GreenFinder_DL-Model_CODE.ipynb

{
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  "metadata": {
    "colab": {
      "name": "GreenFinder_tf_lite.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "v77rlkCKW0IJ"
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      "toc_visible": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hRTa3Ee15WsJ"
      },
      "source": [
        "# **Development of Image Classification Model for (Fruits, Vegetables and Flowers) using Transfer Learning**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dQHMcypT3vDT"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/examples/blob/master/community/en/flowers_tf_lite.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/examples/blob/master/community/en/flowers_tf_lite.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iBMcobPHdD8O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "47337442-5ba6-4e87-f187-f438a37b95f1"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "assert tf.__version__.startswith('2')\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.2.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G97M9jHQ7zwL",
        "colab_type": "text"
      },
      "source": [
        "## **Setting Up Input Connection To Kaggle**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NOG3l_MsBO1A",
        "colab": {}
      },
      "source": [
        "#Install the Kaggle package if it hasn't been installed on your colab before.\n",
        "#!pip install kaggle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wESlgph8Spv",
        "colab_type": "text"
      },
      "source": [
        "**Import API file token**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRMJwZLgveff",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "84f5c1b9-7350-405a-d7cf-f823020c2067"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9a34c31a-17c5-407a-b591-2dc049119e67\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9a34c31a-17c5-407a-b591-2dc049119e67\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"tobiek\",\"key\":\"1fa0910bd02b226ef909b961620fa04a\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mB_4vxYgvf4U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "j4QOy2uA3P_p"
      },
      "source": [
        "**Insert kaggle API command for green finder dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6ljlqy6CjnU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "3e486fef-d20d-41f9-b3af-6319aa71b10b"
      },
      "source": [
        "!kaggle datasets download -d tobiek/green-finder"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading green-finder.zip to /content\n",
            "100% 617M/618M [00:06<00:00, 94.5MB/s]\n",
            "100% 618M/618M [00:06<00:00, 98.6MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2ltoKHsC-Ie",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d59ba1b9-584d-431d-d5e8-8fca8a19c0b9"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "green-finder.zip  kaggle.json  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApIeGN0ek08N",
        "colab_type": "text"
      },
      "source": [
        "**Unzip the kaggle file**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGTCZJB4C_DD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile('green-finder.zip', 'r')\n",
        "zip_ref.extractall('files')\n",
        "zip_ref.close()\n",
        "\n",
        "# Alternative UnZip Method\n",
        "\n",
        "#!unzip \\*.zip && rm *.zip "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "v77rlkCKW0IJ"
      },
      "source": [
        "## **Setup Input Pipeline**\n",
        "Delete the .txt files from Colab Files before running this code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsoXylQB_XOi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1f3649fa-a6ea-4d03-f4d5-80b2575d6773"
      },
      "source": [
        "PATH = \"/content/files/Fruit_Flower_Veg\"\n",
        "\n",
        "import shutil\n",
        "shutil.rmtree('/content/files/Fruit_Flower_Veg/.ipynb_checkpoints', ignore_errors=True)\n",
        "\n",
        "dirs = os.listdir(PATH)\n",
        "for file in dirs:\n",
        "  print (file)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tomato Cherry Maroon\n",
            "Peach Flat\n",
            "Redcurrant\n",
            "Potato Red\n",
            "Grapefruit Pink\n",
            "Pepper Red\n",
            "Quince\n",
            "Pepino\n",
            "Kiwi\n",
