cancer-detect.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ECE228_Project.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vaibhavtiwari33/cancer-detect/blob/main/cancer-detect.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqdH2YBj6v7j"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZX8JhWl3iwHG"
      },
      "source": [
        "from fastai.vision import *\n",
        "from fastai.metrics import error_rate\n",
        "\n",
        "# Import Libraries here\n",
        "import os\n",
        "import json \n",
        "import shutil\n",
        "import zipfile\n",
        "\n",
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wUC1Mtc4_bJ"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import *\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IM8DlN_WG5Xv",
        "outputId": "dae2c9f7-9167-4598-f529-9e42ae2f2b2f"
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-tiUFZY4f-K"
      },
      "source": [
        "# Load Kaggle Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "monfBu9VkRiX",
        "outputId": "c3fa40e9-e1c1-4ac2-f053-9cef0843da88"
      },
      "source": [
        "!pip install --upgrade --force-reinstall --no-deps kaggle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kaggle\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3a/e7/3bac01547d2ed3d308ac92a0878fbdb0ed0f3d41fb1906c319ccbba1bfbc/kaggle-1.5.12.tar.gz (58kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 2.4MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: kaggle\n",
            "  Building wheel for kaggle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaggle: filename=kaggle-1.5.12-cp37-none-any.whl size=73053 sha256=aabb8654eae694450fdd46a36555e01fcf9db8979f81c9a189f162f09ea7fc68\n",
            "  Stored in directory: /root/.cache/pip/wheels/a1/6a/26/d30b7499ff85a4a4593377a87ecf55f7d08af42f0de9b60303\n",
            "Successfully built kaggle\n",
            "Installing collected packages: kaggle\n",
            "  Found existing installation: kaggle 1.5.12\n",
            "    Uninstalling kaggle-1.5.12:\n",
            "      Successfully uninstalled kaggle-1.5.12\n",
            "Successfully installed kaggle-1.5.12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRz9kX3oBOfU",
        "outputId": "e7497039-8253-4044-8373-bde486ea11f8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q_ptAtQji3sg",
        "outputId": "95d5eddd-197c-4afb-da0c-3f59d0ba0bf7"
      },
      "source": [
        "#base_dir = 'drive/MyDrive/Documents/Project/ECE228'\n",
        "base_dir = 'drive/MyDrive/UCSD/ECE228'\n",
        "Path(base_dir).ls()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PosixPath('drive/MyDrive/UCSD/ECE228/histopathologic-cancer-detection.zip'),\n",
              " PosixPath('drive/MyDrive/UCSD/ECE228/mobileNetV2_model_dataGen.h5'),\n",
              " PosixPath('drive/MyDrive/UCSD/ECE228/mobileNetV2_model_dataGen_tuned.h5')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rU5UT8UCi8J_",
        "outputId": "241f4bd2-7f89-4ac1-d1c5-7050ad06bd9b"
      },
      "source": [
        "#!kaggle competitions download -c histopathologic-cancer-detection -p \"{base_dir}\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading histopathologic-cancer-detection.zip to drive/MyDrive/Documents/Project/ECE228\n",
            "100% 6.31G/6.31G [01:33<00:00, 52.1MB/s]\n",
            "100% 6.31G/6.31G [01:36<00:00, 70.5MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsNPBcLnF5-E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea45c099-5574-49ef-94be-5082509b2842"
      },
      "source": [
        "import subprocess\n",
        "subprocess.call(['unzip', '{}/histopathologic-cancer-detection.zip'.format(base_dir)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-dIJgwG4jjR"
      },
      "source": [
        "# Transfer data for flow_from_directory call"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHFiyngLj3Nb"
      },
      "source": [
        "from shutil import copyfile"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iLPiOZG4kOBr"
      },
      "source": [
        "folders = ['train', 'test']\n",
        "labels = ['0', '1']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agQznsazquqm"
      },
      "source": [
        "!mv sample_submission.csv test_labels.csv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvcf0fhCDMbv"
      },
      "source": [
        "!mkdir -p \"dataset_folder/test/0\"\n",
        "!mkdir -p \"dataset_folder/test/1\"\n",
        "!mkdir -p \"dataset_folder/train/0\"\n",
        "!mkdir -p \"dataset_folder/train/1\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eS40IiauCpy2"
      },
      "source": [
        "for folder in folders:\n",
        "    df = pd.read_csv('{}_labels.csv'.format(folder))\n",
        "    for filename, label in zip(df['id'], df['label']):\n",
        "        copyfile('{}/{}.tif'.format(folder, filename), \n",
        "                 '{}/{}/{}/{}.tif'.format('dataset_folder', folder, label, filename))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6Gr5NH8xaEE"
      },
      "source": [
        "!rm -rf test\n",
        "!rm -rf train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lVmOMIhY7yY"
      },
      "source": [
        "# Reference Notebook Code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSV0j-y9Y7NT"
      },
      "source": [
        "tfms = get_transforms(do_flip=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4zYBeL1ZI3K"
      },
      "source": [
        "bs=64 # also the default batch size\n",
        "data = ImageDataBunch.from_csv('.',\n",
        "    ds_tfms=tfms, \n",
        "    size=224, \n",
        "    suffix=\".tif\",\n",
        "    folder=\"train\", \n",
        "    test=\"test\",\n",
        "    csv_labels=\"train_labels.csv\", \n",
        "    bs=bs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHvrdCDazDRr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "239a8bc8-7115-4347-82f9-b607e44670c5"
      },
      "source": [
        "data.normalize(imagenet_stats)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageDataBunch;\n",
              "\n",
              "Train: LabelList (176020 items)\n",
              "x: ImageList\n",
              "Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)\n",
              "y: CategoryList\n",
              "0,1,0,0,0\n",
              "Path: .;\n",
              "\n",
              "Valid: LabelList (44005 items)\n",
              "x: ImageList\n",
              "Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)\n",
              "y: CategoryList\n",
              "0,1,0,0,1\n",
              "Path: .;\n",
              "\n",
              "Test: LabelList (57458 items)\n",
              "x: ImageList\n",
              "Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)\n",
              "y: EmptyLabelList\n",
              ",,,,\n",
              "Path: ."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_mQ1pvgOXr8"
      },
      "source": [
        "data.one_batch()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8NYp9z0y-DD",
        "outputId": "a078da55-b886-4c32-8f92-6ff906e74c59"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageDataBunch;\n",
              "\n",
              "Train: LabelList (176020 items)\n",
              "x: ImageList\n",
              "Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)\n",
              "y: CategoryList\n",
              "0,0,0,0,0\n",
              "Path: .;\n",
              "\n",
              "Valid: LabelList (44005 items)\n",
              "x: ImageList\n",
              "Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)\n",
              "y: CategoryList\n",
              "1,0,0,1,0\n",
              "Path: .;\n",
              "\n",
              "Test: LabelList (57458 items)\n",
              "x: ImageList\n",
              "Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)\n",
              "y: EmptyLabelList\n",
              ",,,,\n",
              "Path: ."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJDVCMOoZUEU"
      },
      "source": [
        "learn = cnn_learner(data, models.resnet50, metrics=error_rate, callback_fns=ShowGraph)#.to_fp16()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wfn_rGTdZV8C"
      },
      "source": [
        "learn.summary"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNujIDqt4HBW",
        "outputId": "0c1be28f-3f82-4100-b3fc-b7749ba896d2"
      },
      "source": [
        "!pip install onnx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting onnx\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/9b/54c950d3256e27f970a83cd0504efb183a24312702deed0179453316dbd0/onnx-1.9.0-cp37-cp37m-manylinux2010_x86_64.whl (12.2MB)\n",
            "\u001b[K     |████████████████████████████████| 12.2MB 5.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnx) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx) (1.15.0)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnx) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx) (3.7.4.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnx) (56.1.0)\n",
            "Installing collected packages: onnx\n",
            "Successfully installed onnx-1.9.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kFgpEL14Tu_",
        "outputId": "e5b7d5b8-b93a-4a61-d1c2-f7d407083dfe"
      },
      "source": [
        "!pip install tensorflow-addons"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-addons\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/66/4b/e893d194e626c24b3df2253066aa418f46a432fdb68250cde14bf9bb0700/tensorflow_addons-0.13.0-cp37-cp37m-manylinux2010_x86_64.whl (679kB)\n",
            "\u001b[K     |████████████████████████████████| 686kB 6.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (2.7.1)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.13.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbZacNXS4Wz1",
        "outputId": "e28c04f9-c372-48e0-b358-ecc403d5cfb5"
      },
      "source": [
        "!pip install git+https://github.com/onnx/onnx-tensorflow.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/onnx/onnx-tensorflow.git\n",
            "  Cloning https://github.com/onnx/onnx-tensorflow.git to /tmp/pip-req-build-zbslp0t_\n",
            "  Running command git clone -q https://github.com/onnx/onnx-tensorflow.git /tmp/pip-req-build-zbslp0t_\n",
            "Requirement already satisfied (use --upgrade to upgrade): onnx-tf==1.8.0 from git+https://github.com/onnx/onnx-tensorflow.git in ./onnx-tensorflow\n",
            "Requirement already satisfied: onnx>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from onnx-tf==1.8.0) (1.9.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from onnx-tf==1.8.0) (3.13)\n",
            "Requirement already satisfied: tensorflow_addons in /usr/local/lib/python3.7/dist-packages (from onnx-tf==1.8.0) (0.13.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.8.0->onnx-tf==1.8.0) (3.7.4.3)\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.8.0->onnx-tf==1.8.0) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx>=1.8.0->onnx-tf==1.8.0) (1.15.0)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnx>=1.8.0->onnx-tf==1.8.0) (3.12.4)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons->onnx-tf==1.8.0) (2.7.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnx>=1.8.0->onnx-tf==1.8.0) (56.1.0)\n",
            "Building wheels for collected packages: onnx-tf\n",
            "  Building wheel for onnx-tf (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for onnx-tf: filename=onnx_tf-1.8.0-cp37-none-any.whl size=219689 sha256=962d0117318f694d5b1313c686fa53628529c1385e09e7468a49f75d8335b01a\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-mac_r0q4/wheels/54/24/31/8873b33d2d560efdfa1ed6f346df67ef793b1897358705a480\n",
            "Successfully built onnx-tf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yN00YIM68Wuf",
        "outputId": "0c2d35e6-8fcd-41ea-fefd-f014468f1fc6"
      },
      "source": [
        "!git clone https://github.com/onnx/onnx-tensorflow.git && cd onnx-tensorflow && pip install -e ."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'onnx-tensorflow'...\n",
            "remote: Enumerating objects: 6136, done.\u001b[K\n",
            "remote: Counting objects: 100% (85/85), done.\u001b[K\n",
            "remote: Compressing objects: 100% (66/66), done.\u001b[K\n",
            "remote: Total 6136 (delta 45), reused 33 (delta 18), pack-reused 6051\u001b[K\n",
            "Receiving objects: 100% (6136/6136), 1.87 MiB | 13.13 MiB/s, done.\n",
            "Resolving deltas: 100% (4770/4770), done.\n",
            "Obtaining file:///content/onnx-tensorflow\n",
            "Requirement already satisfied: onnx>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from onnx-tf==1.8.0) (1.9.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from onnx-tf==1.8.0) (3.13)\n",
            "Requirement already satisfied: tensorflow_addons in /usr/local/lib/python3.7/dist-packages (from onnx-tf==1.8.0) (0.13.0)\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.8.0->onnx-tf==1.8.0) (1.19.5)\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.8.0->onnx-tf==1.8.0) (3.7.4.3)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnx>=1.8.0->onnx-tf==1.8.0) (3.12.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx>=1.8.0->onnx-tf==1.8.0) (1.15.0)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons->onnx-tf==1.8.0) (2.7.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnx>=1.8.0->onnx-tf==1.8.0) (56.1.0)\n",
            "Installing collected packages: onnx-tf\n",
            "  Found existing installation: onnx-tf 1.8.0\n",
            "    Can't uninstall 'onnx-tf'. No files were found to uninstall.\n",
            "  Running setup.py develop for onnx-tf\n",
            "Successfully installed onnx-tf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZ7-P0gTAwR3",
        "outputId": "c0ad7d7b-0ed0-49c6-c004-527bf2ca7178"
      },
      "source": [
        "%cd onnx-tensorflow/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/onnx-tensorflow\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[autoreload of onnx_tf failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ModuleNotFoundError: spec not found for the module 'onnx_tf'\n",
            "]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiwOeiCT7pTi"
      },
      "source": [
        "from torch.autograd import Variable\n",
        "import onnx\n",
        "import onnx_tf\n",
        "from onnx_tf.backend import prepare"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYf-j3m15Ivk"
      },
      "source": [
        "dummy_input = Variable(torch.randn(1, 3, 224, 224))\n",
        "torch.onnx.export(learn.model, dummy_input, \"/content/dummy.onnx\", input_names=['test_input'], output_names=['test_output'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WesBNObf7YL0"
      },
      "source": [
        "tf_rep = prepare(onnx.load('/content/dummy.onnx'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fx3EnXBdBx0d",
        "outputId": "824f772b-4a3c-4a95-ee86-5642945b7656"
      },
      "source": [
        "tf_rep.export_graph('/content/dummy_model')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Found untraced functions such as gen_tensor_dict while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/dummy_model/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/dummy_model/assets\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tx6LdTTIELy8",
        "outputId": "8b32b567-7017-4440-b4cb-31611a6a525e"
      },
      "source": [
        "converted_model = tf.keras.models.load_model('/content/dummy_model')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzmYN0owEWQO",
        "outputId": "b82752bb-65f7-4415-e11c-1863235fda7a"
      },
      "source": [
        "type(converted_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensorflow.python.saved_model.load.Loader._recreate_base_user_object.<locals>._UserObject"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWJQLwBRGCzw"
      },
      "source": [
        "# Data Augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDQSFsHfTlwy"
      },
      "source": [
        "### TODO: \n",
        " * Fit generator on input sample (done with preprocessing function)\n",
        " * Apply learned params to test data (create another generator for test and apply same preprocessing function)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Di4J9q4tFvDX"
      },
      "source": [
        "new_base_dir = 'dataset_folder'\n",
        "training_sub_path = 'train'\n",
        "test_sub_path = 'test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6E4g2E6d-zaA"
      },
      "source": [
        "#IMG_SIZE = (96, 96)\n",
        "\"\"\"\n",
        "Images in the target PCam dataset are square images 96x96. \n",
        "However, when bringing a pre-trained ImageNet model into our\n",
        "network, which was trained on larger images, we need to set\n",
        "the size accordingly to respect the image sizes in that dataset.\n",
        "\"\"\"\n",
        "IMG_SIZE = (96, 96) \n",
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "BATCH_SIZE = 64"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k4QVBZuaU8V",
        "outputId": "ca9eb5f8-7fe5-4295-cf48-4726fdf1b9d7"
      },
      "source": [
        "imagenet_stats"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNly5_vIfn8T"
      },
      "source": [
        "def add_noise_rotate(img_array):\n",
        "    noise = np.random.randint(-3,4, img_array.shape)\n",
        "    noise = noise \n",
        "    img = img_array + noise\n",
        "    np.putmask(img, img < 0, 0)\n",
        "    np.putmask(img, img > 255, 255)\n",
        "    rot_angle = np.random.randint(0,4)\n",
        "    img = np.rot90(img, rot_angle)\n",
        "    return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GB0eddZfz8o"
      },
      "source": [
        "def custom_norm(x, mean=None, std=None):\n",
        "  if mean is None:\n",
        "    mean = [0.485, 0.456, 0.406]\n",
        "  if std is None:\n",
        "    std = [0.229, 0.224, 0.225]\n",
        "\n",
        "  mean, std = np.array(mean), np.array(std)\n",
        "  image = np.array(x)\n",
        "  \n",
        "  return (image - mean[None, None, ...]) / std[None, None, ...]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZagDem6M_tVy"
      },
      "source": [
        "data_generator = ImageDataGenerator(validation_split=0.2) #,rescale=1./255,preprocessing_function=custom_norm)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3g1rVniYHtJ"
      },
      "source": [
        "data_generator = ImageDataGenerator(rotation_range=90, \n",
        "                                    width_shift_range=[0.2, -0.2], \n",
        "                                    height_shift_range=[0.2, -0.2], \n",
        "                                    brightness_range=[0.3, 1], \n",
        "                                    zoom_range=[0.5, 1], \n",
        "                                    horizontal_flip=True, \n",
        "                                    vertical_flip=True, \n",
        "                                    channel_shift_range=150.0, \n",
        "                                    fill_mode='wrap', \n",
        "                                    validation_split=0.2,\n",
        "                                    rescale=1./255,\n",
        "                                    preprocessing_function=custom_norm)\n",
        "#,                                    featurewise_std_normalization=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYZHe2aRGRbE"
      },
      "source": [
        "data_generator = ImageDataGenerator(brightness_range=[0.3, 1], \n",
        "                                    horizontal_flip=True, \n",
        "                                    vertical_flip=True,\n",
        "                                    validation_split=0.2)\n",
        "#,                                    featurewise_std_normalization=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gd0F8Xg2GZzZ",
        "outputId": "9a360d83-ab53-4daa-a8be-72fd881eed20"
      },
      "source": [
        "train_data_iterator = data_generator.flow_from_directory('{}/{}'.format(new_base_dir, 'train'), \n",
        "                                                         target_size=IMG_SIZE,\n",
