2020.02.10 CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2020.02.10  CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CNN\n",
    "[cnn에서 중요한것은 망의 깊이 ]- 얼마나 깊게하는지 - 어떻게 깊게하는지 \n",
    "[cnn에서  중요한것은 이미지를 어떻게 늘릴것인지]\n",
    "\n",
    "\n",
    "[FILTER]\n",
    "- 필터사이즈가 크면 큰특징 작으면 작은특징\n",
    "    - 크게하면 손실이 크다 작게하면 deep하게 할 수 있따 \n",
    "        - 작게한 것 중 deep한것의 대표적인것은 VGG16,19\n",
    "        - identity 시키는 것_ 이전의 것을 고려 _ 깊게하더라도 살아남는다\n",
    "- 인셉션: 망을 깊게하기전에 수평으로한다 . \n",
    "\n",
    "[IMAGE]\n",
    "- image data generator:이미지 증강 \n",
    "    - 이미지 증강을 통해 이미지 구조문제 해결 : 이미지를 다양하게 변화\n",
    "        - pool , scale 등의 방법으로 \n",
    "        - 증강해논애들을 다시 save할 수 있따\n",
    "    - 파일로 많은 데이터를 저장해놨다가 읽어들이면서 fitting\n",
    " \n",
    "[MODEL]\n",
    "- 모델만들기 => compile => fit => evaluATE => PREDICT\n",
    "- 모델만드는 법 3 가지 :\n",
    "    - sequential \n",
    "    - functional : multi => multi\n",
    "        - 이미지가 특성이 다를떄?\n",
    "    - model:class 베이스로 모델을 구성하고 싶을때\n",
    "    \n",
    "- STATIC MODE:모델만들고 실행해야 결과알수있음:\n",
    "    - 속도가 아주빠르다\n",
    "- DIRECT MODE\n",
    "    - 효율성이 높다 \n",
    "    \n",
    "    \n",
    "    \n",
    "#현재 effcient net 에서 transfer learning으로 대세가 바뀌고 있따\n",
    "\n",
    "\n",
    "#http://incredible.ai/artificial-intelligence/2017/05/13/Transfer-Learning/\n",
    "- application : transfer learning: \n",
    "    - 기존의 만들어진 모델을 사용하여 새로운 모델을 만들시 학습을 빠르게 하며,\n",
    "      예측을 더 높이는 방법입니다.\n",
    "    - a를 b에 맵핑해준다?\n",
    "    - 데이터 부족문제 해결\n",
    "    ↓↓↓↓↓↓↓↓↓↓↓↓↓\n",
    "    얘랑 의미가 비슷하다\n",
    "    cf)준지도학습: 라벨이 있는애와 없는애가 있따 : 라벨이 있는애를 기반으로 없는애한테 라벨을 달아준다 \n",
    "    - vert에도 적용 : pre training을 이용해 목적에 맞게 fine tuning한다 \n",
    "    \n",
    "- global average(maximum) pooling : \n",
    "    - covolution 연산이 끝난 후 flatten 해줄때 이미지의 특성을 잃지않게하는것?\n",
    "\n",
    "    \n",
    "- efficient net: 이미지를 잘처리하는 방법 : 동영상 처리에 부적합\n",
    "    - 1 filter의 사이즈 \n",
    "    - 2 depth:layer의 깊이\n",
    "    - 3 원본의 resolution: 원본의 해상도를 어떻게하느냐\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "img_rows, img_cols = 28, 28\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape:  (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "# channel_first, channel_last\n",
    "\n",
    "# x_train = x_train.reshape(x_train.shape[0],1, 28,28,)   <- channel first\n",
    "# x_test = x_test.reshape(x_test.shape[0], 1,  28,28)\n",
    "\n",
    "x_train = x_train.reshape(x_train.shape[0], 28,28,1 )    # <- channel last\n",
    "x_test = x_test.reshape(x_test.shape[0],  28,28, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "print('x_train shape: ', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')   #\n",
    "print(x_test.shape[0], 'test samples')    # \n",
    "\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\site-packages\\ipykernel_launcher.py:6: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", input_shape=(28, 28, 1...)`\n",
      "  \n",
      "C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\")`\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# 아웃풋 필터 개수(32), 필터 사이즈(3x3)\n",
    "# 입력데이터 (28x28) , 나가는 데이터 (128(batch) x 26(28-3+1)x26(28-3+1)x 32(아웃풋 32))\n",
    "# valid(줄어드는것) <- 모서리 특징 노캐어, same(28x28) <- 모서리 특징이 산다\n",
    "model.add(Conv2D(32, 3,3, activation = 'relu', input_shape = (28,28,1)))  # channel이 뭐지에 따라 맞춰줘야한다(last를 써서 뒤에 1)\n",
    "\n",
    "# 128 x 24(26-3+1) x 24(26-3+1) x 64\n",
    "model.add(Conv2D(64, 3,3, activation = 'relu'))\n",
    "\n",
    "# 128,12,12,64\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# 12,12,64 => 1차원 9216( 3수를 다 곱함)\n",
    "model.add(Flatten())\n",
    "\n",
    "# FFNN , FC(fully - connected)\n",
    "# dense (나가는 차수만 지정 : 128)\n",
    "model.add(Dense(128, activation = 'relu'))  # <- 가중치 :9216x128 \n",
    "# 128 x 128\n",
    "model.add(Dropout(0.5)) # 계산 회로만 생략 (차수는 그대로)\n",
    "\n",
    "model.add(Dense(10, activation = 'softmax')) # 128x10\n",
    "\n",
    "# 128x10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/12\n",
      "60000/60000 [==============================] - 56s 926us/step - loss: 0.2684 - accuracy: 0.9174 - val_loss: 0.0537 - val_accuracy: 0.9833\n",
      "Epoch 2/12\n",
      "60000/60000 [==============================] - 55s 909us/step - loss: 0.0866 - accuracy: 0.9742 - val_loss: 0.0499 - val_accuracy: 0.9846\n",
      "Epoch 3/12\n",
      "60000/60000 [==============================] - 54s 898us/step - loss: 0.0663 - accuracy: 0.9803 - val_loss: 0.0333 - val_accuracy: 0.9895\n",
      "Epoch 4/12\n",
      "60000/60000 [==============================] - 54s 897us/step - loss: 0.0521 - accuracy: 0.9842 - val_loss: 0.0330 - val_accuracy: 0.9890\n",
      "Epoch 5/12\n",
      "60000/60000 [==============================] - 54s 900us/step - loss: 0.0466 - accuracy: 0.9866 - val_loss: 0.0329 - val_accuracy: 0.9896\n",
      "Epoch 6/12\n",
      "60000/60000 [==============================] - 54s 900us/step - loss: 0.0412 - accuracy: 0.9877 - val_loss: 0.0282 - val_accuracy: 0.9911\n",
      "Epoch 7/12\n",
      "60000/60000 [==============================] - 54s 902us/step - loss: 0.0369 - accuracy: 0.9887 - val_loss: 0.0272 - val_accuracy: 0.9912\n",
      "Epoch 8/12\n",
      "60000/60000 [==============================] - 54s 898us/step - loss: 0.0336 - accuracy: 0.9896 - val_loss: 0.0270 - val_accuracy: 0.9912\n",
      "Epoch 9/12\n",
      "60000/60000 [==============================] - 54s 898us/step - loss: 0.0312 - accuracy: 0.9906 - val_loss: 0.0269 - val_accuracy: 0.9917\n",
      "Epoch 10/12\n",
      "60000/60000 [==============================] - 54s 898us/step - loss: 0.0306 - accuracy: 0.9901 - val_loss: 0.0331 - val_accuracy: 0.9905\n",
      "Epoch 11/12\n",
      "60000/60000 [==============================] - 54s 901us/step - loss: 0.0302 - accuracy: 0.9910 - val_loss: 0.0329 - val_accuracy: 0.9896\n",
      "Epoch 12/12\n",
      "60000/60000 [==============================] - 54s 898us/step - loss: 0.0263 - accuracy: 0.9920 - val_loss: 0.0261 - val_accuracy: 0.9917\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss = keras.losses.categorical_crossentropy, \n",
    "              optimizer = keras.optimizers.Adadelta(), metrics = ['accuracy'])\n",
    "\n",
    "# train, test 만 하니까 test도 fitting 되는걸 방지하기 위해서\n",
    "# train, test, validation으로 나눠준다\n",
    "model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))\n",
    "\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: \t 0.02610298545975238\n",
      "accuracy : \t 0.991599977016449\n"
     ]
    }
   ],
   "source": [
    "print('loss:', '\\t',score[0])  # loss\n",
    "print('accuracy :', '\\t',score[1])  # accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "first_image = x_train[0, :, :]\n",
    "plt.imshow(first_image, cmap = plt.cm.Greys);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape:  (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ICT01_09\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", input_shape=(28, 28, 1...)`\n",
      "C:\\Users\\ICT01_09\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\")`\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.reshape(x_train.shape[0], 28,28,1 )    # <- channel last\n",
    "x_test = x_test.reshape(x_test.shape[0],  28,28, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "print('x_train shape: ', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')   #\n",
    "print(x_test.shape[0], 'test samples')    # \n",
    "\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# 아웃풋 필터 개수(32), 필터 사이즈(3x3)\n",
    "# 입력데이터 (28x28) , 나가는 데이터 (128(batch) x 26(28-3+1)x26(28-3+1)x 32(아웃풋 32))\n",
    "# valid(줄어드는것) <- 모서리 특징 노캐어, same(28x28) <- 모서리 특징이 산다\n",
    "model.add(Conv2D(32, 3,3, activation = 'relu', input_shape = (28,28,1)))  # channel이 뭐지에 따라 맞춰줘야한다(last를 써서 뒤에 1)\n",
    "\n",
    "# 128 x 24(26-3+1) x 24(26-3+1) x 64\n",
    "model.add(Conv2D(64, 3,3, activation = 'relu'))\n",
    "\n",
    "# 128,12,12,64\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# 12,12,64 => 1차원 9216( 3수를 다 곱함)\n",
    "model.add(Flatten())\n",
    "\n",
    "# FFNN , FC(fully - connected)\n",
    "# dense (나가는 차수만 지정 : 128)\n",
    "model.add(Dense(128, activation = 'relu'))  # <- 가중치 :9216x128 \n",
    "# 128 x 128\n",
    "model.add(Dropout(0.5)) # 계산 회로만 생략 (차수는 그대로)\n",
    "\n",
    "model.add(Dense(10, activation = 'softmax')) # 128x10\n",
    "\n",
    "\n",
    "model.compile(loss = keras.losses.categorical_crossentropy, \n",
    "              optimizer = keras.optimizers.Adadelta(), metrics = ['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# key 이름 확인하기\n",
    "model.history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/12\n",
      "60000/60000 [==============================] - 55s 919us/step - loss: 0.0254 - accuracy: 0.9921 - val_loss: 0.0263 - val_accuracy: 0.9918\n",
