Projects/Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/AllCNN.ipynb

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    "colab": {
      "name": "AllCNN.ipynb",
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYVvm10rKNLZ",
        "colab_type": "text"
      },
      "source": [
        "# Peter Moss Acute Myeloid / Lymphoblastic Leukemia AI Research Project"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9vdvFtKeeUR",
        "colab_type": "text"
      },
      "source": [
        "## Detecting Acute Lymphoblastic Leukemia With Keras & Tensorflow \n",
        "**Using the ACUTE LEUKEMIA CLASSIFICATION USING CONVOLUTION NEURAL NETWORK IN CLINICAL DECISION SUPPORT SYSTEM paper & ALL_IDB2**    \n",
        "\n",
        "![Peter Moss Acute Myeloid / Lymphoblastic Leukemia AI Research Project](https://www.PeterMossAmlAllResearch.com/media/images/banner.png)\n",
        "\n",
        "In this notebook you will create and train a Convolutional Neural Network, (KAllCNN_IDB2), to detect Acute Lymphoblastic Leukemia (ALL) using Keras & Tensorflow on Google Colab. The architecture you will create is based on the network proposed in the [ACUTE LEUKEMIA CLASSIFICATION USING CONVOLUTION NEURAL NETWORK IN CLINICAL DECISION SUPPORT SYSTEM](https://airccj.org/CSCP/vol7/csit77505.pdf \"ACUTE LEUKEMIA CLASSIFICATION USING CONVOLUTION NEURAL NETWORK IN CLINICAL DECISION SUPPORT SYSTEM\") paper by Thanh.TTP, Giao N. Pham, Jin-Hyeok Park, Kwang-Seok Moon, Suk-Hwan Lee, and Ki-Ryong Kwon.\n",
        "\n",
        "This notebook is written by [Adam Milton-Barker](https://www.petermossamlallresearch.com/team/adam-milton-barker/profile \"Adam Milton-Barker\") and based on a notebook written by [Amita Kapoor](https://www.petermossamlallresearch.com/team/amita-kapoor/profile \"Amita Kapoor\"), and [Taru Jain](https://www.petermossamlallresearch.com/students/student/taru-jain/profile \"Taru Jain\"), one of the students from the [AML/ALL AI Student Program](https://www.petermossamlallresearch.com/students/ \"AML/ALL AI Student Program\").\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyMwJj8KwanG",
        "colab_type": "text"
      },
      "source": [
        "# ALL Image Database for Image Processing\n",
        "\n",
        "![Acute Lymphoblastic Leukemia Image Database for Image Processing](https://www.petermossamlallresearch.com/media/images/repositories/ALL_IDB1_Augmented_Slides.png)\n",
        "_Fig 1. Samples of augmented data generated using ALL_IDB1 from the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset._  \n",
        "\n",
        "\n",
        "The [Acute Lymphoblastic Leukemia Image Database for Image Processing](https://homes.di.unimi.it/scotti/all/) dataset created by [Fabio Scotti, Associate Professor Dipartimento di Informatica, Università degli Studi di Milano](https://homes.di.unimi.it/scotti/) is used in this notebook, you will use the **ALL_IDB2** dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pyKxZsy_KJvh",
        "colab_type": "text"
      },
      "source": [
        "## Gain Access To ALL-IDB\n",
        "\n",
        "You you need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YySDuFYdZm3-",
        "colab_type": "text"
      },
      "source": [
        "# Clone AML & ALL Classifiers Repository\n",
        "\n",
        "First of all you should clone the [AML & ALL Classifiers](https://github.com/AMLResearchProject/AML-ALL-Classifiers/ \"AML & ALL Classifiers\") repo to your device. To do this you can navigate to the location you want to clone the repository to on your device using terminal (cd Your/Clone/Location), and then use the following command:\n",
        "\n",
        "```\n",
        "  $ git clone https://github.com/AMLResearchProject/AML-ALL-Classifiers.git\n",
        "```\n",
        "\n",
        "Once you have used the command above you will see a directory called **AML-ALL-Classifiers** in the location you chose to clone the repo to. In terminal, navigate to the **AML-ALL-Classifiers/Python/_Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/** directory, this is your project root directory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sk-PAX1d3zrG",
        "colab_type": "text"
      },
      "source": [
        "# Google Drive / Colab "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUNBpUB1bHky",
        "colab_type": "text"
      },
      "source": [
        "## Upload Project Root To Google Drive\n",
        "Now you need to upload the project root to your Google Drive, placing the tif files from the ALL_IDB2 dataset in the **Model/Data/Training/** directory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0tIn2DdxRQ1f",
        "colab_type": "text"
      },
      "source": [
        "## Mount Google Drive In Colab  \n",
        "\n",
        "![Peter Moss Acute Myeloid / Lymphoblastic Leukemia AI Research Project](https://www.PeterMossAmlAllResearch.com/media/images/repositories/Colab.png)\n",
        "_Fig 2. Example of Colab connected to Google Drive._\n",
        "\n",
        "The first step is to mount your Google Drive in Colab.  \n",
        "\n",
        "**To do this execute the following code block and follow the steps provided:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gunJpXRReMos",
        "colab_type": "code",
        "outputId": "bc47a68d-1b3b-4da1-da42-bb5449c1472a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import sys\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsJef2VNSOp2",
        "colab_type": "text"
      },
      "source": [
        "# Install & Import Requirements\n",
        "**Install and import requirements by executing the following code block:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7QNK6GSeS1t",
        "colab_type": "code",
        "outputId": "fa0d1a83-58e3-4ea5-9282-a0a2687a569c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "!pip install keras_metrics\n",
        "\n",
        "import os,  cv2, keras, keras_metrics, matplotlib.image, random\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' \n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from keras import backend as K\n",
        "from keras import layers\n",
        "from keras.layers import Activation, Dense, Dropout, Conv2D\n",
        "from keras.layers import Flatten, MaxPooling2D, ZeroPadding2D\n",
        "from keras.models import load_model, Model, model_from_json, Sequential\n",
        "from keras.optimizers import Adam\n",
        "from keras.preprocessing import image\n",
        "from keras.utils import np_utils\n",
        "\n",
        "from numpy.random import seed\n",
        "\n",
        "from pathlib import Path\n",
        "\n",
        "from scipy import ndimage\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from tensorflow import set_random_seed\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "seed(3)\n",
        "set_random_seed(3)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting keras_metrics\n",
            "  Downloading https://files.pythonhosted.org/packages/32/c9/a87420da8e73de944e63a8e9cdcfb1f03ca31a7c4cdcdbd45d2cdf13275a/keras_metrics-1.1.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: Keras>=2.1.5 in /usr/local/lib/python3.6/dist-packages (from keras_metrics) (2.2.5)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (1.3.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (1.1.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (2.8.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (1.0.8)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (1.12.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (3.13)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (1.17.3)\n",
            "Installing collected packages: keras-metrics\n",
            "Successfully installed keras-metrics-1.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
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              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rM9PNsciUr_",
        "colab_type": "text"
      },
      "source": [
        "# Program Settings\n",
        "Update the settings below to match the locations of the dataset, augmented and test directories on your Google Drive.. The Classes directory path is added allowing the files in the Classes directory to be executed. \n",
        "\n",
        "**Once you have updated the settings below, execute the following code block:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gIC21U66ifLn",
        "colab_type": "code",
        "outputId": "ae765945-cac3-40ab-a96d-db9e7b49d22f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "local_drive = \"/content/gdrive/My Drive/\"\n",
        "project_root = \"AMLResearchProject/AML-ALL-Classifiers/Python/_Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/\"\n",
        "project_root_full = local_drive + project_root\n",
        "\n",
        "sys.path.append(project_root_full + 'Classes')\n",
        "\n",
        "import Data as AllCnnData\n",
        "import Helpers as AllCnnHelpers\n",
        "\n",
        "core = AllCnnHelpers.Helpers(\"Classifier\", project_root_full)\n",
        "configs = core.confs\n",
        "\n",
        "model_root_path = project_root_full + configs[\"model_root\"]\n",
        "model_path = model_root_path + \"/\" + configs[\"model_file\"]\n",
        "\n",
        "data_dir = model_root_path + configs[\"data_dir\"]\n",
        "training_dir = model_root_path + data_dir + \"/\" + configs[\"training_dir\"]\n",
        "validation_dir = model_root_path + data_dir + \"/\" + configs[\"validation_dir\"]\n",
        "\n",
        "batch_size = configs[\"batch_size\"]\n",
        "epochs = configs[\"epochs\"]\n",
        "val_steps = configs[\"val_steps\"]\n",
        "\n",
        "core.logger.info(\"Class Path: \" + project_root + 'Classes')\n",
        "core.logger.info(\"Data Path: \" + data_dir)\n",
        "core.logger.info(\"Model Path: \" + model_path)\n",
        "core.logger.info(\"Model Root Dir: \" + model_root_path)\n",
        "core.logger.info(\"Project Root: \" + project_root)\n",
        "core.logger.info(\"Program settings setup complete.\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-10-26 20:01:10,599 - Classifier - INFO - Helpers class initialization complete.\n",
            "2019-10-26 20:01:10,613 - Classifier - INFO - Class Path: AMLResearchProject/AML-ALL-Classifiers/Python/_Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/Classes\n",
            "2019-10-26 20:01:10,620 - Classifier - INFO - Data Path: /content/gdrive/My Drive/AMLResearchProject/AML-ALL-Classifiers/Python/_Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/Model/Data/Training\n",
            "2019-10-26 20:01:10,623 - Classifier - INFO - Model Path: /content/gdrive/My Drive/AMLResearchProject/AML-ALL-Classifiers/Python/_Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/Model/model.json\n",