        "                                                         class_mode='binary', \n",
        "                                                         subset='training', \n",
        "                                                         batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 176021 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgiD0KIaGbDa",
        "outputId": "024d3174-0c78-46a4-ef0d-710e7ad568d1"
      },
      "source": [
        "validation_data_iterator = data_generator.flow_from_directory('{}/{}'.format(new_base_dir, 'train'), \n",
        "                                                              target_size=IMG_SIZE,\n",
        "                                                              class_mode='binary', \n",
        "                                                              subset='validation', \n",
        "                                                              batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 44004 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5kVhq_ASGcQF"
      },
      "source": [
        "STEP_SIZE_TRAIN = train_data_iterator.n//train_data_iterator.batch_size\n",
        "STEP_SIZE_VALIDATION = validation_data_iterator.n//validation_data_iterator.batch_size"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGoxNMKuGfM0"
      },
      "source": [
        "# Load Backbone model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjAdj6FtGelF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8502f96a-2aa4-4440-f7d2-59eebb0823aa"
      },
      "source": [
        "conv_base = tf.keras.applications.ResNet50(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwFyR-vtivVv"
      },
      "source": [
        "conv_base.Trainable=True\n",
        "\n",
        "set_trainable=False\n",
        "for layer in conv_base.layers:\n",
        "    if layer.name == 'res5a_branch2a':\n",
        "        set_trainable = True\n",
        "    if set_trainable:\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BJAHOnS_2JY"
      },
      "source": [
        "for layer in conv_base.layers:\n",
        "  layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diuf5LCrGp2i"
      },
      "source": [
        "image_batch, label_batch = next(iter(train_data_iterator))\n",
        "#feature_batch = base_model(image_batch)\n",
        "#print(feature_batch.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAZihPnvpc4Q"
      },
      "source": [
        "image_batch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkzIt_HaGwtZ"
      },
      "source": [
        "base_model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9NGYaiOM4OF"
      },
      "source": [
        "# Add Top Layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueFuZ7XtOlGh"
      },
      "source": [
        "inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
        "x = conv_base(inputs, training=False)\n",
        "avgpool = tf.keras.layers.AveragePooling2D((3,3), padding=\"same\")(x)\n",
        "maxpool = tf.keras.layers.MaxPooling2D((3,3), padding=\"same\")(x)\n",
        "x = tf.keras.layers.Concatenate()([avgpool, maxpool])\n",
        "x = tf.keras.layers.Flatten()(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.25)(x)\n",
        "x = tf.keras.layers.Dense(512, activation='relu')(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.5)(x)\n",
        "x = tf.keras.layers.Dense(1)(x)\n",
        "model = tf.keras.Model(inputs, x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6Ekdh2L8YRa",
        "outputId": "47f6860b-48af-4a10-a19e-25679646ddea"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_5\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_11 (InputLayer)           [(None, 96, 96, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "mobilenetv2_1.00_96 (Functional (None, 3, 3, 1280)   2257984     input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 1, 1, 1280)   0           mobilenetv2_1.00_96[6][0]        \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 1, 1, 1280)   0           mobilenetv2_1.00_96[6][0]        \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 1, 1, 2560)   0           average_pooling2d_1[0][0]        \n",
            "                                                                 max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "flatten_5 (Flatten)             (None, 2560)         0           concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 2560)         10240       flatten_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_10 (Dropout)            (None, 2560)         0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dense_6 (Dense)                 (None, 1)            2561        dropout_10[0][0]                 \n",
            "==================================================================================================\n",
            "Total params: 2,270,785\n",
            "Trainable params: 1,869,121\n",
            "Non-trainable params: 401,664\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZM328swqGTtX"
      },
      "source": [
        "model.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFOopg6OF-t4"
      },
      "source": [
        "# Find Learning Rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDxcubrmL64L"
      },
      "source": [
        "\"\"\"\n",
        "https://github.com/surmenok/keras_lr_finder/blob/master/keras_lr_finder/lr_finder.py\n",
        "\"\"\"\n",
        "\n",
        "from keras.callbacks import LambdaCallback\n",
        "import keras.backend as K\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "class LRFinder:\n",
        "    \"\"\"\n",
        "    Plots the change of the loss function of a Keras model when the learning rate is exponentially increasing.\n",
        "    See for details:\n",
        "    https://towardsdatascience.com/estimating-optimal-learning-rate-for-a-deep-neural-network-ce32f2556ce0\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, model):\n",
        "        self.model = model\n",
        "        self.losses = []\n",
        "        self.lrs = []\n",
        "        self.best_loss = 1e9\n",
        "\n",
        "    def on_batch_end(self, batch, logs):\n",
        "        # Log the learning rate\n",
        "        lr = K.get_value(self.model.optimizer.lr)\n",
        "        self.lrs.append(lr)\n",
        "\n",
        "        # Log the loss\n",
        "        loss = logs['loss']\n",
        "        self.losses.append(loss)\n",
        "\n",
        "        # Check whether the loss got too large or NaN\n",
        "        if batch > 5 and (math.isnan(loss) or loss > self.best_loss * 4):\n",
        "            self.model.stop_training = True\n",
        "            return\n",
        "\n",
        "        if loss < self.best_loss:\n",
        "            self.best_loss = loss\n",
        "\n",
        "        # Increase the learning rate for the next batch\n",
        "        lr *= self.lr_mult\n",
        "        K.set_value(self.model.optimizer.lr, lr)\n",
        "\n",
        "    def find(self, x_train, y_train, start_lr, end_lr, batch_size=64, epochs=1, **kw_fit):\n",
        "        # If x_train contains data for multiple inputs, use length of the first input.\n",
        "        # Assumption: the first element in the list is single input; NOT a list of inputs.\n",
        "        N = x_train[0].shape[0] if isinstance(x_train, list) else x_train.shape[0]\n",
        "\n",
        "        # Compute number of batches and LR multiplier\n",
        "        num_batches = epochs * N / batch_size\n",
        "        self.lr_mult = (float(end_lr) / float(start_lr)) ** (float(1) / float(num_batches))\n",
        "        # Save weights into a file\n",
        "        initial_weights = self.model.get_weights()\n",
        "\n",
        "        # Remember the original learning rate\n",
        "        original_lr = K.get_value(self.model.optimizer.lr)\n",
        "\n",
        "        # Set the initial learning rate\n",
        "        K.set_value(self.model.optimizer.lr, start_lr)\n",
        "\n",
        "        callback = LambdaCallback(on_batch_end=lambda batch, logs: self.on_batch_end(batch, logs))\n",
        "\n",
        "        self.model.fit(x_train, y_train,\n",
        "                       batch_size=batch_size, epochs=epochs,\n",
        "                       callbacks=[callback],\n",
        "                       **kw_fit)\n",
        "\n",
        "        # Restore the weights to the state before model fitting\n",
        "        self.model.set_weights(initial_weights)\n",
        "\n",
        "        # Restore the original learning rate\n",
        "        K.set_value(self.model.optimizer.lr, original_lr)\n",
        "\n",
        "    def find_generator(self, generator, start_lr, end_lr, epochs=1, steps_per_epoch=None, **kw_fit):\n",
        "        if steps_per_epoch is None:\n",
        "            try:\n",
        "                steps_per_epoch = len(generator)\n",
        "            except (ValueError, NotImplementedError) as e:\n",
        "                raise e('`steps_per_epoch=None` is only valid for a'\n",
        "                        ' generator based on the '\n",
        "                        '`keras.utils.Sequence`'\n",
        "                        ' class. Please specify `steps_per_epoch` '\n",
        "                        'or use the `keras.utils.Sequence` class.')\n",
        "        self.lr_mult = (float(end_lr) / float(start_lr)) ** (float(1) / float(epochs * steps_per_epoch))\n",
        "\n",
        "        # Save weights into a file\n",
        "        initial_weights = self.model.get_weights()\n",
        "\n",
        "        # Remember the original learning rate\n",
        "        original_lr = K.get_value(self.model.optimizer.lr)\n",
        "\n",
        "        # Set the initial learning rate\n",
        "        K.set_value(self.model.optimizer.lr, start_lr)\n",
        "\n",
        "        callback = LambdaCallback(on_batch_end=lambda batch,\n",
        "                                                      logs: self.on_batch_end(batch, logs))\n",
        "\n",
        "        self.model.fit_generator(generator=generator,\n",
        "                                 epochs=epochs,\n",
        "                                 steps_per_epoch=steps_per_epoch,\n",
        "                                 callbacks=[callback],\n",
        "                                 **kw_fit)\n",
        "\n",
        "        # Restore the weights to the state before model fitting\n",
        "        self.model.set_weights(initial_weights)\n",
        "\n",
        "        # Restore the original learning rate\n",
        "        K.set_value(self.model.optimizer.lr, original_lr)\n",
        "\n",
        "    def plot_loss(self, n_skip_beginning=10, n_skip_end=5, x_scale='log'):\n",
        "        \"\"\"\n",
        "        Plots the loss.\n",
        "        Parameters:\n",
        "            n_skip_beginning - number of batches to skip on the left.\n",
        "            n_skip_end - number of batches to skip on the right.\n",
        "        \"\"\"\n",
        "        plt.ylabel(\"loss\")\n",
        "        plt.xlabel(\"learning rate (log scale)\")\n",
        "        plt.plot(self.lrs[n_skip_beginning:-n_skip_end], self.losses[n_skip_beginning:-n_skip_end])\n",
        "        plt.xscale(x_scale)\n",
        "        plt.show()\n",
        "\n",
        "    def plot_loss_change(self, sma=1, n_skip_beginning=10, n_skip_end=5, y_lim=(-0.01, 0.01)):\n",
        "        \"\"\"\n",
        "        Plots rate of change of the loss function.\n",
        "        Parameters:\n",
        "            sma - number of batches for simple moving average to smooth out the curve.\n",
        "            n_skip_beginning - number of batches to skip on the left.\n",
        "            n_skip_end - number of batches to skip on the right.\n",
        "            y_lim - limits for the y axis.\n",
        "        \"\"\"\n",
        "        derivatives = self.get_derivatives(sma)[n_skip_beginning:-n_skip_end]\n",
        "        lrs = self.lrs[n_skip_beginning:-n_skip_end]\n",
        "        plt.ylabel(\"rate of loss change\")\n",
        "        plt.xlabel(\"learning rate (log scale)\")\n",
        "        plt.plot(lrs, derivatives)\n",
        "        plt.xscale('log')\n",
        "        plt.ylim(y_lim)\n",
        "        plt.show()\n",
        "\n",
        "    def get_derivatives(self, sma):\n",
        "        assert sma >= 1\n",
        "        derivatives = [0] * sma\n",
        "        for i in range(sma, len(self.lrs)):\n",
        "            derivatives.append((self.losses[i] - self.losses[i - sma]) / sma)\n",
        "        return derivatives\n",
        "\n",
        "    def get_best_lr(self, sma, n_skip_beginning=10, n_skip_end=5):\n",
        "        derivatives = self.get_derivatives(sma)\n",
        "        best_der_idx = np.argmin(derivatives[n_skip_beginning:-n_skip_end])\n",
        "        return self.lrs[n_skip_beginning:-n_skip_end][best_der_idx]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xs-AlS_gGFJf"
      },
      "source": [
        "lr_finder = LRFinder(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZfD8xsHMgas",
        "outputId": "61062f8a-841c-401e-f15d-652df61748e7"
      },
      "source": [
        "lr_finder.find_generator(train_data_iterator, start_lr=0.00001, end_lr=1, epochs=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1940: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "2751/2751 [==============================] - 923s 329ms/step - loss: 1.6837\n",
            "Epoch 2/2\n",
            "2751/2751 [==============================] - 109s 40ms/step - loss: nan\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "DdYtA_8RGnbr",
        "outputId": "173f6393-a923-4a39-898c-48a3951f3a91"
      },
      "source": [
        "lr_finder.plot_loss()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1nTMkw44rH9",
        "outputId": "152d5c8e-b509-4fba-c998-27026074b2c4"
      },
      "source": [
        "lr_finder.get_best_lr(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003162277"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwU8MFfzUGug"
      },
      "source": [
        "# Fit one cycle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJdASdKDUI3O"
      },
      "source": [
        "\"\"\"\n",
        "https://www.avanwyk.com/tensorflow-2-super-convergence-with-the-1cycle-policy/\n",
        "\"\"\"\n",
        "\n",
        "class CosineAnnealer:\n",
        "    def __init__(self, start, end, steps):\n",
        "        self.start = start\n",
        "        self.end = end\n",
        "        self.steps = steps\n",
        "        self.n = 0\n",
        "        \n",
        "    def step(self):\n",
        "        self.n += 1\n",
        "        cos = np.cos(np.pi * (self.n / self.steps)) + 1\n",
        "        return self.end + (self.start - self.end) / 2. * cos\n",
        "\n",
        "\n",
        "class OneCycleScheduler(tf.keras.callbacks.Callback):\n",
        "    def __init__(self, lr_max, steps, mom_min=0.85, mom_max=0.95, phase_1_pct=0.3, div_factor=25.):\n",
        "        super(OneCycleScheduler, self).__init__()\n",
        "        lr_min = lr_max / div_factor\n",
        "        final_lr = lr_max / (div_factor * 1e4)\n",
        "        phase_1_steps = steps * phase_1_pct\n",
        "        phase_2_steps = steps - phase_1_steps\n",
        "        \n",
        "        self.phase_1_steps = phase_1_steps\n",
        "        self.phase_2_steps = phase_2_steps\n",
        "        self.phase = 0\n",
        "        self.step = 0\n",
        "        \n",
        "        self.phases = [[CosineAnnealer(lr_min, lr_max, phase_1_steps), CosineAnnealer(mom_max, mom_min, phase_1_steps)], \n",
        "                 [CosineAnnealer(lr_max, final_lr, phase_2_steps), CosineAnnealer(mom_min, mom_max, phase_2_steps)]]\n",
        "        \n",
        "        self.lrs = []\n",
        "        self.moms = []\n",
        "\n",
        "    def on_train_begin(self, logs=None):\n",
        "        self.phase = 0\n",
        "        self.step = 0\n",
        "\n",
        "        self.set_lr(self.lr_schedule().start)\n",
        "        self.set_momentum(self.mom_schedule().start)\n",
        "        \n",
        "    def on_train_batch_begin(self, batch, logs=None):\n",
        "        self.lrs.append(self.get_lr())\n",
        "        self.moms.append(self.get_momentum())\n",
        "\n",
        "    def on_train_batch_end(self, batch, logs=None):\n",
        "        self.step += 1\n",
        "        if self.step >= self.phase_1_steps:\n",
        "            self.phase = 1\n",
        "            \n",
        "        self.set_lr(self.lr_schedule().step())\n",
        "        self.set_momentum(self.mom_schedule().step())\n",
        "        \n",
        "    def get_lr(self):\n",
        "        try:\n",
        "            return tf.keras.backend.get_value(self.model.optimizer.lr)\n",
        "        except AttributeError:\n",
        "            return None\n",
        "        \n",
        "    def get_momentum(self):\n",
        "        try:\n",
        "            return tf.keras.backend.get_value(self.model.optimizer.momentum)\n",
        "        except AttributeError:\n",
        "            return None\n",
        "        \n",
        "    def set_lr(self, lr):\n",
        "        try:\n",
        "            tf.keras.backend.set_value(self.model.optimizer.lr, lr)\n",
        "        except AttributeError:\n",
        "            pass # ignore\n",
        "        \n",
        "    def set_momentum(self, mom):\n",
        "        try:\n",
        "            tf.keras.backend.set_value(self.model.optimizer.momentum, mom)\n",
        "        except AttributeError:\n",
        "            pass # ignore\n",
        "\n",
        "    def lr_schedule(self):\n",
        "        return self.phases[self.phase][0]\n",
        "    \n",
        "    def mom_schedule(self):\n",
        "        return self.phases[self.phase][1]\n",
        "    \n",
        "    def plot(self):\n",
        "        ax = plt.subplot(1, 2, 1)\n",
        "        ax.plot(self.lrs)\n",
        "        ax.set_title('Learning Rate')\n",
        "        ax = plt.subplot(1, 2, 2)\n",
        "        ax.plot(self.moms)\n",
        "        ax.set_title('Momentum')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smE5yk059U61"
      },
      "source": [
        "epochs = 30\n",
        "#lr = lr_finder.get_best_lr(1)\n",
        "lr = 0.003162277\n",
        "steps = STEP_SIZE_TRAIN * epochs\n",
        "lr_schedule = OneCycleScheduler(lr, steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vavJ4Zex9fY2",
        "outputId": "646e912f-6818-49d9-975b-a4a43ef999dc"
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model.fit(train_data_iterator, validation_data=validation_data_iterator, epochs=epochs, callbacks=[lr_schedule], verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "2751/2751 [==============================] - 801s 281ms/step - loss: 3.3479 - accuracy: 0.7691 - val_loss: 3.0273 - val_accuracy: 0.7888\n",
            "Epoch 2/30\n",
            "2751/2751 [==============================] - 755s 274ms/step - loss: 3.2289 - accuracy: 0.7753 - val_loss: 3.0673 - val_accuracy: 0.7868\n",
            "Epoch 3/30\n",
            "2751/2751 [==============================] - 753s 274ms/step - loss: 3.4025 - accuracy: 0.7653 - val_loss: 3.1915 - val_accuracy: 0.7802\n",
            "Epoch 4/30\n",
            "2751/2751 [==============================] - 749s 272ms/step - loss: 3.2324 - accuracy: 0.7763 - val_loss: 3.0379 - val_accuracy: 0.7912\n",
            "Epoch 5/30\n",
            "2751/2751 [==============================] - 756s 275ms/step - loss: 3.3805 - accuracy: 0.7703 - val_loss: 2.9872 - val_accuracy: 0.7982\n",
            "Epoch 6/30\n",
            "2751/2751 [==============================] - 753s 274ms/step - loss: 3.3093 - accuracy: 0.7768 - val_loss: 3.3475 - val_accuracy: 0.7761\n",
            "Epoch 7/30\n",
            "2751/2751 [==============================] - 752s 273ms/step - loss: 3.3492 - accuracy: 0.7755 - val_loss: 3.4932 - val_accuracy: 0.7656\n",