      "Epoch 2/12\n",
      "60000/60000 [==============================] - 55s 914us/step - loss: 0.0239 - accuracy: 0.9922 - val_loss: 0.0264 - val_accuracy: 0.9924\n",
      "Epoch 3/12\n",
      "60000/60000 [==============================] - 55s 918us/step - loss: 0.0232 - accuracy: 0.9926 - val_loss: 0.0309 - val_accuracy: 0.9899\n",
      "Epoch 4/12\n",
      "60000/60000 [==============================] - 56s 928us/step - loss: 0.0220 - accuracy: 0.9932 - val_loss: 0.0288 - val_accuracy: 0.9906\n",
      "Epoch 5/12\n",
      "60000/60000 [==============================] - 54s 906us/step - loss: 0.0220 - accuracy: 0.9932 - val_loss: 0.0254 - val_accuracy: 0.9918\n",
      "Epoch 6/12\n",
      "60000/60000 [==============================] - 54s 901us/step - loss: 0.0213 - accuracy: 0.9933 - val_loss: 0.0270 - val_accuracy: 0.9918\n",
      "Epoch 7/12\n",
      "60000/60000 [==============================] - 56s 932us/step - loss: 0.0206 - accuracy: 0.9933 - val_loss: 0.0284 - val_accuracy: 0.9920\n",
      "Epoch 8/12\n",
      "60000/60000 [==============================] - 55s 921us/step - loss: 0.0206 - accuracy: 0.9935 - val_loss: 0.0259 - val_accuracy: 0.9927\n",
      "Epoch 9/12\n",
      "60000/60000 [==============================] - 55s 916us/step - loss: 0.0184 - accuracy: 0.9944 - val_loss: 0.0285 - val_accuracy: 0.9922\n",
      "Epoch 10/12\n",
      "60000/60000 [==============================] - 55s 914us/step - loss: 0.0181 - accuracy: 0.9944 - val_loss: 0.0279 - val_accuracy: 0.9916\n",
      "Epoch 11/12\n",
      "60000/60000 [==============================] - 55s 913us/step - loss: 0.0205 - accuracy: 0.9940 - val_loss: 0.0268 - val_accuracy: 0.9905\n",
      "Epoch 12/12\n",
      "60000/60000 [==============================] - 55s 912us/step - loss: 0.0183 - accuracy: 0.9942 - val_loss: 0.0265 - val_accuracy: 0.9922\n"
     ]
    }
   ],
   "source": [
    "class AccuracyHistory(keras.callbacks.Callback):\n",
    "    # 오버라이딩 (재정의)\n",
    "    def on_train_begin(self, logs={}):  # 훈련시작시 이벤트\n",
    "        self.acc = []\n",
    "    def on_epoch_end(self, batch, logs={}):\n",
    "        self.acc.append(logs.get('accuracy'))\n",
    "        \n",
    "\n",
    "history = AccuracyHistory()\n",
    "history.on_train_begin()\n",
    "# train, test 만 하니까 test도 fitting 되는걸 방지하기 위해서\n",
    "# train, test, validation으로 나눠준다\n",
    "# epoch마다 accuracy를 저장 - 12번 저장된다\n",
    "model.fit(x_train, y_train, batch_size = batch_size, \n",
    "          epochs = epochs, verbose = 1, validation_data = (x_test, y_test), callbacks = [history])\n",
    "\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: \t 0.02646493338981254\n",
      "accuracy : \t 0.9922000169754028\n"
     ]
    },
    {
     "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": [
    "print('loss:', '\\t', score[0])  \n",
    "print('accuracy :', '\\t', score[1])  \n",
    "\n",
    "plt.plot(range(1, 13), history.acc)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CNN   ----- ?\n",
    "K.set_image_data_format('channels_first')  # channels_last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "모델이 저장되었습니다.\n",
      "모델이 로딩되었습니다.\n",
      "Model: \"sequential_11\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_15 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 24, 24, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_6 (Flatten)          (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 128)               1179776   \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# HDFS(hadoop file system) : model 구조 전체 저장\n",
    "# 가중치, 구조, optimization state 등이 저장\n",
    "# jason으로 저장가능 : 가중치, 구조를 별도로 저장해야함 => web에서 tensorflow 가능해서\n",
    "# tensorflow.js import\n",
    "model.save(\"model_mnist.h5\")\n",
    "print(\"모델이 저장되었습니다.\")\n",
    "from keras.models import load_model\n",
    "\n",
    "model = load_model('model_mnist.h5')\n",
    "print(\"모델이 로딩되었습니다.\")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv2d_15\n",
      "(None, 28, 28, 1)\n",
      "<function relu at 0x00000203C7F4B1F8>\n",
      "(None, 26, 26, 32)\n"
     ]
    }
   ],
   "source": [
    "# layer 정보 확인\n",
    "l1 = model.layers[0]\n",
    "l2 = model.layers[1]\n",
    "\n",
    "print(l1.name)\n",
    "print(l1.input_shape)\n",
    "print(l1.activation)\n",
    "print(l1.output_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ImageData Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU4AAAD7CAYAAAAFI30bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO2deZAd1XX/PxchkBAgkEBCiH3f7LDIrDbgRTZeiElhuyAbpnBIxXZiUiSBOOXyL4l/sYtUEiexkypScdAvlYI4xo6JFwgGnAQbiACzCwSITSAkgQAJMMag+/tj3rf7vDv39XTPvHnbnM8/M/P6vu6ePt19z3bPCTFGHMdxnPps0+8TcBzHGTb8xek4jtMQf3E6juM0xF+cjuM4DfEXp+M4TkP8xek4jtOQKb04QwhnhBAeCiE8EkK4tFsn5fQXl+vo4rLtDmGyeZwhhFnAamA5sBZYCZwbY3yge6fn9BqX6+jisu0e207hu8cDj8QY1wCEEK4CPgx0FMIOO+wQ58+fP4VDDjfPPvvsczHG3ft9HhPQWK7bb799nDdvHiGEHp3iGDpevxdxbNq0aRjkCg1lO3v27DhnzhzefPPN4rNtthkzUnXN9dPKPvdZStWYrVu3tm3r9X0lXn311Y5yncqLcynwlPl7LXBC1Rfmz5/P+eef3/aZBKGLlX6e2zasfPGLX3yi3+dQg8ZynTdvHsuXL2fWrFkdx1h5ijoyT+8PO0bH0zb7gE/lPJpy5ZVXDoNcoaFs58yZw7Jly3jxxRfbPgN444032n5ut912xZjXX3+97TONgfIlqM+23Xbbts8BXnvttbZtdt+ScfoyzU2euYm1yUv49ttv7yjXqbw4c2cw7uxDCBcCFwLsvPPO477Q6aZtejOnF9R+Xw9Y7sH6+c9/DpRCyqELP3v27Lb9QXkD5B7IIaWxXHfYYQdmzZpVa7KzN66uuX5KFlA+fDvssANQXnP7EGi8tlkZdtJC7T2QvuhzL/46L+MhYkLZWrlut912bNmyJXu/6/pqW+6FlL5A66KXs7Ay6PTCzB1f52rvRd2jVc97HabytK8F9jZ/7wU8kw6KMV4eY1wWY1ymh8AZaBrLNb3RnYFlQtlauU715TLKTOXFuRI4OISwfwhhO+Ac4JrunJbTR1yuo4vLtktMekqJMb4RQvg0cB0wC/hajPH+pvvp5OO05oFUdY21qntqMuTMNpl08+bNa/sbSlOwyhGtz/Q9awbKDEnHDCtTkWuVeyXnm9K1yslVctyyZUvb93Jj9JlcKXa87oOcjzQ1w3Pnlt5Xwyzfyci2k08w9VXaZyK9ZjlTu4mvsWps1bY67jf9bOpqm5IuHmP8HvC9qezDGTxcrqOLy7Y79N2Jkc4YuVlKs4G25WYHffbqq68C7bONtEqNsd9PNRlts5qFZlV9lku9qDr/fqVTDBLSzO11kYz0mZXZz372MwDmzp0LlJFWq9Xq2ucsAck81YKrIqw2+qvzHWYNczpINczcvS3NP9XaLf3OlNG9pvvMUkf7HJlQsOM4Tq/ou8aZppnop9U+NKvltLlddtkFgOeffx4oNQ2rKfz0pz8FYPPmzUD7bLf99tsDsHDhQqCcbaz2oXPRNqVZ2HNKfT52Jh6hVKVKcpp8Si5NTGPt93XNn3lmLOi7xx57ALB48eJx35eG89JLLxXbHn/88bbvVZ1jLnVJ55neczNFlpacdpjKzF4XPQNpnu0gkPrT9fxbzdM1TsdxnGmg7xpnmqSa84ekkfeddtqp2PYf//EfAJxyyikA3H333QAsWrSoGKNZ5aMf/SgA999fBhIPP/xwAP7iL/4CgP322w+AJUuWjDvHJlqH1Zit9jrK5BKNU5+xjXynGRHWSpAFceSRR7Z974UXXijGyA/58ssvA7DnnnsW25Rbqn1q9YvNJdb55u69Tj683P846lSt6Kq6Bk00zV6tFEzlmfOv1zn+zJC84zhOF/EXp+M4TkP6bqrLBJPKnFOT9ZnGXn311cW2K664AijN74MPPnjcfmTKaYxMd4D/+Z//AeDzn/88AH/3d38HtJuNaRK1JQ0sVK2Vn4nITaFrbt0WqfltzaiTTjoJgNWrVwPlNbTm+D333NP2vU2bNhXb9tprL6B06zz77LNAmd4E4xPgc0GiNEE6FwQZdarS/+pUQsp9r1MRl07HE2lAr85x61TPalphyzVOx3GchvRd41QaQDrz2BlIjn4Fgm677bZi2yc/+UkADjnkkLaxGzZsKMb8+Mc/BsqA0apVq4ptp512Wtv4XMKz0plyaTMiTaOy5z9iFXaydNIS0nSe3FJY3QPHH398sU3apDRMpYA9/PDDxRilkGmfVgOUhim5SPPMWQIak7MoOi0JtttGmRhjW6pOWlauacpR+gzpe1bjS/dVte8qKzCtvlXnvNLfOzH6knccx+kyPdU4Y4y8+eabbT6/dBllbgbSLKcEdiVFA1xwwQUA3HDDDQBce+21ALztbW8rxpx++ult+5EGCXDWWWcBsGbNGqBMqM/5rzRzVRUgEVZz7XeF8l4QQshqDVU1L9etWwfAMcccA8BDDz1UbFNa2COPPAKU116FWiw5WWlcqk3mNJS0KIX+Hzt+JmiXKXpebQqZ7nddK1kCVqtLNflckeJ08ULVvWNjEunxc+hcXnnllbaf0J6O1ok6z+vMuxscx3GmiL84HcdxGjKhqR5C+BrwIWBDjPGo1mcLgH8F9gMeBz4WY3yh0z7Mvpg1a1Y2Sz8NHuTGLF++HID3v//9xbYDDzwQKE2797znPUD7enKZclq/LJMf4N577wVg773HCmNrxYpdeaR9pdV47LkJmXTWvLHnMih0U65mn8XvqSmVVq+C8horXWzHHXcstq1duxYozbRcDYI6pOZirjpSzvxLG4blAkiDmmbWLdmGEMa5KHQd9Ezlqv/LHM+1lVmwYEHbmBxpMCfXs0j3geSRW6mn+8muNNSzXxUAqtW7asIRcAVwRvLZpcANMcaDgRtafzvDxRW4XEeVK3DZTisTapwxxv8OIeyXfPxh4PTW7yuAHwKX1DlgOoOlb/5c+odmJ80gZ599tj2/tu9JY7SzlD5T+srFF19cbJMW+sUvfhGA9773vW37zZ2b1TSkTabai50BBzGw0G25hhDaZNap8pDtmCgL4pZbbgFg3333LbZJZmrwlwsAVTXh6xTUyWka+n6uinmqadr9DWrQr1uyjTGydevWtmtmLSko739rxek5kzZqr1laLzf3TCmYk1t0oMCutEjJIFfdSO8NWTTQOWjYlMk+0YtjjOtaJ7AOWNRpYAjhwhDC7SGE262Z5gwkk5JrrhisM3DUkq2V60xZGTUZpj0dKcZ4OXA5wJIlS+Kbb77Z9pbXzJP6TizprGDHKKXl6aefBkqtdP78+cUY9XJ/6qmxltI333xzsU2pMPppzrv4PV1elluOmWo9o37TWbkuXLgwptpXmqaVq6yvfkLyY1ptNPVl5hYfpNaG1fKlCWk/Oa1U37O+VZH6OHPybNr2dhiwcp07d2587bXX2lLAdM0kK/1tl8JW1TDVddRkm2vPXbUYRhqv1SLT7+u4OT9sJ02zqeY5WY1zfQhhCUDr54YJxjvDgct1dHHZdpHJapzXAOcBX2r9/HadL4UQOs7S6RKu3BIobbM1GffZZx+grLUpv4Y0UIDvfW+sN5VmopNPPrnY9ku/9EsA/NEf/VHbMWzUT7NcrhujNJuqzn6DGn3NMCm5iqqIc+666LpKW7CJztqWdkW02ou0Fv2UBgulb1TaRi5yrntOS3jlm7PfV7GQtCPnENJYtttss01xHYTud8ladWvtdVWxFV0zq61LxrqOKrpS5S/OFQBJi8bYzJU00l6n1mfTeqAT3gUhhCuBW4BDQwhrQwgXMHbxl4cQHgaWt/52hgiX6+jisp1+6kTVz+2w6d1dPhenh7hcRxeX7fTTl7Xq1gzPtdyFfLM0mfkKCEFpYqtGp1T23XffvRjz1re+tW2b3bfMtN///d8H4F3veheQTznS92xNx3StbVULkFEmxljZeiKXpvXEE08A+Qo7aZqXggHWJJMZrc8OOuigYlsaeNK9Y+8zyfHMM88E2oOGjz76KABf//rXgbKmgXU1VSVxjxL2eVGqkIKpus7r168vxui66Nrb4EwapKuVbJ5xj8g9I3moxoRFzfusnBToqjqur1V3HMeZBnqucf785z9v00w0Y6TtgdNEW4s0DYDPfOYzABx22GFAORN94xvfKMZoNlLNzV/91V8ttsn5fdxxxwFlvccjjjiiGPPcc88B5bJMpTXB+BksNzs2XSY4SqSapk1tkRape8DKXDJ78skngdJqsNdSstYxclVwVJczV4VH2ueDDz4ItC+skCalMarC9ZGPfKQYMxOsijR9UKjTwvXXXw+0B+aUCpgup25KLhir33UfSQZW45