            "2019-10-26 20:01:10,627 - Classifier - INFO - Model Root Dir: /content/gdrive/My Drive/AMLResearchProject/AML-ALL-Classifiers/Python/_Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/Model\n",
            "2019-10-26 20:01:10,630 - Classifier - INFO - Project Root: AMLResearchProject/AML-ALL-Classifiers/Python/_Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/\n",
            "2019-10-26 20:01:10,632 - Classifier - INFO - Program settings setup complete.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GcyAS5yGSZul",
        "colab_type": "text"
      },
      "source": [
        "# Prepare Your Data\n",
        "Now you need to prepare your training and validation data. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHGp5wXF1bHj",
        "colab_type": "text"
      },
      "source": [
        "## Proposed Training / Validation Sets\n",
        "In the paper the authors use the **ALL_IDB1** dataset. The paper proposes the following training and validation sets proposed in the paper, where **Normal cell** refers to ALL negative examples and **Abnormal cell** refers to ALL positive examples.\n",
        "\n",
        "| | Training Set | Test Set |\n",
        "| --- | --- | --- |\n",
        "| Normal cell | 40 | 19 |\n",
        "| Abnormal cell | 40 | 9 |\n",
        "| **Total** | **80** | **28** |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCPaKVIOwXSA",
        "colab_type": "text"
      },
      "source": [
        "You can view the notebook using **ALL_IDB1** here. In this notebook however, you are going to use the **ALL_IDB2** dataset. On [Fabio Scotti's ALL-IDB website](https://homes.di.unimi.it/scotti/all), Fabio provides a [guideline for reporting your results when using ALL-IDB](https://homes.di.unimi.it/scotti/all/results.php). In this guideline a benchmark is proposed, this benchmark includes testing with both **ALL_IDB1** & **ALL_IDB2**:\n",
        "\n",
        "> \"A system capable to identify the presence of blast cells in the input image can work with different structures of modules, for example, it can processes the following steps: (i) the identification of white cells in the image, (ii) the selection of Lymphocytes, (iii) the classification of tumor cell. Each single step typically contains segmentation/ classification algorithms. In order to measure and fairly compare the identification accuracy of different structures of modules, we propose a benchmark approach partitioned in three different tests, as follows:\"\n",
        "\n",
        "*   Cell test - the benchmark account for the classification of single cells is blast or not (the test is positive if the considered cell is blast cell or not);\n",
        "*   Image level - the whole image is classified (the test is positive if the considered image contains at least one blast cell or not).\n",
        "\n",
        "In the paper the authors do not cover using **ALL_IDB2**. As ALL_IDB2 has an equal amount of images in each class (130 per class) you will use the entire ALL_IDB2 dataset with a test split of 20%.\n",
        "\n",
        "If you haven't already, navigate to the **AML-ALL-Classifiers/Python/_Keras/AllCNN/Paper_1/ALL_IDB2/Non_Augmented/Model/Data/Training/** directory and upload the **tif** files from **ALL_IDB2**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_8rp1G9y5vdF",
        "colab_type": "text"
      },
      "source": [
        "## Sort Your Data\n",
        "**Ensure that you have completed all steps above and then execute the following code block to sort/split your data, recreating the dataset splits proposed in the paper:** \n",
        "\n",
        "(This may take some time)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOQuYSwWecnU",
        "colab_type": "code",
        "outputId": "b6bad497-7b2c-4670-c3d2-780aae0ec494",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "AllData = AllCnnData.Data(core.logger, configs)\n",
        "data, labels = AllData.prepare_data(data_dir)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=3)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-10-26 20:02:59,468 - Classifier - INFO - Sorted data shape: (260, 50, 50, 3)\n",
            "2019-10-26 20:02:59,470 - Classifier - INFO - Sorted labels shape: (260, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0WFNPMdemCS",
        "colab_type": "code",
        "outputId": "6f4bcb9b-6313-4dfe-ee95-4038f384c6e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(208, 50, 50, 3)\n",
            "(52, 50, 50, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ntUYKrae9Uj",
        "colab_type": "code",
        "outputId": "d2488f40-bdc9-44b7-cf88-609748436566",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(208, 2)\n",
            "(52, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZlMAfNn620EF",
        "colab_type": "text"
      },
      "source": [
        "## Shuffle Data \n",
        "Shuffle the new training data, remembering to use our seed of 3.\n",
        "\n",
        "**To shuffle the training date, execute the following code block:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_ZHnUa53bbI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = np.asarray(X_train)\n",
        "labels = np.asarray(y_train)\n",
        "\n",
        "Data, Label = shuffle(data, labels, random_state = 3)\n",
        "data_list = [Data, Label]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KIup5MLn3hP3",
        "colab_type": "text"
      },
      "source": [
        "# View Dataset Sample\n",
        "**To view a sample of your dataset, execute the following code block:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Iu5RJdiH3_2",
        "colab_type": "code",
        "outputId": "d4bf5988-4401-4805-ea3f-19a1f2a6f403",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "y = np.argmax(Label, axis=-1)\n",
        "f, ax = plt.subplots(4, 5, figsize=(30, 7))\n",
        "\n",
        "for i in range(0, 20):\n",
        "    ax[i//5, i%5].imshow(Data[i])\n",
        "    if y[i]==1:\n",
        "        ax[i//5, i%5].set_title(\"Non-ALL\")\n",
        "    else:\n",
        "        ax[i//5, i%5].set_title(\"ALL\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ypAfeZbGKTT12YFIbFaEP4D67eYvHOGDatXvEsGbXU62o0vRZuP94sgVY4bRj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jpor1ebLLqp+rFq+UmK+FDe9/qc38b5pMfJz0E/f/VKyTH0x2vOfLh0TqpbAz7J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\nLvA1IvLtItKIyL9G0dafvcsubgInu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+D/RB15/C+q+vVnvabAYZ8GYBCsStupBQBkHNay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\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fXTFdKJ9Jf8hkMp2vJ8Xy8M46rq7fK8fWZ8++BKeW/9DjMEuQQa5pwTH+iM1N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\nJ9Dpuj1ydxviVOtsdV1cbAt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4Vhsa28GRuHejCYLv81LAOp1rDnZinWTDKMHNweWlMd/Q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\nQmCC\n",
            "text/plain": [
              "<Figure size 2160x504 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JaeNqg-pyfVT",
        "colab_type": "text"
      },
      "source": [
        "# Model Architecture\n",
        "<img src=\"https://www.PeterMossAmlAllResearch.com/media/images/repositories/paper_1_architecture.png\" alt=\"Proposed Architecture\" />\n",
        "\n",
        "_Fig 3. Proposed Architecture  ([Source](https://airccj.org/CSCP/vol7/csit77505.pdf \"Source\"))_  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Twcvifxw6uGJ",
        "colab_type": "text"
      },
      "source": [
        "## Proposed Architecture\n",
        "\n",
        "In the [ACUTE LEUKEMIA CLASSIFICATION USING CONVOLUTION NEURAL NETWORK IN CLINICAL DECISION SUPPORT SYSTEM](https://airccj.org/CSCP/vol7/csit77505.pdf \"ACUTE LEUKEMIA CLASSIFICATION USING CONVOLUTION NEURAL NETWORK IN CLINICAL DECISION SUPPORT SYSTEM\") paper the authors explain the layers they used to create their convolutional neural network.\n",
        "\n",
        "> \"In this work, we proposed a network contains 4 layers. The first 3 layers for detecting features\n",
        "and the other two layers (Fully connected and Softmax) are for classifying the features. The input\n",
        "image has the size [50x50x3]. The receptive field (or the filter size) is 5x5. The stride is 1 then we\n",
        "move the filters one pixel at a time. The zero-padding is 2. It will allow us to control the spatial\n",
        "size of the output image (we will use it to exactly preserve the spatial size of the input volume so\n",
        "the input and output width and height are the same). During the experiment, we found that in our\n",
        "case, altering the size of original image during the convolution lead to decrease the accuracy\n",
        "about 40%. Thus the output image after convolution layer 1 has the same size with the input\n",
        "image.\"\n",
        "\n",
        "> \"The convolution layer 2 has the same structure with the convolution layer 1. The filter size is 5x5,\n",
        "the stride is 1 and the zero-padding is 2. The number of feature maps (the channel or the depth) in\n",
        "our case is 30. If the number of feature maps is lower or higher than 30, the accuracy will\n",
        "decrease 50%. By experiment, we found the accuracy also decrease 50% if we remove\n",
        "Convolution layer 2.\"\"\n",
        "\n",
        "> \"The Max-Pooling layer 25x25 has Filter size is 2 and stride is 2. The fully connected layer has 2\n",
        "neural. Finally, we use the Softmax layer for the classification. \"\n",
        "\n",
        "Like Amita & Taru's notebook, this notebook introduces droupout layers to avoid overfitting. In this case your network has two dropout layers both having different dropout rates. There is no mention of activations for the convolutional layers so **RELU** has been used.  \n",
        "\n",
        "**To recreate the proposed architecture, execute the following code block:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f546l--jIJrf",
        "colab_type": "code",
        "outputId": "583bcd7a-efd8-40c0-f5b4-3510d813a95a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.name=\"KAllCnn_IDB2\"\n",
        "\n",
        "model.add(ZeroPadding2D(padding=(2, 2), input_shape=X_train.shape[1:]))\n",
        "model.add(Conv2D(30, (5, 5), strides=1, padding = \"valid\", input_shape = X_train.shape[1:], activation = 'relu'))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(ZeroPadding2D(padding=(2, 2), input_shape=X_train.shape[1:]))\n",
        "model.add(Conv2D(30, (5, 5), strides=1, padding = \"valid\", activation = 'relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding = 'valid'))\n",
