hair_classification_image_ver.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "hair_classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPuaJowXNDwSN5O/UycckSV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/2020_Image_Lab/blob/master/hair_classification_image_ver.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Me4zvhOxFsm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "760fbe29-c620-4abc-e367-7cf56959905c"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9E3B51tOxpdj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q tensorflow-io\n",
        "!pip install -q pydicom"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKqZAwEHxsb-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image\n",
        "import cv2\n",
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pydicom\n",
        "from pydicom.data import get_testdata_files\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_io as tfio"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhtoGbJLxx9-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q pydicom"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tHcXJKMykrO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set the random seed number for reproducible results\n",
        "seedNum = 1"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mg09uqnXJnsX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set the TensorFlow version to 2.x in Colab\n",
        "%tensorflow_version 2.x"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5t0saa6fJqCD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "random.seed(seedNum)\n",
        "import numpy as np\n",
        "np.random.seed(seedNum)\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(seedNum)\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import pandas as pd\n",
        "import math\n",
        "import os\n",
        "import sys\n",
        "import shutil\n",
        "import urllib.request\n",
        "import zipfile\n",
        "import smtplib\n",
        "from matplotlib import pyplot\n",
        "from matplotlib.image import imread\n",
        "from datetime import datetime\n",
        "from email.message import EmailMessage"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZHrr-DyJsET",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "ecb79567-e931-400d-bac4-ea4a3dd1ddcb"
      },
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime → \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Select the Runtime → \"Change runtime type\" menu to enable a GPU accelerator, \n",
            "and then re-execute this cell.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKkb9iXSJuN5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plot diagnostic learning curves\n",
        "def summarize_diagnostics(history):\n",
        "\tfig, axs = pyplot.subplots(2, 1, figsize=(12,12))\n",
        "\t# plot loss\n",
        "\tpyplot.subplot(211)\n",
        "\tpyplot.title('Cross Entropy Loss')\n",
        "\tpyplot.plot(history.history['loss'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_loss'], color='red', label='test')\n",
        "\t# plot accuracy\n",
        "\tpyplot.subplot(212)\n",
        "\tpyplot.title('Classification Accuracy')\n",
        "\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_accuracy'], color='red', label='test')\n",
        "\tpyplot.show()"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYInvzMSJwcQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5876671c-f447-4e7a-8dc4-0bdfc34b620a"
      },
      "source": [
        "startTimeScript = datetime.now()\n",
        "\n",
        "# Set up the number of CPU cores available for multi-thread processing\n",
        "n_jobs = -1\n",
        "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n",
        "\n",
        "# Set up the flag to stop sending progress emails (setting to True will send status emails!)\n",
        "notifyStatus = False\n",
        "\n",
        "# Set up the mountStorage flag to mount G Drive for storing files (setting True will mount the drive!)\n",
        "mountStorage = False"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Num GPUs Available:  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gwo8RcnbO0IX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set the default optimizer for the remaining portion of the script\n",
        "default_opt = Adam(learning_rate=0.001)"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUjLw4i9O6-Z",
        "colab_type": "text"
      },
      "source": [
        "# **Section 1. Data Preparation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTnrjedEVClW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define location of dataset\n",
        "folder = '/content/gdrive/My Drive/ISIC-melanoma/train/'\n",
        "folder2 = '/content/gdrive/My Drive/ISIC-melanoma/hair_labeling/'\n",
        "# plot first few images\n",
        "hair_label = []\n",
        "image_field = []\n",
        "\n",
        "# start_num = 15719\n",
        "# end_num = 100000\n",
        "\n",
        "# start_num = 100000\n",
