Lecture03 - Fashion MNIST.ipynb

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   "source": [
    "<img src='https://www.anadronestarting.com/wp-content/uploads/intel-main_opt.png' width=50%>\n",
    "\n",
    "# 모바일넷을 이용한 이미지분류 - MNIST\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://upload.wikimedia.org/wikipedia/commons/thumb/2/27/MnistExamples.png/440px-MnistExamples.png' width=60%>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "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---Fashion-MNIST\" data-toc-modified-id=\"예제---Fashion-MNIST-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>예제 - Fashion MNIST</a></span><ul class=\"toc-item\"><li><span><a href=\"#Fashion-MNIST-데이터-불러오기\" data-toc-modified-id=\"Fashion-MNIST-데이터-불러오기-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Fashion MNIST 데이터 불러오기</a></span></li><li><span><a href=\"#Fashion-MNIST-데이터-형태-확인하기\" data-toc-modified-id=\"Fashion-MNIST-데이터-형태-확인하기-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Fashion MNIST 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=\"#데이터-전처리\" data-toc-modified-id=\"데이터-전처리-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>데이터 전처리</a></span></li><li><span><a href=\"#모델링\" data-toc-modified-id=\"모델링-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>모델링</a></span><ul class=\"toc-item\"><li><span><a href=\"#모델-구성\" data-toc-modified-id=\"모델-구성-2.5.1\"><span class=\"toc-item-num\">2.5.1&nbsp;&nbsp;</span>모델 구성</a></span></li><li><span><a href=\"#모델-컴파일\" data-toc-modified-id=\"모델-컴파일-2.5.2\"><span class=\"toc-item-num\">2.5.2&nbsp;&nbsp;</span>모델 컴파일</a></span></li><li><span><a href=\"#모델-확인\" data-toc-modified-id=\"모델-확인-2.5.3\"><span class=\"toc-item-num\">2.5.3&nbsp;&nbsp;</span>모델 확인</a></span></li></ul></li><li><span><a href=\"#신경망-모델-학습\" data-toc-modified-id=\"신경망-모델-학습-2.6\"><span class=\"toc-item-num\">2.6&nbsp;&nbsp;</span>신경망 모델 학습</a></span></li><li><span><a href=\"#신경망-모델-평가\" data-toc-modified-id=\"신경망-모델-평가-2.7\"><span class=\"toc-item-num\">2.7&nbsp;&nbsp;</span>신경망 모델 평가</a></span></li><li><span><a href=\"#신경망-모델-검증하기\" data-toc-modified-id=\"신경망-모델-검증하기-2.8\"><span class=\"toc-item-num\">2.8&nbsp;&nbsp;</span>신경망 모델 검증하기</a></span></li><li><span><a href=\"#다음-예제에서는\" data-toc-modified-id=\"다음-예제에서는-2.9\"><span class=\"toc-item-num\">2.9&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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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 필요한 라이브러리 및 옵션\n",
    "\n",
    "### 기본 라이브러리(Library)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:31.035458Z",
     "start_time": "2019-11-09T07:55:31.026247Z"
    }
   },
   "outputs": [],
   "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,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:31.504692Z",
     "start_time": "2019-11-09T07:55:31.037632Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:31.664692Z",
     "start_time": "2019-11-09T07:55:31.505748Z"
    }
   },
   "outputs": [],
   "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.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\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": "2019-11-09T07:55:32.399727Z",
     "start_time": "2019-11-09T07:55:31.665777Z"
    }
   },
   "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\n",
    "from tensorflow.keras.preprocessing.image import load_img,img_to_array,ImageDataGenerator\n",
    "from tensorflow.keras.applications import MobileNet, MobileNetV2\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\n",
    "from tensorflow.keras.callbacks import Callback\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": "2019-11-09T07:55:32.513082Z",
     "start_time": "2019-11-09T07:55:32.400882Z"
    },
    "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: 1486496404936312892\n",
      ", name: \"/device:XLA_CPU:0\"\n",
      "device_type: \"XLA_CPU\"\n",
      "memory_limit: 17179869184\n",
      "locality {\n",
      "}\n",
      "incarnation: 1494835832195896811\n",
      "physical_device_desc: \"device: XLA_CPU device\"\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 7639764173\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 6498531796935105337\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: 4928824136441955388\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 - Fashion MNIST\n",
    "\n",
    "<img src='https://markusthill.github.io/images/2017-10-12-zalandos-fashion-mnist-dataset/zalando10x10.jpeg' width=70%>\n",
    "\n",
