B&W Flowers Image Colorization.ipynb

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   "source": [
    "# B&W Flower Image Colorization\n",
    "<h4>Elad Shabi, Nadav Dori, Chen Shmilovich</h4>\n",
    "<hr>\n",
    "\n",
    "In the following code we built a convolutional neural network using keras library,<br>\n",
    "that gets as an input 32X32 pixels grayscale images of flowers, and generates an output of 96X96 colored flowers images.\n",
    "\n",
    "<hr>\n",
    "* <b>Data-Sets</b><br>\n",
    "We used a data set that contains 8189 pairs of grayscale and colored flower images<br>\n",
    "The training set is consisted of 70% of the images, while the test set is consisted of 30% of them. 10% of the training images are used for validation.<br>\n",
    "\n",
    "<hr>\n",
    "\n",
    "* <b>Model's Network Architecture</b><br>\n",
    "We've been experimenting with several different designs of the network.<br>\n",
    "In every model we chose to use RELU activation function on the hidden layers of the network.<br>\n",
    "On the output layer we used sigmoid function in order to output values between 0 and 1.<br>\n",
    "Generally the network processed the input image in such a way that it has been shrinked on the first few layers and than upscaled to the desired output dimensions. We used strides and up-sampling in order to do so.<br>\n",
    "We achieved the best results for this assignment using the described method.<br>\n",
    "Overfitting on these kind of networks is a subjective matter, therefore we needed to examine the results and determine wether they match our expectations.\n",
    "<hr>\n",
    "* <b>Model's Training</b><br>\n",
    "We normalized the input pixel values to be between 0 and 1, and used the mean square error loss function to measure our results.<br>\n",
    "After that we also tried using the \"msle\" loss function which we found more suitable for our project.\n",
    "On both occasions we used the \"rmsprop\" optimizer to minimize our loss.\n",
    "<hr>\n",
    "* <b>Performance</b><br>\n",
    "We were suprised by our initial results, it seemed like the network recognized the shapes and textures of the input images, and also colored them nicely.<br>\n",
    "The only issue we had is the lack of sharpness of the output images.<br>\n",
    "We then added more convolutional layers to deepen our network and we increased the amount of filters in each layer dramatically. Furthermore we tested different batch sizes and number of epochs.<br>\n",
    "The output images became more sharp and clear, the results were way over our expectations.\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import load_model\n",
    "from keras.layers import Lambda, Conv2D, UpSampling2D, Dropout, Dense, Flatten, Activation, InputLayer\n",
    "import glob as glob\n",
    "import matplotlib\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rgb2gray(rgb):\n",
    "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bw_folder_path = \"/Users/i/Desktop/Deep Learning/Project/B&W Colorization/JPEG32/\"\n",
    "colored_folder_path = \"/Users/i/Desktop/Deep Learning/Project/B&W Colorization/JPEG96/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bw_images_names=glob.glob(bw_folder_path +\"*.jpg\")\n",
    "bw_images_names.sort()\n",
    "\n",
    "bw_images = []\n",
    "for name in bw_images_names:\n",
    "    bw_image = Image.open(name)\n",
    "    bw_image = np.array(bw_image,dtype=np.float32)\n",
    "    bw_image = rgb2gray(bw_image)\n",
    "    bw_image = bw_image/255\n",
    "    bw_images.append(bw_image)\n",
    "    \n",
    "bw_images = np.array(bw_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colored_images_names=glob.glob(colored_folder_path +\"*.jpg\")\n",
    "colored_images_names.sort()\n",
    "\n",
    "colored_images = []\n",
    "for name in colored_images_names:\n",
    "    colored_image = Image.open(name)\n",
    "    colored_image = np.array(colored_image,dtype=np.float32)\n",
    "    colored_image = colored_image/255\n",
    "    colored_images.append(colored_image)\n",
    "\n",
    "colored_images = np.array(colored_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bw_images = bw_images.reshape(bw_images.shape[0],32,32,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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BAwdm2qMGkgsWLKDaihUrqMbGTAFAeXl5pv2Pf/wj9enRowfVooq5rl27Uo2lPgH+3KIK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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196b2898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FFNCp6bRyfhMGEgjDaSg3nJujIehImByhVKEEkAkrGKQi6b0X5ikVlA3+n06q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c8fe748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(bw_images[0].reshape(32,32),cmap='gist_gray')\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(colored_images[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(bw_images, colored_images, test_size=0.30, random_state=101)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.10, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "# A Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 32, 32, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 16, 8)         208       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 8)         584       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 16)        1168      \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 16)          2320      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8, 8, 16)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 8, 32)          4640      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 16, 16, 32)        9248      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 32, 32, 16)        4624      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 96, 96, 16)        0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 96, 96, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 96, 96, 3)         3891      \n",