        "model.add(Dropout(0.6))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(2))\n",
        "model.add(Activation(\"softmax\"))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjZoldSyvKeG",
        "colab_type": "text"
      },
      "source": [
        "## View Network Architecture (Summary)\n",
        "**To view a summary of your networ architecture, execute the following code block:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQ1PCsxIIUHZ",
        "colab_type": "code",
        "outputId": "4c9be2b9-fbce-4621-9231-c786b5c44bc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"KAllCnn_IDB2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "zero_padding2d_1 (ZeroPaddin (None, 54, 54, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 50, 50, 30)        2280      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 50, 50, 30)        0         \n",
            "_________________________________________________________________\n",
            "zero_padding2d_2 (ZeroPaddin (None, 54, 54, 30)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 50, 50, 30)        22530     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 30)        0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 25, 25, 30)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 18750)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 37502     \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 2)                 0         \n",
            "=================================================================\n",
            "Total params: 62,312\n",
            "Trainable params: 62,312\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6z-rvbNwVYr",
        "colab_type": "text"
      },
      "source": [
        "# Compile &  Fit Your Model\n",
        "\n",
        "In the following code block first the program complies the model, then fits the model. The **validation_data**, _(X_test, y_test)_, and the number of **validation_steps** are passed to the **Keras model.fit** function. This means that in addition to showing training loss, accuracy, precision and recall the program will show validation loss, accuracy, precision and recall in its output.\n",
        "\n",
        "**Assuming you have completed all above steps you can execute the following code block to beging training:**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZzqkS_OJYKl",
        "colab_type": "code",
        "outputId": "7b94c17c-0df1-4b98-a159-703060e4665e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "optimizer = keras.optimizers.rmsprop(lr = 0.0001, decay = 1e-6)\n",
        "model.compile(loss = 'binary_crossentropy', optimizer = optimizer, \n",
        "              metrics = ['accuracy', keras_metrics.precision(), keras_metrics.recall()])\n",
        "\n",
        "history = model.fit(X_train, y_train, validation_data = (X_test, y_test), validation_steps = val_steps, \n",
        "                    steps_per_epoch = int(len(X_train)/batch_size), epochs = epochs)\n",
        "history"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 208 samples, validate on 52 samples\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "Epoch 1/100\n",
            "20/20 [==============================] - 5s 267ms/step - loss: 0.7206 - acc: 0.5135 - precision: 0.5148 - recall: 0.4697 - val_loss: 0.6835 - val_acc: 0.5000 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
            "Epoch 2/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.6621 - acc: 0.6024 - precision: 0.6141 - recall: 0.5510 - val_loss: 0.6575 - val_acc: 0.7500 - val_precision: 0.8421 - val_recall: 0.6154\n",
            "Epoch 3/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.6247 - acc: 0.6466 - precision: 0.6631 - recall: 0.5962 - val_loss: 0.6273 - val_acc: 0.7692 - val_precision: 0.7917 - val_recall: 0.7308\n",
            "Epoch 4/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.5873 - acc: 0.6856 - precision: 0.7075 - recall: 0.6327 - val_loss: 0.6042 - val_acc: 0.7692 - val_precision: 0.7917 - val_recall: 0.7308\n",
            "Epoch 5/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.5572 - acc: 0.7111 - precision: 0.7350 - recall: 0.6601 - val_loss: 0.5957 - val_acc: 0.6731 - val_precision: 0.6452 - val_recall: 0.7692\n",
            "Epoch 6/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.5316 - acc: 0.7238 - precision: 0.7446 - recall: 0.6812 - val_loss: 0.5789 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 7/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.5103 - acc: 0.7397 - precision: 0.7631 - recall: 0.6952 - val_loss: 0.5710 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 8/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.4983 - acc: 0.7406 - precision: 0.7614 - recall: 0.7010 - val_loss: 0.5608 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 9/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.4878 - acc: 0.7546 - precision: 0.7780 - recall: 0.7125 - val_loss: 0.5589 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 10/100\n",
            "20/20 [==============================] - 2s 75ms/step - loss: 0.4749 - acc: 0.7654 - precision: 0.7808 - recall: 0.7380 - val_loss: 0.5518 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 11/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.4700 - acc: 0.7613 - precision: 0.7797 - recall: 0.7284 - val_loss: 0.5506 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 12/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.4555 - acc: 0.7707 - precision: 0.7917 - recall: 0.7346 - val_loss: 0.5424 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 13/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.4432 - acc: 0.7861 - precision: 0.8020 - recall: 0.7596 - val_loss: 0.5332 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 14/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.4378 - acc: 0.7856 - precision: 0.8009 - recall: 0.7601 - val_loss: 0.5266 - val_acc: 0.7115 - val_precision: 0.6897 - val_recall: 0.7692\n",
            "Epoch 15/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.4294 - acc: 0.7906 - precision: 0.8027 - recall: 0.7707 - val_loss: 0.5170 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 16/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.4178 - acc: 0.7998 - precision: 0.8144 - recall: 0.7764 - val_loss: 0.5119 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 17/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.4118 - acc: 0.8026 - precision: 0.8149 - recall: 0.7832 - val_loss: 0.5083 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 18/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.4059 - acc: 0.7978 - precision: 0.8087 - recall: 0.7803 - val_loss: 0.5018 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 19/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.3975 - acc: 0.8130 - precision: 0.8242 - recall: 0.7957 - val_loss: 0.4946 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 20/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.3906 - acc: 0.8106 - precision: 0.8192 - recall: 0.7971 - val_loss: 0.4900 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 21/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.3849 - acc: 0.8137 - precision: 0.8213 - recall: 0.8019 - val_loss: 0.4762 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 22/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.3802 - acc: 0.8236 - precision: 0.8338 - recall: 0.8082 - val_loss: 0.4693 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 23/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.3696 - acc: 0.8286 - precision: 0.8372 - recall: 0.8159 - val_loss: 0.4649 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 24/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.3667 - acc: 0.8255 - precision: 0.8342 - recall: 0.8125 - val_loss: 0.4581 - val_acc: 0.7308 - val_precision: 0.7143 - val_recall: 0.7692\n",
            "Epoch 25/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.3564 - acc: 0.8296 - precision: 0.8346 - recall: 0.8221 - val_loss: 0.4446 - val_acc: 0.7500 - val_precision: 0.7407 - val_recall: 0.7692\n",
            "Epoch 26/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.3498 - acc: 0.8375 - precision: 0.8465 - recall: 0.8245 - val_loss: 0.4601 - val_acc: 0.7308 - val_precision: 0.7000 - val_recall: 0.8077\n",
            "Epoch 27/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.3463 - acc: 0.8382 - precision: 0.8427 - recall: 0.8317 - val_loss: 0.4322 - val_acc: 0.7500 - val_precision: 0.7407 - val_recall: 0.7692\n",
            "Epoch 28/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.3368 - acc: 0.8447 - precision: 0.8522 - recall: 0.8341 - val_loss: 0.4250 - val_acc: 0.7500 - val_precision: 0.7407 - val_recall: 0.7692\n",
            "Epoch 29/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.3306 - acc: 0.8500 - precision: 0.8555 - recall: 0.8423 - val_loss: 0.4260 - val_acc: 0.7308 - val_precision: 0.7000 - val_recall: 0.8077\n",
            "Epoch 30/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.3238 - acc: 0.8507 - precision: 0.8543 - recall: 0.8457 - val_loss: 0.4207 - val_acc: 0.7308 - val_precision: 0.7000 - val_recall: 0.8077\n",
            "Epoch 31/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.3175 - acc: 0.8567 - precision: 0.8634 - recall: 0.8476 - val_loss: 0.4005 - val_acc: 0.7885 - val_precision: 0.8000 - val_recall: 0.7692\n",
            "Epoch 32/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.3091 - acc: 0.8591 - precision: 0.8644 - recall: 0.8519 - val_loss: 0.3943 - val_acc: 0.7692 - val_precision: 0.7692 - val_recall: 0.7692\n",
            "Epoch 33/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.3056 - acc: 0.8659 - precision: 0.8716 - recall: 0.8582 - val_loss: 0.3838 - val_acc: 0.8077 - val_precision: 0.8333 - val_recall: 0.7692\n",
            "Epoch 34/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.2935 - acc: 0.8671 - precision: 0.8730 - recall: 0.8591 - val_loss: 0.3750 - val_acc: 0.8077 - val_precision: 0.8333 - val_recall: 0.7692\n",
            "Epoch 35/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.2897 - acc: 0.8726 - precision: 0.8755 - recall: 0.8687 - val_loss: 0.3689 - val_acc: 0.8077 - val_precision: 0.8333 - val_recall: 0.7692\n",
            "Epoch 36/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.2780 - acc: 0.8784 - precision: 0.8824 - recall: 0.8731 - val_loss: 0.3602 - val_acc: 0.8077 - val_precision: 0.8333 - val_recall: 0.7692\n",
            "Epoch 37/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.2723 - acc: 0.8822 - precision: 0.8867 - recall: 0.8764 - val_loss: 0.3537 - val_acc: 0.8077 - val_precision: 0.8333 - val_recall: 0.7692\n",
