Lecture06 - CIFAR100.ipynb

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    "<img src='https://www.anadronestarting.com/wp-content/uploads/intel-main_opt.png' width=50%>\n",
    "\n",
    "# 모바일넷을 이용한 이미지분류\n",
    "<font size=5><b>(Image Classification using Mobilenet)<b></font>\n",
    "\n",
    "<div align='right'>성  민  석<br>(Minsuk Sung)</div>\n",
    "\n",
    "<img src='https://miro.medium.com/max/356/1*fTQtXyApxWPoW2vzSEk_Pw.png' width=60%>\n",
    "\n",
    "---"
   ]
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   "source": [
    "<h1>강의목차<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#필요한-라이브러리-및-옵션\" data-toc-modified-id=\"필요한-라이브러리-및-옵션-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>필요한 라이브러리 및 옵션</a></span><ul class=\"toc-item\"><li><span><a href=\"#기본-라이브러리(Library)\" data-toc-modified-id=\"기본-라이브러리(Library)-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>기본 라이브러리(Library)</a></span></li><li><span><a href=\"#옵션(Option)\" data-toc-modified-id=\"옵션(Option)-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>옵션(Option)</a></span></li></ul></li><li><span><a href=\"#예제---CIFAR100\" data-toc-modified-id=\"예제---CIFAR100-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>예제 - CIFAR100</a></span><ul class=\"toc-item\"><li><span><a href=\"#CIFAR-100-데이터-불러오기\" data-toc-modified-id=\"CIFAR-100-데이터-불러오기-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>CIFAR-100 데이터 불러오기</a></span></li><li><span><a href=\"#CIFAR10-데이터-형태-확인하기\" data-toc-modified-id=\"CIFAR10-데이터-형태-확인하기-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>CIFAR10 데이터 형태 확인하기</a></span><ul class=\"toc-item\"><li><span><a href=\"#Train-데이터셋\" data-toc-modified-id=\"Train-데이터셋-2.2.1\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>Train 데이터셋</a></span></li><li><span><a href=\"#Validation-데이터셋\" data-toc-modified-id=\"Validation-데이터셋-2.2.2\"><span class=\"toc-item-num\">2.2.2&nbsp;&nbsp;</span>Validation 데이터셋</a></span></li><li><span><a href=\"#Test-데이터셋\" data-toc-modified-id=\"Test-데이터셋-2.2.3\"><span class=\"toc-item-num\">2.2.3&nbsp;&nbsp;</span>Test 데이터셋</a></span></li></ul></li><li><span><a href=\"#데이터-시각화하기\" data-toc-modified-id=\"데이터-시각화하기-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>데이터 시각화하기</a></span></li><li><span><a href=\"#모델링\" data-toc-modified-id=\"모델링-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>모델링</a></span><ul class=\"toc-item\"><li><span><a href=\"#ResNet50모델-생성\" data-toc-modified-id=\"ResNet50모델-생성-2.4.1\"><span class=\"toc-item-num\">2.4.1&nbsp;&nbsp;</span>ResNet50모델 생성</a></span></li><li><span><a href=\"#ResNet50-미세-조정\" data-toc-modified-id=\"ResNet50-미세-조정-2.4.2\"><span class=\"toc-item-num\">2.4.2&nbsp;&nbsp;</span>ResNet50 미세 조정</a></span></li><li><span><a href=\"#전체적인-모델-구성\" data-toc-modified-id=\"전체적인-모델-구성-2.4.3\"><span class=\"toc-item-num\">2.4.3&nbsp;&nbsp;</span>전체적인 모델 구성</a></span></li></ul></li><li><span><a href=\"#모델-컴파일\" data-toc-modified-id=\"모델-컴파일-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>모델 컴파일</a></span></li><li><span><a href=\"#모델-확인하기\" data-toc-modified-id=\"모델-확인하기-2.6\"><span class=\"toc-item-num\">2.6&nbsp;&nbsp;</span>모델 확인하기</a></span></li><li><span><a href=\"#모델-학습하기\" data-toc-modified-id=\"모델-학습하기-2.7\"><span class=\"toc-item-num\">2.7&nbsp;&nbsp;</span>모델 학습하기</a></span></li><li><span><a href=\"#모델-평가하기\" data-toc-modified-id=\"모델-평가하기-2.8\"><span class=\"toc-item-num\">2.8&nbsp;&nbsp;</span>모델 평가하기</a></span><ul class=\"toc-item\"><li><span><a href=\"#Train-/-Validation-Loss\" data-toc-modified-id=\"Train-/-Validation-Loss-2.8.1\"><span class=\"toc-item-num\">2.8.1&nbsp;&nbsp;</span>Train / Validation Loss</a></span></li><li><span><a href=\"#Train-/-Validation-Accuaracy\" data-toc-modified-id=\"Train-/-Validation-Accuaracy-2.8.2\"><span class=\"toc-item-num\">2.8.2&nbsp;&nbsp;</span>Train / Validation Accuaracy</a></span></li><li><span><a href=\"#Train-/-Validation-Top5-Accuracy\" data-toc-modified-id=\"Train-/-Validation-Top5-Accuracy-2.8.3\"><span class=\"toc-item-num\">2.8.3&nbsp;&nbsp;</span>Train / Validation Top5 Accuracy</a></span></li></ul></li><li><span><a href=\"#신경망-모델-검증하기\" data-toc-modified-id=\"신경망-모델-검증하기-2.9\"><span class=\"toc-item-num\">2.9&nbsp;&nbsp;</span>신경망 모델 검증하기</a></span></li><li><span><a href=\"#다음-예제에서는\" data-toc-modified-id=\"다음-예제에서는-2.10\"><span class=\"toc-item-num\">2.10&nbsp;&nbsp;</span>다음 예제에서는</a></span></li></ul></li><li><span><a href=\"#참고\" data-toc-modified-id=\"참고-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>참고</a></span></li></ul></div>"