            "Epoch 8/30\n",
            "2751/2751 [==============================] - 764s 278ms/step - loss: 3.3926 - accuracy: 0.7745 - val_loss: 3.3232 - val_accuracy: 0.7800\n",
            "Epoch 9/30\n",
            "2751/2751 [==============================] - 767s 279ms/step - loss: 3.2764 - accuracy: 0.7836 - val_loss: 3.0893 - val_accuracy: 0.7964\n",
            "Epoch 10/30\n",
            "2751/2751 [==============================] - 764s 278ms/step - loss: 3.3954 - accuracy: 0.7758 - val_loss: 3.3128 - val_accuracy: 0.7811\n",
            "Epoch 11/30\n",
            "2751/2751 [==============================] - 760s 276ms/step - loss: 3.1760 - accuracy: 0.7904 - val_loss: 2.9873 - val_accuracy: 0.8034\n",
            "Epoch 12/30\n",
            "2751/2751 [==============================] - 765s 278ms/step - loss: 3.1721 - accuracy: 0.7909 - val_loss: 3.2902 - val_accuracy: 0.7836\n",
            "Epoch 13/30\n",
            "2751/2751 [==============================] - 761s 276ms/step - loss: 3.2236 - accuracy: 0.7876 - val_loss: 3.1243 - val_accuracy: 0.7942\n",
            "Epoch 14/30\n",
            "2751/2751 [==============================] - 765s 278ms/step - loss: 3.1121 - accuracy: 0.7952 - val_loss: 2.9952 - val_accuracy: 0.8029\n",
            "Epoch 15/30\n",
            "2751/2751 [==============================] - 812s 295ms/step - loss: 3.0903 - accuracy: 0.7968 - val_loss: 2.9734 - val_accuracy: 0.8043\n",
            "Epoch 16/30\n",
            "2751/2751 [==============================] - 752s 273ms/step - loss: 3.2000 - accuracy: 0.7898 - val_loss: 2.9791 - val_accuracy: 0.8044\n",
            "Epoch 17/30\n",
            "2751/2751 [==============================] - 748s 272ms/step - loss: 3.0565 - accuracy: 0.7991 - val_loss: 3.0211 - val_accuracy: 0.8018\n",
            "Epoch 18/30\n",
            "2751/2751 [==============================] - 749s 272ms/step - loss: 3.2001 - accuracy: 0.7897 - val_loss: 2.9437 - val_accuracy: 0.8066\n",
            "Epoch 19/30\n",
            "2751/2751 [==============================] - 749s 272ms/step - loss: 3.0747 - accuracy: 0.7981 - val_loss: 2.9768 - val_accuracy: 0.8045\n",
            "Epoch 20/30\n",
            "2751/2751 [==============================] - 747s 272ms/step - loss: 3.0105 - accuracy: 0.8023 - val_loss: 2.9762 - val_accuracy: 0.8048\n",
            "Epoch 21/30\n",
            "2751/2751 [==============================] - 753s 274ms/step - loss: 2.9893 - accuracy: 0.8039 - val_loss: 2.9703 - val_accuracy: 0.8050\n",
            "Epoch 22/30\n",
            "2751/2751 [==============================] - 753s 274ms/step - loss: 3.0499 - accuracy: 0.7996 - val_loss: 2.8628 - val_accuracy: 0.8121\n",
            "Epoch 23/30\n",
            "2751/2751 [==============================] - 751s 273ms/step - loss: 2.9198 - accuracy: 0.8084 - val_loss: 2.8371 - val_accuracy: 0.8137\n",
            "Epoch 24/30\n",
            "2751/2751 [==============================] - 748s 272ms/step - loss: 2.8928 - accuracy: 0.8100 - val_loss: 2.7957 - val_accuracy: 0.8164\n",
            "Epoch 25/30\n",
            "2751/2751 [==============================] - 748s 272ms/step - loss: 2.8789 - accuracy: 0.8110 - val_loss: 2.8090 - val_accuracy: 0.8155\n",
            "Epoch 26/30\n",
            "2751/2751 [==============================] - 753s 274ms/step - loss: 2.8624 - accuracy: 0.8120 - val_loss: 2.7975 - val_accuracy: 0.8166\n",
            "Epoch 27/30\n",
            "2751/2751 [==============================] - 752s 273ms/step - loss: 2.8508 - accuracy: 0.8130 - val_loss: 2.7958 - val_accuracy: 0.8163\n",
            "Epoch 28/30\n",
            " 156/2751 [>.............................] - ETA: 9:21 - loss: 2.7932 - accuracy: 0.8171"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5J7ldZpTBuB"
      },
      "source": [
        "### Save model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWDnceyUTHxY"
      },
      "source": [
        "model.save('{}/{}'.format(base_dir, 'model.h5'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uo4OR1EGl9_3"
      },
      "source": [
        "# Tensorflow Transfer learning tutorial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qIZq22DCwvhc"
      },
      "source": [
        "## Sans Augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KpbdtJxaw0d2"
      },
      "source": [
        "### Create Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vriO_iGVmC3T"
      },
      "source": [
        "preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAOmwpc0rawb"
      },
      "source": [
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvjFR0qArkmE"
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OmbAANwBr-43",
        "outputId": "32a107be-7036-421f-90b6-62152beca275"
      },
      "source": [
        "image_batch, label_batch = next(iter(train_data_iterator))\n",
        "feature_batch = base_model(image_batch)\n",
        "print(feature_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 3, 3, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIk82qwMroE7",
        "outputId": "29d70f64-4fda-4b5f-a056-57d50e77c9e9"
      },
      "source": [
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "feature_batch_average = global_average_layer(feature_batch)\n",
        "print(feature_batch_average.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jUzznvJJs4vA",
        "outputId": "05b37085-4e8d-4716-c238-408e5d010d9d"
      },
      "source": [
        "prediction_layer = tf.keras.layers.Dense(1)\n",
        "prediction_batch = prediction_layer(feature_batch_average)\n",
        "print(prediction_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ptd2P0JVtBnF"
      },
      "source": [
        "inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
        "x = preprocess_input(inputs)\n",
        "x = base_model(x, training=False)\n",
        "x = global_average_layer(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "outputs = prediction_layer(x)\n",
        "model = tf.keras.Model(inputs, outputs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fK1CDe_8tMME"
      },
      "source": [
        "base_learning_rate = 0.0001\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate), \n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOfZlwONtdlc",
        "outputId": "bca1ef46-ae5b-4e11-fbfd-0a521416ad24"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bJdA8t4cw2-b"
      },
      "source": [
        "### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8wRgqZXtzIE",
        "outputId": "e2d53cd0-f575-446c-c02d-3c5820bef7cf"
      },
      "source": [
        "initial_epochs = 10\n",
        "\n",
        "history = model.fit(train_data_iterator, epochs=initial_epochs, validation_data=validation_data_iterator, verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2751/2751 [==============================] - 316s 114ms/step - loss: 0.4209 - accuracy: 0.8065 - val_loss: 0.3374 - val_accuracy: 0.8513\n",
            "Epoch 2/10\n",
            "2751/2751 [==============================] - 259s 94ms/step - loss: 0.3470 - accuracy: 0.8477 - val_loss: 0.3174 - val_accuracy: 0.8633\n",
            "Epoch 3/10\n",
            "2751/2751 [==============================] - 238s 86ms/step - loss: 0.3316 - accuracy: 0.8548 - val_loss: 0.3103 - val_accuracy: 0.8628\n",
            "Epoch 4/10\n",
            "2751/2751 [==============================] - 232s 84ms/step - loss: 0.3250 - accuracy: 0.8578 - val_loss: 0.3053 - val_accuracy: 0.8680\n",
            "Epoch 5/10\n",
            "2751/2751 [==============================] - 229s 83ms/step - loss: 0.3221 - accuracy: 0.8592 - val_loss: 0.3029 - val_accuracy: 0.8690\n",
            "Epoch 6/10\n",
            "2751/2751 [==============================] - 225s 82ms/step - loss: 0.3201 - accuracy: 0.8604 - val_loss: 0.3011 - val_accuracy: 0.8686\n",
            "Epoch 7/10\n",
            "2751/2751 [==============================] - 227s 83ms/step - loss: 0.3177 - accuracy: 0.8611 - val_loss: 0.3000 - val_accuracy: 0.8685\n",
            "Epoch 8/10\n",
            "2751/2751 [==============================] - 226s 82ms/step - loss: 0.3174 - accuracy: 0.8620 - val_loss: 0.2995 - val_accuracy: 0.8683\n",
            "Epoch 9/10\n",
            "2751/2751 [==============================] - 227s 82ms/step - loss: 0.3164 - accuracy: 0.8622 - val_loss: 0.2980 - val_accuracy: 0.8711\n",
            "Epoch 10/10\n",
            "2751/2751 [==============================] - 225s 82ms/step - loss: 0.3164 - accuracy: 0.8621 - val_loss: 0.2985 - val_accuracy: 0.8749\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z77WEVW1zBYT",
        "outputId": "63da567f-7070-4db0-d483-8279384215f6"
      },
      "source": [
        "model.save('{}/{}'.format(base_dir, 'mobileNetV2_model0.h5'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzd4kkKGw5zM"
      },
      "source": [
        "### Plot loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "XSAlKNDjuNQq",
        "outputId": "376b5b58-5a19-48a2-e945-ace62b225777"
      },
      "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": {
        "id": "fGAoitp9w8ny"
      },
      "source": [
        "### Fine tune model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvcRf05Qze0A"
      },
      "source": [
        "base_model.trainable = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFrIlpCZ2DLe"
      },
      "source": [
        "fine_tune_at = 100\n",
        "\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYXD_8sU2LEO",
        "outputId": "e471cbad-7912-4d65-ea97-eb750a1b4191"
      },
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "              optimizer=tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate/10),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebdKnUs72nTe",
        "outputId": "3d43a740-7aa4-48f3-8488-308b97d1b053"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf.math.truediv (TFOpLambda) (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf.math.subtract (TFOpLambda (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_1.00_96 (Functio (None, 3, 3, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,862,721\n",
            "Non-trainable params: 396,544\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycz1RQES2oNK",
        "outputId": "be33001b-ce82-4426-f502-78115f848d10"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_kEFrrHw_KF"
      },
      "source": [
        "### Train fine tuned model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RdvqdGt2qG6",
        "outputId": "5b3e559d-820a-46ef-ab0b-afa7f54a942f"
      },
      "source": [
        "fine_tune_epochs = 10\n",
        "total_epochs = initial_epochs + fine_tune_epochs\n",
        "\n",
        "history_fine = model.fit(train_data_iterator, \n",
        "                         epochs=total_epochs, \n",
        "                         initial_epoch=history.epoch[-1], \n",
        "                         validation_data=validation_data_iterator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 10/20\n",
            "2751/2751 [==============================] - 258s 91ms/step - loss: 0.2626 - accuracy: 0.8887 - val_loss: 0.2230 - val_accuracy: 0.9074\n",
            "Epoch 11/20\n",
            "2751/2751 [==============================] - 249s 90ms/step - loss: 0.2121 - accuracy: 0.9121 - val_loss: 0.2003 - val_accuracy: 0.9192\n",
            "Epoch 12/20\n",
            "2751/2751 [==============================] - 246s 89ms/step - loss: 0.1875 - accuracy: 0.9237 - val_loss: 0.1926 - val_accuracy: 0.9196\n",
            "Epoch 13/20\n",
            "2751/2751 [==============================] - 248s 90ms/step - loss: 0.1697 - accuracy: 0.9319 - val_loss: 0.1793 - val_accuracy: 0.9292\n",
            "Epoch 14/20\n",
            "2751/2751 [==============================] - 246s 89ms/step - loss: 0.1568 - accuracy: 0.9373 - val_loss: 0.1753 - val_accuracy: 0.9350\n",
            "Epoch 15/20\n",
            "2751/2751 [==============================] - 247s 90ms/step - loss: 0.1453 - accuracy: 0.9424 - val_loss: 0.1734 - val_accuracy: 0.9287\n",
            "Epoch 16/20\n",
            "2751/2751 [==============================] - 245s 89ms/step - loss: 0.1347 - accuracy: 0.9478 - val_loss: 0.1746 - val_accuracy: 0.9336\n",
            "Epoch 17/20\n",
            "2751/2751 [==============================] - 245s 89ms/step - loss: 0.1260 - accuracy: 0.9504 - val_loss: 0.1619 - val_accuracy: 0.9369\n",
            "Epoch 18/20\n",
            "2751/2751 [==============================] - 243s 88ms/step - loss: 0.1177 - accuracy: 0.9541 - val_loss: 0.1633 - val_accuracy: 0.9376\n",
            "Epoch 19/20\n",
            "2751/2751 [==============================] - 244s 89ms/step - loss: 0.1104 - accuracy: 0.9574 - val_loss: 0.1635 - val_accuracy: 0.9378\n",
            "Epoch 20/20\n",
            "2751/2751 [==============================] - 245s 89ms/step - loss: 0.1035 - accuracy: 0.9602 - val_loss: 0.1745 - val_accuracy: 0.9354\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dw0GSp6vxCTJ"
      },
      "source": [
        "### Plot improved loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DfHBDOj2-4G"
      },
      "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']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "jy3Wok9Y3XRA",
        "outputId": "edbb9293-1c7d-43e1-b329-62ab28c31f49"
      },
      "source": [
        "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.ylim([0.8, 1])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1],\n",
        "          plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='lower right')\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.ylim([0, 1.0])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1],\n",
        "         plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='upper right')\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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q6nYAEZkDjAE8A3hX4Lfu9kLgg4oKFOenciFwk5v0OvAkFsCNMaZOKSpSDmTkkHw4m91pWSe8bzuUycGM3JK80WEBdIkJ59ZBrensBup2jcMI8q/bg8lqijcBvAWw22M/GRhQKs96YCxON/vVQAMRiVLVVCBIRFYBBcCzqvoBTrf5EVUt8CizRVkfLiJ3AncCtGrVyquLMsYYUzWKJznxDMzJh48H6r1HcsgrLDrhnCYNAoltFMx5HaJLWtSdm4XTuEFgDV3FuamqbiY8APxTRCYAi4E9QPHwwNaqukdE2gJficgGIN3bglV1OjAdICEhQauovsYYY1xFRcrWgxlsO3iM5MNZ7D58Yku6uLu7WFRoALGNgunWIoJLujejZaMQYhsF0zIyhBYNg61FfZZ4E8D3AC099mPdtBKquhenBY6IhAHXqOoR99ge9327iCwCegPvAQ1FxM9thZ9UpjHGmOqzOy2LJYkpfJuYwtJtqaQdOz6GODzIj5aRIbRvEsbwTk1KgnNxgA4NtIFktYE3P4WVQAd31Pge4EaO37sGQESigTRVLQIewRmRjog0ArJUNdfNMwT4s6qqiCwErsUZiX4b8GEVXZMxxphS0o7l8d22VL5NTGFJYgq70rIAp7t7WMfGDG4fTZeYBsQ2CiEi2L+Ga2u8ccoArqoFIvIrYD7OY2SvqupGEZkMrFLVucAw4BkRUZwu9Hvc07sA/xaRIpy1x5/1GL3+MDBHRJ4G1gL/qcLrMsaYei07r5CVSWklrexN+46iCmGBfgxsG8XtQ+IY0j6a9k3CbLR3HeVVP4iqzgPmlUp73GP7XeDdMs5bCvQop8ztOCPcjTHGnKGCwiI27EkvCdhrdh4hr7AIf1+hT6tG/HZkRwa3jyY+NgK/OjDPtzk1u5FhjDF1kKqy7VAmSxKdbvFl21LJcGcs6xoTzgS3hd0vrpFNfnKOsp+qMcbUAarK9pRjLN+exvIdqSzbnsqBo84z1i0jg7kiPoYh7aMZ1DaKqDB7XKs+sABujDG1kKqSeDCTZTvSWL49leU70jjkTorSuEEgA9pEMqR9NEPaRdMqKqSGa2tqggVwY4ypBYqfxS5uYS/fnkaq+2hXs/AgBreLYkCbKAa2jaRNdKgNPDMWwI0xpiYUFSk/7j/K8u1pLNueysqktJJ1rFs0DGZox8YMbBvFgLaRtIoMsYBtTmIB3BhjzoKCwiI27Tta0sJesSONoznOoLOWkcGM7NKUAW2jGNAmkpaR1iVOUSHkZ0NBzvH3glzQItBC912d96Li/SIvjnseUwhrDBEtIbwF+AfV9FVXigVwY4ypBqrK1gOZLHEnTlmxI61klHib6FAu6xHDgLaRDGgTRfOGwTVc2yp0LAUObYGULZB50CP45kB+DhRkl3ov61gOFOWf/bqHNoaIWPfV0mPb3Q9tDLWoJ8QCuDHGVJE9R7JLAvaSxFRSMp1BZ3FRIVwR35yBbSMZ2DaKpuF1q6V3kqIiSN8NKVuPB+tDW5397LQT8/oGgF+w07r1c1/+QU5aQAiERIFfIPgHu8eCnf2SczzPDQTxBfFxXj6+TkAVnxPTS475lH28+JgqZB6A9GT3tdt5P7QVEr+E/KxS1xIIES3KD/DhLZxrOkssgBtjzGk6knV8etKl21LZkXIMcJbNHNI+miFto7gg+ijNDn4LWUshciSENavhWldCQS6kbnMCdMpPx4N1SqLTWi4WEgXRnaDraOc9uiM07ugENJ9avrBJ445lp6tC9mGP4O4R4NOTYdtXkLEfKLXGVter4PrXq73aYAHcGGO8lp1XyKqdaU7ATkzlh73pqEJogC8D20YxfmBrzm8dQoesNUjiLPjuCzic5J4tsPjPTjdsx1HQ+XJoO8xpcda0wgI4sAEObHJa0cUt68NJzj3jYhGtnIAXd4HzXhysQ6NqrOrVRgRCIp1XTM+y8xTkQcbeEwN8RMuy81YDC+DGGFMOz+lJlySmsnrn4ZLpSXu3bMR9IzoypF0k8cEH8N/+FSR+AQuXQmEe+IdAmwtg0K+g/QgIjoTEBbD5E9j0Iax9w+kebnchdL7MCeqh0WfnwvJzYO8a2LkEdn4Hu5dDXqZzzMcfotpD027QfawTpBt3hKgOZ7V7uE7wC4BGcc6rJj6+Rj7VGGNqqczcAj5av5evNh88YXrSLjHh3Da4NYPbR9M/xp/QPUsg8W34YIHT8gJo3Bn63wntR0KrQSePau5xrfMqyIOd38LmebDlU9jyCSDQcoATzDtdDtHtq+6icjNg9wrYuRR2fQfJq6DQuT9Pk64Qf6NT35heTjDytdBQF9hPyRhT76kq3yenM3vFLuau30tWXiGxjZzpSQe3i2Zw20iijiVC4ufw3QInCBYVQECY0w1+/u+cVnbDVt59oF+A0/JudyFc9hzs/94N5p/AF487r+iO0OlSJ5jHJlTuXnJWmlPHnUud1771Tle4+EJMPPS/A1oPdoJ2SOTpfGWmFrAAboypt47m5PPhur3MXr6LTfuOEuzvy5XxMYzr34pejUG2fw2J/4YFX0LGPuekpt3dbvGRTovZL+DMKiHiBNWYeBj+CBzZ5bbK58F3L8CS50993/zoPqc7vDhoH3RXbfYNdIL/+b91gnXL/hDY4Mzqa2oNC+DGmHpFVVm3+wizV+zio/X7yM4vpGtMOE9d1Z0x8TGE7/0WFj0BOxY7rdbACGg33AnY7UdCeEz1VrBhKxjwC+eVfcS5b75l3sn3zVsPhoM/OoH78A7n3IAw54+K7mOh9RBo3qfOTU5ivOdVABeRUcDzgC/wiqo+W+p4a+BVoDGQBoxX1WQR6QW8BIQDhcAUVX3bPWcGMBRId4uZoKrrzviKjDGmDOnZ+Xy4bg9vLd/F5v0ZhAT4clXv5ozr34oeMWHIj3Ph9b853dlhzeC8+6D9RRDbr+buCQc3rPi+eXAjaDUY+v3cCejNetr963rklD9pEfEFXgAuApKBlSIyV1U3eWSbCsxU1ddF5ELgGeAWIAu4VVV/EpHmwGoRma+qR9zzHlTVd6vygowxppiqsmaX09r++Pu95OQX0aNFBH+8ugejezUnzKcA1r8F701zWrFR7WH0P6DnDc6kIbVJ6fvmGfshrCn4+NR0zUwN8eZPtf5AoqpuBxCROcAYwDOAdwV+624vBD4AUNWtxRlUda+IHMRppR/BGGOqSXpWPv9bm8zsFbvYeiCT0ABfxvaJZVy/VvSIjYCcdFgxDZa9BMcOOl3NF0127jHX9olHwLlvXt1d+abW8yaAtwB2e+wnAwNK5VkPjMXpZr8aaCAiUaqaWpxBRPoDAcA2j/OmiMjjwJfAJFXNrfwlGGOM09petfMws5fv4pMN+8gtKCI+NoJnx/bgyvjmhAb6Oa3WL/4Cq16D3KNOa3bIfc7z2rVojmtjvFFVN0seAP4pIhOAxcAenHveAIhIDPAGcJuqFrnJjwD7cYL6dOBhYHLpgkXkTuBOgFatvHxEwxhTb+TkF/LW8l28tWIXiQczCQv047qEWG7s14ruLSKcTKnb4PNpsO4t5/GvrlfBkN9A8141W3ljzoA3AXwP4Dk3XKybVkJV9+K0wBGRMOCa4vvcIhIOfAL8XlWXeZzjPpNBroi8hvNHwElUdTpOgCchIUHLymOMqX9Ulbnr9/Lnz7aw50g2vVs15M/X9uSKnjGEBLi/2vauhW//7ozg9g2AXjfD4F9DVLuarbwxVcCbAL4S6CAibXAC943ATZ4ZRCQaSHNb14/gjEhHRAKA93EGuL1b6pwYVd0nzir1VwE/nOnFGGPqh9U703jq4x9Zt/sI3ZqHM/W6eAa1c+fjVoVtC2HJ32H7IggMd0aUD7gbGjSt0XobU5VOGcBVtUBEfgXMx3mM7FVV3Sgik4FVqjoXGAY8IyKK04V+j3v69cAFQJTbvQ7HHxebJSKNAQHWAXdV3WUZY85Fu9OyePazzXzy/T6ahgcy9bp4xvZugY+PQFEh/PgRfPs32LfOGaE98g+QMBGCImq66sZUOa/ugavqPGBeqbTHPbbfBU56HExV3wTeLKfMCytVU2PMuaeoCIryoTDffS9w3/M8tvPJzMnmfyuTmP99MgFSyN/6NOayrtEE+qyGjcvg2CFY8TKkbYPItnDl89DzRpvExJzT7Il/Y8zZk3kQvpwMP7wHBTlQMqa1YmHArcCtxb+xNnHig6zgLMRx3evQ5cq68SiYMWfIArgxpvoV5DrPXC+e6gTu+Buc2c58/cHHz33399gPYNPBLN5bd5Dk9HzaNm3I9QPa0qZJhEdev+Pn+AU5U5Dao2CmHrEAboypPqrO+tefP+bMdNbxUrhkSoWjwLfsz2DKvB9ZvPUQcVHteeTmLlzctSliwdmYE1gAN8ZUjwMb4bNJzqIgjTvD+P85S26W41BGLn9bsJU5K3YRFujH/13RlVsGtibAz6YKNaYsFsCNMVXrWAosnAKrZzijvy+bCn0nlrvIRk5+Ia8u2cGLC7eRk1/IrYPi+M2IDjQKPcNlOo05x1kAN8ZUjcJ8ZyT4omchLxP63wlDH4aQyDKzqyoffb+PP326mT1HshnZpSmPXNaZdo3DznLFjambLIAbY87c1s9h/qOQ+hO0GwGX/BGadC43+5pdh3nq402s3XWErjHhPHdtTwa3jz6LFTam7rMAbow5fYe2OIE7cYGzFOdN70CHiyscDT598Tb+OG8zTRoE8udre3JNn1h8fWyAmjGVZQHcGFN5WWnw9Z+cLvOAMKfF3e8OZ83qCny4bg9/nLeZy3vE8OdrezorhBljTov97zHGeK+wAFa/5gxSy0mHvhNg+O8h9NTd38u2p/Lgf79nQJtI/npDPIF+NtmKMWfCArgxxjvbvoLPHoVDPzrrZ1/yDDTr7tWpPx3I4M6Zq2gVFcL0WxIseBtTBSyAG2MqdjgJPp0EWz+FRnFwwyzofLnXs54dPJrDhNdWEujvy4yJ/YgI8a/W6hpTX1gAN8aULy8LZo6BY6nOyl4D7wa/QK9PP5ZbwMQZKzmclcfbdw4itlFINVbWmPrFArgxpnxf/8lpgd/2MbQ5v1KnFhQWcc9ba9i8P4NXbk2gR6wt6WlMVbI5Co0xZdv/Ayz9B/QeX+ngrao89sEPLNpyiKfGdGd45ybVVElj6i8L4MaYkxUVwkf3QnAjuOipSp/+wsJE5qzczT3D23HTgFbVUEFjjFcBXERGicgWEUkUkUllHG8tIl+KyPciskhEYj2O3SYiP7mv2zzS+4rIBrfMaWJLDRlTe6x8Bfashkv/VO5UqOV5f20yUz/fytW9W/DAxZ2qqYLGmFMGcBHxBV4ALgW6AuNEpGupbFOBmaraE5gMPOOeGwk8AQwA+gNPiEgj95yXgDuADu5r1BlfjTHmzKUnw5eTnSlRu19TqVOXJqbw0LvfM6htFH+6pqctAWpMNfKmBd4fSFTV7aqaB8wBxpTK0xX4yt1e6HH8EuALVU1T1cPAF8AoEYkBwlV1maoqMBO46gyvxRhzplRh3oNOF/oVf/X6UTFw1vH+xRuraRMdyr9u6WvLgBpTzbz5H9YC2O2xn+ymeVoPjHW3rwYaiEhUBee2cLcrKtMYc7b9+BFsmQfDH3We+fbS/vQcJry2guAAX16b2J+IYHvW25jqVlV/Ij8ADBWRtcBQYA9QWBUFi8idIrJKRFYdOnSoKoo0xpQlJ91pfTfrAQN/6fVpGTn5TJyxkqPZ+bw2sR8tGgZXYyWNMcW8CeB7gJYe+7FuWglV3auqY1W1N/B7N+1IBefucbfLLdOj7OmqmqCqCY0bN/aiusaY0/LlZDh2EK6cBr7eTRGRX1jEL2etYeuBDF4c35duze1Zb2POFm8C+Eqgg4i0EZEA4EZgrmcGEYkWkeKyHgFedbfnAxeLSCN38NrFwHxV3QccFZGB7ujzW4EPq+B6jDGnY9dyWPkfGHAXtOjj1SmqyqP/28A3P6XwzNU9GNrR/sA25mw6ZQBX1QLgVzjB+EfgHVXdKCKTRWS0m20YsEVEtgJNgSnuuWnAUzh/BKwEJrtpAL8EXgESgW3Ap1V1UcaYSijIg49+AxGxzspiXpr2ZSL/XZ3MvSM6cH2/lqc+wRhTpbzqJ1PVecC8UmmPexIxSToAACAASURBVGy/C7xbzrmvcrxF7pm+CvBuKSNjTPVZ+ryzwti4tyEwzKtT/rtqN39bsJVr+sRy/8gO1VxBY0xZ7DkPY+qz1G3w9XPQ9Sro5N1UDN/8dIhH/reB89pH88zYHvastzE1xAK4MfWVqtN17hfkzLjmhR/3HeXuN9fQvkkYL47vY896G1ODbDUyY+qrdW9B0jdwxd+hQbNTZt+Xns3E11YSFujHaxP7ER5kz3obU5MsgBtTHx1Lgc9/Dy0HQp/bTpn9aE4+E19bSWZuAf+9axAxEfastzE1