CNN-Meetingroom-Augmentation-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataAugment = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(10)\n",
    "import os\n",
    "import glob\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import np_utils\n",
    "#from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers.advanced_activations import PReLU\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "# Plot inline\n",
    "%matplotlib inline\n",
    "\n",
    "if(dataAugment): from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nameDataset = \"misBlock-224\"\n",
    "imgSize = (224, 224)\n",
    "dict_labels = {\"1-0-1-1\": 0,\"0-0-1-1\": 1, \"0-1-0-1\": 2, \"0-1-1-1\": 3, \"1-0-0-1\": 4, \"dark\": 5,\n",
    "              \"1-1-0-0\": 6, \"1-0-0-0\": 7, \"1-0-1-0\": 8, \"0-0-1-0\": 9, \"1-1-1-1\": 10, \n",
    "              \"0-0-0-0\": 11, \"0-0-0-1\": 12, \"1-1-1-0\": 13, \"0-1-0-0\": 14, \"1-1-0-1\": 15, \"0-1-1-0\": 16 }\n",
    "\n",
    "images = []\n",
    "labels = []\n",
    "labels_hot = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_train_history(train_history, train, validation):\n",
    "    plt.plot(train_history.history[train])\n",
    "    plt.plot(train_history.history[validation])\n",
    "    plt.title('Train history')\n",
    "    plt.ylabel(train)\n",
    "    plt.xlabel('Epoch')\n",
    "    legendLoc = 'lower right' if(train=='acc') else 'upper right'\n",
    "    plt.legend(['train', 'validation'], loc=legendLoc)\n",
    "    plt.show()\n",
    "    \n",
    "def load_image(folder):\n",
    "    \n",
    "    global images, labels, labels_hot, dict_labels\n",
    "    \n",
    "    for filename in os.listdir(folder):\n",
    "        label = os.path.basename(folders)\n",
    "        className = np.asarray( label )        \n",
    "        if label is not None:\n",
    "            labels.append(className)\n",
    "            labels_hot.append(dict_labels[label])\n",
    "            #np.append(labels, className , axis=0)\n",
    "            #np.append(labels_hot, np.array(dict_labels[label]), axis=0)\n",
    "            \n",
    "        img = cv2.imread(os.path.join(folder,filename))\n",
    "        if img is not None:\n",
    "            images.append(np.array(img))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load misBlock-224/1-0-1-1 ...\n",
      "Load misBlock-224/0-0-1-1 ...\n",
      "Load misBlock-224/0-1-0-1 ...\n",
      "Load misBlock-224/0-1-1-1 ...\n",
      "Load misBlock-224/1-0-0-1 ...\n",
      "Load misBlock-224/dark ...\n",
      "Load misBlock-224/1-1-0-0 ...\n",
      "Load misBlock-224/1-0-0-0 ...\n",
      "Load misBlock-224/1-0-1-0 ...\n",
      "Load misBlock-224/0-0-1-0 ...\n",
      "Load misBlock-224/1-1-1-1 ...\n",
      "Load misBlock-224/0-0-0-0 ...\n",
      "Load misBlock-224/0-0-0-1 ...\n",
      "Load misBlock-224/1-1-1-0 ...\n",
      "Load misBlock-224/0-1-0-0 ...\n",
      "Load misBlock-224/1-1-0-1 ...\n",
      "Load misBlock-224/0-1-1-0 ...\n"
     ]
    }
   ],
   "source": [
    "for folders in glob.glob(nameDataset+\"/*\"):\n",
    "    print(\"Load {} ...\".format(folders))\n",
    "    load_image(folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images.shape=(1190, 224, 224, 3) , labels_hot.shape==(1190,)\n",
      "[ 0  0  0 ..., 16 16 16]\n"
     ]
    }
   ],
   "source": [
    "images = np.array(images) \n",
    "labels_hot = np.array(labels_hot)\n",
    "print(\"images.shape={} , labels_hot.shape=={}\".format(images.shape, labels_hot.shape))\n",
    "print(labels_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:0-0-1-1 , ID:1, shape:(224, 224, 3)\n"
     ]
    },
    {
     "data": {
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m9vYd3nrzNRq+qr/UMicMQ4K4Qkce//wPfo+/9/d+jSCwzMaCjXde4fnXn2N+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9JP4P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laR6hVU6tFmA7Fnlq/pPxeIzSOV5gI3XGyuo9BoMdWs2fo/sgjuj7oLX+F5ju0v9yavVfBP5b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\nfhKzunmfH/3kHQaDoWm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Lfwn8IfBH5eM3dz3/Xwsh/g8MwNj5WXiC+Q6wRYG2NMI1Jqi2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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2902ec4630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampleID = 126\n",