            "Epoch 38/100\n",
            "20/20 [==============================] - 2s 78ms/step - loss: 0.2640 - acc: 0.8882 - precision: 0.8918 - recall: 0.8837 - val_loss: 0.3421 - val_acc: 0.8462 - val_precision: 0.9091 - val_recall: 0.7692\n",
            "Epoch 39/100\n",
            "20/20 [==============================] - 2s 78ms/step - loss: 0.2539 - acc: 0.8889 - precision: 0.8935 - recall: 0.8832 - val_loss: 0.3379 - val_acc: 0.8654 - val_precision: 0.8519 - val_recall: 0.8846\n",
            "Epoch 40/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.2508 - acc: 0.8947 - precision: 0.8955 - recall: 0.8937 - val_loss: 0.3292 - val_acc: 0.8654 - val_precision: 0.8519 - val_recall: 0.8846\n",
            "Epoch 41/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.2387 - acc: 0.9048 - precision: 0.9025 - recall: 0.9077 - val_loss: 0.3151 - val_acc: 0.8654 - val_precision: 0.9130 - val_recall: 0.8077\n",
            "Epoch 42/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.2267 - acc: 0.9077 - precision: 0.9121 - recall: 0.9024 - val_loss: 0.3039 - val_acc: 0.8654 - val_precision: 0.9130 - val_recall: 0.8077\n",
            "Epoch 43/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.2236 - acc: 0.9130 - precision: 0.9150 - recall: 0.9106 - val_loss: 0.2953 - val_acc: 0.9038 - val_precision: 0.9200 - val_recall: 0.8846\n",
            "Epoch 44/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.2124 - acc: 0.9224 - precision: 0.9209 - recall: 0.9240 - val_loss: 0.2853 - val_acc: 0.8654 - val_precision: 0.9130 - val_recall: 0.8077\n",
            "Epoch 45/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.2029 - acc: 0.9252 - precision: 0.9242 - recall: 0.9264 - val_loss: 0.2752 - val_acc: 0.8654 - val_precision: 0.9130 - val_recall: 0.8077\n",
            "Epoch 46/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.1940 - acc: 0.9327 - precision: 0.9335 - recall: 0.9317 - val_loss: 0.2631 - val_acc: 0.9038 - val_precision: 0.9200 - val_recall: 0.8846\n",
            "Epoch 47/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.1831 - acc: 0.9365 - precision: 0.9324 - recall: 0.9413 - val_loss: 0.2644 - val_acc: 0.8846 - val_precision: 0.9545 - val_recall: 0.8077\n",
            "Epoch 48/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.1741 - acc: 0.9413 - precision: 0.9401 - recall: 0.9428 - val_loss: 0.2491 - val_acc: 0.9038 - val_precision: 0.9565 - val_recall: 0.8462\n",
            "Epoch 49/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.1659 - acc: 0.9433 - precision: 0.9416 - recall: 0.9452 - val_loss: 0.2316 - val_acc: 0.9038 - val_precision: 0.9200 - val_recall: 0.8846\n",
            "Epoch 50/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.1536 - acc: 0.9498 - precision: 0.9478 - recall: 0.9519 - val_loss: 0.2221 - val_acc: 0.9231 - val_precision: 0.9583 - val_recall: 0.8846\n",
            "Epoch 51/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.1477 - acc: 0.9536 - precision: 0.9495 - recall: 0.9582 - val_loss: 0.2177 - val_acc: 0.9038 - val_precision: 0.9565 - val_recall: 0.8462\n",
            "Epoch 52/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.1339 - acc: 0.9591 - precision: 0.9543 - recall: 0.9644 - val_loss: 0.2116 - val_acc: 0.9038 - val_precision: 0.9565 - val_recall: 0.8462\n",
            "Epoch 53/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.1258 - acc: 0.9615 - precision: 0.9598 - recall: 0.9635 - val_loss: 0.2102 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 54/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.1181 - acc: 0.9647 - precision: 0.9644 - recall: 0.9649 - val_loss: 0.1939 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 55/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.1094 - acc: 0.9692 - precision: 0.9665 - recall: 0.9721 - val_loss: 0.1835 - val_acc: 0.9423 - val_precision: 1.0000 - val_recall: 0.8846\n",
            "Epoch 56/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0995 - acc: 0.9760 - precision: 0.9732 - recall: 0.9788 - val_loss: 0.2150 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 57/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0996 - acc: 0.9712 - precision: 0.9716 - recall: 0.9707 - val_loss: 0.2171 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 58/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0905 - acc: 0.9772 - precision: 0.9737 - recall: 0.9808 - val_loss: 0.2276 - val_acc: 0.9038 - val_precision: 1.0000 - val_recall: 0.8077\n",
            "Epoch 59/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0825 - acc: 0.9812 - precision: 0.9803 - recall: 0.9822 - val_loss: 0.1684 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 60/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0769 - acc: 0.9810 - precision: 0.9767 - recall: 0.9856 - val_loss: 0.1438 - val_acc: 0.9423 - val_precision: 1.0000 - val_recall: 0.8846\n",
            "Epoch 61/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0707 - acc: 0.9844 - precision: 0.9818 - recall: 0.9870 - val_loss: 0.1860 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 62/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0652 - acc: 0.9865 - precision: 0.9851 - recall: 0.9880 - val_loss: 0.1553 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 63/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0628 - acc: 0.9870 - precision: 0.9856 - recall: 0.9885 - val_loss: 0.1591 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 64/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0577 - acc: 0.9887 - precision: 0.9866 - recall: 0.9909 - val_loss: 0.1403 - val_acc: 0.9423 - val_precision: 1.0000 - val_recall: 0.8846\n",
            "Epoch 65/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0509 - acc: 0.9911 - precision: 0.9881 - recall: 0.9942 - val_loss: 0.1800 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 66/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0480 - acc: 0.9911 - precision: 0.9890 - recall: 0.9933 - val_loss: 0.1352 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 67/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0438 - acc: 0.9935 - precision: 0.9919 - recall: 0.9952 - val_loss: 0.1188 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 68/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0398 - acc: 0.9945 - precision: 0.9923 - recall: 0.9966 - val_loss: 0.1108 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 69/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0397 - acc: 0.9935 - precision: 0.9914 - recall: 0.9957 - val_loss: 0.1325 - val_acc: 0.9423 - val_precision: 1.0000 - val_recall: 0.8846\n",
            "Epoch 70/100\n",
            "20/20 [==============================] - 2s 78ms/step - loss: 0.0357 - acc: 0.9950 - precision: 0.9938 - recall: 0.9962 - val_loss: 0.1051 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 71/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0320 - acc: 0.9957 - precision: 0.9942 - recall: 0.9971 - val_loss: 0.1510 - val_acc: 0.9231 - val_precision: 1.0000 - val_recall: 0.8462\n",
            "Epoch 72/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0278 - acc: 0.9969 - precision: 0.9966 - recall: 0.9971 - val_loss: 0.1030 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 73/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0294 - acc: 0.9947 - precision: 0.9947 - recall: 0.9947 - val_loss: 0.1042 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 74/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0240 - acc: 0.9981 - precision: 0.9976 - recall: 0.9986 - val_loss: 0.0963 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 75/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0254 - acc: 0.9966 - precision: 0.9952 - recall: 0.9981 - val_loss: 0.1000 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 76/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0229 - acc: 0.9969 - precision: 0.9952 - recall: 0.9986 - val_loss: 0.0996 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 77/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0191 - acc: 0.9981 - precision: 0.9976 - recall: 0.9986 - val_loss: 0.0891 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 78/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0194 - acc: 0.9986 - precision: 0.9976 - recall: 0.9995 - val_loss: 0.1148 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 79/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0175 - acc: 0.9978 - precision: 0.9971 - recall: 0.9986 - val_loss: 0.1005 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 80/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0161 - acc: 0.9983 - precision: 0.9976 - recall: 0.9990 - val_loss: 0.0857 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 81/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0138 - acc: 0.9990 - precision: 0.9981 - recall: 1.0000 - val_loss: 0.0813 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 82/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0127 - acc: 0.9993 - precision: 0.9986 - recall: 1.0000 - val_loss: 0.1541 - val_acc: 0.9423 - val_precision: 1.0000 - val_recall: 0.8846\n",
            "Epoch 83/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0131 - acc: 0.9993 - precision: 0.9990 - recall: 0.9995 - val_loss: 0.0858 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 84/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0107 - acc: 0.9998 - precision: 0.9995 - recall: 1.0000 - val_loss: 0.0864 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 85/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0111 - acc: 0.9990 - precision: 0.9995 - recall: 0.9986 - val_loss: 0.0793 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 86/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0095 - acc: 0.9995 - precision: 0.9990 - recall: 1.0000 - val_loss: 0.0800 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 87/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0087 - acc: 0.9993 - precision: 0.9990 - recall: 0.9995 - val_loss: 0.0745 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 88/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0086 - acc: 0.9998 - precision: 1.0000 - recall: 0.9995 - val_loss: 0.0692 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 89/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0084 - acc: 0.9990 - precision: 0.9990 - recall: 0.9990 - val_loss: 0.0825 - val_acc: 0.9808 - val_precision: 1.0000 - val_recall: 0.9615\n",