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   "source": [
    "## 필요한 라이브러리 및 옵션\n",
    "\n",
    "### 기본 라이브러리(Library)"
   ]
  },
  {
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   "source": [
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import random\n",
    "import warnings\n",
    "import itertools\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:37.830287Z",
     "start_time": "2019-11-09T01:43:37.670241Z"
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   "source": [
    "import cv2\n",
    "from PIL import Image\n",
    "from PIL import ImageFont\n",
    "from PIL import ImageDraw\n",
    "from IPython.display import SVG\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import xml.etree.ElementTree as ET\n",
    "from xml.etree.ElementTree import Element, ElementTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:38.559838Z",
     "start_time": "2019-11-09T01:43:37.831578Z"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import mnist,cifar10,cifar100\n",
    "from tensorflow.keras.preprocessing.image import load_img,img_to_array,ImageDataGenerator\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.models import Model,Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, Conv2D,GlobalAveragePooling2D\n",
    "from tensorflow.keras.optimizers import RMSprop, Adam\n",
    "from tensorflow.keras.utils import to_categorical,plot_model\n",
    "from tensorflow.keras.losses import categorical_crossentropy,binary_crossentropy\n",
    "from tensorflow.keras.callbacks import Callback\n",
    "from tensorflow.keras.metrics import top_k_categorical_accuracy\n",
    "from tensorflow.python.client import device_lib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 옵션(Option)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:38.700833Z",
     "start_time": "2019-11-09T01:43:38.561044Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 6251351141964846220\n",
      ", name: \"/device:XLA_CPU:0\"\n",
      "device_type: \"XLA_CPU\"\n",
      "memory_limit: 17179869184\n",
      "locality {\n",
      "}\n",
      "incarnation: 17933432099555749685\n",
      "physical_device_desc: \"device: XLA_CPU device\"\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 7881329869\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 1496858449212004281\n",
      "physical_device_desc: \"device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1\"\n",
      ", name: \"/device:XLA_GPU:0\"\n",
      "device_type: \"XLA_GPU\"\n",
      "memory_limit: 17179869184\n",
      "locality {\n",
      "}\n",
      "incarnation: 145465887796347891\n",
      "physical_device_desc: \"device: XLA_GPU device\"\n",
      "]\n",
      "WARNING:tensorflow:From /home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['/job:localhost/replica:0/task:0/device:GPU:0']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "warnings.filterwarnings(action='ignore')\n",
    "warnings.filterwarnings(action='default')\n",
    "\n",
    "%matplotlib inline\n",
    "print(device_lib.list_local_devices())\n",
    "keras.backend.tensorflow_backend._get_available_gpus()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 예제 - CIFAR100\n",
    "\n",
    "![](https://miro.medium.com/max/356/1*fTQtXyApxWPoW2vzSEk_Pw.png)\n",
    "\n",
    " 이 데이터 세트는 CIFAR-10과 똑같다. 단, 각각 600개의 이미지가 포함된 100개의 클래스가 있다. 학습용 영상은 500개, 시험용 영상은 클래스당 100개씩이다. CIFAR-100의 100개 클래스는 20개의 슈퍼 클래스로 분류된다. 각 이미지에는 \"fine\"클래스과 \"coarse\" 라벨이 함께 나온다.\n",
    "CIFAR-100의 클래스 목록은 다음과 같다.\n",
    "\n",
    "출처 : [The CIFAR-100 dataset document](https://www.cs.toronto.edu/~kriz/cifar.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:38.706141Z",
     "start_time": "2019-11-09T01:43:38.701794Z"
    }
   },
   "outputs": [],
   "source": [
    "# CIFAR-100 클래스\n",
    "CIFAR100_CLASSES = sorted(['beaver', 'dolphin', 'otter', 'seal', 'whale',  # aquatic mammals\n",
    "                           'aquarium' 'fish', 'flatfish', 'ray', 'shark', 'trout',  # fish\n",
    "                           'orchids', 'poppies', 'roses', 'sunflowers', 'tulips', # flowers\n",