    "plt.imshow(images[sampleID])\n",
    "print(\"Label:{} , ID:{}, shape:{}\".format(labels[sampleID], labels_hot[sampleID], images[sampleID].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(trainData, testData, trainLabels, testLabels) = train_test_split(images, labels_hot, test_size=0.15, random_state=42)\n",
    "(trainData, valiData, trainLabels, valiLabels) = train_test_split(trainData, trainLabels, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainData records: 808\n",
      "testData records: 179\n",
      "valiData records: 203\n",
      "trainData.shape=(808, 224, 224, 3) trainLabels.shape=(808,)\n",
      "testData.shape=(179, 224, 224, 3) testLabels.shape=(179,)\n",
      "valiData.shape=(203, 224, 224, 3) valiLabels.shape=(203,)\n"
     ]
    }
   ],
   "source": [
    "print(\"trainData records: {}\".format(len(trainData)))\n",
    "print(\"testData records: {}\".format(len(testData)))\n",
    "print(\"valiData records: {}\".format(len(valiData)))\n",
    "print(\"trainData.shape={} trainLabels.shape={}\".format(trainData.shape, trainLabels.shape))\n",
    "print(\"testData.shape={} testLabels.shape={}\".format(testData.shape, testLabels.shape))\n",
    "print(\"valiData.shape={} valiLabels.shape={}\".format(valiData.shape, valiLabels.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(808, 17)\n",
      "(179, 17)\n",
      "(203, 17)\n"
     ]
    }
   ],
   "source": [
    "trainLabels_hot = np_utils.to_categorical(trainLabels)\n",
    "testLabels_hot = np_utils.to_categorical(testLabels)\n",
    "valiLabels_hot = np_utils.to_categorical(valiLabels)\n",
    "print(trainLabels_hot.shape)\n",
    "print(testLabels_hot.shape)\n",
    "print(valiLabels_hot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainData_normalize = trainData.astype('float32') / 255.0\n",
    "testData_normalize = testData.astype('float32') / 255.0\n",
    "valiData_normalize = valiData.astype('float32') / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from skimage import exposure\n",
    "\n",
    "def AHE(img):\n",
    "    img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)\n",
    "    return img_adapteq\n",
    "\n",
    "if(dataAugment):\n",
    "    train_datagen = ImageDataGenerator(\n",
    "        preprocessing_function=AHE,\n",
    "        fill_mode='nearest')\n",
    "    \n",
    "    train_datagen.fit(trainData_normalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, ZeroPadding2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add(Conv2D(filters=32, kernel_size=(7, 7), padding='same', input_shape=(imgSize[0], imgSize[1], 3)))\n",
    "#model.add(LeakyReLU(alpha=.001))   # add an advanced activation\n",
    "model.add(PReLU())\n",
    "model.add(Dropout(rate=0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add(MaxPooling2D(pool_size=(2, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add(Conv2D(filters=64, kernel_size=(5, 5), padding='same'))\n",
    "#model.add(LeakyReLU(alpha=.001))   # add an advanced activation\n",
    "model.add(PReLU())\n",
    "model.add(Dropout(rate=0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add(MaxPooling2D(pool_size=(2, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add(Flatten())\n",
    "model.add(Dropout(rate=0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add(Dense(1024))\n",
    "#model.add(LeakyReLU(alpha=.001))   # add an advanced activation\n",
    "model.add(PReLU())\n",
    "model.add(Dropout(rate=0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add(Dense(len(dict_labels), activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 224, 224, 32)      4736      \n",
      "_________________________________________________________________\n",