    "Fashion-MNIST는 60,000 개의 학습 세트와 10,000 개의 테스트 세트로 구성된 Zalando의 기사 이미지 데이터 세트입니다. 각 예제는 28 개의 28 개의 회색조 이미지이며 10 개의 클래스 레이블과 연결되어 있습니다. Fashion-MNIST는 기계 학습 알고리즘 벤치마킹을 위해 원본 MNIST 데이터 세트를 직접 대체하는 역할을합니다.\n",
    "\n",
    "출처 : [zalando research](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fashion MNIST 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.497831Z",
     "start_time": "2019-11-09T07:55:32.513971Z"
    }
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('./data/fashion-mnist/fashion-mnist_train.csv')\n",
    "test = pd.read_csv('./data/fashion-mnist/fashion-mnist_test.csv')\n",
    "\n",
    "X_train = np.array(train.iloc[:, 1:])\n",
    "y_train = np.array(train.iloc[:, 0])\n",
    "X_test = np.array(test.iloc[:, 1:])\n",
    "y_test = np.array(test.iloc[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.735315Z",
     "start_time": "2019-11-09T07:55:35.499212Z"
    }
   },
   "outputs": [],
   "source": [
    "# Train / Validation 데이터 분리하기\n",
    "# Train 데이터를 Train / Validation 으로 나누어줌으로써 Overfitting 여부를 파악\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_train, y_train, test_size=0.2, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.739253Z",
     "start_time": "2019-11-09T07:55:35.736679Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fashion MNIST Train 데이터의 크기 : 48000\n",
      "Fashion MNIST Validation 데이터의 크기 : 12000\n",
      "Fashion MNIST Test 데이터의 크기 : 10000\n"
     ]
    }
   ],
   "source": [
    "# Train / Test 데이터의 크기 확인\n",
    "print('Fashion MNIST Train 데이터의 크기 : {}'.format(len(X_train)))\n",
    "print('Fashion MNIST Validation 데이터의 크기 : {}'.format(len(X_valid)))\n",
    "print('Fashion MNIST Test 데이터의 크기 : {}'.format(len(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fashion MNIST 데이터 형태 확인하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.743283Z",
     "start_time": "2019-11-09T07:55:35.740174Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape :  (48000, 784)\n",
      "y_train Shape :  (48000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train Shape : \",X_train.shape) # 28*28짜리 크기의 숫자 이미지 48000개\n",
    "print(\"y_train Shape : \",y_train.shape) # 각 이미지별 레이블 48000개"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.746601Z",
     "start_time": "2019-11-09T07:55:35.744244Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val Shape :  (12000, 784)\n",
      "y_val Shape :  (12000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_val Shape : \",X_valid.shape) # 28*28짜리 크기의 숫자 이미지 12000개\n",
    "print(\"y_val Shape : \",y_valid.shape) # 각 이미지별 레이블 12000개"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.750184Z",
     "start_time": "2019-11-09T07:55:35.747607Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test Shape :  (10000, 784)\n",
      "y_test Shape :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_test Shape : \",X_test.shape) # 28*28짜리 크기의 숫자 이미지 10000개\n",
    "print(\"y_test Shape : \",y_test.shape) # 각 이미지별 레이블 10000개"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 시각화하기\n",
    "\n",
    "그러면 이제 구체적으로 Fashion MNIST의 Train 데이터셋의 첫번재 데이터를 확인해보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.754019Z",
     "start_time": "2019-11-09T07:55:35.751128Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.861379Z",
     "start_time": "2019-11-09T07:55:35.755043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JmyS9J+kFSXP7qLf/kvSGpJ0aC9b8hnpborGP6Dsl7Si+bm76tUv01ZPXjcNlgSDYQQcEQdiBIAg7EARhB4Ig7EAQhB0IgrADQfwvyfvaPSQscfcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0].reshape(28,28), cmap='Greys', interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "50000개의 중 랜덤으로 한번 살펴보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:35.864909Z",
     "start_time": "2019-11-09T07:55:35.862418Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_classlabel(code):\n",
    "    label = {0: 'T-Shirt',\n",
    "             1: 'Pants',\n",
    "             2: 'Pullover',\n",
    "             3: 'Dress',\n",
    "             4: 'Coat',\n",
    "             5: 'Sandle',\n",
    "             6: 'Shirt',\n",
    "             7: 'Sneakers',\n",
    "             8: 'Bag',\n",
    "             9: 'AnkleBoots'}\n",
    "    return label[code]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:37.138602Z",
     "start_time": "2019-11-09T07:55:35.865892Z"
    }
   },
   "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",
    "# Fashion 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].reshape(28,28), cmap='Greys', interpolation='nearest')\n",