      "=================================================================\n",
      "Total params: 26,683\n",
      "Trainable params: 26,683\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(InputLayer(input_shape=(32, 32,1)))\n",
    "model.add(Conv2D(8, (5, 5), activation='relu', padding='same', strides=2))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', padding='same', strides=2))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((3, 3)))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Conv2D(3, (9, 9), activation='sigmoid', padding='same'))\n",
    "\n",
    "# Finish model\n",
    "model.compile(optimizer='rmsprop',loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "# B Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 32, 32, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 16, 8)         208       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 8)         584       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 16)        1168      \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 32)          4640      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 8, 32)          9248      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 16, 16, 16)        4624      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 32, 32, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 32, 32, 8)         1160      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 96, 96, 8)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 96, 96, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 96, 96, 3)         1947      \n",
      "=================================================================\n",
      "Total params: 23,579\n",
      "Trainable params: 23,579\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(InputLayer(input_shape=(32, 32,1)))\n",
    "model.add(Conv2D(8, (5, 5), activation='relu', padding='same', strides=2))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same', strides=2))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((3, 3)))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Conv2D(3, (9, 9), activation='sigmoid', padding='same'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',loss='msle')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "# C Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 32, 32, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 32, 32, 64)        640       \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2 (None, 64, 64, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 32, 32, 128)       147584    \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 32, 32, 256)       295168    \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 16, 16, 256)       590080    \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 16, 16, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 16, 16, 256)       1179904   \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 16, 16, 128)       295040    \n",
      "_________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2 (None, 48, 48, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 48, 48, 64)        131136    \n",
      "_________________________________________________________________\n",
      "up_sampling2d_6 (UpSampling2 (None, 96, 96, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 96, 96, 32)        18464     \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 96, 96, 3)         867       \n",
      "=================================================================\n",
      "Total params: 3,949,827\n",
      "Trainable params: 3,949,827\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(InputLayer(input_shape=(32, 32, 1)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', padding='same', strides=2))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((4,4)))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', padding='same', strides=2))\n",
    "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(256, (3, 3), activation='relu', padding='same', strides=2))\n",
    "model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
    "\n",
    "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((3, 3)))\n",
    "model.add(Conv2D(64, (4, 4), activation='relu', padding='same'))\n",
    "model.add(UpSampling2D((2, 2)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
    "model.add(Conv2D(3, (3, 3), activation='sigmoid', padding='same'))\n",
    "\n",
    "model.summary()\n",
    "model.compile(optimizer='rmsprop',loss='msle',metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "# Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.fit(x=X_train, y=y_train, batch_size=170, epochs=900,validation_data=(X_val, y_val), verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = load_model(\"./models/d_model.h5py\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:\n"
     ]
    },
    {
     "data": {