      "p_re_lu_4 (PReLU)            (None, 224, 224, 32)      1605632   \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 224, 224, 32)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 112, 112, 64)      51264     \n",
      "_________________________________________________________________\n",
      "p_re_lu_5 (PReLU)            (None, 112, 112, 64)      802816    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 200704)            0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 200704)            0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1024)              205521920 \n",
      "_________________________________________________________________\n",
      "p_re_lu_6 (PReLU)            (None, 1024)              1024      \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 17)                17425     \n",
      "=================================================================\n",
      "Total params: 208,004,817\n",
      "Trainable params: 208,004,817\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps_per_epoch=(808, 224, 224, 3)\n",
      "trainLabels_hot.shape=(808, 17)\n",
      "steps_per_epoch=25\n",
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chtseng/anaconda3/lib/python3.6/site-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint16\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 314s - loss: 2.8742 - acc: 0.0795 - val_loss: 2.7946 - val_acc: 0.1823\n",
      "Epoch 2/15\n",
      "25/25 [==============================] - 303s - loss: 2.7205 - acc: 0.1233 - val_loss: 2.7265 - val_acc: 0.1773\n",
      "Epoch 3/15\n",
      "25/25 [==============================] - 303s - loss: 2.5448 - acc: 0.2439 - val_loss: 2.5305 - val_acc: 0.3547\n",
      "Epoch 4/15\n",
      "25/25 [==============================] - 303s - loss: 2.1693 - acc: 0.3645 - val_loss: 2.3769 - val_acc: 0.4286\n",
      "Epoch 5/15\n",
      "25/25 [==============================] - 303s - loss: 1.5957 - acc: 0.5254 - val_loss: 1.7074 - val_acc: 0.6552\n",
      "Epoch 6/15\n",
      "25/25 [==============================] - 303s - loss: 1.1357 - acc: 0.6351 - val_loss: 1.7242 - val_acc: 0.6601\n",
      "Epoch 7/15\n",
      "25/25 [==============================] - 303s - loss: 0.6267 - acc: 0.8119 - val_loss: 1.0281 - val_acc: 0.7586\n",
      "Epoch 8/15\n",
      "25/25 [==============================] - 303s - loss: 0.5078 - acc: 0.8596 - val_loss: 0.9110 - val_acc: 0.8621\n",
      "Epoch 9/15\n",
      "25/25 [==============================] - 303s - loss: 0.3225 - acc: 0.9051 - val_loss: 0.6938 - val_acc: 0.8719\n",
      "Epoch 10/15\n",
      "25/25 [==============================] - 303s - loss: 0.2533 - acc: 0.9267 - val_loss: 0.6847 - val_acc: 0.8522\n",
      "Epoch 11/15\n",
      "25/25 [==============================] - 303s - loss: 0.1778 - acc: 0.9439 - val_loss: 0.6656 - val_acc: 0.8473\n",
      "Epoch 12/15\n",
      "25/25 [==============================] - 303s - loss: 0.1117 - acc: 0.9692 - val_loss: 0.4555 - val_acc: 0.9064\n",
      "Epoch 13/15\n",
      "25/25 [==============================] - 303s - loss: 0.1290 - acc: 0.9563 - val_loss: 0.4804 - val_acc: 0.8966\n",
      "Epoch 14/15\n",
      "25/25 [==============================] - 307s - loss: 0.1790 - acc: 0.9483 - val_loss: 0.4830 - val_acc: 0.9064\n",
      "Epoch 15/15\n",
      "25/25 [==============================] - 303s - loss: 0.0789 - acc: 0.9764 - val_loss: 0.5875 - val_acc: 0.8424\n"
     ]
    }
   ],
   "source": [
    "epochs = 15\n",
    "batch_size = 64\n",
    "steps_per_epoch=int((len(trainData)*2)/batch_size)\n",
    "print(\"steps_per_epoch={}\".format(trainData_normalize.shape))\n",
    "print(\"trainLabels_hot.shape={}\".format(trainLabels_hot.shape))\n",
    "print(\"steps_per_epoch={}\".format(steps_per_epoch))\n",
    "\n",
    "if(dataAugment):\n",
    "#    train_history = model.fit_generator(train_datagen.flow(trainData_normalize, trainLabels_hot, batch_size=batch_size),\n",
    "#                        steps_per_epoch=round(len(trainData)/batch_size),\n",
    "#                        epochs=epochs,\n",