    "    label_name = \"Label:\" + str(get_classlabel(y_train[n]))\n",
    "    plt.title(label_name)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:37.212298Z",
     "start_time": "2019-11-09T07:55:37.139588Z"
    }
   },
   "outputs": [],
   "source": [
    "# 데이터 크기 조정(Data Reshape)\n",
    "X_train = X_train.reshape(X_train.shape[0],28,28,1)\n",
    "X_valid = X_valid.reshape(X_valid.shape[0],28,28,1)\n",
    "X_test = X_test.reshape(X_test.shape[0],28,28,1)\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, 10) \n",
    "y_valid = to_categorical(y_valid,10)\n",
    "y_test = to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델링\n",
    "\n",
    "간단하게 CNN 모델을 이용하여, MNIST 데이터를 분류해보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:37.215314Z",
     "start_time": "2019-11-09T07:55:37.213353Z"
    }
   },
   "outputs": [],
   "source": [
    "INPUT_SHAPE = (28,28,1)\n",
    "KERNEL_SIZE = (3, 3)\n",
    "DROP_RATE = 0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 모델 구성\n",
    "Keras에서는 add함수를 통해서 레고 블록을 조립하듯이, 원하는 Layer를 추가 가능함  \n",
    "여기서는 Conv Layer와 MaxPooling Layer 그리고 Dense Layer를 이용하여 모델을 구성하도록 하자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:37.296344Z",
     "start_time": "2019-11-09T07:55:37.216331Z"
    }
   },
   "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",
    "# 1번쨰 ConvNet\n",
    "# Conv Layer\n",
    "model.add(Conv2D(filters=64,\n",
    "                 kernel_size=KERNEL_SIZE,\n",
    "                 activation='relu',\n",
    "                 input_shape=INPUT_SHAPE\n",
    "                 ))\n",
    "# Max Pooling Layer\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# 2번쨰 Conv Layer\n",
    "# Conv Layer\n",
    "model.add(Conv2D(filters=128,\n",
    "                 kernel_size=KERNEL_SIZE,\n",
    "                 activation='relu',\n",
    "                 ))\n",
    "# Max Pooling Layer\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# 분류를 하기 위해 shape을 1차원으로 맞추는 Layer\n",
    "model.add(Flatten())\n",
    "model.add(Dense(units=1024, activation='relu'))\n",
    "model.add(Dropout(rate=DROP_RATE))\n",
    "\n",
    "# 마지막 분류를 하기 위한 Fully Connected Layer\n",
    "# One Hot Encoding을 했으므로 activation function은 softmax로 처리함\n",
    "model.add(Dense(units=10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 모델 컴파일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:37.336516Z",
     "start_time": "2019-11-09T07:55:37.297336Z"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(loss=categorical_crossentropy,\n",
    "              optimizer=Adam(),\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 모델 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:37.423731Z",
     "start_time": "2019-11-09T07:55:37.337505Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 64)        640       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 3200)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              3277824   \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 3,362,570\n",
      "Trainable params: 3,362,570\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()\n",
    "\n",
    "plot_model(model, to_file='./img/model/fashion_mnist_cnn_model.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 신경망 모델 학습\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": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:55:37.426855Z",
     "start_time": "2019-11-09T07:55:37.424926Z"
    }
   },
   "outputs": [],
   "source": [
    "EPOCHS = 10\n",
    "BATCH_SIZE = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:56:23.063725Z",
     "start_time": "2019-11-09T07:55:37.428702Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "48000/48000 [==============================] - 5s 109us/sample - loss: 0.4986 - acc: 0.8196 - val_loss: 0.3629 - val_acc: 0.8686\n",
      "Epoch 2/10\n",
      "48000/48000 [==============================] - 4s 92us/sample - loss: 0.3163 - acc: 0.8859 - val_loss: 0.2863 - val_acc: 0.8930\n",
      "Epoch 3/10\n",
      "48000/48000 [==============================] - 4s 92us/sample - loss: 0.2643 - acc: 0.9032 - val_loss: 0.2611 - val_acc: 0.9026\n",
      "Epoch 4/10\n",
      "48000/48000 [==============================] - 4s 92us/sample - loss: 0.2334 - acc: 0.9144 - val_loss: 0.2558 - val_acc: 0.9045\n",
      "Epoch 5/10\n",
      "48000/48000 [==============================] - 4s 92us/sample - loss: 0.2049 - acc: 0.9244 - val_loss: 0.2633 - val_acc: 0.9047\n",
      "Epoch 6/10\n",
