Cnn-mxnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Traffic classification using convultion neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. In this notebook we are going to classify german traffic signs using convolution neural network\n",
    "2. We will learn about implementing convultion using mxnet.\n",
    "3. The notebook assumes that you have fair understanding of convolution neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#some imports along with mxnet\n",
    "%matplotlib inline\n",
    "import mxnet as mx\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import logging\n",
    "\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "# we already have all the data pickled to file. This helps easy loading of train and test set\n",
    "import pickle\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = \"traffic-data/train.p\"\n",
    "validation_file =  \"traffic-data/valid.p\"\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to print few statistics of data. German traffic is a multi class classification problem.t There are 43 different classes of traffic sign in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 34799\n",
      "Number of testing examples = 4410\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n",
      "(34799,)\n"
     ]
    }
   ],
   "source": [
    "### Statistics about the data\n",
    "import numpy as np\n",
    "\n",
    "# Number of training examples\n",
    "n_train = len(X_train)\n",
    "\n",
    "#Number of testing examples.\n",
    "n_test = len(X_valid)\n",
    "\n",
    "# What's the shape of an traffic sign image?\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = len(np.unique(y_train))\n",
    "\n",
    "\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#record the image size, number of channel (RGB) and classes\n",
    "img_size = X_train[0].shape[0]\n",
    "num_channels = X_train[0].shape[2]\n",
    "num_classes = n_classes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual name of classes are given in separate csv file. We need to Load the csv file to map from label id to actual name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'Speed limit (20km/h)', 1: 'Speed limit (30km/h)', 2: 'Speed limit (50km/h)', 3: 'Speed limit (60km/h)', 4: 'Speed limit (70km/h)', 5: 'Speed limit (80km/h)', 6: 'End of speed limit (80km/h)', 7: 'Speed limit (100km/h)', 8: 'Speed limit (120km/h)', 9: 'No passing', 10: 'No passing for vehicles over 3.5 metric tons', 11: 'Right-of-way at the next intersection', 12: 'Priority road', 13: 'Yield', 14: 'Stop', 15: 'No vehicles', 16: 'Vehicles over 3.5 metric tons prohibited', 17: 'No entry', 18: 'General caution', 19: 'Dangerous curve to the left', 20: 'Dangerous curve to the right', 21: 'Double curve', 22: 'Bumpy road', 23: 'Slippery road', 24: 'Road narrows on the right', 25: 'Road work', 26: 'Traffic signals', 27: 'Pedestrians', 28: 'Children crossing', 29: 'Bicycles crossing', 30: 'Beware of ice/snow', 31: 'Wild animals crossing', 32: 'End of all speed and passing limits', 33: 'Turn right ahead', 34: 'Turn left ahead', 35: 'Ahead only', 36: 'Go straight or right', 37: 'Go straight or left', 38: 'Keep right', 39: 'Keep left', 40: 'Roundabout mandatory', 41: 'End of no passing', 42: 'End of no passing by vehicles over 3.5 metric tons'}\n"
     ]
    }
   ],
   "source": [
    "# The actual name of the classes are given in separate file. Here we load the csv file which allows mapping from classes/labels to \n",
    "# file name\n",
    "import csv\n",
    "def read_csv_and_parse():\n",
    "    traffic_labels_dict ={}\n",
    "    with open('signnames.csv') as f:\n",
    "        reader = csv.reader(f)\n",
    "        count = -1;\n",
    "        for row in reader:\n",
    "            count = count + 1\n",
    "            if(count == 0):\n",
    "                continue\n",
    "            label_index = int(row[0])\n",
    "            traffic_labels_dict[label_index] = row[1]\n",
    "    return traffic_labels_dict\n",
    "traffic_labels_dict = read_csv_and_parse()\n",
    "print(traffic_labels_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next , we will visualise the data and see the corresponding image and traffic sign label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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I70aWb2VcUdX9426y/a/i93b5jZRYrG+CGK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\nNu2rFY/Cfbei4+wiRcbybpIOkHSo4o7Ff0iSu59vZm9VJJVda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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15d6396550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data exploration visualization\n",
    "# This gives better understanding of the data\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.figure import Figure\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "#This functions selects one image per class to plot\n",
    "def get_images_to_plot(images, labels):\n",
    "    selected_image = []\n",
    "    idx = []\n",
    "    for i in range(n_classes):\n",
