Projects/Keras/QuantisedCode/QuantisedCode.ipynb

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    "colab": {
      "name": "QuantisedCode.ipynb",
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      "metadata": {
        "id": "8nsx2WMTeKoc",
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      "source": [
        "# Peter Moss Acute Myeloid / Lymphoblastic Leukemia AI Research Project\n",
        "\n",
        "**Exploring novel convolutional network architechture to build a classification system for better assistance in diagonosing Acute Lymphoblastic Leukemia in blood cells.**\n",
        "\n",
        "![Peter Moss Acute Myeloid / Lymphoblastic Leukemia AI Research Project](https://www.PeterMossAmlAllResearch.com/media/images/banner.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EcoBkQrfLv6",
        "colab_type": "text"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "This notebook explores a novel convolutional network architechture as discussed in the following research paper to build a classification system for better assistance in diagonosing Acute Lymphoblastic Leukemia in blood cells.\n",
        "\n",
        "Authors: Peter Moss AML/ALL AI Research Project team member and Student Program Co-Manager, [Amita Kapoor](https://www.petermossamlallresearch.com/team/amita-kapoor/profile \"Amita Kapoor\"), and Student Program student, [Taru Jain](https://www.petermossamlallresearch.com/students/student/taru-jain/profile \"Taru Jain\")."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mB_501h5fWTR",
        "colab_type": "text"
      },
      "source": [
        "# ALL Image Database for Image Processing\n",
        "\n",
        "![Acute Lymphoblastic Leukemia Image Database for Image Processing](https://github.com/AMLResearchProject/AML-ALL-Detection-System/raw/master/Classifiers/Movidius/NCS/Tensorflow/V1/Media/Images/slides.png)\n",
        "_Fig 1. Samples of augmented data generated using the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset._\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.\n",
        "\n",
        "- Here, ALL_IDB2 version of the dataset has been used\n",
        "- This dataset is completely balanced with equal number of samples in both the classes.\n",
        "\n",
        "Although in the [Leukemia Blood Cell Image Classification Using Convolutional Neural Network](http://www.ijcte.org/vol10/1198-H0012.pdf \"Leukemia Blood Cell Image Classification Using Convolutional Neural Network\") paper the ALL_IDB1 dataset is used, in this notebook you will use the ALL_IDB2 dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tTmt1_BfbLV",
        "colab_type": "text"
      },
      "source": [
        "## Data Augmentation\n",
        "\n",
        "Data augmentation ensures that data is large enough and model extracts features efficiently without overfitting and therefore we have analysed two types of data augmentation techniques in this notebook.\n",
        "\n",
        "* Techniques used in the research paper discussing the following parameters:\n",
        "\n",
        "   1. Grayscaling of image\n",
        "   2. Horizontal reflection\n",
        "   3. Vertical reflection\n",
        "   4. Gaussian Blurring\n",
        "   5. Histogram Equalization\n",
        "   6. Rotation\n",
        "   7. Translation\n",
        "   8. Shearing\n",
        "   \n",
        "(Using these methods, the dataset size increased from 260 images to 2340 images)\n",
        "   \n",
        "* Keras in-built ImageDataGenerator\n",
        "\n",
        "**The dataset was split into 80% and 20% for training and testing respectively.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66lXFMqNeEm4",
        "colab_type": "text"
      },
      "source": [
        "# Present Analysis Results \n",
        "The results of our present analysis are:\n",
        "\n",
        "| Data Augmentation    | Accuracy   | Precision   | Recall   |  ROC |\n",
        "|---|---|---|---|--|\n",
        "| Used in paper   | 91%  | 0.93  | 0.88  | 0.97  |\n",
        "| Keras ImageDataGenerator    |  76% | 0.74  |  0.79 | 0.82 |  \n",
        "  \n",
        "  \n",
        "**This result has been recorded for maximum number of epochs that model could be trained for without overfitting**\n",
        "\n",
        "**We can infer that the augmentation used in the paper outperforms the in-built augmentation technique used by Keras**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-IVQ7Iv6xWg6",
        "colab_type": "text"
      },
      "source": [
        "# Clone Peter Moss 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/QuantisedCode** directory, this is your project root directory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dM5oYNKxpeh",
        "colab_type": "text"
      },
      "source": [
        "# Upload Project Root To Google Drive\n",
        "Now you need to upload the project root to your Google Drive."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltdBFSRQfs3E",
        "colab_type": "text"
      },
      "source": [
        "# Detailed Implementation\n",
        "Below is the detailed code implementation of this research paper "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3wkJHL6gKTI",
        "colab_type": "code",
        "outputId": "e661aae2-c0cc-43a2-8712-42c4b045b085",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-68cb48b2-8897-4545-ae3c-6493a007fe99\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-68cb48b2-8897-4545-ae3c-6493a007fe99\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving ALL_Data2.zip to ALL_Data2.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWyAsmJZlvMW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile('ALL_Data2.zip', 'r')\n",
        "zip_ref.extractall()\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwetK3C2pT-C",
        "colab_type": "code",
        "outputId": "91d7737c-fff7-4da0-f36a-6d98524129c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ALL_Data2.zip  logs\t   models\ttensorflow-for-poets-2\tweights.h5\n",
            "img\t       model.json  sample_data\tTF_Model\t\tweights.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CimI3AcDR3kl",
        "colab_type": "code",
        "outputId": "52687be4-ef95-4ab0-91cf-ba7c83a4634e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "!pip install keras_metrics"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: keras_metrics in /usr/local/lib/python3.6/dist-packages (1.1.0)\n",
            "Requirement already satisfied: Keras>=2.1.5 in /usr/local/lib/python3.6/dist-packages (from keras_metrics) (2.2.4)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 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: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (1.3.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (1.16.4)\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: h5py in /usr/local/lib/python3.6/dist-packages (from Keras>=2.1.5->keras_metrics) (2.8.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfZJS4lHhUXE",
        "colab_type": "text"
      },
      "source": [
        "## **Load required packages**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQ8BE0xmnLD6",
        "colab_type": "code",
        "outputId": "a73761a1-5d13-4b34-c180-8f17ed805378",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from pathlib import Path\n",
        "import glob\n",
        "import random\n",
        "import cv2\n",
        "from numpy.random import seed\n",
        "from tensorflow import set_random_seed\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from scipy import ndimage\n",
        "from skimage import exposure\n",
        "import skimage\n",
        "from skimage import io\n",
        "from skimage import transform as tm\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.utils import np_utils\n",
        "from keras.models import Model,Sequential\n",
        "from keras.layers import Dense,Flatten,Activation\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Activation, Convolution2D, Dropout, Conv2D\n",
        "from keras.layers import AveragePooling2D, BatchNormalization\n",
        "from keras.layers import GlobalAveragePooling2D\n",
        "from keras.layers import Input,GaussianNoise\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import SeparableConv2D\n",
        "from keras import layers\n",
        "from keras.regularizers import l2\n",
        "import keras_metrics\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.applications.vgg16 import VGG16,preprocess_input\n",
        "from keras.applications.xception import Xception,preprocess_input\n",
        "from keras.applications.inception_v3 import InceptionV3\n",
        "from keras.optimizers import Adam,RMSprop,SGD\n",
        "from sklearn.metrics import confusion_matrix,precision_score,recall_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from keras import backend as K\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tH2z2eQB6ZpX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# for consistemt results across multiple executions\n",
        "seed(3)\n",
        "set_random_seed(3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpdCO-UDjGnU",
        "colab_type": "text"
      },
      "source": [
        "## Reading data and inserting into a dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJwo22WNpQjI",
        "colab_type": "code",
        "outputId": "8576ebfd-244a-4aae-c979-6046a1d407fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "images_dir = Path('img')\n",
        "images = images_dir.glob(\"*.tif\")\n",
        "\n",
        "train_data = []\n",
        "counter = 0\n",
        "\n",
        "for img in images:\n",
        "  counter += 1\n",
        "  if counter <= 130:    \n",
        "    train_data.append((img,1))    \n",
        "  else:    \n",
        "    train_data.append((img,0))\n",
        "    \n",
        "train_data = pd.DataFrame(train_data,columns=['image','label'],index = None)\n",
        "train_data = train_data.sample(frac=1.).reset_index(drop=True)\n",
        "\n",
        "train_data.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>img/Im237_0.tif</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>img/Im032_1.tif</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257</th>\n",
              "      <td>img/Im131_0.tif</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>img/Im108_1.tif</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>img/Im078_1.tif</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               image  label\n",
              "255  img/Im237_0.tif      1\n",
              "256  img/Im032_1.tif      0\n",