zfq/jKmP5j8KuZlw5fPgU/GvgbyCIu5+czWJBzN5aXwfusSEn6VKGmMqYgHcmPom8Uv4/m04/7fQpHOFWZ11vTewJDGVZ6/pyfkd7FlvY2oLC+DG1Cd5WfDx/RDVHs777Smzv71yN++sSuZXw9tzbd/YU+Y3xpw9dg/cmPrk6z/BkZ0w4RPwD6ow6w970nl87kbO7xDN/Rd1PEsVNMZ4y1rgxtQX+zfA0n9A71sg7rwKs6Zn5XPXm6uJCg3g+Rt74+tjz3obU9tYC9yY+qCoEObeCyGRcNHkirMWKb99Zx0Hjubw9i8GERkacJYqaYypDAvgxtQHK16GvWvgmv84QbwCL329jS83H+QPo7vRp1Wjs1RBY0xlWRe6Mee69GT46iloPxK6X1Nh1iWJKfzl8y1cGd+cWwe1PksVNMacDgvgxpzLVOGTB0CL4PK/QAXzlu9Pz+He2Wtp2ziMZ22Oc2NqPetCN+Zc9uNc2PopXPQUNIorN1t+YRH3vLWG7PxC3h7fh9BA+9VgTG1n/0uNOVflpMO8h6BZDxj4ywqzPjNvM6t3HuYf43rTvkmDs1RBY8yZsABuzLlqwR/g2EEYNxt8y/+v/vH3e3l1yQ4mDI7jyvjmZ7GCxpgz4dU9cBEZJSJbRCRRRCaVcbyViCwUkbUi8r2IXOam3ywi6zxeRSLSyz22yC2z+FiTqr00Y+qx3KOw6j8w4C5o0afcbIkHM3n43e/p06ohj17W5SxW0Bhzpk7ZAhcRX+AF4CIgGVgpInNVdZNHtseAd1T1JRHpCswD4lR1FjDLLacH8IGqrvM472ZVXVVF12KMAWfgWmoiRLSE4b8vN9ux3ALufnM1gf6+vHCzre1tTF3jzf/Y/kCiqm5X1TxgDjCmVB4FipcoigD2llHOOPdcY0x1KSqE9N3OnOeX/wUCw8rMpqo8+v4GEg9lMu3G3rY8qDF1kDf3wFsAuz32k4EBpfI8CXwuIr8GQoGRZZRzAycH/tdEpBB4D3haVbX0SSJyJ3AnQKtWrbyorjH1RG4mHNgIBzY406Tu/8HZj24AodHQ8ZJyT31z2U4+XLeX313UkfM6RJ/FShtjqkpVDWIbB8xQ1b+IyCDgDRHprqpFACIyAMhS1R88zrlZVfeISAOcAH4LMLN0wao6HZgOkJCQcFKAN+acpwpH9zpBuiRYb4C0HTidX0BQQ2e0ecJEyPzeCeDlWLf7CJM/3sTwTo25Z3j7s3MNxpgq500A3wO09NiPddM8/QwYBaCq34lIEBANHHSP3wjM9jxBVfe47xki8hZOV/1JAdyYeqUgD1K2Hg/SxQE7+/DxPI3aQLPuED/OCdpNu0NE7PFJWj6bWG7xacfy+OWbq2kaHsTfbuiFjy1SYkyd5U0AXwl0EJE2OIH7RuCmUnl2ASOAGSLSBQgCDgGIiA9wPXB+cWYR8QMaqmqKiPgDVwALzvBajKk7CnLh8E44vMMZcLb/BydYH9wMRflOHr8gaNIVulwJzXo6wbpJVwgKr7jschQWKb+Zs5aUzDzeu3swDUNskRJj6rJTBnBVLRCRXwHzAV/gVVXdKCKTgVWqOhf4HfCyiNyP06c3weN+9gXAblXd7lFsIDDfDd6+OMH75Sq7KmNqg5x0p5v78I5S70nO/OR43BEKbeIE6EEXOsG6aXeIal/h89uVNe3Ln/jmpxT+eHUPesRGVFm5xpia4dVvB1Wdh/NomGfa4x7bm4Ah5Zy7CBhYKu0Y0LeSdTU1pSDXGTCVl+G+Zx7fL8gFHz/w8QUff2fb189N8wdf/1LHvNgXnwrn7K41VCHzIKRtLyNI74Cs1BPzh0RDZBtoNQgi2zrbjdo422GNq7Wqi7YcZNpXPzG2TwvG9W956hOMMbWezcRWXxTkwp41cGQn5GZ4BOHME4PzCcfctOIu3bNNfMp4+R4P8GUe9yn7uI/v8XN93HJOSPMtI1+ptOL9vGPHW9L5x06sb0SsE5S7XOkGZzdIN4o77a7vM5V8OIv73l5Hp6YNmHKVLVJizLnCAvi5qjhgJ30LSd/A7hVQkH1iHvGBgAbOs8IBYcffw5qcuB8YVipfA+cVEAZ+gc5KV4X5UFTgBPuiwhP3CwvKP1ZWXtQp0/NVVOhul3HshFfp44Wlzi/eLiwjLc8jrRCKikrlc7f9g52g3HboiUG6YSvwq133lXMLCrln1hoKC5WXxvclOMC3pqtkjKkiFsDPFQV5sGd12QG7aQ/oOwHizoMmXY4HZf+QutFVbU7b0x//yPrkdP41vg9tokNrujrGmCpkAbyu8jZgtx4MIZE1WlVTMz5Yu4c3lu3kzgvaMqp7TE1XxxhTxSyA1xUWsE0lZOcV8sj/NtA/LpKHLulU09UxxlQDC+BVQRW2fgZr33S2ff3BN8B9+R3f9vErO93X3x2x7W4Xnw+wb50TtHctt4BtvFJYpGw9kEFooB//vKk3fr62SIkx5yIL4Gfq0Fb4bBJs+xLCW0BwI2dQVmGeOzjLY7swz3lVVtPu0Pc2N2APsYBdTQqLlKy8ArLzCslyX9n5BWTlFXIs9/h2bn4RRaoUqVJYhPvu7BcVKYVuurrphW56kVKyXZyu6pwvgIgcfxcQwKd4W44f9/E4fjyv4OPm23Iwg5yCIl69qTdNwoNq9Ds1xlQfC+CnKycdvv4zLP+XMxjskmeg/x1O67kiqu7I6zw3uHsG+PxS6fnQuFO9D9j5hUXk5BeSW+C85+QX7zvBNKfgeNrxY+5+QSE5eYUcyyt0A3OBG5jdAO2mHcsrJK+gqErq6+sj+Irg4+MEW2db8PVxgqyPFG8778XjCIvcgO68FPVMw01zt4s8gj+l0nxbFNA6KoSBbaOq5HqMMbWTBfDKKiqC9W/BgifhWAr0Hg8jnvB+Ig4Rt/v83P3qVZXcgiKO5uSTkVPgvvLJdLeL0zNznXTPPBm5BU5Qzi90A3ARhUWnv4ZNgK8PwQG+hAT4lryH+PsRGRpAbCNfgv39nDTP4wGeac52sL+zHxroR6CfjxOQxQnCPj7gWxKMa35U/8TP3q7pKhhjzoJzN4pUh+RVMO9B2LsGYvvDTe9Aiz41XatqkV9YxNHsfI5k55Ne/Mo6vn3EYzsjJ98NxscDcoEXQTcs0I+wQD8aBDmvhiEBxEaGEOzvS5C/D0F+vgS670FuWmBxmr+TFujnU3KsOF9xWnGgNcaYc5EFcG9k7IcFf3Ba3mHN4Op/Q4/rnRm96gBV5VBmLkkpWRzMyCkJwEdLBeIj2U5aerYTkCsSGuBLw5AAGgT5ER7kT7PwIDo08aNBkD8NgvwIC3K2w4OKA7S/R7B2tn0tuBpjzGmzAF6RgjxY/hJ8/RwU5MCQ++CCB5xZyGoZVeVwVj47Uo6xI+UYSSnH2JHqvO9MzSozIAf6+RAR7E/DEH8igv1p0TCIrjHhRAT7n5AeUfwe7E/DYH/Cg/3xt5HNxhhToyyAl+enL5zR5amJ0HEUXPJHiGpX07UiPSu/JDDvSDlGksf20ZzjQdrXR4htFExcVCj94iKJiwohLjqUmIjgksAc5G/TahpjTF1lAby01G3w2SPw03xnOceb34UOF531amTlFbB4awpbD2Sc0Jo+nHV8YRERaNEwmDbRoYzp1YK46FDaRIcQFxVKbKMQAvyslWyMMecqC+DFcjNg8VT47gXwC4KLnoIBd53VxSnyCor45qdDzF2/ly82HSArrxCAmIgg4qJCGdU9piRAt4kOpWVkiLWijTGmnvIqgIvIKOB5wBd4RVWfLXW8FfA60NDNM0lV54lIHPAjsMXNukxV73LP6QvMAIJx1hr/jaqe/vNCp6uoCDa8A188AZn7odfNzmNhDZqelY8vLFKW70jlo/V7+fSH/RzJyqdhiD9jerXgyvgYerdsZCtIGWOMOckpA7iI+AIvABcBycBKEZmrqps8sj0GvKOqL4lIV5yAHOce26aqvcoo+iXgDmC5m38U8OnpXshp2bMGPn0YkldA8z5w4yyITaj2j1VVvk9OZ+76vXz8/V4OHM0lJMCXi7o2ZUyv5pzXvrF1fxtzDsvPzyc5OZmcnJyaroqpJYKCgoiNjcXf/xSTgXnwpgXeH0hU1e0AIjIHGAN4BnAFwt3tCGBvRQWKSAwQrqrL3P2ZwFWczQD++f/B0n9AaDSMeQHib6r2x8J+OpDB3PV7+Wj9XpJSswjw9WFop8aMjm/OiC5NCAmwOxrG1AfJyck0aNCAuLi4WjH5j6lZqkpqairJycm0adPG6/O8iRgtgN0e+8nAgFJ5ngQ+F5FfA6HASI9jbURkLXAUeExVv3HLTC5VZguva10VotrDoHtg6EMQFFFtH5N8OIuP1u/jw3V72Lw/Ax+BQe2i+OWw9lzSrRkRId7/tWWMOTfk5ORY8DYlRISoqCgOHTpUqfOqqsk3Dpihqn8RkUHAGyLSHdgHtFLVVPee9wci0q0yBYvIncCdAK1ataqi6uIsDlJNDmXkMm/DPuau38vqnYcB6N2qIU9c2ZXLe8bQpIEtMGFMfWfB23g6nX8P3gTwPUBLj/1YN83Tz3DuYaOq34lIEBCtqgeBXDd9tYhsAzq658eeokzc86YD0wESEhLO/iA3LxUVKe+v3cMH6/awJDGFIoVOTRvw4CWdGB3fnJaRITVdRWOMASA1NZURI0YAsH//fnx9fWnc2FnPYcWKFQQElP/0zapVq5g5cybTpk2r8DMGDx7M0qVLq6zO9913H//973/ZvXs3PnVkFszq5k0AXwl0EJE2OEH2RuCmUnl2ASOAGSLSBQgCDolIYyBNVQtFpC3QAdiuqmkiclREBuIMYrsV+EfVXFLNeOXb7fxx3mZaRgZz97B2jI5vQadmtW/GNmOMiYqKYt26dQA8+eSThIWF8cADD5QcLygowM+v7PCQkJBAQsKpB/tWZfAuKiri/fffp2XLlnz99dcMHz68ysr2VNF110an/DNGVQuAXwHzcR4Je0dVN4rIZBEZ7Wb7HXCHiKwHZgMT3EfCLgC+F5F1wLvAXaqa5p7zS+AVIBHYxtkegV6FsvIK+NfX2zm/QzSLHxzOg5d0tuBtjKlTJkyYwF133cWAAQN46KGHWLFiBYMGDaJ3794MHjyYLVucp4EXLVrEFVdcATjB//bbb2fYsGG0bdv2hFZ5WFhYSf5hw4Zx7bXX0rlzZ26++WaKnxieN28enTt3pm/fvtx7770l5Za2aNEiunXrxt13383s2bNL0g8cOMDVV19NfHw88fHxJX80zJw5k549exIfH88tt9xScn3vvvtumfU7//zzGT16NF27dgXgqquuom/fvnTr1o3p06eXnPPZZ5/Rp08f4uPjGTFiBEVFRXTo0KHk3nVRURHt27ev9L3s0+XVnxqqOg/nUS/PtMc9tjcBQ8o47z3gvXLKXAV0r0xla6s3vttJ2rE87hvZwe5rGWMq5Q8fbWTT3qNVWmbX5uE8cWWlhhsBzuj4pUuX4uvry9GjR/nmm2/w8/NjwYIFPProo7z33sm/zjdv3szChQvJyMigU6dO3H333Sc9CrV27Vo2btxI8+bNGTJkCEuWLCEhIYFf/OIXLF68mDZt2jBu3Lhy6zV79mzGjRvHmDFjePTRR8nPz8ff3597772XoUOH8v7771NYWEhmZiYbN27k6aefZunSpURHR5OWllZuucXWrFnDDz/8UDIC/NVXXyUyMpLs7Gz69evHNddcQ1FREXfccUdJfdPS0vDx8WH8+PHMmjWL++67jwULFhAfH19yO6K62Y2EM5SVV8D0xU7ru2/ryJqujjHGnLbrrrsOX19n4qj09HSuu+46unfvzv3338/GjRvLPOfyyy8nMDCQ6OhomjRpwoEDB07K079/f2JjY/Hx8aFXr14kJSWxefNm2rZtWxI0ywvgeXl5zJs3j6uuuorw8HAGDBjA/PnzAfjqq6+4++67AfD19SUiIoKvvvqK6667jujoaAAiI0/9e7l///4nPL41bdo04uPjGThwILt37+ann35i2bJlXHDBBSX5isu9/fbbmTlzJuAE/okTJ57y86pK3ensr6XeXLaT1GN5/GZEh5quijGmDjqdlnJ1CQ0NLdn+v//7P4YPH877779PUlISw4YNK/OcwMDAkm1fX18KCspY+dCLPOWZP38+R44coUePHgBkZWURHBxcbnd7efz8/CgqKgKcru68vLySY57XvWjRIhYsWMB3331HSEgIw4YNq3DCnZYtW9K0aVO++uorVqxYwaxZsypVrzNhLfAzkJVXwL/de98Jcdb6NsacO9LT02nRwpmeY8aMGVVefqdOndi+fTtJSUkAvP3222Xmmz17Nq+88gpJSUkkJSWxY8cOvvjiC7KyshgxYgQvvfQSAIWFhaSnp3PhhRfy3//+l9TUVICSLvS4uDhWr14NwNy5c8nPzy/z89LT02nUqBEhISFs3ryZZcuWATBw4EAWL17Mjh07TigX4Oc//znjx48/oQfjbLAAfgZmLdtlrW9jzDnpoYce4pFHHqF3796VajF7Kzg4mBdffJFRo0bRt29fGjRoQETEiZNqZWVl8dlnn3H55ZeXpIWGhnLeeefx0Ucf8fzzz7Nw4UJ69OhB37592bRpE926deP3v/89Q4cOJT4+nt/+9rcA3HHHHXz99dfEx8fz3XffndDq9jRq1CgKCgro0qULkyZNYuDAgQA0btyY6dOnM3bsWOLj47nhhhtKzhk9ejSZmZlntfscQGpi/ZDTlZCQoKtWrarpagBO6/uCPy+kc7Nw3vx56YnpjKk5Ez9zfom8Nuq1Gq6JKc+PP/5Ily5daroaNS4zM5OwsDBUlXvuuYcOHTpw//3313S1Km3VqlXcf//9fPPNN2dUTln/LkRktaqW+dyetcBP06xlu0jJzOM3I631bYwxp+Pll1+mV69edOvWjfT0dH7xi1/UdJUq7dlnn+Waa67hmWeeOeufbYPYTkN2XiH/XryNIe2j6Gf3vo0x5rTcf//9dbLF7WnSpElMmjSpRj7bAvhpmLV8JymZebw4omNNV8UYY0w9ZV3olZSdV8i/vt7OkPZR9G9jrW9jjDE1wwJ4JTmt71x+Y61vY4wxNcgCeCUUt74Ht7PWtzHGmJplAbwS3lqxy21928hzY0zdNXz48JLpSIv9/e9/L5mWtCzDhg2j+DHeyy67jCNHjpyU58knn2Tq1KkVfvYHH3zApk2bSvYff/xxFixYUJnqV+i+++6jRYsWJbOuncssgHspJ7+Qf329jUFtoxjQNqqmq2OMMadt3LhxzJkz54S0OXPmVLigiKd58+bRsGHD0/rs0gF88uTJjBw58rTKKq30sqPVpTomtjkdFsC9NGv5Lg5l5Npz38aYOu/aa6/lk08+KZkPPCkpib1793L++edz9913k5CQQLdu3XjiiSfKPD8uLo6UlBQApkyZQseOHTnvvPNKlhwF5xnvfv36ER8fzzXXXENWVhZLly5l7ty5PPjgg/Tq1Ytt27adsMznl19+Se/evenRowe33347ubm5JZ/3xBNP0KdPH3r06MHmzZvLrFd9W3bUHiPzQnHre2DbSAZa69sYU5U+nQT7N1Rtmc16wKXPlns4MjKS/v378+mnnzJmzBjmzJnD9ddfj4gwZcoUIiMjKSwsZMSIEXz//ff07NmzzHJWr17NnDlzWLduHQUFBfTp04e+ffsCMHbsWO644w4AHnvsMf7zn//w61//mtGjR3PFFVdw7bXXnlBWTk4OEyZM4Msvv6Rjx47ceuutvPTSS9x3330AREdHs2bNGl588UWmTp3KK6+8clJ96tuyo9YC98Jbxa1vG3lujDlHeHaje3afv/POO/Tp04fevXuzcePGE7q7S/vmm2+4+uqrCQkJITw8nNGjR5cc++GHHzj//PPp0aMHs2bNKnc50mJbtmyhTZs2dOzo/J697bbbWLx4ccnxsWPHAtC3b9+SBVA81cdlR71qgYvIKOB5wBd4RVWfLXW8FfA60NDNM0lV54nIRcCzQACQBzyoql+55ywCYoBst5iLVfXgGV9RFfNsfQ9qZ61vY0wVq6ClXJ3GjBnD/fffz5o1a8jKyqJv377s2LGDqVOnsnLlSho1asSECRMqXEqzIhMmTOCDDz4gPj6eGTNmsGjRojOqb/GSpOUtR1oflx09ZQtcRHyBF4BLga7AOBHpWirbY8A7qtobuBF40U1PAa5U1R7AbcAbpc67WVV7ua9aF7wBZq/YxUFrfRtjzjFhYWEMHz6c22+/vaT1ffToUUJDQ4mIiODAgQN8+umnFZZxwQUX8MEHH5CdnU1GRgYfffRRybGMjAxiYmLIz88/IVg1aNCAjIyMk8rq1KkTSUlJJCYmAvDGG28wdOhQr6+nPi476k0Xen8gUVW3q2oeMAcYUyqPAuHudgSwF0BV16rqXjd9IxAsIoHUETn5hby0aBsD2ljr2xhz7hk3bhzr168vCeDx8fH07t2bzp07c9NNNzFkyJAKz+/Tpw833HAD8fHxXHrppfTr16/k2FNPPcWAAQMYMmQInTt3Lkm/8cYbee655+jduzfbtm0rSQ8KCuK1117juuuuo0ePHvj4+HDXXXd5dR31ddnRUy4nKiLXAqNU9efu/i3AAFX9lUeeGOBzoBEQCoxU1dVllHOXqo509xcBUUAh8B7wtJ6iMmd7OdEZS3bw5EebeOuOAQxuF33WPteYM2HLidZ+tpxo/XSqZUdrajnRccAMVY0FLgPeEJGSskWkG/AnwHOtuJvdrvXz3dctZRUsIneKyCoRWXWmQ+4rIye/kBcXbaN/m0gG2chzY4wxZ6A6lh31JoDvAVp67Me6aZ5+BrwDoKrfAUFANICIxALvA7eqakl/iarucd8zgLdwuupPoqrTVTVBVRPOdMh9Zcxx733fN7IDInLWPtcYY8y5Z9KkSezcuZPzzjuvysr0JoCvBDqISBsRCcAZpDa3VJ5dwAgAEemCE8APiUhD4BOcUelLijOLiJ+IFAd4f+AK4IczvZiqkpNfyEtfb6N/nLW+jTHG1E6nDOCqWgD8CpgP/Igz2nyjiEwWkeKH/n4H3CEi64HZwAT3fvavgPbA4yKyzn01AQKB+SLyPbAOp0X/clVf3Ol6e+VuDhy11rcxxpjay6vnwFV1HjCvVNrjHtubgJOGK6rq08DT5RTb1/tqnj3FI8/7x9nIc2OMMbWXzcRWyjurdrP/aA6/sda3McaYWswCuIfcgkJeXLiNfnGNGGytb2PMOWzKlCl069aNnj170qtXL5YvXw44y4pmZWVVurwZM2awd+/eMo9NmDCBNm3a0KtXL3r16sW0adOqZBnRDRs2lJQZGRlZ8hmns7pZeUuk1ma2mImHd1Y6re+/XB9vrW9jzDnru+++4+OPP2bNmjUEBgaSkpJSMmXo3//+d8aPH09ISIjX5RUWFjJjxgy6d+9O8+bNy8zz3HPPnbSAyZnq0aMH69atA5w/EspaJMVb8+bNO3WmWsZa4K7cgkJeWLiNhNbW+jbGnNv27dtHdHR0yfzi0dHRNG/enGnTprF3716GDx/O8OHDAcpdXjQuLo6HH36YPn36MHv2bFatWsXNN99Mr169yM7OLvNzPXku21necqHHjh3j9ttvp3///vTu3ZsPP/zQq+sbNmwYxZN+paSkEBcXBzi9BGPHjmXUqFF06NCBhx566ITrSUlJISkpiS5dunDHHXfQrVs3Lr744pLrWblyZUmPxYMPPkj37t29qk91sRa4q7j1PfU6a30bY86eP634E5vTyl7f+nR1juzMw/0fLvf4xRdfzOTJk+nYsSMjR47khhtuYOjQodx777389a9/ZeHChSWrdFW0vGhUVBRr1qwB4JVXXmHq1KkkJJQ5aRgPPvggTz/tjGl+443Sy2KUvVzolClTuPDCC3n11Vc5cuQI/fv3Z+TIkeVOb+qNdevWsXbtWgIDA+nUqRO//vWvadmy5Ql5fvrpJ2bPns3LL7/M9ddfz3vvvcf48eOZOHEiL7/8MoMGDWLSpEmnXYeqYi1w3Hvfi7bRt3UjhrS31rcx5twWFhbG6tWrmT59Oo0bN+aGG25gxowZZeataHlRz3m+T+W5555j3bp1rFu3rmTFME9lLRf6+eef8+yzz9KrV6+S1b527drl/YWWYcSIEURERBAUFETXrl3ZuXPnSXmK76V71ufIkSNkZGQwaNAgAG666aYzqkdVsBY48M6q/2/vzuOjqu7/j78+mawkIQTCvgZFlE1AwLpDbRGXita92AK2Wq1Lbb9u/dXta+tXW7uJtSqtim2tuNaiYrUqitaNxQiCIoggYQ9LSEJCtvP7494kkzBJJpDJzCTv5+Mxj3vvuefeOWfuzHzmnnvnnHw2F5bx63NH6exbRNpUU2fKkRQIBJg4cSITJ05k5MiRPPbYY8yYMaNenuaGFz2YM+GGQg0X6pzj2WefZejQoS3aV/CQoA2H+Kx5nobP1VSecC4JREOHPwPfV1nFAwvWcNTAbI4/VAOWiEj7t2rVKlavXl27nJeXx8CBA4H6w322ZHjRxoYJPRinnHIK9913HzXjXH300UdhbRc8JGjNdfaD1aVLFzIzM2vv1p87d26r7PdgdPgA/vTifDYVlvHjk/W/bxHpGIqLi5k+fTrDhg1j1KhRrFy5kttvvx2Ayy67pn6fTQAAIABJREFUjClTpjBp0qQWDS86Y8YMLr/88rBvYgvHLbfcQkVFBaNGjWL48OHccsstYW133XXX8cADDzBmzBgKCgpapSwADz/8MJdeeimjR4+mpKSErKysVtv3gWh2ONFY0trDie6rrGLSPW/SKyuVZ684VgFc2gUNJxr7NJxofCouLiYjIwPwRhfbvHkz9957b6vtv6XDiXboa+DPLPHOvu8+R9e+RUSkaS+99BJ33XUXlZWVDBw4sNEb/9pKhw3g5ZXV3P/GGsYM6MIJQ3TtW0REmnbBBRe06M77SOuw18CfXrKBTYVlXPuNw3T2LSIicafDBvDPtxQxdkAXTtTZt4hEQTzdfySRdyDvhw7bhP6/U0dQVlGls28RaXOpqans2LGDbt266TtIcM6xY8cOUlNTW7Rdhw3gAKlJgWgXQUQ6oH79+pGfn8/27dujXRSJEampqfTr169F24QVwM1sCnAvEAD+4py7u8H6AcBjQBc/z03Oufn+up8B3weqgGucc6+Es08RkfYqKSmJ3NzcaBdD4lyz18DNLADcD5wKDAMuMrNhDbLdDDzlnBsDXAj8yd92mL88HJgC/MnMAmHuU0RERBoRzk1sE4A1zrm1zrlyYC4wtUEeB3T257OAmlHdpwJznXP7nHNfAmv8/YWzTxEREWlEOAG8L7AhaDnfTwt2O3CxmeUD84Grm9k2nH2KiIhII1rrJraLgDnOud+a2THA38ysVUY6N7PLgMv8xWIzW9Ua+/XlAK3XUW50qS6xJ6r1mMOc1txdezkm0H7q0l7qAapLUwY2tiKcAL4RCB7tvJ+fFuz7eNe4cc69Z2apeJVoatvm9om/v9nA7DDK2WJmtrixPmbjjeoSe9pLPUB1iUXtpR6guhyocJrQFwFDzCzXzJLxbkqb1yDPV8DJAGZ2BJAKbPfzXWhmKWaWCwwBPgxznyIiItKIZs/AnXOVZnYV8AreX74ecc6tMLM7gMXOuXnA/wB/NrOf4N3QNsN53cqsMLOngJVAJXClc64KINQ+I1A/ERGRdimsa+D+f7rnN0i7NWh+JRByoFjn3J3AneHsMwoi0jQfJapL7Gkv9QDVJRa1l3qA6nJA4mo8cBEREfF02MFMRERE4lmHCOBmNsXMVpnZGjO7KcT6FDN70l//gZkNavtSNs/M+pvZAjNbaWYrzOzHIfJMNLNCM8vzH7eG2le0mdk6M1vul3FxiPVmZrP8Y7LMzMZGo5zNMbOhQa91npntMbNrG+SJ2WNiZo+Y2TYz+yQorauZ/cfMVvvT7Ea2ne7nWW1m09uu1KE1Upd7zOwz/z30TzPr0si2Tb4f21Ij9bjdzDYGvYdOa2TbJr/r2lojdXkyqB7rzCyvkW1j5pj45Qn5/RvVz4tzrl0/8G6S+wIYDCQDHwPDGuT5EfCgP38h8GS0y91IXXoDY/35TODzEHWZCLwY7bKGUZd1QE4T608DXgYM+BrwQbTLHEadAsAWYGC8HBPgRGAs8ElQ2q/xxjMAuAn4VYjtugJr/Wm2P58dg3WZDCT6878KVRd/XZPvxxiox+3Adc1s1+x3XSzUpcH63wK3xvox8csT8vs3mp+XjnAGHk63rVPxBmMBeAY42Sz2xvhzzm12zi3154uAT2m/PdhNBf7qPO8DXcysd7QL1YyTgS+cc+ujXZBwOecWAjsbJAd/Hh4Dzgqx6SnAf5xzO51zu4D/4PcFES2h6uKce9U5V+kvvo/X50RMa+SYhCPmuqhuqi7+d+z5wBNtWqgD1MT3b9Q+Lx0hgIfTbWttHv/DXgh0a5PSHSC/mX8M8EGI1ceY2cdm9rKZDW/TgoXPAa+a2RLzettrKB67272Qxr+M4uGY1OjpnNvsz28BeobIE4/H5xK8Vp1Qmns/xoKr/EsBjzTSTBtvx+QEYKtzbnUj62P2mDT4/o3a56UjBPB2x8wygGeBa51zexqsXorXhHskcB/wfFuXL0zHO+fG4o1Id6WZnRjtAh0M8zokOhN4OsTqeDkm+3Fe+1/c/1XFzH6O1xfF441kifX34wPAIcBoYDNe03O8u4imz75j8pg09f3b1p+XjhDAw+kKtjaPmSXijai2o01K10JmloT35nncOfdcw/XOuT3OuWJ/fj6QZGY5bVzMZjnnNvrTbcA/8Zr/goVz3GLJqcBS59zWhivi5ZgE2VpzucKfbguRJ26Oj5nNAM4ApvlfsPsJ4/0YVc65rc65KudcNfBnQpcvno5JIvBt4MnG8sTiMWnk+zdqn5eOEMDD6bZ1HlBzV+C5wBuNfdCjyb9m9DDwqXPud43k6VVz/d7MJuAd45j6MWJm6WaWWTOPd6PRJw2yzQO+Z56vAYVBzVSxqNGziXg4Jg0Efx6mA/8KkecVYLKZZfvNuZP9tJhiZlOAG4AznXN7G8kTzvsxqhrc/3E2ocsXT11UfwP4zDmXH2plLB6TJr5/o/d5ifadfW3xwLuj+XO8OzR/7qfdgfehBq/v9qfxxiv/EBgc7TI3Uo/j8ZpnlgF5/uM04HLgcj/PVcAKvDtQ3weOjXa5Q9RjsF++j/2y1hyT4HoYcL9/zJYD46Jd7ibqk44XkLOC0uLimOD96NgMVOBdl/s+3v0frwOrgdeArn7eccBfgra9xP/MrAFmxmhd1uBde6z5vNT826QPML+p92OM1eNv/udgGV7A6N2wHv7yft91sVYXP31OzecjKG/MHhO/TI19/0bt86Ke2EREROJQR2hCFxERaXcUwEVEROKQAriIiEgcUgAXERGJQwrgIiIicUgBXEREJA4pgIuIiMQhBXCRZvgDkIQ1fm9L8kaTP9byNyKw3zfN7Af+/DQzezWcvAfwPAPMrNjMAgdaVpF4pwAu7ZL/5V7zqDaz0qDlaS3Zl3PuVOfcY83nbFneWGRmN5nZwhDpOWZWbmYjwt2Xc+5x59zkVipXvR8czrmvnHMZzrmq1th/g+dyZnZoa+9XpLUpgEu75H+5ZzjnMoCvgG8FpdWOSOUPqiB1/g4ca2a5DdIvBJY752Kqj3CRjkwBXDoUM5toZvlmdqOZbQEe9QcYeNHMtpvZLn++X9A2wc3CM8zsHTP7jZ/3SzM79QDz5prZQjMrMrPXzOx+M/t7I+UOp4y/MLP/+vt7NXjEMzP7rpmtN7Md5g2tGZLzBpd4A/hug1XfA/7aXDkalHmGmb0TtPxNM/vMzArN7I94/d3XrDvEzN7wy1dgZo+bWRd/3d+AAcALfgvKDWY2yD9TTvTz9DGzeWa208zWmNmlQfu+3cyeMrO/+q/NCjMb19hr0Bgzy/L3sd1/LW82swR/3aFm9pZftwIze9JPNzP7vZltM7M9Zra8Ja0YIk1RAJeOqBfQFRgIXIb3OXjUXx4AlAJ/bGL7o4FVQA7wa+BhM7MDyPsPvMFzugG3s3/QDBZOGb8DzAR6AMnAdQBmNgxvPOnv4g0Y0Q1vOMPGPBZcFjMbijcO9T/CLMd+/B8TzwE3470WXwDHBWcB7vLLdwTe0Iu3Azjnvkv9VpRfh3iKuXiDZfTBG1Hw/8zs60Hrz/TzdMEbDKTZModwH95Qw4OBk/B+1Mz01/0CeBXIxntt7/PTJwMnAof5255PbI9EJ3FEAVw6omrgNufcPudcqXNuh3PuWefcXudcEXAn3hd0Y9Y75/7sX399DOgN9GxJXjMbAIwHbnXOlTvn3qGJoR/DLOOjzrnPnXOlwFN4QRe8gPaic26hc24fcIv/GjTmn34Zj/WXvwe87JzbfgCvVY3TgBXOuWeccxXAH4AtQfVb45z7j39MtgO/C3O/mFl/vB8DNzrnypxzecBf/HLXeMc5N98/Dn8Djgxn30HPEcC7jPAz51yRc24d8FvqfuhU4P2o6eOX4Z2g9EzgcMCcc5+62B4WV+KIArh0RNudc2U1C2bWycwe8ptF9wALgS7W+B3OwYGnZozpjBbm7QPsdPXHqN7QWIHDLOOWoPm9QWXqE7xv51wJTZwF+mV6Gn88dmAa8NcWlCOUhmVwwctm1tPM5prZRn+/f8c7Uw9HzWtZFJS2HugbtNzwtUm1lt3/kAMk+fsN9Rw34LUifOg30V8C4Jx7A+9s/35gm5nNNrPOLXhekUYpgEtH1HAM3f8BhgJHO+c64zV5QtA12gjYDHQ1s05Baf2byH8wZdwcvG//Obs1s81jeM2938Q7g3zhIMvRsAxG/fr+H95xGenv9+IG+2xq3ONNeK9lZlDaAGBjM2VqiQLqzrL3ew7n3Bbn3KXOuT7AD4E/mX8nu3NulnPuKGAYXlP69a1YLunAFMBFvABVCuw2s67AbZF+QufcemAxcLuZJZvZMcC3IlTGZ4AzzOx4M0sG7qD5z/7bwG5gNjDXOVd+kOV4CRhuZt/2z3yvwbsXoUYmUAwUmllf9g9yW/GuPe/HObcBeBe4y8xSzWwU8H28s/gDlezvK9XMUv20p4A7zSzTzAYCP615DjM7L+hmvl14PziqzWy8mR1tZklACVBG05cvRMKmAC7iXY9NwzvLeh/4dxs97zTgGLzm7F8CTwL7Gsl7wGV0zq0ArsS7CW0zXoDJb2Ybh9dsPtCfHlQ5nHMFwHnA3Xj1HQL8NyjL/wJjgUK8YP9cg13cBdxsZrvN7LoQT3ERMAjvbPyfePc4vBZO2RqxAu+HSs1jJnA1XhBeC7yD93o+4ucfD3xgZsV49zL82Dm3FugM/BnvNV+PV/d7DqJcIrXM+5yKSLT5fz36zDkX8RYAEYl/OgMXiRK/efUQM0swsynAVOD5aJdLROJDRAK4mT3id1wQstcmv3ODWX6HC8vMbGwkyiES43oBb+Jd+50FXOGc+yiqJRKRuBGRJnQzOxHvS+mvzrn9eh0ys9PwriedhtfRxb3OuaNbvSAiIiLtVETOwJ1zC4GdTWSZihfcnXPufbz/kfaORFlERETao2hdA+9L/U4r8qnf6YKIiIg0IeZHYjKzy/D6qyY9Pf2oww8/PMolEolt6/asA2BQ50FRLYeIHLwlS5YUOOe6h1oXrQC+kfq9MPWjkV6TnHOz8TqTYNy4cW7x4sWRL51IHJv5b298jUenPBrlkojIwTKz9Y2ti1YT+jz8fpbN7GtAoTr4FxERCV9EzsDN7AlgIpBjZvl43S0mATjnHgTm492BvgZvYIGZofckIiIioUQkgDvnLmpmvcPr2lFEREQOQMzfxCYiIp6Kigry8/MpKytrPrPEldTUVPr160dSUlLY2yiAi4jEifz8fDIzMxk0aBDeiKzSHjjn2LFjB/n5+eTm5oa9nfpCFxGJE2VlZXTr1k3Bu50xM7p169bilhUFcBGROKLg3T4dyHFVABcRkbDs2LGD0aNHM3r0aHr16kXfvn1rl8vLy5vcdvHixVxzzTXNPsexxx7bKmV98803OeOMM1plX7FK18BFRCQs3bp1Iy8vD4Dbb7+djIwMrrvuutr1lZWVJCaGDivjxo1j3LhxzT7Hu+++2zqF7QB0Bi4iIgdsxowZXH755Rx99NHccMMNfPjhhxxzzDGMGTOGY489llWrVgH1z4hvv/12LrnkEiZOnMjgwYOZNWtW7f4yMjJq80+cOJFzzz2Xww8/nGnTplEzeub8+fM5/PDDOeqoo7jmmmtadKb9xBNPMHLkSEaMGMGNN94IQFVVFTNmzGDEiBGMHDmS3//+9wDMmjWLYcOGMWrUKC688MKDf7Famc7ARUTkoOTn5/Puu+8SCATYs2cPb7/9NomJibz22mv8v//3/3j22Wf32+azzz5jwYIFFBUVMXToUK644or9/kL10UcfsWLFCvr06cNxxx3Hf//7X8aNG8cPf/hDFi5cSG5uLhdd1GS3I/Vs2rSJG2+8kSVLlpCdnc3kyZN5/vnn6d+/Pxs3buSTTz4BYPfu3QDcfffdfPnll6SkpNSmxRIFcBGROPS/L6xg5aY9rbrPYX06c9u3hrd4u/POO49AIABAYWEh06dPZ/Xq1ZgZFRUVIbc5/fTTSUlJISUlhR49erB161b69etXL8+ECRNq00aPHs26devIyMhg8ODBtX+3uuiii5g9e3ZY5Vy0aBETJ06ke3dvbJBp06axcOFCbrnlFtauXcvVV1/N6aefzuTJkwEYNWoU06ZN46yzzuKss85q8esSaWpCFxGRg5Kenl47f8sttzBp0iQ++eQTXnjhhUb/GpWSklI7HwgEqKysPKA8rSE7O5uPP/6YiRMn8uCDD/KDH/wAgJdeeokrr7ySpUuXMn78+Ig9/4HSGbiISBw6kDPltlBYWEjfvn0BmDNnTqvvf+jQoaxdu5Z169YxaNAgnnzyybC3nTBhAtdccw0FBQVkZ2fzxBNPcPXVV1NQUEBycjLnnHMOQ4cO5eKLL6a6upoNGzYwadIkjj/+eObOnUtxcTFdunRp9TodKAVwERFpNTfccAPTp0/nl7/8Jaeffnqr7z8tLY0//elPTJkyhfT0dMaPH99o3tdff71es/zTTz/N3XffzaRJk3DOcfrppzN16lQ+/vhjZs6cSXV1NQB33XUXVVVVXHzxxRQWFuKc45prromp4A1gNXf1xQONBy7SPI0H3n59+umnHHHEEdEuRtQVFxeTkZGBc44rr7ySIUOG8JOf/CTaxTpooY6vmS1xzoX8/52ugYuISFz585//zOjRoxk+fDiFhYX88Ic/jHaRokJN6CIiEld+8pOftIsz7oOlM3AREZE4pAAuIiIShxTARURE4pACuIiISBxSABcRkbBMmjSJV155pV7aH/7wB6644opGt5k4cSI1f/897bTTQvYpfvvtt/Ob3/ymyed+/vnnWblyZe3yrbfeymuvvdaS4ocUz8OOKoCLiEhYLrroIubOnVsvbe7cuWEPKDJ//vwD7gylYQC/4447+MY3vnFA+2ovFMBFRCQs5557Li+99BLl5eUArFu3jk2bNnHCCSdwxRVXMG7cOIYPH85tt90WcvtBgwZRUFAAwJ133slhhx3G8ccfXzvkKHj/8R4/fjxHHnkk55xzDnv37uXdd99l3rx5XH/99YwePZovvviCGTNm8MwzzwBej2tjxoxh5MiRXHLJJezbt6/2+W677TbGjh3LyJEj+eyzz8KuazwMO6oALiIiYenatSsTJkzg5ZdfBryz7/PPPx8z484772Tx4sUsW7aMt956i2XLljW6nyVLljB37lzy8vKYP38+ixYtql337W9/m0WLFvHxxx9zxBFH8PDDD3Psscdy5plncs8995CXl8chhxxSm7+srIwZM2bw5JNPsnz5ciorK3nggQdq1+fk5LB06VKuuOKKZpvpa9QMO/rGG2+Ql5fHokWLeP7558nLy6sddnT58uXMnOn1enj33Xfz0UcfsWzZMh588MEWvaYHQx25iIjEo5dvgi3LW3efvUbCqXc3maWmGX3q1KnMnTuXhx9+GICnnnqK2bNnU1lZyebNm1m5ciWjRo0KuY+3336bs88+m06dOgFw5pln1q775JNPuPnmm9m9ezfFxcWccsopTZZn1apV5ObmcthhhwEwffp07r//fq699lrA+0EAcNRRR/Hcc8+F8SLEz7CjOgMXEZGwTZ06lddff52lS5eyd+9ejjrqKL788kt+85vf8Prrr7Ns2TJOP/30RocRbc6MGTP44x//yPLly7ntttsOeD81aoYkbY3hSGNt2FGdgYuIxKNmzpQjJSMjg0mTJnHJJZfU3ry2Z88e0tPTycrKYuvWrbz88stMnDix0X2ceOKJzJgxg5/97GdUVlbywgsv1PZnXlRURO/evamoqODxxx+vHZo0MzOToqKi/fY1dOhQ1q1bx5o1azj00EP529/+xkknnXRQdYyXYUcVwEVEpEUuuugizj777No70o888kjGjBnD4YcfTv/+/TnuuOOa3H7s2LFccMEFHHnkkfTo0aPekKC/+MUvOProo+nevTtHH310bdC+8MILufTSS5k1a1btzWsAqampPProo5x33nlUVlYyfvx4Lr/88hbVJ16HHdVwoiLtjIYTbb80nGj7puFERUREOgAFcBERkTikAC4iIhKHIhbAzWyKma0yszVmdlOI9QPMbIGZfWRmy8zstEiVRUREpL2JSAA3swBwP3AqMAy4yMyGNch2M/CUc24McCHwp0iURUREpD2K1Bn4BGCNc26tc64cmAtMbZDHAZ39+SxgU4TKIiIi0u5EKoD3BTYELef7acFuBy42s3xgPnB1hMoiIiKt5M4772T48OGMGjWK0aNH88EHHwDesKJ79+5t8f7mzJnDpk2hz99mzJhBbm4uo0ePZvTo0cyaNatVhhFdvnx57T67du1a+xwHMrpZY0OktoVoduRyETDHOfdbMzsG+JuZjXDOVQdnMrPLgMsABgwYEIViiogIwHvvvceLL77I0qVLSUlJoaCgoHZksj/84Q9cfPHFtf2bh6Oqqoo5c+YwYsQI+vTpEzLPPffcw7nnntsq5a8xcuRI8vLyAO9HwhlnnHHAzzF//vzWLFqLROoMfCPQP2i5n58W7PvAUwDOufeAVCCn4Y6cc7Odc+Occ+NqOpYXEZG2t3nzZnJycmr7F8/JyaFPnz7MmjWLTZs2MWnSJCZNmgTQ6PCigwYN4sYbb2Ts2LE88cQTLF68mGnTpjF69GhKS0ubLUPwMKKNDRdaUlLCJZdcwoQJExgzZgz/+te/wqrfxIkTqeksrKCggEGDBgFeK8G3v/1tpkyZwpAhQ7jhhhvq1aegoIB169ZxxBFHcOmllzJ8+HAmT55cW59FixbVtlhcf/31jBgxIqzyNCdSAXwRMMTMcs0sGe8mtXkN8nwFnAxgZkfgBfDtESqPiIgcpMmTJ7NhwwYOO+wwfvSjH/HWW28BcM0119CnTx8WLFjAggULAJocXrRbt24sXbqUiy++mHHjxvH444+Tl5dHWlrafs9ZMwb46NGjWb58/9HXQg0Xeuedd/L1r3+dDz/8kAULFnD99ddTUlJyUHXPy8urHbL0ySefZMOGDfvlWb16NVdeeSUrVqygS5cuPPvsswDMnDmThx56iLy8PAKBwEGVI1hEmtCdc5VmdhXwChAAHnHOrTCzO4DFzrl5wP8Afzazn+Dd0DbDxVO/riIiUfSrD3/FZzs/a9V9Ht71cG6ccGOj6zMyMliyZAlvv/02CxYs4IILLuDuu+9mxowZ++VtanjRCy64IOwyNdeEHmq40FdffZV58+bVBvSysjK++uqrg+qG9uSTTyYrKwuAYcOGsX79evr3718vT8219JryrFu3jt27d1NUVMQxxxwDwHe+8x1efPHFAy5HsIhdA3fOzce7OS047dag+ZVA0z3ei4hITAkEAkycOJGJEycycuRIHnvssf0CeM3woosWLSI7O5sZM2bUGxY0PT291coTarhQ5xzPPvssQ4cObdG+EhMTawcqaTiMac3zNHyupvKEc0ngYGg0MhGRONTUmXKkrFq1ioSEBIYMGQJ4zcoDBw4E6ob7zMnJadHwoo0NE3owTjnlFO677z7uu+8+zIyPPvqIMWPGNLvdoEGDWLJkCRMmTKg34tnB6NKlC5mZmXzwwQccffTRtSO4tQYFcBERCUtxcTFXX301u3fvJjExkUMPPZTZs2cDcNlllzFlypTaa+HhDi86Y8YMLr/8ctLS0njvvfdCXgdvqVtuuYVrr72WUaNGUV1dTW5ubljN1tdddx3nn38+s2fP5vTTTz/octR4+OGHufTSS0lISOCkk06qbYo/WBpOVKSd0XCi7ZeGE41PxcXFZGRkAHD33XezefNm7r333v3ytXQ4UZ2Bi4iIRNBLL73EXXfdRWVlJQMHDmTOnDmtsl8FcBERkQi64IILWnTnfbg0nKiIiEgcUgAXEYkj8XTfkoTvQI6rAriISJxITU