Lecture09 - OpenVINO Inference(MobileNet).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\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://gitcdn.xyz/cdn/Tony607/blog_statics/9ddd48eb623175fb734e63865ebe604351c64c61/images/openvino/openvino.png' width=50%>\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=\"#Keras-h5-파일을-Tensorflow-pb파일로-변환\" data-toc-modified-id=\"Keras-h5-파일을-Tensorflow-pb파일로-변환-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Keras h5 파일을 Tensorflow pb파일로 변환</a></span></li><li><span><a href=\"#Tensorflow-pb파일을-OpenVINO-xml,-bin파일로-변환\" data-toc-modified-id=\"Tensorflow-pb파일을-OpenVINO-xml,-bin파일로-변환-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Tensorflow pb파일을 OpenVINO xml, bin파일로 변환</a></span></li><li><span><a href=\"#OpenVINO에서-xml,bin파일로-추론\" data-toc-modified-id=\"OpenVINO에서-xml,bin파일로-추론-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>OpenVINO에서 xml,bin파일로 추론</a></span></li><li><span><a href=\"#Reference\" data-toc-modified-id=\"Reference-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Reference</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://gitcdn.xyz/cdn/Tony607/blog_statics/73e93019d8a9dafd0b5a0fe200e982b75d50bf21/images/keras-tf/keras_tf_pb.png' width=50%>\n",
    "\n",
    "## Keras h5 파일을 Tensorflow pb파일로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/intel/imobilenet/utils/keras_to_freeze_binary.py:18: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/intel/imobilenet/myvenv/lib/python3.5/site-packages/tensorflow/python/ops/init_ops.py:97: calling GlorotUniform.__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",
      "WARNING:tensorflow:From /home/intel/imobilenet/myvenv/lib/python3.5/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",
      "WARNING:tensorflow:From /home/intel/imobilenet/myvenv/lib/python3.5/site-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.__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",
      "WARNING:tensorflow:From /home/intel/imobilenet/myvenv/lib/python3.5/site-packages/tensorflow/python/ops/init_ops.py:97: calling Ones.__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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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/intel/imobilenet/myvenv/lib/python3.5/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "mobilenetv2_1.00_224 (Model) (None, 7, 7, 1280)        2257984   \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1280)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               655872    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                32832     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 20)                1300      \n",
      "=================================================================\n",
      "Total params: 2,947,988\n",
      "Trainable params: 2,216,084\n",
      "Non-trainable params: 731,904\n",
      "_________________________________________________________________\n",
      "Input name:\n",
      "mobilenetv2_1.00_224_input_1:0\n",
      "Output name:\n",
      "dense_2_1/Softmax:0\n",
      "WARNING:tensorflow:From /home/intel/imobilenet/utils/keras_to_freeze_binary.py:47: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
      "WARNING:tensorflow:From /home/intel/imobilenet/myvenv/lib/python3.5/site-packages/tensorflow/python/framework/graph_util_impl.py:270: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
      "INFO:tensorflow:Froze 266 variables.\n",
      "INFO:tensorflow:Converted 266 variables to const ops.\n",
      "COMPLETE CONVERTING h5 to pb FILE\n"
     ]
    }
   ],
   "source": [
    "%run ./utils/keras_to_freeze_binary.py ./bin/mobilenetv2_class20.h5 ./bin/mobilenetv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://software.intel.com/sites/default/files/managed/b7/e3/webops12032-diagram-of-flow-750.jpg'>\n",
    "\n",
    "## Tensorflow pb파일을 OpenVINO xml, bin파일로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Optimizer arguments:\n",
      "Common parameters:\n",
      "\t- Path to the Input Model: \t/home/intel/imobilenet/./bin/mobilenetv2.pb\n",
      "\t- Path for generated IR: \t/home/intel/imobilenet/bin/\n",
      "\t- IR output name: \tmobilenetv2\n",
      "\t- Log level: \tERROR\n",
      "\t- Batch: \t1\n",
      "\t- Input layers: \tNot specified, inherited from the model\n",
      "\t- Output layers: \tNot specified, inherited from the model\n",
      "\t- Input shapes: \tNot specified, inherited from the model\n",
      "\t- Mean values: \tNot specified\n",
      "\t- Scale values: \tNot specified\n",
      "\t- Scale factor: \tNot specified\n",
      "\t- Precision of IR: \tFP32\n",
      "\t- Enable fusing: \tTrue\n",
      "\t- Enable grouped convolutions fusing: \tTrue\n",
      "\t- Move mean values to preprocess section: \tFalse\n",
      "\t- Reverse input channels: \tFalse\n",
      "TensorFlow specific parameters:\n",
      "\t- Input model in text protobuf format: \tFalse\n",
      "\t- Path to model dump for TensorBoard: \tNone\n",
      "\t- List of shared libraries with TensorFlow custom layers implementation: \tNone\n",
      "\t- Update the configuration file with input/output node names: \tNone\n",
      "\t- Use configuration file used to generate the model with Object Detection API: \tNone\n",
      "\t- Operations to offload: \tNone\n",
      "\t- Patterns to offload: \tNone\n",
      "\t- Use the config file: \tNone\n",
      "Model Optimizer version: \t2019.1.1-83-g28dfbfd\n",