    "                           'bottles', 'bowls', 'cans', 'cups', 'plates', # food containers\n",
    "                           'apples', 'mushrooms', 'oranges', 'pears', 'sweet peppers', # fruit and vegetables\n",
    "                           'clock', 'computer' 'keyboard', 'lamp', 'telephone', 'television', # household electrical devices\n",
    "                           'bed', 'chair', 'couch', 'table', 'wardrobe', # household furniture\n",
    "                           'bee', 'beetle', 'butterfly', 'caterpillar', 'cockroach', # insects\n",
    "                           'bear', 'leopard', 'lion', 'tiger', 'wolf', # large carnivores\n",
    "                           'bridge', 'castle', 'house', 'road', 'skyscraper', # large man-made outdoor things\n",
    "                           'cloud', 'forest', 'mountain', 'plain', 'sea', # large natural outdoor scenes\n",
    "                           'camel', 'cattle', 'chimpanzee', 'elephant', 'kangaroo', # large omnivores and herbivores\n",
    "                           'fox', 'porcupine', 'possum', 'raccoon', 'skunk', # medium-sized mammals\n",
    "                           'crab', 'lobster', 'snail', 'spider', 'worm', # non-insect invertebrates\n",
    "                           'baby', 'boy', 'girl', 'man', 'woman', # people\n",
    "                           'crocodile', 'dinosaur', 'lizard', 'snake', 'turtle', # reptiles\n",
    "                           'hamster', 'mouse', 'rabbit', 'shrew', 'squirrel', # small mammals\n",
    "                           'maple', 'oak', 'palm', 'pine', 'willow', # trees\n",
    "                           'bicycle', 'bus', 'motorcycle', 'pickup truck', 'train', # vehicles 1\n",
    "                           'lawn-mower', 'rocket', 'streetcar', 'tank', 'tractor' # vehicles 2\n",
    "                          ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CIFAR-100 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [],
   "source": [
    "# Keras에서 제공하는 CIFAR-100 데이터를 불러오는 함수 : cifar100.load_data()\n",
    "(X_train, y_train), (X_test,y_test) = cifar100.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:39.238792Z",
     "start_time": "2019-11-09T01:43:38.847982Z"
    }
   },
   "outputs": [],
   "source": [
    "# Train / Validation 데이터 분리하기\n",
    "# Train 데이터를 Train / Validation 으로 나누어줌으로써 Overfitting 여부를 파악\n",
    "# X_train, X_valid = X_train[:40000], X_train[40000:]\n",
    "# y_train, y_valid = y_train[:40000], y_train[40000:]\n",
    "X_train,X_valid,y_train,y_valid = train_test_split(X_train,y_train,test_size=0.2,shuffle=True,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:39.243180Z",
     "start_time": "2019-11-09T01:43:39.240488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CIFAR-100 Train 데이터의 크기 : 40000\n",
      "CIFAR-100 Validation 데이터의 크기 : 10000\n",
      "CIFAR-100 Test 데이터의 크기 : 10000\n"
     ]
    }
   ],
   "source": [
    "# Train / Test 데이터의 크기 확인\n",
    "print('CIFAR-100 Train 데이터의 크기 : {}'.format(len(X_train)))\n",
    "print('CIFAR-100 Validation 데이터의 크기 : {}'.format(len(X_valid)))\n",
    "print('CIFAR-100 Test 데이터의 크기 : {}'.format(len(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CIFAR10 데이터 형태 확인하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:39.247090Z",
     "start_time": "2019-11-09T01:43:39.244463Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape :  (40000, 32, 32, 3)\n",
      "y_train Shape :  (40000, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train Shape : \",X_train.shape) # 32*32짜리 크기의 RGB 이미지 40000개\n",
    "print(\"y_train Shape : \",y_train.shape) # 각 이미지별 레이블 40000개"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:39.250499Z",
     "start_time": "2019-11-09T01:43:39.248088Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val Shape :  (10000, 32, 32, 3)\n",
      "y_val Shape :  (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_val Shape : \",X_valid.shape) # 32*32짜리 크기의 RGB 이미지 10000개\n",
    "print(\"y_val Shape : \",y_valid.shape) # 각 이미지별 레이블 10000개"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:39.253991Z",
     "start_time": "2019-11-09T01:43:39.251565Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape :  (10000, 32, 32, 3)\n",