      "image/png": 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F2sc//vHc9nnz5oV9Ojo6Qm39+vWhxpyM6JiLFi0K+7DEHuY6sPclcjJYn/Pnz4cacx2Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15d2ddf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y:\n"
     ]
    },
    {
     "data": {
      "image/png": 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FOU+a2HpqLlWFZ7aOAwDOSWutQwdu0jE33Hg7AODI9aFN1+xASPlNDYS3lnr+\niqk+ZzUlxzGQS0vEWoS8jqIVGdzr4vVcLuV+MHDn+CzEe0/N3yqTTzwGwUoMfJ48HSjIH3j4kzrm\nv/7FnwEAPvA3HwjX40w45saaTa9K4FSg2baeH216Hh01v0HwMGCZpy6zvDn29ksKUjR/kSIrKpe8\nUae2gB44/SPDdR3TWPatlzaWTNteE7IrBTmq+ePpkw/OEdzTpE0gAYB1Fl/80lcDAK65KjSYnBsG\nm1ZaOHvRxp3haqNT7FUzEXZseOkqxkDE5ydfoNHuhOGyMGdi4iOzSdDQ7SRo2lZq/b2lGyLbsFgJ\nTh6DymhDAm6ma8FX3zgQgUyV+O1LASsdOHg9AOC2Z3+BjrnxphvC2A25rlL80sziMZpJCsvtjdbq\nTSW/HeMTLkJyHwi/gBQ/nTsX+RG2zoUCKVo7tdzfSb2uYxjDmRD2bK7VlrDlPnUmALTuf+Q+AMCj\nD9yrY9hG/MrDYT/bZ6VN+tzwG5wK83BaMGX1LoFDjAcQ2myG5Li0IU4Nrjofc+VQiuYvUmRFpfz4\nixRZUdlXs9/LP5vR8Vmqb2DaA1r7nIc4UhMnpYi2ZJBki26mYR3pogwHowYMaRG3QmO9I2ksAJhU\nAdH3Va9+edhGgnpbp02qjnXoEsTqDerNCzknabicI37flmbxegjSb4ROOuZFq/QzgErM/mZKDLm4\nHyYdqJ2EBbbnCd+rhgeppUfexuYBXXNE6MXn83CNDh0Mgb7rn3ODjpkdCtv1ku4iZTXnlR5OztH2\nl9cgngQu1TSOJ9JKOnVrO5jUJ08GSomTT0ZqiW4Z7t/mgTD/2UZwX6bTeD51HVwkBlc7406ekWYb\n56RRCXH8Nz3neTrmji94GQDgye2vBAB84MN/DQD44Ac+qmOOHQ2Vfp08D42pYKzZvVmbmIj5P3bP\nmer02WdIWvgi7P6i+YsUWVG5JAE/i99RzLVGdJJFWIfhd2GFSRnqWAbqTNUVU1mS2qsnQvWcIIGq\nZHsSLG5vR01z4/UhsPX824Om6wXjvmhj9VYjFocYBejaaBWAlXYdm25Ijbp9B3N70eqe1Vv2VpFl\nqCcmP65SwIoE5TopFetNQw5ef2oYBiUrA0qJ4BHR2JOY6rvi8LUyJgTNZhsByDPdNDUGWaou1lEM\naza0PZWlupbr33ky1oRr1y1iMO/M2VA38chjjwAAHnv0AQDA1lZsk7V5IATxrpWg6qZLsf5AtAwr\nR0BTwsXTWiqmAAAgAElEQVQezvlgqFE4dIWQjZr07vokWIS3CSDq9htfDAB4+Ys+oWPe/Rf/NwDg\nox/8ewDAydOR5b4RbgBNGRODZVPiGXc4rVbLKFRP3EUBfYrmL1JkReWSgHzG03kZdDd5LTElxtda\nCtUEIhqUY1pzkFr2eeCqoKHWN4KG2tkxlNdSo35aNP3Zp4KGWW5FbbR2OLz1J9JscTlCnxxFtLzh\n19OUjrzpl2RnMemrWr7TlJ9YN7VtDClpKlbw2drySL9N1SBgHW9ZYUSrIlgslRgupjgw1s2zwabx\nLTc2UiagaspjWUYj0WZNms6DnQdBLaLll120oNqOdODStku4DLfPPq5jHjsWYLT3PxDSb0+dOgEg\nansA2DwY4hObtE5I1wDDiYhtmSOtnHg9N6dhX7NpuOZNsyGTHwJ4/CRco+vXA1vRzdd/hY55/u0B\n9PTHf/57AIA/ePfv6bonHwtgKaYsVbtbfz7NZOsDX5nGp7PNOrEELiRF8xcpsqJyaUA+I5r/fBwk\nfANSu9s2GhRF0crL08JRn/3cAD555ZeGNzE13FNnHtUxT5wMb9/77gvf7Qgry4FrY1T4W9/wLQCA\n69eC/+c1C2F9M4n2s47eD0Ep9Pk7qee3cFRI/KDrt2R/Ycx0bVOHzNYkOu3INmRgubQGkEpvNC61\nKkFHbHs9bUycpEnjCZWB3pJzD1IQFHnyouYmSKfS2IVo+eSeEfLKZbQc2Ip6uQgFNOe2wv144sQj\nOuaBhx4CABw/EaL7k0mY1zVHrtcxN10X4hMbG5vJ+Uxs+zLGggTswyUQ2YanTVg6rjPW1lLaiHdi\n7SzEooKJ6N94dZjTt37N6wEA8y5mkf7gXSEesHVqW3YtxUcmrsCjNXKPZrKsZyb2sFnhyUdiwdGF\npGj+IkVWVMqPv0iRFZVLhO23QYmMosulJj4AjV1pZROtKZPqY2rvwMFAF/XKl32TrvuWf/KPAAC3\n3BiCP5gEs3tuUnTnhGb5Yw/eDwA4diqYlK94wdfqmC//qi8GAMxkzjsCALGADYJ8FMdv05o0fUnC\nKKm25dyY5FIRtiOBrUUXMOUbhzZ0zKakm2YzweavR5eAl7aVoF7r2cQjHoPpR7eQdKBMeemiqwQJ\nXnXicrVJpdxcvhP3QfZdmeCmk4BYLzev7cmhYMx+Gb9Y0sSPx1jsbMt34fy3tkJab/vMEzqmJYDn\nUADuXHt16Bj37GffomOuOBiuDRuCMMhoKcCVykHGTEzAr9aGHKwIFYCYr80YCaqys7Fcatv1uJqE\nL6+6KriM3/QVken+b/7m/QCAex4O9QL1Rtg3A8xANPMPHAqBxyuOBFfF9j5s1oEHPh5p4y4kRfMX\nKbKisq+a3yGkU8aSEaohlaTHaAh5pZJNZnMtvP1uve02HfPyV3wVAODrviRALG+7MdaW1xIs66fy\n1l+TZdIyKfz9bKlDn0xCiueIVO4BQCMafylakHBMmwbzWY05bN02z0O+EowQlttxzPxs0BBPHg+B\nm1OnAiz00NVRux9pA0vOgSuCxptNYoCKmZ9e06EEh9h5CMxYAlW0qDpT5ejaoFnahvXnUatrV16B\nKytJqLfpM1mSSahlcDLqG1oTrdTcL3dMwFCsgL49K/MJ1srEVEBefTBYQ7WQY95w3Y0AgMNXxGs1\nUc0tmp+WhwlgVkLh0wjhpmXgYYBNqwElOFiZBiedMk2xapQWlanHl8NuSGfi59wSn90Xv/BLAQD3\n/F3gCKDVu3k4Vh4ePBC2O3ww3OtDBwXMZajlnW+UMWgvUjR/kSIrKvvr8zsHV1VJqyNtgqAFLIS3\nmiHyar3+WSFl94s/8r8CAO74ki/UMQRjELI7N5pqRzRUNZVmFeLzp004g6xL6qQRP26JmJJpt0Vz\nO7LtyCRtQYpPNb63jRdkHK0MZR1yEV0zFaumF/K+c2fC8RemGWi1EQBIzTq1Yqy1b7SphExDi4aG\nTq6X8yAAaL4wIJs+g9V6q/llHanHCZjp4jGc3LNKCouUL9FYSa00K+lE87fzeHxev6YO362LtdbU\nMfV66FCI79QCwDl0ZdD4E8MWpPfIpQ0/bTPPWjR10yzkGPFn0TQC7qkIySZ8fMgipbU28gx2xh8n\noMuLJbU2jdbaHbe9JMx7/XfC9o3wJByImv9K8fHFcFBuR/s7aWYF3lukSJE9yL5H++uqSkpHey1h\nFaHmNy7q5mbwu3/1J38FAPDyrwzllFum3JagEEUHG99ybRZ8Qz8j592QQZUa0o00hNSpqaZmQwvZ\nxmr+jI/NGR42db9V89MfNVyCouE21sOcezmPna2oFc+eDf7vgStCXKARzRn2SRAKknlYIRNuV6Xt\nvG17KOWR6wg6MlBowpp7tgRj8w6j+eu0yYZjMML47GxT1skxfButC1ZkU9OShWk6idBdIETDK/lu\nQk5AA3mlJanPA58P6/NnzMDOxEecFEQ5tkRjyXbKEhn+p7WlLcbMCHJDyr2fmozCDVeFLBRbuy1a\nxkLs/RBIucyRZdi1sWCW24ukYOlCUjR/kSIrKuXHX6TIisr+pvqcQzOp0SeWSc7UImNNQOWW654L\nALjjZWFJTsydc8ZMlABKxbKtqTFBG7L08FjE1seJdGyYwJ2zN5yx3bSnG9illk3WEmoiWbLZgyXF\n5LlxjLgYs2je1evh77UjEpg6JGw1Z8/omJ3tAABaLkPwq1uaHndeXBy5tXSfGOgCAMfjyUr2gbPF\niTylvtuR/cZrzfr/TqvwiMk3aScG1hio5GU0qSk21KDbYAPBriZ1udrp4XvbX1FM4Hoq96UWE92Y\n9Jpa5EI5FKLEpi40+y1oS+amVYC8jtamz/7QdruWYkmCgOJG2ArGWvkmwj1bSOCUACMA2huQLgGB\nYrVhgWpbn6QXLyRF8xcpsqJyCTR/g7aLb39WxDEjQ21cGc1/621B43O2Z7fk7WtAKZ7VTcIR5xvD\nGENmFNXKRMKYgIp0T1U2GaZ0bGCI+BC+/am5LdmaJx02I11WG7pkWcUdqjQCA73ChbTV9WfD8skT\nseJtbT1ooWpyWg4RWWF6npOXFly+SY4JIAY8GwYnaQlFXcD70ApnXmdSpwpPVgATg2HmWnE7cu4x\nmFibDroMnlGD10MmIM+mHYQeG9IBdvV1UsFJIE7V2GvOJcsTswAzoB2AcwsgjJN5KxOSXDPEZwcZ\nbM3rvbffSZC1HW5xUALaa0KTflaox3tDCb/YIQuUWCIa0LVBQa/3Zi9SNH+RIisq+6z5A7NLbwAr\nzM0xBUJl4k3KYm0tsLC00hudgInOaJpZzz70oila629l7zgWCFkLRNJXvaRXevqPJjXFllF1TcDH\nsJ5f20nVbBs29n6t7DQAA0pRvsGDAbhzza1XyWczppca84kU6PRR86MnI/AhWQqwyUdQSS++aCfz\naOW8uASipl9KYREBOQDQt2w6KcAZJ+ssJSKZjCr69TLGwJ3ZmqxmOs7ep4pALH4WrWy4BDUuoNyM\nbMZi7hnvn0vBPWlTl/S7yjJEqU9Njc9OmRgI4xG6HzPGNeRLDJ8n5mLdfOOzAQC33HwHAOD4E4GR\nqD0Xr3kntf0tuSDEkuqN5m+7mDrfixTNX6TIisqef/zOudo590Hn3B/K5yPOuT91zn1SlocvtI8i\nRYpcPnIxZv9bAdwNiD0JvA3Ae733P+Oce5t8/vEL7cR5ZOYhgyxchu+t8bJxdTB9u/XwrloSbWVc\nA6LVYlzHHISZF6Q13baPXrdMzf6O9fjGFGaAimYlq7ca0yDEK1pMEF0W9QYiC4nwE5N4Yum9g6nX\n9OG79c25zNkQX845Ju3rDsA0teD4NTn3WOnWSxBwSKBpsehhP0upme9g5shafabovCAMbeWgdB5W\n3L9WAFoXSb5y7NYbH8ceYtK7vJ7e9PpjoE++Iya/rm2qruMfsmBwzwYFec8kgDiC3subSXpbEaqd\nplNXz1LD8zFo9bjxWq2vheM+96ZQUfr3H/l/wwjjljIgvaDLq5WuplZi6eE/12a/c+4mAN8A4JfM\n168F8A75+x0A/umej1qkSJFLLnvV/D8P4McAHDTfXee9f0z+PgrgugvuxYdAXpIY0ZxF+sZKmhGs\ni9YQtb6UFFXVGc1dkSlGtJIN2risAUY/NC/49icGnYEd+2btYqmcTIdjbfSoTpZJis1rNFPWSXDO\nnTVDduS4IdBWVYLfn8Q6hmUnmnspNOVd1MqVT1uBQQJ9HhGb33fBGmh7bi/MRMuoCxZST09Szc7b\nY/C8eSyZW22YfMQa6Gnd8NzN9ayVelww+j7i9gmu8pUE+JpgubjmUByjmp8pv5FUH1N0jnOT9GIC\n5GE+UJq5IAYVOTewZkIsqcqMYVq3IaCLgT9jHdRyzxdiwTWm4m+7Dam9rZ3AUtSsEfxlSEJJECUW\ngOLLzPPVtf1YHHJXuaDmd859I4Bj3vu/3W2MD6H50eM6597inLvLOXdXP1JkUqRIkUsje9H8rwTw\nzc65r0d47R1yzv06gMedczd47x9zzt0A4NjYxt77OwHcCQCTSeMx6NqRafzBH1H7dtq/nH6XZdCh\nVqfTbzVuui6CfYz/KW9p9TGVUci8H8lKQ78RBPTY+nFWlmXNKhDBNKy8Uo4328giEhSGechYe6PU\ncHFDfj4FgTD9Rtiyj74yU32tpOyW9CeX1udnK7JhPX9NtSOxl8aRr9BAiGV8p+2xBL5tUqee4B7x\nte2TUGkFpTQedUHjNnWsca8rgXSrry/3Lqmmk5Qt6bTpayfxibTKEoalh+w8ZJGi726r57Saj4+e\nbmtr/oM0GzJ/48/f96mPAwDu/8xHwhjyBJoTUSg54e8KFU95BS6inP/Cmt97/3bv/U3e+1sBfDuA\nP/PevwHAuwC8UYa9EcDvX8RxixQpconl6YB8fgbAbzvnvhfAAwBed4HxGGp9mChs+FiJyq0MPPeQ\ncJmxjp6atzGR34q+oWJ5zTHIGdCx7loyA5ZjTf5sGHEmgMf6bVS5zCwoXDceKiJ2OQGj6SQC33OZ\nsd8CQCsaphN/fmdHWoXPTT39nLX2YmV0pniopaYXDjxmLUyBERMHrWj6xUKOZQqEtJGGQEwte692\njpL2XB19ftsMlLX6FQu25MIYAE6njEhhO0Odh06i/U1FZqMQbvK1ySizMYuAfVzDrIFh31UrjRYA\nQTqmdboWlaVMS+FLseA6sh8NwVtOLBcthiKzj3kwFnzA5J5/6uhndN3P/0qIo993/MEw5wPkIDCc\nirzXGiOjtWIKpZxDDjU+n1zUj997/z4A75O/nwDwmovZvkiRIpePFIRfkSIrKvtO4+WQQu2ZJiOe\nmk0eqmk0X9Y0oBL+mEitPjvaAkBDl4DxHBO0YRhEiTfVPjPbS3Cl5b6VSNSYd5riYqqOKwwQiEsx\n5ZNmGZ1QVLcEErE9bjSpG6Fr2pE6htNnJPC2Y9JoPEf1LGwhPjsHs+6b1zUew8/lXMVdWLbhvLaX\n8Zq3kltaLFi7bzoJKx0Z01Z61vEYsq5VbL0ER011IKnQCYhaM8dQHgIBJNXi1tUu1ig0jt8Jtt8x\n5WbMfr1HWZWmt6lPVi6Sp0FXxYAfSD0u1ZJVnEfkThD3SZ7LrXOndMzxE6H+4u/u/gAA4D1//ju6\n7oH7ggvALr/rM3FnTAtHSk4BZ8FKvnIXFfIrmr9IkRWVfafuriqHPknFMIUibzRCNdfioCdPB+aa\nHSHsnB5hf3gDGJFX/ETezEzZAVHTs4rN92FM38W39w5po3umyoZBFwbzmFZ0GtyzVYpp9VZvAn6d\nMLR0C0n1zVldF+faSnfeJ0+GZh1PHg0ao0Ks8GqEuWZKDWFry0VTLZlO5DE7U48/l4YcEsBcEuSz\nMFBRsU4Wc2nQYQJ+JAWqQHpt8ELEaTDFKI0wmDq19ecMfLKhxsLArden2kMLQGyWYeLAmMo9nki3\n4pqWQBKMk3sOphylWnFpGHBIRb5ModkA0IiGZ1oRYi1t78TrceLEAwCAj3wspOz+/lOfAAB88uFP\n6JinngqZ8NOnA6Br27QmmwqY58CmgJw6NlOJ56qpbE31DSsha9S4mIBf0fxFiqyo7Hs9/7SpE41N\nH4XpGfpvzSy+wR58JLxB7/rgXQCA5/2DUAAxncVilUPr4u+JozTfipruhPRvf+JkaGK4JrTJX/D8\n5+qYdWmAyLL1pWg8Z96sWuedvTLdGGhJ3t5sWAkAniaPJ6uLUDXP47lu7wTN8MjRAPV88vEQJ1ib\nRU11YDP8vSm+bWOoqpleomJTngSjEQh9jhktsUCMs7sUDb3oWOATLQ83Z5wmjJlO1EGPY6ip9BhM\nQRpabNH8rcCCaWUAwLIhp6LMVa6nN7x2naTYpjMBAHFpNDcZiU48FSyohx67Pyw/+Wkdc/xYsLJ2\nRBtffcUVuu7aawNqfW0q8GK5vg8cjVr9r/7i3QCAj97zMADgrMRyUvakcK9nUpfPJjMAQCPHkVNC\nKQMsTBmJKN14wg9efP4iRYrsQfadw28yaRSWCsQ3WPTZwxtybTMWTvQ7wQ9+718FEOFHPxaKOyaz\nCFyhJ3nyxHEAwNFjsZXzQyceAQCcOP5k2E6+/4df/eU65ge+5ycAAFdJs0c6+EsDrmHhhr5d9XU8\nZH5h+zELeWXdS7dgtD2c49YiaqrTZ8O602cEgLMMFolR7uhbYejtpLFHEyGvXvzfXvxoT+isj2Oc\nFPu4lrdftlkYP5iaX6Y/N+u8wIEbNv3AiP+pMZe0hNbWbxHkwxbl5+pY4FQ5YSearMv24Z7PTG0Z\nLYdKYiBb87D9U2dim+r7PnU3AOBvPxSi7B//xIcBAMePH40TEVVLnr31WbxWVx8OLbU3NwJT8kJK\nvh995HEd86hYlnxWZuvhvk6mpvxYnh02FKlNi2+QPzIrOffe/k7knFmApttnMamL6NdVNH+RIisq\n5cdfpMiKyv6a/XBwdY3GmCaNmNfNhKw44X00W4tTWyxDbft99weT7eMfDwGVhentduZ0cA22t4X5\nxlZdiYW0IE5d8PL/z7vfp2OWTTCFv/O1bwYA3HjzrQCASRPdDwbEKhJXjr07eW49STpNZRb3I+Yu\ne911SwPAkUjdVDDt7WRD5mFANkw/SaVbVRmzX8AwUzYNkWq+vo/dbXtJh3aOABYJHE4MkGhBPgPp\nG+cNgSc7EMsxlMjUMjT51NzvtBuzqafgd7K/uY+NSRbLQGJ5ToJgZ86EezabReDMjgR3T564DwDw\nyXuCiX/33Z/SMQ8dDa7eXIKTtQR7J9P4fG1uiJku12E+P63rHj0a6hfYybeT9Oj2VuQ3mE3D9jOh\n3ia/gE0ZMpWtPUiMq6iVqBlWKq1aJbhHJCUW+qykaP4iRVZU9hnkA7jaaeUeAEzIzcY3MovqDA/b\nXKrYCIygxrS8JbNNSX3wrWverLUEwah9CNDoDADnv/3Z+wAAj98fgoNf95pvBQDc8aIX65ibnxWC\ngetrQdMSmpwAV0QzVLLvpY+XeC7pqi2B6p47F6yVs2ciSw+BLxubga581rBtWNS8E+k6DAn0VSbl\n6UE2GjZwl+shbbwAwEvQiVSItFbW+qjNtnfOyXzCtdppYzBuKem6BmQCIgAn6pKJBAMJBY7hPxMc\nFUaipZpm0brZWgSNvTgnTEYLSccZq/FhCbp94p5QDXfi2CmZqwE9SfpxY0MCyZJim9gIqgRee87D\nUKkrIxNBSgLdna0bmDGp0DtWLuYBYUTzc4SLgnwVuiRQzKB8lPeRAT82M/E21ecvItFXNH+RIisr\n+17YAwfUtvCipq9PBhxJd5g0R7ckQAQyJky7MYCPpiPghWPNW1MKRipJu63zzWxe/qxFf+CBTwIA\nfvP/+t8BANe8L1ITfuEdLwEAPO95XwwAePYttwEArrwq1phvCNDES0rolNHqW9KMYetcSGPVixNy\nPoYlh80+NgPQpDlAv94UxEh8guy1TRO1etcHDddpQlM4DXvDS8ciJpfyGvTmcVhfiHWyHqyKfiem\nthYIwKPOkYlHYg6V5VcgGw0vMuMLxtdV7gWxlkxjy064/1phET72VDjmY8ejz//wQyGld/qMcBfI\nKa6txXOlr054sJOUW+etz81aeT6DBhrOWn2eD4u6TEGNViGJmaMsTolqJeiLm9hisDQNykffxouU\np4KxJL1WBrzlfU6FeV4pmr9IkRWV8uMvUmRFZf/r+Ssfu9Mi9O4DLDqMQTQTEKEdRAJOpo9GTCet\n27b0RkollVIgNU0072az8Hcr5vrp08EkP3E0IsE+8dGQSprO/hgAcOBgCModluAcAGxsBDO5Xic2\n/pyu25Tg3QueE2oTXvKiLwz7ORKx5K02kg+LRisI47k2E1YuEh0ZceK+J6UUac3E7G+tmStVdNKP\nsBN3yFlaNDFd55CgZB/PY3k6MLazDl4rzEwgl/3zWLPRyH3pLGUZyUHB1KepjmQhPpsvb0iqbRIf\n2V5cvLUZK/fCObOuPnwghRtTj+xibCr/tH8f6dmiy1mzazTpuLU/ozlXVoBm3LGpa0AUoaD5OhOU\nZB9ABfWzAY0JjpIWgXUZS56zpfHqcTF2f9H8RYqsqOx/qs+lGHAGV+J38hZOKv9kLNKUiH3JEVNf\njbQrYoVZlyp+GEWlqcGpAGD8mryNjTJaCHZ750zAcj/5ZNCA99mXuByrluBPbcgkifk+ezZo0dtu\nuxkAcOBITNVNWJNOXoKaKR5jLeWav4lpJ6b2lKqbrbWWNgAq59ywiowF+oakU7gCniXHXZ/GINox\nqTs/fTpYRQwY1gYQ1QiunSk1pj59b+lp0nZhlgCUmt8pOSmp0A1ATKyAji3WlpmFBygIxmUptkQr\n87nis2eYkVxFslR2DWbqzwB4qPmZDsRQiNPv5Vq1hoWq12YyMrYni5QJ5snOyc+glZk2HVj7i8L8\nFM1fpMiKyv77/C59e+v7L3bSCOMsLZ2CH/i2k+/90DrI9yM7kDGk/h5aF8qUAvIKhM9r0/iGn4im\nFWwKumbIb8cGilPhIKyNeUFlceLJEE/4m78LcOV/uPYqHXP4KrECmGLSmv2o3Z20jnJVaiUAMQ3K\nlJamhgwvNtuWda1cV/G9G5NyXEoV4EwARLODsU3WVOIax9fCd4utUEG5NBqTYCtHfjuCZYzmd30A\nDnkvgCLTtqzKYh6NAG86A+mmCdMrlFi+tkgXvcVVMg+bh+s1tUfsrX0+8y5Tcl8Tnz/TodTyFsjD\