    "#                        validation_data=(valiData_normalize, valiLabels_hot))\n",
    "#     train_history = model.fit_generator(train_datagen.flow(trainData_normalize, trainLabels_hot, batch_size=batch_size),\n",
    "#                        samples_per_epoch=(len(trainData)*2),\n",
    "#                        nb_epoch=epochs,\n",
    "#                        validation_data=(valiData_normalize, valiLabels_hot))\n",
    "\n",
    "     train_history = model.fit_generator(train_datagen.flow(trainData_normalize, trainLabels_hot, batch_size=batch_size),\n",
    "                        steps_per_epoch=steps_per_epoch, epochs=epochs,\n",
    "                        validation_data=(valiData_normalize, valiLabels_hot))\n",
    "else:\n",
    "    train_history = model.fit(x=trainData_normalize, y=trainLabels_hot, validation_split=0.15, epochs=epochs, batch_size=batch_size, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_train_history(train_history, train, validation):\n",
    "    plt.plot(train_history.history[train])\n",
    "    plt.plot(train_history.history[validation])\n",
    "    plt.title('Train history')\n",
    "    plt.ylabel(train)\n",
    "    plt.xlabel('Epoch')\n",
    "    legendLoc = 'lower right' if(train=='acc') else 'upper right'\n",
    "    plt.legend(['train', 'validation'], loc=legendLoc)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PB+xzkV7lPlJXwf7VcMGL4FX754WP16SxKuUYz1/eh5bBeuU0pZqDup5o/lZE\nErGCYB3wBdYIJNWcrHgD/MOg71W1Nj2WV8S/FmwhsUOErnqqVDNSp1AQkVuAe7AuqbkOGAwsB0Y5\nrjTlVNn7rfMJg28H/5Bamz/37VZyCkt4+tJeuqaRUs1IXc8p3AOcCew1xowE+gMZDqtKOd+qtwFj\nzWCuRVLKUeauSuXmoQl0a6PzEZRqTuoaCoXGmEIAEfE3xmwFujquLOVURfnW3ISuF0BEfI1Ni0vL\neOTzTcSGB3LPmC7OqU8p5TR1PdGcZpun8DmwSESOAQccV5Zyqo3zoOBYnYahvrN0D1sP5TD92oEE\n+enyV0o1N3U90Xyp7eYTIrIYCAO+dVhVynmMgRVvQpve0OGcGpvuzyrg5e93MKZ7K/7Qs42TClRK\nOVO9P+oZY36uvZVqMnb/BBlb4JJpUMtV0Z6cn4wx8MT4ns6pTSnldHrlE0+3YhoER0Ovy2ts9sOW\nwyzcfJi7R3chLkJXP1WqudJQ8GSZu2DHd5B4E/hUf92DgqJSHvsimS6tQrh5aIITC1RKOZueKfRk\nK/8LXr6QeHONzab+uIP9WQV8OGWwXlZTqWZO/8I9VWE2rJtldRuFtq622fbD1oJ3EwfGcZYuia1U\ns6eh4KnWzoSiXBh8W7VNjDE88vkmgv19eGhc3ZbRVko1bRoKnqisFFa+Ce3Phrb9q232yZr9/Lbn\nKA+N66bXWlbKQ2goeKJt30DWPmudo2pk5RfxzwVbGNA+nCsS2zmxOKWUK2koeKIV0yCsHXS9sNom\nz327leyCYp65tLcueKeUB9FQ8DQHN8DeX62F77yrHny2eu9R5vyWyk1D4ukeowveKeVJNBQ8zcr/\ngm8QDLiuyqdLSst4+LNNxIQFcO+YM5xcnFLK1TQUPEnGNmvxu75XQWBElU3eXZbC1kM5PH5xT4L9\ndRqLUp5GQ8FTbJgH00eCXwicc1eVTQ5kFfDSou2M6taK83tWP3dBKdV86UfB5q64AL55ANa8Zw1B\nvfx/EBZbZdOnvtxMmTE8Ob4nUsvieEqp5klDoTnL2A4f3QDpyTD0LzDy4WpPLv+49TDfJh/ib+d3\npV1LXfBOKU/l0O4jERkrIttEZKeIPFhDu4kiYkQk0ZH1eJQN82D6CMg9BNd8AmMerzYQTi5417lV\nCLcO6+jcOpVSbsVhRwoi4g28DpwHpAGrRGS+MWZzpXahwN3ASkfV4lGK8uGbv8PaD6D9OTDxf9Ci\nbY2bvLZ4B2nHCpirC94p5fEc+Q4wCNhpjNltjCkC5gITqmj3D+B5oNCBtXiGjO3w9mgrEIb9Fa7/\nstZA2Jmew/Qlu7lsQCyDdcF3IEDJAAAW90lEQVQ7pTyeI0MhFkitcD/N9lg5EekPtDPGfFXTjkRk\niogkiUhSRkaG/SttDtZ/aOsuOgyTP4HRj1XbXXSSMYaHP9tEkJ8P/3dBd+fUqZRya44MhaqGr5jy\nJ0W8gP8Af61tR8aY6caYRGNMYnR0tB1LbAaK8uGLu+CzKdC2H9z2K3QeU6dNP12zn5V7jvLA2G5E\n6YJ3SikcO/ooDai4kloccKDC/VCgF/CTbfhjG2C+iIw3xiQ5sK7mI2ObbXTRFhh2P4x4qNajg5NO\nLnjXv304k87UBe+UUhZHhsIqoIuIJAD7gUnA1SefNMZkA1En74vIT8D9Ggh1tH4ufHWftWTF5E+g\n8+h6bf78d9s4ll/E+zcP0gXvlFLlHBYKxpgSEbkL+A7wBmYYY5JF5CkgyRgz31Gv3awV5cM3f7Mu\nktNhKFz+NrSIqdcu1uw7xuyV+7h5aAI924Y5qFClVFPk0MlrxpgFwIJKjz1WTdsRjqylWUjfanUX\nZWyF4X+Dcx+sc3fRSScXvGvTIoD7ztMF75RSp9IZzU3Fujnw9V+s7qJrP4VOoxq0m3eXpbDl4HGm\nXTOAEF3wTilVib4ruLuifFjwN1jX8O6ik1KP5vOfRdsZ0TWasb3a2LlQpVRzoKHgzk7pLvo7nPtA\nvbuLTsopLOaW95Lw8hL+MaGXLninlKqShoK72jAPvrwH/ILh2s+g08gG76q0zHDP3HXszMjlvRsH\n6YJ3SqlqaSi4o8PJ8NmfrKWuJ86A0MZ19Tz7zRZ+3JrOPy7pxdAuUbVvoJTyWBoK7mjho+DfAq6c\nCUEtG7WrD1ft461f9nD92R24dnAHOxWolGqudElMd7Pze9j1A5z790YHwordmTzy+SaGdYni0Yt6\n2KlApVRzpqHgTspKraOEiHg485ZG7WpvZh63z1xNu5ZBvHb1AHy89Z9aKVU77T5yJ+tmQfpm+ON7\n4NPwBeqOFxZz83tJlBmYcf2ZhAX62rFIpVRzph8f3cWJXPjxaYgbBD2quuxE3ZSUlnHX7LWkHMlj\n2uQBxEcF27FIpVRzp0cK7mLZq9a1EK6cCY2YQ/D011tYsj2Df13Wm3M66UgjpVT96JGCOzh+EJZN\nhZ6XQrtBDd7NzBV7eXdZCjcNSeCqQe3tWKBSylNoKLiDxU9DWQmMfrzBu1i28wiPz09mZNdoHr5Q\nr6KmlGoYDQVXO7QJ1s6CQVOgZUKDdrE7I5fbZ62hY1QwU6/qj7deH0Ep1UAaCq5kDCx8BALCYPj9\nDdpFdr61ppG3l/C/688kNEBHGimlGk5DwZV2/gC7F1sL3QVG1Hvz4tIy7pi9mtRj+bw5eSDtI3VN\nI6VU4+joI1cpLbGOEiISGjxR7akvN7N0ZyYvTOzDoITGzX5WSinQUHCddTMhYwtc8T74+NV78/eW\npfDBir38aXhH/pjYzgEFKqU8kXYfucKJXPjxGWg3GLqPr/fmS7Zn8OSXyYzp3oq/j+3mgAKVUp5K\njxRcYdlUyEuHq+bUe6LazvQc7py9hjNah/LyJB1ppJSyLz1ScLbjB2DpVOh5GcQl1mvTY3lF3Pxe\nEv4+Xrx9faJeY1kpZXf6ruJsPz4DphTG1G+iWlFJGbfNXM3BrELmTBlMXISONFJK2Z8eKTjTwQ3W\nSqhn/claHruOjDE8Pn8TK/cc5bmJvRnYof7DV5VSqi40FJzl5ES1wHAY9td6bTpjaQpzfkvlzpGd\nuLR/nIMKVEopDQXn2fk97PkZzn2wXhPVFm9N55mvN3N+z9b89byuDixQKaU0FJzj5ES1lh0h8aY6\nb7b9cA5/nrOWbm1a8J8r++GlI42UUg6mJ5qdYe0HkLHVulZCHSeqZeae4Ob3VhHo583b1ycS5Kf/\nVEopx9MjBUc7kQOL/wntz4ZuF9Vpk5MjjdKPn+Ct6xJpGx7o4CKVUsqiHz8dbekrtolqc+s8Ue2p\nr5JZlXKMVyb1o1+7cAcXqJRSv9MjBUfK3g/LXoNeEyFuYJ02mfvbPmau2MefhndkQr9YBxeolFKn\n0lBwpB+fBlMGox+rU/M1+47x2BfJDOsSpWsaKaVcQkPBUQ6uh/VzYPBtENGh1ubpxwu57YPVtAkL\n4FW9eppSykU0FByhfKJaBAz9S63Ni0rKuH3WGnIKS5h+3UDCg+q/lLZSStmDhoIj7FgIe5bAiAet\nGcy1eOLLZFbvPcYLf+xDtzYtnFCgUkpVTUPB3kpLYOGj0LJTnSaqzV65j9kr93HbuZ24qE9bJxSo\nlFLVc2goiMhYEdkmIjtF5MEqnv+LiGwWkQ0i8oOI1N757u7Wvg9HtsF5T4G3b41NV+89yuPzNzH8\njGj+dr4uYaGUcj2HhYKIeAOvA+OAHsBVItKjUrO1QKIxpg/wMfC8o+pxisLj1kS1DkOg24U1Nj18\nvJDbZq6hbXggr+rFcpRSbsKRk9cGATuNMbsBRGQuMAHYfLKBMWZxhfYrgMkOrMfxlr4CeRlw9Yc1\nTlQ7UVLKbTNXk3eihJk3n0VYUM1HFEo1Z8XFxaSlpVFYWOjqUpqFgIAA4uLi8PVt2PuKI0MhFkit\ncD8NOKuG9jcD31T1hIhMAaYAtG/f3l712Vd2Gix/DXr/EWJrnqj2xPxk1u7L4o1rBtC1TaiTClTK\nPaWlpREaGkp8fDxSz8vTqlMZY8jMzCQtLY2EhIQG7cOR5xSq+tc1VTYUmQwkAi9U9bwxZroxJtEY\nkxgdHW3HEu3ox6etoai1TFSbtXJv+bURLugd46TilHJfhYWFREZGaiDYgYgQGRnZqKMuRx4ppAHt\nKtyPAw5UbiQiY4CHgXONMSccWI/jHFgH6+fCkHsgvPojmaSUozwxP5kRXaP5i14bQalyGgj209jf\npSNDYRXQRUQSgP3AJODqig1EpD/wX2CsMSbdgbXAoU1wcB34hVhf/hW/h4JfMPj413nRunInJ6oF\ntYRh1U9UO5RtnViODQ/kFT2xrJRyUw4LBWNMiYjcBXwHeAMzjDHJIvIUkGSMmY/VXRQCfGRLt33G\nmPEOKWjHQvjhyZrbePnYgiK0QmAEV/NYqHU79zCk/AIXvAgBYVXu9uSJ5YKiEmbfehZhgXpiWSl3\nkZWVxezZs7njjjvqtd0FF1zA7NmzCQ9vXisZO3TpbGPMAmBBpcceq3B7jCNf/xSDpkCvy6AoD07k\nQlGO7XseFOVa1z0oyq36+dz0Ux8rKz5131FnwMAbqnxZYwyPfZ7MutQs3pw8gDNa64llpdxJVlYW\nb7zxxmmhUFpaire3d7XbLViwoNrnmjLPuZ6Cv+1Tvj2UFJ0aJC3aVjtRbebKfXyYlMqfR3VmbC89\nsaxUTZ78MpnNB47bdZ892rbg8Yt7Vvv8gw8+yK5du+jXrx++vr6EhIQQExPDunXr2Lx5M5dccgmp\nqakUFhZyzz33MGXKFADi4+NJSkoiNzeXcePGMXToUJYtW0ZsbCxffPEFgYFN8+JYusxFQ/j4WecQ\nIjpA657WwndV+G3PUZ6cn8yobq24b8wZTi5SKVUXzz77LJ06dWLdunW88MIL/PbbbzzzzDNs3mxN\nqZoxYwarV68mKSmJqVOnkpmZedo+duzYwZ133klycjLh4eF88sknzv4x7MZzjhSc7GB2AXfMWk27\nlkH858p+eOmJZaVqVdMnemcZNGjQKWP8p06dymeffQZAamoqO3bsIDIy8pRtEhIS6NevHwADBw4k\nJSXFafXam4aCAxQWl3LbB6spKCplzq2D9cSyUk1IcHBw+e2ffvqJ77//nuXLlxMUFMSIESOqnAPg\n7+9fftvb25uCggKn1OoIGgp2Zozh0c83sT4tm/9eO5AuemJZKbcWGhpKTk5Olc9lZ2cTERFBUFAQ\nW7duZcWKFU6uzvk0FOzsgxV7+Wh1GneP7sL5Pdu4uhylVC0iIyMZMmQIvXr1IjAwkNatW5c/N3bs\nWN5880369OlD165dGTx4sAsrdQ4xpsqVJ9xWYmKiSUpKcnUZVVq5O5Nr3l7JiK7RTL82Uc8jKFUH\nW7ZsoXv37q4uo1mp6ncqIquNMYm1baujj+zkQFYBd85eQ/vIIF7SE8tKqSZKu4/soLDYmrFcWFzG\n3CmJtAjQE8tKqaZJQ6GRjDE8/NkmNqRl89Z1iXRuZacJckop5QIaCo1wvLCYNxbv4pM1adw7pgvn\n9Whd+0ZKKeXGNBQaIPVoPu8sTeHDVfvIKyrl0v6x3D2qi6vLUkqpRtNQqIf1qVm89ctuvtl0CAEu\n6hPDLcM60iu26tVRlVKqqdHRR7UoKzMs2nyYK/67nAmvL+XnbRncMjSBXx4YycuT+msgKOVhQkKs\n84YHDhxg4sSJVbYZMWIEtQ2df/nll8nPzy+/f8EFF5CVlWW/QhtIjxSqUVBUyidr0pjx6x52H8kj\nNjyQRy7szpVntiNURxcp5fHatm3Lxx9/3ODtX375ZSZPnkxQUBDgPktxayhUkpFzgg+Wp/DBir0c\nyy+mb1wYr17Vn3G92uDjrQdWSjnUNw/CoY323Web3jDu2WqffuCBB+jQoUP59RSeeOIJRIQlS5Zw\n7NgxiouLefrpp5kwYcIp26WkpHDRRRexadMmCgoKuPHGG9m8eTPdu3c/Ze2j22+/nVWrVlFQUMDE\niRN58sknmTp1KgcOHGDkyJFERUWxePHi8qW4o6KieOmll5gxYwYAt9xyC/feey8pKSlOWaJbQ8Fm\nZ3oOb/+yh0/X7qe4tIzR3Vpz67AEBiW01OvHKtWMTZo0iXvvvbc8FObNm8e3337LfffdR4sWLThy\n5AiDBw9m/Pjx1b4XTJs2jaCgIDZs2MCGDRsYMGBA+XPPPPMMLVu2pLS0lNGjR7NhwwbuvvtuXnrp\nJRYvXkxUVNQp+1q9ejXvvPMOK1euxBjDWWedxbnnnktERAQ7duxgzpw5vPXWW1xxxRV88sknTJ48\n2a6/D48OBWMMy3dn8taS3SzeloG/jxcTB8Zx89AEOkXrfAOlnK6GT/SO0r9/f9LT0zlw4AAZGRlE\nREQQExPDfffdx5IlS/Dy8mL//v0cPnyYNm2qXs9syZIl3H333QD06dOHPn36lD83b948pk+fTklJ\nCQcPHmTz5s2nPF/Zr7/+yqWXXlq+Wutll13GL7/8wvjx452yRLdHhkJxaRlfbzjIW7/sJvnAcSKD\n/bhvzBlMHtyeyBD/2neglGpWJk6cyMcff8