      "y_train Shape :  (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train Shape : \",X_test.shape) # 32*32짜리 크기의 RGB 이미지 10000개\n",
    "print(\"y_train Shape : \",y_test.shape) # 각 이미지별 레이블 10000개"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 시각화하기\n",
    "\n",
    "CIFAR100의 Train 데이터셋의 첫번재 데이터를 확인해보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:39.258020Z",
     "start_time": "2019-11-09T01:43:39.254908Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 31,  43,  48],\n",
       "        [ 72,  86,  94],\n",
       "        [151, 165, 170],\n",
       "        ...,\n",
       "        [ 19,  36,  41],\n",
       "        [ 18,  34,  38],\n",
       "        [ 17,  32,  34]],\n",
       "\n",
       "       [[ 47,  55,  53],\n",
       "        [ 63,  74,  75],\n",
       "        [104, 117, 116],\n",
       "        ...,\n",
       "        [ 18,  37,  43],\n",
       "        [ 19,  35,  41],\n",
       "        [ 18,  33,  38]],\n",
       "\n",
       "       [[ 76,  87,  86],\n",
       "        [107, 119, 115],\n",
       "        [ 82,  96,  86],\n",
       "        ...,\n",
       "        [ 17,  34,  42],\n",
       "        [ 16,  35,  43],\n",
       "        [ 17,  33,  42]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[  3,   2,   3],\n",
       "        [  3,   3,   3],\n",
       "        [  3,   5,   4],\n",
       "        ...,\n",
       "        [ 85,  91,  90],\n",
       "        [ 70,  78,  77],\n",
       "        [ 57,  65,  65]],\n",
       "\n",
       "       [[  3,   5,   5],\n",
       "        [  3,   4,   3],\n",
       "        [  3,   5,   4],\n",
       "        ...,\n",
       "        [100, 105, 101],\n",
       "        [ 84,  92,  91],\n",
       "        [ 45,  54,  56]],\n",
       "\n",
       "       [[  5,   7,   7],\n",
       "        [  5,   7,   8],\n",
       "        [  3,   5,   5],\n",
       "        ...,\n",
       "        [ 94, 100,  98],\n",
       "        [ 67,  77,  76],\n",
       "        [ 21,  29,  29]]], dtype=uint8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = 0\n",
    "X_train[idx] # 3채널 RGB 이미지 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:39.375034Z",
     "start_time": "2019-11-09T01:43:39.258990Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[idx], interpolation='nearest')\n",
    "plt.title('Label : {}'.format(CIFAR100_CLASSES[y_train[idx][0]]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "조금 더 많은 CIFAR 데이터셋을 확인해보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:40.688040Z",
     "start_time": "2019-11-09T01:43:39.376057Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 재연성을 위하여 랜덤시드 고정\n",
    "np.random.seed(1234) \n",
    "\n",
    "# random 함수를 통해서 임의의 16개 데이터 가져오기\n",
    "samples = np.random.randint(0,len(X_train)+1,size=16)\n",
    "\n",
    "# MNIST를 그릴 Figure 준비\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "# 16개의 이미지 시각화\n",
    "for count, n in enumerate(samples,start=1):\n",
    "    plt.subplot(4, 4, count)\n",
    "    plt.imshow(X_train[n], interpolation='nearest')\n",
    "    label_name = \"Label:\" + str(CIFAR100_CLASSES[y_train[n][0]])\n",
    "    plt.title(label_name)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:40.845871Z",
     "start_time": "2019-11-09T01:43:40.689134Z"
    }
   },
   "outputs": [],
   "source": [
    "# 데이터 크기 조정(Data Reshape)\n",
    "X_train = X_train.reshape(X_train.shape[0],32,32,3)\n",
    "X_valid = X_valid.reshape(X_valid.shape[0],32,32,3)\n",
    "X_test = X_test.reshape(X_test.shape[0],32,32,3)\n",
    "\n",
    "# 데이터 포맷 바꾸기\n",
    "# 정수(int)인 데이터에서 실수(float)으로 변환\n",
    "X_train = X_train.astype('float32')\n",
    "X_valid = X_valid.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# 데이터 정규화(Data Regularization)\n",
    "# 이 과정을 통해서 추후 학습할 신경망이 조금 더 학습이 원할히 될 수 있게함\n",
    "X_train = X_train / 255\n",
    "X_valid = X_valid / 255\n",
    "X_test = X_test / 255\n",
    "\n",
    "# 원-핫 인코딩(One Hot Encoding)\n",
    "# Keras의 to_categorical함수를 통해서 모든 Train 데이터의 레이블을 벡터화(Vectorize)\n",
    "# ex) [3] -> [0 0 0 1 0 0 0 0 0 0]\n",
    "y_train = to_categorical(y_train, 100) \n",
    "y_valid = to_categorical(y_valid,100)\n",
    "y_test = to_categorical(y_test, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델링\n",
    "\n",