      "48000/48000 [==============================] - 4s 92us/sample - loss: 0.1814 - acc: 0.9317 - val_loss: 0.2417 - val_acc: 0.9123\n",
      "Epoch 7/10\n",
      "48000/48000 [==============================] - 4s 92us/sample - loss: 0.1605 - acc: 0.9401 - val_loss: 0.2389 - val_acc: 0.9162\n",
      "Epoch 8/10\n",
      "48000/48000 [==============================] - 4s 90us/sample - loss: 0.1417 - acc: 0.9480 - val_loss: 0.2420 - val_acc: 0.9151\n",
      "Epoch 9/10\n",
      "48000/48000 [==============================] - 4s 92us/sample - loss: 0.1214 - acc: 0.9550 - val_loss: 0.2341 - val_acc: 0.9211\n",
      "Epoch 10/10\n",
      "48000/48000 [==============================] - 4s 92us/sample - loss: 0.1059 - acc: 0.9602 - val_loss: 0.2570 - val_acc: 0.9183\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": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:56:23.187186Z",
     "start_time": "2019-11-09T07:56:23.068416Z"
    }
   },
   "outputs": [],
   "source": [
    "# 학습된 모델 weight 저장 \n",
    "model.save('./bin/fashion_mnist_cnn.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 신경망 모델 평가\n",
    "\n",
    "위에서 학습된 결과를 시각화 해보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:56:25.388984Z",
     "start_time": "2019-11-09T07:56:23.188270Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss : 0.07145159787257822\n",
      "Train Accuracy : 0.9743541479110718\n",
      "Validation Loss : 0.2570084097534418\n",
      "Validation Accuracy : 0.9183333516120911\n"
     ]
    }
   ],
   "source": [
    "# Train 데이터로 평가하기\n",
    "train_loss, train_acc = model.evaluate(X_train,y_train,verbose=0)\n",
    "print('Train Loss : {}'.format(train_loss))\n",
    "print('Train Accuracy : {}'.format(train_acc))\n",
    "\n",
    "# Validation 데이터로 평가하기\n",
    "valid_loss, valid_acc = model.evaluate(X_valid,y_valid,verbose=0)\n",
    "print('Validation Loss : {}'.format(valid_loss))\n",
    "print('Validation Accuracy : {}'.format(valid_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:56:25.529230Z",
     "start_time": "2019-11-09T07:56:25.389930Z"
    }
   },
   "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": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:56:25.732194Z",
     "start_time": "2019-11-09T07:56:25.530299Z"
    }
   },
   "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(loss)+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": [
    "### 신경망 모델 검증하기\n",
    "\n",
    "MNIST 데이터셋을 학습한 모델을 놀랍게도 정확도 99% 이상의 성능을 보인다.  \n",
    "그렇다면 이러한 신경망이 학습에 전혀 사용되지 않는 데이터인 Test 데이터셋에서는 어떤 결과를 보이는지 살펴보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:56:26.187487Z",
     "start_time": "2019-11-09T07:56:25.733266Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss : 0.23800334943830967\n",
      "Validation Accuracy : 0.9211999773979187\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test,y_test,verbose=0)\n",
    "print('Validation Loss : {}'.format(test_loss))\n",
    "print('Validation Accuracy : {}'.format(test_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test 데이터에 대해서도 99%의 정확도를 보여준다.  \n",
    "\n",
    "그러면 과연 학습된 신경망이 어떠한 데이터를 잘 못 예측했는지 살펴보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T07:57:25.939778Z",
     "start_time": "2019-11-09T07:57:23.529586Z"
    }
   },
   "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_test) \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",
    "# 잘못 예측한 데이터 찾기\n",
    "wrong_result = []\n",
    "for n in range(0, len(test_labels)):\n",
    "    if predicted_labels[n] != test_labels[n]:\n",
    "        wrong_result.append(n)\n",
    "\n",
    "# random 함수를 통해서 임의의 16개 데이터 가져오기\n",
    "samples = random.choices(population=wrong_result, k=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_test[n].reshape(28, 28), cmap='Greys', interpolation='nearest')\n",
    "    tmp = \"Label:\" + str(get_classlabel(test_labels[n])) + \", Prediction:\" + str(get_classlabel(predicted_labels[n]))\n",
    "    plt.title(tmp)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('./img/result/fashion_mnist_wrong_result.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 다음 예제에서는\n",
    "\n",
    "CNN을 통해서 MNIST와 같은 간단한 예제를 분류할 수 있었다. 다음 예제에서는 RGB 이미지를 한번 분류하는 예제를 더 깊은 CNN으로 수행해보도록 하자."
   ]
  },
  {
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   "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",
    "    - https://pinkwink.kr/1121\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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