      "WARNING:tensorflow:From /opt/intel/openvino/deployment_tools/model_optimizer/mo/front/tf/partial_infer/tf.py:148: The name tf.NodeDef is deprecated. Please use tf.compat.v1.NodeDef instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ WARNING ]  From %s: The name %s is deprecated. Please use %s instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/intel/openvino/deployment_tools/model_optimizer/mo/front/tf/loader.py:35: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ WARNING ]  From %s: The name %s is deprecated. Please use %s instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/intel/openvino/deployment_tools/model_optimizer/mo/front/tf/loader.py:109: The name tf.MetaGraphDef is deprecated. Please use tf.compat.v1.MetaGraphDef instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ WARNING ]  From %s: The name %s is deprecated. Please use %s instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[ SUCCESS ] Generated IR model.\n",
      "[ SUCCESS ] XML file: /home/intel/imobilenet/bin/mobilenetv2.xml\n",
      "[ SUCCESS ] BIN file: /home/intel/imobilenet/bin/mobilenetv2.bin\n",
      "[ SUCCESS ] Total execution time: 9.09 seconds. \n"
     ]
    }
   ],
   "source": [
    "%run /opt/intel/openvino/deployment_tools/model_optimizer/mo.py \\\n",
    "    --framework tf \\\n",
    "    --input_model ./bin/mobilenetv2.pb \\\n",
    "    --batch 1 \\\n",
    "    --data_type FP32 \\\n",
    "    --output_dir bin/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OpenVINO에서 xml,bin파일로 추론"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# assert 'computer_vision_sdk' in os.environ['PYTHONPATH']\n",
    "\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "try:\n",
    "    from openvino import inference_engine as ie\n",
    "    from openvino.inference_engine import IENetwork, IEPlugin\n",
    "except Exception as e:\n",
    "    exception_type = type(e).__name__\n",
    "    print(\"The following error happened while importing Python API module:\\n[ {} ] {}\".format(exception_type, e))\n",
    "    sys.exit(1)\n",
    "    \n",
    "    \n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.preprocessing.image import load_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process_image(imagePath, img_height=224):\n",
    "    # Model input format\n",
    "    n, c, h, w = [1, 3, img_height, img_height]\n",
    "    image = Image.open(imagePath)\n",
    "    processedImg = image.resize((h, w), resample=Image.BILINEAR)\n",
    "\n",
    "    # Normalize to keep data between 0 - 1\n",
    "    processedImg = (np.array(processedImg) - 0) / 255.0\n",
    "\n",
    "    # Change data layout from HWC to CHW\n",
    "    processedImg = processedImg.transpose((2, 0, 1))\n",
    "    processedImg = processedImg.reshape((n, c, h, w))\n",
    "\n",
    "    return image, processedImg, imagePath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plugin initialization for specified device and load extensions library if specified.\n",
    "plugin_dir = None\n",
    "model_xml = './bin/mobilenetv2.xml'\n",
    "model_bin = './bin/mobilenetv2.bin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Devices: GPU (intel), CPU, MYRIAD\n",
    "# plugin = IEPlugin(\"MYRIAD\", plugin_dirs=plugi)\n",
    "plugin = IEPlugin(\"CPU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/intel/imobilenet/myvenv/lib/python3.5/site-packages/ipykernel_launcher.py:2: DeprecationWarning: from_ir() method of IENetwork is deprecated. Please use IENetwork class constructor to create valid IENetwork instance\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Read IRn_dir\n",
    "net = IENetwork.from_ir(model=model_xml, weights=model_bin)\n",
    "assert len(net.inputs.keys()) == 1\n",
    "assert len(net.outputs) == 1\n",
    "input_blob = next(iter(net.inputs))\n",
    "out_blob = next(iter(net.outputs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load network to the plugin\n",
    "exec_net = plugin.load(network=net)\n",
    "del net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['boat', 'dog', 'person', 'aeroplane', 'person_bike', 'sofa', 'cat']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "test_list = list(map(lambda x:x.split('.')[0],os.listdir('./img/test/')))\n",
    "print(test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select Test File : cat\n"
     ]
    }
   ],
   "source": [
    "# Run inference\n",
    "input_file = input('Select Test File : ')\n",
    "\n",
    "fileName = './img/test/{}.jpg'.format(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "image, processedImg, imagePath = pre_process_image(fileName)\n",
    "res = exec_net.infer(inputs={input_blob: processedImg})\n",
    "# Access the results and get the index of the highest confidence score\n",
    "output_node_name = list(res.keys())[0]\n",
    "res = res[output_node_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicted class index.\n",
    "idx = np.argsort(res[0])[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-25T16:56:11.472194Z",
     "start_time": "2019-11-25T16:56:10.203650Z"
    }
   },
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted : CAT (100.00%)\n"
     ]
    }
   ],
   "source": [
    "# decode the predictions\n",
    "with open('./bin/class20.pickle', 'rb') as f:\n",
    "    labels = pickle.load(f)\n",
    "    \n",
    "plt.imshow(load_img(fileName))\n",
    "plt.show()    \n",
    "print('Predicted : {} ({:.2f}%)'.format(labels[idx].upper(),res[0][idx]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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": [
    "import pandas as pd\n",
    "pd.DataFrame(res,columns=labels.values()).T.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "강의목차",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}