yhQ4eYNGkSs2bNIiMjg9WrV+Pr60t8fHyVS2ZXVNVR\nxJ49e3jxxRdZtWoVERER3HDDDbXup6b16JyxRLdHdZIfLyzmvz/vYvjzi7n3w3UUFpfy7GW9Wfrg\nKO4Z00UDQSkPNWnSJObOncvHH3/MxIkTyc7OplWrVvj6+rJ48WL27t1b4/bDhw9n1qxZAGzatIkN\nGzYAcPz4cYKDgwkLC+Pw4cN888035dtUt2T38OHD+fzzz8nPzycvL4/PPvuMYcOG2fGnrZnHHCl8\nuGof//hqC7knSji7YyTPXNqLEWe00oXrlFL07NmTnJwcYmNjiYmJ4ZprruHiiy8mMTGRfv360a1b\ntxq3v/3227nxxhvp06cP/fr1Y9CgQQD07duX/v3707NnTzp27MiQIUPKt5kyZQrjxo0jJiaGxYsX\nlz8+YMAAbrjhhvJ93HLLLfTv399pV3PzmKWzl+48wrykVG7VyWZKuRVdOtv+GrN0tsccKQzpHMWQ\nzlG1N1RKKQ/mUecUlFJK1UxDQSnlck2tG9udNfZ3qaGglHKpgIAAMjMzNRjswBhDZmYmAQEBDd6H\nx5xTUEq5p7i4ONLS0sjIyHB1Kc1CQEAAcXFxDd5eQ0Ep5VK+vr4kJCS4ugxlo91HSimlymkoKKWU\nKqehoJRSqlyTm9EsIhlAzQuRVC8KOGLHchytKdXblGqFplVvU6oVmla9TalWaFy9HYwx0bU1anKh\n0BgiklSXad7uoinV25RqhaZVb1OqFZpWvU2pVnBOvdp9pJRSqpyGglJKqXKeFgrTXV1APTWleptS\nrdC06m1KtULTqrcp1QpOqNejzikopZSqmacdKSillKqBhoJSSqlyHhMKIjJWRLaJyE4RedDV9VRH\nRNqJyGIR2SIiySJyj6trqgsR8RaRtSLylatrqYmIhIvIxyKy1fY7PtvVNdVERO6z/T/YJCJzRKTh\ny186gIjMEJF0EdlU4bGWIrJIRHbYvke4ssaTqqn1Bdv/hQ0i8pmIhLuyxpOqqrXCc/eLiBERh1w1\nzCNCQUS8gdeBcUAP4CoR6eHaqqpVAvzVGNMdGAzc6ca1VnQPsMXVRdTBK8C3xphuQF/cuGYRiQXu\nBhKNMb0Ab2CSa6s6zbvA2EqPPQj8YIzpAvxgu+8O3uX0WhcBvYwxfYDtwEPOLqoa73J6rYhIO+A8\nYJ+jXtgjQgEYBOw0xuw2xhQBc4EJLq6pSsaYg8aYNbbbOVhvWrGurapmIhIHXAi87epaaiIiLYDh\nwP8AjDFFxpgs11ZVKx8gUER8gCDggIvrOYUxZglwtNLDE4D3bLffAy5xalHVqKpWY8xCY0yJ7e4K\noOFrTttRNb9XgP8AfwccNkLIU0IhFkitcD8NN3+jBRCReKA/sNK1ldTqZaz/qGWuLqQWHYEM4B1b\nV9fbIhLs6qKqY4zZD7yI9anwIJBtjFno2qrqpLUx5iBYH3KAVi6up65uAr5xdRHVEZHxwH5jzHpH\nvo6nhIJU8Zhbj8UVkRDgE+BeY8xxV9dTHRG5CEg3xqx2dS114AMMAKYZY/oDebhP18ZpbH3xE4AE\noC0QLCKTXVtV8yQiD2N13c5ydS1VEZEg4GHgMUe/lqeEQhrQrsL9ONzsMLwiEfHFCoRZxphPXV1P\nLYYA40UkBatbbpSIzHRtSdVKA9KMMSePvD7GCgl3NQbYY4zJMMYUA58C57i4pro4LCIxALbv6S6u\np0Yicj1wEXCNcd+JW52wPhyst/2txQFrRKSNvV/IU0JhFdBFRBJExA/rZN18F9dUJRERrD7vLcaY\nl1xdT22MMQ8ZY+KMMfFYv9cfjTFu+WnWGHMISBWRrraHRgObXVhSbfYBg0UkyPb/YjRufGK8gvnA\n9bbb1wNfuLCWGonIWOABYLwxJt/V9VTHGLPRGNPKGBNv+1tLAwbY/k/blUeEgu1E0l3Ad1h/VPOM\nMcmurapaQ4BrsT5xr7N9XeDqopqRPwOzRGQD0A/4p4vrqZbtiOZjYA2wEevv1a2WZRCROcByoKuI\npInIzcCzwHkisgNrpMyzrqzxpGpqfQ0IBRbZ/tbedGmRNtXU6pzXdt+jJaWUUs7mEUcKSiml6kZD\nQSmlVDkNBaWUUuU0FJRSSpXTUFBKKVVOQ0GpSkSktMJw4HX2XFVXROKrWvlSKXfh4+oClHJDBcaY\nfq4uQilX0CMFpepIRFJE5DkR+c321dn2eAcR+cG2Jv8PItLe9nhr2xr9621fJ5eo8BaRt2zXSVgo\nIoEu+6GUqkRDQanTBVbqPrqywnPHjTGDsGbCvmx77DXgfdua/LOAqbbHpwI/G2P6Yq2xdHIWfRfg\ndWNMTyALuNzBP49SdaYzmpWqRERyjTEhVTyeAowyxuy2LVp4yBgTKSJHgBhjTLHt8YPGmCgRyQDi\njDEnKuwjHlhkuwANIvIA4GuMedrxP5lStdMjBaXqx1Rzu7o2VTlR4XYpem5PuRENBaXq58oK35fb\nbi/j98tkXgP8arv9A3A7lF/DuoWzilSqofQTilKnCxSRdRXuf2uMOTks1V9EVmJ9oLrK9tjdwAwR\n+RvWld1utD1+DzDdtsJlKVZAHHR49Uo1gp5TUKqObOcUEo0xR1xdi1KOot1HSimlyumRglJKqXJ6\npKCUUqqchoJSSqlyGgpKKaXKaSgopZQqp6GglFKq3P8D82kfogHK5e8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29028dac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_train_history(train_history, 'acc', 'val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LyArgJWCcLXiUh4gMCeCJSxJZtzuPVxwxNlKvMdCyAyx8xf5tK+WlGjvz2uXA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f290283ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_train_history(train_history, 'loss', 'val_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179/179 [==============================] - 9s     \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>predict</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "predict  0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16\n",
       "label                                                                      \n",
       "0         3   0   0   0   6   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "1         0   9   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0\n",
       "2         0   0  13   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "3         0   0   0  14   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "4         0   0   0   0  11   0   0   0   0   0   0   0   0   0   0   0   0\n",
       "5         0   0   0   0   0  14   0   0   0   0   0   0   0   0   0   0   0\n",