    "        selected = np.where(labels == i)[0][0]\n",
    "        selected_image.append(images[selected])\n",
    "        idx.append(selected)\n",
    "    return selected_image,idx\n",
    " \n",
    "# function to plot the images in a grid    \n",
    "def plot_images(selected_image,y_val,row=5,col=10,idx = None):     \n",
    "    count =0;\n",
    "    f, axarr = plt.subplots(row, col,figsize=(50, 50))\n",
    "   \n",
    "    for i in range(row): \n",
    "         for j in range(col):\n",
    "                if(count < len(selected_image)):\n",
    "                    axarr[i,j].imshow(selected_image[count])\n",
    "                    if(idx != None):\n",
    "                        axarr[i,j].set_title(traffic_labels_dict[y_val[idx[count]]], fontsize=20)\n",
    "                axarr[i,j].axis('off')\n",
    "                count = count + 1\n",
    "           \n",
    "            \n",
    "\n",
    "selected_image,idx = get_images_to_plot(X_train,y_train)\n",
    "plot_images(selected_image,row=10,col=4,idx=idx,y_val=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lP1uR6f46k6UKlf/Sil82S5pEPc/ZQBlvFb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\nvni4DjObZ2aPrvz/YYVlDnKOfTWv89/yxfKq/1S6wP3VymdGk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15d63c7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_image,idx = get_images_to_plot(X_valid,y_valid)\n",
    "plot_images(selected_image,row=10,col=4,idx=idx,y_val=y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f15d5f19b38>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15cffae978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[28])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Above is the plot of various traffic sign along with the corresponding images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 180 1980 2010 1260 1770 1650  360 1290 1260 1320 1800 1170 1890 1920  690\n",
      "  540  360  990 1080  180  300  270  330  450  240 1350  540  210  480  240\n",
      "  390  690  210  599  360 1080  330  180 1860  270  300  210  210]\n"
     ]
    }
   ],
   "source": [
    "#count the number of data associated with each label. We can the data is not evenly distributed across the label\n",
    "print(np.bincount(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## we can clearly see the dataset is not balanced. For example there are 210 images with label 0 and 2220 images with label 1. This will cause convultion neural network to favor images with higher occurence(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(86516,)\n",
      "(86516, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "# randomly translates the images (shifts the image)\n",
    "# Generates additional data with translated image\n",
    "# Image is input image and trans_range is range of translation\n",
    "def random_trans(image, trans_range):\n",
    "    rows,cols,_ = image.shape;\n",
    "    tr_x = trans_range * np.random.uniform() - trans_range / 2\n",
    "    tr_y = trans_range * np.random.uniform() - trans_range / 2\n",
    "    Trans_M = np.float32([[1, 0, tr_x], [0, 1, tr_y]])\n",
    "    image_tr = cv2.warpAffine(image, Trans_M, (cols, rows))\n",
    "    return image_tr\n",
    "\n",
    "\n",
    "# Code for balancing dataset\n",
    "def get_additional(count, label, X_train, y_train):\n",
    "    selected = np.where(y_train == label)[0]\n",
    "    counter = 0;\n",
    "    m = 0;\n",
    "    # just select the first element in selected labels\n",
    "    X_mqp = X_train[selected[0]]\n",
    "    X_mqp = X_mqp[np.newaxis, ...]\n",
    "    while m < (len(selected)):\n",
    "        aa =  random_trans(X_train[selected[m]],20)\n",
    "        # ignore the first element, since it already selected\n",
    "        X_mqp = np.vstack([X_mqp, aa[np.newaxis, ...]])\n",
    "        if (counter >= count):\n",
    "            break\n",
    "        if (m == (len(selected) - 1)):\n",
    "            m = 0\n",
    "        counter = counter + 1\n",
    "        m = m + 1\n",
    "    Y_mqp = np.full((len(X_mqp)), label, dtype='uint8')\n",
    "\n",
    "    return X_mqp, Y_mqp\n",
    "\n",
    "def balance_dataset(X_train_extra, Y_train_extra):\n",
    "    hist = np.bincount(y_train)\n",
    "    max_count = np.max(hist)\n",
    "    for i in range(len(hist)):\n",
    "        X_mqp, Y_mqp = get_additional(max_count - hist[i], i, X_train, y_train)\n",
    "        X_train_extra = np.vstack([X_train_extra, X_mqp])\n",
    "        Y_train_extra = np.append(Y_train_extra, Y_mqp)\n",
    "        \n",
    "    return X_train_extra,Y_train_extra\n",
    "\n",
    "X_train_extra,y_train_extra = balance_dataset(X_train,y_train)\n",
    "print(y_train_extra.shape)\n",
    "print(X_train_extra.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012\n",
      " 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012\n",
      " 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012]\n"
     ]
    }
   ],
   "source": [
    "print(np.bincount(y_train_extra))\n",