        "# end_num = 405555\n",
        "\n",
        "start_num = 405652\n",
        "end_num = 695029\n",
        "\n",
        "# start_num = 695059\n",
        "# end_num = 999642\n",
        "\n",
        "# start_num = 1000328\n",
        "# end_num = 1307396\n",
        "\n",
        "# start_num = 1307419\n",
        "# end_num = 1601764\n",
        "\n",
        "# start_num = 1601784\n",
        "# end_num = 1894741\n",
        "\n",
        "# start_num = 1894782\n",
        "# end_num = 2188465\n",
        "\n",
        "# start_num = 2188566\n",
        "# end_num = 2491594\n",
        "\n",
        "file_name = str(start_num) + '_' + str(end_num) + '.csv'\n",
        "\n",
        "target_row = 1"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJaAV61IVFAU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "######hair이 있는 사진들에 대해서!###########\n",
        "import csv\n",
        "\n",
        "hair_image_file = []\n",
        "\n",
        "ct = 1\n",
        "\n",
        "with open(folder2 + file_name, newline='') as myfile:\n",
        "    reader  = csv.reader(myfile, delimiter=',')\n",
        "    for i in reader:\n",
        "        if i[1] == '1':\n",
        "          hair_image_file.append(i[0])\n",
        "        ct += 1"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybQNecoIXCPs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "27be1b6a-2dd7-47e6-f957-32e176e0aad4"
      },
      "source": [
        "from torchvision import transforms\n",
        "\n",
        "for i in range (1, 10):#원랜 (1, ct + 1)\n",
        "  pyplot.subplot(330 + i)\n",
        "  if os.path.isfile(folder + hair_image_file[i] + '.dcm'):\n",
        "    filename = folder + hair_image_file[i] + '.dcm'\n",
        "    dataset = pydicom.dcmread(filename)\n",
        "\n",
        "    image_bytes = tf.io.read_file(filename)\n",
        "\n",
        "    image = tfio.image.decode_dicom_image(image_bytes, dtype=tf.uint16)\n",
        "\n",
        "    skipped = tfio.image.decode_dicom_image(image_bytes, on_error='skip', dtype=tf.uint8)\n",
        "\n",
        "    image_info = np.squeeze(image.numpy())\n",
        "\n",
        "    rows = int(dataset.Rows) #높이\n",
        "    cols = int(dataset.Columns) #너비\n",
        "\n",
        "    cropped = image_info[int(rows/2 - cols/3) :int(rows/2 + cols/3), 0:cols]\n",
        "    if cols/rows > 1.5:\n",
        "      cropped = image_info[0 : rows, int(cols/2 - rows*3/4):int(cols/2 + rows*3/4)]\n",
        "    \n",
        "    cropped = cv2.resize(cropped, dsize=(600, 400), interpolation=cv2.INTER_CUBIC)             #resize 진행\n",
        "    \n",
        "  plt.imshow(cropped)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gG62I3wcG97B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hair_image_file = []\n",
        "\n",
        "ct = 1\n",
        "\n",
        "with open(folder2 + file_name, newline='') as myfile:\n",
        "    reader  = csv.reader(myfile, delimiter=',')\n",
        "    for i in reader:\n",
        "        if i[1] == '0':\n",
        "          hair_image_file.append(i[0])\n",
        "        ct += 1"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXRHMm65xzAF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "fcc0c2a5-5593-43ff-cfc4-9e06699dcece"
      },
      "source": [
        "from torchvision import transforms\n",
        "\n",
        "##center crop size ratio\n",
        "width_ratio = 1\n",
        "height_ratio = 2/3\n",
        "\n",
        "\n",
        "for i in range (1, 10):#원랜 (1, ct + 1)\n",
        "  pyplot.subplot(330 + i)\n",
        "  if os.path.isfile(folder + hair_image_file[i] + '.dcm'):\n",
        "    filename = folder + hair_image_file[i] + '.dcm'\n",
        "    dataset = pydicom.dcmread(filename)\n",
        "\n",
        "    image_bytes = tf.io.read_file(filename)\n",
        "\n",
        "    image = tfio.image.decode_dicom_image(image_bytes, dtype=tf.uint16)\n",
        "\n",
        "    skipped = tfio.image.decode_dicom_image(image_bytes, on_error='skip', dtype=tf.uint8)\n",
        "\n",
        "    image_info = np.squeeze(image.numpy())\n",
        "\n",
        "    rows = int(dataset.Rows) #높이\n",
        "    cols = int(dataset.Columns) #너비\n",
        "\n",
        "    # left = 0 # 여기 작성하다 맘\n",
        "    # top = int((rows - (cols*height_ratio)) / 2)\n",
        "    # right = 0\n",
        "    # bottom = int((rows + (cols*height_ratio)) / 2)\n",
        "\n",
        "    # image = image_info.crop((left, top, right, bottom))   #center crop 진행\n",
        "    cropped = image_info[int(rows/2 - cols/3) :int(rows/2 + cols/3), 0:cols]\n",
        "    if cols/rows > 1.5:\n",
        "      cropped = image_info[0 : rows, int(cols/2 - rows*3/4):int(cols/2 + rows*3/4)]\n",
        "    # print(cropped)\n",
        "    cropped = cv2.resize(cropped, dsize=(600, 400), interpolation=cv2.INTER_CUBIC)             #resize 진행\n",
        "    \n",
        "    # print(image_info.shape)\n",
        "    # print(cropped.shape)\n",