            "Raspberry\n",
            "Pepper Yellow\n",
            "Grapefruit White\n",
            "Pear Red\n",
            "Roses\n",
            "Apple Red\n",
            "Pear Kaiser\n",
            "Fig\n",
            "Beetroot\n",
            "Star fruit or Carambola\n",
            "Cherry Black\n",
            "Papaya\n",
            "Potato Sweet\n",
            "Daisy\n",
            "Cucumber Ripe\n",
            "Onion White\n",
            "Cantaloupe\n",
            "Dandelion\n",
            "Tomato Green Unripe\n",
            "Granadilla\n",
            "Tamarillo\n",
            "Grape White\n",
            "Pecan Nut\n",
            "Huckleberry\n",
            "Pear Forelle\n",
            "Hazelnut\n",
            "Physalis or Gooseberry with Husk\n",
            "Onion Red\n",
            "Pomelo Sweetie\n",
            "Rambutan\n",
            "Avocado\n",
            "Mango Red\n",
            "Nectarine\n",
            "Walnut\n",
            "Dates\n",
            "Orange\n",
            "Lemon Meyer\n",
            "Ginger Root\n",
            "Apple Red Delicious\n",
            "Apple Golden\n",
            "Apple Golden Rotten\n",
            "Cauliflower\n",
            "Lemon\n",
            "Apple Granny Smith\n",
            "Maracuja\n",
            "Plum\n",
            "Pear Stone\n",
            "Tomato Cherry Yellow\n",
            "Pepper Green\n",
            "Kumquats\n",
            "Pomegranate\n",
            "Salak\n",
            "Pear Williams\n",
            "Grape Pink\n",
            "Mango\n",
            "Pear\n",
            "Kaki\n",
            "Banana Lady Finger\n",
            "Mandarine\n",
            "Pear Abate\n",
            "Passion Fruit\n",
            "Cactus fruit\n",
            "Apple Crimson Snow\n",
            "Strawberry\n",
            "Eggplant\n",
            "Grape Blue\n",
            "Sunflowers\n",
            "Pepper Orange\n",
            "Tomato Beefsteak\n",
            "Pineapple Mini\n",
            "Cherry Yellow\n",
            "Blueberry\n",
            "Banana\n",
            "Guava\n",
            "Tulips\n",
            "Corn or Maize\n",
            "Pitahaya Red\n",
            "Limes\n",
            "Avocado ripe\n",
            "Melon or Piel de Sapo\n",
            "Tangelo\n",
            "Pineapple\n",
            "Corn or Maize Husk\n",
            "Tomato Plum or Roma\n",
            "Kohlrabi\n",
            "Coconut\n",
            "Clementine\n",
            "Banana Red\n",
            "Apple Braeburn\n",
            "Physalis or Gooseberry\n",
            "Apricot\n",
            "Grape Green\n",
            "Lychee\n",
            "Potato White\n",
            "Mangostan\n",
            "Mulberry\n",
            "Tomato Campari\n",
            "Apple Pink Lady\n",
            "Tomato Brandywine\n",
            "Forest Nut\n",
            "Apple Red Yellow\n",
            "Chestnut\n",
            "Tomato Cherry Red\n",
            "Cherry Red\n",
            "Peach\n",
            "Watermelon\n",
            "Cherry Rainier\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLKpGWqNApf_",
        "colab_type": "text"
      },
      "source": [
        "**Dataset Summary**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bp98nWfbGbvI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0d4beaa9-f82a-44eb-f921-a159c963a376"
      },
      "source": [
        "from pathlib import Path\n",
        "\n",
        "ds = len(dirs)\n",
        "data_dir = Path(PATH)\n",
        "image_count = len(list(data_dir.glob('*/*.jpg')))\n",
        "print(\"There are a total of \" + str(image_count) +\n",
        "      \" Images and \" + str(ds) + \" Classes in this dataset.\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are a total of 94045 Images and 116 Classes in this dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "z4gTv7ig2vMh"
      },
      "source": [
        "# **Preparing the data**\n",
        "\n",
        "Use **`ImageDataGenerator`** to rescale the images.\n",
        "\n",
        "*   Create the train generator and specify where the train dataset directory, image size, batch size.\n",
        "*   Create the validation generator with similar approach as the train generator with the flow_from_directory() method.\n",
        "\n",
        "**PLEASE NOTE:**\n",
        "*   Image Size for **MobileNetV2** = (224, 224, 3)\n",