    "#We can see now the dataset is balanced. The labels are equally distributed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#making sure the images are plotted correctly\n",
    "#reshape = np.transpose(:,1,2,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(86516, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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XzewIgG8CuA2jll33uvvlpo4wsSMSNZjcQZuCknEsmSLqMkzr3JWxrUOSXGYXl2I/BvE+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\n/wPuD3qde1ApeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15d589d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#change the image dimensioning from 32 X 32 X 3 to 3 X 32 X 32 for train\n",
    "\n",
    "X_train_reshape = np.transpose(X_train_extra, (0, 3, 1, 2))\n",
    "plt.imshow(X_train_reshape[0].transpose((1,2,0)))\n",
    "print(X_train_reshape.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4410, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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SAFcBeBnAXnc/njV9jMFtgRDiPGHkx3vNbAnAkwDucPfTmxNsuLub5SeaN7NDAA5lr8ez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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15d63d4cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#change the image dimensioning from 32 X 32 X 3 to 3 X 32 X 32 for validation\n",
    "X_valid_reshape = np.transpose(X_valid, (0, 3, 1, 2))\n",
    "plt.imshow(X_valid_reshape[1].transpose((1,2,0)))\n",
    "print(X_valid_reshape.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = mx.symbol.Variable('data')\n",
    "conv1 = mx.sym.Convolution(data=data, pad=(1,1), kernel=(3,3), num_filter=24, name=\"conv1\")\n",
    "relu1 = mx.sym.Activation(data=conv1, act_type=\"relu\", name= \"relu1\")\n",
    "pool1 = mx.sym.Pooling(data=relu1, pool_type=\"max\", kernel=(2,2), stride=(2,2),name=\"max_pool1\")\n",
    "# second conv layer\n",
    "conv2 = mx.sym.Convolution(data=pool1, kernel=(3,3), num_filter=48, name=\"conv2\", pad=(1,1))\n",
    "relu2 = mx.sym.Activation(data=conv2, act_type=\"relu\", name=\"relu2\")\n",
    "pool2 = mx.sym.Pooling(data=relu2, pool_type=\"max\", kernel=(2,2), stride=(2,2),name=\"max_pool2\")\n",
    "\n",
    "conv3 = mx.sym.Convolution(data=pool2, kernel=(5,5), num_filter=64, name=\"conv3\")\n",
    "relu3 = mx.sym.Activation(data=conv3, act_type=\"relu\", name=\"relu3\")\n",
    "pool3 = mx.sym.Pooling(data=relu3, pool_type=\"max\", kernel=(2,2), stride=(2,2),name=\"max_pool3\")\n",
    "# first fullc layer\n",
    "flatten = mx.sym.Flatten(data=pool3)\n",
    "fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500, name=\"fc1\")\n",
    "relu3 = mx.sym.Activation(data=fc1, act_type=\"relu\" , name=\"relu3\")\n",
    "# second fullc\n",
    "fc2 = mx.sym.FullyConnected(data=relu3, num_hidden=43,name=\"final_fc\")\n",
    "# softmax loss\n",
    "mynet = mx.sym.SoftmaxOutput(data=fc2, name='softmax')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data',\n",
       " 'conv1_weight',\n",
       " 'conv1_bias',\n",
       " 'conv2_weight',\n",
       " 'conv2_bias',\n",
       " 'conv3_weight',\n",
       " 'conv3_bias',\n",
       " 'fc1_weight',\n",
       " 'fc1_bias',\n",
       " 'final_fc_weight',\n",
       " 'final_fc_bias',\n",
       " 'softmax_label']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynet.list_arguments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set :  (86516, 3, 32, 32)\n",
      "validation set :  (4410, 3, 32, 32)\n",
      "y train set :  (86516,)\n",
      "y validation set : (4410,)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "X_train_set_as_float = X_train_reshape.astype('float32')\n",
    "X_train_set_norm = X_train_set_as_float[:] / 255.0;\n",
    "\n",
    "X_validation_set_as_float = X_valid_reshape.astype('float32')\n",
    "X_validation_set_norm = X_validation_set_as_float[:] / 255.0 ;\n",
    "\n",
    "\n",
    "train_iter =mx.io.NDArrayIter(X_train_set_as_float, y_train_extra, batch_size, shuffle=True)\n",
    "val_iter = mx.io.NDArrayIter(X_validation_set_as_float, y_valid, batch_size,shuffle=True)\n",
    "\n",
    "\n",
    "print(\"train set : \", X_train_set_norm.shape)\n",
    "print(\"validation set : \", X_validation_set_norm.shape)\n",
    "\n",
    "\n",
    "print(\"y train set : \", y_train_extra.shape)\n",
    "print(\"y validation set :\", y_valid.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sookinoby/miniconda2/envs/mxnet/lib/python3.5/site-packages/mxnet/module/base_module.py:451: UserWarning: Optimizer created manually outside Module but rescale_grad is not normalized to 1.0/batch_size/num_workers (1.0 vs. 0.015625). Is this intended?\n",
      "  optimizer_params=optimizer_params)\n",
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      "INFO:root:Epoch[0] Time cost=8.453\n",
      "INFO:root:Saved checkpoint to \"models/chkpt-0001.params\"\n",
      "INFO:root:Epoch[0] Validation-accuracy=0.697917\n",
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      "INFO:root:Epoch[1] Time cost=8.196\n",
      "INFO:root:Saved checkpoint to \"models/chkpt-0002.params\"\n",