na96x0ebQEmPKMcJ97YmkwCHyGyRjKjNuD1I0f5EiKyr7rvl7H4s+AKBvWfCRViokba68z5bJUPkz\nK46wzTKU+CybjAUC6Yb030TL29iDaISa7ZQYHTZR6lq030SyBzNT012JFVELP+EDx0LM4K4PR1aY\nF70w7PuwMMgcmAYAj6kRUR+T3HWM/gOxmMQp9wBbhZvgBTEpjh9ljAWMiN9J13g6iZbHgY0Aatrp\nQibgNDMB820dw+0I325ke81CIHLw1dI0ZGF4BsmDMBETYEMi+humIEd5FejHU5cl6FzeszpZWutT\ngWXKxRC354nYJwVItWbv0mdvrB6fxiF9/iSbpaAe+vrxDONhU0ammAkwgCRnrskepGj+IkVWVMqP\nv0iRFZV9Nfu999JD3ATBGKzhmBGzP26f/tEb08lAWGSIMftpujHQp/XnBuih4Jr0fehNb7leA4W0\nl4eBmYrNFARfXs2iuby2HlJks3XSUgcz94Gj9+iYMzshfXbN9dcDAJ594y0AgFtvjmSjG1cGs3si\n5r63Jizx6TT/tVbfmrkybVJ9iWcyaS0AR1yac2HM3FyXToJ5G5Ow4VMd6bjitdqUdQc20hr3xSKa\n9q1MxE+IuzcVkLSF5ZrPZCyr84DI/eDV3JbnwgTTtLZAqeGTj2H7ivwEshwhZOUtZoqwN2SldBG9\nppTFtDfziGY/n11zBH2ud3FjYH4ffOTo4ZhrFir+itlfpEiRC8j+Bvx8SIvZ5IlzeZAmTdskm2cM\nJ34UyjgSDeQLlco9f9MiNvQYgI0MSKdyyarYWdgGHqkhtKrNalweg62fBEJrIMAPHg1Vaw88FmrU\nPyzsNM9/zgt1zCtf9lUAgJtvClbBxByDaUen3WQ5d6OpmO3KrqetZuslMDZbDym/c/MYqNs6HVJ7\nxx4J3Af33hNosOfbMY13+EiwTp59czjXqw8Lk5BJOTIwpq3nTeC0lwYeLQOGctEtMCtaYNS03K8Z\no007+AVvkAnhuZRWvB/R/BRC023BHdvAxSdvJNXnsyWGovwQMW9tTiSt4vNq4ZmAcuPHfzi7SNH8\nRYqsqOyvzw+PvvcJuKbTVldMqbBn+xCpEV1+VVmDMWOf1bhwmQlgtq+o/bImnDbFRp8/Kih5w9vU\nkhbb8HM8BoFDqqnE/W2txpUv5/OQRjtzNlgCd52MVOSPPRHiAv/wJV8NAHjBP3iRrrvmcNC4rQB4\nlmyBlcQFeDDGWyR9ZCjAyStA7XzydKzn/8u73gcA+FtponLieOAn8KYg5tAVAch0WuZ/281XAwDW\nD8QiJF7cVjTu0mYjyTHA9JdoV1uM1WhcQ8a0KW8gEAt61Byg6jVMUYwP0VdPgD006wiuqfh8xiH6\ngbeXz4lBqqVPeZpWHYCLCO+1MSmFE8tPVijubcPQuvFJCvNCUjR/kSIrKvvu8/e9T7ShMrhqeyfx\nWW05Y/piNdFY62ynmt9bH9cRlMM3LPc7LJyI31ELDIFAca6yHwv1ZBMSLVWOc2LxEZ3TpbD2etss\ng629GSmuhb9w+6SOufujQeMefeAhAMDLH4zNR770pa8AAFx7bYgHrK8TJGQmItqTSo2+8plzkXXo\n8WOPAgA+/ekAQf7Lv363rvvoJ0J2Yi6Re/YytVr51CmB7LbBStk6G6C7R47Ephvrm8JINCWTjmmb\nLbrSKR+eXA7TOPXARrBUTteB37Bz5HYchtJ93pbdsgg7ltQy2m+sArUEM2i5jcTzAVUjY5iNikAe\n7krqUqkAACAASURBVNg+GPLsIXs+rQWjj1ialbKNZF3Vo0T7ixQpckEpP/4iRVZU9r+qDy5JseUG\nvJpFLjXjZWMRsaEsv2E2xAY+CGbRtJ3UgXvDCuNlnQJfZI5dQjgp5pyCjIbH4pzG6JeXbZ9stxCa\nbkvm2GasRU7NOnOygoE/djSkA//o3Sd01d33fggA8NwbAlfA4WuvCae8FgNtjQSP5tuBqvvMqdAs\n48FjkbXoMw/eDwB49NHw3fa2we0LV8G64Owb4su7aPYvxW053YVjLIVP4MxWPNcN6ce4tia02mvx\nWk2kS/NUYpBtTfBXvNYbB8M5ra8F92G54PUcBtO8AoCGTE/0f9RcH2Pg6VP3AxZEhszsJ5DHBDA1\n/eiHZjndBmUU0u/tmHSbyPNgfwQdLkaK5i9SZEVlT5rfOXclgF8C8EIEJfs9AO4F8FsAbgVwP4DX\nee9P7rIL7giucgmHX1YQBb737BveKTmaz8YkOw9DCPm0wRKCWhjomrDBhzkG0SNVBiAa4RIcBClH\nwEZM5Vgl1LLHfJZq7C2rC+HO3DcRt1ZRCRyWqcN2HkFCn/rkvQCAe+/9pJwY05vxGEz/LZdCj83U\nowW+aHo1bG/o/bSKkNBZP5JeqjILaCHBzXOxZB+tWDCLNuyv7aMuWhPuQd4zanMbzFtbC+s2DwTN\nvyOt2nxvqgNlSa3eKZcfBmOQt8RCtACj0ZkGe8OYDGQk98zCjNVyGKnVj4G91Lo5n+bXbWyFbN8B\ng5T37rJXzf8LAN7tvX8BgC8CcDeAtwF4r/f+dgDvlc9FihR5hsgFNb9z7goAXwnguwDAe78AsHDO\nvRbAq2XYOwC8D8CPn3df4FvN+vx82+kBw8JYB1VF/5dAi7CsE7ZYac4wG1ZuELyh39WaS4lz09eg\nvNm1kYWBnBIM0pPXbZhyrAYAJJuuEe3l2cBBtJHR/GR44TqusW6oa8kkG9TxdGaaVGzIOZGlR9VR\nPNeJaNFWlstlll5MRGr9EzwVtVjGjmMLtsTJZZqVmpfNUsMUCe6hBWJiBtJ8hFbF2kw+W14CidPM\n2GhTuBNo0djjkoOPPH9JVEAsQB7dxnlMEEp2iIHQutFLTZ+/NZpfrnVMYduYgQhbccnzXiWpPlqZ\nPH9Ja/qYnvXtMilou5DsRfM/B8BxAL/qnPugc+6XnHObAK7z3j8mY44CuG5sY+fcW5xzdznn7qLJ\nVaRIkUsve/nxNwC+GMAveu9fAuAcMhPfB6d31Nnw3t/pvX+p9/6ldV3ii0WKXC6yl4DfwwAe9t6/\nXz6/E+HH/7hz7gbv/WPOuRsAHNvLATOrPyKw8iCHDXZkTSGqaXiJTNfi9Buhlp7O2I8+br+QOnXW\nEfQazLLCdE8a1EtRWn02RtfEuRKXzfQRhsE8rddWqL9xLTIaZw1umpmSA4HukDeVXXpLXRpUTKmt\nwnc1m0XwuhiTmnOKxppFzXEeMreeqSpbAZnO22nKLO6GlXsL0mfBmOsS6SQPQcN2v01yJWRMRtFl\nTpZVjp3OdRicrNXVZH2HmWOWmhsmqWMwz2uKb3iu+rfeDuvH0fVlMDKdDxDvtdKsaxw3junadpdK\n13G5oCr23h8F8JBz7vny1WsAfBzAuwC8Ub57I4Df3/NRixQpcsllryCfHwTwG865KYDPAPhuhBfH\nbzvnvhfAAwBet6c9OWQqNwU2jG5S8Q0vAZ51CXStR41XM/0lb/+ETptH0qYIQ62uqa0+tQBsnGKY\n6pP5GY2nuHBNEdn3a/pW7jHUyjom0xT9WDqRZpJNWer1ZFVebknEc43nQc1rYzJiXXAbS3aqjDM8\n6DA9q2ddpxZIgkPJqiSXiySqCQCY7oQU4ZTpWUtYKX/Xso5Le6W0qC9L3Vo2KYVRke3IVHJqCUA0\nYezphL8zBqH4DA3HqLU0BjJit+KMpjt8KbTzOh3OIwZQ+65Nb/QFZE8/fu/9hwC8dGTVa/Z8pCJF\nilxWckngvQlTis//GPFx2ShBUkraw96m6lSziMZujR/d0n9le6oh00qcT06tPDbX9ItRzresBRQA\nrUn3+ZvdZpZcuhzv/cQFNY1JFapLmIJaEoWQwZS1lZTV7nlLNAtKofYim4ynVt6dVlvTgqaaLqYz\nh9qqE21IyO5iLhWElh+BQCK2y1JOP+OZZ+3b+Nmm81iJWWVTDgcRjZ036BxpCbY7Vt2A2XQ+Zo6K\n+2F1oXxOgg8cm8KNLZTZf55APkWKFPn/mey75vfeZ6X32Zt5ROHyZdmJhlsuqalMY0cyrBDjYoAz\nhK9GppahVna530q0r6Vs0Vbf9C2p5U1ttwI10gwFEPnfFMA0VuTBN7n6nZl/DWSIm1QJqd+ZNYa0\n8Q21HDL/MPXruZ+RaIz6rbxm4TGqYMBGGsEnM+4Qp6wxB87Z3g+BQi8WYfutnaD5p2Y6DWMdAgVu\nNNofnXZCfdWq0efLWEt6y4dAHjU0lFeAXwz1pkMaIHDJ9eRyJHZD8Bj7pQ4NQn0eGMuJllkc1fcd\nLqZVZ9H8RYqsqJQff5EiKyr7bvYHGZomgzp+a8qKicOeEn4uYAzbsDUz262ZS7y89t2jpWQ76BLu\nz9IApWgeM9tlXt3QBGTwqKkZnIwmKKm91DVg/zUL3BcbVK25rCGEnJyctAyxqUa1L2l2D/0o/csN\nvhkRpqjyb2zaijRcBnSVA6q096GNbspiJLhJd6UTs38xQplNy51XeCLXtzF06zkYJlr7Nq1IvH+O\nv4+XSHH2VbYi25Vd5ccGnc/8z4K0SAg8vY4K2yNZ6j73bvUXzV+kyKrKpdH8NkU3ALqMWAXyImRF\nlqZLErafNEqSwEiz1BgjKkk9P6sCG1Jvp4E7IFoOrVTV9WPtuuRtPRGN3xjNPxFWnhgEFK1uKauz\n2nAnlW6dSeeR+UdTdfZcBxDklHsg/K1/hWNgTNKUlB9ZFwlN08Cb3WelRKj5fk3gVS0ZA6iS5WLB\n44slYIk3RUPOiPViR96JbXPlkl2PYWC0Gi/TzmH+KexcuSHGLlqfWg424Ke3LyfyhLk3BJbFrcx5\nyHOpZLYu3Tb7ey9SNH+RIisq+w/ycUiZfDK9o/omKWBPV8Y0xxBco59GWiVRUxEcZKGibHk1E009\nFTppC50lO89iyXp+YkdHNH81ovmZitLqRll28R3c8tzI0UbNb2DGrVBca9GKKQxSz9CnFsDFaoUo\n3vyffZcXDe1+O8b3TJDPKLxYrJIlPw1BQgQ3aWsyiTlYCnGlLG/Zxoz7GyKrGCdKtDpbiamvP4Qy\nx/3k6UQ3WOezZzj8zeHpRbOFPTxXxk5ypiV72L1K0fxFiqyo7K/md8Hf9TbKLk6UMvqMwGpzVtOo\n3uKu87eeLcX02RuZjEC1ixpiTUjq1qfSRlscSTezVR5hu5YalxDihAEnjKHmt80yFITCaL9cft/G\nY3ScUy/ZArEKlq1pbb0I7C3zZeCsm9sipjza74dOZsQx7a7FYvFP+tmeI+GoPdJl2I7gHtlPVkwE\nQNtc92MwX59qYQUtjbApO7nW06zkGwAqNk9ZpJmVFD7O5TDar/EMWnfMxlQj14yQWy79iHbn0sR5\nFFAVvwnzMEA1hbJnpe9jFtlepWj+IkVWVMqPv0iRFZV