        "*   Image Size for **InceptionV3** = (299, 299, 3)\n",
        "*   Image Size for **VGG19** = (224, 224, 3)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aCLb_yV5JfF3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "b03522b2-f361-4ab0-fb1a-b82d05ee74ab"
      },
      "source": [
        "IMAGE_SIZE = 224\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale=1./255, \n",
        "    validation_split=0.2)\n",
        "\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    PATH,\n",
        "    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    batch_size=BATCH_SIZE,\n",
        "    seed=120,\n",
        "    subset='training')\n",
        "\n",
        "val_generator = datagen.flow_from_directory(\n",
        "    PATH,\n",
        "    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    batch_size=BATCH_SIZE, \n",
        "    seed=120,\n",
        "    subset='validation')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 75274 images belonging to 116 classes.\n",
            "Found 18771 images belonging to 116 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tx1L7fxxWA_G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8d6ce077-b226-4101-ad9d-f980b038b818"
      },
      "source": [
        "for image_batch, label_batch in train_generator:\n",
        "  break\n",
        "image_batch.shape, label_batch.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((32, 224, 224, 3), (32, 116))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZrFFcwUb3iK9"
      },
      "source": [
        "**Save the labels in a file which will be downloaded later.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-QFZIhWs4dsq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "5c53a0ca-6904-46a6-c5b0-90c793458049"
      },
      "source": [
        "print (train_generator.class_indices)\n",
        "\n",
        "labels = '\\n'.join(sorted(train_generator.class_indices.keys()))\n",
        "\n",
        "with open('labels.txt', 'w') as f:\n",
        "  f.write(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Apple Braeburn': 0, 'Apple Crimson Snow': 1, 'Apple Golden': 2, 'Apple Golden Rotten': 3, 'Apple Granny Smith': 4, 'Apple Pink Lady': 5, 'Apple Red': 6, 'Apple Red Delicious': 7, 'Apple Red Yellow': 8, 'Apricot': 9, 'Avocado': 10, 'Avocado ripe': 11, 'Banana': 12, 'Banana Lady Finger': 13, 'Banana Red': 14, 'Beetroot': 15, 'Blueberry': 16, 'Cactus fruit': 17, 'Cantaloupe': 18, 'Cauliflower': 19, 'Cherry Black': 20, 'Cherry Rainier': 21, 'Cherry Red': 22, 'Cherry Yellow': 23, 'Chestnut': 24, 'Clementine': 25, 'Coconut': 26, 'Corn or Maize': 27, 'Corn or Maize Husk': 28, 'Cucumber Ripe': 29, 'Daisy': 30, 'Dandelion': 31, 'Dates': 32, 'Eggplant': 33, 'Fig': 34, 'Forest Nut': 35, 'Ginger Root': 36, 'Granadilla': 37, 'Grape Blue': 38, 'Grape Green': 39, 'Grape Pink': 40, 'Grape White': 41, 'Grapefruit Pink': 42, 'Grapefruit White': 43, 'Guava': 44, 'Hazelnut': 45, 'Huckleberry': 46, 'Kaki': 47, 'Kiwi': 48, 'Kohlrabi': 49, 'Kumquats': 50, 'Lemon': 51, 'Lemon Meyer': 52, 'Limes': 53, 'Lychee': 54, 'Mandarine': 55, 'Mango': 56, 'Mango Red': 57, 'Mangostan': 58, 'Maracuja': 59, 'Melon or Piel de Sapo': 60, 'Mulberry': 61, 'Nectarine': 62, 'Onion Red': 63, 'Onion White': 64, 'Orange': 65, 'Papaya': 66, 'Passion Fruit': 67, 'Peach': 68, 'Peach Flat': 69, 'Pear': 70, 'Pear Abate': 71, 'Pear Forelle': 72, 'Pear Kaiser': 73, 'Pear Red': 74, 'Pear Stone': 75, 'Pear Williams': 76, 'Pecan Nut': 77, 'Pepino': 78, 'Pepper Green': 79, 'Pepper Orange': 80, 'Pepper Red': 81, 'Pepper Yellow': 82, 'Physalis or Gooseberry': 83, 'Physalis or Gooseberry with Husk': 84, 'Pineapple': 85, 'Pineapple Mini': 86, 'Pitahaya Red': 87, 'Plum': 88, 'Pomegranate': 89, 'Pomelo Sweetie': 90, 'Potato Red': 91, 'Potato Sweet': 92, 'Potato White': 93, 'Quince': 94, 'Rambutan': 95, 'Raspberry': 96, 'Redcurrant': 97, 'Roses': 98, 'Salak': 99, 'Star fruit or Carambola': 100, 'Strawberry': 101, 'Sunflowers': 102, 'Tamarillo': 103, 'Tangelo': 104, 'Tomato Beefsteak': 105, 'Tomato Brandywine': 106, 'Tomato Campari': 107, 'Tomato Cherry Maroon': 108, 'Tomato Cherry Red': 109, 'Tomato Cherry Yellow': 110, 'Tomato Green Unripe': 111, 'Tomato Plum or Roma': 112, 'Tulips': 113, 'Walnut': 114, 'Watermelon': 115}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "duxD_UDSOmng",
        "colab": {}
      },
      "source": [
        "!cat labels.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OkH-kazQecHB"