1lx44dCuLtjHOOHTt2kJqa2qLtdA1cRCRO9OvXj/z8fLZvV6/T7U1qair9+vVr0TYK4CIicSIpKYnc3NxoF0NihJrQRURE4pACuIiISBxSABcREYlDCuAiIiJxSAFcREQkDimAi4iIxCEFcBERkTikAC4iIhKHFMBFRETikAK4iIhIHFIAFxERiUMK4CIiInFIAVxERCQOKYCLiIjEIQVwERGROKQALiIiEocUwEVEROKQAriIiEgcUgAXERGJQwrgIiIicShiAdzMppjZKjNbY2Y3NZLnfDNbaWYrzOwfkSqLiIhIe5MYiZ2aWQC4H/gmkA8sMrN5zrmVQXmGAD8DjnPO7TKzHpEoi4iISHsUqTPwCcAa59xa51w5MBeY2iDPpcD9zrldAM65bREqi4iISLsTqQDeF9gQtJzvpwU7DDjMzP5rZu+b2ZQIlUVERKTdiUgTegueewgwEegHLDSzkc653cGZzOwy4DKAAQMGtHUZRUREYlKkzsA3Av2Dlvv5acHygXnOuQrn3JfA53gBvR7n3Gzn3Djn3Lju3btHqLgiIiLxJVIBfBEwxMxyzSwZuBCY1yDP83hn35hZDl6T+toIlUdERKRdiUgAd85VAlcBrwCfAk8551aY2R1mdqaf7RVgh5mtBBYA1zvndkSiPCIiIu1NxK6BO+fmA/MbpN0aNO+An/oPERERaQH1xCYiIhKHFMBFRETikAK4iIhIHFIAFxERiUMK4CIiInFIAVxERCQOKYCLiIjEIQVwERGROKQALiIiEocUwEVEROKQAriIiEgcUgAXERGJQwrgIiIicUgBXEREJA4pgIuIiMQhBXAREZE4pAAuIiIShxTARURE4pACuIiISBxSABcREYlDCuAiIiJxSAFcREQkDimAi4iIxCEFcBERkTikAC4iIhKHFMBFRETikAK4iIhIHFIAFxERiUMK4CIiInGowwbwbXvK2Li7NNrFEBEROSAdNoDf/PwnnPqHhby0bHO0iyIiItJiEQvgZjbFzFaZ2Rozu6mJfOeYmTOzcZEqSyg/P/0IcrtncOU/lnLDMx+zt7yyLZ9k86i7AAAeF0lEQVReRETkoEQkgJtZALgfOBUYBlxkZsNC5MsEfgx8EIlyNGVgt3SeufwYrpx0CE8vyeeMWe/wycbCti6GiIjIAYnUGfgEYI1zbq1zrhyYC0wNke8XwK+AsgiVo0lJgQSuP+Vw/vGDr7G3vIqz//Rf/rxwLdXVLhrFERERCVukAnhfYEPQcr6fVsvMxgL9nXMvRagMYTvmkG68/OMTmDS0B3fO/5Tpj37ItqKo/KYQEREJS1RuYjOzBOB3wP+EkfcyM1tsZou3b98esTJlpyfz0HeP4s6zR7Bo3U5O/cPbvPHZ1og9n4iIyMGIVADfCPQPWu7np9XIBEYAb5rZOuBrwLxQN7I552Y758Y558Z17949QsX1mBnTjh7IC1cdT/fMFC6Zs5jb562grKIqos8rIiLSUpEK4IuAIWaWa2bJwIXAvJqVzrlC51yOc26Qc24Q8D5wpnNucYTK0yJDemby/JXHMfO4Qcx5dx1n3f9fVm8tinaxREREakUkgDvnKoGrgFeAT4GnnHMrzOwOMzszEs/Z2lKTAtz2reE8OmM824v2ccZ97/D399fjnG5wExGR6EuM1I6dc/OB+Q3Sbm0k78RIleNgTTq8By9fewL/89TH3Pz8Jyz8fDu/OmcU2enJ0S6aiIh0YB22J7aW6JGZymMzJ3Dz6UewYNU2pty7kHfXFES7WCIi0oEpgIcpIcH4wQmD+eePjiM9JZFpD3/Ar/79GRVV1dEumoiIdEAK4C00om8WL159PBeM688Db37BuQ+8y/odJdEuloiIdDAK4AegU3Iid58zij9NG8uXBSWcdu/bPLc0Xze4iYhIm4nYTWwdwWkje3Nk/y78ZG4eP33qY/6zciuThvYgt3s6g3PS6ZqejJlFu5giItIOKYAfpL5d0njisq9x/4I13L9gDS9/sqV2XefURHK7Z3BITjq5Oel+YM9gUE4nOiXrpRcRkQPXcaPIh3+GrSug95Heo8cwSEo9oF0FEoxrTh7CjyYewsbdpawtKOHL7SWsLSjmy4IS3l+7g+c+2lhvm95ZqeTmpDO4ezq5ORkM9uf7dkkjMaArGyIi0rSOG8B3r4dPnoMlj3rLCYnQ/Yi6gN77SOg1ApLTw95lYiCBgd3SGdgtnUlD668rLa/iy4IS/1HM2u0lrC0oYV7eJvaU1Y1FnhQwBnTt5AX17unkZCSTlZZE59Qkb5pWN81MSSQhQU30IiIdUccN4JN/Cd/8BexaB5s/rnt8/jLk/d3LYwnQbUj9oN57FKRmtfjp0pIDDOvTmWF9OtdLd86xs6ScLwu8gP5lQQlrt3tn7gtXb6e8svG/qZlBZkpiXVCvDfKJdcud6gf/TskBUhITSPYfKYFA7XxAPwZEROJGxw3g4EXArrneY/hZXppzsGdT/aC+7h1Y/lTddtm5DYL6aEjvdoBFMLplpNAtI4Vxg7rWW+eco6S8isLSCgr3VrCnrILC0gr2lPrTssq6eX+6tqDYX66ktIWDsAQSjORAUHCvCfSBhHpBvy5PgORAAkkBIzFgJCbUzCeQlOBNEwNGUoI3DU5P8vMnBoykgJEUSKjdPimQQFIggeTEuvnEgPnPVfMw3SAoIh1axw7goZhBVl/vcfhpdenF22DzMtic5wX1TR/Byufr1nfuBz2HQ3p3SOviP7Ih1Z/WLKdlQ0oWJDR/ndvMyEhJJCMlkb5d0lpclX2VVewprawN/IWlFZSWV1FeWU15ZTX7qqpr58srqymvqmJfRTXlQekN85RVVLOntNJbV1lFRZWjoqqaymp/WuWorK6moiryf6kLDvZJgQSSA0ZSoh/wE4xkfz5gBn6sN7xDbJg3DZqvYWZB+eov16wPmJGQAAlmJJgRSPD2EfCXExKMBPN+FNXkqVk2MwJB2yaE+UPEEd5runFXKV06JYX7MopInFIAD1dGDxjyDe9Ro3QXbFled6a+7VNvuWw3VOxtYmfmNcMHB/WGgb5mOSUTkjMgJcO7Hp+c4T0CzR+6lMQA3TMDdM9MOfj6t5BzjqpqVy+wV/iBvbLKn1b76SF+AJRXess1j/IqR0VldW3e8srqoPWO8qrq2vW1y/6jqtr5ZQIHuGpwVNctu7rQWJOGn+YtO2r+4u8cVDtvuco5qp2jutpR7aCq2nn1dt6yl+789OD83j6qXN1+W1PagL1s2AU/enwJP/3mYRzaI7P1n0REok4B/GCkZUPuid6joYoyL5CX7vYCfZk/Ld0VOm3X+ro0F0b3rImpfjBPrwvyyel+oM9osJxZN5+YBoEkCCT7j6bm/eWEQItfGjO/WT3gjewmoTk/2Id7MSCck/XpLz/N5sIy3vp0O//+ZAvfHtuPH588hP5dOx1UWUUktiiAR0pSKiT1gsxeLduuuhrKi+qCfHkx7Cv2prXzJV6e8pL6y6W7oDC/fl7XsuvgIVlCiCBfE9yTvNaAhERvPiGxkeWm1jVYtoD3o8ES6s8nBEKsC84TvC4ovyX4kc/qTy3Bn6eJdaG28583wS+7Bfx5//nrpTd9qcRrjj/4QxQskGD0y05j7g2TePCtL3jsvfX8K28j35kwgCu/fig9Mg/s75IiElsUwGNNQoLXvJ6aBdkDD25fzkHlvvoBvbIMqsr9R2XQfEXT89UVjeeprvKWqyu9R1UFlO/1tqm3ruFyUP7qitZ5/WKO1QX04ABvQYE+IcE7Vji/zT5ovjatuok06q/LyYSMnnRLcfz89GFccnwus15fw98/+IqnFucz47hBXH7iIWTpOrlIXLN46r973LhxbvHixdEuhkRKTXB3VV5wqg6eVtVNm11XvX/afkERfxpqnWt6Xb3nrfkhUl3/R0nN81dX1p/W2yYoT8izfkK3BDSTNnPbAijZzqP0hHMfhZwhAKwrKOH3r33OvI83kZGSyOUnHcKMYweRnqLf8SKxysyWOOfGhVynAC7Svsz890zYu5NHVy/zWmBO/y2Mvqh2/aeb9/DbVz/ntU+3kpORzJWTDuU7Rw8gJVH3KojEmqYCuPrsFGmPOnWFy9+BPqPh+cvhuR96l1CAI3p35i/Tx/HsFcdyaI8M/veFlXz9N2/x1KINVGp8e5G4oQAu0l5l9YXpL8BJN3kdEc0+yevLwHfUwGyeuPRr/P37R5OTkcwNzy5j8u8X8uKyTVRXx0/LnEhHpQAu0p4lBGDSz+B787x/K/zlZPhgNjV/QDczjh+Sw/NXHsdD3z2KxIBx1T8+4lt/fIcFq7ZpjHuRGKYALtIR5J4Al/8XBk+Cl6+HJy+GvTtrV5sZpwzvxcs/PpHfnX8ke8oqmPnoIs5/6D0+/HJnEzsWkWhRABfpKNK7wXeehFP+Dz5/BR48Ab56v16WQILx7bH9eP2nE/nFWSNYv2Mv5z/0Ht99+AOWrFcgF4klCuAiHYkZHHMlfP9VrzOeR0+Dhfd4f2cLkpyYwHe/NpC3rp/Ez049nBWb9nDOA+8x7S/v88HaHVEqvIgEUwAX6Yj6joUfLvRG4Xvjl/C3s6Foy37Z0pID/PCkQ3jnxkn8/LQjWLWlmAtmv88FD73Hu2sKdI1cJIoUwEU6qtTOcM7DcOZ9sOFDeOA4WP1ayKydkhO59MTBvH3DJG49YxhfFpTwnb98wHkPvsfCz7crkItEgQK4SEdmBmO/B5e96Y249/g58J9bvR7xQkhLDnDJ8bksvGESd0wdzsbdpXzvkQ85+0/vsuAz3bUu0pYUwEUEehwOl74BR82E/94Lj0yBXesazZ6aFOB7xwzizesncufZI9hetI+ZcxYx9f7/8trKrQrkIm1AAVxEPElp8K0/wHlzoOBzePBEWPF8k5ukJAaYdvRA3rx+Ir8+ZxS791bwg78u5vRZ7/DvT7aoQxiRCFIAF5H6hp8Nl78NOYfC09PhxZ9ARWmTmyQFEjh/fH9e/5+T+M15R1JaUcXlf1/CabPe5qVlmxXIRSJAwxCJyP6yB8Elr8Abv/Ca1Jc/C/3GQb/x0H889D0K0rL32ywpkMC5R/XjrNF9eHHZZu57YzVX/mMpQ3pkcNXXD+WMUX0IJLTyAOgiHZRGIxNpZ2b+eyYAj055tHV2+OVCWP4M5C+GbSvxx2KFnKF1Ab3feOh+uNd1a5Cqasf85V4g/3xrMYNz0rnq64dy5pF9SAyoAVCkOVEZTtTMpgD3AgHgL865uxus/ynwA6AS2A5c4pxb39Q+FcBFmtfqATxY2R7YtBTyF8GGRd601O+hLTnT+395/wleQO87zuv9DaiudryyYgv3vr6az7YU0ScrleMOzWFCblcm5HZlQNdOmOnMXKShpgJ4RJrQzSwA3A98E8gHFpnZPOfcyqBsHwHjnHN7zewK4NfABZEoj4i0ktTOMHii9wBvUJSda/2A/qE3fft34Pye3boeAv3Gk9B/PKf2G88pVx3Da6t28PSSfP7z6VaeXpIPQM/OKYwf1JWjc7syPrcrh/XIJEFN7SJNitQ18AnAGufcWgAzmwtMBWoDuHNuQVD+94GLI1QWEYkUM+h2iPc48kIvrbwENn1Ud5b+xeuwbC4ACUmdmNxnLJN7H0l1bne2VXbisz3J5BUk8P6X+dy7LJndpJOelsb4QdmMH+SdoY/om0WSmtxF6olUAO8LbAhazgeObiL/94GXQ60ws8uAywAGDBjQWuUTkUhJTodBx3sP8M7Sd6/3rqHXnKkvfpiEyjJ6Ab2AiTXbpnqTUstg17oMtn2Rzi6XwcvWmaTOOWR17UnPXn3o36cvyZ27Q6eukNYVOnWDpNQ2r6pINEX9LnQzuxgYB5wUar1zbjYwG7xr4G1YNBFpDWbeXe3Zg2DkuV6ac1Cx1xvStHQn7N3hz++CvTtI27uTtNKddCsqoGz3Nqr3riGleDGdikvhq0aep1MOZA+ELgO9afaguvms/t7gLSLtSKQC+Eagf9ByPz+tHjP7BvBz4CTn3L4IlUVEYo2Zd6aenA5d+jeaLcV/1CgsLuGT1V/y6dr1rM/fwK7tm8l0RXSliKH7dnHojh30LviQrPJ5JLjKoOdLgM596wJ6w2lGL0hQE73El0gF8EXAEDPLxQvcFwLfCc5gZmOAh4ApzrltESqHiLQjWRnpHDdmBMeNGQFAaXkVeRt2s2T9Tl7ZUsR9W4pYW1CCq66iFzvJDWxnTOdChnfaTW5gOz2Lt5JZ8DqJJQ1GXgukQJcB9YN6pxyvd7qkTkHT1KBlPy2Q7P0gEWljEQngzrlKM7sKeAXvb2SPOOdWmNkdwGLn3DzgHiADeNr/+8hXzrkzI1EeEWmf0pIDHHNIN445pFtt2r7KKtZuL+HzrUWs2lLEp1uKeH5rEfm76nqTy0qq4rhuexmbtYcjUncyMGE7OZVbSCnegOUvhrLd4RfCEvYP6sHTRD/oJ6Z4zfgJSf40sW5aO58UIq2RdQmJ4Kq9O/6rq/xpdYi0Ki+t3nLN1NXP06mr90OmywCvxUKXHZrmnHfpp2gT7PEfGT3h8NPa5Okjdg3cOTcfmN8g7dag+W9E6rlFpONKSQxwRO/OHNG7c7304n2VfL61iM+3FLHKD+4PbiyioLhvbZ7sTkkM7ZXJqBzjsM6V9E2H3unV9EhzdKLCu25fURpiGjRfWVqXVroraF0ZVFdCdQVU1UwrqO0YJ9ZYAmT28QN6f2+a1b8uwGf1836UtFeV5VC8BfZshj0boWizF6Brpns2QdEWqGpw9XfI5PgP4CIisSQjJZGxA7IZO6B+F7AFxftqg/rnW4v4bEsRj+cVUVJeVS9fl05J9MvOpl+XvvTLTvMeOZ3o1zWNftmdyEg5wK/T6uq6YB4c3Ksr/bTKBuuC1lsCWMDrAa92mlA3rbcuVN6adP9Rsh12fwW7N3jTQn+6/j1Y/rR3ll7LILPX/oG9S3/vMkRWP68FooZz3pl+daV/1l/pLzeSVrtc6b9GlXX9C9R2QObqzzdc11ze6mqvzns2+WfRm+vOpku273+sEtOgc2/vh03/CdC5jzffubfXYpHZ2zsDbyMK4CLSoeVkpJBzaArHHppTm+acY0dJOfm7SsnftbfedM32Yt78fBtlFdX19pPdKYl+2Z3qgnvtfCf6Zqc1HuATEiAhJTbOZlMyoevg0OuqKrzAVhPUa4P8V7BxMax83guywRLT6gKvqw6931iR1tULyJ37QO/RfnD2A3Pn3t58WnZM3e+gAC4i0oCZeYE9I4XR/bvst76pAL96WzELVu0f4LPSkuidlUqfLmn1pr2z0ujTJZVeWamkJAb2e66YEUjy/543MPT66iqvSbn2zH09lBXWXa+3gD/vn/3XpjVcDpUnsa4FoTaA+lOzBvMN1zWV17zufjN7128tiBMK4CIiLXSgAX5LYRmbdpfx0Ve72LW3Yr/tcjKS6Z2VRq+sVPpkpdK7QbDv2Tk1dnukSwhAVl/vwTHRLk2HoAAuItLKmgvw4P0FbnNhKZsLy9i0u9QL7oVlbC4s5asde3l/7Q6Kyiob7Bd6ZKbQKyuNPn5A79k5lV5ZKfTMTKVH51R6dk4hM1V3j3cECuAiIlGQlhxgcPcMBnfPaDRP8b5KNu8uZVNhGVsKS9m0u6w26H++tYh3VhdQtK9yv+3SkwP07JxKj84ptUG+px/ce3ZO9YN9CqlJMdxkL81SABcRiVEZKYkM6ZnJkJ6ZjeYp2VfJtqJ9bN1TFvSoW/7oq91s3VPGvsr9byLr0impNpj37JxK98wUuqUnk5ORQtf0ZLpl1M3HbNN9B6YALiISx9JTEslNSSQ3J73RPM459pRWsiUoyG8r2seWQn+5aB+rtxawo2QfFVWh/5eelZZEt4xkuqUn0y09xZvPSCEnw1vump7szWek0CUtScPBtgEFcBGRds7MyOqURJbfUU1jnHPsKatkR/E+dpSUB029+QI/7YvtxXy4rpxde8vr/lodJJBgZHfygn3XRh7d0pPJDprqDL/lFMBFRATwA31aEllpSQzu3nz+yqpqdpdW7BfgdxSXs6PEm+4sKefTLXvYWVLO7hB33tfITE2sF9S71ptPoWt6El3TvbP7Lp2S6Jyqs3wFcBEROSCJgYTau+2h8TP7GjUBf6d/Vr9rbzk7SsrZGTxfso+Nu8tYvrGQnSXljTbpm3nN+l3SksjqlFwb2GuWszvVLCeT5ad36ZRMVloSgXYS+BXARUSkTdQL+GH0OOqco3hfJTtLymsfhaUV7NpbQeHecnaXVrB7b4U/LWfdjhJ2762gsLTxM32AzqmJdOmUTJdOSbUtDjWPurTk/dI6JQcw9cQmIiLSNDMjMzWJzNQkBnZr/Ca9hqqqHXtK6wL77tIKCvd687v8AB88v3FXqZdWWkFVdeODyyQFvEsMnWvO/IPO6mvShvTM4IQhYVx/aAUK4CIi0q4EEoxs/xo6hB/4nXOUlFexe693pl/oB/6a4N4wbXvxPtZsL6ZwbwV7/E53pgzvpQAuIiLSlsyMjJREMlIS6ZfdfP5gVdWOorKmz+BbmwK4iIjIQQokGF06Jbfpc+qPdyIiInFIAVxERCQOKYCLiIjEIQVwERGROKQALiIiEocUwEVEROKQAriIiEgcUgAXERGJQwrgIiIicUgBXEREJA4pgIuIiMQhBXAREZE4pAAuIiIShxTARURE4lDEAriZTTGzVWa2xsxuCrE+xcye9Nd/YGaDIlUWERGR9iYiAdzMAsD9wKnAMOAiMxvWINv3gV3OuUOB3wO/ikRZRERE2qNInYFPANY459Y658qBucDUBnmmAo/5888AJ5uZRag8IiIi7UqkAnhfYEPQcr6fFjKPc64SKAS6Rag8IiIi7UpitAvQHDO7DLjMXyw2s1WtuPscoKAV9xdNqkvsiWo95jCnNXfXXo4JtJ+6tJd6gOrSlIGNrYhUAN8I9A9a7uenhcqTb2aJQBawo+GOnHOzgdmRKKSZLXbOjYvEvtua6hJ72ks9QHWJRe2lHqC6HKhINaEvAoaYWa6ZJQMXAvMa5JkHTPfnzwXecM65CJVHRESkXYnIGbhzrtLMrgJeAQLAI865FWZ2B7DYOTcPeBj4m5mtAXbiBXkREREJQ8SugTvn5gPzG6TdGjRfBpwXqecPU0Sa5qNEdYk97aUeoLrEovZSD1BdDoip1VpERCT+qCtVERGRONQhAnh76dbVzPqb2QIzW2lmK8zsxyHyTDSzQjPL8x+3htpXtJnZOjNb7pdxcYj1Zmaz/GOyzMzGRqOczTGzoUGvdZ6Z7TGzaxvkidljYmaPmNk2M/skKK2rmf3HzFb70+xGtp3u51ltZtND5WlLjdTlHjP7zH8P/dPMujSybZPvx7bUSD1uN7ONQe+h0xrZtsnvurbWSF2eDKrHOjPLa2TbmDkmfnlCfv9G9fPinGvXD7yb6L4ABgPJwMfAsAZ5fgQ86M9fCDwZ7XI3UpfewFh/PhP4PERdJgIvRrusYdRlHZDTxPrTgJcBA74GfBDtModRpwCwBRgYL8cEOBEYC3wSlPZr4CZ//ibgVyG26wqs9afZ/nx2DNZlMpDoz/8qVF38dU2+H2OgHrcD1zWzXbPfdbFQlwbrfwvcGuvHxC9PyO/faH5eOsIZeLvp1tU5t9k5t9SfLwI+Zf8e7tqLqcBfned9oIuZ9Y52oZpxMvCFc259tAsSLufcQrx/gQQL/jw8BpwVYtNTgP8453Y653YB/wGmRKygYQhVF+fcq87r6RHgfbw+KWJaI8ckHOF817Wppurif8eeDzzRpoU6QE18/0bt89IRAni77NbVb+YfA3wQYvUxZvaxmb1sZsPbtGDhc8CrZrbEvN72GgrnuMWaC2n8yygejkmNns65zf78FqBniDzxeHwuwWvVCaW592MsuMq/FPBII8208XZMTgC2OudWN7I+Zo9Jg+/fqH1eOkIAb3fMLAN4FrjWObenweqleE24RwL3Ac+3dfnCdLxzbizeiHVXmtmJ0S7QwTCvw6IzgadDrI6XY7If57X/xf1fVczs50Al8HgjWWL9/fgAcAgwGtiM1/Qc7y6i6bPvmDwmTX3/tvXnpSME8JZ064o10a1rLDCzJLw3z+POuecarnfO7XHOFfvz84EkM8tp42I2yzm30Z9uA/6J1/wXLJzjFktOBZY657Y2XBEvxyTI1prLFf50W4g8cXN8zGwGcAYwzf+C3U8Y78eocs5tdc5VOeeqgT8TunzxdEwSgW8DTzaWJxaPSSPfv1H7vHSEAN5uunX1rxk9DHzqnPtdI3l61Vy/N7MJeMc4pn6MmFm6mWXWzOPdaPRJg2zzgO+Z52tAYVAzVSxq9GwiHo5JA8Gfh+nAv0LkeQWYbGbZfnPuZD8tppjZFOAG4Ezn3N5G8oTzfoyqBvd/nE3o8oXzXRcrvgF85pzLD7UyFo9JE9+/0fu8RPvOvrZ44N3R/DneHZo/99PuwPtQA6TiNX2uAT4EBke7zI3U43i85pllQJ7/OA24HLjcz3MVsALvDtT3gWOjXe4Q9Rjsl+9jv6w1xyS4Hgbc7x+z5cC4aJe7ifqk4wXkrKC0uDgmeD86NgMVeNflvo93/8frwGrgNaCrn3cc8JegbS/xPzNrgJkxWpc1eNceaz4vNf826QPMb+r9GGP1+Jv/OViGFzB6N6yHv7zfd12s1cVPn1Pz+QjKG7PHxC9TY9+/Ufu8qCc2ERGRONQRmtBFRETaHQVwERGROKQALiIiEocUwEVEROKQAriIiEgcUgAXkYNm3ohrL0a7HCIdiQK4iIhIHFIAF+lAzOxiM/vQH2P5ITMLmFmxmf3eH+P4dTPr7ucdbWbvW91Y2tl++qFm9po/OMtSMzvE332GmT1j3vjbj8fiiH4i7YkCuEgHYWZHABcAxznnRgNVwDS8nuQWO+eGA28Bt/mb/BW40Tk3Cq8XsJr0x4H7nTc4y7F4PW2BNzrTtXhjJA8Gjot4pUQ6sMRoF0BE2szJwFHAIv/kOA1v4IVq6gaV+DvwnJllAV2cc2/56Y8BT/v9U/d1zv0TwDlXBuDv70Pn921tZnnAIOCdyFdLpGNSABfpOAx4zDn3s3qJZrc0yHeg/SvvC5qvQt8vIhGlJnSRjuN14Fwz6wFgZl3NbCDe98C5fp7vAO845wqBXWZ2gp/+XeAt51wRkG9mZ/n7SDGzTm1aCxEB9AtZpMNwzq00s5uBV80sAW+EqCuBEmCCv24b3nVy8IZGfNAP0GuBmX76d4GHzOwOfx/ntWE1RMSn0chEOjgzK3bOZUS7HCLSMmpCFxERiUM6AxcREYlDOgMXERGJQwrgIiIicUgBXEREJA4pgIuIiMQhBXAREZE4pAAuIiISh/4/iO58gti26wgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DD3hPS4W4iyd",
        "outputId": "31556838-7ecc-4a2e-c22c-cd2e6718b727"
      },
      "source": [
        "model.save('{}/{}'.format(base_dir, 'mobileNetV2_model_tuned.h5'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rpdfzwpv7tBA"
      },
      "source": [
        "## Data Augmentation (with replacement)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKYtlLsvv9BI"
      },
      "source": [
        "### Create Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8bNadcw7luO"
      },
      "source": [
        "preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BhbDGBZ7luO"
      },
      "source": [
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40MrfM2u7luO"
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xlr_nUS27luP",
        "outputId": "829e179b-0598-4053-ba95-714bccd245f3"
      },
      "source": [
        "image_batch, label_batch = next(iter(train_data_iterator))\n",
        "feature_batch = base_model(image_batch)\n",
        "print(feature_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 3, 3, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mg-lgfiA7luQ",
        "outputId": "927f7b89-659d-476e-d3de-af5803c5a24a"
      },
      "source": [
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "feature_batch_average = global_average_layer(feature_batch)\n",
        "print(feature_batch_average.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YlKWXeJy7luQ",
        "outputId": "70707657-b8d3-4895-ea77-02954a83a0c5"
      },
      "source": [
        "prediction_layer = tf.keras.layers.Dense(1)\n",
        "prediction_batch = prediction_layer(feature_batch_average)\n",
        "print(prediction_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgXOwSbU7luQ"
      },
      "source": [
        "inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
        "x = preprocess_input(inputs)\n",
        "x = base_model(x, training=False)\n",
        "x = global_average_layer(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Dense(1)(x)\n",
        "#outputs = prediction_layer(x)\n",
        "model = tf.keras.Model(inputs, x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fp289CXCFIrB",
        "outputId": "531b4531-8020-4bbf-aed7-6cf4739d63a9"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf.math.truediv_1 (TFOpLambd (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf.math.subtract_1 (TFOpLamb (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_1.00_96 (Functio (None, 3, 3, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,281\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcaliSij7luQ"
      },
      "source": [
        "base_learning_rate = 0.0001\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate), \n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJ5G1E_m7luR",
        "outputId": "be23ce66-241c-423e-fa67-a07be3db9af7"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LviJkm3xwEaY"
      },
      "source": [
        "### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KSXW8SM7luR",
        "outputId": "30546b7c-69f3-4c4c-c627-0d1639ebca35"
      },
      "source": [
        "initial_epochs = 10\n",
        "\n",
        "history = model.fit(train_data_iterator, epochs=initial_epochs, validation_data=validation_data_iterator, verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2751/2751 [==============================] - 339s 122ms/step - loss: 0.4344 - accuracy: 0.7943 - val_loss: 0.3559 - val_accuracy: 0.8405\n",
            "Epoch 2/10\n",
            "2751/2751 [==============================] - 327s 119ms/step - loss: 0.3662 - accuracy: 0.8350 - val_loss: 0.3381 - val_accuracy: 0.8537\n",
            "Epoch 3/10\n",
            "2751/2751 [==============================] - 317s 115ms/step - loss: 0.3517 - accuracy: 0.8419 - val_loss: 0.3296 - val_accuracy: 0.8570\n",
            "Epoch 4/10\n",
            "2751/2751 [==============================] - 315s 114ms/step - loss: 0.3445 - accuracy: 0.8466 - val_loss: 0.3255 - val_accuracy: 0.8540\n",
            "Epoch 5/10\n",
            "2751/2751 [==============================] - 317s 115ms/step - loss: 0.3409 - accuracy: 0.8475 - val_loss: 0.3226 - val_accuracy: 0.8574\n",
            "Epoch 6/10\n",
            "2751/2751 [==============================] - 317s 115ms/step - loss: 0.3399 - accuracy: 0.8489 - val_loss: 0.3228 - val_accuracy: 0.8563\n",
            "Epoch 7/10\n",
            "2751/2751 [==============================] - 315s 114ms/step - loss: 0.3390 - accuracy: 0.8494 - val_loss: 0.3216 - val_accuracy: 0.8640\n",
            "Epoch 8/10\n",
            "2751/2751 [==============================] - 316s 115ms/step - loss: 0.3373 - accuracy: 0.8502 - val_loss: 0.3188 - val_accuracy: 0.8579\n",
            "Epoch 9/10\n",
            "2751/2751 [==============================] - 318s 116ms/step - loss: 0.3370 - accuracy: 0.8501 - val_loss: 0.3169 - val_accuracy: 0.8597\n",
            "Epoch 10/10\n",
            "2751/2751 [==============================] - 312s 113ms/step - loss: 0.3368 - accuracy: 0.8504 - val_loss: 0.3180 - val_accuracy: 0.8585\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdHSYO527luR",
        "outputId": "d6b7481f-bb41-4846-cb20-e7f8828dbf5e"
      },
      "source": [
        "model.save('{}/{}'.format(base_dir, 'mobileNetV2_model_dataAug.h5'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NsiCVT67wIdd"
      },
      "source": [
        "### Plot loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "zPGgL4CB7luS",
        "outputId": "723054ff-04b7-4d8d-bc94-9a9b0c5b83ae"
      },
      "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": {
        "id": "C2IWfNoOwNyR"
      },
      "source": [
        "### Fine tune model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HFLvxo17luS"
      },
      "source": [
        "base_model.trainable = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEqYY9Ck7luS"
      },
      "source": [
        "fine_tune_at = 100\n",
        "\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2TOTqIx7luT"
      },
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "              optimizer=tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate/10),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_K3wd9Zd7luT",
        "outputId": "75c26805-bac4-4c09-b198-a81c632de180"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf.math.truediv_1 (TFOpLambd (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf.math.subtract_1 (TFOpLamb (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_1.00_96 (Functio (None, 3, 3, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,862,721\n",
            "Non-trainable params: 396,544\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLy00sHx7luT",
        "outputId": "50cea5ac-03d5-48a5-8943-3a5210b9d247"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGNZB0TUwSYl"
      },
      "source": [
        "### Train fine tuned model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsPWJ_6h7luT",
        "outputId": "9f703c8f-7648-47cf-d8f6-fbc93f81ba82"
      },
      "source": [
        "fine_tune_epochs = 10\n",
        "total_epochs = initial_epochs + fine_tune_epochs\n",
        "\n",
        "history_fine = model.fit(train_data_iterator, \n",
        "                         epochs=total_epochs, \n",
        "                         initial_epoch=history.epoch[-1], \n",
        "                         validation_data=validation_data_iterator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 10/20\n",
            "2751/2751 [==============================] - 356s 123ms/step - loss: 0.2826 - accuracy: 0.8791 - val_loss: 0.2433 - val_accuracy: 0.8920\n",
            "Epoch 11/20\n",
            "2751/2751 [==============================] - 331s 120ms/step - loss: 0.2362 - accuracy: 0.9011 - val_loss: 0.2231 - val_accuracy: 0.9028\n",
            "Epoch 12/20\n",
            "2751/2751 [==============================] - 329s 120ms/step - loss: 0.2164 - accuracy: 0.9107 - val_loss: 0.2126 - val_accuracy: 0.9190\n",
            "Epoch 13/20\n",
            "2751/2751 [==============================] - 332s 121ms/step - loss: 0.2036 - accuracy: 0.9174 - val_loss: 0.2162 - val_accuracy: 0.9204\n",
            "Epoch 14/20\n",
            "2751/2751 [==============================] - 330s 120ms/step - loss: 0.1947 - accuracy: 0.9212 - val_loss: 0.1944 - val_accuracy: 0.9217\n",
            "Epoch 15/20\n",
            "2751/2751 [==============================] - 333s 121ms/step - loss: 0.1879 - accuracy: 0.9242 - val_loss: 0.1864 - val_accuracy: 0.9263\n",
            "Epoch 16/20\n",
            "2751/2751 [==============================] - 332s 121ms/step - loss: 0.1822 - accuracy: 0.9272 - val_loss: 0.1850 - val_accuracy: 0.9273\n",
            "Epoch 17/20\n",
            "2751/2751 [==============================] - 332s 121ms/step - loss: 0.1783 - accuracy: 0.9292 - val_loss: 0.1880 - val_accuracy: 0.9232\n",
            "Epoch 18/20\n",
            "2751/2751 [==============================] - 329s 119ms/step - loss: 0.1743 - accuracy: 0.9312 - val_loss: 0.1855 - val_accuracy: 0.9322\n",