            "Epoch 90/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0067 - acc: 1.0000 - precision: 1.0000 - recall: 1.0000 - val_loss: 0.0703 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 91/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0072 - acc: 0.9990 - precision: 0.9990 - recall: 0.9990 - val_loss: 0.0705 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 92/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0068 - acc: 0.9998 - precision: 1.0000 - recall: 0.9995 - val_loss: 0.0732 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 93/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0055 - acc: 1.0000 - precision: 1.0000 - recall: 1.0000 - val_loss: 0.0745 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 94/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0051 - acc: 0.9998 - precision: 1.0000 - recall: 0.9995 - val_loss: 0.0816 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 95/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0059 - acc: 0.9990 - precision: 0.9981 - recall: 1.0000 - val_loss: 0.1114 - val_acc: 0.9615 - val_precision: 1.0000 - val_recall: 0.9231\n",
            "Epoch 96/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0045 - acc: 0.9995 - precision: 1.0000 - recall: 0.9990 - val_loss: 0.0783 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 97/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0046 - acc: 0.9998 - precision: 1.0000 - recall: 0.9995 - val_loss: 0.0825 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 98/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0043 - acc: 0.9998 - precision: 1.0000 - recall: 0.9995 - val_loss: 0.0864 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 99/100\n",
            "20/20 [==============================] - 2s 77ms/step - loss: 0.0035 - acc: 0.9998 - precision: 0.9995 - recall: 1.0000 - val_loss: 0.0868 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n",
            "Epoch 100/100\n",
            "20/20 [==============================] - 2s 76ms/step - loss: 0.0042 - acc: 0.9995 - precision: 0.9995 - recall: 0.9995 - val_loss: 0.0840 - val_acc: 0.9615 - val_precision: 0.9615 - val_recall: 0.9615\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc926a76be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVh6O0zyNlKC",
        "colab_type": "text"
      },
      "source": [
        "# Evaluate Your Model\n",
        "Now we will evaluate how well our model has done."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TY64b46pfe1s",
        "colab_type": "text"
      },
      "source": [
        "## View Metrics Names\n",
        "**Execute the following code block to view the names of the metrics used during training:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hi8TJxq_SmLb",
        "colab_type": "code",
        "outputId": "ed1a17bb-0019-4a7c-f09c-19ada1f66a1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.metrics_names"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loss', 'acc', 'precision', 'recall']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmyFkSCnTN3u",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate Model & Print Metrics\n",
        "**Execute the following code block to evaluate your model and print the training metrics:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtplOWRhJqm0",
        "colab_type": "code",
        "outputId": "185fb4d2-9bd3-47a3-dbb3-4cb2a5f7eb08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "score = model.evaluate(X_test, y_test, verbose=0)\n",
        "score"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08396839837615307,\n",
              " 0.9615384615384616,\n",
              " 0.9615384578402366,\n",
              " 0.9615384578402366]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rs7RjY25TuQF",
        "colab_type": "text"
      },
      "source": [
        "## Generate AUC Score\n",
        "**Execute the following code block to generate your AUC score:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMeKHZ89bX-V",
        "colab_type": "code",
        "outputId": "00f72079-4cf4-4c5b-d75f-4f634d117fa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "roc_auc_score(y_test, model.predict_proba(X_test))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9970414201183433"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QWlYTjriVVHT",
        "colab_type": "text"
      },
      "source": [
        "# Results\n",
        "\n",
        "Below are the training results for 100 epochs.\n",
        "\n",
        "| Loss   |  Accuracy   | Precision   | Recall   | AUC |\n",
        "|------|---|---|--|--|\n",
        "| 0.083 (~0.84)  | 0.961 (~96%)   | 0.961 (~0.96)   | 0.961 (~0.96) | 0.997 (~1.0) |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPZyb9RDTzhl",
        "colab_type": "text"
      },
      "source": [
        "## Visualise Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLRWNFmXEcUm",
        "colab_type": "text"
      },
      "source": [
        "### Training Loss & Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqrPzxbmbmdR",
        "colab_type": "code",
        "outputId": "53ab740d-d384-49dc-d39c-6f51cdf4820c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "source": [
        "training_acc = history.history['acc']\n",
        "training_loss = history.history['loss']\n",
        "\n",
        "plt.figure(figsize = (8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(training_acc, label = 'Training Accuracy')\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Training Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(training_loss, label = 'Training Loss')\n",
        "plt.legend(loc = 'upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,max(plt.ylim())])\n",
        "plt.title('Training Loss')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yEdDoKG4d0dw1pfzyxYWMy83g5rP1OJmIiBw8LRns5QvgODNLDS/viHhVMaKq\nppbvP/UpfdNTuOeifBLjW/PYvYiISOvsM3XM7Hdmlu7uO9x9h5l1N7PftmTjZjbBzJaZ2Qozm9rI\n6382s3nhr+VmVtaaHyJa/jF/A1sqdvO7SUeS3lm3CYiIyMHVkq7jme5eF67uvg34xr7eZGbxwD3A\nmYQGiLnQzPZ67srdr3f30e4+GrgbeHF/io+2Jz9aw+BeqYw9PCPapYiISAfUkhCPN7PkPQtm1glI\nbqb9HmOAFe6+MnxH+3RgYjPtLwSmtWC77cL8dWUsKCrn0rEDMNNkJiIicvC15Ma2p4G3zOxRwIAr\ngMdb8L4sYF295SLg2MYamtkAQuOxv92C7bYLT360hi5J8XxLc4KLiEiUtOTGtt+b2Xzg64TGUH8D\nGNDGdVwAPO/utY29aGZTgCkA/fv3b+Nd779tO6v5x/wNnFeQTVqKBnUREZHoaOnt1JsIBfh5wCnA\nkha8Zz3Qr95ydnhdYy6gmVPp7v6guxe4e0HPnj1bVnEE/W3uOnYHglx63MBolyIiIh1Ykz1xMzuC\n0HXqCwkN7vIsYO5+cgu3PRsYbGY5hML7AuCiRvYzFOgOfLh/pUdHMOg89dFaxuT0YEiftGiXIyIi\nHVhzPfGlhHrdZ7n7Ce5+N6Fx01vE3QPAtYROvy8BnnP3RWZ2W/0R4AiF+3R398a20978p3ALa0t3\ncelxbX1FQUREZP80d018EqGAfcfMXid0d/l+3Ybt7q8CrzZY9+sGy7fszzaj7akP15CZmswZw/tE\nuxQREengmuyJu/vL7n4BMBR4B/gx0MvM7jOz0w9Wge3J5ooq3l62mQvH9CMpQaOziYhIdO0zidx9\np7s/4+5nE7o57TPg5xGvrB16v7AEd9QLFxGRdmG/upPuvi18p/ipkSqoPZtZWEJGlyTyDusa7VJE\nRET2L8Q7MndnZmEJJwzOJC5OI7SJiEj0KcRbaOnGCkp27OaE3MxolyIiIgIoxFtsZuEWAMYPjv5g\nMyIiIqAQb7GZhSUc0TuVPt1Sol2KiIgIoBBvkaqaWj5ZVapeuIiItCsK8RaYvbqU3YEgJwzW9XAR\nEWk/FOItMLOwhKT4OI7N6RHtUkREROooxFvgveVbKBjYnc5JLZl+XURE5OBQiO/D5ooqlm6s0Kl0\nERFpdxTi+/DBihIATtRNbSIi0s4oxPdh5vISemioVRERaYcU4s1wd2auKGFcroZaFRGR9kch3ozl\nm3awpWI34zXUqoiItEMK8WZ8smorAGMHZUS5EhERka9SiDfj41Wl9OmaQnb3TtEuRURE5CsU4k1w\nd2avLuWYnB6Y6Xq4iIi0P4PpKwYAACAASURBVArxJqwrrWTT9t2M0ShtIiLSTinEm/Bx+Hr4mIEK\ncRERaZ8U4k2YvbqUbp0SGdwrNdqliIiINEoh3oTZq7dxzMAeej5cRETaLYV4IzZXVLGqZCdjcrpH\nuxQREZEmKcQbMXvVNgCO0fVwERFpxxTijZi9upROifGMyOoW7VJERESapBBvxCerSskfkE5ivA6P\niIi0X0qpBsora1iycbtOpYuISLunEG/g0zXbcEeDvIiISLunEG/gk9WlJMYbR/XTnekiItK+RTTE\nzWyCmS0zsxVmNrWJNt82s8VmtsjMnolkPS3xyapSRmR1o1NSfLRLERERaVbEQtzM4oF7gDOBPOBC\nM8tr0GYw8AtgnLsPB34cqXpaoqqmlgVFZRpqVUREYkIke+JjgBXuvtLdq4HpwMQGbb4H3OPu2wDc\nfXME69mnBUXl1NS6bmoTEZGYEMkQzwLW1VsuCq+r7wjgCDP7wMw+MrMJEaxnn1aX7ARgSJ+0aJYh\nIiLSIgntYP+Dga8B2cB7Znaku5fVb2RmU4ApAP37949YMevLKjGD3l1TIrYPERGRthLJnvh6oF+9\n5ezwuvqKgBnuXuPuq4DlhEJ9L+7+oLsXuHtBz549I1dwWSW901JIStBN+yIi0v5FMq1mA4PNLMfM\nkoALgBkN2rxMqBeOmWUSOr2+MoI1NWtDWSV909ULFxGR2BCxEHf3AHAt8AawBHjO3ReZ2W1mdk64\n2RvAVjNbDLwD3OjuWyNV076sL6ukb3qnaO1eRERkv0T0mri7vwq82mDdr+t978BPwl9RFQw6xWVV\nTBjRJ9qliIiItIgu/oaV7NhNdW2QLPXERUQkRijEw9aXVQIoxEVEJGYoxMP2hLiuiYuISKxQiIdt\n2NMT764QFxGR2KAQD1u/rZK05AS6piRGuxQREZEWUYiHrS+rUi9cRERiikI8TM+Ii4hIrFGIh20o\nq9Sd6SIiElMU4sCO3QHKK2vUExcRkZiiEEd3pouISGxSiBO6Mx0gS5OfiIhIDFGIU3+0ts5RrkRE\nRKTlFOKEQjwhzuiZlhztUkRERFpMIU7omvhh6SnEx1m0SxEREWkxhTiha+J9u+mmNhERiS0RnU88\nVmwoq+S4QRnRLkNEpE5NTQ1FRUVUVVVFuxQ5SFJSUsjOziYxseXDf3f4EK+pDbJxe5UGehGRdqWo\nqIi0tDQGDhyImS71Hercna1bt1JUVEROTk6L39fhT6dv2l5F0DUFqYi0L1VVVWRkZCjAOwgzIyMj\nY7/PvHT4EP/yGXGFuIi0LwrwjqU1f98dPsQ3lIdCXD1xEZEvbd26ldGjRzN69Gj69OlDVlZW3XJ1\ndXWLtnHllVeybNmyZtvcc889PP30021RMgCbNm0iISGBhx56qM222Z51+Gvi6omLiHxVRkYG8+bN\nA+CWW24hNTWVG264Ya827o67ExfXeH/w0Ucf3ed+rrnmmgMvtp7nnnuOsWPHMm3aNK666qo23XZ9\ngUCAhIToR2iH74mvL6uiR5ckOiXFR7sUEZF2b8WKFeTl5XHxxRczfPhwiouLmTJlCgUFBQwfPpzb\nbrutru0JJ5zAvHnzCAQCpKenM3XqVEaNGsXYsWPZvHkzADfddBN33HFHXfupU6cyZswYhgwZwqxZ\nswDYuXMnkydPJi8vj3PPPZeCgoK6XzAamjZtGnfccQcrV66kuLi4bv0rr7xCfn4+o0aN4vTTTweg\noqKCyy+/nJEjRzJy5Ehefvnlulr3mD59et0vA5dccgk/+MEPGDNmDL/85S/56KOPGDt2LEcddRTj\nxo2jsLAQCAX89ddfz4gRIxg5ciT33nsv//rXvzj33HPrtvvaa69x3nnnHfDfR/R/jYiy9ZqCVETa\nuVv/sYjFG7a36Tbz+nbl5rOHt+q9S5cu5YknnqCgoACA22+/nR49ehAIBDj55JM599xzycvL2+s9\n5eXlnHTSSdx+++385Cc/4ZFHHmHq1Klf2ba788knnzBjxgxuu+02Xn/9de6++2769OnDCy+8wPz5\n88nPz2+0rtWrV1NaWsrRRx/Neeedx3PPPcd1113Hxo0b+cEPfsDMmTMZMGAApaWlQOgMQ8+ePVmw\nYAHuTllZ2T5/9uLiYj766CPi4uIoLy9n5syZJCQk8Prrr3PTTTfx7LPPct9997Fhwwbmz59PfHw8\npaWlpKenc+2117J161YyMjJ49NFH+c53vrO/h/4rOnxPfENZJX018YmISIsNGjSoLsAh1PvNz88n\nPz+fJUuWsHjx4q+8p1OnTpx55pkAHH300axevbrRbU+aNOkrbd5//30uuOACAEaNGsXw4Y3/8jF9\n+nTOP/98AC644AKmTZsGwIcffsjJJ5/MgAEDAOjRowcAb775Zt3pfDOje/fu+/zZzzvvvLrLB2Vl\nZUyePJkRI0Zwww03sGjRorrtfv/73yc+Pr5uf3FxcVx88cU888wzlJaWMnfu3LozAgeiQ/fE3Z31\n2yo5cXDPaJciItKk1vaYI6VLly513xcWFnLnnXfyySefkJ6eziWXXNLoY1JJSUl138fHxxMIBBrd\ndnJy8j7bNGXatGmUlJTw+OOPA7BhwwZWrly5X9uIi4vD3euWG/4s9X/2//mf/+GMM87ghz/8IStW\nrGDChAnNbvs73/kOkydPBuD888+vC/kD0aF74mW7aqisqVVPXESklbZv305aWhpdu3aluLiYN954\no833MW7cOJ577jkAFi5c2GhPf/HixQQCAdavX8/q1atZvXo1N954I9OnT+f444/nnXfeYc2aNQB1\np9NPO+007rnnHiDUqdu2bRtxcXF0796dwsJCgsEgL730UpN1lZeXk5WVBcBjjz1Wt/60007j/vvv\np7a2dq/99evXj8zMTG6//XauuOKKAzsoYR06xPdMQZrdXdfERURaIz8/n7y8PIYOHcpll13GuHHj\n2nwfP/rRj1i/fj15eXnceuut5OXl0a1bt73aTJs2jW9961t7rZs8eTLTpk2jd+/e3HfffUycOJFR\no0Zx8cUXA3DzzTezadMmRowYwejRo5k5cyYAv//97znjjDM4/vjjyc7ObrKun//859x4443k5+fv\n1Xu/+uqr6dOnDyNHjmTUqFF1v4AAXHTRReTk5HDEEUcc8HEBsPo7jgUFBQU+Z86cNtnWG4s2cvWT\nc5lx7ThGZqfv+w0iIgfJkiVLGDZsWLTLaBcCgQCBQICUlBQKCws5/fTTKSwsbBePeO2v73//+4wd\nO5bLL7+80dcb+3s3s7nuXtBY+9g7Am1oR1WAzknxujtdRKQd27FjB6eeeiqBQAB354EHHojJAB89\nejTdu3fnrrvuarNtxt5RaEOTj85mUn5WtMsQEZFmpKenM3fu3GiXccCaerb9QET0mriZTTCzZWa2\nwsy+8kCgmV1hZlvMbF74K3LD6zRdo8YnFhGRmBSxnriZxQP3AKcBRcBsM5vh7g1vK3zW3a+NVB0i\nIrHK3dXJ6EBac49aJHviY4AV7r7S3auB6cDECO5PROSQkZKSwtatW1v1H7vEnj3ziaek7N8jz5G8\nJp4FrKu3XAQc20i7yWZ2IrAcuN7d1zVsYGZTgCkA/fv3j0CpIiLtS3Z2NkVFRWzZsiXapchBkpKS\n0uwjbY2J9o1t/wCmuftuM7saeBw4pWEjd38QeBBCj5gd3BJFRA6+xMREcnJyol2GtHORPJ2+HuhX\nbzk7vK6Ou291993hxYeAoyNYj4iIyCElkiE+GxhsZjlmlgRcAMyo38DMDqu3eA6wJIL1iIiIHFIi\ndjrd3QNmdi3wBhAPPOLui