              "257  img/Im131_0.tif      0\n",
              "258  img/Im108_1.tif      0\n",
              "259  img/Im078_1.tif      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6_eBeXbKyKj",
        "colab_type": "code",
        "outputId": "6264c8ce-15f2-4ba3-f4b9-99748c443feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(train_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "260"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zewhaj6Ul0Y_",
        "colab_type": "text"
      },
      "source": [
        "## Data Exploration and Augmentation as presented in the paper\n",
        "\n",
        "### 8 augmentation techniques have been used here\n",
        "1. Grayscaling of image\n",
        "2. Horizontal reflection \n",
        "3. Vertical reflection\n",
        "4. Gaussian Blurring \n",
        "5. Histogram Equalization\n",
        "6. Rotation\n",
        "7. Translation\n",
        "8. Shearing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f71MR6OdWyJO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# histogram equalization function\n",
        "def hist(img):\n",
        "  \n",
        "  img_to_yuv = cv2.cvtColor(img,cv2.COLOR_BGR2YUV)\n",
        "  img_to_yuv[:,:,0] = cv2.equalizeHist(img_to_yuv[:,:,0])\n",
        "  hist_equalization_result = cv2.cvtColor(img_to_yuv, cv2.COLOR_YUV2BGR)\n",
        "  return hist_equalization_result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81rPbbtegU2h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function to perform rotation on an image\n",
        "def rotation(img):\n",
        "  rows,cols = img.shape[0],img.shape[1]\n",
        "  randDeg = random.randint(-180, 180)\n",
        "  matrix = cv2.getRotationMatrix2D((cols/2, rows/2), randDeg, 0.70)\n",
        "  rotated = cv2.warpAffine(img, matrix, (rows, cols), borderMode=cv2.BORDER_CONSTANT,\n",
        "                                     borderValue=(144, 159, 162))\n",
        "  return rotated     "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Urz26j6qZJFG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function to perform shearing of an image\n",
        "def shear(img):\n",
        "  # Create Afine transform\n",
        "  afine_tf = tm.AffineTransform(shear=0.5)\n",
        "\n",
        "  # Apply transform to image data\n",
        "  modified = tm.warp(img, inverse_map=afine_tf)\n",
        "  \n",
        "  return modified"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYS6DhKxKrvJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def aug_method(dataframe,dim,method):\n",
        "  \n",
        "  if method == 'paper':    \n",
        "    n = len(dataframe)\n",
        "\n",
        "    data = np.zeros((n*9,dim,dim,3),dtype = np.float32)\n",
        "    labels = np.zeros((n*9,2),dtype = np.float32)\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    for j in range(0,n):\n",
        "\n",
        "      img_name = dataframe.iloc[j]['image']\n",
        "      label = dataframe.iloc[j]['label']\n",
        "\n",
        "      encoded_label = np_utils.to_categorical(label, num_classes=2)\n",
        "\n",
        "      img = cv2.imread(str(img_name))\n",
        "      img = cv2.resize(img, (dim,dim))\n",
        "\n",
        "      if img.shape[2]==1:\n",
        "\n",
        "        img = np.dstack([img, img, img])\n",
        "\n",
        "      orig_img = img.astype(np.float32)/255.\n",
        "\n",
        "      data[count] = orig_img\n",
        "      labels[count] = encoded_label      \n",
        "      \n",
        "      aug_img1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "      aug_img2 = cv2.flip(img, 0) \n",
        "      aug_img3 = cv2.flip(img,1)\n",
        "      aug_img4 = ndimage.gaussian_filter(img, sigma= 5.11)\n",
        "      aug_img5 = hist(img)\n",
        "      aug_img6 = rotation(img)\n",
        "      aug_img7 = cv2.warpAffine(img, np.float32([[1, 0, 84], [0, 1, 56]]), (img.shape[0], img.shape[1]),\n",
        "                                  borderMode=cv2.BORDER_CONSTANT, borderValue=(144, 159, 162))\n",
        "      aug_img8 = shear(img)\n",
        "\n",
        "      aug_img1 = np.dstack([aug_img1, aug_img1, aug_img1])\n",
        "\n",
        "      aug_img1 = aug_img1.astype(np.float32)/255.                 \n",
        "      aug_img2 = aug_img2.astype(np.float32)/255.\n",
        "      aug_img3 = aug_img3.astype(np.float32)/255. \n",
        "      aug_img4 = aug_img4.astype(np.float32)/255.\n",
        "      aug_img5 = aug_img5.astype(np.float32)/255.\n",
        "      aug_img6 = aug_img6.astype(np.float32)/255.\n",
        "      aug_img7 = aug_img7.astype(np.float32)/255.\n",
        "      aug_img8 = aug_img8.astype(np.float32)/255.\n",
        "\n",
        "      data[count+1] = aug_img1\n",
        "      labels[count+1] = encoded_label\n",
        "      data[count+2] = aug_img2\n",
        "      labels[count+2] = encoded_label\n",
        "      data[count+3] = aug_img3\n",
        "      labels[count+3] = encoded_label\n",
        "      data[count+4] = aug_img4\n",
        "      labels[count+4] = encoded_label\n",
        "      data[count+5] = aug_img5\n",
        "      labels[count+5] = encoded_label\n",
        "      data[count+6] = aug_img5\n",
        "      labels[count+6] = encoded_label\n",
        "      data[count+7] = aug_img5\n",
        "      labels[count+7] = encoded_label\n",
        "      data[count+8] = aug_img5\n",
        "      labels[count+8] = encoded_label\n",
        "\n",
        "      count +=9\n",
        "      \n",
        "  elif method == 'keras':    \n",
        "    n = len(dataframe)\n",
        "  \n",
        "    data = np.zeros((n,dim,dim,3),dtype = np.float32)\n",
        "    labels = np.zeros((n,2),dtype = np.float32)  \n",
        "  \n",
        "    count = 0  \n",
        "    \n",
        "    for j in range(0,n):\n",
        "    \n",
        "      img_name = dataframe.iloc[j]['image']\n",
        "      label = dataframe.iloc[j]['label']\n",
        "      \n",
        "      encoded_label = np_utils.to_categorical(label, num_classes=2)\n",
        "            \n",
        "      img = cv2.imread(str(img_name))\n",
        "      img = cv2.resize(img, (dim,dim))\n",
        "      \n",
        "      if img.shape[2]==1:\n",
        "        img = np.dstack([img, img, img])\n",
        "            \n",
        "      orig_img = img.astype(np.float32)/255.\n",
        "                        \n",
        "      data[count] = orig_img\n",
        "      labels[count] = encoded_label\n",
        "    \n",
        "      count +=1   \n",
        "      \n",
        "  return data,labels                  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MllJOUPlItHB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data,labels = aug_method(train_data,dim=100,method='paper')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tn-7I1xAIrqS",
        "colab_type": "code",
        "outputId": "5a2814c5-866b-4533-b2a1-419226a1c683",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2340, 100, 100, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBBN9vhqI0JB",
        "colab_type": "code",
        "outputId": "a0b69fb1-bb4d-4cab-c6d9-9eef4897d245",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "labels.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2340, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IomV1Dx-I6xD",
        "colab_type": "code",
        "outputId": "9892113f-f08a-4be3-fb53-52c2d06d4a13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = np.asarray(data)\n",
        "labels = np.asarray(labels)\n",
        "\n",
        "Data,Label = shuffle(data,labels, random_state=3)\n",
        "data_list = [Data,Label]\n",
        "type(data_list)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLuFjhqsDsAd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = np.argmax(Label, axis=-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viodvfyvmDSS",
        "colab_type": "text"
      },
      "source": [
        "## **Visualizing dataset images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aB2BiOAmJg5t",
        "colab_type": "code",
        "outputId": "34b19c53-cc36-4d98-a594-b25516963578",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "f, ax = plt.subplots(4,5, figsize=(30,7))\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": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dltttMuN54fTjzB46jFfLWKXKWK1KpuR3T01U6W32MGq49443kKhQrdZ4+Atf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SLfiNKy3BgDmoKHXarvpZZoUVHhU4j9JKxU91pjhLhNUO3br7p/x/TWtyPrNxOLhZTFVLFi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efpharcbJl84g0UgZgg3vfO+7KG2pkLIEUpiRTQaDYX3i39UqWVQ8Jt5fm5HNzvjSsfUj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\n/Q+qWqvqLwJfAr49avOzqvplVR0CHwLu3ewAInIX8I3AL6jqC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NEBs7HMRCKRuGW5JF/b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VqbXP92/FWXZv9WW/wlndtVnNbyCI3BaVW1WhPvm9prvuoT7e0Ewnb72KoqVhAEYRucq1G9\nfZ4z/oShX7zfKj75n/ya+jZZFT/GtsauvLdD7PWK9H7s/ZnJJCK8Jb7fky1Fe3mmFQThClRdl0g4\nSFegn0Jmg2QyyejPj1MqF3HLij/78z9jYyXD4sVl5mbSBAY66OjvQ2dzFKsubqZAWbehqwECXSFy\nrotbqvLm5JscuvsOtOvw3N+e4CNPPo7ruKRmpmte9wtLHBg8wDdee4ljDx5lamqKoIb4YAIHKJTK\nHBkdZ2khzYGRmn98PB4HILW4yNC+EVylGUom6OyOceb7k1yanue+sffjUOWuw0dAO8xOp3CA9Pw8\nB4b3MdDZxdzcHI5WOLo2OlR2fZ3942PE43Fm04v0JgZYmV9i6swpXO3Q0dnNyYkJVheXOHHiOVwF\ns3MLrK+v88ixY0xMTDB2+H27dg0FQWhttNaUSiW6urqaCoSNGG8L7X4fd/84o0CTrmkXZ5uiZVtv\ntecLhQL5fN4T8o3OmslkiEajRKNRr2DbryEbLde2L4daxf76+jrZbJZ8Pg/gVdbbvQT8837rG9d1\nyeVy5HI5T7xXShGNRr2xSHfK474lxhJ3XZfl5WXvpPn9j/wZE4M9HwqF6OzspLe3l/b2dq/bghkU\nQGvtZWgcx/Gq3guFgud7ZDIspgeA2b65CexuDkaM7+npoaenx8uwmAtpKvpDoZB3wWzh37Tf3Ey2\nX5Q5/mq1Sj6f9xISuVyOjY0NLyNk3wT2ORMEQdgStYWfsl+xsQShJnHFXuUqdrWVOON5ydtx3Yjm\n9V5G2resad6q0PRP9rquvT17v7phPaZ1s79yk4jkF5K0FtFeEISrxi46UY7TPNlJxtrKtS/ZCcxt\nJv9n+GKW/dq83LRsczzf2ZSkv4dT47js5KhfzG/6gjzLCoJwFeQ3iszNzJAYSRDuj3H0kYeIdHbw\n999+jUohx8bqOnOpOSquxgmFWM3mKLtldLlEWySEU9I4gTBurki5VICAwsXh0ptvsfLWEsXlDZ77\n2+dxgSOjozjVIGde/z6udjh46A4crdi/b4Qjo2PMLsxxcvIUf//t79Qr5fupKLg0k2I2vYiLqonu\nQDIeZ35+nrVshsuX5/nBD78PKKi2MTz0HgAcpXHQjI+PcXlmmorSHH/yCZ5/9WWSIyP8/AMPcPsd\nh7iUmuHvnnuBb7/yCj+cnGJi6nugFeP3jLG6sIyjFQMDNV98M7334EFOTkywvpbZzcsnCMIeYGNj\no8n1wx4/FBpjkAKbLGHsSnhjL16pVLzxRm0bGtf/9zjNPUOLxaLnOV+tVikWixQKBVZWVpibm2N1\ndbXp2dvs3wj2RnM12y0UCqTTadbX170kwVaarNGNHev5HfCOxVTrmx4Ic3NzLC0tcfnyZZaWlpo0\n5XdKS1TcQ03oXl5eplwue1kTc3OYA7ZvDBtzM5hRg002xr7RzOi+ZjttbW0EAgFKpRKxWIz29nZv\nZGR7hGP/4AfBYNCr2I9EIrS3t3sWOwZz8xn/JDPAQtMfcnUR3wj5toBfKBQoFove/u3P29ravC4p\nZt7f+0AQBGErTJW90rq58n6