XsNSa35DK1cnKLHyrPrck/4jiO4/RY41SRDzuDpT5O+UVymoF8WXYGkf9EM45mmQ9+8IPFmDTZ9lvf+lax7Y//+I+BUpO45JKxVWgqGgKltiJfidWM0jSbXNLuEKewNKJqmWqaUA5lwQf50OyyPfmoldqifdtUNFkLkq9qd0Kp4aY9rezx09bUv/d7v1ds0/2gdDctC/2t3/qtYoxaF4861kcpzVJFWHIFWnJanOhUXCPnH9fzbv3Kknlaa9OeY6rpymKEfD3X9LzqWIgz44l2HMfpIv7idBzHaUjPm7XFGNtMV6nYKgCSC7LIFEvXtVt22203oFTTbbM2IXPCttW46KKLAPjsZz8LwMc+9jGgXG0EcOKJJwKlO8CaJelKCrkabNqMXREzqmzdujXbUiR1tFs3y9133w3AO97xDqCsVAVlFRwFAmWS2dYHGq97SKtYLFVtp2UCSj62vbCqM0mOuk/sevqZEByCfEtnXXMFgjZu3FiM2XXXXYF6prrkkysUkzOr5apJVxDZAFLqGrN1K3LutsngGqfjOE5Deq5xplWl0yTiXHAlrY2Yq1wkzSBdV26/r1lNMyKUSdPSWOUYtmOkDUvbyc3AqUZjZ9aZUk7P/s+5VBJon+nl6FfKkNK9oKw1ICtDWr5d/KDPcnJJ62emzcGg1Cplndh6oGlC9Y033jhuzEwghNB2XfXs6blVVSR7XRU4ylWHT4Opkp0dq98VeLLBmqq6ESJtINe0A0OdYK5rnI7jOA3pi4/TaiZ1KnXXaQkqf5W0SjtW+9Y2O4OqIs6KFSuAcsnlHnvsUYyRv01aiPXHpFVzNLvZmTA3844anWbpXIVvoXQkLW+0moUsAGmY0k6ttSF/l7Qfe8+kSz4lQ2mZAIceeigA73znO4F2v7juy6OPPhooOwTYxReTTeweJlQFXshH/f3vfx8oZXjAAQcUY3Rd9ZzlEtiF9m0XL9i0NGiPEaQWqe4v+0zLWpSlZ58/3QdVsutKdSTHcRynnb74OHM1GaUZVC2B0qxSRxu135eGqJnH+mPkS9PSzccee6xtv7nj55J17f8Ho1kdvIqtW7dW+oZyCeiStfyGdtmcti1durRtmxLS7e/SIqusDHVDtdqLfKta1mkT2tesWQPAF77wBQBOPfXUtvOy/9Mok/aSkt9QC0RkYdnFByJXW7eTZVnVo6tKA0wXx0CpVUrW1pKQ1ZhW9M8tuKmiTj3OvUMIN4UQVoUQ7g8hfKb1+YIQwvUhhIdbP3edaF/O4OByHU1crr2hjqn+BnBxjPFw4ETgUyGEI/B2o8OOy3U0cbn2gDqFjNcB6o63JYSwCljKFFrJ5hKlUydvzuzLNc5K00z00wZwFFiQ6m63KbCwcuVKoGyr8aMf/agYo5SLXO3QND0iTYjv9L/0m+mQa460PmnO1NVP27oiTRVS/UfVWITxFZes6ZyaWwoy2QR8uQGU+G4XPWif73//+4HxzckGlemWq54ByVMysEnmaWCubluMJqTpSLm18nKXWTdAJ+w7qSumuqXVq/kY4DYm0W50puQzDhtTletMKeg7bExVrqPeqXUq1A4OhRB2BK4GLooxbq7zVobx7YFb+yq2V7USTcdIU7Tf12eqkalUBjsDaTZUQq2tlvK7v/u7QOncVoUcuywzTX3ILddKZ9VcG9tBpBtyXbhwYdxmm23a/s/0mqWV4O22HOkS2jvvvBNoT0yX9qMUoVwCvAKC0jQfeOCBYsy9994LwOLFiwHYf//9x52HtNFcPc9BphtynTdvXkyX0qZB3H6j88h1jJD1V8dKqLJWctTSOEMIsxkTwr/EGL/Z+tjbjQ45LtfRxOU6/UyocYax1+8/AqtijH9pNk26lWxuCVdaKTxX5EPYGVC+SfWEkaaoAhIAzzzzDFD2mDn99NOLbTfddBMAZ5xxBlDWgVRiL4yv72e1jvSznC+vjo+l10yHXJvW40ytjFyqkpBWmNMM1Lso9/30vrK9qLTIQWNypmmqvVhrYxD9nd2Wa/o/pvLsN7kFNPJt5tIXO1mGNp2pzvNa54k+Bfg14N4Qwl2tzz7LmAC+3mo9+iTw0Rr7cgYHl+to4nLtAXWi6jcDnYx+bzc6pLhcRxOXa2/ouw2ZrhzIBYmkelfV0lOAQO1+tSoEShNdQSKZ7lDWgvzEJz4BwHnnnQeUgSQYv9bcmos679RJnWvRMMqkpltq0uVMqtQMzK3WEqkrxP6uNLMqk0yBJHsMySW37/T/0jZ7zjNBrpD/nwfRTSH0ftAznAv2pC7Cpgzuf+84jjOg9F3jTDU2Yf9OG9zbGUTV2f/pn/4JgBNOOAEo25dCWedRDmBbiUXpSOeffz5QJjrbteap1pRba6/PUsf0TCfV2HJInlVaTJ1karutUw0BS2rl2KCANNR0zExH8kzv+16TytPKTqln9913H9D+LtA7IF1M01SDdo3TcRynIX3XOFNtLu3dA6WPUbNEbsnmscceC5QJ8LZ/jPyewu5b7V41XonzVRqjnZ2kmaRVnmY6qQVR1X9mqtcst+902V+60CL3PXseuYR9p7yOVRr5dD4DqW9SFp6NSegZVkxD1ZLs+abPd9Nzdo3TcRynIQOjcQppc1arSyOydgZJ+xDlOuNpmV6u1qdmKs1cOa1W49Nq71D6TDRmUBKDB42crzNdPJDrMyVyWmWVVpv6RHM+0vS+shXo0z5XYqb7rtPrmPMRNinyUedYuc90XFV7X716dTHm7LPPBsqCMPfcc0+xzVbwB4+qO47j9Ax/cTqO4zSk76Z6ioIstlRZWunGmktpgEHr2q0Z18kdUHV8uz5eDuWqdegeRJg8VWZ81droTua0HV+V6pRbP58e38mTXs9cdSKRWzAicgsjUrnUqW2rNEKAT33qU0BZl8AumFm2bBlQ1jeYLK5xOo7jNCT0sr5gCGEj8Arw3ERjB5DdmPp57xtj3H3iYcOFy9XlOoBMq1x7+uIECCHcHmNc1tODdoFhPe9eMazXZ1jPu1cM6/WZ7vN2U91xHKch/uJ0HMdpSD9enJf34ZjdYFjPu1cM6/UZ1vPuFcN6fab1vHvu43Qcxxl23FR3HMdpiL84HcdxGtKzF2cI4YwQwkMhhEdCCJf26rhNCSHsHUK4KYSwKoRwfwjhM63PF4QQrg8hPNz6uWu/z3VQGAbZulyb43KtOG4vfJwhhFnAamA5sBZYCZwbY3xg2g/ekFbP6SUxxjtDCDsBdwBnAR8HNsUYv9S6iXaNMV7Sx1MdCIZFti7XZrhcq+mVxnk88EiMcU2M8XXgKuDDPTp2I2KM62KMd7Z+3wKsApYydr4rWsNWMCYcZ0hk63JtjMu1gim9OBuo8kuBp8zfa1ufDTQhhP2AY4DbgMUxxnUwJixgUf/ObHppaKINnWxnqlxhtJ/ZXsp10i/Olir/VeD9wBHAuSGEIzoNz3w20HlQIYQdgauBi2KMm/t9Pr2ioVxhyGQ7U+UKo/3M9lquk/ZxhhBOAv5PjPF9rb//ECDG+MVOY+fMmfPenXfeedIVoRueX/F7WpKsX32BNm7c+NygF4NoIleN32677X6sPi819l/8PtX7YKoVxrvFpk2bBl6u0PyZnT179o/nzp1bq/uopaqXU6fq8HaMjpfrDNBJ5lXnUXX8HBrz4osvdpTrVOpx5lT5E9JBIYQLgQuBt8yePZtzzz23stZh1UOQu2idLmSuvYVqBs6dO7fjMXLnkV7kOi0acnzlK1+ZWhHA3tBUrmy77ba8733vq2wBLKrqLlZR9YD1u13JP//zPw+DXKGGbK1cZ82axUknnYSdFNNrLZlbuabPd+7FlcrOfl/Pp1qZ2Nq8nWqwTseL8+qrr+4o16m8OGup8jHGy4HLQwgfmDNnzncn6i5X9Q+lnfUsqQByxU/VV8h2udT5SNj62x4jvRFyhYz7pcVOA43kCrBw4cKYk0mO3KSTahj296r7I314qjpp5o6Rkuub0++XcpeZULZWrgsWLIjqUS46aZ+5flF6bnKKkp7lnBLz6quvdjxmp+fMfq59q/C5ndD1fE+1d9RUgkNrgb3N33sBz3QaHGP83hSO5fSORnJ1hgqXbZeYyotzJXBwCGH/EMJ2wDnANd05LaePuFxHF5dtl5i0qR5jfCOE8GngOmAW8LUY4/0Tfa+TvzD1FeZMJJnato1rur9cu1KZkTvssAMADz74YLHtrrvuAuDoo48GyvahOVO9ykmejhlW032ycp2Iqp4/ubbNGq/rqr4x1uxLe0DV8VvlPtP3cibloASgukFT2cYYO8YOqvoCpb7upUvLLKbrrrsOKHt6vf3tbwfanze1/5Y87DbFK/SZnvdcO29ts/3D0vbj+tnUdJ9Ss7aW+e0m+Ijhch1dXLbdoe9dLqsicEKzgtU0O+1HM4+NxO25554ArFgxtpDg85//fLHt3//939uO+5u/+ZsAvPLKK8UYaTs5bTINJuW0lzrR5lGjUzDGXpeqWT+Nvua0wapOmFWZEJ2oSmEblAh+v9G9rPtdGqC9dnr2dtttNwD+5m/+pti2cuVKAO644w4ALr10LAf/zDPPLMYoOKRjWNnp+KmGaMekz2Lu+dO7RFZsrsNqFV4dyXEcpyF90TirZpBc7/KqBPZ0dtBMIn8mwAMPjNUleN/73gfACy+8UGz7xje+AZS+lpyvI9VeqvxsOV/eKPjHmtLpf67Kt8tdu1RzzaUs5bTAdJ86bu74VT7WdIz9/rD6sZtir2/qF9a2nFa3bt06AM4444zis1tvvRWAhx9+GChjCi+//HIxJvUr2+et0zXP3QNVz2QaL7Epiq5xOo7jTAN90TjtrCEfiWaAOiuH7OyWags5f6i0yF/4hV8A2v2XV199NQC777572/dz/rLcDJbOuDqu/KIzlVRryGU7dPoOlH6qVOOs8kNatE3HzUV/U3LJ+ammOROthyqZ5bId9Cw/88xYiuipp55abHvooYcAeP755wFYsmQJUK0x5mSeWhB2jJ5FfWatyPT5zvlMq/7fYsyEIxzHcZw2/MXpOI7TkL6nI0nVr2MC5ZzF6badd94ZgNtuu63Y9sEPfhCAxx57DCjXsEJpMhxwwAHA+ARbu++qNdW5Ne5iJqawpNcxt6ZfZpJMO+tCee6559rGK4k5l86kfVv3iApTaC10TgbaVy7wlB6jKggyE5F8c+6rNIVs06ZNxTY9n08++SRQmuq5VMOceyQ1o3MuGHsfAey6a9k1Q/eKzk33lX0neHDIcRxnGui5xhljzFYuqqOVVaWyaCaRBnnCCWW1rM2bx+qaaunXt771rWKbllraZVl2v/a4OWd1GrTIJcnXrRw0zMQYs9WFhK6dLVG2ceNGAO677z4ADjvssGLbW97yFgA2bNgAwLJly4BSllAuzZOGIdnbfT/66KMA7L33WG0Lm6ZWtcwuvdfSJX65/3HUCCEQQmi7f/WcyErQ9bDPrz5TwNXKXLJKg8F1S7+lz5nSmKxclHgvLVKpT1DKX2NywaE67yLXOB3HcRrSF1XIzmBpmkrubV/lZ9LspNnl8ccfB2DfffctxigRV8m28+bN63huuVSEqtSHVOtIl3LlxswElFAsmUnmd999dzFG/q4vf/nLQLlQAdq1RyitBHt/SCuVFip/GcBBBx3U9v0f/OAHALz00kvFZ9KI6mg9OV/eqCfAhxCYPXt2mzYmOaYFWnKFOFTL0y440XOqn7nnvlOxYjtOflPJUD+htETk27TpUCrw88gjjwClFWoT4OvI1TVOx3GchviL03EcpyETmuohhK8BHwI2xBiPan22APhXYD/gceBjMcYXOu3DMpHjtc4KkZzpUGUOSx1XitKBBx5YbJNpXaeFR53PcpV+BtGk66ZcQwhss802lf2AZE4feuihxRhVzTn//PMBWLVqVbFt0aKxbq4y53fZZRegNMMA7r9/rJSkZG+DAEpNOuWUU4CyTsGPfvSjYoyq8MikrFoTnat6NYhyhe4/s3WCNNaFka7Wsm4rBZdUQUnytaQ1COzxJbP99tuvbYy9L3Sv3HLLLQCcdNJJxbbjjjuu7TxyqUe1emdNOAKuAM5IPrsUuCHGeDBwQ+tvZ7i4ApfrqHIFLttpZUKNM8b432Gs0bvlw8Dprd9XAD8ELqlzwDQdKXUEpwmquTG57yu5/dd//dcBuOGGG4oxe+yxB1CmqNhZTvtM05Gss7uqsk6umtMw0G25KnVFpAsCFi5cCJTpQVCmginIY534up5porVNVK5Cmokqjqv6lY4JpfapIIK1WtIaCDlLaFA1zm7KdtasWW3J6Z2qTll07XWtlewOZQBPwZiqxQfSVLUYAmD//fcHSo1V6UVaFw+wZs0aoFz8oC4PACeffDJQBohz6VR16kxM1se5OMa4DqD1c1GngSGEC0MIt4cQbrfqtDOQTEqutmi0M7DUkq2Va6pMOCXTno5k240uWrQohhDafAid+r1U9aixpLPSPffcA7T3OdGMJ/+afdA1K6VL86Tp2M+qKuTUqagySli57rbbbjHVONO0FV1PmzKkZa4aY5fKddLg6y53nT9/PgAvvvgikF/Sly73zVXESmWe8+OOEmnb507PZerLty9ZWRdbtmwB2n2GWtCQ9k6311Vy0f0gLRPKZ1fPvfZnj6FUxPXr1wPt7wJpwzpffc9+v86ClclKfpW3d/YAACAASURBVH0IYQlA6+eGSe7HGSxcrqOLy7aLTFbjvAY4D/hS6+e3J3sCaeX3qm6EaZ09KBOa5cPSEqxDDjmkGCNf1r333gvAMcccM26bZjDNdupTBGXUVcevKgJQtVxsCJi0XLdu3ZqNRlZpbHLd5Ao1pAVVcr7kOp1RdX9of7fffnuxTb7uXAX49Bi5cxyyhQ2NZRtj7Hivd1ogAKX/UBqjNFAoF6hoGWaaSG+RzO0yWZ2PsjT0/tAzCqUWK+1SUXYoNda0Z5I04LpMqHGGEK4EbgEODSGsDSFcwNjFXx5CeBhY3vrbGSJcrqOLy3b6qRNVP7fDpnd3+VycHuJyHV1cttNPz9eqp4nSnUz0qjXr1nmrwI8cwAoS2RSIs88+GyhVd/0EOOKII4DSdNC266+/vhgjdV7mey5VKk36nYnYAEzarrmqJUkutaVT69+qhRF2m5z/SjVSQv1TTz1VjFFqU64GQnq89P+BoTPVJ8XWrVvb0nP0LKQpOzaYKjNa1/fZZ58ttnVqcWOfaQWVlORuK2Lpewr6KdXQBnxl2uucbNBRgSO9J+x52/95IkYvLOg4jjPN9Fzj3Lp1a3bJZKpZ5JZw5f5Ol+bp+7baiQIE0kLs7CYNUzOYZqBzzy2tne985ztAOXPZ6koaX5UIP4ppK5YYY8d6nLouVSkeueBMej1zmry0QG2zWpCWX77zne8EysCg1TBs0CE9flrFvE6zt1EkvXdTC0vX3Gp8Rx11FFAGgpQalo6DfIpfqgXac5D2qX3mqpFpjL5nF7woOKiEedVptXLNpa6ljPYT7TiOMw30xcdZpy+PnWXSfjV2m2YOtf6VX0Q1OKHUNqSF5nxhOob8mCtXrizGnHbaaQDcdNNNQHvqQ6cUpdzyvVGnqshHzgdcVVk/vWZVmqdkJ6sBYPHixUCZiqL6nvKJwfg0plzb5zQtysp1JlT2T9PMquQg0hSwqvq1Irc/Pa/yeULpN1Uqm2Sg5dRQap/vfvdYLMw+myoMo5REPf9W9tO55NJxHGfG4i9Ox3GchvTc1ggh1ErjyKWtSC237Q/U4EsrhtQe45prrinGqOqOgjtWLZda//TTTwP5NdVaXaRgQm69dO7/nGjMKJFWveq0rj/nwshVn6oKFgqZVHLm6x6AMih04403AmUwIXf8XKpUet5VwalRRUE/G4zV/5yas9ZNInJurE7ph7nmiJJrTmY6p5wLRm00FIhShSyAI488sm2fOfdfHVzjdBzHaUhfvNu5mbrOGz8XBFDFI1VCUYKr0pSgnHl0DJuqtHr1aqAMAElrWbBgQTFGDmRpLTalok7C+0xIik+rIzWpKKUxVrPpVO/RBipkLcjh/4u/+IvFNiW66zy0FrmqJXD6/9jxucUXM4Vcc8VUm7QWmsYrbc8GatMqZBMdD9prsMra1HHVzcHuT9tUAf7ggw8utqVpbrlUOE+AdxzHmQZ6Pn2++eablWkfoq5fUDOQ/BryR+aqG8nHadvQfuhDHwLKZWFqO2qTo9OafVZr6eQryVX6GVVijLz++uttmnyd/zlN8cktZ0y32URn1V5dvnw50H7N1QY27U2Toyq5PdV+ct0HRhVZEVaWqVxz10wan37mfKTSIiVfW51Iz7SsP3vMXDvg9Dz+93//F4B99tln3LY0BS1dgFOX0X6iHcdxpoGea5x1Z+lc/5qcb1TbpO1II7EdDzWDKUr3rne9q9gmX5i0UWkvl112WTFGnRLl28xFf3VuVcnUo0oIoU0ThM6J0nUT4FNNU9fTZlSoV5G+91//9V/FNlUNr5OAn/ox7WciVyl81OWqqHrVwoRcpouWMWo5s+QE5ZLHdDGK7Ssk7VMZLlbrVxRdclA2jK1Ar8I9el5tTCJd0JCTYa7wR0qdepx7hxBuCiGsCiHcH0L4TOvzBSGE60MID7d+7jrh0ZyBweU6mrhce0MdU/0N4OIY4+HAicCnQghH4O1Ghx2X62jicu0BdQoZrwPUHW9LCGEVsJQptJK11UfSpk/p51Dd2kBl+dW0SZVZrAqu1CI5lH/yk58U22RWyDz4whe+AJRBIyjNgxwyNVKTMlfpZ5CYDrnm/s+qRPLUHK9KR1IwwdYQUAUrBfZsFZxOx89VQMq5V9J0mzr/2yDQK7mmz6tN/1O6oOoF2JbQhx9+OFC6xtKWNVCuTde1V61cGC9