        "  plt.imshow(cropped)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piSrCna8SvDv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e71cd7e7-708b-41bc-d92c-272ada69ea20"
      },
      "source": [
        "# organize dataset into a useful structure\n",
        "from os import makedirs\n",
        "from os import listdir\n",
        "from shutil import copyfile\n",
        "from random import seed\n",
        "from random import random\n",
        "import scipy.misc\n",
        "import imageio\n",
        "# create directories\n",
        "dataset_home = '/content/gdrive/My Drive/dataset_nonhair_vs_hair/'\n",
        "subdirs = ['train/', 'test/']\n",
        "for subdir in subdirs:\n",
        "\t# create label subdirectories\n",
        "\tlabeldirs = ['nonhair/', 'hair/']\t\t#dog == nonhair, cat == hair\n",
        "\tfor labldir in labeldirs:\n",
        "\t\tnewdir = dataset_home + subdir + labldir\n",
        "\t\tmakedirs(newdir, exist_ok=True)\n",
        "# seed random number generator\n",
        "seed(seedNum)\n",
        "# define ratio of pictures to use for validation\n",
        "val_ratio = 0.25\n",
        "# copy training dataset images into subdirectories\n",
        "# src_directory = 'train/'\n",
        "src_directory = '/content/gdrive/My Drive/ISIC-melanoma/train/'\n",
        "\n",
        "for file in listdir(src_directory):\n",
        "\tsrc = src_directory + '/' + file\n",
        "\tdst_dir = 'train/'\n",
        "\tif random() < val_ratio:\n",
        "\t\tdst_dir = 'test/'\n",
        "\t# if file.startswith('hair'):\t\t\t\t\t\t##요기 if문을 label 0, 1 배열 안에 있는 파일 숫자인지 확인하는걸 만들어서 바꾸면 될 듯. csv에서 해당 파일 레이블이 1인가. 아래코드로!\n",
        "\twith open(folder2 + file_name, newline='') as myfile:\n",
        "\t\treader  = csv.reader(myfile, delimiter=',')\n",
        "\t\tct = 1\n",
        "\t\tfor i in reader:\n",
        "\t\t\tif i[1] == '1':\n",
        "\t\t\t\tfile = i[0]\n",
        "\t\t\t\tdst = dataset_home + dst_dir + 'hair/'  + file + '.jpg'\n",
        "\t\t\t\t# copyfile(src, dst)\n",
        "\t\t\t\tdataset = pydicom.dcmread(filename)\n",
        "\n",
        "\t\t\t\timage_bytes = tf.io.read_file(filename)\n",
        "\n",
        "\t\t\t\timage = tfio.image.decode_dicom_image(image_bytes, dtype=tf.uint16)\n",
        "\n",
        "\t\t\t\tskipped = tfio.image.decode_dicom_image(image_bytes, on_error='skip', dtype=tf.uint8)\n",
        "\n",
        "\t\t\t\timage_info = np.squeeze(image.numpy())\n",
        "\n",
        "\t\t\t\trows = int(dataset.Rows) #높이\n",
        "\t\t\t\tcols = int(dataset.Columns) #너비\n",
        "\n",
        "\t\t\t\tcropped = image_info[int(rows/2 - cols/3) :int(rows/2 + cols/3), 0:cols]\n",
        "\t\t\t\tif cols/rows > 1.5:\n",
        "\t\t\t\t\tcropped = image_info[0 : rows, int(cols/2 - rows*3/4):int(cols/2 + rows*3/4)]\n",
        "\t\t\t\t\t\n",
        "\t\t\t\tcropped = cv2.resize(cropped, dsize=(600, 400), interpolation=cv2.INTER_CUBIC)\n",
        "\t\t\n",
        "\t\t\t\tscipy.misc.toimage(cropped, cmin=0.0, cmax=...).save(dst)\n",
        "\t\t\t\t# imageio.imwrite(dst, cropped[:, :, 0])\n",
        "\t\t\telif i[1] == '0':\n",
        "\t\t\t\tfile = i[0]\n",
        "\t\t\t\tdst = dataset_home + dst_dir + 'hair/'  + file + '.jpg'\n",
        "\t\t\t\t# copyfile(src, dst)\n",
        "\t\t\t\tdataset = pydicom.dcmread(filename)\n",
        "\n",
        "\t\t\t\timage_bytes = tf.io.read_file(filename)\n",
        "\n",
        "\t\t\t\timage = tfio.image.decode_dicom_image(image_bytes, dtype=tf.uint16)\n",
        "\n",
        "\t\t\t\tskipped = tfio.image.decode_dicom_image(image_bytes, on_error='skip', dtype=tf.uint8)\n",
        "\n",
        "\t\t\t\timage_info = np.squeeze(image.numpy())\n",
        "\n",
        "\t\t\t\trows = int(dataset.Rows) #높이\n",
        "\t\t\t\tcols = int(dataset.Columns) #너비\n",
        "\n",
        "\t\t\t\tcropped = image_info[int(rows/2 - cols/3) :int(rows/2 + cols/3), 0:cols]\n",
        "\t\t\t\tif cols/rows > 1.5:\n",
        "\t\t\t\t\tcropped = image_info[0 : rows, int(cols/2 - rows*3/4):int(cols/2 + rows*3/4)]\n",
        "\t\t\t\t\t\n",
        "\t\t\t\tcropped = cv2.resize(cropped, dsize=(600, 400), interpolation=cv2.INTER_CUBIC)\n",
        "\t\t\n",
        "\t\t\t\tscipy.misc.toimage(cropped, cmin=0.0, cmax=...).save(dst)\n",
        "\t\t\t\t# imageio.imwrite(dst, cropped[:, :, 0])\n",
        "\t\t\n",
        "\t\t\tprint(ct)\n",
        "\t\t\tct = ct + 1\n",
        "  #         hair_image_file.append(i[0])\n",
        "\t\t# dst = dataset_home + dst_dir + 'hair/'  + file\n",
        "\t\t# copyfile(src, dst)\n",
        "\t# elif file.startswith('nonhair'):\n",
        "\t# \tdst = dataset_home + dst_dir + 'nonhair/'  + file\n",
        "\t# \tcopyfile(src, dst)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "2\n"
          ],
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        },
        {
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
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        },
        {
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            "3\n"