            "Epoch 19/20\n",
            "2751/2751 [==============================] - 332s 121ms/step - loss: 0.1714 - accuracy: 0.9325 - val_loss: 0.1965 - val_accuracy: 0.9300\n",
            "Epoch 20/20\n",
            "2751/2751 [==============================] - 330s 120ms/step - loss: 0.1694 - accuracy: 0.9334 - val_loss: 0.1921 - val_accuracy: 0.9173\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YFYGIhUwWXf"
      },
      "source": [
        "### Plot improved loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxAYOvLk7luU"
      },
      "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']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "8inNkJhA7luU",
        "outputId": "ecb7b119-93c3-46ee-9bf7-bed6843aa6dc"
      },
      "source": [
        "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.ylim([0.8, 1])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1],\n",
        "          plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='lower right')\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.ylim([0, 1.0])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1],\n",
        "         plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='upper right')\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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t4d1TOVi7luU",
        "outputId": "15be6be3-8052-4b15-f51a-2dd457acbece"
      },
      "source": [
        "model.save('{}/{}'.format(base_dir, 'mobileNetV2_model_dataAug_tuned.h5'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rey4EbeGh19z"
      },
      "source": [
        "## Data Augmentation (with image generation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z85nmdZPwjGN"
      },
      "source": [
        "### Generate additional images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_e0bwvhndBk"
      },
      "source": [
        "aug = ImageDataGenerator(brightness_range=[0.3, 1], \n",
        "                         horizontal_flip=True, \n",
        "                         vertical_flip=True, \n",
        "                         channel_shift_range=150)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YgHMIi8mY_J"
      },
      "source": [
        "folders = ['train']#, 'test']\n",
        "labels = ['0', '1']\n",
        "# provide degree of data expansion required, 1x, 2x, 1.5x, 0.5x\n",
        "p = 1.5 #not used currently due to uncertain behaviour of imagedatagenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQiNOwbOmY_O"
      },
      "source": [
        "for folder in folders:\n",
        "    df = pd.read_csv('{}_labels.csv'.format(folder))\n",
        "    for filename, label in zip(df['id'], df['label']):\n",
        "        image = tf.keras.preprocessing.image.load_img('{}/{}/{}/{}.tif'.format('dataset_folder', folder, label, filename))\n",
        "        image = tf.keras.preprocessing.image.img_to_array(image)\n",
        "        image = np.expand_dims(image, axis=0)\n",
        "        for x, var in zip(aug.flow(image, batch_size=1, save_to_dir='{}/{}/{}/'.format('dataset_folder', folder, label), save_prefix='aug_{}'.format(filename), save_format='tif'), range(0)):\n",
        "            pass\n",
        "        #imageGen = aug.flow(image, batch_size=1, save_to_dir='{}/{}/{}/'.format('test_folder', folder, label), save_prefix='aug_{}'.format(filename), save_format='tif')\n",
        "\n",
        "        \"\"\"for itr in range(int(p)):\n",
        "          next(imageGen)\"\"\"\n",
        "        \"\"\"\n",
        "        if random.random() < (p -  int(p)):\n",
        "          next(imageGen)\n",
        "        \"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mr6LgHaz8_qt"
      },
      "source": [
        "#!find dataset_folder/train/0/ -name '*.tiff' -type f -delete\n",
        "#!find dataset_folder/train/1/ -name '*.tiff' -type f -delete"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nED2Gh-Kue0t"
      },
      "source": [
        "### Create data generators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f64cFO6Bue0t"
      },
      "source": [
        "new_base_dir = 'dataset_folder'\n",
        "training_sub_path = 'train'\n",
        "test_sub_path = 'test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJavmP7tue0t"
      },
      "source": [
        "IMG_SIZE = (96, 96) \n",
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "BATCH_SIZE = 64"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYNgFUUyvnv6"
      },
      "source": [
        "data_generator = ImageDataGenerator(validation_split=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCqTnpIavgiU",
        "outputId": "9d7614f2-cd24-4864-ca43-f781e7d188d1"
      },
      "source": [
        "train_data_iterator = data_generator.flow_from_directory('{}/{}'.format(new_base_dir, 'train'), \n",
        "                                                         target_size=IMG_SIZE,\n",
        "                                                         class_mode='binary', \n",
        "                                                         subset='training', \n",
        "                                                         batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 352041 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJFE-i_7vgiV",
        "outputId": "edcbe37d-da6c-48cc-ecc4-5f9458c686c3"
      },
      "source": [
        "validation_data_iterator = data_generator.flow_from_directory('{}/{}'.format(new_base_dir, 'train'), \n",
        "                                                              target_size=IMG_SIZE,\n",
        "                                                              class_mode='binary', \n",
        "                                                              subset='validation', \n",
        "                                                              batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 88009 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TskBvlgHue0v"
      },
      "source": [
        "STEP_SIZE_TRAIN = train_data_iterator.n//train_data_iterator.batch_size\n",
        "STEP_SIZE_VALIDATION = validation_data_iterator.n//validation_data_iterator.batch_size"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kpczeJiwfwM"
      },
      "source": [
        "### Create Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyci8IMQwfwM"
      },
      "source": [
        "preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQ_iB2PMwfwN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75a58fb6-d3a2-4033-f43d-db278c571f3e"
      },
      "source": [
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')"
      ],
      "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_96_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrDnHCXlwfwN"
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJB20yC0wfwN",
        "outputId": "f12b30f3-30b0-447b-a722-d328b3fa1137"
      },
      "source": [
        "image_batch, label_batch = next(iter(train_data_iterator))\n",
        "feature_batch = base_model(image_batch)\n",
        "print(feature_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 3, 3, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xRM66S0RwfwO",
        "outputId": "a53d569f-f2e9-48b7-c9b8-6f26bf603d54"
      },
      "source": [
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "feature_batch_average = global_average_layer(feature_batch)\n",
        "print(feature_batch_average.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NPF3VHGWwfwO",
        "outputId": "984f38cc-bdf0-4b90-c3e6-0ee4facd16e0"
      },
      "source": [
        "prediction_layer = tf.keras.layers.Dense(1)\n",
        "prediction_batch = prediction_layer(feature_batch_average)\n",
        "print(prediction_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCLvwajQwfwO"
      },
      "source": [
        "inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
        "x = preprocess_input(inputs)\n",
        "x = base_model(x, training=False)\n",
        "x = global_average_layer(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Dense(1)(x)\n",
        "#outputs = prediction_layer(x)\n",
        "model = tf.keras.Model(inputs, x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjyF6W1mwfwP",
        "outputId": "893be80f-4d76-4147-c718-434fbbf8b459"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf.math.truediv (TFOpLambda) (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf.math.subtract (TFOpLambda (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_1.00_96 (Functio (None, 3, 3, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,281\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7t1GCmA4EpD"
      },
      "source": [
        "base_learning_rate = 0.0001"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-N5zxEBwfwP"
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate), \n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wsCdFWeYwfwP",
        "outputId": "5cef3d74-ef1b-4235-be19-b883edaa117e"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9X458RhwfwP"
      },
      "source": [
        "### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HoI48L34RfY"
      },
      "source": [
        "initial_epochs = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jW-HjdUOwfwP",
        "outputId": "0015d253-626b-45bc-cd90-b021f3309778"
      },
      "source": [
        "history = model.fit(train_data_iterator, epochs=initial_epochs, validation_data=validation_data_iterator, verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "5501/5501 [==============================] - 863s 156ms/step - loss: 0.4220 - accuracy: 0.8064 - val_loss: 0.3362 - val_accuracy: 0.8549\n",
            "Epoch 2/10\n",
            "5501/5501 [==============================] - 810s 147ms/step - loss: 0.3644 - accuracy: 0.8365 - val_loss: 0.3290 - val_accuracy: 0.8623\n",
            "Epoch 3/10\n",
            "5501/5501 [==============================] - 789s 143ms/step - loss: 0.3565 - accuracy: 0.8408 - val_loss: 0.3203 - val_accuracy: 0.8613\n",
            "Epoch 4/10\n",
            "5501/5501 [==============================] - 795s 144ms/step - loss: 0.3547 - accuracy: 0.8410 - val_loss: 0.3182 - val_accuracy: 0.8577\n",
            "Epoch 5/10\n",
            "5501/5501 [==============================] - 799s 145ms/step - loss: 0.3531 - accuracy: 0.8428 - val_loss: 0.3175 - val_accuracy: 0.8640\n",
            "Epoch 6/10\n",
            "5501/5501 [==============================] - 788s 143ms/step - loss: 0.3524 - accuracy: 0.8425 - val_loss: 0.3156 - val_accuracy: 0.8608\n",
            "Epoch 7/10\n",
            "5501/5501 [==============================] - 779s 142ms/step - loss: 0.3518 - accuracy: 0.8431 - val_loss: 0.3153 - val_accuracy: 0.8615\n",
            "Epoch 8/10\n",
            "5501/5501 [==============================] - 778s 141ms/step - loss: 0.3518 - accuracy: 0.8433 - val_loss: 0.3150 - val_accuracy: 0.8627\n",
            "Epoch 9/10\n",
            "5501/5501 [==============================] - 772s 140ms/step - loss: 0.3520 - accuracy: 0.8437 - val_loss: 0.3159 - val_accuracy: 0.8639\n",
            "Epoch 10/10\n",
            "5501/5501 [==============================] - 768s 140ms/step - loss: 0.3516 - accuracy: 0.8433 - val_loss: 0.3172 - val_accuracy: 0.8647\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikStufLmwfwQ",
        "outputId": "59433090-8c52-4059-fc04-178d2700e54b"
      },
      "source": [
        "model.save('{}/{}'.format(base_dir, 'mobileNetV2_model_dataGen.h5'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_bqXNAGTvSeX"
      },
      "source": [
        "model = tf.keras.models.load_model('{}/{}'.format(base_dir, 'mobileNetV2_model_dataGen.h5'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uEcajwzJwfwQ"
      },
      "source": [
        "### Plot loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "31y0Gi9HwfwQ",
        "outputId": "9274c7d8-5130-44dd-8cc3-a5b1b2cae4f1"
      },
      "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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mAYfXqTMKeDydfqJqeUT8OyLeSqffAz4CBuQwVjMzs7yQy8Q/EJifmS9Ly7JeBo5Mp78M9JTUL1tB0gSgGHg7U3xlegrgZ5I61/fhkk6TVCqpdOHChZ9mO8zMzDqMtr6473xgP0kvAvsBC4CKqoWStgF+D5wcEZVp8cXASGB3oC/w3foajojrI6IkIkoGDPBggZmZGUBRDtteAAzOzA9Ky6qlw/hHAkjqARwVEUvT+V7AX4HvRcSzmXXeTyfXSrqJ5ODBzMzMmmCjPX5JX5K0KSMDzwPDJQ2TVAwcC9xfp+3+mbYvBm5My4uBe0ku/Lu7zjrbpO8CjgBe24TYzMzM8lJTEvpXgbck/UTSyKY2HBHlwLeAR4A3gDsjYpakKyQdllabBLwp6d/AVsCVaflXgH2BqfX8bO82Sa8CrwL9gR83NSYzM7N8p4jYeKVk2H0KcDIQwE3A7RGxIrfhtYySkpIoLS1t6zDMzMxahaSZEVFS37ImDeFHxHLgbpKf5G1DcgX+C5LObrEozczMLOeaco7/MEn3AtOBTsCEiDgIGAN8J7fhmZmZWUtqylX9RwE/i4gZ2cKIWCXp67kJy8zMzHKhKYn/cqDqJ3RI6gpsFRHzIuKxXAVmZmZmLa8p5/jvAioz8xVpmZmZmW1mmpL4i9J77QOQThfnLiQzMzPLlaYk/oWZ390j6XBgUe5CMjMzs1xpyjn+M0humvNLQCQP3jkxp1GZmZlZTmw08UfE28Ae6b30iYiVOY/KzMzMcqJJD+mRdAiwM9AluUU+RMQVOYzLzMzMcqApN/D5Dcn9+s8mGeo/Btgux3GZmZlZDjTl4r69IuJE4OOI+CGwJ7BjbsMyMzOzXGhK4l+Tvq+StC2wnuR+/WZmZraZaco5/gck9QZ+CrxA8nS+G3IalZmZmeVEo4lfUgHwWEQsBe6R9BegS0Qsa5XozMzMrEU1OtQfEZXArzLza530zczMNl9NOcf/mKSjVPU7vmaQdKCkNyXNkXRRPcu3k/SYpFckTZc0KLPsJElvpa+TMuXjJb2atnntpsRlZmaWr5qS+E8neSjPWknLJa2QtHxjK0kqJBktOAgYBUyRNKpOtWuAWyNiV+AK4Kp03b7AZcBEYAJwmaQ+6TrXAacCw9PXgU3YBjMzM6MJiT8iekZEQUQUR0SvdL5XE9qeAMyJiLnpg32mAYfXqTMKeDydfiKz/IvA3yNiSUR8DPwdOFDSNkCviHg2IgK4FTiiCbGYmZkZTbiqX9K+9ZVHxIyNrDqQ5L7+VcpIevBZLwNHAr8Avgz0lNSvgXUHpq+yesrNzMysCZryc74LMtNdSHryM4HPtcDnnw/8UtJUYAawAKhogXaRdBpwGsCQIUNaokkzM7PNXlMe0vOl7LykwcDPm9D2AmBwZn5QWpZt+z2SHj/pQ4COioilkhYAk+qsOz1df1Cd8lptZtq+HrgeoKSkJJoQr5mZWYfXlIv76ioDdmpCveeB4ZKGSSoGjgXuz1aQ1D+9VwDAxcCN6fQjwAGS+qQX9R0APBIR7wPLJe2RXs1/IvDnTdgGMzOzvNSUc/z/S3K3PkgOFMaS3MGvURFRLulbJEm8ELgxImZJugIojYj7SXr1V0kKkqH+s9J1l0j6EcnBA8AVEbEknf4mcDPQFXgofZmZmVkTKLk4vpEKmd/QA+XAvIh4OqdRtbCSkpIoLS1t6zDMzMxahaSZEVFS37KmXNx3N7AmIirSxgoldYuIVS0ZpJmZmeVek+7cRzKsXqUr8GhuwjEzM7Ncakri7xIRK6tm0uluuQvJzMzMcqUpif8TSbtVzUgaD6zOXUhmZmaWK005x38ucJek9wABWwNfzWlUZmZmlhNNuYHP85JGAiPSojcjYn1uwzIzM7Nc2OhQv6SzgO4R8VpEvAb0kPTN3IdmZmZmLa0p5/hPjYilVTPp0/JOzV1IZmZmlitNSfyF6e1xgeR3/EBx7kIyMzOzXGnKxX0PA3dI+n/p/On4NrlmZmabpaYk/u+SPN72jHT+FZIr+83MzGwzs9Gh/oioBP4FzAMmAJ8D3shtWGZmZpYLDfb4Je0ITElfi4A7ACJicuuEZmZmZi2tsaH+2cBTwKERMQdA0nmtEpWZmZnlRGND/UcC7wNPSLpB0v4kd+4zMzOzzVSDiT8i7ouIY4GRwBMkt+7dUtJ1kg5orQDNzMys5TTl4r5PIuKPEfElYBDwIsmV/hsl6UBJb0qaI+miepYPkfSEpBclvSLp4LT8OEkvZV6Vksamy6anbVYt27JZW2xmZpbHFBG5aTi50c+/gS8AZcDzwJSIeD1T53rgxYi4TtIo4MGIGFqnndHAfRGxQzo/HTg/IkqbGktJSUmUlja5upmZ2WZN0syIKKlvWVPu3LepJgBzImJuRKwDpgGH16kTQK90egvgvXramZKua2ZmZp9SLhP/QGB+Zr4sLcu6HDheUhnwIHB2Pe18Fbi9TtlN6TD/97O3EzYzM7PG5TLxN8UU4OaIGAQcDPxeUnVMkiYCq9KnAlY5LiJGA59NXyfU17Ck0ySVSipduHBh7rbAzMxsM5LLxL8AGJyZH5SWZX0duBMgIv4JdAH6Z5YfS53efkQsSN9XAH8kOaWwgYi4PiJKIqJkwIABn2IzzMzMOo5cJv7ngeGShkkqJkni99ep8x9gfwBJO5Ek/oXpfAHwFTLn9yUVSeqfTncCDgVew8zMzJqkKQ/p2SQRUS7pW8AjQCFwY0TMknQFUBoR9wPfAW5I7wgYwNSo+ZnBvsD8iJibabYz8Eia9AuBR4EbcrUNZmZmHU3Ofs7XnvjnfGZmlk/a6ud8ZmZm1s448ZuZmeURJ34zM7M84sRvZmaWR3J2Vb+ZbQYiYP0qKOoKBe4HWAdXWQmV66Fiffpenpkvz5Rn5ivWNbxsgzbqa3Nd4+1XrTNkD/j8Za2yG5z4zTq6CFi1GJbMhcVvJ+9L5sKSdHrNMlABdOkN3fpC1z7QtW+d6T51ytNlxd3Bd802SP6dVayD8rVpwlybzq9Lk19anrPl62o+v26SrZqOytbZFyqEwk5Q0AkKi9L34sx0JygoytRJX63Eib+jqyiHj9+BFR/U/EOr+4+tvumCTu4Bbk4i4JOFNUm9VoJ/B9Yuq6mrAthiMPTbAUYfA70GJr3+VUtg9ZLkfcX78NHryfT6Txr+3MLimoOA6gOFPrUPDuqbLirO/T7pqCKSxFa+NpNo16YJMJsMM2W16tazTt33DdpoQuKtXN/y21rYGYo6p99P6XtR5zSJpq+iYujco2Z59bKiBr7bihqZL6r9HdjUpJ2dLyhq99+dTvwdRflaWDwHFr6ZvmYn74vnbPp/yFpHrU08YGjJ6aIu0K0fdOsP3fslPdJ87l1GwMqPanrqtRL8O7BuRU1dFULvIdB3exg8IXnvuz303SEpb07iLV8Lqz9ODww+rjk42GD64ySeqrLG/t0V96hzkFDfKEOd6S5bQEHhpu8/SJNmRRJbZXn6qqiZrlhfe77W8gbWqSxPh3cbW6ei8WRdXjexNpKUy9eS3O+shdRNrkXFdd47Q3E3KOzTQOLdWGLexOUFRfn9/z2HnPg3N+tWwaJ/J0l9USbJL3kHoiKpowLoMxQGjIQRBybvvbZNvnzqG/6qex5rU6fL18LaFTXnrKp6AQ1NN1dBUZIEuvdPDgiq3rv1z0xXlafzhZvZP/GIZHQmOxS/ZC4sTt+zve+CIui9XZLQh+yZ9OCrEnzvIS03dFjUGXpunbyasx3rPkkOAqoPGqqmP96wfFlZMr1maSPDsUqSf7e+yUEgpIm1gURdXxKv+j/SVgrrJtXiDcuqkmw28VYnyKr3jbTR1HULOzm55qHN7Fsxj6xZnib42ekrnV76H6qP9guKkh7clqNg5yNhwIgkyff7DHTq0qbhb1RVz6veg4P0VTX8vGoxrFoEnyxK3xcnZR+8mpStWdrw53TpXftgoHu/OgcL/ZNEUjVd3C33215ZmQyl1z3XvuSdNLmvqqlb0Ck5iOu7PQzdJ3nvlyb3LYa03wMbKRl+7dwjOQhpqsrK5LREdhQhewqiapRh9dLkALegKB2Czb4KM9Od6sxn6lQN0W6wTn2vzPLChtrcyDoqcJK1dqGdfmvkkVVLag/NV72veK+mTmFn6L8jDCqBccfXJPi+27fqBSEtSkq+sFsicVWsTxJC9YHBovRgYXHtso/nwYLSpLyyvP62OnXbcOSge3pwUOtgoV/jpx8qK5O/Ya1z7Zlz7uWra+oWFqfJfQcYth/0HVbTe+81qP0m91woKKg5BWBmOZFH3yhtqOrc7MLZmV58muQ/WVhTr1N3GLAjbL9fkugHjEySfJ+hn/7cZkdW2Al6bJm8miIiGSVYtaTxg4VVi5ORllWLavfCswqKag4UuvVLDhyWvpsk94q1mRg7Jwm97/aww+dqhuT77ZBcXOe/r5m1Eif+lhQByxfU6b2niT47HN15iySh73hgTXIfMCLp3bXzq0E7BKmmV9lvh6ats25VzcHAJ4vrHCwsqilbszQ51TL8C0kPvirB9xrov62ZtQtO/JuisiLp1S2s03tf9G9Yt7KmXrd+SWLf5ciaBN9/RHKRlM/1bV6Ku0HxkOadrzYza4ec+JvrwQvhhVugfE1NWc9tkqH5scfVnH8fMCI5H2xmZtaOOPE319ajYfdv1CT4/jtC195tHZWZmVmT5DTxSzoQ+AVQCPw2Iq6us3wIcAvQO61zUUQ8KGko8AbwZlr12Yg4I11nPHAz0BV4EPiviGjBu1lsxG4ntNpHmZmZtbScXW0kqRD4FXAQMAqYImlUnWqXAndGxDjgWODXmWVvR8TY9HVGpvw64FRgePo6MFfbYGZm1tHk8jLjCcCciJgbEeuAacDhdeoE0Cud3gJ4j0ZI2gboFRHPpr38W4EjWjZsMzOzjiuXiX8gMD8zX5aWZV0OHC+pjGTY/uzMsmGSXpT0pKTPZtos20ibZmZm1oC2/mHxFODmiBgEHAz8XlIB8D4wJD0F8G3gj5J6NdLOBiSdJqlUUunChQs3voKZmVkeyGXiXwAMzswPSsuyvg7cCRAR/wS6AP0jYm1ELE7LZwJvAzum6w/aSJuk610fESURUTJgwIAW2BwzM7PNXy4T//PAcEnDJBWTXLx3f506/wH2B5C0E0niXyhpQHpxIJK2J7mIb25EvA8sl7SHJAEnAn/O4TaYmZl1KDn7OV9ElEv6FvAIyU/1boyIWZKuAEoj4n7gO8ANks4judBvakSEpH2BKyStByqBMyJiSdr0N6n5Od9D6cvMzMyaQK35E/i2UlJSEqWlpW0dhpmZWauQNDMiSupb5jv3mZm1AxFBBFRGEKTvQXVZVXlU1q5TGQEBlQFBUBlQWZl06JLlSdtV79XrVSb16/2M6vqZ8qhpL5lP2qj7GY3Wjzr1K7PLm1A/6tSvrL++BAUSBVJmmnReFBbUTBdklitdp7qsoPHl1W0X1Hxe7bo1y7PrFmY/O/2M3t2K2WFAj1b5t+bEb7YZq6wM1pRXsGpdBavXVbBmfQWr1yfTq9cn86vW1ZTVLK9k9frytKyy+gtMdb6wRM28VPvLMvvlCpkv14KNrEdNvex6G9ZL20rjyH7homTbK9LkUV4ZVFQmiaGiEioqK5P3iOrpZFnmlVm3qq3s8qr65Znpykoor6ykIk062XU3jKPmcyoq6yQ5aifjyo4/8PqpFaixZF6VQJN/K1D3IKLmgKTmYKV97fcDRm3F9SfW20FvcU78ZjlSXlGZJNn1FaxZVzNdlYCrE/L6CtZkplevq0ncVcl79braCb1qem15ZbPjKiwQ3ToV0qW4kK6dCunSKbnGt+pLkg16XpkkxYY9yAbXo30kt8ICUZj2uooKCpIeV4FqXmnPLjtfmCaRosLkPVteXFSU1BcUFhRQWFDVXgGFaQIqrLNudRuNHBRVHdwkZUmdbIITqnWAVlVO3QOq9MCovgOqej+DTK+0zmeIur3ZOj3Zenu9jfSMG2qjoJ5EXqd+rjQ0UlFR2YSRjMpkvqJ6WaZuZT0HHZn16rbdt3txzraxLid+a1PryitZtHItC1es5aMVVe9rWLhiLUtXr68eTqwakkySR5qMyCQmaoZKa+rWt0WHBk0AACAASURBVCybxIBMQssOeaaLaiWwSMuyw6VV60DNl8Wa9Ukvel1F85NycWEBXToV0DVNyl2Li+iazvfu1okunZLybsU1iTupV1hrWddOGy6varNTYdvdviM7nF2Z2df1DUk3OESdfnE2lLyLCrIJN7dJwzZ/UnoQR/78O3HitxYXESxfXc7ClWuqk3k2sWeT+8er1tfbRt/uxfTu2inpIWV6O1U9lNq9kpreUa3eFFU9H5AK0vWpHprODinXtFvTTs3Q94afn+0tVfWuRNLz69KpJsnWTcDdqhJ0reUFdCsuoktRAUVtmJRbQ3UvNo++ZFvS+vXrKSsrY82aNRuvbHmhS5cuDBo0iE6dOjV5HSd+a7L6eufZJF5dtnIt6+oZgi4uKmDLnp0Z0LMzw/p3Z8KwvmzZswsDenZmQI/ObNkrWda/R+c27ZWatVdlZWX07NmToUOHeiTDiAgWL15MWVkZw4YNa/J6Tvx5LiJYvqachStq987rG3pvqHfep1un6gS+ff/uSSLPvKqW9epS5C8rs09hzZo1TvpWTRL9+vWjubeld+LPAx8uX8Pjsz/io+Vrk+H35UmvvOq9od55VS98aL+kdz6gR5c0kacJvVdn+nXvTHGRe+dmrcVJ37I25d+DE38HFhHc88ICfvjALFasKQeS3nlVL3zCsO7VSbymd96ZAT27uHduZhtYvHgx+++/PwAffPABhYWFVD0L5bnnnqO4uOEr00tLS7n11lu59tprG/2Mvfbai2eeeabFYj733HO56667mD9/PgUF7qSAE3+H9eHyNVz8p1d5fPZH7D60D1ccvgs7DOjh3rmZbbJ+/frx0ksvAXD55ZfTo0cPzj///Orl5eXlFBXVn1ZKSkooKdn479RbMulXVlZy7733MnjwYJ588kkmT57cYm1nNbbd7ZGzQAcTEdz7Yhlf+J8neebtRXz/0FHccdqe7LRNLyd9M2txU6dO5YwzzmDixIlceOGFPPfcc+y5556MGzeOvfbaizfffBOA6dOnc+ihhwLJQcMpp5zCpEmT2H777WuNAvTo0aO6/qRJkzj66KMZOXIkxx13HFW3mH/wwQcZOXIk48eP55xzzqlut67p06ez8847c+aZZ3L77bdXl3/44Yd8+ctfZsyYMYwZM6b6YOPWW29l1113ZcyYMZxwwgnV23f33XfXG99nP/tZDjvsMEaNGgXAEUccwfjx49l55525/vrrq9d5+OGH2W233RgzZgz7778/lZWVDB8+vPrcfGVlJZ/5zGeafa5+U20+hyi2UR+tWMP37n2Nv7/+IeO368NPj96V7VvpFpBm1rp++MAsXn9veYu2OWrbXlz2pZ2bvV5ZWRnPPPMMhYWFLF++nKeeeoqioiIeffRRLrnkEu65554N1pk9ezZPPPEEK1asYMSIEZx55pkb/CTtxRdfZNasWWy77bbsvffePP3005SUlHD66aczY8YMhg0bxpQpUxqM6/bbb2fKlCkcfvjhXHLJJaxfv55OnTpxzjnnsN9++3HvvfdSUVHBypUrmTVrFj/+8Y955pln6N+/P0uWLGmw3SovvPACr732WvUV9TfeeCN9+/Zl9erV7L777hx11FFUVlZy6qmnVse7ZMkSCgoKOP7447nttts499xzefTRRxkzZgyt9Qh5dwE7gIjgzy8t4ICfzeDJfy/kewfvxJ2n7+mkb2at4phjjqGwsBCAZcuWccwxx7DLLrtw3nnnMWvWrHrXOeSQQ+jcuTP9+/dnyy235MMPP9ygzoQJExg0aBAFBQWMHTuWefPmMXv2bLbffvvqZNtQ4l+3bh0PPvggRxxxBL169WLixIk88sgjADz++OOceeaZABQWFrLFFlvw+OOPc8wxx9C/f38A+vbtu9HtnjBhQq2f0V177bWMGTOGPfbYg/nz5/PWW2/x7LPPsu+++1bXq2r3lFNO4dZbbwWSA4aTTz55o5/XUtzj38wtWrmWS+99jYdnfcDYwb255pgxfGZLJ3yzjm5Teua50r179+rp73//+0yePJl7772XefPmMWnSpHrX6dy5c/V0YWEh5eXlm1SnIY888ghLly5l9OjRAKxatYquXbs2eFqgIUVFRVRWJr98qqysZN26ddXLsts9ffp0Hn30Uf75z3/SrVs3Jk2a1OiNlgYPHsxWW23F448/znPPPcdtt93WrLg+Dff4N2N/feV9DvjZDB6f/REXHTSSu8/Y00nfzNrUsmXLGDhwIAA333xzi7c/YsQI5s6dy7x58wC444476q13++2389vf/pZ58+Yxb9483nnnHf7+97+zatUq9t9/f6677joAKioqWLZsGZ/73Oe46667WLx4MUD1UP/QoUOZOXMmAPfffz/r19d/P5Nly5bRp08funXrxuzZs3n22WcB2GOPPZgxYwbvvPNOrXYBvvGNb3D88cfXGjFpDU78m6Eln6zjrD++wFl/fIFBfbryl3P24Yz9dujwt3s1s/bvwgsv5OKLL2bcuHHN6qE3VdeuXfn1r3/NgQceyPjx4+nZsydbbLFFrTqrVq3i4Ycf5pBDDqku6969O/vssw8PPPAAv/jFL3jiiScYPXo048eP5/XXX2fnnXfme9/7Hvvttx9jxozh29/+NgCnnnoqTz75JGPGjOGf//xnrV5+1oEHHkh5eTk77bQTF110EXvssQcAAwYM4Prrr+fII49kzJgxfPWrX61e57DDDmPlypWtOswPoKqrJHPSuHQg8AugEPhtRFxdZ/kQ4Bagd1rnooh4UNIXgKuBYmAdcEFEPJ6uMx3YBlidNnNARHzUWBwlJSVRWlraYtvVlh5+7QMuve9Vlq1ez7mf35HT993eCd8sT7zxxhvstNNObR1Gm1u5ciU9evQgIjjrrLMYPnw45513XluH1WylpaWcd955PPXUU5+qnfr+XUiaGRH1/n4yZ+f4JRUCvwK+AJQBz0u6PyJez1S7FLgzIq6TNAp4EBgKLAK+FBHvSdoFeAQYmFnvuIjoGJm8iT7+ZB2XPzCLP7/0Hjtv24s/fGMiI7fu1dZhmZm1uhtuuIFbbrmFdevWMW7cOE4//fS2DqnZrr76aq677rpWPbdfJZcX900A5kTEXABJ04DDgWziD6Aqe20BvAcQES9m6swCukrqHBFrcxhvu/X31z/kkntf5eNP1nHe53fkm5N38ENszCxvnXfeeZtlDz/roosu4qKLLmqTz85l4h8IzM/MlwET69S5HPibpLOB7sDn62nnKOCFOkn/JkkVwD3AjyOX5yva0LJV6/nhA7P404sLGLl1T24+eXd23naLja9oZmbWgLb+Od8U4OaI+L+S9gR+L2mXiKgEkLQz8H+AAzLrHBcRCyT1JEn8JwC31m1Y0mnAaQBDhgzJ8Wa0vMdnf8jFf3qVRSvXcc7+w/nW5M/4zntmZvap5TKTLAAGZ+YHpWVZXwfuBIiIfwJdgP4AkgYB9wInRsTbVStExIL0fQXwR5JTChuIiOsjoiQiSlrrbkgtYdnq9Vxw18uccnMpvbsWc9839+bbX9jRSd/MzFpELnv8zwPDJQ0jSfjHAl+rU+c/wP7AzZJ2Ikn8CyX1Bv5KcpX/01WVJRUBvSNikaROwKHAoznchlb15L8XctE9r/Dh8jWcNXkHztl/OJ2LWu+3nWZm1vHlrBsZEeXAt0iuyH+D5Or9WZKukHRYWu07wKmSXgZuB6am5+u/BXwG+IGkl9LXlkBn4BFJrwAvkRxQ3JCrbWgtK9as56J7XuGkG5+je+ci7v3m3lzwxZFO+mbWrkyePLn6trdVfv7zn1ff/rY+kyZNourn1AcffDBLly7doM7ll1/ONddc0+hn33fffbz+es214T/4wQ949NGW6/ede+65DBw4sPoufR1ZTs/xR8SDJD/Ry5b9IDP9OrB3Pev9GPhxA82Ob8kY29o/3lrEhXe/zAfL13DGfjtw7ueH06WTE76ZtT9Tpkxh2rRpfPGLX6wumzZtGj/5yU+atP6DDz648UoNuO+++zj00EOrn4R3xRVXbHJbdeXb43t94riNrFxbzvfufZXjf/cvuhQXcveZe3HRQSOd9M2s3Tr66KP561//Wn2/+nnz5vHee+/x2c9+ljPPPJOSkhJ23nlnLrvssnrXHzp0KIsWLQLgyiuvZMcdd2SfffapfnQvJL/R33333RkzZgxHHXUUq1at4plnnuH+++/nggsuYOzYsbz99tu1Hpf72GOPMW7cOEaPHs0pp5zC2rVrqz/vsssuY7fddmP06NHMnj273rjy7fG9bX/okYeeeXsRF979CguWrubUzw7jOweMcMI3s+Z56CL44NWWbXPr0XDQ1Q0u7tu3LxMmTOChhx7i8MMPZ9q0aXzlK19BEldeeSV9+/aloqKC/fffn1deeYVdd9213nZmzpzJtGnTeOmllygvL2e33XZj/PhkMPfII4/k1FNPBeDSSy/ld7/7HWeffTaHHXYYhx56KEcffXStttasWcPUqVN57LHH2HHHHTnxxBO57rrrOPfccwHo378/L7zwAr/+9a+55ppr+O1vf7tBPPn2+F73+FvRJ2vL+cGfX+NrN/yLogJx1+l78r1DRjnpm9lmo2q4H5Jh/qrH4t55553stttujBs3jlmzZtU6H1/XU089xZe//GW6detGr169OOyww6qXvfbaa3z2s59l9OjR3HbbbQ0+1rfKm2++ybBhw9hxxx0BOOmkk5gxY0b18iOPPBKA8ePHVz/YJysfH9/rHn8r+dfcxVxw9yvM/3gVp+w9jAu+OIKuxU74ZraJGumZ59Lhhx/OeeedxwsvvMCqVasYP34877zzDtdccw3PP/88ffr0YerUqY0+krYxU6dO5b777mPMmDHcfPPNTJ8+/VPFW/Vo34Ye65uPj+91jz/HVq+r4IcPzOLYG5JHNE47dQ9+8KVRTvpmtlnq0aMHkydP5pRTTqnu7S9fvpzu3buzxRZb8OGHH/LQQw812sa+++7Lfffdx+rVq1mxYgUPPPBA9bIVK1awzTbbsH79+lpJrmfPnqxYsWKDtkaMGMG8efOYM2cOAL///e/Zb7/9mrw9+fj4Xif+HCqdt4SDfjGDm56ex4l7bMfD536Widv3a+uwzMw+lSlTpvDyyy9XJ/4xY8Ywbtw4Ro4cyde+9jX23nuDH2vVsttuu/HVr36VMWPGcNBBB7H77rtXL/vRj37ExIkT2XvvvRk5cmR1+bHHHstPf/pTxo0bx9tvV9/TjS5dunDTTTdxzDHHMHr0aAoKCjjjjDOatB35+vjenD6Wt71o7cfyrllfwTWPvMnvnn6Hgb278pOjd2WvHfq32uebWcfkx/Lmp409vrfdPJY3X73wn485/86XmbvoE46bOISLD96JHp29m83MrPly8fheZ6QWsmZ9BT979N/cMGMu22zRlT98fSL7DHcv38zMNl0uHt/rxN8CXp6/lO/c9TJzPlrJlAmDueTgnejZpVNbh2VmZrYBJ/5PYW15Bb949C1+8+TbbNWrC7ecMoH9dtx8ngRoZpufiEBSW4dh7cSmXKfnxL+JXi1bxvl3vcybH67gKyWDuPTQUfRyL9/McqhLly4sXryYfv36OfkbEcHixYvp0qVLs9Zz4m+mdeWV/PLxt/jV9Lfp36OYm6buzuSRW7Z1WGaWBwYNGkRZWdmnvle7dRxdunRh0KBBzVrHib+Zzrn9RR6e9QFH7jaQyw7dmS26uZdvZq2jU6dOtW79arYpnPib6dR9h3HU+EF8YdRWbR2KmZlZsznxN9P47Tb+QAYzM7P2yrfsNTMzyyN5ccteSQuBd1uwyf7AohZsz+rn/dw6vJ9bj/d16/B+hu0iot7fl+dF4m9pkkobugeytRzv59bh/dx6vK9bh/dz4zzUb2Zmlkec+M3MzPKIE/+mub6tA8gT3s+tw/u59Xhftw7v50b4HL+ZmVkecY/fzMwsjzjxN5OkAyW9KWmOpJZ9SLIBIGmwpCckvS5plqT/auuYOjJJhZJelPSXto6lo5LUW9LdkmZLekPSnm0dU0ck6bz0O+M1SbdLat7Ta/KEE38zSCoEfgUcBIwCpkga1bZRdUjlwHciYhSwB3CW93NO/RfwRlsH0cH9Ang4IkYCY/D+bnGSBgLnACURsQtQCBzbtlG1T078zTMBmBMRcyNiHTANOLyNY+pwIuL9iHghnV5B8iU5sG2j6pgkDQIOAX7b1rF0VJK2APYFfgcQEesiYmnbRtVhFQFdJRUB3YD32jiedsmJv3kGAvMz82U4IeWUpKHAOOBfbRtJh/Vz4EKgsq0D6cCGAQuBm9JTKr+V1L2tg+poImIBcA3wH+B9YFlE/K1to2qfnPit3ZLUA7gHODcilrd1PB2NpEOBjyJiZlvH0sEVAbsB10XEOOATwNcHtTBJfUhGYIcB2wLdJR3ftlG1T078zbMAGJyZH5SWWQuT1Ikk6d8WEX9q63g6qL2BwyTNIzlt9TlJf2jbkDqkMqAsIqpGre4mORCwlvV54J2IWBgR64E/AXu1cUztkhN/8zwPDJc0TFIxyYUj97dxTB2OJJGcD30jIv6nrePpqCLi4ogYFBFDSf4tPx4R7iG1sIj4AJgvaURatD/wehuG1FH9B9hDUrf0O2R/fBFlvYraOoDNSUSUS/oW8AjJFaM3RsSsNg6rI9obOAF4VdJLadklEfFgG8Zk9mmcDdyWdhjmAie3cTwdTkT8S9LdwAskvwx6Ed/Br16+c5+ZmVke8VC/mZlZHnHiNzMzyyNO/GZmZnnEid/MzCyPOPGbmZnlESd+MzOzPOLEb2Zmlkec+M1yRNJDkk5q6bptSdI8SZ/PQbvTJX0jnT5OUoMPV8nW3YTPGSJpZfqIbbO85MRvlpEmhapXpaTVmfnjmtNWRBwUEbe0dN32SNJFkmbUU95f0jpJuzS1rYi4LSIOaKG4ah2oRMR/IqJHRFS0RPt1Piskfaal2zVraU78ZhlpUugRET1I7v39pUzZbVX10ud9W40/AHtJGlan/Fjg1Yh4rQ1iMrN6OPGbNYGkSZLKJH1X0gckz1bvI+kvkhZK+jidHpRZJzt8PVXSPyRdk9Z9R9JBm1h3mKQZklZIelTSrxp6ql4TY/yRpKfT9v4mqX9m+QmS3pW0WNL3Gto/EVEGPE7yjIWsE4FbNxZHnZinSvpHZv4LkmZLWibpl4Ayy3aQ9Hga3yJJt0nqnS77PTAEeCAdsblQ0tC0Z16U1tlW0v2SlkiaI+nUTNuXS7pT0q3pvpklqaShfdAQSVukbSxM9+WlkgrSZZ+R9GS6bYsk3ZGWS9LPJH0kabmkV5szamLWGCd+s6bbGugLbAecRvL/56Z0fgiwGvhlI+tPBN4E+gM/AX4nSZtQ94/Ac0A/4HI2TLZZTYnxayQPjdkSKAbOB5A0CrgubX/b9PPqTdapW7KxKHka3dg03ubuq6o2+pM8XvVSkn3xNslDnKqrAFel8e1E8tjsywEi4gRqj9r8pJ6PmEby2NxtgaOB/5b0uczyw9I6vUmexLnRmOvxv8AWwPbAfiQHQ1UP6fkR8DegD8m+/d+0/ABgX2DHdN2vAIs34bPNNuDEb9Z0lcBlEbE2IlZHxOKIuCciVkXECuBKki/2hrwbETek55dvAbYBtmpOXUlDgN2BH0TEuoj4B408GrqJMd4UEf+OiNXAnSTJGpJE+JeImBERa4Hvp/ugIfemMVY9A/1E4KH0+ejN3VdVDgZmRcTd6TPWfw58kNm+ORHx9/RvshD4nya2i6TBJAcR342INRHxEvDbNO4q/4iIB9O/w++BMU1pO/MZhSSnOy6OiBURMQ/4v9QcIK0nORjaNo3hH5nynsBIkoepvRER7zfns80a4sRv1nQLI2JN1YyS537/v3T4djkwA+ithq8YzyasVelkj2bW3RZYkikDmN9QwE2M8YPM9KpMTNtm246IT2ik15nGdBdwYjo6cRxwazPiqE/dGCI7L2krSdMkLUjb/QPJyEBTVO3LFZmyd4GBmfm6+6aLmnd9R3+gU9pufZ9xIcmoxXPpqYRTACLicZLRhV8BH0m6XlKvZnyuWYOc+M2aru4zrL8DjAAmRkQvkqFZyJyDzoH3gb6SumXKBjdS/9PE+H627fQz+21knVtIhqW/QNJjfeBTxlE3BlF7e/+b5O8yOm33+DptNvbc8fdI9mXPTNkQYMFGYmqORdT06jf4jIj4ICJOjYhtgdOBXyv9ZUBEXBsR44FRJEP+F7RgXJbHnPjNNl1PknPVSyX1BS7L9QdGxLtAKXC5pGJJewJfylGMdwOHStpHUjFwBRv/zngKWApcD0yLiHWfMo6/AjtLOjLtaZ9Dcq1FlZ7ASmCZpIFsmBw/JDm3voGImA88A1wlqYukXYGvk4wabKritK0ukrqkZXcCV0rqKWk74NtVnyHpmMxFjh+THKhUStpd0kRJnYBPgDU0fprFrMmc+M023c+BriS9umeBh1vpc48D9iQZdv8xcAewtoG6mxxjRMwCziK5OO99ksRUtpF1gmR4f7v0/VPFERGLgGOAq0m2dzjwdKbKD4HdgGUkBwl/qtPEVcClkpZKOr+ej5gCDCXp/d9Lcg3Ho02JrQGzSA5wql4nA2eTJO+5wD9I9ueNaf3dgX9JWklyrcZ/RcRcoBdwA8k+f5dk23/6KeIyq6bk/6mZba7Sn4DNjoicjziY2ebPPX6zzUw6DLyDpAJJBwKHA/e1dVxmtnloV4lf0o3pDSvqvctXelOLa9MbbbwiabfWjtGsHdgamE5ybvta4MyIeLFNIzKzzUa7GuqXtC/Jl9mtEbHBXaokHUxyvuxgkhuc/CIiJrZulGZmZpuvdtXjj4gZwJJGqhxOclAQEfEsye+At2md6MzMzDZ/7SrxN8FAat+spIzaN9swMzOzRnTYJ4xJOo3kfup07959/MiRI9s4IjMzs9Yxc+bMRRExoL5lm1viX0Dtu3YNooG7bEXE9SQ3EaGkpCRKS0tzH52ZmVk7IOndhpZtbkP995PeB1zSHsAyP7jCzMys6dpVj1/S7cAkoL+kMpLbenYCiIjfAA+SXNE/h+SBGSfX35KZmZnVp10l/oiYspHlQXILUTMzM9sE7Srxm5lZ21m/fj1lZWWsWbNm45WtXejSpQuDBg2iU6dOTV7Hid/MzAAoKyujZ8+eDB06lOQJyNaeRQSLFy+mrKyMYcOGNXm9ze3iPjMzy5E1a9bQr18/J/3NhCT69evX7BEaJ34zM6vmpL952ZS/lxO/mZm1C4sXL2bs2LGMHTuWrbfemoEDB1bPr1u3rtF1S0tLOeecczb6GXvttVeLxDp9+nQOPfTQFmmrtfkcv5mZtQv9+vXjpZdeAuDyyy+nR48enH/++dXLy8vLKSqqP22VlJRQUlKy0c945plnWibYzZh7/GZm1m5NnTqVM844g4kTJ3LhhRfy3HPPseeeezJu3Dj22msv3nzzTaB2D/zyyy/nlFNOYdKkSWy//fZce+211e316NGjuv6kSZM4+uijGTlyJMcddxxVT6t98MEHGTlyJOPHj+ecc85pVs/+9ttvZ/To0eyyyy5897vfBaCiooKpU6eyyy67MHr0aH72s58BcO211zJq1Ch23XVXjj322E+/s5rIPX4zM2vXysrKeOaZZygsLGT58uU89dRTFBUV8eijj3LJJZdwzz33bLDO7NmzeeKJJ1ixYgUjRozgzDPP3OAnby+++CKzZs1i2223Ze+99+bpp5+mpKSE008/nRkzZjBs2DCmTGn09jK1vPfee3z3u99l5syZ9OnThwMOOID77ruPwYMHs2DBAl577TUAli5dCsDVV1/NO++8Q+fOnavLWoMTv5mZbeCHD8zi9feWt2ibo7btxWVf2rnZ6x1zzDEUFhYCsGzZMk466STeeustJLF+/fp61znkkEPo3LkznTt3Zsstt+TDDz9k0KBBtepMmDChumzs2LHMmzePHj16sP3221f/PG7KlClcf/31TYrz+eefZ9KkSQwYkDwb57jjjmPGjBl8//vfZ+7cuZx99tkccsghHHDAAQDsuuuuHHfccRxxxBEcccQRzd4vm8pD/WZm1q517969evr73/8+kydP5rXXXuOBBx5o8KdsnTt3rp4uLCykvLx8k+q0hD59+vDyyy8zadIkfvOb3/CNb3wDgL/+9a+cddZZvPDCC+y+++45+/y63OM3M7MNbErPvDUsW7aMgQMHAnDzzTe3ePsjRoxg7ty5zJs3j6FDh3LHHXc0ed0JEyZwzjnnsGjRIvr06cPtt9/O2WefzaJFiyguLuaoo45ixIgRHH/88VRWVjJ//nwmT57MPvvsw7Rp01i5ciW9e/du8W2qy4nfzMw2GxdeeCEnnXQSP/7xjznkkENavP2uXbvy61//mgMPPJDu3buz++67N1j3scceq3X64K677uLqq69m8uTJRASHHHIIhx9+OC+//DInn3wylZWVAFx11VVUVFRw/PHHs2zZMiKCc845p1WSPoCqrmLsyEpKSqK0tLStwzAza9feeOMNdtppp7YOo82tXLmSHj16EBGcddZZDB8+nPPOO6+tw2pQfX83STMjot7fN/ocv5mZ2f9v797j5CrrPI9/vl3dSSckBgIBNAkQ7oZLLjRBbhJWFISZRBSUeBkCLigrjrgzOrCLhMVlRwfWCyuKCLoMopFhkI1LEEfAiMolF0JigtEkZCDcCSQEQi7d/Zs/6nSnurq6uzqpU1Xp832/XkWd8zzPeZ6nTof6PedS5ynwgx/8gIkTJ3LEEUewYcMGPvOZz9S6SxXlU/1mZmYFvvjFL9b1Ef7O8hG/mZlZhjjwm5mZZYgDv5mZWYY48JuZmWWIA7+ZmdWFU089lfvvv79L2re+9S0uueSSHreZOnUqHT/XPvPMM0s+8/7qq6/m+uuv77Xte+65h+XLl3euX3XVVfz617/uT/dLqsfpl74RkgAAG6JJREFUex34zcysLsyYMYPZs2d3SZs9e3bZE+XMnTt3hx+CUxz4r7nmGk477bQdqqveOfCbmVldOOecc7j33nvZunUrAGvWrOH555/n5JNP5pJLLqGlpYUjjjiCWbNmldz+gAMO4NVXXwXg2muv5dBDD+Wkk07qnLoX8r/RP/bYY5kwYQIf+chH2LRpE3/4wx+YM2cOX/rSl5g4cSKrVq1i5syZ3HXXXUD+CX2TJk3iqKOO4sILL2TLli2d7c2aNYvJkydz1FFH8ac//ansz1rL6Xsd+M3MrC6MHDmSKVOmcN999wH5o/2PfvSjSOLaa69lwYIFLFmyhHnz5rFkyZIe61m4cCGzZ89m8eLFzJ07l/nz53fmffjDH2b+/Pk8+eSTvPvd7+bWW2/lhBNOYNq0aVx33XUsXryYgw46qLP85s2bmTlzJj/72c9YunQpra2tfO973+vM32uvvVi0aBGXXHJJn5cTOnRM3/vggw+yePFi5s+fzz333MPixYs7p+9dunQpF1xwAZCfvveJJ55gyZIl3HTTTf3ap6X4AT5mZtbdfZfDi0srW+e+R8EHv9ZrkY7T/dOnT2f27NnceuutANx5553cfPPNtLa28sILL7B8+XKOPvroknU8/PDDnH322QwdOhSAadOmdeb98Y9/5Morr2T9+vW8+eabnH766b32Z8WKFYwbN45DDz0UgPPPP58bb7yRyy67DMgPJACOOeYY7r777jJ2Qu2n7/URv5mZ1Y3p06fzwAMPsGjRIjZt2sQxxxzD008/zfXXX88DDzzAkiVLOOuss3qcjrcvM2fO5Dvf+Q5Lly5l1qxZO1xPh46pfSsxrW+1pu/1Eb+ZmXXXx5F5WoYNG8app57KhRde2HlT3xtvvMFuu+3GiBEjeOmll7jvvvuYOnVqj3W8973vZebMmVxxxRW0trbyi1/8ovN5+xs3buSd73wn27Zt44477uic4nf48OFs3LixW12HHXYYa9asYeXKlRx88MHcfvvtnHLKKTv1GWs9fa8Dv5mZ1ZUZM2Zw9tlnd97hP2HCBCZNmsThhx/O2LFjOfHEE3vdfvLkyXzsYx9jwoQJ7L333l2m1v3qV7/Kcccdx6hRozjuuOM6g/15553HRRddxA033NB5Ux9Ac3MzP/rRjzj33HNpbW3l2GOP5bOf/Wy/Pk+9Td/raXnNzAzwtLy7Kk/La2ZmZj1y4DczM8sQB34zM7MMqbvAL+kMSSskrZR0eYn8/SQ9JOkJSUsknVmLfpqZDURZuO9rINmRv1ddBX5JOeBG4IPAeGCGpPFFxa4E7oyIScB5wHer20szs4GpubmZdevWOfjvIiKCdevW0dzc3K/t6u3nfFOAlRGxGkDSbGA6sLygTADvSJZHAM9XtYdmZgPUmDFjWLt2La+88kqtu2Jlam5u7vJTwXLUW+AfDTxbsL4WOK6ozNXAryR9HtgNGJjTJ5mZVVlTUxPjxo2rdTcsZXV1qr9MM4D/GxFjgDOB2yV1+xySLpa0QNICj17NzMzy6i3wPweMLVgfk6QV+jRwJ0BEPAI0A3sVVxQRN0dES0S0dEyEYGZmlnX1FvjnA4dIGidpEPmb9+YUlXkGeB+ApHeTD/w+pDczMytDXQX+iGgFLgXuB54if/f+MknXSOqYV/HvgIskPQn8FJgZvgXVzMysLPV2cx8RMReYW5R2VcHycqD3GRrMzMyspLo64jczM7N0OfCbmZlliAO/mZlZhjjwm5mZZYgDv5mZWYY48JuZmWWIA7+ZmVmGOPCbmZlliAO/mZlZhjjwm5mZZYgDv5mZWYY48JuZmWWIA7+ZmVmGOPCbmZlliAO/mZlZhjjwm5mZZYgDv5mZWYY48JuZmWWIA7+ZmVmGOPCbmZlliAO/mZlZhqQS+CV9XtIeadRtZmZmOy6tI/59gPmS7pR0hiSl1I6ZmZn1QyqBPyKuBA4BbgVmAn+R9L8kHZRGe2ZmZlae1K7xR0QALyavVmAP4C5J/5RWm2ZmZta7xjQqlfQF4G+AV4FbgC9FxDZJDcBfgC+n0a6ZmZn1LpXAD4wEPhwR/16YGBHtkv4qpTbNzMysD6kE/oiYJWmypOlAAL+PiEVJ3lNptGlmZmZ9S+vnfF8BbgP2BPYCfiTpyjTaMjMzs/Kldar/k8CEiNgMIOlrwGLgf6bUnpmZmZUhrbv6nweaC9YHA8+Vs2Hyu/8VklZKuryHMh+VtFzSMkk/qUB/zczMMiGtI/4NwDJJ/0b+Gv/7gccl3QAQEX9baiNJOeDGpPxa8g8BmhMRywvKHAJcAZwYEa9L2julz2BmZjbgpBX4f568OvymzO2mACsjYjWApNnAdGB5QZmLgBsj4nWAiHh5p3trZmaWEWnd1X+bpEHAoUnSiojYVsamo4FnC9bXAscVlTkUQNLvgRxwdUT8cie7bGZmlglpPcBnKvm7+tcAAsZKOj8ifluB6hvJPw54KjAG+K2koyJifVEfLgYuBthvv/0q0KyZmdmuL62b+/438IGIOCUi3gucDnyzjO2eA8YWrI+h+02Ba4E5EbEtIp4G/kx+INBFRNwcES0R0TJq1Kgd+hBmZmYDTVqBvykiVnSsRMSfgaYytpsPHCJpXHKp4DxgTlGZe8gf7SNpL/Kn/ldXotNmZmYDXVo39y2UdAvw42T9E8CCvjaKiFZJlwL3k79+/8OIWCbpGmBBRMxJ8j4gaTnQRn4egHWpfAozM7MBRvlJ9CpcqTQY+BxwUpL0MPDdiNhS8cbK0NLSEgsW9DnuMDMzGxAkLYyIllJ5FT/iT36L/2REHA58o9L1m5mZ2Y6r+DX+iGgDVkjyrfRmZmZ1Jq1r/HuQf3Lf48BbHYkRMS2l9szMzKwMaQX+r6RUr5mZme2EtAL/mRHxD4UJkr4OzEupPTMzMytDWr/jf3+JtA+m1JaZmZmVqaJH/JIuAf4LcKCkJQVZw4E/VLItMzMz679Kn+r/CXAf8I/A5QXpGyPitQq3ZWZmZv1U0cAfERuADcCM5Pf8+yRtDJM0LCKeqWR7ZmZm1j9pzc53KXA18BLQniQHcHQa7ZmZmVl50rqr/zLgMD9D38zMrL6kdVf/s+RP+ZuZmVkdSeuIfzXwG0n3Ap0T80SEn91vZmZWQ2kF/meS16DkZWZmZnUglcAfEf+jOE1SWoMMMzMzK1NFr/FL+l3B8u1F2Y9Xsi0zMzPrv0rf3LdbwfKRRXmqcFtmZmbWT5UO/NHDcql1MzMzq7JKX3ffXdLZ5AcUu0v6cJIuYESF2zIzM7N+qnTgnwdMK1j+64K831a4LTMzM+unSj+r/4JK1mdmZmaVldaT+8zMzKwOOfCbmZlliAO/mZlZhqQS+CWdK2l4snylpLslTU6jLTMzMytfWkf8X4mIjZJOAk4DbgW+l1JbZmZmVqa0An9b8n4WcHNE3Isn6zEzM6u5tAL/c5K+D3wMmCtpcIptmZmZWZnSCsYfBe4HTo+I9cBI4EsptWVmZmZlSmuq3HcC90bEFklTgaOBf06prap6bPU6Nm5uZcqBI3lHc1Otu2NmZtYvaQX+fwVaJB0M3Az8P+AnwJkptVc1P/z909y/7CUaBEe8awTHH7Qnxx+4J8eOG8mwwWntTjMzs8pQROUnzZO0KCImS/oy8HZE/B9JT0TEpDK2PQP4NpADbomIr/VQ7iPAXcCxEbGgtzpbWlpiwYJei5Rt87Y2nnhmPY+uXscjq9ex+Jn1bG1rJ9cgjhw9guMP3JPjD9qTlv33YDcPBMzMrAYkLYyIllJ5aUWmbZJmAH/D9ol6+jwvLikH3Ai8H1gLzJc0JyKWF5UbDnwBeKyivS5Dc1Muf5R/0J58EXh7axuLnnk9PxBYtY5bHl7NTfNW0dggjh6TPyPwngP3pGX/kQwZlKt2d83MzLpIK/BfAHwWuDYinpY0Dri9jO2mACsjYjWApNnAdGB5UbmvAl+nDm4YHDIox4kH78WJB+8FwKatrSz899d5ZFX+jMBN81Zz40OraMqJiWN35/gD8wOByfvvQXOTBwJmZlZdqQT+iFgu6e+BQyUdCayIiK+Xselo4NmC9bXAcYUFkicAjo2IeyXVPPAXGzqokZMPGcXJh4wC4M0trSxY8xqPrF7Ho6vW8Z2HVnLDgysZlGtg0n67857k0sCk/XZncKMHAmZmlq5UAn9yJ/9twBpAwFhJ50fEb3ey3gbgG8DMMspeDFwMsN9+++1Msztl2OBGph62N1MP2xuAjZu3MX/Nazyyah2Prn6NGx78C99+4C8Mbmxg8n57dF5GmDBmdwY1+tEHZmZWWWnd3LcQ+HhErEjWDwV+GhHH9LHd8cDVEXF6sn4FQET8Y7I+AlgFvJlssi/wGjCttxv8KnlzX6VteHsbjz/9Wuc9Ak+9+AYR0NzUQMv+IzvvETh6zAiach4ImJlZ32pxc19TR9AHiIg/SyrnR+/zgUOSewKeA84DPl5QzwZgr451Sb8B/r6vu/rr2YghTbx//D68f/w+AKzftJXHnu44I7CO6+7P78ahg3K0HDAyuUdgJEeNHkGjBwJmZtZPaQX+hZJuAX6crH8C6DM4R0SrpEvJP/UvB/wwIpZJugZYEBFzUupv3dh96CBOP2JfTj9iXwBee2srjyU/HXx09Tq+/ss/AflLCMcesEfnPQJHvGsEuQbVsutmZrYLSOtU/2Dgc8BJSdLDwHcjYkvFGytDPZ/q769X39zCo8kg4JFV61j1ylsADB/cyJRx2y8NjH/nO2jwQMDMLJN6O9Vf8cCf/BZ/WUQcXtGKd8JACvzFXn5jc3I2IH+fwNOv5gcCI4Y05QcCyRmBw/YZ7oGAmVlGVPUaf0S0SVohab+IeKbS9VtXe7+jmekTRzN94mgAXtywufNswCOr1/Fvy18CYFCugeamBgY35WhuaqC5MUdzU47BjQ00J2mDm3I0N+YY3Jnf0K1Mfr23MkldjTlfejAzq0NpXePfA1gm6XHgrY7EiJiWUnuW2HdEMx+aNJoPTcoPBJ5b/zaPrlrHn1/eyJZt7WxpbWPztnY2b2tj87Y2trS2s2lrK6+91c7m1rZuZVrbd/yMUFNOyUCiYzDQdXBQOMgYXDBg6BhMDMo1kGsQDQJJNCi/3CDR0LB9WR1pKixLsm3P+Q0NRXUWlm2gS/lcYX4Pbeck1JD//Wq3+gralTwgMrPaSSvwfyWleq2fRu8+hI8cM2aHt29ta2dLazJQaO06YNi8LT9QKFwvLLc9rZ0thevJwGL9pm0ly2xta6/gHqhPhQMHlRh8iK4DE0kFA4pkm2Rw0pGu4jq61JvUUWogQkf5fN861jt0tJ1fzvetY/AiOrZTUX73upS0RWde6bq7tFlUT9eyBW2W6EPJcgX1U1RGXfrV/bPTY13dt6fb/um+XyutGsPJwsOA4qvEUZDbPa/nDXuqMygq1yWPHvOKdfm7dEtTl/Uu5Qr/jfSwXZ/1l/iD91THAXvuxhlH7tvzB6mgigb+ZDa+fSJiXlH6ScALlWzLqqMx10BjrqGqEw61tQdbWtvY2tpOe0B7BO0RROTzOpbz6STr25fb2nvJb8+/RwRtxfnt27fpKN/Wue32/FJ1t7UXrue/tArXO9ulsP6O5a7rnXUUrHdsE1Hcx456u3+OoK86OtLaO784O7bp+B6NKPiS7ayzo2xHP7tuS5dto0s9HfmR/Gd7O8VtxvZ6Y3uZ7e0U9YHCsl3bLFl/P/rRfT/0/rlSuF96wCmOh12DZveAW2o7FeeWGvkU/DvpTIouWV3/zRaVqaYPjN9n1wz8wLeAK0qkb0jy/rpEnlkXuQYxdFAjQwfVuidmO6dzQFA4cEixnYrXS6ngW2ZgHmCXtAr/lp1pRXld0wrLdd+WonLV3F2VDvz7RMTS4sSIWCrpgAq3VRurfwOvr4HGZmgcnLw3F60P7p6W8xS9ZlnTeUkk9S/1gRVk61Hvf8tda/9XOhrt3kvekAq3VRtP3AFL7+z/dsr1MDgo972f2zQN2b6cG+yBh5mZAZUP/AskXRQRPyhMlPSfgYUVbqs2zroeTrsaWjdD65Z+vvdRZvOGZP3t7vmxkze8NTRC09DtA4KO5aah0NRcsJy8dylTWK64TNF2uabqnrMyM7N+qXTgvwz4uaRPsD3QtwCDgLMr3FZtNI/Iv6opAtpbd2ww0fG+bRNs25wfVGx7O1l/O5+26bWuaa2bYetbEG3976saug4YGocUDQ6GFA0mCssUlWscnD9T0pDL16uG/LoaoKGhRFphuYaitHK386DFzAa2igb+iHgJOEHSqcCRSfK9EfFgJdvJHCl/JJ1rgsHDq9du27btA4bOgUIyQGgtTCunzNv5MxobX+ya1vo2tG2t3mfqk0oPGLoMGnJFAwt1TZOSelRQZ5IGBfmUSOtrm57S6Lrcr3oKB0PqPnjq9uqrTIn84sHVTrdRNEFVlwGbSqSXSisjvT9l+10HRXaw/rS3iSD/U4Y+3ruUpbxten3vR9u9vXcutyfr7QV57UV50Uteqe16q7+9RF4v9b9rEpxwKdWQyoXfiHgIeCiNuq2Kck2Qq8IZjrbWgjMRhQOHLfmzDtGef7W3Jf/DtBWsJ3mR5HVLKy7XXpTWtv1/vOK09oK8stosKN/ti7DwFt/itJ6+PHurp4c629vL2KY4reBLqXg/dXntaH6yX8x2Sdo+EO04MOiyXrzcU17hQUHypK/CvCG93SJXWb7jy2ov1wi54dU9m2HV1+fAoZzBRcEgbXvFXdsoTi+VVlZ6f8oWp/ej7E71sQrbFJ8l6ukdeilDeXX0+t5b/SXeuwVfSgTf3gJz4dm0gcWB38yqo+OSCLla98Qs0xr6LmJmZmYDhQO/mZlZhjjwm5mZZYgDv5mZWYY48JuZmWWIA7+ZmVmGOPCbmZlliAO/mZlZhjjwm5mZZYgDv5mZWYY48JuZmWWIA7+ZmVmGOPCbmZlliAO/mZlZhjjwm5mZZYgDv5mZWYY48JuZmWVI3QV+SWdIWiFppaTLS+T/V0nLJS2R9ICk/WvRTzMzs11RXQV+STngRuCDwHhghqTxRcWeAFoi4mjgLuCfqttLMzOzXVddBX5gCrAyIlZHxFZgNjC9sEBEPBQRm5LVR4ExVe6jmZnZLqveAv9o4NmC9bVJWk8+DdxXKkPSxZIWSFrwyiuvVLCLZmZmu656C/xlk/RJoAW4rlR+RNwcES0R0TJq1Kjqds7MzKxONda6A0WeA8YWrI9J0rqQdBrw34FTImJLlfpmZma2y6u3I/75wCGSxkkaBJwHzCksIGkS8H1gWkS8XIM+mpmZ7bLqKvBHRCtwKXA/8BRwZ0Qsk3SNpGlJseuAYcC/SFosaU4P1ZmZmVmRejvVT0TMBeYWpV1VsHxa1TtlZmY2QNTVEb+ZmZmly4HfzMwsQxz4zczMMsSB38zMLEMc+M3MzDLEgd/MzCxDHPjNzMwyxIHfzMwsQxz4zczMMsSB38zMLEMc+M3MzDLEgd/MzCxDHPjNzMwyxIHfzMwsQxz4zczMMsSB38zMLEMc+M3MzDLEgd/MzCxDHPjNzMwyxIHfzMwsQxz4zczMMsSB38zMLEMc+M3MzDLEgd/MzCxDHPjNzMwyxIHfzMwsQxz4zczMMsSB38zMLEMc+M3MzDLEgd/MzCxD6i7wSzpD0gpJKyVdXiJ/sKSfJfmPSTqg+r00MzPbNdVV4JeUA24EPgiMB2ZIGl9U7NPA6xFxMPBN4OvV7aWZmdmuq64CPzAFWBkRqyNiKzAbmF5UZjpwW7J8F/A+SapiH83MzHZZ9Rb4RwPPFqyvTdJKlomIVmADsGdVemdmZraLa6x1B9Ii6WLg4mT1TUkrKlj9XsCrFazPSvN+rg7v5+rxvq4O72fYv6eMegv8zwFjC9bHJGmlyqyV1AiMANYVVxQRNwM3p9FJSQsioiWNum077+fq8H6uHu/r6vB+7l29neqfDxwiaZykQcB5wJyiMnOA85Plc4AHIyKq2EczM7NdVl0d8UdEq6RLgfuBHPDDiFgm6RpgQUTMAW4Fbpe0EniN/ODAzMzMylBXgR8gIuYCc4vSripY3gycW+1+FUnlEoJ14/1cHd7P1eN9XR3ez72Qz5KbmZllR71d4zczM7MUOfD3U1+PFLadJ2mspIckLZe0TNIXat2ngUxSTtITkv5/rfsyUEnaXdJdkv4k6SlJx9e6TwORpC8m3xl/lPRTSc217lM9cuDvhzIfKWw7rxX4u4gYD7wH+Jz3c6q+ADxV604McN8GfhkRhwMT8P6uOEmjgb8FWiLiSPI3iPvm7xIc+PunnEcK206KiBciYlGyvJH8l2TxExytAiSNAc4Cbql1XwYqSSOA95L/RRIRsTUi1te2VwNWIzAkecbLUOD5GvenLjnw9085jxS2CkpmX5wEPFbbngxY3wK+DLTXuiMD2DjgFeBHySWVWyTtVutODTQR8RxwPfAM8AKwISJ+Vdte1ScHfqtbkoYB/wpcFhFv1Lo/A42kvwJejoiFte7LANcITAa+FxGTgLcA3x9UYZL2IH8GdhzwLmA3SZ+sba/qkwN//5TzSGGrAElN5IP+HRFxd637M0CdCEyTtIb8Zav/JOnHte3SgLQWWBsRHWet7iI/ELDKOg14OiJeiYhtwN3ACTXuU11y4O+fch4pbDspmWb5VuCpiPhGrfszUEXEFRExJiIOIP9v+cGI8BFShUXEi8Czkg5Lkt4HLK9hlwaqZ4D3SBqafIe8D99EWVLdPbmvnvX0SOEad2sgOhH4FLBU0uIk7b8lT3U02xV9HrgjOWBYDVxQ4/4MOBHxmKS7gEXkfxn0BH6CX0l+cp+ZmVmG+FS/mZlZhjjwm5mZZYgDv5mZWYY48JuZmWWIA7+ZmVmGOPCbWc1ImupZAc2qy4HfzMwsQxz4zaxPkj4p6XFJiyV9X1JO0puSvpnMf/6ApFFJ2YmSHpW0RNLPk2eoI+lgSb+W9KSkRZIOSqofVjBX/R3JU9fMLCUO/GbWK0nvBj4GnBgRE4E24BPAbsCCiDgCmAfMSjb5Z+AfIuJoYGlB+h3AjRExgfwz1F9I0icBlwHjgQPJP7nRzFLiR/aaWV/eBxwDzE8OxocAL5OfyvdnSZkfA3cnc8/vHhHzkvTbgH+RNBwYHRE/B4iIzQBJfY9HxNpkfTFwAPC79D+WWTY58JtZXwTcFhFXdEmUvlJUbkef/72lYLkNfy+Zpcqn+s2sLw8A50jaG0DSSEn7k//+OCcp83HgdxGxAXhd0slJ+qeAeRGxEVgr6UNJHYMlDa3qpzAzwCNrM+tDRCyXdCXwK0kNwDbgc8BbwJQk72Xy9wEAnA/clAT2wpnoPgV8X9I1SR3nVvFjmFnCs/OZ2Q6R9GZEDKt1P8ysf3yq38zMLEN8xG9mZpYhPuI3MzPLEAd+MzOzDHHgNzMzyxAHfjMzswxx4DczM8sQB34zM7MM+Q/J7rQijjZaugAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhffPuwPwfwQ"
      },
      "source": [
        "### Fine tune model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_l9rRMSVwfwQ"
      },
      "source": [
        "base_model.trainable = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGOtjCCiwfwQ"
      },
      "source": [
        "fine_tune_at = 100\n",
        "\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWECEpNnwfwR"
      },
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "              optimizer=tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate/10),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kevAIiOawfwR",
        "outputId": "f092f6a6-d3f1-4aa2-ffa6-1143c6b144e7"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf.math.truediv (TFOpLambda) (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf.math.subtract (TFOpLambda (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_1.00_96 (Functio (None, 3, 3, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,862,721\n",
            "Non-trainable params: 396,544\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iefNjFg9wfwR",
        "outputId": "bedf3268-545a-4943-a0e8-68eebe5f125b"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "howFYywBwfwR"
      },
      "source": [
        "### Train fine tuned model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RS5IkrVwfwR",
        "outputId": "681291f2-79e7-48d8-d57c-9d919c62badf"
      },
      "source": [
        "fine_tune_epochs = 10\n",
        "total_epochs = initial_epochs + fine_tune_epochs\n",
        "\n",
        "history_fine = model.fit(train_data_iterator, \n",
        "                         epochs=total_epochs, \n",
        "                         initial_epoch=history.epoch[-1], \n",
        "                         validation_data=validation_data_iterator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 11/20\n",
            "5501/5501 [==============================] - 825s 149ms/step - loss: 0.2808 - accuracy: 0.8788 - val_loss: 0.2150 - val_accuracy: 0.9068\n",
            "Epoch 12/20\n",
            "5501/5501 [==============================] - 812s 148ms/step - loss: 0.2310 - accuracy: 0.9038 - val_loss: 0.1890 - val_accuracy: 0.9233\n",