rlX0V700/Ov5q+LehSbSxhfX6q739K1rlbNUWs6x+XF6GXamGfY4l\nxG8niG2XoPASrUjFvSAIb49dUeTH9A6qzWxvy+Wvrrfj16bt1eOxievmVVvLTcV9sz3OTka1rY7B\nWm4lGZrEef+rIAjC1aA1Xe2dlBdydMf7+I3f+rcEIyEKxSJTU6fJZrNcfOunEGyjN95HPlQlUA1Q\nzJUZ6o5TUIrezl4KAc364gLt0TDZYoFKETKZDOXzZUqFAiN3vYe//MqzJDq6ibiK+8bu4+TEBPf/\n46MsLSwxdWqKZGKQ8fFxUqkUjoaVhTT9iTguigcffoRz584xn06TTAwQdBXt7d089YlP8Nprr3Hf\n6BjJW/p59itf5pH7HyGVSuEqePP8T3jo2FGef/453vu+u0kOxRkeSBByHUJVh7fe+CkH9o3w+sQk\n4NDX0UPEhdzaOk4iyZnJ00TawjiuQ3IoTv/+QYYG4qzMLzExMYGjIIgmlZ7d7SspCEKLYmuRQFPV\nunnOtSva7Yp0o3+ainVbxzXV9kbAN7qp2afRdv0uK2Y9WzzXWnvF18VikZ6eHk8fNfs2Vj2mfevr\n6ywtLaG19qr97YFl7eMyuq9pvzl+Y5lue98bzdocm9leLBbbkevxthX3Sqk/VUotKKXOWsv6lFIv\nKKXO119768uVUuqPlVJvKKXOKKXGr6UxGxsbrK2tkc1mKRaLTZ73tshtMDdEfd9A44+aUCjUNDit\nfTMY0dwMdGAGdXUch0gk4l1cMwCu67rEYjE6Ojro7e0lHo+TSCTo6uoiFos1BCfrYpuL7DgOnZ2d\n3s1i8PcoMAmK9fV1NjY2yOVyFAoFcrkc5XLZy0qVy2Wv8t74QdkDPQiCsDe5KbHWqrjfstq+jt/s\nwFtuidsNIcn3Xb21OO4X65sq77VlWbPdtM02vSy9b1v29puqPNm6fU0Vq/6Dsv3zd8pTQhCEm87N\neqb1F2psmsw6+KRzKxbZ1fXo5thrV9XjW7dpfXt7mPjmtfLaT+B10LR/63VLWzJBEPY8NyPOulWX\n9Wye4YPDtHWF6WiPEHNd3vrhOcq5PBupZXQpQDUUotjmUM7l6e/upq+rk/5kH4l9cZIHkvQP9HLn\noYOMDA0znBikRIVSKEimkmPmzctszOcJuYqhxCDvu3ec754+Sf/QAF/98tdJXZ7jY5/8BMmRYZ5/\n/gUOj42S3D/E1MQpXCAeH+DcuXM4Gu647TbSC4v83QvPEekMk8lkGL9njIkzJ0m/tcCxBx7m0sw0\nruOyND/HQ8eO4Sq46+734VSDBCtBvvzMV3jgwaP0JuK4wKXZyyT39bO4MkclUKHgaG49dJChkWFm\nF+b48MP3k8uskp6bZzaVYmpqiomJCZLJQbJrGUqOi6vlmVYQ9io3OtYqpbYUyo1YbjzmbQ3SaK1G\nyDaCtxHo7Up1o22az+znWOObb75v7G/MPuy/1Y3onslkWFlZaSq8NqK8V9jnumSzWTY2NpraaO/L\nWKe3tbU1abp+XdmRMwTjAAALCklEQVR818ybdYxFjuu6Xpt2gquxynkaeNy37AvAi1rrg8CL9XmA\nJ4CD9elzwJ9cbUPMCc/lciwsLLC8vEwmk/EGrbVPiD3ggJk3J9q+MOaE5fN5r5rdXORAIEA0GvUG\nOjD2M+FwmIGBATo6OmhvbyeRSHDgwAF6e3vp7+8nFosRjUa9fRifJftmsy+g1pqBgQESiQSdnZ2b\nRj82lfMAq6urrKysUC6XvfNivJJMDwHbiiebzbK+vk4ulxMxSRD2Pk9zg2OtX7TfNmrYVZDbVKNv\nzxaWZj7R3gju1foPsWt+zC0h/oqTvZ5PwDcivn89v5XOVjY5TVWuW5w37FdBEPYiT3MTnmmVMs+m\nzZNjifb4EoFN4vYVEot2xbqdkMR8z17elARotO/GaeXaat7mJIT9KoK9ILxreZobHWeVIhFr59Yj\nt/OF3/33KCdARjl89Zt/x8QLr/Pji8sQjRKLObiZNcpLOfb3x7ntztsI93Uw1NVJrLuDRGc77T0R\n+vraGR4YYCSxj+7OPtyeGE5XJ2fP/oRSscKR8fez7+AtHH3iOMmRYX79X32eIJoLb16kouCR48c5\nOzVFauYt4vv6WUqnCWrFUH8CRysy61mCGoIaXnzuBOnFBU6enqTkwPzcAvOp6Zo1ztgRRu8bpT8x\ngKMV4/eMM78wx/DwMMceup/b7tjP0tIMOFXmF+bYP7yPgXgfaMX8/Dw//v6PeOXVl0gOJnh98hQf\neugoM4vzvHnuIud/8ialQJXT505z+6Hb+fDRh4knB3fieguCsDs8zQ2OtbYIbk+mat7oA7YNDeBV\nn9u2OkbAN4PAGs3T1nRtgd6sb3RVY1VuD4BrdFezr0wm4xU2G8HdtK1SqTA9Pc3i4qJ3TCbpYKrx\njYNLLBbzdFjzXYNxYLGt0O3t2Hp0IBDYscFp31a411p/G1j2Lf4o8KX6+y8BH7OW/7mu8V2gRyk1\ndDUNsU+G1trzdodGtwxzIe2si8mUGIwoHgqF6Ovro7u7m66uLtrb22lvb/cuni32m4sSDocJhUJE\nIpGmzIp/sFpjuWNEemiMNOwfcdi0ra2tzUsGGBHeziSZrJPZnu3Db5IXZrk5X2bQ3Fwu15QFEgRh\n73FTYu1WMeJtqu6vtNyIM5vW8XT/zfY3nohjvXp2D1ch5DR937dDTxja6jNramqkf90rtUFirCDs\naW7WM62d5zNT47PNccRvheOPU9tZ6lwtN1ciF0FeEH6WuRlxNqAceob6+c3/+NsQUOTLRc6fPo27\ntkFXTz+9nR0EVBslAhSrcPfdd+NEAoRjASKRMG19YWIdmkB/mI7OGGGliYYh2t/OQF8fqlJlLbtO\nMbNGvlyhfWCAtUyGdDpNb7KfEy++REU7xAcSHLrrvbgoQloDDvMzy5ycmGApvcDS0ix33HEAR7lU\nAlX+ySc+SQVF70CS8XvGiVQCdA8lSY4M42jF1ETNQz89v8yhO+/izOQUHxi9jzMnz5DsH+TvX/0u\nrg6CVozfO8rlVIqh+BDx5CDxRO20VQoVlubmGBqMc3H2MgQUXT3tFMpFltNLoBU/+sEPmJo8SXpB\nrHIEYa9yo2OtEbNtfdK82lqp+dxez4jqRq/1V+AD3qu/QNBon+a7toW6XyQPBAJNvvSVSoVisejN\nmzYHAgGKxaLnhW/b45htK6UIh8O0tbXR2dnpJRJsLdY+J1vZnptCa39B+U5wvR73Sa21ifRzQLL+\nfhi4bK03XV+26VdBKfU5atkegOIf/dEfnfWv04IMAIu73Yi34b273QBBEHaMdxRrfXE2+/u/93tL\ntH4M2wtx9oBS6nNa6y/udkMEQXjH7Pgz7ec//3l5pn3nSJwVhHcPOx1ns//ti3+49N+++IdXFcO+\n9dp1tRmA7/zOy/z+7/yH6/16q8dZEO1AEN5N7Kh28PnPf160g53hHT/TvuPBabXWWil1zeU19UZ/\nEUApdVJrfd87bcuNZi+0Uyl1crfbIAjCznM9sdaOs7B3YlirtxG8WCuCkiC8i5Bn2tZC4qwgvPvY\niTgLeyeG7YU27nYbBEHYeUQ7aC3e6TPt1Xjcb8W86VpRf12oL08B+631RurLBEEQhGtHYq0gCMKN\nReKsIAjCjUXirCAIwo1HYu27lOsV7v8G+HT9/aeBv7aW/2p91OIPAmtWVw1BEATh2pBYKwiCcGOR\nOCsIgnBjkTgrCIJw45FY+y7lba1ylFJ/CRwDBpRS08DvAn8APKOU+ixwCXiqvvo3gI8AbwA54DNX\n2Y690g12L7RzL7RREAQfEms99kIbYe+0UxCEOhJnm9gL7dwLbRQEweImxVnYG/FB2igIwg1Bnmk9\n9kIb4R22U9kj+AqCIAiCIAiCIAiCIAiCIAiCsLtcr1WOIAiCIAiCIAiCIAiCIAiCIAg3ABHuBUEQ\nBEEQBEEQBEEQBEEQBKGF2HXhXin1uFLqx0qpN5RSX9jt9hiUUj9VSn1fKTWllDpZX9anlHpBKXW+\n/tq7C+36U6XUglLqrLVsy3bVB5/44/q5PaOUGr/Z7RUEYfdp1TgLrRlrJc4KgnA9tGqsbcU4W2+D\nxFpBEK4JibPX3C6Js4IgXBOtGmehNWPtzYizuyrcK6UCwP8AngAOA/9MKXV4N9vk40Gt9ajW+r76\n/BeAF7XWB4EX6/M3m6eBx33LtmvXE8DB+vQ54E9uUhsFQWgR9kCchdaLtU8jcVYQhGtgD8TaVouz\nILFWEIRrQOLsdfE0EmcFQbhK9kCchdaLtU9zg+PsblfcfwB4Q2t9QWtdAv4K+Ogut+lKfBT4Uv39\nl4CP3ewGaK2/DSz7Fm/Xro8Cf65rfBfoUUoN3ZyWCoLQIuy1OAu7HGslzgqCcB3stVgrz7SCIOw1\nJM5eIxJnBUG4RvZanIWfAe1gt4X7YeCyNT9dX9YKaOB5pdQppdTn6suSWuvZ+vs5ILk7TdvEdu1q\n5fMrCMLNodXjwF6JtRJnBUG4Eq0cC/ZKnAWJtYIgbE8rxwGJs4IgvBto9TiwV2LtjsbZ4M627V3F\nh7XWKaVUAnhBKXXO/lBrrZVSepfati2t2i5BEIRt2HOxthXbJAiCcAX2XJyF1m2XIAjCFkicFQRB\nuPHsuVi7E23a7Yr7FLDfmh+pL9t1tNap+usC8HVqXUbmTTeG+uvC7rWwie3a1bLnVxCEm0ZLx4E9\nFGslzgqCcCVaNhbsoTgLEmsFQdielo0DEmcFQXiX0NJxYA/F2h2Ns7st3H8POKiUulUpFQI+BfzN\nLrcJpVS7UqrTvAeOA2epte3T9dU+Dfz17rRwE9u162+AX62PXPxBYM3qriEIws8GLRlnYc/FWomz\ngiBciZaMtXsszoLEWkEQtkfi7M4gcVYQhO1oyTgLey7W7mic3VWrHK11RSn168AJIAD8qdb6B7vZ\npjpJ4OtKKaido/+jtX5OKfU94Bml1GeBS8BTN7thSqm/BI4BA0qpaeB3gT/Ypl3fAD4CvAHkgM/c\n7PYKgrC7tHCchRaNtRJnBUG4Vlo41rZknAWJtYIgXBsSZ68dibOCIFwLLRxnoUVj7c2Is0rrlrL/\nEQRBEARBEARBEARBEARBEISfaXbbKkcQBEEQBEEQBEEQBEEQBEEQBAsR7gVBEARBEARBEARBEARB\nEAShhRDhXhAEQRAEQRAEQRAEQRAEQRBaCBHuBUEQBEEQBEEQBEEQBEEQBKGFEOFeEARBEARBEARB\nEARBEARBEFoIEe4FQRAEQRAEQRAEQRAEQRAEoYUQ4V4QBEEQBEEQBEEQBEEQBEEQWoj/Dw2oNfzk\nLIGDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 2160x504 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qh2eNGw2U9jK",
        "colab_type": "text"
      },
      "source": [
        "## **Splitting into training and test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVhGNDOp4SpL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X, y) = (data_list[0],data_list[1])\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qrAQNIXMpPv7",
        "colab_type": "text"
      },
      "source": [
        "## Replicating The Model\n",
        "The following model was used in the [Leukemia Blood Cell Image Classification Using Convolutional Neural Network](http://www.ijcte.org/vol10/1198-H0012.pdf \"Leukemia Blood Cell Image Classification Using Convolutional Neural Network\") paper, additionaly three dropout layers with different dropout rates have been used to reduce overfitting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6-Em20CpBof",
        "colab_type": "code",
        "outputId": "e71ff0bc-5012-4b8d-c801-9eaab1dc1d80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(16,(5,5),padding='valid',input_shape = X_train.shape[1:]))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=2,padding = 'valid'))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(Conv2D(32,(5,5),padding='valid'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=2,padding = 'valid'))\n",
        "model.add(Dropout(0.6))\n",
        "\n",
        "model.add(Conv2D(64,(5,5),padding='valid'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.8))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(2,activation = 'softmax'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0724 19:56:28.391399 139762540324736 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0724 19:56:28.395263 139762540324736 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0724 19:56:28.398960 139762540324736 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0724 19:56:28.414140 139762540324736 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0724 19:56:28.416520 139762540324736 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "W0724 19:56:28.425667 139762540324736 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: 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",
            "W0724 19:56:28.460555 139762540324736 nn_ops.py:4224] 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",
            "W0724 19:56:28.493752 139762540324736 nn_ops.py:4224] Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnjtIKJqt28j",
        "colab_type": "code",
        "outputId": "a231148f-178d-485c-c6ac-52e5d1178a29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 96, 96, 16)        1216      \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 96, 96, 16)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 48, 48, 16)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 48, 48, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 44, 44, 32)        12832     \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 44, 44, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 22, 22, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 22, 22, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 18, 18, 64)        51264     \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 18, 18, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 18, 18, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 20736)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 41474     \n",
            "=================================================================\n",
            "Total params: 106,786\n",
            "Trainable params: 106,786\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I60o6qEQleL4",
        "colab_type": "text"
      },
      "source": [
        "### Model compilation and fitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Tw9CHEPMz1K",
        "colab_type": "code",
        "outputId": "89738bd4-71ce-477f-a0b5-2ce98cc00aea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "source": [
        "batch_size = 100\n",
        "epochs= 300\n",
        "\n",
        "optimizer = keras.optimizers.rmsprop(lr = 0.0001, decay = 1e-6)\n",
        "\n",
        "model.compile(loss = 'binary_crossentropy',optimizer = optimizer, metrics = ['accuracy',keras_metrics.precision(), keras_metrics.recall()])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0724 19:56:35.583545 139762540324736 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "W0724 19:56:35.592297 139762540324736 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "W0724 19:56:35.599432 139762540324736 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (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"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_zPlEYfsAGvn",
        "colab_type": "code",
        "outputId": "89545161-d508-4c16-f555-c4a13ee4ef1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model.fit(X_train,y_train,steps_per_epoch = int(len(X_train)/batch_size),epochs=epochs)\n",
        "history"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/300\n",
            "18/18 [==============================] - 8s 418ms/step - loss: 0.7205 - acc: 0.5058 - precision: 0.5078 - recall: 0.5115\n",
