      "source": [
        "!tensorboard dev list"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W10EIiMjFCIr"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}