      "INFO:root:Epoch[1] Validation-accuracy=0.807745\n",
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      "INFO:root:Epoch[2] Time cost=8.154\n",
      "INFO:root:Saved checkpoint to \"models/chkpt-0003.params\"\n",
      "INFO:root:Epoch[2] Validation-accuracy=0.833560\n",
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      "INFO:root:Epoch[3] Time cost=8.217\n",
      "INFO:root:Saved checkpoint to \"models/chkpt-0004.params\"\n",
      "INFO:root:Epoch[3] Validation-accuracy=0.852808\n",
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      "INFO:root:Epoch[4] Validation-accuracy=0.874094\n",
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      "INFO:root:Epoch[5] Validation-accuracy=0.865716\n",
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      "INFO:root:Saved checkpoint to \"models/chkpt-0008.params\"\n",
      "INFO:root:Epoch[7] Validation-accuracy=0.853714\n",
      "INFO:root:Epoch[8] Batch [64]\tSpeed: 10541.78 samples/sec\tTrain-accuracy=0.962740\n",
      "INFO:root:Epoch[8] Batch [128]\tSpeed: 10534.21 samples/sec\tTrain-accuracy=0.960693\n",
      "INFO:root:Epoch[8] Batch [192]\tSpeed: 10604.50 samples/sec\tTrain-accuracy=0.971191\n",
      "INFO:root:Epoch[8] Batch [256]\tSpeed: 10646.46 samples/sec\tTrain-accuracy=0.970703\n",
      "INFO:root:Epoch[8] Batch [320]\tSpeed: 10575.87 samples/sec\tTrain-accuracy=0.944824\n",
      "INFO:root:Epoch[8] Batch [384]\tSpeed: 10512.23 samples/sec\tTrain-accuracy=0.955566\n",
      "INFO:root:Epoch[8] Batch [448]\tSpeed: 10418.38 samples/sec\tTrain-accuracy=0.965576\n",
      "INFO:root:Epoch[8] Batch [512]\tSpeed: 10484.16 samples/sec\tTrain-accuracy=0.961182\n",
      "INFO:root:Epoch[8] Batch [576]\tSpeed: 10423.94 samples/sec\tTrain-accuracy=0.964600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Epoch[8] Batch [640]\tSpeed: 10443.65 samples/sec\tTrain-accuracy=0.954346\n",
      "INFO:root:Epoch[8] Batch [704]\tSpeed: 10451.66 samples/sec\tTrain-accuracy=0.966797\n",
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      "INFO:root:Epoch[8] Batch [960]\tSpeed: 10487.21 samples/sec\tTrain-accuracy=0.965088\n",
      "INFO:root:Epoch[8] Batch [1024]\tSpeed: 10431.29 samples/sec\tTrain-accuracy=0.968750\n",
      "INFO:root:Epoch[8] Batch [1088]\tSpeed: 10425.53 samples/sec\tTrain-accuracy=0.971436\n",
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      "INFO:root:Epoch[8] Batch [1216]\tSpeed: 10674.53 samples/sec\tTrain-accuracy=0.972900\n",
      "INFO:root:Epoch[8] Batch [1280]\tSpeed: 10585.69 samples/sec\tTrain-accuracy=0.965576\n",
      "INFO:root:Epoch[8] Batch [1344]\tSpeed: 10577.67 samples/sec\tTrain-accuracy=0.964355\n",
      "INFO:root:Epoch[8] Train-accuracy=0.975446\n",
      "INFO:root:Epoch[8] Time cost=8.252\n",
      "INFO:root:Saved checkpoint to \"models/chkpt-0009.params\"\n",
      "INFO:root:Epoch[8] Validation-accuracy=0.876585\n",
      "INFO:root:Epoch[9] Batch [64]\tSpeed: 10559.98 samples/sec\tTrain-accuracy=0.959856\n",
      "INFO:root:Epoch[9] Batch [128]\tSpeed: 10599.77 samples/sec\tTrain-accuracy=0.955078\n",
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      "INFO:root:Epoch[9] Batch [1024]\tSpeed: 10757.40 samples/sec\tTrain-accuracy=0.967773\n",
      "INFO:root:Epoch[9] Batch [1088]\tSpeed: 10793.34 samples/sec\tTrain-accuracy=0.967285\n",
      "INFO:root:Epoch[9] Batch [1152]\tSpeed: 10759.93 samples/sec\tTrain-accuracy=0.972412\n",
      "INFO:root:Epoch[9] Batch [1216]\tSpeed: 10773.00 samples/sec\tTrain-accuracy=0.977051\n",
      "INFO:root:Epoch[9] Batch [1280]\tSpeed: 10787.49 samples/sec\tTrain-accuracy=0.969727\n",
      "INFO:root:Epoch[9] Batch [1344]\tSpeed: 10772.73 samples/sec\tTrain-accuracy=0.974854\n",
      "INFO:root:Epoch[9] Train-accuracy=0.982143\n",
      "INFO:root:Epoch[9] Time cost=8.082\n",
      "INFO:root:Saved checkpoint to \"models/chkpt-0010.params\"\n",
      "INFO:root:Epoch[9] Validation-accuracy=0.886549\n"
     ]
    }
   ],
   "source": [
    "#create adam optimiser\n",
    "adam = mx.optimizer.create('adam')\n",
    "\n",
    "#checking point (saving the model). Make sure there is folder named models exist\n",
    "model_prefix = 'models/chkpt'\n",
    "checkpoint = mx.callback.do_checkpoint(model_prefix)\n",
    "                                       \n",
    "#loading the module API. Previously mxnet used feedforward (deprecated)                                       \n",
    "model =  mx.mod.Module(\n",
    "    context = mx.gpu(0),     # use GPU 0 for training if you dont have gpu use mx.cpu()\n",
    "    symbol = mynet,\n",
    "    data_names=['data']\n",
    "   )\n",
    "                                       \n",
    "#actually fit the model for 10 epochs. Can take 5 minutes                                      \n",
    "model.fit(\n",
    "    train_iter,\n",
    "    eval_data=val_iter, \n",
    "    batch_end_callback = mx.callback.Speedometer(batch_size, 64),\n",
    "    num_epoch = 10, \n",
    "    eval_metric='acc',\n",
    "    optimizer = adam,\n",