          ],
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        },
        {
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
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        },
        {
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            "4\n"
          ],
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
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            "5\n"
          ],
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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          ],
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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          ],
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        },
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "47\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
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            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "52\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "53\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "54\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "55\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "56\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "57\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "58\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "59\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "60\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "61\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "62\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "63\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:Lossy conversion from uint16 to uint8. Losing 8 bits of resolution. Convert image to uint8 prior to saving to suppress this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "65\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-107-6eec4abc869e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     69\u001b[0m                                 \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode_dicom_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m                                 \u001b[0mskipped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode_dicom_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'skip'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m                                 \u001b[0mimage_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_io/core/python/ops/dicom_ops.py\u001b[0m in \u001b[0;36mdecode_dicom_image\u001b[0;34m(contents, color_dim, on_error, scale, dtype, name)\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m         \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m     )\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<string>\u001b[0m in \u001b[0;36mio_decode_dicom_image\u001b[0;34m(contents, dtype, color_dim, on_error, scale, name)\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PB4EheXzrHH2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pydicom.dcmread(filename)\n",
        "\n",
        "image_bytes = tf.io.read_file(filename)\n",
        "\n",
        "image = tfio.image.decode_dicom_image(image_bytes, dtype=tf.uint16)\n",
        "\n",
        "skipped = tfio.image.decode_dicom_image(image_bytes, on_error='skip', dtype=tf.uint8)\n",
        "\n",
        "image_info = np.squeeze(image.numpy())\n",
        "\n",
        "rows = int(dataset.Rows) #높이\n",
        "cols = int(dataset.Columns) #너비\n",
        "\n",
        "cropped = image_info[int(rows/2 - cols/3) :int(rows/2 + cols/3), 0:cols]\n",
        "if cols/rows > 1.5:\n",
        "  cropped = image_info[0 : rows, int(cols/2 - rows*3/4):int(cols/2 + rows*3/4)]\n",
        "  \n",
        "cropped = cv2.resize(cropped, dsize=(600, 400), interpolation=cv2.INTER_CUBIC)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTLBFg1oSpdm",
        "colab_type": "text"
      },
      "source": [
        "# **Section 2. Fit and Evaluate Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T13x8rU1SoIs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# baseline model with One Block VGG Model\n",
        "startTimeModule = datetime.now()\n",
        "tf.random.set_seed(seedNum)\n",
        "\n",
        "# define cnn model\n",
        "def define_model1():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(400, 600, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\tmodel.compile(optimizer=default_opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        "\n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "\t# define model\n",
        "\tmodel1 = define_model1()\n",
        "\t# create data generator\n",
        "\tdatagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "\t# prepare iterators\n",
        "\ttrain_it = datagen.flow_from_directory('/content/gdrive/My Drive/dataset_nonhair_vs_hair/train/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(400, 600))\n",
        "\ttest_it = datagen.flow_from_directory('/content/gdrive/My Drive/dataset_nonhair_vs_hair/test/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(400, 600))\n",