    " 앞선 예제에서는 직접 CNN 모델을 구성하여 학습을 진행하였지만, 이번 예제에서는 VGGNet, ResNet, MobileNet과 같이 보편적으로 성능이 좋다고 알려져있는 CNN 모델을 사용해보자. Keras에서는 application 모듈에서 Pretrained된 모델을 사용할 수 있다. 이미지 분류에 사용할 수 있는 모델의 목록은 아래와 같다.\n",
    " \n",
    "- Xception\n",
    "- VGG16\n",
    "- VGG19\n",
    "- ResNet, ResNetV2\n",
    "- InceptionV3\n",
    "- InceptionResNetV2\n",
    "- MobileNet\n",
    "- MobileNetV2\n",
    "- DenseNet\n",
    "- NASNet\n",
    "\n",
    "출처 : [Keras Documentation](https://keras.io/applications/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이번 예제에서는 **`ResNet50`을 사용**해보도록 하자.\n",
    "\n",
    "<img src='https://i.stack.imgur.com/XTo6Q.png' width=50%>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ResNet50모델 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:45.603926Z",
     "start_time": "2019-11-09T01:43:40.846994Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    }
   ],
   "source": [
    "# CIFAR-10의 이미지 형태\n",
    "INPUT_SHAPE = (32, 32, 3)\n",
    "\n",
    "# VGG16 객체 생성\n",
    "base_model = ResNet50(input_shape=INPUT_SHAPE,  # CIFAR-10의 이미지 형태\n",
    "                      include_top=False,  # FC Layer를 제거\n",
    "                      weights='imagenet')\n",
    "\n",
    "# ResNet50의 모든 Layer를 Freezing\n",
    "base_model.trainable = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ResNet50 미세 조정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:45.612117Z",
     "start_time": "2019-11-09T01:43:45.604884Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 175/175 [00:00<00:00, 61428.00it/s]\n"
     ]
    }
   ],
   "source": [
    "# ResNet의 상단 Layer를 학습가능여부\n",
    "set_trainable = False\n",
    "\n",
    "# res5c_branch2a, res5c_branch2b, res5c_branch2c 라는 이름의 Layer를 만나면 학습가능하게끔\n",
    "for layer in tqdm(base_model.layers):\n",
    "    if layer.name in ['res5a_branch2a','res5a_branch2b','res5a_branch2c',\n",
    "                      'res5b_branch2a','res5b_branch2b','res5b_branch2c',\n",
    "                      'res5c_branch2a','res5c_branch2b','res5c_branch2c']:\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:45.624364Z",
     "start_time": "2019-11-09T01:43:45.613195Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Layer Type</th>\n",
       "      <th>Layer Name</th>\n",
       "      <th>Layer Trainable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>&lt;tensorflow.python.keras.engine.input_layer.In...</td>\n",
       "      <td>input_1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.convolutional....</td>\n",
       "      <td>conv1_pad</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.convolutional....</td>\n",
       "      <td>conv1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.normalization....</td>\n",
       "      <td>bn_conv1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.core.Activatio...</td>\n",
       "      <td>activation</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.core.Activatio...</td>\n",
       "      <td>activation_47</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.convolutional....</td>\n",
       "      <td>res5c_branch2c</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.normalization....</td>\n",
       "      <td>bn5c_branch2c</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.merge.Add obje...</td>\n",
       "      <td>add_15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>&lt;tensorflow.python.keras.layers.core.Activatio...</td>\n",
       "      <td>activation_48</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Layer Type      Layer Name  \\\n",
       "0    <tensorflow.python.keras.engine.input_layer.In...         input_1   \n",
       "1    <tensorflow.python.keras.layers.convolutional....       conv1_pad   \n",
       "2    <tensorflow.python.keras.layers.convolutional....           conv1   \n",
       "3    <tensorflow.python.keras.layers.normalization....        bn_conv1   \n",
       "4    <tensorflow.python.keras.layers.core.Activatio...      activation   \n",
       "..                                                 ...             ...   \n",
       "170  <tensorflow.python.keras.layers.core.Activatio...   activation_47   \n",
       "171  <tensorflow.python.keras.layers.convolutional....  res5c_branch2c   \n",
       "172  <tensorflow.python.keras.layers.normalization....   bn5c_branch2c   \n",
       "173  <tensorflow.python.keras.layers.merge.Add obje...          add_15   \n",