            "Epoch 2/300\n",
            "18/18 [==============================] - 7s 407ms/step - loss: 0.6999 - acc: 0.5052 - precision: 0.5071 - recall: 0.5158\n",
            "Epoch 3/300\n",
            "18/18 [==============================] - 7s 406ms/step - loss: 0.6947 - acc: 0.5137 - precision: 0.5154 - recall: 0.5303\n",
            "Epoch 4/300\n",
            "18/18 [==============================] - 7s 408ms/step - loss: 0.6921 - acc: 0.5191 - precision: 0.5203 - recall: 0.5413\n",
            "Epoch 5/300\n",
            "18/18 [==============================] - 7s 408ms/step - loss: 0.6907 - acc: 0.5253 - precision: 0.5258 - recall: 0.5576\n",
            "Epoch 6/300\n",
            "18/18 [==============================] - 7s 410ms/step - loss: 0.6884 - acc: 0.5359 - precision: 0.5353 - recall: 0.5737\n",
            "Epoch 7/300\n",
            "18/18 [==============================] - 7s 412ms/step - loss: 0.6846 - acc: 0.5521 - precision: 0.5511 - recall: 0.5824\n",
            "Epoch 8/300\n",
            "18/18 [==============================] - 7s 413ms/step - loss: 0.6821 - acc: 0.5544 - precision: 0.5531 - recall: 0.5865\n",
            "Epoch 9/300\n",
            "18/18 [==============================] - 7s 414ms/step - loss: 0.6754 - acc: 0.5776 - precision: 0.5732 - recall: 0.6218\n",
            "Epoch 10/300\n",
            "18/18 [==============================] - 7s 415ms/step - loss: 0.6679 - acc: 0.5844 - precision: 0.5796 - recall: 0.6280\n",
            "Epoch 11/300\n",
            "18/18 [==============================] - 7s 415ms/step - loss: 0.6571 - acc: 0.6057 - precision: 0.5999 - recall: 0.6449\n",
            "Epoch 12/300\n",
            "18/18 [==============================] - 7s 417ms/step - loss: 0.6484 - acc: 0.6164 - precision: 0.6110 - recall: 0.6495\n",
            "Epoch 13/300\n",
            "18/18 [==============================] - 8s 418ms/step - loss: 0.6378 - acc: 0.6284 - precision: 0.6234 - recall: 0.6566\n",
            "Epoch 14/300\n",
            "18/18 [==============================] - 8s 419ms/step - loss: 0.6269 - acc: 0.6431 - precision: 0.6409 - recall: 0.6577\n",
            "Epoch 15/300\n",
            "18/18 [==============================] - 8s 420ms/step - loss: 0.6166 - acc: 0.6517 - precision: 0.6500 - recall: 0.6637\n",
            "Epoch 16/300\n",
            "18/18 [==============================] - 8s 420ms/step - loss: 0.6087 - acc: 0.6633 - precision: 0.6636 - recall: 0.6683\n",
            "Epoch 17/300\n",
            "18/18 [==============================] - 8s 421ms/step - loss: 0.5988 - acc: 0.6715 - precision: 0.6726 - recall: 0.6735\n",
            "Epoch 18/300\n",
            "18/18 [==============================] - 8s 421ms/step - loss: 0.5924 - acc: 0.6774 - precision: 0.6767 - recall: 0.6849\n",
            "Epoch 19/300\n",
            "18/18 [==============================] - 8s 421ms/step - loss: 0.5803 - acc: 0.6876 - precision: 0.6888 - recall: 0.6893\n",
            "Epoch 20/300\n",
            "18/18 [==============================] - 8s 422ms/step - loss: 0.5721 - acc: 0.6923 - precision: 0.6925 - recall: 0.6965\n",
            "Epoch 21/300\n",
            "18/18 [==============================] - 8s 422ms/step - loss: 0.5643 - acc: 0.7018 - precision: 0.7029 - recall: 0.7033\n",
            "Epoch 22/300\n",
            "18/18 [==============================] - 8s 422ms/step - loss: 0.5565 - acc: 0.7066 - precision: 0.7112 - recall: 0.6999\n",
            "Epoch 23/300\n",
            "18/18 [==============================] - 8s 423ms/step - loss: 0.5490 - acc: 0.7118 - precision: 0.7117 - recall: 0.7162\n",
            "Epoch 24/300\n",
            "18/18 [==============================] - 8s 423ms/step - loss: 0.5419 - acc: 0.7202 - precision: 0.7261 - recall: 0.7111\n",
            "Epoch 25/300\n",
            "18/18 [==============================] - 8s 423ms/step - loss: 0.5329 - acc: 0.7228 - precision: 0.7258 - recall: 0.7198\n",
            "Epoch 26/300\n",
            "18/18 [==============================] - 8s 423ms/step - loss: 0.5220 - acc: 0.7319 - precision: 0.7356 - recall: 0.7275\n",
            "Epoch 27/300\n",
            "18/18 [==============================] - 8s 424ms/step - loss: 0.5155 - acc: 0.7362 - precision: 0.7406 - recall: 0.7304\n",
            "Epoch 28/300\n",
            "18/18 [==============================] - 8s 425ms/step - loss: 0.5087 - acc: 0.7411 - precision: 0.7472 - recall: 0.7320\n",
            "Epoch 29/300\n",
            "18/18 [==============================] - 8s 425ms/step - loss: 0.4987 - acc: 0.7494 - precision: 0.7539 - recall: 0.7437\n",
            "Epoch 30/300\n",
            "18/18 [==============================] - 8s 425ms/step - loss: 0.4915 - acc: 0.7520 - precision: 0.7585 - recall: 0.7424\n",
            "Epoch 31/300\n",
            "18/18 [==============================] - 8s 426ms/step - loss: 0.4844 - acc: 0.7555 - precision: 0.7611 - recall: 0.7479\n",
            "Epoch 32/300\n",
            "18/18 [==============================] - 8s 426ms/step - loss: 0.4810 - acc: 0.7587 - precision: 0.7644 - recall: 0.7509\n",
            "Epoch 33/300\n",
            "18/18 [==============================] - 8s 427ms/step - loss: 0.4708 - acc: 0.7652 - precision: 0.7715 - recall: 0.7563\n",
            "Epoch 34/300\n",
            "18/18 [==============================] - 8s 427ms/step - loss: 0.4658 - acc: 0.7685 - precision: 0.7734 - recall: 0.7622\n",
            "Epoch 35/300\n",
            "18/18 [==============================] - 8s 426ms/step - loss: 0.4541 - acc: 0.7770 - precision: 0.7828 - recall: 0.7694\n",
            "Epoch 36/300\n",
            "18/18 [==============================] - 8s 427ms/step - loss: 0.4481 - acc: 0.7805 - precision: 0.7872 - recall: 0.7715\n",
            "Epoch 37/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.4441 - acc: 0.7828 - precision: 0.7915 - recall: 0.7704\n",
            "Epoch 38/300\n",
            "18/18 [==============================] - 8s 427ms/step - loss: 0.4366 - acc: 0.7873 - precision: 0.7952 - recall: 0.7764\n",
            "Epoch 39/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.4289 - acc: 0.7896 - precision: 0.7994 - recall: 0.7758\n",
            "Epoch 40/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.4252 - acc: 0.7947 - precision: 0.8001 - recall: 0.7879\n",
            "Epoch 41/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.4159 - acc: 0.7982 - precision: 0.8084 - recall: 0.7840\n",
            "Epoch 42/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.4115 - acc: 0.8012 - precision: 0.8090 - recall: 0.7907\n",
            "Epoch 43/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.4081 - acc: 0.8055 - precision: 0.8124 - recall: 0.7966\n",
            "Epoch 44/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.4012 - acc: 0.8087 - precision: 0.8175 - recall: 0.7969\n",
            "Epoch 45/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3951 - acc: 0.8136 - precision: 0.8235 - recall: 0.8002\n",
            "Epoch 46/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3930 - acc: 0.8138 - precision: 0.8219 - recall: 0.8032\n",
            "Epoch 47/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.3858 - acc: 0.8153 - precision: 0.8238 - recall: 0.8040\n",
            "Epoch 48/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3791 - acc: 0.8237 - precision: 0.8339 - recall: 0.8102\n",
            "Epoch 49/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3755 - acc: 0.8245 - precision: 0.8316 - recall: 0.8158\n",
            "Epoch 50/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3713 - acc: 0.8269 - precision: 0.8368 - recall: 0.8141\n",
            "Epoch 51/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3654 - acc: 0.8312 - precision: 0.8399 - recall: 0.8201\n",
            "Epoch 52/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3584 - acc: 0.8361 - precision: 0.8441 - recall: 0.8261\n",
            "Epoch 53/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3569 - acc: 0.8341 - precision: 0.8443 - recall: 0.8210\n",
            "Epoch 54/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3530 - acc: 0.8368 - precision: 0.8455 - recall: 0.8260\n",
            "Epoch 55/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3500 - acc: 0.8389 - precision: 0.8486 - recall: 0.8267\n",
            "Epoch 56/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3458 - acc: 0.8434 - precision: 0.8514 - recall: 0.8336\n",
            "Epoch 57/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.3401 - acc: 0.8436 - precision: 0.8532 - recall: 0.8316\n",
            "Epoch 58/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3330 - acc: 0.8470 - precision: 0.8567 - recall: 0.8349\n",
            "Epoch 59/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3304 - acc: 0.8475 - precision: 0.8564 - recall: 0.8366\n",
            "Epoch 60/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3278 - acc: 0.8506 - precision: 0.8589 - recall: 0.8405\n",
            "Epoch 61/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.3251 - acc: 0.8526 - precision: 0.8632 - recall: 0.8396\n",
            "Epoch 62/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3188 - acc: 0.8558 - precision: 0.8641 - recall: 0.8459\n",
            "Epoch 63/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.3154 - acc: 0.8572 - precision: 0.8658 - recall: 0.8469\n",
            "Epoch 64/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.3150 - acc: 0.8587 - precision: 0.8683 - recall: 0.8470\n",
            "Epoch 65/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.3085 - acc: 0.8609 - precision: 0.8686 - recall: 0.8520\n",
            "Epoch 66/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.3065 - acc: 0.8641 - precision: 0.8721 - recall: 0.8546\n",
            "Epoch 67/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.3036 - acc: 0.8623 - precision: 0.8725 - recall: 0.8499\n",
            "Epoch 68/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.3004 - acc: 0.8671 - precision: 0.8740 - recall: 0.8592\n",
            "Epoch 69/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2967 - acc: 0.8677 - precision: 0.8762 - recall: 0.8578\n",
            "Epoch 70/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.2954 - acc: 0.8676 - precision: 0.8766 - recall: 0.8569\n",
            "Epoch 71/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.2904 - acc: 0.8713 - precision: 0.8789 - recall: 0.8625\n",
            "Epoch 72/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2892 - acc: 0.8717 - precision: 0.8789 - recall: 0.8634\n",