    "    epoch_end_callback=checkpoint\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EvalMetric: {'accuracy': 0.88654891304347827}\n"
     ]
    }
   ],
   "source": [
    "acc = mx.metric.Accuracy()\n",
    "model.score(val_iter,acc)\n",
    "print(acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sookinoby/miniconda2/envs/mxnet/lib/python3.5/site-packages/mxnet/module/base_module.py:64: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "#load the model from the checkpoint , we are loading the 10 epoch\n",
    "sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, 10)\n",
    "\n",
    "# assign the loaded parameters to the module\n",
    "mod = mx.mod.Module(symbol=sym, context=mx.cpu())\n",
    "mod.bind(for_training=False, data_shapes=[('data', (1,3,32,32))])\n",
    "mod.set_params(arg_params, aux_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class=Keep right\n",
      "class=Go straight or right\n",
      "class=Turn left ahead\n",
      "class=Ahead only\n",
      "class=Roundabout mandatory\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZniBtQ4n/W7HT1hZ7LamARaw+Ea213PGOd+Re97pX5V/Z1keGQOXeB7pL2dT7FkVRgetq\nKgtry21+AHyhoSlUB4Rjb9J7IVhR3kl1DNsv/MwDq9/ydzodTjnlFISIuP766yvbQ9MbwOtd7USw\n/p8mk9rXS7/f5653vStaw65duw7YM9cHsEqQ0rk+WOvdaCxaW0CXgFpakY234PvB7FbSBz7wPlzw\nxS9jjOG0k08D2cNYg4wkmnL1LxvYSIGKXNXVKtsqjcUI6ADS5AglkRYkGW5DniFtadEnYZQbikIz\nygt2jccsZ2MW0zHjwjFQohgja0OEZz9SSbrdPt2uC0KAkonZAo1GCkGEcY0k6q2l0QJrdemo3wUh\niGy9ONz//vfnP77/febm5rjT0cc6BlVKYQoGySYWR5dz82U/4ka7m9887VRe9dnPceyd70CcSHSu\n6Xa75PmpCCF41vOeyzXXXMP5H/son/jQ33Hqo54KoxHj0YjBcKGawFCDnDGhV0STgYaTd4Vhyi+s\n1Malql1YaUyQQlYuOyFLkVKQ5wUnnngiF154IcYYzjzzzMrI5Ps7BDwPZu6vqgC+MviZptuVf+9Z\nnZTSbZONU7qHwK1EWL5SLSNqHWojIECBkBZRfi+kM6r5+ocLj9f/how/tCV4XWlRFJx66ml84hOf\nAuDUU091xsVR1lgY2n0Ufu6+DBe9pkrnQEl7AZlk11rNWOXF960V9ZgKVWEAJ5xwV77zne+SJAn3\nuMc9iOO41IdPcKrfD1kXwOqV39aaBhMI/y+0G5SZ9u4bbjDnQmBR7F5OOeGEE1BKMcxTUqsRcQRS\nVoaFiuLLlXqq1cQ9q3QU1mOUyBBWIK1zBdPCUlinzB/mlsVRzuIoZ2lcMM4NhS5Q8cpOqoA8jul0\nOlXkj3toM0JHlOoJN6FbK+8qg+umm27i8MMPJ45jtm3bxvZrbwgU9wlRbDnuoCkGP72G57z05Zxy\nnwdw0NYZpqam0CardsbeRavTlRyx7WCe9axncdyJd+NLX7yQyIzIi0Fl/FhLx7e6K1OofywnbMsd\nZzVAndSeIXAJISo3saOOOor5+fmGDruaeH4rv2LnUntQKG0nPt/3kxsnkydjXTb/f+CWJVYyVfe3\nyWjbz63mRRAF1TY8+e9CYLXWsm3bNmZnZ5FSsry8jLHNhaINsP7Z1UKySj3Xq0xiqv6zKIo45JBD\nXKRjywB6W2RdAKvvcGNKHU/pAqVzx1nctth9NvZhcNo5yVjrVvlL/vMndDodhBCM84J+f5pUlwyi\n1FkK6VxBlJTVCmhbriChDiYqjRkYkFhkbDCFxUhNofPSQT4io6CwkixLGZucVBvGaakGEG4YhsDh\nFxLXuRFCSVQcIeKE2AqKInMgWjIhKSyxccxXBM77Qghi63WtoCVE5e/fdP11yKTD1NQUl1xyCfMH\nHVZbk4Xiwk9/nI+9/294/xFHcMQRdyKWOb1ej0xndCNJmhckFiIFQ1O6EMURnVhx+q+fzJ++/v9y\n3xMPLd3WNMYUDXBrT8j2NrO9xWzoYD2AePW6j64rde4WN16sgW63y3hc1BZeAQiJFC7UOcsyvv71\nrzI3N8dVV+3i8st/TpIk6MD3tXZXmxw26tq7NtaEYOYXnobuGIEVTSYsyr5zut16UVdSNZ7Vtkp7\nlop1DD5sN1+Oyie7jHoKDVCesVrrXOE++9nPMjMzwy+3OyNNGG4aqixCIMU4l0e35slqJ+Fr2/x7\n20ApXDwang+3gRBPAkzfzt/97nfodrssLe3huuuuxhhTE5zbIOsCWL2OxwNrYRz7ya3AaxqN9oPP\nbWl1yQJVVE4oKRmVYZ5Ruc33EyYc4PsqxhiMcMPFGOcNWbmjmAypU1IEmY3IpWCsJcuFYJDBWAsX\nAGAVapVx5oEgjFGOkg4i1xjj6qon3GNNU19kWldVgIatGMzU1FTD1zWKIvLd13L1Dbdw3F3vyfT0\nNHNzc84LIo4RmEplYaxZ8fvWWk477TTSXb+owMkDTaP9JrDUsIzh9lKImgX5iWBadLwoCrrdLsPF\njM2bN3P3u9+DY445hqOPvlOVG0KV9dRFxi233EKapuzatYvrr7+eG2+8kRtuuIFxuoC1lm6329i5\nyNIjpGb2tcHDejVLCVyhbrgt1XZeNsedH34hsK728uLd2aA2lHkLtgfSNrB6QPUsNgTM8XjMeDzG\nlr+dJElVn0kLomeofnH43yK+PTxWGGNK1Ve+31gxSdYHsI6zYLJZcgsg0BKELS2kVWKRcqsnXaSL\nwq08hRQkSVmdCJASGeigrLWVJdFoqi20bSVs8BIFiKitITWGxBaIPIPCRW9pAVoJrCqd/tFY6XSz\nVkVExsfqK7QxiMJgk3qQK6VQUVSFL/qyunpmCGmxaCQRUgaWb9H0sWzUsdTjCVm3WW4081G9CksM\n03OzTPemmeoXzM7MBOxeI4VCGIuVAkyENGOKsp2TJKHILfPTlp2LUWOyhb6SIZCGyVlqvWANpG5h\n9IDhyqG1Znk543d+53c4++yzufe97023N0uapQjhEp4sLjk3oaXhgG6367blZZCJEpJjy3aampqi\nE0Uuv0KimJrqEamIqBwEC4u7+cpXvsLnL7yAyy67jOmphOnpaXbs3Em/1yOK6ommS0YTLhoVyxTO\nbaoCRqlASkTpF+v6uGbpYWIV/6rngWgsWm2LvWembSANw0qttehyYTRaYIwGISiCxSDNhqAj3MCh\nZKcWY2pXNs9Qw91cW7x3wFrqmv2R26K3bRsmw0WhrVv3ZfVj7kDpi9cFsC7naVU5Y+rEKoIO2lvt\nZVS6RLkIHVXqq7xVX7YaY5J1vwajva9IxthyS1cONOt0qWY8pihKp2qlnAMrlqLFuKpt916eUwGs\nUqUedd/abG9ijKk0YW0DTGwFj3nEoxgOh/T7/aqtnFtOp7Ftt+VfGcWV+5SUqjLUhPVsMyRaz/f1\n9eXz9xVFgZDOePAXf/EWDj3yTu53o5jl5SFCCEYadi0suzoYhTGKPIpZGo3RImHnggsAQQqWlpaI\npAu4mJqaIrddDA6AOp0O8WLuvDBiyeLiIp1uzH0ecDqnn/FbWGuZn+1hsWy/4jJe+5pXOReysh7G\n2kYdQ3D1/RhGwzn1hDdgtSK+QvVBYASbpD5pJ0HxSYJC536/3fe/FwKrNc3EKnW/SKwuqvKEz2+P\n3f8NjLWttrm9ZF0Aqy4ZqjXldowIrQRKSCJvQFBOV6qCrT3UbsntlSZkqPWH5edtKyfBKidrnZ/w\nW9JCoxUMckuRFVXmrDiOETYiFhFSFgjlEpZENI1jfjDnwhKXzCBSrk5+2+hZViEMVoCyVJt8KyBX\nzkPBi4nqwIFQF+gZq9fH+cnp09kJIciKlHRpicHCHtTsQcQqQlICa+nF4BazwjnNR3GtGwYsBbpk\nuGGopGdMhWlOTA82g8GAmek5BwCF5t1/825+84yHIaVkMMy4/sadTB80z+6hQQjLaJhy4403cuml\nl/Ktb32LosjYtm0bR9/xSI455himp+aYnp4GWzA11SkzcEm2btlUjYeiDMd0ZY3YtWM3i3t2ctNN\nN/Gzn/+Y7du3MxhK7nGPe3Dn44/hASfdl+XMMcGD7nAs7z3vn0DkTE1Nkw738IEPfIAPfOAD9Pv9\nauvd7/erbXUcx0QqKdlo2Vkqcq6DZqVaJQSzELAnGabaWaMm6a29odf/fg2SPqihfG6llljdlSrc\nVTTLHbLWA599al+l7SlQ3WtXft42mkK9mE36jUltsj9lWx/AWhYjKa31NlYOFIXLuyqEQIuavrct\nfHDbtx6+sdUEx+NcWKzWxEYyzi3DrMAYS4ElSSRtVXfYWau6rATXhgaLtbwUXJ33rZ5t1pHnOSj3\nuc81OxwOmZoOslJNKF/47E6nw9LioCp3Q9dbAoDWGkSTqXsgAHjuc1/AS//wpRREpHnODTuXmZ+f\n52fbf0V3epavfPUHXPCFC0mShFNPPYVjjz2Wk+5zD+5/8klEkWOhpswsJaTfGpcO/VFCFNf5UV2i\njagqrzGGgw/ZwtaDD+LoY47j9DPPZDQaoaRl9+7dECXccOO1fPuz3+fiiy9mbmaaZz7jKdzxjkci\nFpY5ZNMsz37u83neC16CIudTn/4UL3/5yysDmE/IUu+W1u4rrysNGX/IVtvAGrpUhdFfK3TZtl5o\n20ZC74UR5jmdNHdcmdYs/u0qB0ql8N8l6wJYZWkZ1arePlU+p5TbZVm7qEBt2KgSOjiO5q7xWy7j\nzcomeL/2quNZhPBME4uwEcYUZPmYkdYUUlIYg5IShUYLt3pqKykqFlmWyjZdgOo6OD9Jb4l2DNF5\nLQhhyZHk1ulqEYI4cFq31kKuyY2uPA68FD51oJEUWqONYTwe0+ksYcSUY5VlMu84lhhT0Ou7sF1j\nM6ztN54jhQMkpdz23yv7ZTkxfdmVUgjlvC2KIkOKLuN0mfFIc8aZZ/LJT3yKHYvLFFnCjnFGUYxZ\nHCb87PLr+OQn38H8zBRnnHEGJ97lGE68++9XbZYkCcrWqh1jXPpEY/OKsRtjSJKkUmeo0giF9VF9\nsmGUsCZ3XhRFjpLOtWnTpk0YY5jpHc7ZRx3J2b/9WPK8IEsLLvrmD/jc5y7kiCOO4Om/8yRmZ2eZ\nm+3zkIefxQ8e9himuz3e8tZzOe+89zI9Pe3A35gydU7dP23WZHTTIBV+F7pPNe4J2FYIpNY69ZUf\nb/4aD6B+iLgZIBqGwZqk1GN03/EsCL0+gFvs1QB1f3KihrrVkGjsC1hPqstaxKMt6wJYRZxU+tOQ\nwQErHHWrRvENNjHqxutonRizspG8m0xb3yUnMNbCaKQx5KYgxRmyhFJESYRIIqSMSzB1oYPhZPGl\naLORKjWZamYxcuWofR0dkCgX4isirM0bOr0iMCj4tqsmnS3Qut6iW1mm/MszisIl/z7s4DsAkGcl\neBIRWcMoS+nGM9hqkajbXwhBNlqsyue3rYkyDKWgK6d58EPP4L3veQ9WdtixuMD2PUP27Fkk0nN8\n+J0fZljs4ayzzuKowzbzRy95bhViKoTLiVA52wNSBW0h60TfURQxGo3o9epw3TBxsWd1lUHQNhNt\neBWBs/j7cFK3MDg9aUK/3+OUB9yPk0+5b5XU5z9++AP+4QMf4fjjj+dlL3spS3rMM1/wBzzvnJcT\nC8MF//xx3vaXf0YiOoxVTj+JS52oaFjr86w2OrXBs1Lh2Fof2rbYO72pxZbuicL670zj2r3LbVfs\n+x3BbZVJ+mfYN0ANXbKaxkAbzO/adzkcF/sia+0m27IugLX2r1w/CnJjDNaztcBh3btoeADw6cdW\nk1B1Malua313e0kI8N6fsxt3SdPlMmSzHnjhPeGWNU3TBghEUcR4EPG2t76DxzzqMYgoYtfSiGG6\nyGhs+dFPf8onPvEJnnL243jSkx+D6pbJR2wN1n6Ah6AKKw0O3jthNBq5gIYgYUb7WillFYbqv/fi\nQbjNYqSUpGlKp9Op2scvZL1ej3ve857c/W33oCgK/vUbX+NDH/xHHvqwM3jEIx7O5tkpTj7tTL74\nW49h++WX8bSnPonO1hmyLCOOnZY8tOCHRqkVjNaY2pPFNHWhxhiwHjg8E7f7CagbcnvJugBWpADp\nnNxDVuEncahTNfi8irXRRAR/gTojeUsm6W3aK2T1vrLgGpTL419mRo2IylSCne6Uc5eKI/f8vLwn\nSA+HlMioTgVXAa2Uzh0KBTLGiohISoR0+s/IQFqBmyS3EcLUztu5FWAKItM03FXgJGy9giuJLfXX\nDkwKhO6hiwIJxEIxzEegNePBMp25OYyBQqfOmX5s6fUSBoMBURSxPM5AWMbZHqI4RgjFwx/9eP74\ndW8gFQmDrGBp126WRgmf/cyXufmWq3nCWY/iNa/6w8qQpbVGWCo1g7W2wVqB6viZsL/8d0690alA\nWGtdnnFmHIuLCqyyCGNRyjE7D17dbrfS+3rPklwXdKO4XCgsUZSQps57wINVuItKEoXWEfe+9724\n+91/jTRNedtb/5qFhQVe+of/h2PmNrP5sMP41n/8hB2/uprffcbTkEazXAwZDrOS5ZvqnKi2Fb/M\nW4nOvQmzVmN5MHUBMgJR6kybPrV+i970s13NILWWhFmjnAGzBnCl/DwRjV3CrZVqHt4KnuGxwRtv\npWepohlJ2cXYAAAgAElEQVQCXKn7yrrseyDCvrfdugDW1VhbtRWc4HNmb+OuI9RXhVIBFGV6OrWS\nKfkDDPv9vnOrQVQ6vLDMjoU3Hfon1X1vRqt9KfckCdUD/iA8V0eN0C2/WO2s1t7avbCwwMGbZhrM\nNuyf5eVlFhcy3vTGN3LSyQ/k5j0L3LIn55bFRb77vR9z0UUX8fwXPovHnPVg12emGWff9t/0dQuf\nEZW+p2E5PRh6Xa9frDxz9jkR8nIiSYrqmZ6h5nleLXCOJa70Y7TWpV4cjUZV2/n7wh2KV+nEccwr\nX/VH7NmzB2sLXv2qN2Ct4I1vehW6N8cX/vU7KODxj3kkV2z/WZD5qxmFVgVktJBlrT7fnzFxIERK\nGQDQ+mHGB8Kx/0DJugDW0CreFimisu/KyQUTQXXiVlq0LrT1BG4PxFp/V7/3pxsaIiSpc/dSCqXc\nRPMO4ZFSGJtjAiCr6lWeWhR+5hYGU62oVThmwJ6NMSgDOiuIOhGRkI1J32ZRK8VFC7nfVSiZECm3\nFZWxZFgClmNfMQWC4UDTSZzu2es5hRAsLCwwPT1dJ+GOLHe/22/wp+e+gaW8YHE0ZjSy/Hz71Xzr\nm//OIx/2G9zvpLu5ZORZeYRMAKZhHeM4DtyhlPP+KJnRrsVFpmdnyEdLTM/PUAyHTE33UWKaOI74\n+TU72H7DLr576ZVcdvmVLGYRN481Y1OwtGdAVxp6kaTX69FPFP2phOMOmeO4bUdx1B23codD57jD\n5g7jhRuJpzcjul2yYozOLHEnYpxlxLHCmIJB6nZOcaknDeviJ/R4PGZ6ehqtNS980e+BVfzdeR/k\nmmuu4ZxzzmFmWvLOfzifQ+b7PPzM07n00kvp9XosLy839H5u/K30IW3vSryrVM3Gwrys/v7brj/1\nIFrNTyHqueXHn24ujLcW4EJis79AubrbWH0ahF88K//j9m94tR8rda+ijSdryLoB1hBUb42+MQTK\n2sDVBtb6+zaw1iqApmdB+xp/Vk632yXpdMoQUMjyyWUOXcQmvdaqa9ubYH+smuEzwjN+wGXu8mDu\nB5q3wIc+sV6P6fWp1lqsTjnx8MN5w7lv5Lqb95AWlit/dQMf+uD5nP3Ex/Hosx7EpunZcvFaGUDg\n2Z5/Tp3j0/VhLGq1xcGbOoh4np8vpHzwXd/jqz/dzk27h8jZDnQ3YfIhGEOsOijhcu0SdSDXmJke\nI5My6vbKUGBFsTDiBzsXKS7/Cegcmcxidt8E44wTj97KA+90OA970EmccPQ0sdHoOIEyb8OUdAuA\nVs3DB8P+C7OWufrmPPasR7KwZ8j555/PTy77GW9605vYs/0G3vf353PYIQdz6EGzzM3NMRgM9tqn\nXmrw2udbDrg0fKc3ZIWsD2BFNbLASzEpZG6l20QoxphVgbS+pozx1jUQe52QS0AtXeYqWSZusaX7\niijASpRPA6cUUZmVSilFkecIEqxNG9bGkJ0ppYitQFmDsgYhS72sDM5A0qZKOWdU4eojDNbmaJ2D\nVBXbltqiyzbwAQlCuNSB1kIRJAHxRjZfDqszlIzRxiCjCJnERJmhUEVjq+3B1RlZUg7ud7DTh3Dj\njTtIteSWpWU+ev4nePJTHs9L/+jFxMJHcJlSl9pk6Q5ga8ZgjGFsI+IoQgrLlF3iZzsi/ujtX+aK\nGxZJlaIz2yWTPin1NGpT34GzsaB6oNz59BoJhuqUCSKwNkah0Nqz4B4yNgibubSSeYGa3oLtG36+\nCJdfcj1/d8n1YFx02jjP2Dy8lic95kE8/wmnMZUN2G0VRkTEZdJqFYxD76vrFzMPPtMzPX777Mfx\n2Mzwsx/9hPe973284hWvoLARV968h9k4ZnbaJYbpdrtO7yv8aXwrj+nZLxYXMLDbKtVCL9y8CK3q\nTdXKrVdLVHMGykThrTLsTzVMk5gYY5x+OAAGWe4oJ7XobTEorwtg9bKWnjHsrPD/MD69DayVz2sA\nosYYdNFMywdu4nlAcWoBgbEG65kfYErrd3WcRskEbctiO0kmsfL90a36ejSjadZOjBHqMX3dpJSY\nIFO9vy6OY5YHdZiqP4mzEyds2rSJrf0eO25ZYGwMexaGvPtvP8RjH38Wz3r209AmI+kolKldbrxq\nxbO4tnj2P9/r8cMrlnn+m/+B5emj0TKn0+kynM/oRV2GqSbpJK2MSvVvTGqjUM/tpSjxSXjfeAvS\nRyiVR6kI4/SzBgPaYpOE4aa78s4v/ZJ3fuEXRDrjXkdO8a6XPY3eXFEeO6MrA5xXn7Qt8/XuAU48\n8UTOPfdcfvGLX/Cxj32MP3jJCxlPdbjs6uv49y99nee+5PdWNQCtJx1iKO6oof86He//BFkXwLra\ntrid49PrSULXE0sdgQJNfW24enqjRRXFkpUZfcpHKqXodDrVhFeiPKoF4Q6XowZCD1DefcZlg6oz\nxPtyOuAzgIu5L4StGlxZQ4Q7/qM2KtV1jQ3VqVpaazAuqXfl4lQMEdYSS4WRgZ+mEs4qbiyRjVew\nZlfGCISh13NnqysLQ5uxZ88eiuJghBBsnpsvvS/g6EMPYvt1N7C8lHHTjt187rNf5PnP+12SjiiZ\nbYQwTXbqxYP/crpEHPXoT0UUCgZjyzNf8Xkuv+VGxOYtFJ2DUGaMsTBYLohEh9xIolhSlM7nqky7\nV7klex0nAlkmvClE46u6DL6jjUDYEkD9mVkTQiC1AkFEOiqQvT4Obnt8b7fl/q94D2ZkeNR9j+PN\nzz0Nne/Bqtkq2UplpDOuHaK49LuNJVk+Ik4UdznxOI499lj+5M1v4ZRT7s+jHn4mpzz8YVx13Y1s\nmplGSsn8/Dyj8bgq00omaGiGlK5M3VfNqTXweEVoaOvaatsvSss7IKIyuxy+XP54lnocwH+xqsAn\njvF1DuwldsJR9LenwW9dAKuXSQalNrC2jTeWtjvJSnCz1laJK9I0xYwzsvIkAm/hj+O4ckcCMFJh\nEC5zfymT9KOTmOf+sFCg8gNd7RTVdps0/Bz3wQOkXebwvdexpmnK1q1bqwiruX5Ch4KduxYZRJZB\nGvGWv/ob/uD3X8Dvn/PcykruDTm+bI3orPL3pZTMRX2KzhRPf/P5fOvqLkwt0R30sLNbMKZHoXJy\nq0luhTsQQOG0N8ggEsiXSSm1IgXhrRahEXoGsanPBZfdwAXnfBSVDzlxc5dPvemplc7Yj6WQfXqX\nMD8eu50p/vClL2YwWOJF55zD4856Eg/69ZO49obdbJ3vsm3btv8egNqQ2yzrAli9YSN0u3G+aDVj\nrQwcdmVYX1uvGRp9vBtOlmVk4xSTZozSIUXm+KASoDOF6XTc6atCIkpdqvNHDVUQzg+wqTN0rFdO\nsOK6Vx22Ck6fbI2gwL0Mddkzk6KE022mGArXAmW2L1sdI6O1RuMO/3M6S9tYUKy1RNjGd6EFW4oY\nrMQUfWI1X29j8wVkpOjHMZEVLI8zdiyl/PiSa9mx61p+/yXPZ3rGsfTxeIy1doWfZxzHFaAWQtOJ\nOxTRDCc++68oeoeA6BAlApv1yWNwytC8ipWzuEVt0hjxNFSYpgrECJw+Tgp0sNC6etdZpdxnLoS6\nZlm1EShksH4cVT7R1TEGEt0B8hF+VdPRLD9dFJzwvC9xULSd817+25x4+Cw20wyiWh8ZJqzpdDqk\nJkXJhKn+PK/4P69kx44dvPXtf80555zDnpHlhht20Eki8kIzOze1AmRDn80V7dUkbWvKviaRDseS\nMN7LwyJEnZnOGF1204ENeFlLt7pa+SssEAK0XIEXodvcgV7A1gWwauNALtzuTIqICT+vIlWKGnwr\nnWGkKuOLMXXuytHSIqPRCJvn5NmwVMQrZ+EXCtNdmepOSElsPRN2gwicGmASw2y7VVWA6kHRnQeI\n8HpSI8AYTFGgjUWbFGMLQioa6lLb6pH6eZZIa5cLtrUYqQiUqg1YCI2Qlqg/w2g8wKCJrUEimBaC\n3Aqu37mD1MS8928/ytOe/lju3jm6oZP1g9ZFFDmLOaaAqXlkOqIbwQcv2sEbPv5xYrUNM7UFazKk\nkGVmrCqpYXMsYFcwflW6OBnpdzXONa1KFanqLFv+5NxqoggX4imCia7LNHlGCCLj+gTcomqtxZSq\nGylC3+p6F+Qe0Cgi2hbI7hI3cRBnvfUbZFnGI3/tCN7xgtPIUkvGMrGcBr2LWM0yHo+RSYxUbkGY\nUl2mprfx5Cc9lfe+52/pdrucffbZXH7djRx58FYOPWgGhFNVpWXKwLWOw6nWgYnfri2hn7GTWsUQ\nkhh/rRCiyqZWuzaVdwTGtvoev/hNypDV7HsrWFVP35bV7A0NQ7KlCkzZX9kfY9YByv552yQM8fNh\nfj7ULzTUeKD0143HY9I0bfwNj6Tw94SJgNvhg14qy3zAfEVjYtWAclsOGms/d1L52tfszS1rNQnr\nFNbNy2AwQAgX3KCU4s53vhO7l3K2X3sLFJt41zvfz9Of+SQ2b9o6MfzTb3fH4zHdbpdoao5kNMRE\n0/zGSz/Mn3zmO3Snjqx0wL7t9rf9QjetSa5rvr3a7bq/7MPvboAqZWT43Vp9EF6f6QKZxFz4i938\n2ks+yQU/2sFsX2FsBsYx/F6v18gP4V/dbpfHPe5xnH766fzZn/0ZUWeGG3bt4eZbltixY0+VG/b/\nL+KZ8H93GfZXvbcugNWDiwfUSQekWWsbp0+m6QgzHpMOlsmGA8bLS2TDASZLwVisDkDAOGartcZS\nlFmc6onntseCOHCRUQgSKxDWMZrQ0lwBnVAYf42odYx+sofs1Vi3YWsvEu6eFGNyimJMkZsqWbGy\nEAuJMrXXQgWQ2sWRW2uJjLu2nWum7fLlrfRCSKyJGAiDVhYjDMcctonldMxNo53sWMj56r9dyO89\n+6nM9ruVmsOlxRNVXldfh0IKxss5XZHzuDd/hV875x+5zsxgrSLPJBbpjthRkkxrjBDu2BvhGKqV\nAiMEtADXSMojy8toLev6JRICYQxWCqwUDYCiDP309/r3jhl7oC2TrkiJjYIpICWdKEEI6dpIito9\npwwL9q92GwsU1p2MhowjkAJpDGMpeNWHv8mxL7iQL126yAwZI6MZy24VjdZwWVKWXj9hdm6KF77o\nebz4RX/AlVffwrU7l9gzGPLkJz2VIs8pyki/UM8ftNxt2tJWASveOOZDzoPnGGEwExzm636oVS3h\nDseVyycZahvfVkrbztJ4lq3VNmstemHI8KTyTip/W7W4v+25LoDVn8PjX/6sc3/MRAhCnqmOx2OW\nl5cZDAYMBgPG4/GK4ypWGHpKCZmTT07snePXWp1Wc/IPy7e/4YXt+ybdu3Li7Ju0y9mWfrfHMQfP\ncPh8n2t2Dvjl1bfw5U/9kISCU045pbrOL3T+JUR93LKUks0HHcKffvK7HPvCj3Dp7tF+rfBrMZJI\nu9feZNI2cV/vDcUv3g0RouliUH28cjKvNsELJbBJxMv+9l85+qWf55+/dTUdOa7KHobjeiOqlC5g\n47WvfS1RFPHaN/0pu5fHvOaP38wVV1zBeLzSyr0h60fWBbDmaUaR5WTjlHQ0ZjwcUGQpOq+ZK5Rs\nTxusNohxTpFmZPmIcTrAZOPyIL6V4CSUdOxOQETsAhKUS45S+aRGkTvJtaU7UpQnoYa/54FOGOIJ\n56r7Vdlax3hCxXulIzYGE+ir2nrRUKxKnKI0EC3q1XoSiLlVVoNPHiMsQrgTQq01SKU5fkvEve6y\nhZtvXmDP0h4+8qFPcNIpxzI979LwOR2yBkywFXfGOG0NMleI6Xnu9vg/4/zv3YSOphGqZm4uEtmx\nUWlro1PTB9exx9roqBw7D/RghXUHO4YqESlldV0kXP8aLEa6l8adPdbGRa+zcy5Y/lW3pVEhu22C\nZMgQrSxP0S2ZN0q6vpYCg8QgG7sIIQQiVoikz5sv+DEPfMHHMf0enShhsJwhk5i0PNo9TNYTxYL+\nVIenP/2ZPOMFL2dkY2xnirwYY60gzyetHrdOdbRiDAmqtJzhvDCCqu1Dxtx8Zp36cpIdwl1bX1Pf\ns7ZUfTWRQDYZsDEFxhT184LdYlXH28nbYl0Aa5qmDAYDRqNRg7WmabrifCHPWqtzf7ICpZvb69Wk\nYpiRbJxNFJ4zvy/S1vN5CTttrS3MpHJ5fWV4muqBkvag11qzZcsWzjz9vly/Y8wggy99+Yc86SmP\nZm5urlIZVFbnCW27eWaOv//6Tznx9z7I8tzByGTfJrLdi65yzXoYSyIVioJORPWSUhJbtwDGxr3a\ni92avxvsPNplC/t50thaq69yVapzbEpkxsTlq6sVu3qS+z37g/z1hZfQO2y2cZ/3uPCAEEURU1NT\nvO3Nb+DP//zP2bU7Zfu1u1hcXEQXG47561HWhVdAOlh2BqliXG01oyhCCYGOImIVOXca6yyK7VRo\nXtor5womKQQ6kkgrncO/ihDKJZtGSRenXt6HMFgyrE2C56kma/Ggg8baMv2cNUgVU5iRyy+7aq3r\n45aFiIiiDkprdDRGFU1vA2ULbJFjRdOAJKh1v77ulVeFLfWCgR9uJ3ZuRg898yEcf/zx/OLanQwL\ny3vP+yBPfdoTmZmqDSre/7QGjfL39YCoczhHPP4vkYccBAJ3kqdZqa8ywkKeEfWmKMZDOt0+OohM\nmiy2yfKEQFlJ4r6hZwy5gFTGzKQDZBxRyBxhIlCgZUSqCyLv1F5ockUVMi1wPq5iFZcgay3KJ99p\nGcOc7tWXzbvvBN9Xpzz4uwTCpiTpsGSsXbLBHpKpaRiNEcLyvouv5LzP/4zzXvFoTjrMEvWnMLqL\nFj4owo29JElIkoTnPO/5fP0b38QYw1mPfRjLo5SzfusR/Pu//zvj8TjIZXvrVEcQuGoJ7x5WnrJb\njiUlZZUJyXsBWOF2hJPchdsLnF+kffYyb/WftD6t5Qomqi/b/ej7rf5BZ1/5r8sEti6A1VvzKfwp\noC6Cd18YR2hUChXOk1hEZfxRyhlBgvDUirVUYLJvzzam1nVZazn11FMZjlIKY7nyyivZvn37qvdW\n272SqRpcqrys/Py2ZbZcWW9/SukxxxzN9YtDbNzlb887j2c882l0uqxpRzDGZaI6/zs38if//G04\n7GBMXp7uuZqVPxecdf8TyE1OR+T88JoFrtpdu9PtTfw1iYCuArFzJxed9xymewlXXHYt/7l9F1/+\nzve5/IYRNj6IZQrAkqhAjSDdoX6JcYEPOrp9wkI9411LnvnI+0A+Yvsty3zjO9sxYkgUz2M2jXnG\n27/M0eoWvv7Ol2HMLWQ6aQRfoN0JrJ1OhzPPPJOdO3fytre/ixc/7zm8593v50Mf+hBvfNNr9inI\n5NbUDfZmZtp3Mcbw2Mc+lvF4zPz8PB/72McOiOpiPcm6ANa8GGOyHMqVUYiE3AoSUyekFcqtkh6M\nCmUQMdisBCnlrLFCrb41qyy8MnaMVApkHLm/wT1OzyVcAowJ/T1pm2lxWZD+7d/+jbww3HjzLTzy\nkY9cAaxtA0d1QqtSSCkgF41J6tiARZShhJYCbTIsOdYpwCo/zDBGXQqBtJKYpNbfKslTnvwErr5l\nB6ozxwc/8Eme/OTHMNPrkCQJWZZVZfN+wJQJXjp2xB+9/et87sohMEaaDjZwgQrbw3g9aDfmU9+8\nhKg/QzHYzaMffH+u/N4vG+0gSo+D0KNBW0NkAGnpSEGhLZvTJb5+/kvYc8vNDLOYbYdt4tCDp3jo\nyUciVMyUEvRm57nhpgV+fMW1XPCty/