8zsNmCOu88A/tvMzgECQClwRaTqEREROdTE3LCrZrYFWNOGm8wEStpw\nex2VjmPb0HFsGzqObUPHsW0c6HEc4O6NTrcZcyHe1sxsTlNj0krL6Ti2DR3HtqHj2DZ0HNtGJI9j\nh57FTEREJJYpxEVERGKUQjz8/LkcMB3HtqHj2DZ0HNuGjmPbiNhx7PDXxEVERGKVeuIiIiIxqkOH\n+L6mSpXGmVk/M3vHzBab2SIzuy68voeZ/dvMCsN/do92rbHAzOLN7DMz+2d4OcfMPg5/Lp8ND5Yk\nzTCzdDN73syWmtkSMxurz+P+M7Prw/+mPzezaWaWos/jvpnZI2a22cw+r7eu0c+fhdwVPp4LzCz/\nQPbdYUO83lSpZwJ5wIVmlhfdqmJGAPipu+cBxwHXhI/dVOAtdx8MvBVeln27jr1HK/w98Gd3zwW2\nAd+NSlWx5U7gdXcfCowidDz1edwPZpYF/DdQ4O4jCA3SdQH6PLbEY8CEBuua+vydCQwOf00B7juQ\nHXfYEEdTpbaauxe7+6fh7ysI/YeZRej4PR5u9jjwzehUGDvMLBv4L0JzB2ChyaNPAZ4PN9Fx3Acz\n6wacCDwM4O7V7l6GPo+tkQB0MrMEoDNQjD6P++Tu7xEadbS+pj5/E4EnPOQjIL3BEOT7pSOHeGNT\npWZFqZaYZWYDgaOAj4He7l4cfmkj0DtKZcWSO4CfAcHwcgZQ5u6B8LI+l/uWA2wBHg1flnjIzLqg\nz+N+cff1wB+BtYTCuxyYiz6PrdXU569Ns6cjh7gcIDNLBV4Afuzu2+u/5qHHHvToQzPM7Cxgs7vP\njXYtMS4ByAfuc/ejgJ00OHWuz+O+ha/ZTiT0S1FfoAtfPUUsrRDJz19HDvF9TpUqTTOzREIB/rS7\nvxhevWnPaaHwn5ujVV+MGAecY2arCV3OOYXQtd308OlM0OeyJYqAInf/OLz8PKFQ1+dx/3wdWOXu\nW9y9BniR0GdUn8fWaerz16bZ05FDfJ9TpUrjwtdtHwaWuPuf6r00A7g8/P3lwN8Pdm2xxN1/4e7Z\n7j6Q0OfvbXe/GHgHODfcTMdxH9x9I7DOzIaEV50KLEafx/21FjjOzDqH/43vOY76PLZOU5+/GcBl\n4bvUjwPK6512328derAXM/sGoWuSe6ZK/f+iXFJMMLMTgJnAQr68lvtLQtfFnwP6E5pp7tvu3vBm\nD2mEmX0NuMHdzzKzwwn1zHsAnwGXuPvuaNbX3pnZaEI3ByYBK4ErCXVS9HncD2Z2K3A+oSdQPgOu\nInS9Vp/HZpjZNOBrhGYr2wTcDLxMI5+/8C9IfyF0qWIXcKW7z2n1vjtyiIuIiMSyjnw6XUREJKYp\nxEVERGKUQlxERCRGKcRFRERilEJcREQkRinERUREYpRCXEREJEYpxEUOUeF5yneYWf+2bCsi7YcG\nexFpJ8xsR73FzsBuoDa8fLW7P33wqzpwZvZbINvdr4h2LSKHmoR9NxGRg8HdU/d8H54U5Sp3f7Op\n9maWUG+KSBHpgHQ6XSRGmNlvzexZM5tmZhXAJWY21sw+MrMyMys2s7vCM8xhZglm5uE53zGzp8Kv\nv2ZmFWb2oZnl7G/b8OtnmtlyMys3s7vN7AMzu6IVP9NwM/tPuP6FZvZf9V47y8yWhPdfZGbXh9f3\nMrNXw+8pNbP3WntMRWKdQlwktnwLeAboBjxLaKKK6whNvDCO0KQKVzfz/ouAXxGazGIt8Jv9bWtm\nvQhN7HBjeL+rgDH7+4OEZw/8J/AK0BO4HnjWzHLDTR4FvuvuacBI4D/h9TcSmuSkJ9AHuGl/9y1y\nqFCIi8SW9939H+4edPdKd5/t7h+7e8DdVwIPAic18/7n3X1OeL7op4HRrWh7FjDP3f8efu3PQEkr\nfpZxhGYd+4O714QvHbxGaFpWgBogz8zS3L3U3T+tt74v0N/dq91dPXHpsBTiIrFlXf0FMxtqZq+Y\n2UYz2w7cRqh33JSN9b7fBaQ21bCZtn3r1+Ghu2OLWlB7Q32Btb733bVrCE19CaGzDucAa83sXTM7\nNrz+9nC7t8zsCzO7sRX7FjkkKMRFYkvDx0keAD4Hct29K/BrwCJcQzGQvWchPD9yVtPNm7QB6Bd+\n/x79gfUA4TMM5wC9CJ12nx5ev93dr3f3gcA3gZ+bWXNnH0QOWQpxkdiWBpQDO81sGM1fD28r/wTy\nzexsM0sgdE2+5z7eE29mKfW+koFZhK7p/9TMEs3sFOAbhK6LdzKzi8ysa/iUfQUQBAjvd1A4/MsJ\nPYYXjMyPKtK+KcRFYttPgcsJhdwDhG52iyh33wScD/wJ2AoMAj4j9Fx7Uy4BKut9LXP33cDZwERC\n19TvAi5y98Lwey4H1oQvE3w3vA2AIcDbwA7gA+BOd5/ZZj+gSAzRYC8ickDMLJ7QqfFzFaYiB5d6\n4iKy38xsgpmlh0+L/4rQHeOfRLkskQ5HIS4irXECoWe1twBnAN8Knx4XkYNIp9NFRERilHriIiIi\nMUohLiIiEqNibhazzMxMHzhwYLTLEBEROSjmzp1b4u6NjsUQcyE+cOBA5syZE+0yREREDgozW9PU\nazqdLiIiEqMU4iIiIjFKIS4iIhKjYu6auIiIHLiamhqKioqoqqqKdikSlpKSQnZ2NomJiS1+j0Jc\nRKQDKioqIi0tjYEDB7L3bLASDe7O1q1bKSoqIicnp8Xv0+l0EZEOqKqqioyMDAV4O2FmZGRk7PeZ\nkQ4d4uWVNby1ZBMaelZEOiIFePvSmr+PDh3iM+Zv4LuPz2HN1l3RLkVEpEPZunUro0ePZvTo0fTp\n04esrKy65erq6hZt48orr2TZsmXNtrnnnnt4+umn26JkTjjhBObNm9cm22orHfqa+PjcTABmrihh\nYGaXKFcjItJxZGRk1AXiLbfcQmpqKjfccMNebdwddycurvH+5qOPPrrP/VxzzTUHXmw7FtGeeHjO\n4WVmtsLMpjby+p/NbF74a7mZlUWynoYGZHQmK70THxSWHMzdiohIE1asWEFeXh4XX3wxw4cPp7i4\nmClTplBQUMDw4cO57bbb6tru6RkHAgHS09OZOnUqo0aNYuzYsWzevBmAm266iTvuuKOu/dSpUxkz\nZgxDhgxh1qxZAOzcuZPJkyeTl5fHueeeS0FBQYt73JWVlVx++eUceeSR5Ofn89577wGwcOFCjjnm\nGEaPHs3IkSNZuXIlFRUVnHnmmYwaNYoRI0bw/PPPH/DxiliIm1k8cA9wJpAHXGhmefXbuPv17j7a\n3UcDdwMvRqqeJmpk/OBMZn1RQm1Q18VFRNqDpUuXcv3117N48WKysrK4/fbbmTNnDvPnz+ff//43\nixcv/sp7ysvLOemkk5g/fz5jx47lkUceaXTb7s4nn3zCH/7wh7pfCO6++2769OnD4sWL+dWvfsVn\nn33W4lrvuusukpOTWbhwIU8++SSXXnop1dXV3Hvvvdxwww3MmzeP2bNn07dvX1599VUGDhzI/Pnz\n+fzzzznttNNad4DqieTp9DHACndfCWBm04GJwFePfsiFwM0RrKdR43IzmT57HQuKyjiqf/eDvXsR\nkai79R+LWLxhe5tuM69vV24+e3ir3jto0CAKCgrqlqdNm8bDDz9MIBBgw4YNLF68mLy8vfqEdOrU\niTPPPBOAo48+mpkzZza67UmTJtW1Wb16NQDvv/8+P//5zwEYNWoUw4e3vO7333+fG2+8EYDhw4fT\nt29fVqxYwfHHH89vf/tb1qxZw6RJk8jNzWXkyJFMnTqVqVOncvbZZzNu3LgW76cpkTydngWsq7dc\nFF73FWY2AMgB3o5gPY0al5uJGXywQqfURUTagy5dvrxHqbCwkDvvvJO3336bBQsWMGHChEYfw0pK\nSqr7Pj4+nkAg0Oi2k5OT99mmLVx66aW89NJLJCcnM2HCBN577z2GDRvGnDlzGD58OFOnTuV3v/vd\nAe+nvdzYdgHwvLvXNvaimU0BpgD079+/TXfco0sSw/t2ZWZhCdeeMrhNty0iEgta22M+GLZv305a\nWhpdu3aluLiYN954gwkTJrTpPsaNG8dzzz3H+PHjWbhwYaOn65syfvx4nn76aU488USWLFlCcXEx\nubm5rFy5ktzcXK677jpWrVrFggULGDRoEJmZmVx66aWkpaXx1FNPHXDtkQzx9UC/esvZ4XWNuQBo\n8hZCd38QeBCgoKCgzS9ej8vN5JH3V7Fzd4Auye3l9xoREcnPzycvL4+hQ4cyYMCANjkF3dCPfvQj\nLrvsMvLy8uq+unXr1mjbM844o25Y1PHjx/PII49w9dVXc+SRR5KYmMgTTzxBUlISzzzzDNOmTSMx\nMZG+fftyyy23MGvWLKZOnUpcXBxJSUncf//9B1y7RWqgEzNLAJYDpxIK79nARe6+qEG7ocDrQI63\noJiCggJv6/nE3y8s4ZKHP+bRK4/h5CG92nTbIiLt0ZIlSxg2bFi0y2gXAoEAgUCAlJQUCgsLOf30\n0yksLCQh4eB36hr7ezGzue5e0Fj7iFXo7gEzuxZ4A4gHHnH3RWZ2GzDH3WeEm14ATG9JgEdKwcDu\nJCXE8X5hiUJcRKSD2bFjB6eeeiqBQAB354EHHohKgLdGRKt091eBVxus+3WD5VsiWUNLpCTGM2Zg\nD97X8+IiIh1Oeno6c+fOjXYZrdKhh12t74TBmSzbVMHmCk3LJyIisUEhHnZCeAhWPWomIh2FJn9q\nX1rz96EQD8s7rCs9uiQxU6fURaQDSElJYevWrQrydmLPfOIpKSn79b7YuHJ/EMTFGccPyuCDFSW4\nu6boE5FDWnZ2NkVFRWzZsiXapUhYSkoK2dnZ+/UehXg9J+Rm8s8FxSzdWMGww7pGuxwRkYhJTEwk\nJycn2mXIAdLp9HpOy+tNUnwc0z5ZG+1SRERE9kkhXk9GajJnjTyMF+YWUVFVE+1yREREmqUQb+DS\nsQPYWV3LS581NUKsiIhI+6AQb2B0v3RGZnfjiQ/X6K5NERFp1xTiDZgZlx43gBWbd/DhF1ujXY6I\niEiTFOKNOHtUX7p3TuSJD9dEuxQREZEmKcQbkZIYz7eP6ce/Fm9kQ1lltMsRERFplEK8CZccOwAH\nnvlYj5uJiEj7pBBvQr8enTl1aC+mz17L7kBttMsRERH5CoV4My4dO5CSHdW8tnBjtEsRERH5CoV4\nM8bnZpKT2YUnPlwd7VJERES+QiHejLg445LjBvDp2jI+X18e7XJERET2EtEQN7MJZrbMzFaY2dQm\n2nzbzBab2SIzeyaS9bTGuUdn0ykxXr1xERFpdyIW4mYWD9wDnAnkAReaWV6DNoOBXwDj3H048ONI\n1dNa3Tol8s2jsvj7vA2U7aqOdjkiIiJ1ItkTHwOscPeV7l4NTAcmNmjzPeAed98G4O6bI1hPq102\ndgC7A0H+Nqco2qWIiIjUiWSIZwHr6i0XhdfVdwRwhJl9YGYfmdmECNbTasMO68oxA7vz5EdrCAY1\nnrqIiLQP0b6xLQEYDHwNuBD4q5mlN2xkZlPMbI6ZzdmyZctBLjHksrEDWVu6i/8sj87+RUREGopk\niK8H+tVbzg6vq68ImOHuNe6+ClhOKNT34u4PunuBuxf07NkzYgU354zhfeiZlqwb3EREpN2IZIjP\nBgabWY6ZJQEXADMatHmZUC8cM8skdHp9ZQRrarWkhDguHNOfd5dvYc3WndEuR0REJHIh7u4B4Frg\nDWAJ8Jy7LzKz28zsnHCzN4CtZrYYeAe40d3b7fyfFx/bn4Q44/7/tMvfM0REpIMx99i6UaugoMDn\nzJkTtf3f/PfPefrjtbz105MYkNElanWIiEjHYGZz3b2gsdeifWNbzLnmlFwS4o073iyMdikiItLB\nKcT3U6+0FK44PoeX561n2caKaJcjIiIdmEK8Fb5/0uGkJiXwp38vi3YpIiLSgSnEWyG9cxLfO/Fw\n3li0ifnryqJdjoiIdFAK8Vb6zgk59OiSxB//pd64iIhEh0K8lVKTE/jh1wYxs7CED79ot0/FiYjI\nIUwhfgAuOW4AfbulcOs/FhGoDUa7HBER6WAU4gcgJTGeX589nKUbK3hs1upolyMiIh2MQvwAnTG8\nNycP6cmf/72c4vLKaJcjIiIdiEL8AJkZt54zgkDQ+e0/l0S7HBER6UAU4m2gf0Znrj05l1cWFmuq\nUhEROWgU4m1kykmHc3jPLvz6759TVVMb7XJERKQDUIi3keSEeH4zcQRrtu7i7rc1rrqIiESeQrwN\njcvN5Lyjs7n33S/+//buO06q+tzj+OeZme0LywJL26WDFJEuCCq2oFgCUaOCN8Z6jYlEklyTa3pv\naoyaGBPsJkYSuRYUI1E0gqLSe28CS1vawgLbn/vHDGYlbGHZ2dnZ/b5fr3kx58yZcx7O68Azv86c\nDXtiHY6IiDRySuJ17EdjT6dr6zS+NmUxewqKYh2OiIg0YkridSwtKcQj1w/mwNES/ufvSygvj6/1\n2kVEJH4oiUdBn/bN+cEVfXl3bR6Pzd4Y63BERKSRimoSN7MxZrbGzNab2T0n+PwmM8szs8WR123R\njKc+/dfwTlx2Rjvum7GGBR/vj3U4IiLSCEUtiZtZEHgEuBToC0wws74nOPRv7j4w8no8WvHUNzPj\nl1f1p32LZG58ci6vL9sR65BERKSRqTaJR5JxbQwD1rv7RncvBqYA42p5rriUkZLA324fQY826Xzl\nuYX89LWVlGihFBERqSM1KYmvM7P7KilFVyUb2Fphe1tk3/GuNrOlZjbVzDqe5DUavA4tUvj7l0Zw\n08guPPHeJsZP/pCd+YWxDktERBqBmiTxAcBa4HEz+9DMbjez5nV0/VeBLu7eH3gTeOZEB0WuOd/M\n5uflxd+0pomhAD8aezoPTxjEqh0HGfPQLF5ZnIu7eq6LiEjtVZvE3f2Quz/m7iOB/wV+COwws2fM\nrEcVX80FKpascyL7Kp57r7sfG0z9ODCkkhgmu/tQdx+alZVVXcgN1tgBHXj1q+fQtXUak6Ys5vY/\nL2D3IZXKRUSkdmrUJm5mY83sJeBB4DdAN8Kl6Ner+Oo8oKeZdTWzRGA8MO24c7evsDkWaPTLgHXP\nSmfqHSP57mV9mLU2j9EPhEvlIiIiJytUg2PWAe8A97n7nAr7p5rZqMq+5O6lZjYRmAEEgSfdfYWZ\n/QSY7+7TgLvMbCxQw3t1swAAIABJREFUCuwDbqrl3yOuBAPGf4/qxoV92vDNF5Ywacpilm3L59uX\n9SEYsFiHJyIiccKqa5c1s3R3L6ineKo1dOhQnz9/fqzDqDMlZeX8fPoqnp6zmfNOy+LhCYPISEmI\ndVgiItJAmNkCdx96os9q0rGtjZm9amZ7zGy3mb1iZt3qOMYmKyEY7vT2y6vO4P31e7jyD++zac/h\nWIclIiJxoCZJ/K/A34F2QAfgBeD5aAbVFE0Y1onnbhvOgSMlXPLgLG55eh5/m7dFi6iIiEilalKd\nvjQyBKziviXuPiCqkVWisVWnH2/b/iM8+d5mZqzYSe6BowQMRnRvxf3XDKB9RkqswxMRkXpWVXV6\nTZL4r4H9hGdcc+A6IBO4D8Dd99VptNVo7En8GHdn5Y6DzFixiyff20RGSgJ/vnUY3bLSYx2aiIjU\no1NN4puq+NjdvV7bx5tKEq9o2bZ8bnxqLgY8c8sw+mVnxDokERGpJ6fUsc3du1bxUge3enBGTgYv\n3DGC5IQg4yd/yAcb9sY6JBERaQBqMtlLgpndFZnbfKqZTTQzjYGqZ92z0pn65RG0y0jmxqfmMnXB\ntliHJCIiMVaT3umPEp4O9Q+R15DIPqln7TNSeOFLIxjcqQV3v7CE77+8nOJSrYomItJU1WTGtjOP\n64n+tpktiVZAUrXMtET+cutw7p2xhsmzNrJiez6PfmEIbZsnxzo0ERGpZzUpiZeZWfdjG5GJXsqi\nF5JUJxQM8J3L+vD76wexeuchLn/4PX7/9jo2a5IYEZEmpSa90y8CngI2AgZ0Bm5293eiH95/aoq9\n06uydtchvvfycuZuCo/065+TwWf7d+CaoTm0SE2McXQiInKqaj3EzMwCwFnAAqBXZPeaCsuH1jsl\n8RPbfuAo05fu4NWl21m6LZ9mySHuOK87t5zdlZTEYKzDExGRWjrVceKL3H1QVCKrBSXx6q3acZD7\nZ6xh5urdtGmWxKTP9OS6oR0JBWvSeiIiIg3JqS6AMtPMrjYzrZEZJ/q0b84TN53JC3eMoGPLVL77\n0nKu+dMHbN13JNahiYhIHapJSfwQkEZ4ze9Cwu3i7u7Nox/ef1JJ/OS4O9OWbOd7Ly3HDO79/ADG\n9GsX67BERKSGTnXGtmbuHnD3RHdvHtmOSQKXk2dmjBuYzfS7zqVL6zTu+MsCfvDKcgpLNMBARCTe\n1WTGtpk12ScNW6dWqUy9YyS3ntOVZz/4mJG/epufvraS1TsPxjo0ERGppUonezGzZCAVaG1mmYSr\n0QGaA9n1EJvUscRQgO9f0ZfP9GnLnz/czLMfbOaJ9zbRPyeDa4bk8NkBHTQsTUQkjlTaJm5mk4Cv\nAR2AXP6dxA8Cj7n776s9udkY4CEgCDzu7r+q5LirgamEZ4erssFbbeJ1Z9/hYl5elMvf529l9c5D\nJAYDjO7blquHZDOqZ5Z6s4uINACnOsTsq+7+u1pcNAisBUYD24B5wAR3X3nccc2A6UAiMFFJvP65\nOyu2H+T/Fm7jlcXb2Xe4mI4tU7j74l58tn8HAgENTBARiZVTSuKRE4wEulCh+t3dn63mOyOAH7n7\nJZHtb0e+98vjjnsQeBP4JnC3knhsFZeW8/bqXfzu7fWs2H6Qvu2bc8+lvRl1WlasQxMRaZJOqXe6\nmf0ZuB84Bzgz8jrhyY6TDWytsL2N49rSzWww0NHdp1cTw+1mNt/M5ufl5dXg0lJbiaEAY/q159WJ\n5/DQ+IEcLCzhi0/OZfzkD5i5ahfl5dX/6BMRkfpRk1XMhgJ9vSZF9pMQmdL1AeCm6o5198nAZAiX\nxOsyDjmxQCA8NG1Mv3Y89+EWJs/ayK3PzKdr6zRuPrsLVw/OIS2pJo+PiIhES016Li0HajM7SC7Q\nscJ2TmTfMc2AfsC/zGwz4Tnap5lZTUr5Uk+SQkFuOacrs//3Ah6eMIjmKQn84JUVDP/FTL75whLm\nbNij0rmISIzUpGPbO8BAYC7wycIn7j62mu+FCHdsu4hw8p4HXO/uKyo5/l+oTTwuLPh4P8/P3cIb\ny3dSUFRK+4xkxg7owMWnt2Vgx0yC6ggnIlJnqmoTr0l96I9qc1F3LzWzicAMwkPMnnT3FWb2E2C+\nu0+rzXkl9oZ0zmRI50x+Oq4fb67axcuLcnnivU38adZGWqYlckGvNlxyels+06eteraLiERRVePE\ne7v76sj7pIrLj5rZWe7+YT3F+CkqiTdM+UdLeHdtHjNX7eKd1bs5WFjKiG6tuPfz/enYMjXW4YmI\nxK1aDTEzs4XuPvj49yfark9K4g1fSVk5Uxds4+fTV+HufPfyvkwY1hEthCcicvJqW51ulbw/0bbI\nJxKCASYM68S5PVvzralL+c5Ly/jH8h2MG5hNn/bN6NmmGYkhzQYnInKqqkriXsn7E22L/IeczFT+\ncutwnpu7hXv/sZrZ6/YAEAoYPds247ZzunLV4GyV0EVEaqmq6vTdwBTCpe7rIu+JbF/r7m3rJcLj\nqDo9PpWVO5v2HGbVjoOs2nGQ2ev2sCw3n/N7ZfGLK8+gQ4uUWIcoItIg1bZN/MaqTuruz9RBbCdN\nSbxxKCt3nv1gM/e+sYZgwPj2Zb2ZcGYn9WYXETnOKc+d3pAoiTcuW/Ye4Z4XlzJnw156tEnnxhGd\nuUqzwYmIfEJJXBo0d2faku08PnsTy3LzaZYU4vNDc7iif3tO75BBckIw1iGKiMSMkrjEBXdn4ZYD\nPDNnM68v20FpuZMYDNC3Q3MGd8rk8v7tGdI5M9ZhiojUKyVxiTt7C4qY//F+Fm7Zz6KPD7Bk2wGK\nSsu5anA23760D1nNkmIdoohIvTilJG5m9wI/A44CbwD9ga+7+1/qOtCaUBJvmo4Ul/L7t9fz2OyN\nJCcEufviXnzhrM6ap11EGr1TWk8cuNjdDwJXAJuBHsA36y48keqlJob41pje/GPSKPrnZPDDaSsY\n/dt3eXz2RvYdLo51eCIiMVGTJH6sm/DlwAvunh/FeESq1KNNOn+5dTh/+K/BtEhJ4GfTV3HWL2by\n1ecX8cGGvcRb85CIyKmoyTie18xsNeHq9C+bWRZQGN2wRCpnZlx2RnsuO6M9a3Ye4vm5W3hx4TZe\nXbKd3u2acfPZXRg3MFu92kWk0atRxzYzawnku3uZmaUCzd19Z9SjOwG1icuJFJaUMW3xdp58fxOr\ndx4iMzWB8cM6cf2wTlpFTUTi2ql2bLsGeMPdD5nZ94DBwM/cfWHdh1o9JXGpirvz0aZ9PPX+Jt5c\nuQsHRvXM4vrhnbiodxtCQS28IiLx5VST+FJ3729m5xDupX4f8AN3H173oVZPSVxqavuBo/xt3lb+\nNm8rOw8W0j4jma9e2JNrh+YomYtI3DjV3ullkT8vBya7+3Qgsa6CE4mWDi1S+Pro03jvfy9g8