jPdP2GGlwyAaSdFwdT/9HzpyvolFwKIz1aj4GuI1JtBv19sCDyVTl6m1kBxOX6/RROzgUQtgRuBq4KMa4uW5St203unjx4nFtZNMlWLmE6bRatG2+pORnLbG79dZbx43RrLJq1SqgbEoP5ZI8HUPBIatl1tE+RK751CBqnKIbcl24cGGE6jSt9POJPtP3FESQnM8888xijORx9913A+2JzulyvVxCfZqSkpNZWgHeaiODvLChG3JdsGBBDCG0BV50rdLnNlf/VhWLjj322GKbFqrofHLLoJU+uHbtWqC9ar8sTFmRWvCy2267FWMU/P37v//7tvOBMmhoKy6l51/HkqilcYYQZjMmhH+JMX6z9bG3Gx1yXK6jict1+plQ4wxjU8M/AqtijH9pNk26laxNL6jyQXXC+jy0nEu+r/e85z1A+yxzww03AGVaxFVXXVVs05JNnZPtRySkSckfk0uVqqonOohMh1w7HAeoruOY8zFKHko3kb9KPwH+9m//FihbQ6fVxSc6blWhhzp1JwdR45wOudo0wE49h+xzK2tPmqqsBSiXLetZku/aFmiRhqkxdjGLNFT5NGVFHn300cUY3QdKSTzooIOKbdJQUyswt6y0ijqm+inArwH3hhDuan32WcYE8PUw1nr0SeCjNfblDA4u19HE5doD6kTVbwY6Ta3ebnRIcbmOJi7X3tDTlUNaiWCpE3BJgzPWLFb9TQUDrrzySqC9oZpaxK5ZswZod1bLnEhrAOZSU3TudmWBTJZRX0UyWXJBFZGaw3aM5KBrLYf/V7/61WLMCSecAJTulVzgp1M1nHR8uk2/y1yU+VanFuuooOfV/p+q4ZCuR7ekNTtt4EXXMQ3OWFM5bbxo0XOtn8rUUW0CKNOhFCw8/vjji20y43W8nFy9OpLjOM400Pfigp2qCuWqC2kGs7NcWh9Qzt+c9qH95III6Sxpj1G13rpTUGhY0pGmizStLLfQIZW9DThokcKv/MqvAPDEE0+MG6PrKu00p02mFkTVWnWr9Wh8p2CI/d9mErrGuq66LnWvRZrOJOz3Uy2wat/SZm11JVmhsibtPSeNWVaKglN1WgJbXON0HMdpSN81zlQTqVNpO5c2UkezyC2HTMdXzW45zaRTReyZjq5LToMXuuYaY1eWKc3sjDPOAMq+UWeddVYxRlpDLn2kU93IXAWktP00lPeBNBKX6xi6DtL09NzZZyK9VlXanK557trnrLf0OdVPe0ylLGmbtRZ0LraqvB1bF9c4HcdxGtJ3jTMXUYV89LIqeTqNfOe65uWiZXV8G+msWqdfykz0f1nSa6VZ3/qXdV0POeQQAD73uc8V23T9Pv3pTwNlhNTW/XzhhReA8QVWLGkhkZzvOWelpPdFztqYieh6pvVNbaZJWqPWUmdhSPqc2+90WrRgj5X6LauWb08W1zgdx3Ea4i9Ox3GchgyM3ZGq5zk1vyoAU1UKv8k68ty2tFJOrm5keh7OGKkzP2fqaj36Jz7xieIzValS1RstWti8eXMxRlV0cqTBoKrgUHquufOc6SZ6SnqfWxdK2nqizrXLudY6HWsiqszwuu2hJ8I1TsdxnIaEXmpIIYSNwCvAcxONHUB2Y+rnvW+McfeJhw0XLleX6wAyrXLt6YsTIIRwe4xxWU8P2gWG9bx7xbBen2E9714xrNdnus/bTXXHcZyG+IvTcRynIf14cV7eh2N2g2E9714xrNdnWM+7Vwzr9ZnW8+65j9NxHGfYcVPdcRynIf7idBzHaUjPXpwhhDNCCA+FEB4JIVzaq+M2JYSwdwjhphDCqhDC/SGEz7Q+XxBCuD6E8HDr5679PtdBYRhk63Jtjsu14ri98HGGEGYBq4HlwFpgJXBujPGBaT94Q1o9p5fEGO8MIewE3AGcBXwc2BRj/FLrJto1xnhJH091IBgW2bpcm+FyraZXGufxwCMxxjUxxteBq4AP9+jYjYgxrosx3tn6fQuwCljK2PmuaA1bwZhwnCGRrcu1MS7XCqb04mygyi8FnjJ/r219NtCEEPYDjgFuAxbHGNfBmLCARf07s+mloYk2dLKdqXKF0X5meynXSb84W6r8V4H3A0cA54YQjug0PPPZQOdBhRB2BK4GLooxbp5o/KjQUK4wZLKdqXKF0X5mey3XSfs4QwgnAf8nxvi+1t9/CBBj/GKnsXPnzn3v/PnzZ2zptfXr1z836MUgmshV42fPnv3jOXPmVJYPq+otkyOt9J3rApD2kKrqmT6dbNq0aeDlCs2f2Tlz5vxYPc77QZ0+YNPJhg0bOsp1KkUGc6r8CemgEMKFwIXAW2bPns3HP/7xWg3fq+rz1WGq358OLrvssif6fQ41aCpXZs2axQknnFDUzoSyZqquvWqa5hpn5UhbXmh/9uU8b948oKz/qOZtUK9FQ7e48sorh0GuUEO2Vq6zZ88u2jP3g1yztl7yV3/1Vx3lOpUXZy1VPsZ4OXB5COEDc+fO/e5EF6GO9mD/zhWoTb+f9pSp6pqX602j7ou5Hsz6bIS06EZyBVi4cGHcfff2ibmT9mmvna61xub63atArq6v/b7kIvna3tp60aY9j6xcNUbfszLUuNw5DTETytbKdfHixbVvantdda3TvkSt/bd9VlXgWvKxhcvT8bmXa6d3QjeZyp7XAnubv/cCnuk0OMb4vSkcy+kdjeTqDBUu2y4xlRfnSuDgEML+IYTtgHOAa7pzWk4fcbmOLi7bLjFpUz3G+EYI4dPAdcAs4GsxxvsnsZ/2E2qp4NYkS/1kuXaf6d85syvdD5Rmhfxka9asAWDDhg3FmBNPPBEofWgLFiwotqlFrfxtuX5Iw0S35KprLDnusMMOQHs/mtQUs/5PyUzjdV9Ysy/9vt2Wundy/lSNeeWVV8Zt032UthCealvZftIt2U6E5KFrl+vRlcrcPjfpGHvP6L6QC6fKLK/jI61qPV7FlDpQtcxvN8FHDJfr6OKy7Q59b92nN75+SqvbfvvtO46xs1MadU3H5j6zHfkefvhhAPbZZx8Afud3fgeAt7/97cUYzUaXXXYZAE89VQYmpQlpVk07YqbnO1OQJpEGZWwA5+WXXwZKeVR1NtX1zWkWue+lVkb6t91XTjORXHU/7rjjjuPGzES5dtLQbHBH10g/c9vSgK1Ne5LM7733XqC89gC77LJL2/d0f+WsUH2Wu2fS+6FpFo5XR3Icx2lIXzRO+3ZP/ZY5n0U6s1f5M+qkBT3yyCPF70cffTRQznjf/va3gVITBfizP/szAB566CEAFi0qV2/J/5LOYDmfzaiT0/LlE9R1sf4q+YWF9T2nWkv6OYzvkZ07fmoB5FLZct//2c9+BpSWj/ygVmMeoRS02nRKSq/yI+Z8nPpM1/P73/9+MeY3fuM3APjc5z4HwP77719sO+ecc4DSEkg1V0sutTA9j/T/gnqWxMx4oh3HcbpIXzTO3OyQrgyxs0Q6c9jZIp35cpHzjRs3AqU/RFqm3efjjz/e9j17DPlalOSdS4rWeU82SjcK2P9ZGpu0c2UfrFu3rhiTanFWZtJUpfFJTlYb0OIDReyroupNtX59X5qRjqX/C9p95TMde33T5zQXk5DlIUvvmWfKdFL5MVevXg20+zjTpHr9bTMyRC7ekfq8c1aD+zgdx3GmAX9xOo7jNKTv6UhSmVMTObf2tMrZm657tgGg0047DSjTX5577rlim0wvmXsHHngg0G46yByQaWad3WkqTc5ZPVOCQ5Y99tgDgL/+678G4IgjxqqXXXDBBcWYTZs2AeX12XXXsruBZCRTSmb9888/X4yRPNevXw/A3nuXqwlTMy1nfuleySXQp0nc+tumyc1kdF1zCezp85kL9Oq6yu110EEHFWP0LD722GNA6Sax3696ptK0xaqAYNOqXcX/NOEIx3Ecp42+pyOln+XSUNIk6tzsIM1PKUPLli0rxjz77LNAqbVYjVGl0BT4+d73xhZV7LTTTsWYJUuWtB0rd/xUM7Ez4kwJFNn0on/4h38Aymu+ZcsWAP7kT/6kGCNtQ5qjDbwoaJBq+TaZ+pBDDgHKQMOLL75YbFOAYbKkaXJpUGOmkj4Dufu9aqljmpSu+8PKVVq9rA5poPZ7Ch6mixg6HVfkNMxOY6pwjdNxHKchPdc4U20zp0lAPhk6NxNIG1U60Vvf+lagTEGCcjbSMfbbb79i29q1awF48skngXK2szNY6lutKiCS83H2qxBrrwghsP3227elhEhjl39qxYqxvlnyN0N5zaU92Ot0//1jtSekee65555AKWcoC7FI81y5cmWxTYVY0iR5S5VfOl3CmytEMhOXXKbPYK5+bop9tlNNMaexpu+CqlqfORmkz2JVbd+cFWnfPZ1wjdNxHKch/uJ0HMdpyISmegjha8CHgA0xxqNany0A/hXYD3gc+FiM8YW6B82lEqRO21wASSq3TQmRuadgkAIEOdVf5uMPfvCDYtu+++7bdgyltNj1sddcM1brVSZh7tzSNIum6Q29pptyDSGw7bbbtq2ouvDCC4ttAAcccADQLheZ07l0JJlkkqfMcFu1SqlJuh9scKqTeyfnAhJWTmnVq9x3cqtVBoFuyTaEwDbbbJNd+ZOrydCJ3MqdtG2JDbppdZnuh5y7JZVvnXY6VeeWW7VWRR2N8wrgjOSzS4EbYowHAze0/naGiytwuY4qV+CynVYm1DhjjP/davRu+TBweuv3FcAPgUvqHtTOIFWpRkKfKTVFCc8Ab3vb29o+08xltVKlMyj1QbU3oQxe6DykGSloBGVqi1Jq5s+fX2zT7KTvV9V2HCS6KdcYI2+88Ubb/yxL4JJLxr6ua281FGmoVTUIlCT/3ve+F2hfmLBw4UKgDCRZuaT7EVVN/HLfS7WWQeyemtLtZ9ZqY53qolatVc8FZ9JK7vad8PTTTwNlQNBaMnrOOlkEnY6bksrX3pd1ntfJ+jgXxxjXtU5gHbCo08AQwoUhhNtDCLerK6EzsExKriPWCXJUqSVbK1fbbtlpZ9pVIdtudI899ohQvQRL5GZzaR92lpFwpWlqP7Yv0C233AKUlXpscrv2pSRsaZXW5/KRj3wEgKuuugpo98Wlie/CzlqjmLZi5bpgwYL42muvtSWw6xpL8xQ5f1nOFyUN9fDDDwfGV6+CUvtQ9Ry7HHPx4sVt+6uqIJ9LqUm10dySy0H0XU+VtD1wpyWKKblr12H/2b/tgoW09a99gesdkOthlZJLK0qXYeZ8nHXSByerca4PISxpncASYMME453hwOU6urhsu8hkNc5rgPOAL7V+frvOlxSly80SabQuV81bBTikHUIZdZWvRMsjb7755mKMIufat42a6bjaj/62bgX5Ozdv3gy0z3JpN8TcbDWovrAMk5LrrFmz2HHHHdtm7U7/c66TpbCahRYySIvMdTiVL+ywww4DSl8nlDJTpF0acNVy2TpLBK1WnVawH3AmJdsYY606p7nK/LmId/p8p7267O8q8vHbv/3bxbb//M//BMqiMbnOD7l7JaVKK64TjZ9wRAjhSuAW4NAQwtoQwgWMXfzlIYSHgeWtv50hwuU6urhsp586UfVzO2x6d5fPxekhLtfRxWU7/fQ0TybGSIwxa9Kl6Ui2LYECBVLdbXK6vqdgwLXXXgvAUUcdVYxJ10LbAEVaTzAXyJFprkpKNopsK7dYrLo/6hV1tm7dyk9/+tPKpP8q8ymtpQpw6623AqWbRXKyx9C1V+1VW49TqSwy/5944om270CZvpQz0Tu1sR3FgFAVcq91IldrIh1fdX3T59/uS8+9DTAqfbBO0CqX5tYpmNsUX3LpOI7TkL5XRxJpXT07gzz66KNAmRa0atWqYptSg2666SYAjjzySCAfwEkrs1iq6gtq5vvABz4AwHe/+91im1Jh0hkvl3YzymzdurWtUre08jozfNqoD0ptUNqGNE4bnNFyPS3DtC2dFUCUhnnssccC8OCDDxZj1qxZA8DSpUvbjgHjrRSdmz1H3RejirTNqva6ueaKncbacWlAzqYYpoFie81Ty6WOBVCnsn/TZ9Q1TsdxnIb0vT1w6jtKe8wAnHLKKUC53M4urbvjjjsA2GuvvYB8n5E0+bnKj5lqGHZf3/zmN4Ey5QnqzVyjXo9T5DT51JKw176qAId+V2X+nPah9LDrrrsOKFNUoOx5pJqdSqA/+OCDizGqy3rXXXe1HQvGLwXOLcEcsnSkxsQY2bp1a2V7XWFllz4LddKC7H2RLkiw6Yf6TPLJxQ/Sc8s97+lnTS1E1zgdx3Ea4i9Ox3GchgxMszaZ03Lm2wpIct6/9NJLQBmQgc4pJbmaejquXW8sE86207DnA+V6abkFbBBEpoJMmFzazKinsCjNrCrtKudCSVed5FqS2GOkqK2GTGabtqLAjyphqZbq6tWrizGqrqQKWzfeeGOxTe1qU5PdmnSjWIMgJV05lLaaqHLP1AkICvu8KrVQjRf1/EEZGNZnqi3RNLhTVbOzW/U4HcdxHEPPNc40IKC/q9ITtG48rX1pv5/OgDaBXvvSZ1rjDHD00Ue3nY+CCVarVPqRkrFz9QXT4JadAeusfR1mQgiEENqumRLPc+ldoo4mnmov9tqnmoGtsKN9SwtVYPG4444rxmhBhc5b6WYAP/zhD4GyPoKOZc95JmicaQJ8aglUaW5VqWjpeFvNStdeFoGVmbox5CyYlFyN305r7e37pitr1R3HcZx2+p4An84cSpxWehGU/iktw7MzgjQbfU9J0XaMtAVV2rFapqoiyT+nKjp2BvzJT34ClCkuucK96UxmZ7BR93EC4xKl01SUqtk/5y+TzCQX+b5zdROrltJq6aV8Yd/5zneKMUpzU4+bk08+udim+0r7kcztOQ5qz6FuUtfHaWVf5QftVBdV8QuAX/7lXwbgvvvuA+ArX/lKsU3vhdSnmXvGqpLz0yrvTa1C1zgdx3EaUqfL5d7A/wP2ALYCl8cY/3oqnS5zC/o1Y0hrsFH1tD+J7Ttz6qmnAqVGsXHjRp13MUYFP6Rx2n3Lf6rovCJ5NvKupOmcnytNjNbfg14Bvtty7ZR0nM7s9tpJ+5DmZmugKnNC+1GU3FoiklFaE9Wej5KnVZ/zHe94RzEmrfavKL3dV9pZIKcVDxLdlmsIIZscnmr79tqnVkadmECuer/kY5fSyi+tTJtcQn7qW60qAJKeM3Sv59AbwMUxxsOBE4FPhRCOwLvmDTsu19HE5doDJnxxxhjXxRjvbP2+BVgFLGWsa96K1rAVwFnTdZJO93G5jiYu197QKDjUajl6DHAbSde8EELHjojJPtr+ljqdOtptasmf//mfA/AHf/AHAKxcubLYJjVe6rVMbltFR+PTFr5QBnzSBHbVeIQyLSKn+qcO8JzpMOitM7ohV2iXoYIrVW2f08UCNuimBQkyx1VrU64UgBdeeKFtP1bmMvV1HynVSCY/lGa41rjffffdxTYFleROyLlbBtFUt3RLrhZdh04uKhhvvufqE6TBHJneAF/+8pcBuPjii4EyRRBKmcmtk9bTteSO1elZbLpgpXZwKISwI3A1cFGMcXOD73l74AGmG3L19sCDhz+v00stjTOEMJsxIfxLjPGbrY/XhxCWtGavjl3zcu2Bc61ENbPrbW+1F6WNqC6ntAEo24UqnUHCtrOGUlmUqmS3PfDAA0A5O2rms8sHq+p4pmk3ueZTg5oA3y25Lly4MEL7rK9AXqoJ2DSt1Alvqw0pCCDNU4ECWQh2m91nehxV2VJKmU3SVycBNfazGquW9Cm4pPtx0LVM6J5cFy9ePE71SqtUVWlnuZqZqYWm62k7Nvzpn/4pAJ/85CeB9iWXqrurRSxaUpt7yadpUdA5Ta5quW+OOs3aAvCPwKoY41+aTeqaBw265jmDgct1NHG59oY6GucpwK8B94YQ7mp99lnGuuR9vdVB70ngo3UPapdDpikLOZ+Y/FzqG/PUU08V28455xygrM0o35oS2e339NPOKFaDgXxye3quOW0y9dUOYgpSQtflausmdurVU7Vc1mqD0v606OEtb3nLuDFKYclpFjq+/NPSLm2itfzhkqet1an7SNt0X1iteEB7SXVdrjnS+ztXZKNOsQ9dZ8kJSr+00sNs8RVpo0qST4vzwPhlz1W1PuucY446XS5vBjpFN7xr3pDich1NXK69YTCdb47jOANM3+txpuaazPhc2oDU+UWLykwKOfZlSqXVlqA029XeN7f6I1fVKCWX3pAGjnItFmYikqOc9umKE6hO01Lw4bDDDgPK9eTWHSDzXQFCG2zScW+55RaglL39vuqrytyzbpq0DqcCjE2DCKNGp+CKNXXTgG+urUantuAACxYsAODf/u3fADjvvPOKbXLPKGCkfVfVMLD77pQq1TRlcGY/3Y7jOJOgLxqnpVO7Tjuza/bXZ9Ypr3WtVetj03XwOUdwLvCTnqM0mlx6w6AnufcLaXE2WCc61XaEUmuRhqigjA3OKJk9F5CTxqtK7mn1KzteAQpbnyBtDzxEQb9pZTL/f+5ZTNsL26DdueeeC5TWwrXXXltsk6apltCqTZE7Ri5lKtWQc+lUdZ5l1zgdx3Ea0neNM9U0c7NEmlxusW2EJ0PavrbKR1mllaY+0plQg7OKVNOUz9GmE0meuWo0qX8q50+U31HYVCfJIa3/aOWqe07nZJPb9VnOgnFKcqlgqTZnn4W0ylQuZUi/aylsruq+UgtzNGlLnO63Lq5xOo7jNKTvGqeoM0vktNH0s9wsJ3KfQ62NyAAAAyJJREFUpVpkbkydDnqpr3Oma5xCWpw0ODuzy6coGVjNM822yBWD0b7TZYB2fFXUtCqDQ+c0oEnuA0NVtkRuW6f6rFUavX3+6lTdr9pXp2wXj6o7juNMM/7idBzHacjAmOopOdU/NdssVSZyHcd+VaCiUw1RGB9UchO9nSpTOw36qXoVlMElySMN9thtOTMrdZ3ILFeVJTtGMswFrpzJk2sd3GSNeJqyBOOf85zs05bU9vnvVpDPNU7HcZyGhF5qSCGEjcArwHM9O2j32I2pn/e+Mcbdu3Eyg4TL1eU6gEyrXHv64gQIIdweY1zW04N2gWE9714xrNdnWM+7Vwzr9Znu83ZT3XEcpyH+4nQcx2lIP16cl/fhmN1gWM+7Vwzr9RnW8+4Vw3p9pvW8e+7jdBzHGXbcVHccx2lIz16cIYQzQggPhRAeCSFc2qvjNiWEsHcI4aYQwqoQwv0hhM+0Pl8QQrg+hPBw6+eu/T7XQWEYZOtybY7LteK4vTDVQwizgNXAcmAtsBI4N8b4wLQfvCGtntNLYox3hhB2Au4AzgI+DmyKMX6pdRPtGmO8pI+nOhAMi2xdrs1wuVbTK43zeOCRGOOaGOPrwFXAh3t07EbEGNfFGO9s/b4FWAUsZex8V7SGrWBMOM6QyNbl2hiXawW9enEuBZ4yf69tfTbQhBD2A44BbgMWxxjXwZiwgEWdvzmjGDrZulxr4XKtoFcvzlyxu4EO54cQdgSuBi6KMW7u9/kMMEMlW5drbVyuFfTqxbkW2Nv8vRfwTI+O3ZgQwmzGhPAvMcZvtj5e3/KnyK+yoV/nN2AMjWxdro1wuVbQqxfnSuDgEML+IYTtgHOAa3p07EaEsTpV/wisijH+pdl0DaAGz+cB3+71uQ0oQyFbl2tjXK5Vx+1VAnwI4QPAl4FZwNdijP+3JwduSAjh7cD/APcCKt73Wcb8Jl8H9gGeBD4aY9zUl5McMIZBti7X5rhcK47rK4ccx3Ga4SuHHMdxGuIvTsdxnIb4i9NxHKch/uJ0HMdpiL84HcdxGuIvTsdxnIb4i9NxHKch/uJ0HMdpyP8HrIHm0IKne2wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from matplotlib import pyplot\n",