            "Epoch 73/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.2849 - acc: 0.8738 - precision: 0.8819 - recall: 0.8644\n",
            "Epoch 74/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.2863 - acc: 0.8731 - precision: 0.8820 - recall: 0.8627\n",
            "Epoch 75/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2779 - acc: 0.8784 - precision: 0.8887 - recall: 0.8663\n",
            "Epoch 76/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2789 - acc: 0.8783 - precision: 0.8851 - recall: 0.8706\n",
            "Epoch 77/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2758 - acc: 0.8791 - precision: 0.8868 - recall: 0.8703\n",
            "Epoch 78/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2748 - acc: 0.8791 - precision: 0.8883 - recall: 0.8684\n",
            "Epoch 79/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2719 - acc: 0.8815 - precision: 0.8881 - recall: 0.8742\n",
            "Epoch 80/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2666 - acc: 0.8814 - precision: 0.8881 - recall: 0.8739\n",
            "Epoch 81/300\n",
            "18/18 [==============================] - 8s 437ms/step - loss: 0.2667 - acc: 0.8820 - precision: 0.8897 - recall: 0.8732\n",
            "Epoch 82/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2618 - acc: 0.8858 - precision: 0.8918 - recall: 0.8791\n",
            "Epoch 83/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.2621 - acc: 0.8868 - precision: 0.8940 - recall: 0.8788\n",
            "Epoch 84/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2586 - acc: 0.8859 - precision: 0.8938 - recall: 0.8770\n",
            "Epoch 85/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.2572 - acc: 0.8903 - precision: 0.8968 - recall: 0.8832\n",
            "Epoch 86/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.2557 - acc: 0.8884 - precision: 0.8949 - recall: 0.8811\n",
            "Epoch 87/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.2525 - acc: 0.8889 - precision: 0.8959 - recall: 0.8813\n",
            "Epoch 88/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2482 - acc: 0.8922 - precision: 0.8983 - recall: 0.8856\n",
            "Epoch 89/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2457 - acc: 0.8926 - precision: 0.9009 - recall: 0.8832\n",
            "Epoch 90/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2469 - acc: 0.8919 - precision: 0.8985 - recall: 0.8846\n",
            "Epoch 91/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2502 - acc: 0.8904 - precision: 0.8962 - recall: 0.8841\n",
            "Epoch 92/300\n",
            "18/18 [==============================] - 8s 433ms/step - loss: 0.2426 - acc: 0.8967 - precision: 0.9039 - recall: 0.8887\n",
            "Epoch 93/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2418 - acc: 0.8972 - precision: 0.9054 - recall: 0.8880\n",
            "Epoch 94/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2393 - acc: 0.8969 - precision: 0.9030 - recall: 0.8903\n",
            "Epoch 95/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2335 - acc: 0.9006 - precision: 0.9072 - recall: 0.8934\n",
            "Epoch 96/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2380 - acc: 0.8971 - precision: 0.9029 - recall: 0.8908\n",
            "Epoch 97/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.2344 - acc: 0.9000 - precision: 0.9054 - recall: 0.8943\n",
            "Epoch 98/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2327 - acc: 0.8993 - precision: 0.9058 - recall: 0.8923\n",
            "Epoch 99/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.2284 - acc: 0.9033 - precision: 0.9088 - recall: 0.8974\n",
            "Epoch 100/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2301 - acc: 0.9007 - precision: 0.9068 - recall: 0.8943\n",
            "Epoch 101/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2248 - acc: 0.9047 - precision: 0.9107 - recall: 0.8983\n",
            "Epoch 102/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.2225 - acc: 0.9040 - precision: 0.9111 - recall: 0.8963\n",
            "Epoch 103/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2209 - acc: 0.9058 - precision: 0.9124 - recall: 0.8986\n",
            "Epoch 104/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2248 - acc: 0.9054 - precision: 0.9114 - recall: 0.8989\n",
            "Epoch 105/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2200 - acc: 0.9049 - precision: 0.9111 - recall: 0.8983\n",
            "Epoch 106/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2205 - acc: 0.9056 - precision: 0.9121 - recall: 0.8985\n",
            "Epoch 107/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2142 - acc: 0.9103 - precision: 0.9154 - recall: 0.9050\n",
            "Epoch 108/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2160 - acc: 0.9087 - precision: 0.9164 - recall: 0.9002\n",
            "Epoch 109/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2114 - acc: 0.9111 - precision: 0.9169 - recall: 0.9049\n",
            "Epoch 110/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2122 - acc: 0.9094 - precision: 0.9147 - recall: 0.9038\n",
            "Epoch 111/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2139 - acc: 0.9105 - precision: 0.9173 - recall: 0.9032\n",
            "Epoch 112/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2074 - acc: 0.9133 - precision: 0.9197 - recall: 0.9065\n",
            "Epoch 113/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2116 - acc: 0.9101 - precision: 0.9150 - recall: 0.9050\n",
            "Epoch 114/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2059 - acc: 0.9124 - precision: 0.9172 - recall: 0.9076\n",
            "Epoch 115/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2072 - acc: 0.9135 - precision: 0.9186 - recall: 0.9082\n",
            "Epoch 116/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.2071 - acc: 0.9106 - precision: 0.9163 - recall: 0.9047\n",
            "Epoch 117/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.2053 - acc: 0.9142 - precision: 0.9187 - recall: 0.9096\n",
            "Epoch 118/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.2033 - acc: 0.9160 - precision: 0.9212 - recall: 0.9107\n",
            "Epoch 119/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1998 - acc: 0.9177 - precision: 0.9241 - recall: 0.9108\n",
            "Epoch 120/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1988 - acc: 0.9171 - precision: 0.9239 - recall: 0.9099\n",
            "Epoch 121/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1974 - acc: 0.9174 - precision: 0.9219 - recall: 0.9129\n",
            "Epoch 122/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1984 - acc: 0.9206 - precision: 0.9245 - recall: 0.9168\n",
            "Epoch 123/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1951 - acc: 0.9179 - precision: 0.9236 - recall: 0.9120\n",
            "Epoch 124/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1961 - acc: 0.9171 - precision: 0.9228 - recall: 0.9112\n",
            "Epoch 125/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1934 - acc: 0.9196 - precision: 0.9257 - recall: 0.9132\n",
            "Epoch 126/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1918 - acc: 0.9200 - precision: 0.9258 - recall: 0.9140\n",
            "Epoch 127/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.1903 - acc: 0.9225 - precision: 0.9269 - recall: 0.9180\n",
            "Epoch 128/300\n",
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            "Epoch 129/300\n",
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            "Epoch 130/300\n",
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            "Epoch 131/300\n",
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            "Epoch 132/300\n",
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            "Epoch 133/300\n",
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            "Epoch 134/300\n",
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            "Epoch 135/300\n",
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            "Epoch 136/300\n",
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            "Epoch 137/300\n",
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            "Epoch 138/300\n",
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            "Epoch 139/300\n",
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            "Epoch 140/300\n",
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            "Epoch 141/300\n",
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            "Epoch 142/300\n",
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            "Epoch 143/300\n",
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            "Epoch 144/300\n",
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            "Epoch 145/300\n",
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            "Epoch 146/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1743 - acc: 0.9276 - precision: 0.9317 - recall: 0.9236\n",
            "Epoch 147/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1755 - acc: 0.9280 - precision: 0.9334 - recall: 0.9224\n",
            "Epoch 148/300\n",
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            "Epoch 149/300\n",
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            "Epoch 150/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1713 - acc: 0.9295 - precision: 0.9349 - recall: 0.9239\n",
            "Epoch 151/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1691 - acc: 0.9325 - precision: 0.9372 - recall: 0.9277\n",
            "Epoch 152/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1702 - acc: 0.9313 - precision: 0.9364 - recall: 0.9262\n",
            "Epoch 153/300\n",
            "18/18 [==============================] - 8s 434ms/step - loss: 0.1668 - acc: 0.9309 - precision: 0.9359 - recall: 0.9258\n",
            "Epoch 154/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1682 - acc: 0.9303 - precision: 0.9351 - recall: 0.9254\n",
            "Epoch 155/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1687 - acc: 0.9309 - precision: 0.9356 - recall: 0.9261\n",
            "Epoch 156/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1636 - acc: 0.9344 - precision: 0.9383 - recall: 0.9306\n",
            "Epoch 157/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1668 - acc: 0.9323 - precision: 0.9367 - recall: 0.9278\n",
            "Epoch 158/300\n",