g1D\naJ+RzHdeWsbo387itaXbKS9XJzgRiW81KYm/BuQCowlXpR8F5rr7gGpPbjYGeAgIAo+7+6+O+/wO\n4E7CPxQKgNvdfWVV51RJXGrL3Xlr1W7um7GatbsK6NO+ORf2zmJI50wGdcwkM02/TUWk4TnV6vRU\nYAywzN3XmVl74Ax3/2c13wsCawkn/23APGBCxSRtZs0jY9Axs7HAV9x9TFXnVRKXU1VW7ryyOJen\n52xmxfaDlEVK5D3apPO9y/twfq82MY5QROTfqkri1Q4xc/cjZrYBuMTMLgFmV5fAI4YB6919YySI\nKcA44JMkfiyBR6QBqt+UqAsGjKsG53DV4ByOFJeydFs+C7fs5+VFudz89DzuurAnd13UU7PBiUiD\nV22buJlNAp4D2kRefzGzr9bg3NnA1grb2yL7jj//nZEfCfcCd1USw+1mNt/M5ufl5dXg0iI1k5oY\n4qxurfjK+T145c5zuHJQNg/NXMfNT8/TTHAi0uDVqHc6MMLdD0e204AP3L1/Nd/7PDDG3W+LbN8A\nDHf3iZUcfz1wibvfWNV5VZ0u0eTuPD93Kz+atoKsZkncMKIzw7q25IzsDBLUo11EYuCUqtMB4989\n1Im8r0k9Yy7QscJ2TmRfZaYAj9bgvCJRY2ZcP7wT/bKb862pS/nVP1YDkJIQZEjnTL50XjfO7ZkV\n4yhFRMJqksSfAj4ys5ci258DnqjB9+YBPc2sK+HkPR64vuIBZtbT3ddFNi8H1iHSAPTPacEbXxtF\n3qEi5m3ex9xN+5i5ehc3PDGXr5zfnW+MPk1jzUUk5mo67epg4JzI5mx3X1Sjk5tdBjxIeIjZk+7+\nczP7CTDf3aeZ2UPAZ4ASYD8w0d1XVHVOVadLrBSWlPHjV1fw/NytDO2cycMTBtGhRUqswxKRRq7W\nQ8wiw8RWuHvvaAV3spTEJdZeWZzLd15cRkIowDdGn8Zn+3fQGHMRiZpaz9jm7mXAGjPrFJXIROLQ\nuIHZvHbXuXRtncYPXlnBsF+8xe3PzueN5TsoKi2r/gQiInWkJm3imcAKM5sLHD62093HRi0qkQau\na+s0XvzySFbuOMhLC3N5Zcl2/rlyFx0ykpn0mZ5cPVjzs4tI9NVkiNl5J9rv7u9GJaJqqDpdGqLS\nsnJmr9vDQzPXsXjrAbplpXH3xb24tF87zDRpjIjUXq2GmJlZD6Dt8ck6sprZjroNUSS+hYIBLujd\nhvN7ZfHPlbu4f8YavvLcQgZ1asGvr+7PaW2bxTpEEWmEqqrvexA4eIL9+ZHPROQ4ZsYlp7fjja+N\n4t7P92fznsNc/vBsHnxrLcWl5bEOT0QamaqSeFt3X3b8zsi+LlGLSKQRCAaMa4d25K1vnMdlZ7Tn\nwbfWccXvZvOvNbs5VFgS6/BEpJGoqmNbiyo+0+BYkRpolZ7EQ+MHMW5gB7770nJuemoeAN1ap3F6\ndgZnd2/FtUM7EtBiKyJSC1Ul8flm9t/u/ljFnWZ2G7AgumGJNC4X9m7LW99oxdxN+1iem8+y3HwW\nbN7Hq0u2M2PFTn573UBapGqsuYicnEp7p5tZW+AloJh/J+2hQCJwpbvvrJcIj6Pe6dJYuDt/+fBj\nfvLaSto2T+aPXxhCv+yMWIclIg1MrSZ7cfdd7j4S+DGwOfL6sbuPiFUCF2lMzIwbRnTh718aQXm5\nc9Wjc5gydws1mQpZRARqOHd6Q6KSuDRG+w4XM2nKImav28PVg3P42ef6kZIYjHVYItIA1HraVRGp\nHy3TEnn65mFMuqgnLy7axuceeZ8NeQWxDktEGjglcZEGIhgwvj76NJ65eRh5BUWM/d17vLRom+Zj\nF5FKqTpdpAHakX+UiX9dxIKP95MQNE5r24wzsjPon9OCKwdlq6pdpAmp9VKkDZGSuDQVJWXlvLVy\nF0u25bNie3hY2oEjJfRok87D4wfRt0PzWIcoIvVASVykEXB33l2bxzenLiX/SAnfGtOLW87uqoli\nRBo5dWwTaQTMjPN7teGNSecy6rQsfjZ9FTc+NZcd+UdjHZqIxEhUk7iZjTGzNWa23szuOcHn3zCz\nlWa21MxmmlnnaMYj0hi0Sk/isS8O4Wef68e8zfu46DfvMnnWBkrKtMCKSFMTtSRuZkHgEeBSoC8w\nwcz6HnfYImCou/cHpgL3RisekcbEzPjCWZ158+vnMaJbK37x+mouf3g2czfti3VoIlKPolkSHwas\nd/eN7l4MTAHGVTzA3d9x9yORzQ+BnCjGI9LodGyZyhM3ncljXxzK4aIyrv3TB0yasojcA6piF2kK\nopnEs4GtFba3RfZV5lbgH1GMR6TRGt23LW994zy+emEP3li+kwvv/xe/+ecaDheVxjo0EYmiBtGx\nzcy+QHhxlfsq+fx2M5tvZvPz8vLqNziROJGSGOR/Lu7F23efz5h+7fjd2+s5//5/MW3J9liHJiJR\nEs0kngt0rLCdE9n3KWb2GeC7wFh3LzrRidx9srsPdfehWVlZUQlWpLHIbpHCQ+MH8eJXRtIhI5m7\nnl/ExL8u5MCR4liHJiJ1LJpJfB7Q08y6mlkiMB6YVvEAMxsE/IlwAt8dxVhEmpzBnTL5vy+P5O6L\nT+ON5Tu55MFZzFqrmiyRxiRqSdzdS4GJwAxgFfB3d19hZj8xs7GRw+4D0oEXzGyxmU2r5HQiUguh\nYICJF/bk5TvPpllyAl98ci7ffnEpewtOWOklInFGM7aJNBGFJWX85p9reOr9zaQkBpl0UU++OKIL\niaEG0TVGRCqhGdtEhOSEIN+9vC9vfO1cBnfK5GfTVzHmwVm8tXIX8fZjXkTClMRFmpgebZrxzC3D\neOqmM8Hgtmfnc9Wjc5izfk+sQxORk6QkLtJEXdC7DTO+NopfXnUGO/MLuf7xj7j+sQ9ZsvVArEMT\nkRpSEhdpwhKCASYM68Q7d5/P96/oy5qdh7jq0Tk88d4mVbGLxAElcREhOSHIred05Z1vns9Fvdvw\n09dWcteUxRwp1oxvIg2ZkriIfKJ5cgJ//MIQvnlJL6Yv3c6Vj8xh057DsQ5LRCqhJC4inxIIGHde\n0INnbhnGrkOFXPLbWdz53ELeWbObUi13KtKgaJy4iFQq98BRHpu1kVcW57L/SAltmydx3dCOTLyw\np8aXi9STqsaJK4mLSLWKS8t5e/UuXpi/jZmrdzOiWyv+eMMQMlISYh2aSKOnyV5E5JQkhgKM6dee\nJ246kweuHcC8zfu45o9z2K51y0ViSklcRE7KVYNzeOaWYew4UMiVf3ifldsPxjokkSZLSVxETtrZ\nPVrzwpdHEDDjmj/O4eGZ6zhUWBLrsESaHCVxEamV3u2a89JXzmZkj9Y88OZaRt37Dn96dwNHi8ti\nHZpIk6GObSJyypZsPcADb67l3bV5tE5P4o7zunH98E6kJoZiHZpI3FPvdBGpF/M37+OBN9cyZ8Ne\nWqUlctu53bhhRGfSk5TMRWpLSVxE6tX8zft4+O31zFqbR4vUBCZe0IMbRnQmKRSMdWgicUdJXERi\nYnGkmn3W2jw6t0rlnjG9GdOvHWYW69BE4kbMxomb2RgzW2Nm683snhN8PsrMFppZqZl9PpqxiEj9\nG9ixBc/eMoynbz6TpFCALz+3kGv/9AHLc/NjHZpIoxC1JG5mQeAR4FKgLzDBzPoed9gW4Cbgr9GK\nQ0Ri7/xebXj9rnP5xZVnsGnPYcY98j4/n75Sq6SJnKJolsSHAevdfaO7FwNTgHEVD3D3ze6+FNCq\nCiKNXCgY4PrhnZj5jfO5dmgOj83exOgHZvHOmt2xDk0kbkWzy2g2sLXC9jZgeBSvJyJxICM1gV9e\n1Z8rB+XwnZeWcfNT80hNDFJW7pS7U+4wpFMm37+iL2fkZMQ6XJEGLS7GfZjZ7cDtAJ06dYpxNCJS\nF4Z1bcn0u87hrx9tIXf/UYIBIxAwysqdFxduY+wj73HtkI7cfUkvspolxTpckQYpmkk8F+hYYTsn\nsu+kuftkYDKEe6efemgi0hAkhYLcfHbX/9g/8cIe/G7mOp56fzPTl+3gzgt6cOPIzpo8RuQ40WwT\nnwf0NLOuZpYIjAemRfF6ItJINE9O4LuX92XG10cxrGtLfv3Gakbd+y+efG8ThSWa1lXkmKiOEzez\ny4AHgSDwpLv/3Mx+Asx392lmdibwEpAJFAI73f30qs6pceIiTU/FmeDaNU/mv0d143MDO9AqXdXs\n0vhpshcRaRTmbNjDb99cy7zN+wkGjFE9W/O5QdmM7ttWVe3SaFWVxPXUi0jcGNm9NSO7t2bNzkO8\nvDiXVxblMmnKYtKTQnx2QAfGn9mR/jkZmhFOmgyVxEUkbpWXOx9t2sfUBduYvmw7hSXl9GnfnOuG\n5nDFgA60VnW7NAKqTheRRu9gYQnTFm/nb/O2siw3n2DAOKdHa8YN7MDFp7fTSmoSt5TERaRJWb3z\nINMWb+eVxdvJPXCUUMDo3CqV7lnpdG+TTo+sdEadlqXx5xIXlMRFpElydxZuOcDbq3exfncBG/IO\n8/Hew5SUOWZwZpeWXNavHWP6taddRnKswxU5ISVxEZGI0rJy1u4qYMaKnfxj+Q7W7ioA4IJeWdx8\ndlfO7dlaHeOkQVESFxGpxPrdBUxbnMtf525lT0ER3bPSuOnsrlzQK4vsFilK6BJzSuIiItUoKi1j\n+tIdPPX+ZpZF1jtvlhyiT7vm9GnfjMGdMzmnR2tNMCP1TklcRKSG3J0V2w+yeOsBVu04yOqdh1i9\n4yCHi8PTvfZt35xze7ZmZI/WnNklU5PMSNQpiYuInIKycmdZbj7vr9/D7HV5LPh4PyVlTkLQGJDT\ngpHdW9G9TToAZoYBrdITGdwpk+SEYGyDl7inJC4iUoeOFJcyf/N+Pti4lzkb9rJs2wHKT/BfaWIo\nwJBOmZzdoxUjureiX3YGSSEldTk5mnZVRKQOpSaGGHVaFqNOywLgUGEJuw8VES4TOe6wZd8RPtgQ\nTvL3/3MtAEmhAAM6tuDMLpmc2aUlw7q2VHW8nBKVxEVEomzf4WLmbtrHgo/3MXfzflbk5lNaHq6O\nH9Qp3GFuYMcWHCosZdfBQnYdKuRQYSlnd2/Nhb3bkJKo0ntTpup0EZEG5EhxKQs+3s/76/fy3vo8\nVmw/SMX/ihODAZJCAQ4VlZKWGGR037ZcekZ7gmbsKSgi71ARew8Xk9Usie5Z6fRok07nVqkkBAOx\n+0tJ1Kg6XUSkAUlNDHFuzyzO7ZkF9Gbf4WJW7zxIy7RE2jZLpkVqAuUOH23cy6tLt/P6sp28vHj7\np86RnhSioKj0k+2EoNG3fXOGd2vF8K4tGdqlJRkpCZ98XlbuBAyNe29kVBIXEWngikvLWbz1AEmh\nAFnNkmiVnkhSKEhBUSkbdhewfncBa3cfYuHH+1myNZ/isnLMIC0xRElZOSVl5ZR7uE0+OzOF7BYp\n5GSm0j4jmVbpibRKS6J1eiJtmyfToUUKwYASfUOikriISBxLDAUY1rXlf+xPTwoxoGMLBnRs8cm+\nwpIyFm05wNxN+zhYWEIoaCQGA4QCAQqKSsg9cJRt+4+ycvtO9h4uPuG1urRKpVvrcBV9RmoCGSnh\nV3pSiNIyp/iTHwZOx8xUumWl0zItMar3QE4sqknczMYADwFB4HF3/9VxnycBzwJDgL3Ade6+OZox\niYg0ZskJQUZ0Dw9pq05xaTn7DhezpyDcxr7jwFE27jnMxrzDrN19iLdX76a4rLxG181MTaBL6zQC\nZhSXllNcGk70GakJtG2WTLuMZNo2TyYjJYG0pCCpiSHSEoMkhgKYGcGAETQjEICAWeQFqUkh2jRL\nUnt/JaKWxM0sCDwCjAa2AfPMbJq7r6xw2K3AfnfvYWbjgV8D10UrJhER+bfEUIB2GcmVruDm7hSW\nlJN/tIT8oyUUFJWSEDQSQwESgwGc8FC6DRVWiDOD5skhkkJBQkHjwJES1ucV8N76PZ9qwz8ZZtA6\nPYn2GeEfAcWl5RRFfiiUlTsJoXBtQ0IwQHJCkJZpiZ96JSeEPwu/jMKScgqKSjlSVMqRkjLSk0K0\nTk+iVVoirZslkRgMUO5OWblT7hAwSAgGCAWNUCBAckKAtMQQgRM0O5SWhWNLq6f166N5lWHAenff\nCGBmU4BxQMUkPg74UeT9VOD3ZmYebw31IiKNkJmRkhgkJTFYaaLvnpXOBb3a1Oh8BUWlHCos4Uhx\nGUeKyigoKv2kWj6cNKHcw+Ps3cMJ9FBhCTsPFrIzv5Ad+YXkHy0hKRSgWXKIpFCAYMAoKfNw6b+s\nnP1HitmQV8C+w8UciUyVGy0pCUHSkkIEA3C0uIzCknAMLVITWPyDi6N67WOimcSzga0VtrcBwys7\nxt1LzSwfaAXsiWJcIiISA+lJIdLrqYQK4f4B+48UUxRJrseq+FMSg6QlhkhLCpGaGORQYWm4SaGg\nmL2HiygpC/fkD5hhBu5QUlZOablTUlZO0bGSfHEpBUVllJf7Jz92UhKCNEuuv79jXHRsM7Pbgdsj\nmwVmtqYOT98a/WioC7qPdUP3sW7oPtYN3cdauuXTm6d6HztX9kE0k3gu0LHCdk5k34mO2WZmISCD\ncAe3T3H3ycDkaARpZvMr67ovNaf7WDd0H+uG7mPd0H2sG9G8j9Hs7jcP6GlmXc0sERgPTDvumGnA\njZH3nwfeVnu4iIhIzUStJB5p454IzCA8xOxJd19hZj8B5rv7NOAJ4M9mth7YRzjRi4iISA1EtU3c\n3V8HXj9u3w8qvC8ErolmDDUQlWr6Jkj3sW7oPtYN3ce6oftYN6J2H+Nu2lUREREJ0xQ4IiIicapJ\nJ3EzG2Nma8xsvZndE+t44oWZdTSzd8xspZmtMLNJkf0tzexNM1sX+TMz1rHGAzMLmtkiM3stst3V\nzD6KPJd/i3QMlSqYWQszm2pmq81slZmN0PN48szs65F/08vN7HkzS9bzWD0ze9LMdpvZ8gr7Tvj8\nWdjDkfu51MwGn8q1m2wSrzAt7KVAX2CCmfWNbVRxoxT4H3fvC5wF3Bm5d/cAM929JzAzsi3VmwSs\nqrD9a+C37t4D2E94emKp2kPAG+7eGxhA+H7qeTwJZpYN3AUMdfd+hDskH5sOW89j1Z4Gxhy3r7Ln\n71KgZ+R1O/DoqVy4ySZxKkwL6+7FwLFpYaUa7r7D3RdG3h8i/B9mNuH790zksGeAz8UmwvhhZjnA\n5cDjkW0DLiQ8DTHoPlbLzDKAUYRHu+Duxe5+AD2PtRECUiLzdqQCO9DzWC13n0V4hFVFlT1/44Bn\nPexDoIWZta/ttZtyEj/RtLDZMYolbplZF2AQ8BHQ1t13RD7aCbSNUVjx5EHgW8CxpaJaAQfc/dhK\nEXouq9cVyAOeijRLPG5maeh5PCnungvcD2whnLzzgQXoeaytyp6/Os09TTmJyykys3Tg/4CvufvB\nip9FJu3R0IcqmNkVwG53XxDrWOJcCBgMPOrug4DDHFd1ruexepE223GEfxR1ANL4zypiqYVoPn9N\nOYnXZFpYqYSZJRBO4M+5+4uR3buOVQtF/twdq/jixNnAWDPbTLg550LCbbstItWZoOeyJrYB29z9\no8j2VMJJXc+mB7pKAAABU0lEQVTjyfkMsMnd89y9BHiR8DOq57F2Knv+6jT3NOUkXpNpYeUEIu22\nTwCr3P2BCh9VnEb3RuCV+o4tnrj7t909x927EH7+3nb3/wLeITwNMeg+VsvddwJbzaxXZNdFhJc8\n1vN4crYAZ5lZauTf+LH7qOexdip7/qYBX4z0Uj8LyK9Q7X7SmvRkL2Z2GeE2yWPTwv48xiHFBTM7\nB5gNLOPfbbnfIdwu/negE/AxcK27H9/ZQ07AzM4H7nb3K8ysG+GSeUtgEfAFdy+KZXwNnZkNJNw5\nMBHYCNxMuJCi5/EkmNmPgesIj0BZBNxGuL1Wz2MVzOx54HzCq5XtAn4IvMwJnr/ID6TfE26qOALc\n7O7za33tppzERURE4llTrk4XERGJa0riIiIicUpJXEREJE4piYuIiMQpJXEREZE4pSQuIiISp5TE\nRURE4pSSuIiISJz6fwXQTeWgBPkgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fsmstZgUm1Q",
        "colab_type": "text"
      },
      "source": [
        "### Validation Loss & Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nA29J5mjdcx6",
        "colab_type": "code",
        "outputId": "ddbab586-7762-42f7-d129-0a50d9b24224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "source": [
        "validation_acc = history.history['val_acc']\n",
        "validation_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize = (8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(validation_acc, label = 'Validation Accuracy')\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(validation_loss, label = 'Validation Loss')\n",
        "plt.legend(loc = 'upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,max(plt.ylim())])\n",
        "plt.title('Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ngbJm1wA3xpj2ZEncmHa0PDMXgCnWqc0YcwRYEjemHS3PzGVI91j6dOkU6FCM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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzHuKBkVrFDW",
        "colab_type": "text"
      },
      "source": [
        "## Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_0rEhYFrJIF",
        "colab_type": "code",
        "outputId": "492747de-f43c-4ee2-9b52-a800de608d45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        }
      },
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.9999940e-01, 5.8897302e-07],\n",
              "       [9.9999976e-01, 2.0343715e-07],\n",
              "       [9.9199718e-01, 8.0027673e-03],\n",
              "       [9.5849141e-02, 9.0415090e-01],\n",
              "       [8.7243891e-01, 1.2756117e-01],\n",
              "       [3.6466299e-09, 1.0000000e+00],\n",
              "       [3.3179110e-06, 9.9999666e-01],\n",
              "       [1.2063768e-04, 9.9987936e-01],\n",
              "       [4.9350643e-07, 9.9999952e-01],\n",
              "       [9.9999702e-01, 2.9465098e-06],\n",
              "       [9.9976021e-01, 2.3979064e-04],\n",
              "       [9.8710191e-01, 1.2898162e-02],\n",
              "       [9.9998748e-01, 1.2529519e-05],\n",
              "       [9.9997604e-01, 2.3915021e-05],\n",
              "       [5.8868172e-04, 9.9941134e-01],\n",
              "       [2.8293201e-04, 9.9971706e-01],\n",
              "       [1.8060839e-09, 1.0000000e+00],\n",
              "       [9.9976581e-01, 2.3424970e-04],\n",
              "       [9.9549943e-01, 4.5005716e-03],\n",
              "       [9.9999976e-01, 2.3427557e-07],\n",
              "       [2.4395626e-05, 9.9997556e-01],\n",