      },
      "source": [
        "## **Create the base model from the pre-trained convnets**\n",
        "\n",
        "Create the base model from either **MobileNet V2, Inception V3 or VGG19** model developed at Google, and pre-trained on the ImageNet dataset.\n",
        "\n",
        "First, pick which intermediate layer of the model will be used for feature extraction. A common practice is to use the output of the very last layer before the flatten operation, the so-called \"bottleneck layer\". The reasoning here is that the following fully-connected layers will be too specialized to the task the network was trained on, and thus the features learned by these layers won't be very useful for a new task. The bottleneck features, however, retain much generality.\n",
        "\n",
        "Let's instantiate an MobileNet V2 model pre-loaded with weights trained on ImageNet. By specifying the `include_top=False` argument, we load a network that doesn't include the classification layers at the top, which is ideal for feature extraction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsB2hWSYaFGc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "0638fc09-faaf-4851-e3d0-4dae8e113bb7"
      },
      "source": [
        "IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)\n",
        "\n",
        "# Create the base model from the pre-trained model of your choice\n",
        "\n",
        "MobileNetV2_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                              include_top=False, \n",
        "                                              weights='imagenet')\n",
        "InceptionV3_model = tf.keras.applications.InceptionV3(input_shape=IMG_SHAPE,\n",
        "                                              include_top=False, \n",
        "                                              weights='imagenet')\n",
        "VGG19_model = tf.keras.applications.VGG19(input_shape=IMG_SHAPE,\n",
        "                                              include_top=False, \n",
        "                                              weights='imagenet')\n",
        "MODEL_SELECTION = (MobileNetV2_model) #@param [\"(MobileNetV2_model)\", \"(InceptionV3_model)\", \"(VGG19_model)\"] {type:\"raw\", allow-input: true}\n",
        "\n",
        "base_model = MODEL_SELECTION\n",
        "print(\"\\n Your selected Model has an input size of {} \".format(IMG_SHAPE))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80142336/80134624 [==============================] - 1s 0us/step\n",
            "\n",
            " Your selected Model has an input size of (224, 224, 3) \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rlx56nQtfe8Y"
      },
      "source": [
        "## **Feature extraction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2zu78cK3aSl",
        "colab_type": "text"
      },
      "source": [
        "Freeze the convolutional base created from the previous step and use that as a feature extractor, add a classifier on top of it and train the top-level classifier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Tts8BbAtRGvk",
        "colab": {}
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wdMRM8YModbk"
      },
      "source": [
        "**Add a classification head**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eApvroIyn1K0",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "  base_model,\n",
        "  tf.keras.layers.Conv2D(32, 3, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.2),\n",
        "  tf.keras.layers.GlobalAveragePooling2D(),\n",
        "  tf.keras.layers.Dense(116, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "g0ylJXE_kRLi"
      },
      "source": [
        "**Compile the model**\n",
        "\n",
        "You must compile the model before training it.  Since there are multiple classes, use a categorical cross-entropy loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RpR8HdyMhukJ",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(), \n",
        "              loss='categorical_crossentropy', \n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "I8ARiyMFsgbH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "41f166a5-1885-4f6e-92df-98eb9e8fdb2f"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_224 (Model) (None, 7, 7, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "conv2d_94 (Conv2D)           (None, 5, 5, 32)          368672    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 116)               3828      \n",