9NfudEzPMAme0SUb4rHX4I/G+3NA6n5Fj\nrTKF4oi5PWnCac8mscPpbBr+Xp+I2S/01FXCWU2gipi5LVOPw/xRzZ59pqkC/6aZ7b3su4q3oYO4\nG334zi25n7mOWbjUTGzbWOPglvybABytyI9juFQeU461QdI0ipfg1DPXzGMYsFNwT9b51qYstdYi\nWwLQGnd2G6b5bWnCaQrXk3A/anFDbAp3IoHb5YJp1bxA31wPWtY2IJ2Z26ovxx6+3o0vgZjiUy/M\npgEHTisAoDPMSgowc+nntF3wxUnR/EWKrKjss+Z3oRLLvJnrhlpQgjV801tQS4bpyZm4wxgGiIaB\nGH7XMJ0nnG9TAwNtZOe1vNJJ6jI1qTpSPGvP+Hr4+tdqPrZgGg0tBel6aX7ho3XRS991WgW1m8j+\nzK2S6kL2uGgMzrmuCIYhrJZQ0QS5ks11GGStNFAna2ylW6T3kQUDfiYoqQFHApKYlrTWQcojkPA8\nZOAvHzeKxxCNWYsFxnN35r6ubYbrR4tSqcOT1KcEBYeM7vrsxGeNAUOTAs6wtuPQ2/S0Rvn90iFp\nuy8GR/N04Bjueo9SNH+RIisq+w7yqes60fyVav7wWV+QNhXSpyvpf/YjSpVv5ITlhxqSDEBNml4E\ngAUbV5wNDSnnoiFma9Gfnglb8ETSgEzd1QZUwqKSSlOY8f2qzTvJ8ko4qHV1BUDk6et3wkZsrkct\n8YBa0oGMD4QNZS5eYgaqKodpNGXYzYpGrKjGM2qi71ILJvr1cZJs2qF8idTylg15NF2le5B1znzK\nwDlInx1eX9vMZU0aehBwU++kACm7/RBqbjT8GJAJ6Xd5qi4jrki+Oy+nxcjB8tZwmoLc3bC8oBTN\nX6TIisoliPZnIJ86fV0R0WjBHBqVTitZkxdmZNKV/VgCHpcX6ww2x1I07s5cNO+ZHRkbm2AS+LOx\nGfpGU6usrZuswTpbRwmAx5gn9DcXOyFyv9wRINDSlP1uh2NMVDvLGKP6a/nbca5zw2g0F79XrIs6\nuy7hS7GOMsXkE9h1EMYVKmsUKPCG++aNifPwmpHIMwHDrIPCYK21pl+lPndlnovYGyCzHs0zVUsz\nzxnvvbT+7i3Ho1opI/GmDCPlRzR35DRM55PGn/Kc1YivnsUVxop/dI68eT69MYXJp0iRIheU8uMv\nUmRFZZ8Dfg5VlWP7MzOIJv5o1icN/iSWEzNaNA9t6yimtEjRrDXew9rwJZljxJSmOwBEnP5sLbgC\n69I0ZH1jTcesrUmKTmr3LZHotvSPnwsZZSdBvYmLxJcbOAAA2KyDa9HXQkBpes4vuuCSbO+EeezM\nY6dW1gB0WZdc1wyx/XkD2kQyc7u3LkFG+a3pPDfY3Jj/8n0CoEkbeoz1o1csi7pzlt0mz82pb6DC\nex9rLcTs76xpL+lRsgeZYGAv95/r1PuyZr8+sy49x96eK3aXbOXgOU8kT8vaY/iLivkVzV+kyIrK\nvlf1uapKIk1RQaev+gQco1o9DaSM9SLnS7c2GoY19fr2ZysrE1FZCrioW4blYkE6ZcOmIn+3wii0\nsx207NmzUStrzT4568z2S0kndlqHHsauVTFQRo67tg4qZo6FzDVqo0UbvttZ7shnA67JVJNXtaRD\nIt+hMiJhV1Eg0FhwVVNcbPgZpWOEUDU3wTHWAsmgxyPpWVel2rxKOBXJHZhxI1pNqg1emOYd5he1\n9RUtABMMZNOQJS1BfS7sM0wINJ9PHtOkNXNGoNFrnq0cARtpVV+V/hZ23+fuUjR/kSIrKvub6kN4\nc/f2lZP5/GN1ClH7pKAYZ6hcCQ2ljzsxdfTU+BOByk5kzNKAWhaO8FMCcMbSLbJkyyfRFG0XNe+8\nImOMWCem1j+3VNgaemEKOHwf4gKEcy5E89v4RCvdHXhcfgYwaEFG7eMsLYFm3XhCBNSMSNZqO3yV\nWgzUfN5YJ5E1SbRgnWqsZAfITAnEFHCMHYRlnfjzVbrUOIWxDtLTMJM2+yFLUE2+BXOxtOFp+NTJ\ntU7SgV0O+R25khqWGEsDpuvyGFWYUxa3cmPX018U0Kdo/iJFVlTKj79IkRWVfQ74ObiqQmWJCQdW\n2BCix3U1cfuykaVxpsVJM3Nm7MM1qYibSBXduoxZGNTcUnZwNsfbJySM2XeDlrrQyj+aZUnGkj3+\nNFAmaSdjQi69BPgktbfsidUfpiX7nl1dbQUkze3wmajGOul2kTYCiUmjYa5Nv0uaZci5KQ2XrtEx\nbFAymcm1n05lPvEYnl2TlcDTIBUz81XdOmOR127c3E+8Ss4p4yBIWd+ZmuPBksKSsemMU6HndHHJ\nU525JMZc7zVFKOdF0948w7xuufuUuMnejcx0dymav0iRFZV9r+qrqirikoFho1q1BIYYcGp+Bsos\nSw4Lymp5n62bdWtS9TbtQ8BvU8Z0Ro0sJbV2Wmix5/pKHenyEClf5KN9h3KfHGNzSilQPJJkGq3O\nikXZbNkN688j5fUQ381rRT4Cp9fKTFGuf6eBO9Hkbqip/EgQzQ9UxhBwMpXahk0BQM02NmQeRuP1\nkj6T1GXbLeI6pg8ZaGM9gxs+FzkdwWhDDO1rPzpI5iNL2+FZ/u5I1072pm54P/Lq0wT/r39QYxsQ\nmhKRyrXOGsfYvyPnwFjAcPjV+aRo/iJFVlT2P9XnXKJhYoNNqUJD+pnbAUDD6jx1aC0AJ4yfiObd\nsJq/pc8f1q17wnyt5g9a6KSke7blqMb729VKSSiFqmzdyOtfIa+a4Rr6jwrUzKrJwoeUAWe0Dp/n\nT64B25qMfiNPfwQw4jPLIYnK0P90TI3xmHHM2lrw8Q8cDDDldVlaY225CHENQTsrlwGgPTYUJNW3\njKEYK4nBBuXMk/mN0GKrUmbT1s7GUFKNb4FZbTZex1jN36Uaf0DbAxiNj4EQHER/vpmM+PyDe5TC\n4QFaHqWqr0iRIheQPWl+59y/APAmhNfKRwB8N4ANAL8F4FYA9wN4nff+5B52lrgmsbiE2kSPqWNy\n0ENTqQrVMbX4sVPR5hMTFW7kbVgv6UCGxdRsvyn7XpeCHAKBFpZNhQohR8wmFkEGgLFrBlx5onEw\nlJzQNYkus0Z+jHuPPr6c/1Q0vs1+9LQClO+P52Gj/Zyzsx/l7zQDwNthW5NN15tkOZtJhsEmTwjI\nopXVWz9YQFc5L4Bl/82vtWOmYwirZQCf1gKbvgJR0xO2vVyOWAV5i+8hMdLweRiBK1djD4ZIrZpf\nYMsj7eE1vqA73rumz+WCmt85dyOAHwLwUu/9CxEiWt8O4G0A3uu9vx3Ae+VzkSJFniGyV7O/AbDu\nnGsQNP6jAF4L4B2y/h0A/unnfnpFihT5fMkFzX7v/SPOuZ8F8CCAbQB/4r3/E+fcdd77x2TYUQDX\n7eWAzqcxsAEn17AIzdo44eMIG7X2K5fPlgaM8aBaTS9p7mC2n8iW65JmIa33VmvNbWK40zSg7e3G\ndaMugabWaEKyHn54rjHwJ2PdcB46n8RFCstGUm1TAdnMmnirl8Lt1eWv/uRz6lKkDWdJyimb0UUw\nrpaX9rpLHyoPd1jJaC5IK6k+Un57E6nrXUoKyqpGC4hS/I6mTMW1MIFcmsvcTAN+JqjHNF67GLoE\nPjf79UKYez6ox5dj2681AzxMnbrM3Fe6uZF0HgOPrKNIH52LcwH2YvYfRtDyzwHwLACbzrk32DE+\nOKSjR3bOvcU5d5dz7i5LjFGkSJFLK3sJ+H0NgPu898cBwDn3uwC+HMDjzrkbvPePOeduAHBsbGPv\n/Z0A7gSAA4fWvPdegyiAVfzZG9bGnlTRZtrDaOWqSwMphktSUSlTYWjROnYTVePhZvK6XhdNecbk\nptheKodW2vRNTiCavrwzy2UER+RjkfxgjvlkI6zVpDUVXBNIRTdnYTmxk5QqwmWekrLgK/2ztyPk\nb36Xg5XiMRg0296i5g4AniqBtTKNxopKU51I6C8hzH4Y8IsAnnSZkpUSyuySbUatrT5dhuNz1y5Z\nuhHNr5BufRSH947PTm0bxkiglJqfAKBEk2fAMO1qZw9xkcG/vfj8DwJ4uXNuw4WzfA2AuwG8C8Ab\nZcwbAfz+RR25SJEil1T24vO/3zn3TgB/B6AF8EEETX4AwG87574XwAMAXnfhfYWilLQogumu1B/u\nkze88NEx1cdXq2XZYWFPVvwDAGsd3/bSSEM2W5oX5bIiA0/4PBWgxdTkDJl20je9Qi6Nz13vvi6t\nvQbYXzPtS49U1KEepvO479k0tvs6uBk0/RUCq12XhqN2t4STQoAzTq0tM6pLNaTdXv3vfKWxUhaE\nw4rPTphyUtijFH7De86/h7TaJo2nTESMvch+kowl75ncX14DM6bPTbARdhzNSGNobSn0VjR4rXyB\nhpK9dsnYuhl5ZpjOU2DS8HpEkJNYB0kR0kj+8Dyypzy/9/5fAfhX2ddzBCugSJEiz0DZ58Iej77r\nMsCKLLM2za3hUfP01Qm84UZJcQX/oDaLb8GzEhs4J2/Lg/Km3UwYW8KYJd/MovHXZvEStVmZbs4k\nA5jijCq1AOx3ykWokeixct0R31SEPuJM5ra5FtmDD7KRyGzKg4b9WbBSRy0oWQ9CaY2myYuOEhc5\nXuxkjr1VudppnfsRAM6QJGegXQGj6cfdevk7tTxiRa6xCNVKIgCI197el9SyrEypuDYt0Qd1eM/Z\nrm0yEaYoaf0+m0ZW5umUrM7kG7RAJMkySKCq1xiI0eosXJPttQzYaPuLrOgt8N4iRVZVyo+/SJEV\nlX01+70P5nwCBuFSzLyWJo/BBPRKdR0+a5rFpsFSSxQma6RAjS0xkbaqcNoHpvHdN1Nqadlezf94\niZrMvKSMpfpiPwkzR4KNMtPTmstM4XQacBNz1ZiZNCE3pEfg+iz2CnRyTvOa+H+6U4a1SKjCSRKq\nprmtdFNTlKm286SRRkzNvIpOAUnm4uXVgCmoJUvfjRbrc8FgoBzbuIMa8ONt1IpEM9dB6tYE4+iS\n6LxTtw6Irh27Nk8zgFX4joE+CTobs3/JZ1WWrFmwgWANxlapy5iUY1TuYqz+ovmLFFlV2V/ND4/O\nd0nnWtW0GYtKP5LSydGOYy29IvPLyPYK4hBtZlQ2U30Nq8C4vzGwETNM+UmYv73LB1nLhftjWtIe\nIw0I5cFFAGhEi1CbdS6aOYyTqobV+QyDeb7KzSUbiB0H8iTnoVaO063MDmQh15Pa2Gp+0hNSqX22\nqijLOPok+5WmCGM6M8ErJ7sbA2ZFNTm0UoZgL14zayPSyiNnQLxny6VAoVvyN2apVEQLcmABXVx2\nL5Gi+YsUWVHZ31SfD764Mww8eSMM9RE7q0XoAxFayTerddy4n3Rp993nb3+rIeRF3Lh0zMI0xGCT\nDp+pPuvXR0