              "       [1.3532301e-06, 9.9999869e-01],\n",
              "       [1.8464297e-02, 9.8153573e-01],\n",
              "       [1.7592744e-07, 9.9999988e-01],\n",
              "       [9.9998844e-01, 1.1562862e-05],\n",
              "       [9.9274492e-01, 7.2551421e-03],\n",
              "       [3.1911328e-02, 9.6808863e-01],\n",
              "       [9.9293876e-01, 7.0612002e-03],\n",
              "       [7.4821728e-01, 2.5178277e-01],\n",
              "       [4.5970170e-04, 9.9954033e-01],\n",
              "       [3.2316162e-03, 9.9676836e-01],\n",
              "       [1.8428310e-04, 9.9981576e-01],\n",
              "       [2.0736766e-06, 9.9999797e-01],\n",
              "       [8.9549303e-01, 1.0450692e-01],\n",
              "       [2.0583182e-05, 9.9997938e-01],\n",
              "       [9.9732876e-01, 2.6712343e-03],\n",
              "       [4.5759953e-07, 9.9999952e-01],\n",
              "       [4.0742294e-03, 9.9592578e-01],\n",
              "       [1.0924477e-05, 9.9998903e-01],\n",
              "       [9.9452412e-01, 5.4758405e-03],\n",
              "       [9.9999607e-01, 3.8868529e-06],\n",
              "       [8.3622377e-04, 9.9916375e-01],\n",
              "       [1.1713677e-03, 9.9882859e-01],\n",
              "       [9.9997532e-01, 2.4702402e-05],\n",
              "       [9.9860650e-01, 1.3934982e-03],\n",
              "       [9.8792899e-01, 1.2070973e-02],\n",
              "       [2.8750760e-09, 1.0000000e+00],\n",
              "       [8.9042455e-01, 1.0957541e-01],\n",
              "       [1.3443053e-01, 8.6556953e-01],\n",
              "       [9.8939872e-01, 1.0601314e-02],\n",
              "       [9.9998975e-01, 1.0281275e-05],\n",
              "       [8.0821660e-09, 1.0000000e+00]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwEulbUTEkfz",
        "colab_type": "text"
      },
      "source": [
        "### Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRPExZhDtA5I",
        "colab_type": "code",
        "outputId": "52d7127c-a845-4f6c-9c40-c18dab807248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "matrix = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))\n",
        "matrix"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[25,  1],\n",
              "       [ 1, 25]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-Pa_ytfvQoq",
        "colab_type": "code",
        "outputId": "337549a2-2fe7-4d47-b967-310dbf94fb1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "plt.imshow(matrix, cmap=plt.cm.Blues)\n",
        "plt.xlabel(\"Predicted labels\")\n",
        "plt.ylabel(\"True labels\")\n",
        "plt.xticks([], [])\n",
        "plt.yticks([], [])\n",
        "plt.title('Confusion matrix ')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8IfBTRhhK/RFxRvPVM7Ne0UQmTZa0sPR9XkTMa3DMlIjoT58fpuKdIA3HlMxsbBLV5gQl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\nZGZZcSiZWVb+H0gW5NaQjmLjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABl7pPxwKWjC",
        "colab_type": "text"
      },
      "source": [
        "## Results on ALL-IDB (Images)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vyjHpRrhOvb7",
        "colab_type": "code",
        "outputId": "82c30808-2734-47a6-8e8c-90b8834a5e7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "TN = matrix[0][0]\n",
        "FN = matrix[0][1]\n",
        "TP = matrix[1][0]\n",
        "FP = matrix[1][1]\n",
        "\n",
        "(TP, FP, TN, FN)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 25, 25, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lVEDjCb5RnqO",
        "colab_type": "code",
        "outputId": "175ed0e0-6bb3-4bf8-f22e-f673a351254e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_len = len(X_test)\n",
        "\n",
        "TPP = (TP * 100) / test_len\n",
        "FPP = (FP * 100) / test_len\n",
        "FNP = (FN * 100) / test_len\n",
        "TNP = (TN * 100) / test_len\n",
        "\n",
        "(TPP, FPP, TNP, FNP)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.9230769230769231, 48.07692307692308, 48.07692307692308, 1.9230769230769231)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iodAfPQeNFBu",
        "colab_type": "code",
        "outputId": "7ce9d239-9101-40e1-d81c-93d0160b61c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "specificity = TN/(TN+FP) \n",
        "specificity"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58W0lm17nrc2",
        "colab_type": "code",
        "outputId": "c9f110fd-383f-4dab-d43b-d041c8ff3421",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "specificity = (specificity * 100) / test_len\n",
        "specificity"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9615384615384616"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wsq1dDM-NJiQ",
        "colab_type": "code",
        "outputId": "732237e2-aab8-404d-fe88-7f7c7b4c530f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "misc = FP + FN\n",
        "misc"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "6b6b5fe6-929b-49e8-c173-6ad7c14649ab",
        "id": "HIBNGPJkjt0X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "misc = (misc * 100) / test_len\n",
        "misc"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-DdbP3BknaA2",
        "colab_type": "text"
      },
      "source": [
        "### Figures Of Merit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lXHj5vILLvE",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "| Figures of merit  | Value | Percentage |\n",
        "| ----------------  | ----- | ---------- |\n",
        "| True Positives | 1 | 1.92% |\n",
        "| False Positives | 25 | 48.08% \n",
        "| True Negatives | 25 | 48.08% |\n",
        "| False Negatives | 1 | 1.92% |\n",
        "| Misclassification | 26 | 50.00% |\n",
        "| Sensitivity / Recall | 0.96 | 96% |\n",
        "| Specificity | 0.5 | 50% |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBFCWOCposL7",
        "colab_type": "text"
      },
      "source": [
        "# Save Your Keras Model\n",
        "\n",
        "Now you will save your Keras model and weights so that they can be used again. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrgjTj8ApKuy",
        "colab_type": "text"
      },
      "source": [
        "## Save Model As Json"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYrOPH1apCU4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open(model_path, \"w\") as file:\n",
        "    file.write(model.to_json())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-UiwZMXpPpr",
        "colab_type": "text"
      },
      "source": [
        "## Save Weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4o3alUuspEK6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save_weights(model_root_path + \"/weights.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_b4v8LZ-9AT",
        "colab_type": "text"
      },
      "source": [
        "# Load Your Saved Keras Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7YTAT-O_GOU",
        "colab_type": "code",
        "outputId": "a6a3ba64-39a0-46e3-9b33-6f75b3ea6527",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "with open(model_path, \"r\") as file:\n",
        "    jmodel = file.read()\n",
        "\n",
        "K.set_learning_phase(0)\n",
        "model = model_from_json(jmodel)\n",
        "model.load_weights(model_root_path + \"/weights.h5\")\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"KAllCnn_IDB2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "zero_padding2d_1 (ZeroPaddin (None, 54, 54, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 50, 50, 30)        2280      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 50, 50, 30)        0         \n",
            "_________________________________________________________________\n",
            "zero_padding2d_2 (ZeroPaddin (None, 54, 54, 30)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 50, 50, 30)        22530     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 30)        0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 25, 25, 30)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 18750)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 37502     \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 2)                 0         \n",
            "=================================================================\n",
            "Total params: 62,312\n",
            "Trainable params: 62,312\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5JA27GH_c4yR",
        "colab_type": "text"
      },
      "source": [
        "# Contributing\n",
        "\n",
        "The Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research project encourages and welcomes code contributions, bug fixes and enhancements from the Github.\n",
        "\n",
        "**Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/AML-ALL-Classifiers/blob/master/CONTRIBUTING.md \"CONTRIBUTING\") document for a full guide to forking our repositories and submitting your pull requests. You will also find information about our code of conduct on this page.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gx76M0YiyU8x",
        "colab_type": "text"
      },
      "source": [
        "## Acute Myeloid & Lymphoblastic Leukemia Classifiers Contributors\n",
        "\n",
        "- [Adam Milton-Barker](https://github.com/AdamMiltonBarker \"Adam Milton-Barker\") - Bigfinite IoT Network Engineer & Intel Software Innovator, Barcelona, Spain\n",
        "- [Salvatore Raieli](https://github.com/salvatorera \"Salvatore Raieli\") - PhD Immunolgy / Bioinformaticia, Bologna, Italy\n",
        "- [Dr Amita Kapoor](https://github.com/salvatorera \"Dr Amita Kapoor\") - Delhi University, Delhi, India\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZKmkJrPyHPr",
        "colab_type": "text"
      },
      "source": [
        "# Versioning\n",
        "\n",
        "We use SemVer for versioning. For the versions available, see [Releases](https://github.com/AMLResearchProject/AML-ALL-Classifiers/releases \"Releases\")."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ih7zGxLyyNdk",
        "colab_type": "text"
      },
      "source": [
        "# License\n",
        "\n",
        "This project is licensed under the **MIT License** - see the [LICENSE](https://github.com/AMLResearchProject/AML-ALL-Classifiers/blob/master/LICENSE \"LICENSE\") file for details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbJhAHK7yQLb",
        "colab_type": "text"
      },
      "source": [
        "# Bugs/Issues\n",
        "\n",
        "We use the [repo issues](https://github.com/AMLResearchProject/AML-ALL-Classifiers/issues \"repo issues\") to track bugs and general requests related to using this project."
      ]
    }
  ]
}