            "=================================================================\n",
            "Total params: 2,630,484\n",
            "Trainable params: 372,500\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "krvBumovycVA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "06279e37-4ebc-42d1-de7d-36b7e50c0384"
      },
      "source": [
        "print('Number of trainable variables = {}'.format(len(model.trainable_variables)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of trainable variables = 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RxvgOYTDSWTx"
      },
      "source": [
        "**Train the model**\n",
        "\n",
        "<!-- TODO(markdaoust): delete steps_per_epoch in TensorFlow r1.14/r2.0 -->"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JsaRFlZ9B6WK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "0510b8d9-3d1a-4050-f6f6-167c9f163298"
      },
      "source": [
        "epochs = 10\n",
        "\n",
        "history = model.fit(train_generator, \n",
        "                    steps_per_epoch=len(train_generator), \n",
        "                    epochs=epochs, \n",
        "                    validation_data=val_generator, \n",
        "                    validation_steps=len(val_generator))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2353/2353 [==============================] - 377s 160ms/step - loss: 1.3629 - accuracy: 0.6351 - val_loss: 0.5307 - val_accuracy: 0.8387\n",
            "Epoch 2/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.3537 - accuracy: 0.8912 - val_loss: 0.4199 - val_accuracy: 0.8738\n",
            "Epoch 3/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.2195 - accuracy: 0.9320 - val_loss: 0.4031 - val_accuracy: 0.8789\n",
            "Epoch 4/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.1626 - accuracy: 0.9487 - val_loss: 0.4485 - val_accuracy: 0.8759\n",
            "Epoch 5/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.1334 - accuracy: 0.9580 - val_loss: 0.4661 - val_accuracy: 0.8765\n",
            "Epoch 6/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.1088 - accuracy: 0.9650 - val_loss: 0.3739 - val_accuracy: 0.9037\n",
            "Epoch 7/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.0980 - accuracy: 0.9684 - val_loss: 0.4060 - val_accuracy: 0.8965\n",
            "Epoch 8/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.0854 - accuracy: 0.9728 - val_loss: 0.4173 - val_accuracy: 0.9031\n",
            "Epoch 9/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.0778 - accuracy: 0.9748 - val_loss: 0.3522 - val_accuracy: 0.9160\n",
            "Epoch 10/10\n",
            "2353/2353 [==============================] - 374s 159ms/step - loss: 0.0721 - accuracy: 0.9754 - val_loss: 0.4213 - val_accuracy: 0.8987\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Hd94CKImf8vi"
      },
      "source": [
        "**Plot learning curves of the training and validation accuracy/loss**\n",
        "\n",
        "Let's take a look at the learning curves of the training and validation accuracy/loss when using the base model as a fixed feature extractor. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "53OTCh3jnbwV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "f61e4ee2-9bb7-4e74-9a57-4e687397a552"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,1.0])\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CqwV-CRdS6Nv"
      },
      "source": [
        "## **Fine tuning**\n",
        "In our feature extraction experiment, you were only training a few layers on top of the base model. The weights of the pre-trained network were **not** updated during training.\n",
        "\n",
        "One way to increase performance even further is to train (or \"fine-tune\") the weights of the top layers of the pre-trained model alongside the training of the classifier you added. The training process will force the weights to be tuned from generic features maps to features associated specifically to our dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CPXnzUK0QonF"
      },
      "source": [
        "### **Un-freeze the top layers of the model**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rfxv_ifotQak"
      },
      "source": [
        "All you need to do is unfreeze the `base_model` and set the bottom layers be un-trainable. Then, recompile the model (necessary for these changes to take effect), and resume training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4nzcagVitLQm",