Jf0Xhjmj83GazmVwAREkl8zIZgljC3eW/TotylaAr61bZpR+Y3RvPLngevuerTwRxR\nD7Vg3EG6XZ/dZ8BYYrKfpIGrFmqlFlCCYxk1vTKNyYYaCpseFlNpg04Ci2xaNFuOCdNuLWNT5Bu0\nlVP6GIWx3TLyFS6XaUs2Tf1m/ryVsdTnRdb1FM1fpMiqSvnxFymyorLv1N2+92k/OUWHpcSItmw7\nr/n32qxh1yGjPfZyE7Q1B5lLkwwljJSNlsakVii5plmG5APaow/p0o7LOTETS5bxpSyIhSrOlQWM\nir4bIayMcSpJMSU1/yRClaEaVTT7ybgHrE0ZSVfT87AyuB+Z62VX8jbYxiJa756RnY7ZtoPD2www\nU4UdqcDTgC5gyDm1nsDsKjvcWK1DFPZsHAZAvdDHR/fHVDDymc8PZk6M1yPSkQ3dY9/7UUToblI0\nf5EiKyqXAOSTvpq0Wom13YrpHkstMeB3vvxGGiCyf3ukGiuh5Za3/7KlNsrpsWOwSTHgvHo2GKeW\nxzBXqIoy79RqQUJ1lmocqT9Xjc9+HkmKjoE+BhxlnakxyHu96/FttyylIB8eH9mcfJ7DtEN1znJd\nLZAoCwpaOu1YBbe7ecE5+kHGchjwy2sFEnyYVgwyvTk0KYeW5YhpqadTDQ7C5ys+w8Oq1VizH6RK\nTbHkXJVhqEtuDC5GiuYvUmRFZZ81v0fX9qk/nmn+PvNZAauFZJFZAnad15p7c+C80YHOx1bTcT6p\nb5r4YfTns45cFoCjqb7R2EMaBxik3GBSUuKbdn2m1jC0ChLrROGwMjeNDwytA7VkRnvGpxp3TNNp\nGpOU2eamDZQh526O4NXH5jU32/fpzY68dmYHmTWQa3dgyDkQuQPMdvkfI8fQ8xhp9xUNmFyrD60V\n1vgn8Spq8zyWYq+nI0go28Ze0IsE/BTNX6TIisq+g3zaRZdp/tTHVxfZAD74moytvPI3JHJFlYXQ\nx32hMbdt0KTB+o/0MRUeO9Q0ucYffzHTOqHmN2OycL+X0L6NkjsF8qT7y84uWYxdqiq7jmNNIgam\nTPpncnRv1VkarNe6c1t/Hhl1ubTkeQJ2YmZCrC3Lj0CLyUucptVTtgw46Y04H0/CAPSEYXxJz9Ua\nhPl3Cic390zOu3Fk+zFPA7kGtOhoSAAZfx/CdyHFWZW9L0XzFylSZC9SfvxFiqyo7H/Ar0+pu4fF\n8WGRWJB9CqoZT/74ke847sL20CA1F5sImkHj+xkzqUftfq0KTPdj38BqemrcL3URwof0XLPuh8li\nNEWWBfgiIWic7KDG3s45N50zYJM9D62n12YmxhTWWg3Z7dDq1yCpgpUMKSbNZGLxWa3ZJYHHNNUX\n3ScbZOUydTXkKHJusr+Rak11rTQFOrz3NOUnwg9Byq+wjkFN9ugLYzsTAG3bACBqhXOtlrqOJF3t\nO4z/AsalaP4iRVZU9rmeH+hanwavsqYUETJqm0zI9go4yXJ+MOSNg7xNHBcTfrsH6hSMkhkCdvts\nWhlcmcGe9FhhIGv1hTBSjzECwMnryO0hqBWV+tocQvfDsWMRvyzaNZKyVNrpkWBpTCcSeFInn+3x\no0U2tMy0Rp3U224k/+aZ4iNbTnxkCQDqyDrEm7awQKI0VTkKf1VLaEjOmTyHMBYEhhc9J9W08UtN\n48n8m7V1XTcRtFhTC8mnlGCS1QoAFovQ9qyV2v/5YsEJZadRNH+RIkUuIPuc6vNo2xYp5DVLSdFX\ntCk2VWfUqkz92X1nSyMDamg95AjwhZ8zzTV6OiOMLdEqGZoOXuu1Sb8sQ21coM42c+QQGNHA0dnU\n78ZaRCVjYYAubD/mU792fHsjQ2ICPXouWuhEM6ey9zWP5fjBdjEekfrDQPT5uVXVDguttM1X1u4q\nYeLh6Yxw+EUodFjUGowYSSdGfDHXxP3IvR/JJqqmrxE0fwWmA+PPk/EYV8kYoXGy16yzFV571mM9\nvwAABSBJREFUkKL5ixRZUdn3kl4Hj868c5q8KEShvCNRdi679I0PGI2rBzKQ22wOMRBuw7HU1NSG\nI3PPmm2MNZugL+jULx5z1ukkjkzQp9djNz2eTmxkjvp5bJt0jsgi4nankVPQbs3tqvGlPQ/ujS3X\nRmIPBK7UI2xDTS3+sCMsts43HzjyaRl1jjYaUb3ZfU2bsKTYcl+PxYuyZ0ZZhOOYzqf7mZiM11Q2\naIje0uzJ0PqNsGeWCMcxPfxFIX2K5i9SZEWl/PiLFFlR2Vez3zmHadPYTMwg1afm5ghn9gBnbcfk\n9Mt++LcGe7jJGFAjt5oSDHdmXqaeQrYjpEtAa7A7poToxYwcTju26gobJOVcc0DP7pJcO60+q5LP\nCdsozXQw8GbTXylQRoNyZnP+rZilKh0LRFM4bhfXMZjHBhjsnVgnabj0OjJVWBkq83gdtTBDxtpj\npQCiyhKJZgAmHr63dRAcoy6bsAYlNQYhRdeTEHZqbprOkW4Qn4g4hsdlqrNu2BhkR8fMmoujPy+a\nv0iRFRV33jZMn+uDOXccwDkAJ/btoJ8buRrPvDkDz8x5lzk/PbnFe3/NXgbu648fAJxzd3nvX7qv\nB32a8kycM/DMnHeZ8/5JMfuLFFlRKT/+IkVWVC7Fj//OS3DMpyvPxDkDz8x5lznvk+y7z1+kSJHL\nQ4rZX6TIisq+/vidc//YOXevc+5Tzrm37eex9yrOuZudc3/unPu4c+5jzrm3yvdHnHN/6pz7pCwP\nX+q55uKcq51zH3TO/aF8vqzn7Jy70jn3TufcPc65u51zr7jc5wwAzrl/Ic/GR51z/9k5t/ZMmHcu\n+/bjd87VAP49gH8C4A4Ar3fO3bFfx78IaQH8iPf+DgAvB/D9Ms+3AXiv9/52AO+Vz5ebvBXA3ebz\n5T7nXwDwbu/9CwB8EcLcL+s5O+duBPBDAF7qvX8hQhH2t+Myn/eoeO/35R+AVwB4j/n8dgBv36/j\nP415/z6ArwVwL4Ab5LsbANx7qeeWzfMmhIfuqwH8oXx32c4ZwBUA7oPEncz3l+2cZU43AngIwBEE\nePwfAvi6y33eY//20+znRaM8LN9dtuKcuxXASwC8H8B13vvHZNVRANddomntJj8P4MeQUoZeznN+\nDoDjAH5VXJVfcs5t4vKeM7z3jwD4WQAPAngMwCnv/Z/gMp/3mJSA3y7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15c0adcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_true:\n"
     ]
    },
    {
     "data": {
      "image/png": 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YWWqvROGtqzFg2GGwakNpoDPVXKaPO80LrSxLtUusUWYZkan1JdEl93z6/rCt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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15c3b0d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference:\n"
     ]
    },
    {
     "data": {
      "image/png": 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7DQK4AQQZ14XjE2reVKGupsY9YbBFATyTiiViJt6n1V5PDeStf+VWAPCMzzKI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dAAAA\nEUlEQVTJkl1SSz/+ZMkuqf0/lmqI09A94/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x156dff8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_array = []\n",
    "test_index = 20\n",
    "test_image = X_test[test_index]\n",
    "plt.imshow(test_image.reshape(32,32), cmap='gist_gray')\n",
    "print(\"X:\")\n",
    "plt.show()\n",
    "test_array.append(test_image)\n",
    "test_array = np.array(test_array)\n",
    "output = model.predict(test_array)\n",
    "\n",
    "plt.imshow(output[0])\n",
    "print(\"y:\")\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(y_test[test_index])\n",
    "print(\"y_true:\")\n",
    "plt.show()\n",
    "\n",
    "print(\"difference:\")\n",
    "diff = abs(output[0] - y_test[test_index])\n",
    "plt.imshow(diff)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<table style=\"float:left\">\n",
    "    <tr>\n",
    "        <th style=\"text-align:center\">Input</th>\n",
    "        <th style=\"text-align:center\">Model A</th>\n",
    "        <th style=\"text-align:center\">Model B</th>\n",