        "\t# fit model\n",
        "\thistory = model1.fit(train_it, steps_per_epoch=len(train_it),\n",
        "\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=1)\n",
        "\t# evaluate model\n",
        "\t_, acc = model1.evaluate(test_it, steps=len(test_it), verbose=0)\n",
        "\tprint('> %.3f' % (acc * 100.0))\n",
        "\t# learning curves\n",
        "\tsummarize_diagnostics(history)\n",
        "\n",
        "# entry point, run the test harness\n",
        "run_test_harness()\n",
        "print('Total time for the model processing:', (datetime.now() - startTimeModule))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9YMcqNzAS_L0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# baseline model with Two Block VGG Model\n",
        "startTimeModule = datetime.now()\n",
        "tf.random.set_seed(seedNum)\n",
        "\n",
        "# define cnn model\n",
        "def define_model2():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\tmodel.compile(optimizer=default_opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        "\n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "\t# define model\n",
        "\tmodel2 = define_model2()\n",
        "\t# create data generator\n",
        "\tdatagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "\t# prepare iterators\n",
        "\ttrain_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\ttest_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\t# fit model\n",
        "\thistory = model2.fit(train_it, steps_per_epoch=len(train_it),\n",
        "\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=1)\n",
        "\t# evaluate model\n",
        "\t_, acc = model2.evaluate(test_it, steps=len(test_it), verbose=0)\n",
        "\tprint('> %.3f' % (acc * 100.0))\n",
        "\t# learning curves\n",
        "\tsummarize_diagnostics(history)\n",
        "\n",
        "# entry point, run the test harness\n",
        "run_test_harness()\n",
        "print('Total time for the model processing:', (datetime.now() - startTimeModule))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0OORFWSTBwl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# baseline model with Three Block VGG Model\n",
        "startTimeModule = datetime.now()\n",
        "tf.random.set_seed(seedNum)\n",
        "\n",
        "# define cnn model\n",
        "def define_model3():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\tmodel.compile(optimizer=default_opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        "\n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "\t# define model\n",
        "\tmodel3 = define_model3()\n",
        "\t# create data generator\n",
        "\tdatagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "\t# prepare iterators\n",
        "\ttrain_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\ttest_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\t# fit model\n",
        "\thistory = model3.fit(train_it, steps_per_epoch=len(train_it),\n",
        "\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=1)\n",
        "\t# evaluate model\n",
        "\t_, acc = model3.evaluate(test_it, steps=len(test_it), verbose=0)\n",
        "\tprint('> %.3f' % (acc * 100.0))\n",
        "\t# learning curves\n",
        "\tsummarize_diagnostics(history)\n",
        "\n",
        "# entry point, run the test harness\n",
        "run_test_harness()\n",
        "print('Total time for the model processing:', (datetime.now() - startTimeModule))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67jD0r7STHlr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# baseline model with VGG-3 and dropout (variation #1)\n",
        "startTimeModule = datetime.now()\n",
        "tf.random.set_seed(seedNum)\n",
        "\n",
        "# define cnn model\n",
        "def define_model4():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dropout(0.5))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\tmodel.compile(optimizer=default_opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        "\n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "\t# define model\n",
        "\tmodel4 = define_model4()\n",
        "\t# create data generator\n",
        "\tdatagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "\t# prepare iterator\n",
        "\ttrain_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\ttest_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\t# fit model\n",
        "\thistory = model4.fit(train_it, steps_per_epoch=len(train_it),\n",
        "\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=50, verbose=1)\n",
        "\t# evaluate model\n",
        "\t_, acc = model4.evaluate(test_it, steps=len(test_it), verbose=0)\n",
        "\tprint('> %.3f' % (acc * 100.0))\n",
        "\t# learning curves\n",
        "\tsummarize_diagnostics(history)\n",
        "\n",
        "# entry point, run the test harness\n",
        "run_test_harness()\n",
        "print('Total time for the model processing:', (datetime.now() - startTimeModule))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}