       "6         0   0   0   0   1   0   9   0   0   0   0   0   0   0   0   0   0\n",
       "7         0   0   0   0   0   0   1   2   0   0   0   6   0   0   0   0   0\n",
       "8         0   0   0   0   0   0   0   0  11   2   0   0   0   0   0   0   0\n",
       "9         0   0   0   0   0   0   0   0   0   4   0   1   0   0   0   0   0\n",
       "10        0   0   0   0   0   0   0   0   0   0  13   0   0   1   0   0   0\n",
       "11        0   0   0   0   0   0   0   0   0   0   0   9   0   0   0   0   0\n",
       "12        0   0   0   0   1   0   0   0   0   0   0   1   7   0   0   0   0\n",
       "13        0   0   0   0   0   0   1   0   0   0   0   0   0   7   0   0   0\n",
       "14        0   0   0   0   0   0   0   0   0   0   0   1   0   0  11   0   0\n",
       "15        0   0   1   0   1   0   0   0   0   0   0   0   0   0   0   6   0\n",
       "16        0   0   0   0   0   0   0   0   0   0   0   1   0   0   1   0   9"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "prediction = model.predict_classes(testData_normalize)\n",
    "pd.crosstab(testLabels, prediction, rownames=['label'], colnames=['predict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.33      0.50         9\n",
      "          1       1.00      0.90      0.95        10\n",
      "          2       0.93      1.00      0.96        13\n",
      "          3       1.00      1.00      1.00        14\n",
      "          4       0.55      1.00      0.71        11\n",
      "          5       1.00      1.00      1.00        14\n",
      "          6       0.82      0.90      0.86        10\n",
      "          7       1.00      0.22      0.36         9\n",
      "          8       1.00      0.85      0.92        13\n",
      "          9       0.57      0.80      0.67         5\n",
      "         10       1.00      0.93      0.96        14\n",
      "         11       0.47      1.00      0.64         9\n",
      "         12       1.00      0.78      0.88         9\n",
      "         13       0.88      0.88      0.88         8\n",
      "         14       0.92      0.92      0.92        12\n",
      "         15       1.00      0.75      0.86         8\n",
      "         16       1.00      0.82      0.90        11\n",
      "\n",
      "avg / total       0.91      0.85      0.85       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(testLabels, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imagesGave = []\n",
    "labelsGave = []\n",
    "labelidGave = []\n",
    "\n",
    "def load_image_predict(folder):    \n",
    "    global images, labels, labels_hot, dict_labels\n",
    "    \n",
    "    for filename in os.listdir(folder):\n",
    "        label = os.path.basename(folders)      \n",
    "        if label is not None:\n",
    "            labelidGave.append(dict_labels[label])\n",
    "            labelsGave.append(label)\n",
    "            \n",
    "        img = cv2.imread(os.path.join(folder,filename))\n",
    "        if img is not None:\n",
    "            imagesGave.append(np.array(img))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load 20170929/1-0-1-1 ...\n",
      "Load 20170929/0-1-1-1 ...\n",
      "Load 20170929/1-0-0-1 ...\n",
      "Load 20170929/1-1-0-0 ...\n",
      "Load 20170929/1-0-1-0 ...\n",
      "Load 20170929/1-1-1-1 ...\n",
      "Load 20170929/0-0-0-0 ...\n",
      "Load 20170929/1-1-1-0 ...\n",
      "Load 20170929/0-1-0-0 ...\n",
      "Load 20170929/1-1-0-1 ...\n",
      "Load 20170929/0-1-1-0 ...\n"
     ]
    }
   ],
   "source": [
    "for folders in glob.glob(\"20170929/*\"):\n",
    "    print(\"Load {} ...\".format(folders))\n",
    "    load_image_predict(folders)\n",
    "\n",
    "normalizeGave = np.array(imagesGave).astype('float32') / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "165/165 [==============================] - 8s     \n",
      "Score for the prediction: 0.8000000003612403\n",
      "165/165 [==============================] - 8s     \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>predict</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "predict  2   3   4   6   8   9   10  11  13  14  15  16\n",