    "from keras import backend as K\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "X_train = X_train.reshape(X_train.shape[0], 28, 28,1)\n",
    "X_test = X_test.reshape(X_test.shape[0], 28, 28,1)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# 이미지 조금씩 변화주기\n",
    "#1. 방법# datagen = ImageDataGenerator(horizontal_flip = True, vertical_flip = True)\n",
    "#2. 방법# datagen = ImageDataGenerator(width_shift = True, heigh_shift = True)   \n",
    "\n",
    "#3. 방법# 이미지 백색화 => noise 제거 (변수간 상관도를 없앰 : PCA)\n",
    "datagen = ImageDataGenerator(zca_whitening = True)\n",
    "\n",
    "datagen.fit(X_train)\n",
    "\n",
    "for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9,\n",
    "                                     # augment\n",
    "                                     save_to_dir = 'images', save_prefix = 'aug', save_format = 'png'):\n",
    "    \n",
    "    for i in range(0,9):\n",
    "        pyplot.subplot(330 + 1 + i)\n",
    "        pyplot.imshow(X_batch[i].reshape(28, 28), cmap = pyplot.get_cmap('gray'))\n",
    "    \n",
    "    pyplot.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow' has no attribute 'name_scope'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-43-07f672058a7b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcifar10\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizers\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSGD\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAdam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRMSprop\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnp_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_test\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcifar10\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mactivations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mapplications\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\utils\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mio_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconv_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlosses_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmetrics_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\utils\\conv_utils.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoves\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbackend\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mset_epsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfloatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mset_floatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcast_to_floatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\load_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0m_BACKEND\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'tensorflow'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m     \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Using TensorFlow backend.\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mtensorflow_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m     \u001b[1;31m# Try and load external backend.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[0mget_graph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_keras_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;31m# learning_phase_scope = tf_keras_backend.learning_phase_scope  # TODO\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m \u001b[0mname_scope\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'name_scope'"
     ]
    }
   ],
   "source": [
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.optimizers import SGD, Adam, RMSprop\n",
    "from keras.utils import np_utils\n",
    "\n",
    "(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()\n",
    "X_train = X_train.reshape(X_train.shape[0], 32, 32, 3 )\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')\n",
    "\n",
    "NB_CLASSES = 10\n",
    "Y_train = np_utils.to_categorical(Y_train, NB_CLASSES)\n",
    "Y_test = np_utils.to_categorical(Y_test, NB_CLASSES)\n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow' has no attribute 'name_scope'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-efac853b50c1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmnist\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mactivations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mapplications\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\utils\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mio_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconv_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlosses_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmetrics_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\utils\\conv_utils.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoves\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbackend\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mset_epsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfloatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mset_floatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcast_to_floatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\load_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0m_BACKEND\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'tensorflow'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m     \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Using TensorFlow backend.\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mtensorflow_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m     \u001b[1;31m# Try and load external backend.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[0mget_graph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_keras_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;31m# learning_phase_scope = tf_keras_backend.learning_phase_scope  # TODO\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m \u001b[0mname_scope\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'name_scope'"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "plt.imshow(X_train[5])\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "plt.imshow(X_train[6])\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RMSprop' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-9228f8fa2968>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mVERBOSE\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mVALIDATION_SPLIT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mOPTIM\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRMSprop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'RMSprop' is not defined"
     ]
    }
   ],
   "source": [
    "IMG_CHANNELS = 3\n",
    "IMG_ROWS = 32\n",
    "IMG_COLS = 32\n",
    "BATCH_SIZE = 128\n",
    "NB_EPOCH = 40\n",
    "VERBOSE = 1\n",
    "VALIDATION_SPLIT = 0.2\n",
    "OPTIM = RMSprop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ICT01_09\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# residual망의 영향\n",
    "# 32x32x3(kernel) =>  32x32x32 (padding이 same이라서) \n",
    "model.add(Conv2D(32, kernel_size = 3, padding = 'same', input_shape = (IMG_ROWS, IMG_COLS, IMG_CHANNELS)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(32, kernel_size = 3, padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "model.add(Dropout(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ICT01_09\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3))`\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "model.add(Conv2D(64, kernel_size = 3, padding = 'same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(64, 3,3))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size = (2,2)))\n",
    "model.add(Dropout(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 14, 14, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 3136)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               1606144   \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 1,676,842\n",