       "174  <tensorflow.python.keras.layers.core.Activatio...   activation_48   \n",
       "\n",
       "     Layer Trainable  \n",
       "0              False  \n",
       "1              False  \n",
       "2              False  \n",
       "3              False  \n",
       "4              False  \n",
       "..               ...  \n",
       "170             True  \n",
       "171             True  \n",
       "172             True  \n",
       "173             True  \n",
       "174             True  \n",
       "\n",
       "[175 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layers = [(layer, layer.name, layer.trainable) for layer in base_model.layers]\n",
    "pd.DataFrame(layers, columns=['Layer Type', 'Layer Name', 'Layer Trainable']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 전체적인 모델 구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:46.979501Z",
     "start_time": "2019-11-09T01:43:45.625292Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/minsuk/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(base_model) # ResNet50 추가\n",
    "model.add(GlobalAveragePooling2D()) # Transfer Learning을 진행할때 항상 GAP Layer 추가\n",
    "model.add(Dense(len(CIFAR100_CLASSES), activation='softmax')) # 분류할 FC Layer 추가"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델 컴파일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:47.048271Z",
     "start_time": "2019-11-09T01:43:46.980581Z"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(loss=categorical_crossentropy, \n",
    "              optimizer=Adam(learning_rate=0.0001), \n",
    "              metrics=['acc','top_k_categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:47.128525Z",
     "start_time": "2019-11-09T01:43:47.049322Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "resnet50 (Model)             (None, 1, 1, 2048)        23587712  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               204900    \n",
      "=================================================================\n",
      "Total params: 23,792,612\n",
      "Trainable params: 15,180,900\n",
      "Non-trainable params: 8,611,712\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()\n",
    "\n",
    "plot_model(model, to_file='./img/model/cifar100_resnet_model.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델 학습하기\n",
    "\n",
    "Keras의 `fit`의 메소드를 통해서 간단하게 학습가능하다. 이번 예제에서는 여기서는 10번의 epoch만으로 학습을 진행하도록 한다. 그리고 앞서 준비한 검증 데이터(Validation Set)을 통해서 신경망의 오버피팅 여부를 판단하도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:43:47.131661Z",
     "start_time": "2019-11-09T01:43:47.129704Z"
    }
   },
   "outputs": [],
   "source": [
    "EPOCHS = 10\n",
    "BATCH_SIZE = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:47:38.650692Z",
     "start_time": "2019-11-09T01:43:47.133613Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "40000/40000 [==============================] - 25s 627us/sample - loss: 3.9952 - acc: 0.1555 - top_k_categorical_accuracy: 0.3420 - val_loss: 7.3771 - val_acc: 0.0096 - val_top_k_categorical_accuracy: 0.0501\n",
      "Epoch 2/10\n",
      "40000/40000 [==============================] - 22s 560us/sample - loss: 2.5992 - acc: 0.3553 - top_k_categorical_accuracy: 0.6415 - val_loss: 7.6171 - val_acc: 0.0098 - val_top_k_categorical_accuracy: 0.0503\n",
      "Epoch 3/10\n",
      "40000/40000 [==============================] - 23s 571us/sample - loss: 1.9979 - acc: 0.4816 - top_k_categorical_accuracy: 0.7682 - val_loss: 7.4113 - val_acc: 0.0101 - val_top_k_categorical_accuracy: 0.0471\n",
      "Epoch 4/10\n",
      "40000/40000 [==============================] - 23s 569us/sample - loss: 1.5467 - acc: 0.5907 - top_k_categorical_accuracy: 0.8507 - val_loss: 7.6248 - val_acc: 0.0103 - val_top_k_categorical_accuracy: 0.0541\n",
      "Epoch 5/10\n",
      "40000/40000 [==============================] - 23s 571us/sample - loss: 1.2171 - acc: 0.6778 - top_k_categorical_accuracy: 0.9041 - val_loss: 8.8476 - val_acc: 0.0111 - val_top_k_categorical_accuracy: 0.0518\n",
      "Epoch 6/10\n",
      "40000/40000 [==============================] - 23s 564us/sample - loss: 0.9377 - acc: 0.7540 - top_k_categorical_accuracy: 0.9385 - val_loss: 8.7705 - val_acc: 0.0105 - val_top_k_categorical_accuracy: 0.0536\n",
      "Epoch 7/10\n",