        "colab": {}
      },
      "source": [
        "base_model.trainable = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-4HgVAacRs5v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "33ebe514-fbbb-4d62-f22e-d456104ff5a0"
      },
      "source": [
        "# Let's take a look to see how many layers are in the base model\n",
        "print(\"Number of layers in the base model: \", len(base_model.layers))\n",
        "\n",
        "# Fine tune from this layer onwards\n",
        "fine_tune_at = 100\n",
        "\n",
        "# Freeze all the layers before the `fine_tune_at` layer\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable =  False"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of layers in the base model:  155\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4Uk1dgsxT0IS"
      },
      "source": [
        "### **Compile the model**\n",
        "\n",
        "Compile the model using a much lower training rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NtUnaz0WUDva",
        "colab": {}
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer = tf.keras.optimizers.Adam(1e-5),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WwBWy7J2kZvA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "c485c2dd-1acb-456f-896c-acc5671fbbde"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_224 (Model) (None, 7, 7, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "conv2d_94 (Conv2D)           (None, 5, 5, 32)          368672    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 116)               3828      \n",
            "=================================================================\n",
            "Total params: 2,630,484\n",
            "Trainable params: 2,235,092\n",
            "Non-trainable params: 395,392\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bNXelbMQtonr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c941dd55-d3ed-48c7-b95c-3e308b0612b2"
      },
      "source": [
        "print('Number of trainable variables = {}'.format(len(model.trainable_variables)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of trainable variables = 60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4G5O4jd6TuAG"
      },
      "source": [
        "### **Train the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ECQLkAsFTlun",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "2d55ff81-9b6c-49a7-d9a7-d74fc17d1045"
      },
      "source": [
        "history_fine = model.fit(train_generator, \n",
        "                         steps_per_epoch=len(train_generator), \n",
        "                         epochs=10, \n",
        "                         validation_data=val_generator, \n",
        "                         validation_steps=len(val_generator))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2353/2353 [==============================] - 724s 308ms/step - loss: 0.7926 - accuracy: 0.8428 - val_loss: 0.2160 - val_accuracy: 0.9457\n",
            "Epoch 2/10\n",
            "2353/2353 [==============================] - 722s 307ms/step - loss: 0.0679 - accuracy: 0.9788 - val_loss: 0.1543 - val_accuracy: 0.9629\n",
            "Epoch 3/10\n",
            "2353/2353 [==============================] - 722s 307ms/step - loss: 0.0333 - accuracy: 0.9895 - val_loss: 0.1441 - val_accuracy: 0.9697\n",
            "Epoch 4/10\n",
            "2353/2353 [==============================] - 722s 307ms/step - loss: 0.0217 - accuracy: 0.9933 - val_loss: 0.1112 - val_accuracy: 0.9761\n",
            "Epoch 5/10\n",
            "2353/2353 [==============================] - 722s 307ms/step - loss: 0.0178 - accuracy: 0.9945 - val_loss: 0.1046 - val_accuracy: 0.9761\n",
            "Epoch 6/10\n",
            "2353/2353 [==============================] - 722s 307ms/step - loss: 0.0131 - accuracy: 0.9956 - val_loss: 0.0965 - val_accuracy: 0.9793\n",
            "Epoch 7/10\n",
            "2353/2353 [==============================] - 721s 307ms/step - loss: 0.0097 - accuracy: 0.9972 - val_loss: 0.0923 - val_accuracy: 0.9792\n",
            "Epoch 8/10\n",
            "2353/2353 [==============================] - 723s 307ms/step - loss: 0.0121 - accuracy: 0.9962 - val_loss: 0.0982 - val_accuracy: 0.9809\n",
            "Epoch 9/10\n",
            "2353/2353 [==============================] - 722s 307ms/step - loss: 0.0085 - accuracy: 0.9975 - val_loss: 0.0712 - val_accuracy: 0.9832\n",