            "Epoch 13/20\n",
            "5501/5501 [==============================] - 802s 146ms/step - loss: 0.2097 - accuracy: 0.9140 - val_loss: 0.1896 - val_accuracy: 0.9293\n",
            "Epoch 14/20\n",
            "5501/5501 [==============================] - 795s 145ms/step - loss: 0.1969 - accuracy: 0.9201 - val_loss: 0.1758 - val_accuracy: 0.9276\n",
            "Epoch 15/20\n",
            "5501/5501 [==============================] - 791s 144ms/step - loss: 0.1875 - accuracy: 0.9245 - val_loss: 0.1680 - val_accuracy: 0.9322\n",
            "Epoch 16/20\n",
            "5501/5501 [==============================] - 800s 145ms/step - loss: 0.1801 - accuracy: 0.9278 - val_loss: 0.1762 - val_accuracy: 0.9393\n",
            "Epoch 17/20\n",
            "5501/5501 [==============================] - 793s 144ms/step - loss: 0.1747 - accuracy: 0.9305 - val_loss: 0.1688 - val_accuracy: 0.9394\n",
            "Epoch 18/20\n",
            "5501/5501 [==============================] - 808s 147ms/step - loss: 0.1689 - accuracy: 0.9330 - val_loss: 0.1636 - val_accuracy: 0.9403\n",
            "Epoch 19/20\n",
            "5501/5501 [==============================] - 808s 147ms/step - loss: 0.1640 - accuracy: 0.9348 - val_loss: 0.1642 - val_accuracy: 0.9423\n",
            "Epoch 20/20\n",
            "5501/5501 [==============================] - 806s 147ms/step - loss: 0.1599 - accuracy: 0.9370 - val_loss: 0.1721 - val_accuracy: 0.9410\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQapXUxTwfwR"
      },
      "source": [
        "### Plot improved loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejRcZ2jswfwS"
      },
      "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']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfhz8vfywfwS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "0f575070-f8cc-4269-8911-d7df321c96a4"
      },
      "source": [
        "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.ylim([0.8, 1])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1],\n",
        "          plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='lower right')\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.ylim([0, 1.0])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1],\n",
        "         plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='upper right')\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": "code",
      "metadata": {
        "id": "-cGRH-QqwfwS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "888284b5-51f7-4183-8ead-bc7de5e84e2a"
      },
      "source": [
        "model.save('{}/{}'.format(base_dir, 'mobileNetV2_model_dataGen_tuned.h5'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3EY95Yasc-V"
      },
      "source": [
        "## One Cycle policy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7vxNRm6tHlq"
      },
      "source": [
        "### One cycle scheduler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5aUs5Xt-soSh"
      },
      "source": [
        "\"\"\"\n",
        "https://www.avanwyk.com/tensorflow-2-super-convergence-with-the-1cycle-policy/\n",
        "\"\"\"\n",
        "\n",
        "class CosineAnnealer:\n",
        "    def __init__(self, start, end, steps):\n",
        "        self.start = start\n",
        "        self.end = end\n",
        "        self.steps = steps\n",
        "        self.n = 0\n",
        "        \n",
        "    def step(self):\n",
        "        self.n += 1\n",
        "        cos = np.cos(np.pi * (self.n / self.steps)) + 1\n",
        "        return self.end + (self.start - self.end) / 2. * cos\n",
        "\n",
        "\n",
        "class OneCycleScheduler(tf.keras.callbacks.Callback):\n",
        "    def __init__(self, lr_max, steps, mom_min=0.85, mom_max=0.95, phase_1_pct=0.3, div_factor=25.):\n",
        "        super(OneCycleScheduler, self).__init__()\n",
        "        lr_min = lr_max / div_factor\n",
        "        final_lr = lr_max / (div_factor * 1e4)\n",
        "        phase_1_steps = steps * phase_1_pct\n",
        "        phase_2_steps = steps - phase_1_steps\n",
        "        \n",
        "        self.phase_1_steps = phase_1_steps\n",
        "        self.phase_2_steps = phase_2_steps\n",
        "        self.phase = 0\n",
        "        self.step = 0\n",
        "        \n",
        "        self.phases = [[CosineAnnealer(lr_min, lr_max, phase_1_steps), CosineAnnealer(mom_max, mom_min, phase_1_steps)], \n",
        "                 [CosineAnnealer(lr_max, final_lr, phase_2_steps), CosineAnnealer(mom_min, mom_max, phase_2_steps)]]\n",
        "        \n",
        "        self.lrs = []\n",
        "        self.moms = []\n",
        "\n",
        "    def on_train_begin(self, logs=None):\n",
        "        self.phase = 0\n",
        "        self.step = 0\n",
        "\n",
        "        self.set_lr(self.lr_schedule().start)\n",
        "        self.set_momentum(self.mom_schedule().start)\n",
        "        \n",
        "    def on_train_batch_begin(self, batch, logs=None):\n",
        "        self.lrs.append(self.get_lr())\n",
        "        self.moms.append(self.get_momentum())\n",
        "\n",
        "    def on_train_batch_end(self, batch, logs=None):\n",
        "        self.step += 1\n",
        "        if self.step >= self.phase_1_steps:\n",
        "            self.phase = 1\n",
        "            \n",
        "        self.set_lr(self.lr_schedule().step())\n",
        "        self.set_momentum(self.mom_schedule().step())\n",
        "        \n",
        "    def get_lr(self):\n",
        "        try:\n",
        "            return tf.keras.backend.get_value(self.model.optimizer.lr)\n",
        "        except AttributeError:\n",
        "            return None\n",
        "        \n",
        "    def get_momentum(self):\n",
        "        try:\n",
        "            return tf.keras.backend.get_value(self.model.optimizer.momentum)\n",
        "        except AttributeError:\n",
        "            return None\n",
        "        \n",
        "    def set_lr(self, lr):\n",
        "        try:\n",
        "            tf.keras.backend.set_value(self.model.optimizer.lr, lr)\n",
        "        except AttributeError:\n",
        "            pass # ignore\n",
        "        \n",
        "    def set_momentum(self, mom):\n",
        "        try:\n",
        "            tf.keras.backend.set_value(self.model.optimizer.momentum, mom)\n",
        "        except AttributeError:\n",
        "            pass # ignore\n",
        "\n",
        "    def lr_schedule(self):\n",
        "        return self.phases[self.phase][0]\n",
        "    \n",
        "    def mom_schedule(self):\n",
        "        return self.phases[self.phase][1]\n",
        "    \n",
        "    def plot(self):\n",
        "        ax = plt.subplot(1, 2, 1)\n",
        "        ax.plot(self.lrs)\n",
        "        ax.set_title('Learning Rate')\n",
        "        ax = plt.subplot(1, 2, 2)\n",
        "        ax.plot(self.moms)\n",
        "        ax.set_title('Momentum')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0V8nwN1LtDFr"
      },
      "source": [
        "### Create data generators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRDP_6jguCIA"
      },
      "source": [
        "new_base_dir = 'dataset_folder'\n",
        "training_sub_path = 'train'\n",
        "test_sub_path = 'test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOMN_9S8uCIB"
      },
      "source": [
        "IMG_SIZE = (96, 96) \n",
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "BATCH_SIZE = 64"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_5Cfa9jsokE"
      },
      "source": [
        "data_generator = ImageDataGenerator(validation_split=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t86V4PsMsokF",
        "outputId": "d0409b65-c50c-4e88-e7e6-a20658c0dbc7"
      },
      "source": [
        "train_data_iterator = data_generator.flow_from_directory('{}/{}'.format(new_base_dir, 'train'), \n",
        "                                                         target_size=IMG_SIZE,\n",
        "                                                         class_mode='binary', \n",
        "                                                         subset='training', \n",
        "                                                         batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 176021 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57Ujl828sokH",
        "outputId": "a5f3b8b6-e74a-4951-da98-8038ed4744b3"
      },
      "source": [
        "validation_data_iterator = data_generator.flow_from_directory('{}/{}'.format(new_base_dir, 'train'), \n",
        "                                                              target_size=IMG_SIZE,\n",
        "                                                              class_mode='binary', \n",
        "                                                              subset='validation', \n",
        "                                                              batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 44004 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnUHH3h0uLal"
      },
      "source": [
        "STEP_SIZE_TRAIN = train_data_iterator.n//train_data_iterator.batch_size\n",
        "STEP_SIZE_VALIDATION = validation_data_iterator.n//validation_data_iterator.batch_size"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHItcrkqsokH"
      },
      "source": [
        "### Create Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-1kpAEosokH"
      },
      "source": [
        "preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afqr4TEBsokI",
        "outputId": "0fefe82c-3b7a-47d0-b5d1-10fabb96d704"
      },
      "source": [
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')"
      ],
      "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_96_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tD5ujmk3sokI"
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abZefO_esokI",
        "outputId": "700cab77-2965-415f-94b3-3aa0be8d4d1d"
      },
      "source": [
        "image_batch, label_batch = next(iter(train_data_iterator))\n",
        "feature_batch = base_model(image_batch)\n",
        "print(feature_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 3, 3, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjQ45xYJsokJ",
        "outputId": "6a18a630-e6d3-4437-fbc2-7a433e38f28b"
      },
      "source": [
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "feature_batch_average = global_average_layer(feature_batch)\n",
        "print(feature_batch_average.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zfMMgBqEsokJ",
        "outputId": "c40686d3-f03b-4726-ce20-a56b63255c7c"
      },
      "source": [
        "prediction_layer = tf.keras.layers.Dense(1)\n",
        "prediction_batch = prediction_layer(feature_batch_average)\n",
        "print(prediction_batch.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPurfPqNsokJ"
      },
      "source": [
        "inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
        "x = preprocess_input(inputs)\n",
        "x = base_model(x, training=False)\n",
        "x = global_average_layer(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Dense(1)(x)\n",
        "model = tf.keras.Model(inputs, x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oIOkw_zXsokJ",
        "outputId": "893be80f-4d76-4147-c718-434fbbf8b459"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf.math.truediv (TFOpLambda) (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf.math.subtract (TFOpLambda (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_1.00_96 (Functio (None, 3, 3, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,281\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUPU3mthsokK"
      },
      "source": [
        "base_learning_rate = 0.0001"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJzj46c3sokK"
      },
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate), \n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0V96ZUyzsokK",
        "outputId": "5cef3d74-ef1b-4235-be19-b883edaa117e"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXrGB4iNsokL"
      },
      "source": [
        "### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVGU4JYzsokL"
      },
      "source": [
        "initial_epochs = 10\n",
        "steps = STEP_SIZE_TRAIN * initial_epochs\n",
        "lr_schedule = OneCycleScheduler(base_learning_rate, steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FD9aVotVsokL",
        "outputId": "98fbd8f5-4697-459c-a9de-8c5be7448312"
      },
      "source": [
        "history = model.fit(train_data_iterator, \n",
        "                    epochs=initial_epochs, \n",
        "                    validation_data=validation_data_iterator, \n",
        "                    callbacks=[lr_schedule], \n",
        "                    verbose=True)\n",
        "\n",
        "model.save('{}/{}'.format(base_dir, 'mobileNetV2_model_onecycle.h5'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2751/2751 [==============================] - 348s 125ms/step - loss: 0.7286 - accuracy: 0.6101 - val_loss: 0.4688 - val_accuracy: 0.7670\n",
            "Epoch 2/10\n",
            "2751/2751 [==============================] - 283s 103ms/step - loss: 0.4286 - accuracy: 0.8012 - val_loss: 0.3546 - val_accuracy: 0.8402\n",
            "Epoch 3/10\n",
            "2751/2751 [==============================] - 262s 95ms/step - loss: 0.3583 - accuracy: 0.8409 - val_loss: 0.3234 - val_accuracy: 0.8579\n",
            "Epoch 4/10\n",
            "2751/2751 [==============================] - 253s 92ms/step - loss: 0.3360 - accuracy: 0.8526 - val_loss: 0.3135 - val_accuracy: 0.8607\n",
            "Epoch 5/10\n",
            "2751/2751 [==============================] - 244s 89ms/step - loss: 0.3280 - accuracy: 0.8567 - val_loss: 0.3076 - val_accuracy: 0.8666\n",
            "Epoch 6/10\n",
            "2751/2751 [==============================] - 241s 88ms/step - loss: 0.3233 - accuracy: 0.8587 - val_loss: 0.3051 - val_accuracy: 0.8658\n",
            "Epoch 7/10\n",
            "2751/2751 [==============================] - 246s 89ms/step - loss: 0.3208 - accuracy: 0.8604 - val_loss: 0.3036 - val_accuracy: 0.8669\n",
            "Epoch 8/10\n",
            "2751/2751 [==============================] - 245s 89ms/step - loss: 0.3200 - accuracy: 0.8598 - val_loss: 0.3026 - val_accuracy: 0.8686\n",
            "Epoch 9/10\n",
            "2751/2751 [==============================] - 244s 89ms/step - loss: 0.3192 - accuracy: 0.8603 - val_loss: 0.3024 - val_accuracy: 0.8687\n",
            "Epoch 10/10\n",
            "2751/2751 [==============================] - 243s 88ms/step - loss: 0.3189 - accuracy: 0.8617 - val_loss: 0.3023 - val_accuracy: 0.8694\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8pJ6jPIsokM"
      },
      "source": [
        "model = tf.keras.models.load_model('{}/{}'.format(base_dir, 'mobileNetV2_model_onecycle.h5'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2Sr4ND7sokM"
      },
      "source": [
        "### Plot loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "WMhXtP_UsokM",
        "outputId": "0a084f31-a73e-476e-ca9f-9a4a3548defc"
      },
      "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": {
        "id": "Dmmo9yyusokM"
      },
      "source": [
        "### Fine tune model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHFSsIQ9sokN"
      },
      "source": [
        "base_model.trainable = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWImxnwMsokN"
      },
      "source": [
        "fine_tune_at = 100\n",
        "\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkBJ3k2PsokN"
      },
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "              optimizer=tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate/10),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvWVULt8sokN",
        "outputId": "f092f6a6-d3f1-4aa2-ffa6-1143c6b144e7"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf.math.truediv (TFOpLambda) (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf.math.subtract (TFOpLambda (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_1.00_96 (Functio (None, 3, 3, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,862,721\n",
            "Non-trainable params: 396,544\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZ2SwqFpsokN",
        "outputId": "bedf3268-545a-4943-a0e8-68eebe5f125b"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2CSUJ4VsokN"
      },
      "source": [
        "### Train fine tuned model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXbOqCjTsokO",
        "outputId": "4d6246cf-0462-4020-c581-10ed5a1bdea5"
      },
      "source": [
        "fine_tune_epochs = 10\n",
        "total_epochs = initial_epochs + fine_tune_epochs\n",
        "\n",
        "history_fine = model.fit(train_data_iterator, \n",
        "                         epochs=total_epochs, \n",
        "                         initial_epoch=history.epoch[-1], \n",
        "                         validation_data=validation_data_iterator)\n",
        "\n",
        "model.save('{}/{}'.format(base_dir, 'mobileNetV2_model_onecycle_tune.h5'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 10/20\n",
            "2751/2751 [==============================] - 268s 95ms/step - loss: 0.2635 - accuracy: 0.8874 - val_loss: 0.2187 - val_accuracy: 0.9085\n",
            "Epoch 11/20\n",
            "2751/2751 [==============================] - 259s 94ms/step - loss: 0.2113 - accuracy: 0.9129 - val_loss: 0.1954 - val_accuracy: 0.9199\n",
            "Epoch 12/20\n",
            "2751/2751 [==============================] - 257s 93ms/step - loss: 0.1868 - accuracy: 0.9242 - val_loss: 0.1897 - val_accuracy: 0.9207\n",
            "Epoch 13/20\n",
            "2751/2751 [==============================] - 256s 93ms/step - loss: 0.1695 - accuracy: 0.9321 - val_loss: 0.1755 - val_accuracy: 0.9285\n",
            "Epoch 14/20\n",
            "2751/2751 [==============================] - 256s 93ms/step - loss: 0.1555 - accuracy: 0.9376 - val_loss: 0.1773 - val_accuracy: 0.9268\n",
            "Epoch 15/20\n",
            "2751/2751 [==============================] - 259s 94ms/step - loss: 0.1439 - accuracy: 0.9433 - val_loss: 0.1781 - val_accuracy: 0.9288\n",
            "Epoch 16/20\n",
            "2751/2751 [==============================] - 260s 94ms/step - loss: 0.1338 - accuracy: 0.9472 - val_loss: 0.1669 - val_accuracy: 0.9339\n",
            "Epoch 17/20\n",
            "2751/2751 [==============================] - 258s 94ms/step - loss: 0.1252 - accuracy: 0.9515 - val_loss: 0.1609 - val_accuracy: 0.9396\n",
            "Epoch 18/20\n",
            "2751/2751 [==============================] - 257s 93ms/step - loss: 0.1167 - accuracy: 0.9543 - val_loss: 0.1750 - val_accuracy: 0.9350\n",
            "Epoch 19/20\n",
            "2751/2751 [==============================] - 258s 94ms/step - loss: 0.1101 - accuracy: 0.9570 - val_loss: 0.1671 - val_accuracy: 0.9406\n",
            "Epoch 20/20\n",
            "2751/2751 [==============================] - 258s 94ms/step - loss: 0.1027 - accuracy: 0.9604 - val_loss: 0.1864 - val_accuracy: 0.9400\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZ0lwIs2sokO"
      },
      "source": [
        "### Plot improved loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5V0X10LsokO"
      },
      "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']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "0s6ebyXMsokO",
        "outputId": "d4d727a9-1a25-4ebf-b506-2b461cfe0e6d"
      },
      "source": [
        "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.ylim([0.8, 1])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1],\n",
        "          plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='lower right')\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.ylim([0, 1.0])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1],\n",
        "         plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='upper right')\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": {
        "id": "IfYE7PPRTYhv"
      },
      "source": [
        "# References\n",
        "\n",
        "  * https://github.com/priya-dwivedi/Deep-Learning/blob/master/resnet_keras/Residual_Networks_yourself.ipynb"
      ]
    }
  ]
}