       "label                                                  \n",
       "0         0   0   1   0   0   0   0   0   0   0   0   0\n",
       "3         1   5   0   0   0   0   0   0   0   0   0   0\n",
       "4         0   0   1   0   0   0   0   0   0   0   0   0\n",
       "6         0   0   0   4   0   0   0   0   0   0   0   0\n",
       "8         0   0   0   0  14   2   0   1   0   0   0   0\n",
       "10        0   9   2   0   0   0  62   0   2   0   6   1\n",
       "11        0   0   0   0   0   0   0   2   0   0   0   0\n",
       "13        0   0   0   2   0   0   0   0  30   2   0   2\n",
       "14        0   0   0   0   0   0   0   0   0   1   0   0\n",
       "15        1   0   0   0   0   0   0   0   0   0   5   0\n",
       "16        0   0   0   0   0   0   0   0   0   1   0   8"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LabelsGave_hot = np_utils.to_categorical(labelidGave)\n",
    "scores = model.evaluate(normalizeGave, LabelsGave_hot)\n",
    "print(\"Score for the prediction: {}\".format(scores[1]) )\n",
    "\n",
    "predictionGave = model.predict_classes(normalizeGave) \n",
    "pd.crosstab(np.array(labelidGave), predictionGave, rownames=['label'], colnames=['predict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. [0]: 1-0-1-1 =>1-0-0-1\n",
      "2. [1]: 0-1-1-1 =>0-1-0-1\n",
      "3. [20]: 1-0-1-0 =>0-0-1-0\n",
      "4. [22]: 1-0-1-0 =>0-0-1-0\n",
      "5. [25]: 1-0-1-0 =>0-0-0-0\n",
      "6. [33]: 1-1-1-1 =>0-1-1-1\n",
      "7. [36]: 1-1-1-1 =>1-0-0-1\n",
      "8. [37]: 1-1-1-1 =>0-1-1-1\n",
      "9. [40]: 1-1-1-1 =>1-1-0-1\n",
      "10. [46]: 1-1-1-1 =>1-1-0-1\n",
      "11. [47]: 1-1-1-1 =>1-0-0-1\n",
      "12. [57]: 1-1-1-1 =>0-1-1-1\n",
      "13. [58]: 1-1-1-1 =>1-1-0-1\n",
      "14. [69]: 1-1-1-1 =>0-1-1-1\n",
      "15. [73]: 1-1-1-1 =>1-1-1-0\n",
      "16. [76]: 1-1-1-1 =>1-1-0-1\n",
      "17. [82]: 1-1-1-1 =>0-1-1-1\n",
      "18. [85]: 1-1-1-1 =>0-1-1-1\n",
      "19. [97]: 1-1-1-1 =>0-1-1-1\n",
      "20. [102]: 1-1-1-1 =>0-1-1-1\n",
      "21. [104]: 1-1-1-1 =>1-1-0-1\n",
      "22. [105]: 1-1-1-1 =>1-1-1-0\n",
      "23. [107]: 1-1-1-1 =>0-1-1-1\n",
      "24. [108]: 1-1-1-1 =>0-1-1-0\n",
      "25. [110]: 1-1-1-1 =>1-1-0-1\n",
      "26. [116]: 1-1-1-0 =>1-1-0-0\n",
      "27. [123]: 1-1-1-0 =>0-1-1-0\n",
      "28. [125]: 1-1-1-0 =>0-1-0-0\n",
      "29. [128]: 1-1-1-0 =>0-1-1-0\n",
      "30. [138]: 1-1-1-0 =>1-1-0-0\n",
      "31. [141]: 1-1-1-0 =>0-1-0-0\n",
      "32. [150]: 1-1-0-1 =>0-1-0-1\n",
      "33. [162]: 0-1-1-0 =>0-1-0-0\n"
     ]
    }
   ],
   "source": [
    "ii = 1\n",
    "\n",
    "for sampleID in range(0,len(imagesGave)): \n",
    "    \n",
    "    if(labelidGave[sampleID] != predictionGave[sampleID]):\n",
    "        keys = [ key for key,value in dict_labels.items() if value==predictionGave[sampleID] ]\n",
    "    \n",
    "        title = (\"{}. [{}]: {} =>{}\".format(ii, sampleID, labelsGave[sampleID], keys[0]))\n",
    "        print(title)\n",
    "        \n",
    "        ii += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22]: 1-0-1-0 =>0-1-0-0\n"
     ]
    },
    {
     "data": {
      "image/png": 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zKIVwELoc4SzCObQTGCsZD2Nm6hV6vR7j4ZBGo4FCMRwl/OZvfZ3j5+pUay1O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O+bBoAVg7A6qdybHr3Oj1u5H8lHnGFMmklM14UoHzEKZyUym8ertqvq+2l95n5VRr2WepOSVstx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\nJEV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38BO0Rov+RKbxo7SkoR1LEVAoldjc2ODKlStMzs5QKpVNJhanpGkGvkXYTfA8j3qt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\nsQ7/DbJdJgYhkQDfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f290280b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampleID = 22\n",
    "plt.imshow(imagesGave[sampleID])\n",
    "print(\"[{}]: {} =>{}\".format(sampleID, labelsGave[sampleID], keys[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}