            "18/18 [==============================] - 8s 432ms/step - loss: 0.1619 - acc: 0.9350 - precision: 0.9413 - recall: 0.9285\n",
            "Epoch 159/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1648 - acc: 0.9339 - precision: 0.9381 - recall: 0.9297\n",
            "Epoch 160/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1625 - acc: 0.9351 - precision: 0.9393 - recall: 0.9309\n",
            "Epoch 161/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1627 - acc: 0.9340 - precision: 0.9381 - recall: 0.9300\n",
            "Epoch 162/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1614 - acc: 0.9352 - precision: 0.9403 - recall: 0.9300\n",
            "Epoch 163/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1582 - acc: 0.9365 - precision: 0.9404 - recall: 0.9326\n",
            "Epoch 164/300\n",
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            "Epoch 165/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1580 - acc: 0.9368 - precision: 0.9396 - recall: 0.9343\n",
            "Epoch 166/300\n",
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            "Epoch 167/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1577 - acc: 0.9364 - precision: 0.9403 - recall: 0.9325\n",
            "Epoch 168/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1565 - acc: 0.9386 - precision: 0.9421 - recall: 0.9352\n",
            "Epoch 169/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1570 - acc: 0.9383 - precision: 0.9422 - recall: 0.9344\n",
            "Epoch 170/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1559 - acc: 0.9379 - precision: 0.9419 - recall: 0.9338\n",
            "Epoch 171/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1539 - acc: 0.9384 - precision: 0.9425 - recall: 0.9343\n",
            "Epoch 172/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1541 - acc: 0.9395 - precision: 0.9434 - recall: 0.9357\n",
            "Epoch 173/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1554 - acc: 0.9385 - precision: 0.9416 - recall: 0.9355\n",
            "Epoch 174/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1521 - acc: 0.9395 - precision: 0.9430 - recall: 0.9361\n",
            "Epoch 175/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1514 - acc: 0.9385 - precision: 0.9431 - recall: 0.9339\n",
            "Epoch 176/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1519 - acc: 0.9382 - precision: 0.9411 - recall: 0.9355\n",
            "Epoch 177/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1546 - acc: 0.9388 - precision: 0.9433 - recall: 0.9344\n",
            "Epoch 178/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1488 - acc: 0.9410 - precision: 0.9446 - recall: 0.9374\n",
            "Epoch 179/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1458 - acc: 0.9420 - precision: 0.9448 - recall: 0.9394\n",
            "Epoch 180/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1472 - acc: 0.9414 - precision: 0.9447 - recall: 0.9383\n",
            "Epoch 181/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1493 - acc: 0.9402 - precision: 0.9444 - recall: 0.9361\n",
            "Epoch 182/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1432 - acc: 0.9446 - precision: 0.9477 - recall: 0.9415\n",
            "Epoch 183/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1454 - acc: 0.9416 - precision: 0.9443 - recall: 0.9392\n",
            "Epoch 184/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1450 - acc: 0.9425 - precision: 0.9456 - recall: 0.9395\n",
            "Epoch 185/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1450 - acc: 0.9430 - precision: 0.9451 - recall: 0.9411\n",
            "Epoch 186/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1469 - acc: 0.9421 - precision: 0.9467 - recall: 0.9375\n",
            "Epoch 187/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1455 - acc: 0.9421 - precision: 0.9453 - recall: 0.9391\n",
            "Epoch 188/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1431 - acc: 0.9445 - precision: 0.9481 - recall: 0.9410\n",
            "Epoch 189/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1438 - acc: 0.9435 - precision: 0.9467 - recall: 0.9405\n",
            "Epoch 190/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1430 - acc: 0.9432 - precision: 0.9466 - recall: 0.9400\n",
            "Epoch 191/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1428 - acc: 0.9436 - precision: 0.9457 - recall: 0.9417\n",
            "Epoch 192/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1409 - acc: 0.9437 - precision: 0.9461 - recall: 0.9415\n",
            "Epoch 193/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1422 - acc: 0.9453 - precision: 0.9500 - recall: 0.9406\n",
            "Epoch 194/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1418 - acc: 0.9439 - precision: 0.9473 - recall: 0.9405\n",
            "Epoch 195/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1406 - acc: 0.9455 - precision: 0.9477 - recall: 0.9434\n",
            "Epoch 196/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1400 - acc: 0.9433 - precision: 0.9467 - recall: 0.9400\n",
            "Epoch 197/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1399 - acc: 0.9452 - precision: 0.9483 - recall: 0.9423\n",
            "Epoch 198/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1419 - acc: 0.9454 - precision: 0.9498 - recall: 0.9410\n",
            "Epoch 199/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1402 - acc: 0.9451 - precision: 0.9489 - recall: 0.9414\n",
            "Epoch 200/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1366 - acc: 0.9473 - precision: 0.9496 - recall: 0.9452\n",
            "Epoch 201/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1367 - acc: 0.9455 - precision: 0.9488 - recall: 0.9424\n",
            "Epoch 202/300\n",
            "18/18 [==============================] - 8s 440ms/step - loss: 0.1373 - acc: 0.9469 - precision: 0.9490 - recall: 0.9450\n",
            "Epoch 203/300\n",
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            "Epoch 204/300\n",
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            "Epoch 205/300\n",
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            "Epoch 206/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1350 - acc: 0.9473 - precision: 0.9501 - recall: 0.9447\n",
            "Epoch 207/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1314 - acc: 0.9483 - precision: 0.9517 - recall: 0.9450\n",
            "Epoch 208/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1325 - acc: 0.9483 - precision: 0.9515 - recall: 0.9452\n",
            "Epoch 209/300\n",
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            "Epoch 210/300\n",
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            "Epoch 211/300\n",
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            "Epoch 212/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1326 - acc: 0.9479 - precision: 0.9502 - recall: 0.9457\n",
            "Epoch 213/300\n",
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            "Epoch 214/300\n",
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            "Epoch 215/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1301 - acc: 0.9494 - precision: 0.9524 - recall: 0.9465\n",
            "Epoch 216/300\n",
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            "Epoch 217/300\n",
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            "Epoch 218/300\n",
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            "Epoch 219/300\n",
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            "Epoch 220/300\n",
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            "Epoch 221/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1319 - acc: 0.9480 - precision: 0.9501 - recall: 0.9461\n",
            "Epoch 222/300\n",
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            "Epoch 223/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1280 - acc: 0.9518 - precision: 0.9522 - recall: 0.9518\n",
            "Epoch 224/300\n",
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            "Epoch 225/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1243 - acc: 0.9511 - precision: 0.9522 - recall: 0.9503\n",
            "Epoch 226/300\n",
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            "Epoch 227/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1236 - acc: 0.9517 - precision: 0.9553 - recall: 0.9482\n",
            "Epoch 228/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1270 - acc: 0.9504 - precision: 0.9535 - recall: 0.9473\n",
            "Epoch 229/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1258 - acc: 0.9514 - precision: 0.9547 - recall: 0.9483\n",
            "Epoch 230/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1253 - acc: 0.9520 - precision: 0.9553 - recall: 0.9489\n",
            "Epoch 231/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1243 - acc: 0.9522 - precision: 0.9545 - recall: 0.9502\n",
            "Epoch 232/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1247 - acc: 0.9512 - precision: 0.9545 - recall: 0.9480\n",
            "Epoch 233/300\n",
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            "Epoch 234/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1220 - acc: 0.9539 - precision: 0.9562 - recall: 0.9519\n",
            "Epoch 235/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1238 - acc: 0.9517 - precision: 0.9553 - recall: 0.9482\n",
            "Epoch 236/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1223 - acc: 0.9536 - precision: 0.9560 - recall: 0.9514\n",
            "Epoch 237/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1222 - acc: 0.9526 - precision: 0.9557 - recall: 0.9496\n",
            "Epoch 238/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1222 - acc: 0.9528 - precision: 0.9560 - recall: 0.9498\n",
            "Epoch 239/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1178 - acc: 0.9532 - precision: 0.9560 - recall: 0.9505\n",
            "Epoch 240/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1228 - acc: 0.9521 - precision: 0.9540 - recall: 0.9505\n",
            "Epoch 241/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1272 - acc: 0.9502 - precision: 0.9513 - recall: 0.9494\n",
            "Epoch 242/300\n",
            "18/18 [==============================] - 8s 433ms/step - loss: 0.1194 - acc: 0.9534 - precision: 0.9574 - recall: 0.9495\n",
            "Epoch 243/300\n",
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            "Epoch 244/300\n",