      "40000/40000 [==============================] - 23s 565us/sample - loss: 0.7364 - acc: 0.8077 - top_k_categorical_accuracy: 0.9604 - val_loss: 8.3413 - val_acc: 0.0103 - val_top_k_categorical_accuracy: 0.0518\n",
      "Epoch 8/10\n",
      "40000/40000 [==============================] - 23s 565us/sample - loss: 0.5862 - acc: 0.8505 - top_k_categorical_accuracy: 0.9741 - val_loss: 10.4077 - val_acc: 0.0107 - val_top_k_categorical_accuracy: 0.0528\n",
      "Epoch 9/10\n",
      "40000/40000 [==============================] - 23s 572us/sample - loss: 0.4665 - acc: 0.8819 - top_k_categorical_accuracy: 0.9840 - val_loss: 8.0091 - val_acc: 0.0099 - val_top_k_categorical_accuracy: 0.0501\n",
      "Epoch 10/10\n",
      "40000/40000 [==============================] - 23s 572us/sample - loss: 0.3953 - acc: 0.8992 - top_k_categorical_accuracy: 0.9876 - val_loss: 9.3926 - val_acc: 0.0105 - val_top_k_categorical_accuracy: 0.0511\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,  # 학습할 데이터\n",
    "                    y_train,  # 학습할 레이블\n",
    "                    epochs=EPOCHS,  # 전체 학습할 횟수\n",
    "                    batch_size=BATCH_SIZE,  # 배치 사이즈\n",
    "                    use_multiprocessing=True,\n",
    "                    validation_data=(X_valid, y_valid) # 검증 데이터로 확인\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:47:39.980877Z",
     "start_time": "2019-11-09T01:47:38.654212Z"
    }
   },
   "outputs": [],
   "source": [
    "model.save('./bin/cifar100_resnet50.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델 평가하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T01:51:34.217542Z",
     "start_time": "2019-11-09T01:51:08.850208Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss : 9.426516875839233\n",
      "Train Accuracy : 0.009949999861419201\n",
      "Train Top5 Accuracy : 0.04987499862909317\n",
      "Validation Loss : 9.39263415222168\n",
      "Validation Accuracy : 0.010499999858438969\n",
      "Validation Top5 Accuracy : 0.051100000739097595\n"
     ]
    }
   ],
   "source": [
    "# Train 데이터로 평가하기\n",
    "train_loss, train_acc, train_top5_acc= model.evaluate(X_train,y_train,verbose=0)\n",
    "print('Train Loss : {}'.format(train_loss))\n",
    "print('Train Accuracy : {}'.format(train_acc))\n",
    "print('Train Top5 Accuracy : {}'.format(train_top5_acc))\n",
    "\n",
    "# Validation 데이터로 평가하기\n",
    "valid_loss, valid_acc , valid_top5_acc= model.evaluate(X_valid,y_valid,verbose=0)\n",
    "print('Validation Loss : {}'.format(valid_loss))\n",
    "print('Validation Accuracy : {}'.format(valid_acc))\n",
    "print('Validation Top5 Accuracy : {}'.format(valid_top5_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train / Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T02:06:58.031937Z",
     "start_time": "2019-11-09T02:06:57.901776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train / Validation 데이터에 대해서 Loss 시각화\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1,len(loss)+1)\n",
    "\n",
    "plt.plot(epochs,loss,label='Training Loss')\n",
    "plt.plot(epochs,val_loss,label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train / Validation Accuaracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T02:06:59.754304Z",
     "start_time": "2019-11-09T02:06:59.623734Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train / Validation 데이터에 대해서 Accuracy 시각화\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "epochs = range(1,len(acc)+1)\n",
    "\n",
    "plt.plot(epochs,acc,label='Training Accuarcy')\n",
    "plt.plot(epochs,val_acc,label='Validation Accuarcy')\n",
    "plt.title('Training and Validation Accuarcy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuarcy')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train / Validation Top5 Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T02:07:39.807376Z",
     "start_time": "2019-11-09T02:07:39.674779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train / Validation 데이터에 대해서 Accuracy 시각화\n",
    "acc = history.history['top_k_categorical_accuracy']\n",
    "val_acc = history.history['val_top_k_categorical_accuracy']\n",
    "epochs = range(1,len(loss)+1)\n",
    "\n",
    "plt.plot(epochs,acc,label='Training Top5 Accuarcy')\n",
    "plt.plot(epochs,val_acc,label='Validation Top5 Accuarcy')\n",