    "        <th style=\"text-align:center\">Model C</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/12_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/12_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/12_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/12_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/19_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/19_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/19_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/19_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/20_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/20_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/20_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/20_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/24_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/24_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/24_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/24_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/29_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/29_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/29_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/29_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/94_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/94_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/94_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/94_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/101_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/101_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/101_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/101_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/103_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/103_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/103_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/103_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/160_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/160_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/160_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/160_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/202_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/202_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/202_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/202_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/322_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/322_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/322_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/322_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/341_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/341_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/341_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/341_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/342_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/342_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/342_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/342_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/399_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/399_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/399_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/399_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/400_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/400_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/400_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/400_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/790_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/790_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/790_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/790_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/850_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/850_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/850_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/850_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/900_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/900_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/900_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/900_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/920_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/920_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/920_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/920_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/2065_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/2065_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/2065_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/2065_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"b_model_examples/2058_x_test.png\"></img></td>\n",
    "        <td><img src=\"b_model_examples/2058_prediction.png\"></img></td>\n",
    "        <td><img src=\"c_model_examples/2058_prediction.png\"></img></td>\n",
    "        <td><img src=\"d_model_examples/2058_prediction.png\"></img></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fun experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"float:left\">\n",
    "    <tr>\n",
    "        <th style=\"text-align:center\">Input</th>\n",
    "        <th style=\"text-align:center\">Model C</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td><img src=\"d_model_experiments/face_test_1.png\"></img></td>\n",
    "        <td><img src=\"d_model_experiments/face_test_1_pred.png\"></img></td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td><img src=\"d_model_experiments/face_test_2.png\"></img></td>\n",
    "        <td><img src=\"d_model_experiments/face_test_2_pred.png\"></img></td>\n",
    "    </tr>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td><img src=\"d_model_experiments/face_test_3.png\"></img></td>\n",
    "        <td><img src=\"d_model_experiments/face_test_3_pred.png\"></img></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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