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            "Epoch 245/300\n",
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            "Epoch 246/300\n",
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            "Epoch 247/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1183 - acc: 0.9538 - precision: 0.9550 - recall: 0.9528\n",
            "Epoch 248/300\n",
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            "Epoch 249/300\n",
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            "Epoch 250/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1175 - acc: 0.9546 - precision: 0.9574 - recall: 0.9518\n",
            "Epoch 251/300\n",
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            "Epoch 252/300\n",
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            "Epoch 253/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1140 - acc: 0.9560 - precision: 0.9584 - recall: 0.9539\n",
            "Epoch 254/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1181 - acc: 0.9543 - precision: 0.9572 - recall: 0.9515\n",
            "Epoch 255/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1149 - acc: 0.9565 - precision: 0.9593 - recall: 0.9539\n",
            "Epoch 256/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1133 - acc: 0.9567 - precision: 0.9598 - recall: 0.9536\n",
            "Epoch 257/300\n",
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            "Epoch 258/300\n",
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            "Epoch 259/300\n",
            "18/18 [==============================] - 8s 431ms/step - loss: 0.1162 - acc: 0.9557 - precision: 0.9577 - recall: 0.9538\n",
            "Epoch 260/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1164 - acc: 0.9570 - precision: 0.9589 - recall: 0.9554\n",
            "Epoch 261/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1154 - acc: 0.9554 - precision: 0.9579 - recall: 0.9530\n",
            "Epoch 262/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1159 - acc: 0.9552 - precision: 0.9577 - recall: 0.9529\n",
            "Epoch 263/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1138 - acc: 0.9564 - precision: 0.9587 - recall: 0.9543\n",
            "Epoch 264/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1126 - acc: 0.9569 - precision: 0.9596 - recall: 0.9544\n",
            "Epoch 265/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1114 - acc: 0.9569 - precision: 0.9601 - recall: 0.9538\n",
            "Epoch 266/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1142 - acc: 0.9559 - precision: 0.9577 - recall: 0.9544\n",
            "Epoch 267/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1118 - acc: 0.9572 - precision: 0.9610 - recall: 0.9535\n",
            "Epoch 268/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1137 - acc: 0.9567 - precision: 0.9587 - recall: 0.9548\n",
            "Epoch 269/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1110 - acc: 0.9566 - precision: 0.9593 - recall: 0.9540\n",
            "Epoch 270/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1114 - acc: 0.9571 - precision: 0.9596 - recall: 0.9548\n",
            "Epoch 271/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1129 - acc: 0.9558 - precision: 0.9599 - recall: 0.9517\n",
            "Epoch 272/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1122 - acc: 0.9568 - precision: 0.9596 - recall: 0.9541\n",
            "Epoch 273/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1104 - acc: 0.9571 - precision: 0.9599 - recall: 0.9545\n",
            "Epoch 274/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1116 - acc: 0.9578 - precision: 0.9596 - recall: 0.9562\n",
            "Epoch 275/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1086 - acc: 0.9591 - precision: 0.9615 - recall: 0.9570\n",
            "Epoch 276/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1134 - acc: 0.9575 - precision: 0.9597 - recall: 0.9555\n",
            "Epoch 277/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1101 - acc: 0.9582 - precision: 0.9617 - recall: 0.9548\n",
            "Epoch 278/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1092 - acc: 0.9581 - precision: 0.9605 - recall: 0.9560\n",
            "Epoch 279/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1084 - acc: 0.9596 - precision: 0.9617 - recall: 0.9577\n",
            "Epoch 280/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1060 - acc: 0.9595 - precision: 0.9615 - recall: 0.9577\n",
            "Epoch 281/300\n",
            "18/18 [==============================] - 8s 430ms/step - loss: 0.1055 - acc: 0.9595 - precision: 0.9615 - recall: 0.9576\n",
            "Epoch 282/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1075 - acc: 0.9583 - precision: 0.9595 - recall: 0.9574\n",
            "Epoch 283/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1077 - acc: 0.9594 - precision: 0.9627 - recall: 0.9563\n",
            "Epoch 284/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1070 - acc: 0.9587 - precision: 0.9610 - recall: 0.9565\n",
            "Epoch 285/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1095 - acc: 0.9581 - precision: 0.9608 - recall: 0.9556\n",
            "Epoch 286/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1087 - acc: 0.9593 - precision: 0.9614 - recall: 0.9574\n",
            "Epoch 287/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1045 - acc: 0.9610 - precision: 0.9631 - recall: 0.9590\n",
            "Epoch 288/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1055 - acc: 0.9605 - precision: 0.9621 - recall: 0.9592\n",
            "Epoch 289/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1084 - acc: 0.9585 - precision: 0.9617 - recall: 0.9554\n",
            "Epoch 290/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1038 - acc: 0.9613 - precision: 0.9633 - recall: 0.9595\n",
            "Epoch 291/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1062 - acc: 0.9600 - precision: 0.9621 - recall: 0.9581\n",
            "Epoch 292/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1037 - acc: 0.9598 - precision: 0.9619 - recall: 0.9577\n",
            "Epoch 293/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1060 - acc: 0.9594 - precision: 0.9618 - recall: 0.9573\n",
            "Epoch 294/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1056 - acc: 0.9586 - precision: 0.9607 - recall: 0.9567\n",
            "Epoch 295/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1016 - acc: 0.9613 - precision: 0.9625 - recall: 0.9604\n",
            "Epoch 296/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1035 - acc: 0.9602 - precision: 0.9628 - recall: 0.9579\n",
            "Epoch 297/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1037 - acc: 0.9599 - precision: 0.9617 - recall: 0.9583\n",
            "Epoch 298/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1040 - acc: 0.9602 - precision: 0.9623 - recall: 0.9584\n",
            "Epoch 299/300\n",
            "18/18 [==============================] - 8s 429ms/step - loss: 0.1017 - acc: 0.9621 - precision: 0.9640 - recall: 0.9605\n",
            "Epoch 300/300\n",
            "18/18 [==============================] - 8s 428ms/step - loss: 0.1017 - acc: 0.9606 - precision: 0.9618 - recall: 0.9596\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f00800d2588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbqWD-vXGM6s",
        "colab_type": "code",
        "outputId": "2f50e8ea-1770-4e46-9e81-1eb7434dbfb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "score = model.evaluate(X_test,y_test,verbose=0)\n",
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.2491266246025379,\n",
              " 0.9081196571007754,\n",
              " 0.9308755756078915,\n",
              " 0.8782608691833649]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32dFEYLzJ379",
        "colab_type": "code",
        "outputId": "69363066-b006-483c-acb9-c845f833195a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred = model.predict_proba(X_test)\n",
        "roc_auc_score(y_test, y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.968551333576909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iquoDxPyscPR",
        "colab_type": "text"
      },
      "source": [
        "**Result for 300 epochs**\n",
        "\n",
        "**1.Accuracy -91%**\n",
        "\n",
        "**2.Precision -0.93**\n",
        "\n",
        "**3.Recall -0.88**\n",
        "\n",
        "**4. AUC score -0.97**\n",
        "\n",
        "The model stops learning after 300 epochs\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rziaoBkmq2I",
        "colab_type": "text"
      },
      "source": [
        "### Visualizing accuracy and loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObK148TAr20z",
        "colab_type": "code",
        "outputId": "1ec034a7-1ae9-43c6-f51a-98d014ff673b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        }
      },
      "source": [
        "acc = history.history['acc']\n",
        "loss = history.history['loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(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(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": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L44+LdDgiIhKlWk3qoXvGK9y9FHgPGHpMoupifvV2Hruq9vL0DblouXoRETlS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\ndRERkQShUBcREUkQCnUREZEE8f8BuyYvumB4syoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2keg8LDnBFu",
        "colab_type": "text"
      },
      "source": [
        "## Saving model into \".json\" format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDJ_sV-sAvjo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open(\"model.json\", \"w\") as file:\n",
        "    file.write(model.to_json())\n",
        "model.save_weights(\"weights.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-Kn7lu45Dig",
        "colab_type": "text"
      },
      "source": [
        "## Convert model to Tensorflow\n",
        "Turning Keras model into Tensorflow since Movidius NCS compiles Tensorflow or Caffe models only"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCGF7y9jBPco",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import model_from_json\n",
        "from keras import backend as K\n",
        "import tensorflow as tf\n",
        "\n",
        "model_file = \"model.json\"\n",
        "weights_file = \"weights.h5\"\n",
        "\n",
        "with open(model_file, \"r\") as file:\n",
        "    config = file.read()\n",
        "\n",
        "K.set_learning_phase(0)\n",