            "Epoch 10/10\n",
            "2353/2353 [==============================] - 722s 307ms/step - loss: 0.0063 - accuracy: 0.9980 - val_loss: 0.0385 - val_accuracy: 0.9916\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TfXEmsxQf6eP"
      },
      "source": [
        "**Plot learning curves of the training and validation accuracy/loss**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "chW103JUItdk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "ddffa028-b0b2-4542-cd53-a432a159624b"
      },
      "source": [
        "acc = history_fine.history['accuracy']\n",
        "val_acc = history_fine.history['val_accuracy']\n",
        "\n",
        "loss = history_fine.history['loss']\n",
        "val_loss = history_fine.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,1.0])\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kRDabW_u1wnv"
      },
      "source": [
        "## **Convert to TFLite**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hNvMl6CM6lG4"
      },
      "source": [
        "Saved the model using **`tf.saved_model.save`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_LZiKVInWNGy",
        "colab": {}
      },
      "source": [
        "saved_model_dir = 'save/fine_tuning'\n",
        "tf.saved_model.save(model, saved_model_dir)\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PuoKK0JGvIyX",
        "colab_type": "text"
      },
      "source": [
        "**Post-Training Quantization**\n",
        "\n",
        "This technique is enabled as an option in the TensorFlow Lite converter to reduce the precision of the numbers in weight and biases in the model.\n",
        "\n",
        "The simplest form of post-training quantization quantizes weights from floating point to 8-bits of precision. At inference, weights are converted from 8-bits of precision to floating point and computed using floating-point kernels. This conversion is done once and cached to reduce latency.\n",
        "\n",
        "To further improve latency, hybrid operators dynamically quantize activations to 8-bits and perform computations with 8-bit weights and activations. This optimization provides latencies close to fully fixed-point inference. However, the outputs are still stored using floating point, so that the speedup with hybrid ops is less than a full fixed-point computation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-MHkANQqKRS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1RjcPuCayJJD",
        "colab_type": "text"
      },
      "source": [
        "**Convert the saved model to a TF lite compatible format**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QarV5r-KqKiZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tflite_model = converter.convert()\n",
        "\n",
        "with open('model.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GE4w-9S410Dk"
      },
      "source": [
        "**Download the converted model and labels**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "x47uW_lI1DoV",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('model.tflite')\n",
        "files.download('labels.txt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_TZTwG7nhm0C"
      },
      "source": [
        "## Summary:\n",
        "\n",
        "* **Using a pre-trained model for feature extraction**:  When working with a small dataset, it is common to take advantage of features learned by a model trained on a larger dataset in the same domain. This is done by instantiating the pre-trained model and adding a fully-connected classifier on top. The pre-trained model is \"frozen\" and only the weights of the classifier get updated during training.\n",
        "In this case, the convolutional base extracted all the features associated with each image and you just trained a classifier that determines the image class given that set of extracted features.\n",
        "\n",
        "* **Fine-tuning a pre-trained model**: To further improve performance, one might want to repurpose the top-level layers of the pre-trained models to the new dataset via fine-tuning.\n",
        "In this case, you tuned your weights such that your model learned high-level features specific to the dataset. This technique is usually recommended when the training dataset is large and very similar to the orginial dataset that the pre-trained model was trained on.\n"
      ]
    }
  ]
}