      "Trainable params: 1,676,842\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(NB_CLASSES))\n",
    "model.add(Activation('softmax'))\n",
    "model.summary()\n",
    "\n",
    "\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer = OPTIM, metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ICT01_09\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "40000/40000 [==============================] - 59s 1ms/step - loss: 1.7803 - accuracy: 0.3581 - val_loss: 1.3732 - val_accuracy: 0.5040\n",
      "Epoch 2/10\n",
      "40000/40000 [==============================] - 59s 1ms/step - loss: 1.3199 - accuracy: 0.5348 - val_loss: 1.1495 - val_accuracy: 0.6150\n",
      "Epoch 3/10\n",
      "40000/40000 [==============================] - 58s 1ms/step - loss: 1.1147 - accuracy: 0.6063 - val_loss: 1.1759 - val_accuracy: 0.5815\n",
      "Epoch 4/10\n",
      "40000/40000 [==============================] - 58s 1ms/step - loss: 0.9814 - accuracy: 0.6594 - val_loss: 0.9617 - val_accuracy: 0.6674\n",
      "Epoch 5/10\n",
      "40000/40000 [==============================] - 58s 1ms/step - loss: 0.8876 - accuracy: 0.6869 - val_loss: 0.9411 - val_accuracy: 0.6686\n",
      "Epoch 6/10\n",
      "40000/40000 [==============================] - 59s 1ms/step - loss: 0.8254 - accuracy: 0.7134 - val_loss: 0.8067 - val_accuracy: 0.7203\n",
      "Epoch 7/10\n",
      "40000/40000 [==============================] - 58s 1ms/step - loss: 0.7598 - accuracy: 0.7358 - val_loss: 0.7770 - val_accuracy: 0.7301\n",
      "Epoch 8/10\n",
      "40000/40000 [==============================] - 58s 1ms/step - loss: 0.7075 - accuracy: 0.7547 - val_loss: 0.7896 - val_accuracy: 0.7292\n",
      "Epoch 9/10\n",
      "40000/40000 [==============================] - 58s 1ms/step - loss: 0.6695 - accuracy: 0.7687 - val_loss: 0.7101 - val_accuracy: 0.7550\n",
      "Epoch 10/10\n",
      "40000/40000 [==============================] - 64s 2ms/step - loss: 0.6336 - accuracy: 0.7820 - val_loss: 0.7419 - val_accuracy: 0.7472\n"
     ]
    }
   ],
   "source": [
    "# 모델 피팅\n",
    "history = model.fit(X_train, Y_train, batch_size = BATCH_SIZE, epochs = NB_EPOCH, \n",
    "                    validation_split = VALIDATION_SPLIT, verbose = VERBOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 5s 533us/step\n",
      "\n",
      "Test score: 0.7576467494964599\n",
      "\n",
      "Test accuracy: 0.7501999735832214\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, batch_size = BATCH_SIZE, verbose = VERBOSE)\n",
    "print('\\nTest score:', score[0])\n",
    "print('\\nTest accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from matplotlib import pyplot\n",
    "from keras import backend as K\n",
    "\n",
    "datagen = ImageDataGenerator(   # 똑같은 데이터 : random 순서\n",
    "    featurewise_center = False, \n",
    "    samplewise_center = False, \n",
    "    featurewise_std_normalization=False,\n",
    "    samplewise_std_normalization = False, \n",
    "    zca_whitening=False,  # 이미지 PCA 노이즈 제거\n",
    "    rotation_range = 0, \n",
    "    width_shift_range=0.1, height_shift_range=0.1,\n",
    "    horizontal_flip = True, vertical_flip = False)\n",
    "\n",
    "\n",
    "datagen.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ICT01_09\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: The semantics of the Keras 2 argument `steps_per_epoch` is not the same as the Keras 1 argument `samples_per_epoch`. `steps_per_epoch` is the number of batches to draw from the generator at each epoch. Basically steps_per_epoch = samples_per_epoch/batch_size. Similarly `nb_val_samples`->`validation_steps` and `val_samples`->`steps` arguments have changed. Update your method calls accordingly.\n",
      "  \n",
      "C:\\Users\\ICT01_09\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras.pre..., verbose=1, steps_per_epoch=390, epochs=10)`\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "390/390 [==============================] - 71s 182ms/step - loss: 0.9002 - accuracy: 0.6896\n",
      "Epoch 2/10\n",
      "390/390 [==============================] - 71s 183ms/step - loss: 0.8673 - accuracy: 0.6994\n",
      "Epoch 3/10\n",
      "390/390 [==============================] - 69s 177ms/step - loss: 0.8490 - accuracy: 0.7089\n",
      "Epoch 4/10\n",
      "390/390 [==============================] - 67s 173ms/step - loss: 0.8276 - accuracy: 0.7133\n",
      "Epoch 5/10\n",
      "390/390 [==============================] - 66s 170ms/step - loss: 0.8123 - accuracy: 0.7247\n",
      "Epoch 6/10\n",
      "390/390 [==============================] - 66s 170ms/step - loss: 0.8074 - accuracy: 0.7234\n",
      "Epoch 7/10\n",
      "390/390 [==============================] - 66s 170ms/step - loss: 0.8030 - accuracy: 0.7275s - loss: 0.8026 - accuracy: 0.\n",
      "Epoch 8/10\n",
      "390/390 [==============================] - 66s 169ms/step - loss: 0.7886 - accuracy: 0.7350\n",
      "Epoch 9/10\n",
      "390/390 [==============================] - 68s 173ms/step - loss: 0.7878 - accuracy: 0.7334\n",
      "Epoch 10/10\n",
      "390/390 [==============================] - 67s 173ms/step - loss: 0.7957 - accuracy: 0.7317\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1fab323a408>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(datagen.flow(X_train, Y_train, batch_size = BATCH_SIZE), samples_per_epoch = X_train.shape[0], \n",
    "                   nb_epoch=NB_EPOCH, verbose = VERBOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# json으로 모델 저장\n",
    "\n",
    "model_json = model.to_json()\n",
    "open('cifar10_architecture.json', 'w').write(model_json)\n",
    "model.save_weights('cifar10_weights.h5', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "# 모델 읽어오기\n",
    "\n",
    "from keras.models import model_from_json\n",
    "\n",
    "json_file = open('cifar10_architecture.json','r')\n",
    "loaded_model_json = json_file.read()\n",
    "\n",
    "json_file.close()\n",
    "\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "loaded_model.load_weights(\"cifar10_weights.h5\")\n",
    "\n",
    "print(\"Loaded model from disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'datagen' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-5e6c5a424b62>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatagen\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'datagen' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "문제       \n",
    "1.accuracy graph를 출력해보시오       \n",
    "2.이미지 cat, dog 를 다운로드 한 다음 위의 모델로 예측해 보시오"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-b0efccb3e971>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'val_acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'model accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'epoch'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'history' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow' has no attribute 'name_scope'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-fc2a9a988222>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mimageio\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mimread\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mskimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mresize\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmodel_from_json\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mimg_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'cat.jpg'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'dog.jpg'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m imgs = [resize(imread(img_name), (32, 32)).astype('float32')\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mactivations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mapplications\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\utils\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mio_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconv_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlosses_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmetrics_utils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\utils\\conv_utils.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoves\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbackend\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mset_epsilon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfloatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mset_floatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mload_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcast_to_floatx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\load_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0m_BACKEND\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'tensorflow'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m     \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Using TensorFlow backend.\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mtensorflow_backend\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m     \u001b[1;31m# Try and load external backend.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[0mget_graph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_keras_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;31m# learning_phase_scope = tf_keras_backend.learning_phase_scope  # TODO\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m \u001b[0mname_scope\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'name_scope'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from imageio import imread\n",