        "model = model_from_json(config)\n",
        "model.load_weights(weights_file)\n",
        "\n",
        "saver = tf.train.Saver()\n",
        "sess = K.get_session()\n",
        "saver.save(sess, \"./TF_Model/tf_model\")\n",
        "\n",
        "fw = tf.summary.FileWriter('logs', sess.graph)\n",
        "fw.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjcrpKbvYL0q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras.models.save_model(model,weights_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XAOByainK1k",
        "colab_type": "text"
      },
      "source": [
        "## Convert to TFLite\n",
        "Converting the model into tensorflow lite so that it can run on embedded systems "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuU_ZRlDmD7q",
        "colab_type": "code",
        "outputId": "ccb0f98b-b925-4aa2-8332-3f984c5b7653",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        }
      },
      "source": [
        "converter = tf.lite.TocoConverter.from_keras_model_file(weights_file)\n",
        "tflite_model = converter.convert()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0724 19:59:06.432994 139762540324736 deprecation.py:323] From <ipython-input-19-b7fadcd17f69>:1: TocoConverter.from_keras_model_file (from tensorflow.lite.python.lite) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `lite.TFLiteConverter.from_keras_model_file` instead.\n",
            "W0724 19:59:06.597624 139762540324736 hdf5_format.py:221] No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
            "W0724 19:59:06.614048 139762540324736 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/util.py:238: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "W0724 19:59:06.614983 139762540324736 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:270: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmN2Uje1xPSj",
        "colab_type": "code",
        "outputId": "58ba47c7-08c4-4e4d-9ced-8063b2fee185",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "open(\"weights.tflite\",\"wb\").write(tflite_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "429272"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZYjIkBX1YpS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tflite_models_dir = Path(\"weights.tflite\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2ADgVUnVxxc",
        "colab_type": "code",
        "outputId": "15415062-4009-4c8d-88f2-3e1f286a01d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tflite_models_dir.write_bytes(tflite_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "429272"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJEovBbtE2qX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "interpreter = tf.lite.Interpreter(model_path=str(tflite_models_dir))\n",
        "interpreter.allocate_tensors()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BG_FRbYXnoyF",
        "colab_type": "text"
      },
      "source": [
        "## Convert model to quantised TFLite\n",
        "Converting the model into 8 bit quantised tensorflow lite version"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "or0d87We4QBs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.logging.set_verbosity(tf.logging.INFO)\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "#converter.post_training_quantize = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-wb2k_s4XVF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tflite_quant_model = converter.convert()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9YC57ekg42eM",
        "colab_type": "code",
        "outputId": "7a39252c-7735-4d4b-d6b7-56c38244690d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tflite_models_dir.write_bytes(tflite_quant_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "109368"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kEnLxAXMsbNF",
        "colab_type": "text"
      },
      "source": [
        "We can see that the quantised version consumes approximately 1/4th of the memory consumed by non-quantised version"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOlzju98FK3Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "interpreter_quant = tf.lite.Interpreter(model_path=str(tflite_models_dir))\n",
        "interpreter_quant.allocate_tensors()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbq9z8tBoB4o",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate TFLite versions\n",
        "The following two code snippets are to evaluate the tensorflow lite non-quantised and quantised versions. These worked well for the first time and gave similar outputs(which shows that the precision loss when converting to quantised version is not much and is still accurate).\n",
        "\n",
        "After that, these two cells are crashing without giving error log and according to tensorflow-lite github issues, it seems that there have been a couple of cases where the tflite interpreter crashes when running on web server."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-uH3KjHJDQB3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get input and output tensors.\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()\n",
        "\n",
        "# Test model on random input data.\n",
        "input_shape = input_details[0]['shape']\n",
        "# change the following line to feed into your own data.\n",
        "input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\n",
        "#input_data = np.array(X_test)\n",
        "interpreter.set_tensor(input_details[0]['index'], input_data)\n",
        "\n",
        "interpreter.invoke()\n",
        "output_data = interpreter.get_tensor(output_details[0]['index'])\n",
        "print(output_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRpvjktCPvoh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get input and output tensors.\n",
        "input_details = interpreter_quant.get_input_details()\n",
        "output_details = interpreter_quant.get_output_details()\n",
        "\n",
        "# Test model on random input data.\n",
        "input_shape = input_details[0]['shape']\n",
        "# change the following line to feed into your own data.\n",
        "input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\n",
        "#input_data = np.array(X_test[0])\n",
        "interpreter_quant.set_tensor(input_details[0]['index'], input_data)\n",
        "\n",
        "interpreter_quant.invoke()\n",
        "output_data = interpreter_quant.get_tensor(output_details[0]['index'])\n",
        "print(output_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mr_mUUk_ogAA",
        "colab_type": "text"
      },
      "source": [
        "## Obtain accuracy & evaluate model\n",
        "This is to obtain accuracy and evaluate the model over a number of images in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADqW4Lb5W62o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def eval_model(interpreter, train_data):\n",
        "  total_seen = 0\n",
        "  num_correct = 0\n",
        "\n",
        "  input_index = interpreter.get_input_details()[0][\"index\"]\n",
        "  output_index = interpreter.get_output_details()[0][\"index\"]\n",
        "  for img, label in train_data:\n",
        "    total_seen += 1\n",
        "    interpreter.set_tensor(input_index, img)\n",
        "    interpreter.invoke()\n",
        "    predictions = interpreter.get_tensor(output_index)\n",
        "    if predictions == label.numpy():\n",
        "      num_correct += 1\n",
        "\n",
        "    if total_seen % 500 == 0:\n",
        "      print(\"Accuracy after %i images: %f\" %\n",
        "            (total_seen, float(num_correct) / float(total_seen)))\n",
        "\n",
        "  return float(num_correct) / float(total_seen)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3MPamumRMQf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(eval_model(interpreter, train_data))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-aaENJwfRNhJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(eval_model(interpreter_quant, train_data))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_qbKtbH5pI3",
        "colab_type": "text"
      },
      "source": [
        "## Neural Compute Stick\n",
        "\n",
        "This is the compilation command to be written on command line tool provided with Neural Compute Stick SDK toolkit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnkfGqE6CTSR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# compilation command\n",
        "#mvNCCompile TF_Model/tf_model.meta -in=conv2d_1_input -on=dense_1/Softmax\n",
        "\n",
        "mvNCCompile tflite_quant_model -in=conv2d_1_input -on=dense_1/Softmax"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBwo8AlW58Ym",
        "colab_type": "text"
      },
      "source": [
        "### Deploying the graph on Neural Compute Stick to make an inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyArrqHvLyMI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mvnc import mvncapi as mvnc\n",
        "# get the first NCS device by its name. \n",
        "devices = mvnc.enumerate_devices()\n",
        "# get the first NCS device by its name.  \n",
        "dev = mvnc.Device(devices[0])\n",
        "# Read a compiled network graph from file (set the graph_filepath correctly for your graph file)\n",
        "with open(\"graph\", mode='rb') as f:\n",
        "    graphFileBuff = f.read()\n",
        "\n",
        "graph = mvnc.Graph('graph1')\n",
        "\n",
        "# Allocate the graph on the device and create input and output Fifos\n",
        "in_fifo, out_fifo = graph.allocate_with_fifos(dev, graphFileBuff)\n",
        "\n",
        "# Write the input to the input_fifo buffer and queue an inference in one call\n",
        "graph.queue_inference_with_fifo_elem(in_fifo, out_fifo, input_img.astype('float32'), 'user object')\n",
        "\n",
        "# Read the result to the output Fifo\n",
        "output, userobj = out_fifo.read_elem()\n",
        "print('Predicted:',output.argmax())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIVkcJludQcm",
        "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.\n",
        "\n",
        "## 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",
        "\n",
        "&nbsp;\n",
        "\n",
        "# Versioning\n",
        "\n",
        "We use SemVer for versioning. For the versions available, see [Releases](https://github.com/AMLResearchProject/AML-ALL-Classifiers/releases \"Releases\").\n",
        "\n",
        "# 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.\n",
        "\n",
        "# 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.\n"
      ]
    }
  ]
}