    "plt.title('Training and Validation Top5 Accuarcy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuarcy')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 신경망 모델 검증하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T02:14:35.828625Z",
     "start_time": "2019-11-09T02:14:30.641965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss : 9.417670503234863\n",
      "Test Accuracy : 0.009999999776482582\n",
      "Test Accuracy : 0.05079999938607216\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc, test_top5_acc = model.evaluate(X_test,y_test,verbose=0)\n",
    "print('Test Loss : {}'.format(test_loss))\n",
    "print('Test Accuracy : {}'.format(test_acc))\n",
    "print('Test Accuracy : {}'.format(test_top5_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RenNet으로 학습한 신경망이 과연 어떠한 이미지를 잘 못 예측했는지 확인해보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T05:39:55.345400Z",
     "start_time": "2019-11-09T05:39:41.360689Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 재연성을 위하여 랜덤시드 고정\n",
    "random.seed('intel')\n",
    "\n",
    "# TEST 데이터 예측하기\n",
    "predicted_result = model.predict(X_train)\n",
    "predicted_labels = np.argmax(predicted_result, axis=1)\n",
    "\n",
    "# TEST 데이터의 정답 가져오기\n",
    "test_labels = np.argmax(y_test, axis=1)\n",
    "\n",
    "# random 함수를 통해서 임의의 16개 데이터 가져오기\n",
    "samples = random.choices(population=test_labels, k=16)\n",
    "\n",
    "# CIFAR100를 그릴 Figure 준비\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# 16개의 이미지 시각화\n",
    "for count, n in enumerate(samples, start=1):\n",
    "    plt.subplot(4, 4, count)\n",
    "    plt.imshow(X_test[n], interpolation='nearest')\n",
    "    label = \"Label:\" + CIFAR100_CLASSES[test_labels[n]]\n",
    "    pred = \"Prediction:\" + CIFAR100_CLASSES[predicted_labels[n]]\n",
    "    plt.title(label+', '+pred)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('./img/result/cifar100_wrong_result.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 다음 예제에서는"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://miro.medium.com/max/5250/1*n16lj3lSkz2miMc_5cvkrA.jpeg)\n",
    "\n",
    "ResNet50모델을 통해서 이제 VGG-16모델에서 가장 큰 이슈였던 많은 양의 파라미터는 해결됐다. ResNet50은 VGG-16모델보다 적은 파라미터로 더 좋은 성능을 낼 수 있다. 하지만 여전히 이정도의 파라미터는 작은 디바이스에서 학습하기엔 문제가 된다. 그렇기 때문에 이러한 이슈를 해결하고자 다음 예제에서는 `MobileNet`을 이용하는 예제를 해보자."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 참고\n",
    "\n",
    "- Intel\n",
    "    - https://www.intel.co.kr/\n",
    "- Intel OpenVINO\n",
    "    - https://software.intel.com/en-us/openvino-toolkit\n",
    "- MNIST\n",
    "    - http://yann.lecun.com/exdb/mnist/\n",
    "- CIFAR10\n",
    "    - https://www.cs.toronto.edu/~kriz/cifar.html\n",
    "- ImageNet\n",
    "    - http://www.image-net.org\n",
    "- Tensorflow\n",
    "    - https://www.tensorflow.org/?hl=ko\n",
    "- Keras\n",
    "    - https://keras.io/\n",
    "    - https://tensorflow.blog/2019/03/06/tensorflow-2-0-keras-api-overview/\n",
    "    - https://tykimos.github.io/2017/02/22/Integrating_Keras_and_TensorFlow/\n",
    "    - https://tykimos.github.io/2017/03/08/CNN_Getting_Started/\n",
    "    - https://raw.githubusercontent.com/keras-team/keras-docs-ko/master/sources/why-use-keras.md\n",
    "- Keras to Caffe\n",
    "     - https://github.com/uhfband/keras2caffe\n",
    "     - http://www.deepvisionconsulting.com/from-keras-to-caffe/\n",
    "- Fully Connected Layer\n",
    "    - https://sonofgodcom.wordpress.com/2018/12/31/cnn%EC%9D%84-%EC%9D%B4%ED%95%B4%ED%95%B4%EB%B3%B4%EC%9E%90-fully-connected-layer%EB%8A%94-%EB%AD%94%EA%B0%80/\n",
    "- Convultional Nueral Network\n",
    "    - http://aikorea.org/cs231n/convolutional-networks/\n",
    "    - http://cs231n.stanford.edu/\n",
    "- CNN Models\n",
    "    - https://ratsgo.github.io/deep%20learning/2017/10/09/CNNs/\n",
    "\n",
    "- VOC2012\n",
    "    - https://blog.godatadriven.com/rod-keras-multi-label\n",
    "    - https://gist.github.com/rragundez/ae3a17428bfec631d1b35dcdc6296a85#file-multi-label_classification_with_keras_imagedatagenerator-ipynbhttps://fairyonice.github.io/Part_5_Object_Detection_with_Yolo_using_VOC_2012_data_training.html\n",
    "    - http://research.sualab.com/introduction/2017/11/29/image-recognition-overview-1.html"
   ]
  }
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