    "from skimage.transform import resize\n",
    "from keras.models import model_from_json\n",
    "img_names = ['cat.jpg', 'dog.jpg']\n",
    "imgs = [resize(imread(img_name), (32, 32)).astype('float32')\n",
    "       for img_name in img_names]\n",
    "imgs = np.array(imgs) /255\n",
    "predictions = model.predict_classese(imgs)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전이 (transfer learning) 학습 Application\n",
    "- 반지도 학습 ( 일부 데이터의 라벨이 없음): KNN, Trenductive SVM\n",
    "- (이미 남이 만들어진 가중치)\n",
    "- 가중치 활용 : 그대로 적용\n",
    "- 가중치 중 일부만 활용\n",
    "- FFNN 부분만 학습해서 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-trained, fine-tuning(FFNN 부분만 특정 Domain knowledge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PIL image size (224, 224)\n"
     ]
    },
    {
     "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": [
      "numpy array size (224, 224, 3)\n",
      "image array size (1, 224, 224, 3)\n",
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
      "102858752/102853048 [==============================] - 12s 0us/step\n",
      "WARNING:tensorflow:From C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "[[('n07753592', 'banana', 0.9993026), ('n07749582', 'lemon', 0.00014120796), ('n07716358', 'zucchini', 0.00013493975), ('n07720875', 'bell_pepper', 7.893238e-05), ('n07747607', 'orange', 7.704725e-05)]]\n"
     ]
    },
    {
     "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 PIL\n",
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "from keras.applications.imagenet_utils import decode_predictions\n",
    "import numpy as np\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.applications import resnet50\n",
    "filename = 'banana.jpg'\n",
    "original = load_img(filename, target_size=(224,224))\n",
    "print('PIL image size', original.size)\n",
    "plt.imshow(original)\n",
    "plt.show()\n",
    "numpy_image = img_to_array(original)\n",
    "plt.imshow(np.uint8(numpy_image))\n",
    "print('numpy array size' , numpy_image.shape)\n",
    "image_batch=np.expand_dims(numpy_image, axis=0)\n",
    "print('image array size' , image_batch.shape)\n",
    "#prepare the image for the resnet50 model\n",
    "precessed_image = resnet50.preprocess_input(image_batch.copy())\n",
    "resnet_model = resnet50.ResNet50(weights = 'imagenet')\n",
    "predictions = resnet_model.predict(precessed_image)\n",
    "label = decode_predictions(predictions)\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tf.keras\n",
    "# fine-tuning : FFNN 부분만 학습\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import randn\n",
    "import pathlib\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from matplotlib.image import imread\n",
    "from keras.preprocessing import image\n",
    "\n",
    "#assert tf.executing_eagerly()\n",
    "#tf.enable_eager_execution()\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "\n",
    "data_dir = 'C:/Users/ICT01_09/Documents/jeju_h/전달/flowers/flower_photos'\n",
    "data_dir = pathlib.Path(data_dir)\n",
    "label_names ={'daisy':0,'dandelion':1,'roses':2,'sunflowers':3,'tulips':4}\n",
    "label_key=['daisy','dandelion','roses','sunflowers','tulips']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_images = list(data_dir.glob('*/*'))  # 파일명 로딩\n",
    "all_images = [str(path) for path in all_images]\n",
    "random.shuffle(all_images)\n",
    "\n",
    "# 이미지 주소 라벨 할당\n",
    "# 디렉토리의 경로를 관리하는 라이브러리\n",
    "all_labels=[label_names[pathlib.Path(path).parent.name] for path in all_images]\n",
    "data_size = len(all_images)\n",
    "\n",
    "train_test_split=(int)(data_size*0.2)  # 20 : 80 \n",
    "\n",
    "x_train = all_images[train_test_split:]\n",
    "x_test = all_images[:train_test_split]\n",
    "y_train = all_labels[train_test_split:]\n",
    "y_test = all_labels[:train_test_split]\n",
    "\n",
    "img_size = 160\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _parse_data(x,y):\n",
    "    image = tf.read_file(x)  # 파일에는 헤더파일(압축방법)\n",
    "    image = tf.image.decode_jpeg(image, channels= 3)  # color\n",
    "    image = tf.cast(image,tf.float32)  \n",
    "    image = (image/127.5) -1   # 이미지를 절반으로 나누면 0~2 -1 => -1~1 사이로 만들어주려고\n",
    "    # 이미지 사이즈 통일 160x160\n",
    "    image = tf.image.resize(image, (img_size, img_size))\n",
    "    return image,y \n",
    "\n",
    "def _input_fn(x,y):\n",
    "    # 가장 기본단위 요소로 분해\n",
    "    ds = tf.data.Dataset.from_tensor_slices((x,y))\n",
    "    # from_tensor_slices  : dataset을 생성\n",
    "    \n",
    "    ds = ds.map(_parse_data)\n",
    "    # map : 함수를 적용할때 (데이터마다)\n",
    "    \n",
    "    # buffer : 거쳐가는 기억공간\n",
    "    ds = ds.shuffle(buffer_size=data_size) # 데이터가 shuffle 될ㄹ수 있는 충분한 공간확보\n",
    "    ds = ds.repeat()  # 데이터가 부족하면 처음부터 다시 시작하기 위해서 리핏\n",
    "    ds = ds.batch(batch_size) # 미니배치\n",
    "    ds = ds.prefetch(buffer_size=AUTOTUNE)  # 미니배치 사이즈를 어떤 사이즈(몇배)가 유리할지 결정은 자동으로 결정해라\n",
    "    return ds                               # 32(미니배치)x3(자동)  ..or 32x5...  etc\n",
    "\n",
    "train_ds = _input_fn(x_train,y_train)\n",
    "validation_ds = _input_fn(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ICT01_09\\Anaconda3\\envs\\tf_test\\lib\\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",
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 18s 0us/step\n"
     ]
    }
   ],
   "source": [
    "IMG_SHAPE = (img_size,img_size,3)\n",
    "# include_top = flase : \n",
    "VGG16_MODEL = tf.keras.applications.VGG16(input_shape = IMG_SHAPE,\n",
    "                                         include_top=False,\n",
    "                                         weights='imagenet')\n",
    "\n",
    " # include_top=False : pooling 으로 1000개 분류하기 전에 모델셋팅 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "VGG16_MODEL.trainable =False\n",
    "# flatten 가중치 -> false  니까 학습안한다는 뜻\n",
    "\n",
    "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "# ffnn 의 가중치는 학습\n",
    "prediction_layer = tf.keras.layers.Dense(len(label_names),activation='softmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([ VGG16_MODEL, global_average_layer, prediction_layer])\n",
    "\n",
    "# model (vgg16 가중치 + ffnn 가중치 학습)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tf.train.AdamOptimizer()\n",
    "# sparse : 희소행렬 (많은 걸 나눌때)\n",
    "# 00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000000000 \n",
    "# 열개로 나눌때는 희소가 아님(천개정도 나눌때가 희소)\n",
    "# 0000000100   <- 희소 아님\n",
    "\n",
    "model.compile(optimizer = tf.train.AdamOptimizer(), loss = tf.keras.losses.sparse_categorical_crossentropy, metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2/2 [==============================] - 17s 8s/step - loss: 1.6296 - acc: 0.1562\n",
      "Epoch 2/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.6474 - acc: 0.1250\n",
      "Epoch 3/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.6268 - acc: 0.2969\n",
      "Epoch 4/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.5894 - acc: 0.2969\n",
      "Epoch 5/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.5949 - acc: 0.2969\n",
      "Epoch 6/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.5719 - acc: 0.3750\n",
      "Epoch 7/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.6015 - acc: 0.2969\n",
      "Epoch 8/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.4813 - acc: 0.4219\n",
      "Epoch 9/100\n",
      "2/2 [==============================] - 8s 4s/step - loss: 1.4622 - acc: 0.4688\n",
      "Epoch 10/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.5065 - acc: 0.3750\n",
      "Epoch 11/100\n",
      "2/2 [==============================] - 8s 4s/step - loss: 1.4605 - acc: 0.4531\n",
      "Epoch 12/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.4646 - acc: 0.4219\n",
      "Epoch 13/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.4325 - acc: 0.4531\n",
      "Epoch 14/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.5126 - acc: 0.3594\n",
      "Epoch 15/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.4392 - acc: 0.5000\n",
      "Epoch 16/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.4776 - acc: 0.4062\n",
      "Epoch 17/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.4267 - acc: 0.4531\n",
      "Epoch 18/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.4028 - acc: 0.4844\n",
      "Epoch 19/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.4509 - acc: 0.4219\n",
      "Epoch 20/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3793 - acc: 0.5625\n",
      "Epoch 21/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3324 - acc: 0.5469\n",
      "Epoch 22/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3806 - acc: 0.5469\n",
      "Epoch 23/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3011 - acc: 0.6406\n",
      "Epoch 24/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3628 - acc: 0.5781\n",
      "Epoch 25/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3375 - acc: 0.5469\n",
      "Epoch 26/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3646 - acc: 0.4844\n",
      "Epoch 27/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3670 - acc: 0.5000\n",
      "Epoch 28/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3335 - acc: 0.6250\n",
      "Epoch 29/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3319 - acc: 0.5156\n",
      "Epoch 30/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2620 - acc: 0.5312\n",
      "Epoch 31/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2917 - acc: 0.5156\n",
      "Epoch 32/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.3069 - acc: 0.5469\n",
      "Epoch 33/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2548 - acc: 0.5938\n",
      "Epoch 34/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2956 - acc: 0.5000\n",
      "Epoch 35/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1825 - acc: 0.6719\n",
      "Epoch 36/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1546 - acc: 0.6406\n",
      "Epoch 37/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2398 - acc: 0.6406\n",
      "Epoch 38/100\n",
      "2/2 [==============================] - 8s 4s/step - loss: 1.2308 - acc: 0.5469\n",
      "Epoch 39/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1313 - acc: 0.6406\n",
      "Epoch 40/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2048 - acc: 0.5625\n",
      "Epoch 41/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2466 - acc: 0.6250\n",
      "Epoch 42/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2436 - acc: 0.6406\n",
      "Epoch 43/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2387 - acc: 0.5938\n",
      "Epoch 44/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1327 - acc: 0.7031\n",
      "Epoch 45/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2123 - acc: 0.6562\n",
      "Epoch 46/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1671 - acc: 0.6250\n",
      "Epoch 47/100\n",
      "2/2 [==============================] - 8s 4s/step - loss: 1.2558 - acc: 0.5781\n",
      "Epoch 48/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0746 - acc: 0.7500\n",
      "Epoch 49/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.2107 - acc: 0.6094\n",
      "Epoch 50/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1149 - acc: 0.7031\n",
      "Epoch 51/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1088 - acc: 0.7344\n",
      "Epoch 52/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1322 - acc: 0.6875\n",
      "Epoch 53/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0704 - acc: 0.7188\n",
      "Epoch 54/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1957 - acc: 0.5625\n",
      "Epoch 55/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0279 - acc: 0.7812\n",
      "Epoch 56/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1685 - acc: 0.6094\n",
      "Epoch 57/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0759 - acc: 0.6094\n",
      "Epoch 58/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0929 - acc: 0.6719\n",
      "Epoch 59/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0346 - acc: 0.6719\n",
      "Epoch 60/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0881 - acc: 0.6406\n",
      "Epoch 61/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0438 - acc: 0.6562\n",
      "Epoch 62/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9911 - acc: 0.7188\n",
      "Epoch 63/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1528 - acc: 0.6250\n",
      "Epoch 64/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1423 - acc: 0.6562\n",
      "Epoch 65/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1057 - acc: 0.6094\n",
      "Epoch 66/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0782 - acc: 0.7031\n",
      "Epoch 67/100\n",
      "2/2 [==============================] - 8s 4s/step - loss: 0.9433 - acc: 0.8125\n",
      "Epoch 68/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9722 - acc: 0.8281\n",
      "Epoch 69/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0285 - acc: 0.6875\n",
      "Epoch 70/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0118 - acc: 0.7656\n",
      "Epoch 71/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1326 - acc: 0.6250\n",
      "Epoch 72/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0286 - acc: 0.6719\n",
      "Epoch 73/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1258 - acc: 0.6250\n",
      "Epoch 74/100\n",
      "2/2 [==============================] - 8s 4s/step - loss: 0.9866 - acc: 0.7344\n",
      "Epoch 75/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1556 - acc: 0.5781\n",
      "Epoch 76/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0250 - acc: 0.6406\n",
      "Epoch 77/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0023 - acc: 0.7031\n",
      "Epoch 78/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9560 - acc: 0.7344\n",
      "Epoch 79/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.8812 - acc: 0.7812\n",
      "Epoch 80/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9053 - acc: 0.7656\n",
      "Epoch 81/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1319 - acc: 0.6094\n",
      "Epoch 82/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9030 - acc: 0.7969\n",
      "Epoch 83/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0251 - acc: 0.6406\n",
      "Epoch 84/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9775 - acc: 0.7656\n",
      "Epoch 85/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9918 - acc: 0.6875\n",
      "Epoch 86/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.8731 - acc: 0.7500\n",
      "Epoch 87/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9981 - acc: 0.6562\n",
      "Epoch 88/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.1022 - acc: 0.6406\n",
      "Epoch 89/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9950 - acc: 0.6719\n",
      "Epoch 90/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 9s 4s/step - loss: 1.0588 - acc: 0.6250\n",
      "Epoch 91/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.7263 - acc: 0.8750\n",
      "Epoch 92/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9093 - acc: 0.7500\n",
      "Epoch 93/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9056 - acc: 0.7031\n",
      "Epoch 94/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9070 - acc: 0.7500\n",
      "Epoch 95/100\n",
      "2/2 [==============================] - 8s 4s/step - loss: 1.0094 - acc: 0.7031\n",
      "Epoch 96/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9370 - acc: 0.7031\n",
      "Epoch 97/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 1.0833 - acc: 0.5781\n",
      "Epoch 98/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9153 - acc: 0.7188\n",
      "Epoch 99/100\n",
      "2/2 [==============================] - 8s 4s/step - loss: 0.8764 - acc: 0.7812\n",
      "Epoch 100/100\n",
      "2/2 [==============================] - 9s 4s/step - loss: 0.9731 - acc: 0.6719\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_ds, epochs = 100, steps_per_epoch=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'val_accuracy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-57-fd3e52df845a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'val_accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'model accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'train'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'test'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'uppper left'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'val_accuracy'"
     ]
    },
    {
     "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.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend(['train', 'test'], loc = 'uppper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}