nxL65lmM2QRjPs7hWMlIV8QBxNVe0lhLN4hyDmvTd88Aa4\nxEDWWqqM/uX/kxlVrQcWpm6bD1/4HaSKefaj7scF37mUPopxmdXKxJKr063c4xnn8s2/fxlyeRdF\nHGFyFzgTIzHSEMcSXWhmZmZ42lN+h89c8GXOevTDee6LXsDn/+UzfP/i76EBWSbA2ZfkK6vNF2+c\nrCdBEM02gdU3JWT7zecopfj0pz+NUorZ2dkqx8L+LAi1B0ijJqteK1XzCHjviXRbXCfXknUBrKvJ\nWnrGioUlEUjZYJ0V+1zlHrBEapV79oOxevHbiyzLuN/97keaFeR6si6x/bwwoYsx+x8O27bW+8/C\nZ3im86ynnc3Nu5cYZIb3n/c+zn7CY5mecnlWR6MRxpgqUbQ3onjAefA57+P65M4U40WItyBVtmqZ\nKoYzGvCb978bcx1NZu+ETvfNBzMyLleCSQz9NOOVT7wPD7lnwvLygEh1yIRGSoH2AKY1YzTjxUWS\nDtz/bkdw1+MOYm6qiyk0UueYRJHLhJ0jxWn/93NVOY1pBg9ADSir6VRhsifCWu0hhOCFT3wQO27Z\nxY7dQ+a7W8jS3dU1wljyjmKX2MS9nvNPPP4kyeuf+WBS67wDsrR+Xuheduqpp/Ks576Qt7/lXD7y\nic9QDEbc5YRj9rls/xUyaUw/4QlPYDAYkOc5F1100a36Xd8/B9IecaBknZWo1juqCCKpKou+Fysk\nVkg0CitjlJRESiEihY4kyNpCPElEqQuypf4xNNSsuK5M3LDS/86JzovKkgs1A/rGN77BRRddVIXp\nhqoJr1+q9MFRXJUh1BFbssbEjkotoS5wyT2sxrbUEO2ILGs1QmriJOI5z3o6Ny2OGGjB+R/9LM9+\n5u8wP92lG9XbfO/qY61FC8it4fBDtvCAF57HNck2xHgEyRSIcaXr9cyhDh11LFQYSyxyeiLlMz+8\niS9++z+rOPmmJ4R7SWtROGNPX+WMzJjDi4LvvPsZ/NYp2+j1DkbqHKlcuG6EJSYiso7tFzauQipT\nbUiimMEoZ2hgiYjR4GZ+dXXGKa/8VMMgF+rjm23XPOUA6XZNxmiMCUC1/NwKKn9P/3JJS6CfWO5w\n2Ayfu+hHfPDrl9KZ7ZCrpkFRoJBGIqMp8q7hI5eM+P/e/k3oJNjBIoWw2LxAlX7VWoJRgs50nz8/\n9894/RvPJSsUotPj+z+4hCSKq6Qk+y/Our6aATAScmXUl7H7nJpw8+bNfOELX+CrX/0q3/72t2+V\nzrM9jsLXpGvD3VzYv7eXrAtgbTvk1lvj1ZNBt1lfCEz7+ry2XnZf9TxtB//w/yiKOPTQQznttNPW\nHDCrPf/WdnbbC8EzrizLeN3Lz2FhMCKzkquvv4FHP/rRaLNcsVl/rdfDhi5NBz/idezsbCYeR+Qt\nlrpa33jxrO6wvuWMB52KSZfXbA/f512lObov+Npbn44d3Vz5Jbef6yeHT/riv2uoJco+ecff/4IH\nvfcLbDadfWzRlWHJQAOQ/WtvwJBlGVdffTVn3vcu/N6j7sevLvsRYvirvTy94OKf38BJz/xzmD0S\nqNVGXvz7Xq/Hq1/9al7zxjejUUxtOoh73vOe65LJAQyHQx760IeyefNmzjjjDA477LB1W9ZbK+L2\n8uPaH+kfvNW61HhuwCbdDt1ul87UDHGv786ZKp23ffBAljnWhHFp/rrdbmU5jTvdagAWRVG5Zo3H\nwwpIYhW5c6sS9ywRR40JGitFIhQ9FdGVeTWI+/0+nW6fbreLUi7D02B5kYWFBZaWFhgOhywuDUhz\n99ypXp9+v0+/78rXLZNjd3s95ufn2bT5UObm5kmShDQdMxztYWnPAnsWbqEYlZmSOjEyngKrWFjc\nxcLCAoPBEiLXdLru7K1+4hmnm+RR1GHL5oN46zvfgUqmuGHnAt/94c857rjjSDpOV6tMcEij8fdF\nZFlGMrWJ4856Axx2NLEVFLYE3iAJuWglS7GiGdrpdMAFUmcYF5oGvV4JfqWRsmRVSkJiBR0h+NDz\nTudux44YZDNoDUniz7Wq/XO9H+QkVmWMwSq3SGzudrjbSz/F7iIi7mRYneBj3v1OqGIvPhJKyebn\nVX2agA1rb0ONMUQmp5O5BcVgme1Ms5gOneonTRn0NjXUDj65ScWwsgLVifn6q3+T+dmkUQ6/aJC7\nRWA0GvHa176WN5/7JjZNd9j+85/w0DPOXJXJeanrUC4QrSPg24tnyPK904lPHj/Z6t70Z63qWi6m\n7iwxlxynLVW5ZR3rv5qsltRbKWeo3rRlni2b59i6dSsHHXQQU1NTwS7Nt8HaKp53v+v9+8S+1sUy\nYcqTSokVIokqp2srRGVEqdw6WoxBRgoZeYf/GAJnc2gmJLG2eRJoeysBK41LITttD84q5NYY5/Bv\nQGuLUK7MLs+qwZiCSCmkcCcIJFKgpKzcpSqfUem20cqAMiuZdWPLQ4QVTuGhgnFcsSylOOuJv02u\nBb+47mZu3JURxZq8WCYCRNH0TPDMb6nbY3rKcuKz3wuHH4HQgsKAlQod5KRtgyq4Ae3UAAZhjHOf\nERITdRHdLqLfR4H7rnIwlyQyQudD5rIZ/vEPfp1j79xnVGxCCEUUKbR2apjqtAcZIWV9MKCUsooO\ny6PU1UmM2D3sccJz/4lMRUQRFaj6bXo1/nwAinDBJOFYmBS4Ee6WwnFS/6ALZFFCYlWHUX+Le3U3\nc5NIGHfmGUQzDKe3rvQmUKoy/gHITowFTnv1J7hiD0zZvBovHRVBEeQjjhWv+ePX839f/Tpu3jHg\nqGPuyif/6R+xppgArm6776rQdKwPnfCdi2NzzoV13htot5/lMmGVGruy7q4+qlGGqixr7CT3dZdp\njFmh1gt3mU40bVD1ARK3RtYFsIYTvK0K8LJaB661jQ+BuD0xQkCZNFj2R9rgu7cghQOx9d+bFEXB\nA057MLsHQ5Ik4eKLL+aoo45iampqBRPzOl+tNcdYzZkv/hgi6UGh9nnw3lpR5Cibcyetueyjj+aE\nbUmVEyKUScaicCvuyxjrGQbZkE/9y5U85I2fZjw3vdfwT3k7WYa9+OQ6+yPtdhfxFE9/28W848u/\nqL73/RjOm06nw+tf/3r+8p3vYWmYcpeTTkVru2IhuDWy70C6IesCWI0U7iUirIxBJRgREW4hQvDy\n0tZPhoMxVFKHgNdmq6GO1pSss95ylvdUIZqydHpyr5rRuPc1c20ezQGgdY4Q4cpvUbLTAHaFS07i\njr+mG9QAACAASURBVJip3VmkdglIGvUXRdU+IUBPTc2SJD0+8enPsGNXirGWT//z1zjz9FOY7vZQ\ngeuLB1NXPs18N+FOz/g7LieiMM6gsqKvvF5ZN/vBtZfB5a91Rqxqu2xBWmegMsqFjWpg2hbMqIjf\nPWUrF7zjcVx/wxKZ6dQeGhVTsnhjStin4ftefwZpod+Hp7/+M7zxy1c7T/1cl8DpyhSGaHoDjP+N\n1XTv4Vjzbjqh4asqSxAaK6ULcdXWrLABhEzIQHVEuhszrg2tFM5Q5kNmo5jlbAfv+pef8Rcf/w96\nwqI6PbRQ5ORo6RhXFAlUN+H3X/g8XvO6P2Yhzbl2xy6OO/5OJHF/zXnoZYUh1IeQluexeZWJ2znt\n3X8mbKfwd1djwo2yrMEa1zJWtQ254c6grZJYbaFYPXR277IugDVcdVdjkqtFTEzalt2We/anvLf1\nN8K6thmkf+3t99vtNBwOef3rX8+eQcZYLPKTn9/Eb5x+b4Sg4aXgmV54csLdnvM3LPQ6cDtmppOF\nQRaGns1IRrv5yMsfyYse9WvoIqYwSw2DoJeQka42YQbpIjNbtnDsE9/BT8bz5MKSy7UnbZWf9naS\nEHxvzb0EfS+lRNqEIkr48Pd28sK//pcV4zBUXUxPT/PKV76SN537FvYsjfjXr13MYLRwm+rzXxW1\ndHtJe0zdnnVZF8DqnPZrl5ZwmxPmVXWf1yGtbRWC1+GEjeYnXqhiCAe8Fu7VdqWatBqGjMOnUQuv\nB4nvq5VqiJLVls+zhSYvGUnwBKenFVQJm6F0rQnCPD1jmzSxRuOCQ7cdw+7FBSxbGKV76Pd7DcbV\nrl/GMpf+8mZGM3dGa4GI3JlYCIMRjmUiypepw0Ot0VijnY7SunO2RGDcqvWogJEIq1AGEiNJFnbx\nb+95BkfNG1LZQUpBpPrVbqG5qFLqWeu2Dj0XdDpEqx7HPfFtFFuPJIrLkxe0pbDuVfn62uY2O1Ql\nNHTYq7GYCSocKaVLXC5qhqW9cS5cEErdayiRVEgERpuS+gko200Gp++6gEyNQKH1iC9eOebZf/JR\nTDEijrsYlwkGY+qyTU9P86Lnvpirr72eq26+mSuvua5Ss+wLKfDtoLV7udMz2pFdew+fbdsvVtPV\nriX7wh5XSyO4mprOG6tvj8V1XQCrl7WY4G1hrAd6dbo1jLVdltuq81rt/gv+5YvcsryAlglf+9rX\nuOtd71qpObz4geytz9+9VPO0d/874/GeW12evYpIQaT0ZMZLTz+M73/g98h11jAojsfj6mDFUNr6\ndr/g+ro8+y1f4dQ/uABz2LZ9Kko7sfi+ZqjfV9mfvvW7CKnUPut6/bUXXVPwR+/+FlAv+tWZbSWQ\nzMxGDAc5V11/E4PMMB6POfjgg29dxTZkn2VdAOskXZYQtZP/JBbR1oVB7TYSXhtOwHCLWaUSrFbl\n1Y+5CFdcKSVSREgRVUy7ZiaGyB+lHKyC7W0+4M51svnESSgK0KUVs6ELDuvsV2eco3iRG+73gFNJ\nC8vScs41V+3ilPvdnQhTRZi129sUGYU2PPuNH2U02om1AmvcqzwT1dFN3y7GgGe9wq5QgIlKL1rq\nWsuXMQYtBNNa8rqz78mzHn0MVhsSG1VtL4TLnj8YDCqgDY0+oT7Y98fBm+Z57MvewSXFHEViyYdZ\nUEfZYM9hX1ZjpnRq94vMJEbl262th612UdaCMc2zUid4kDQMUQH7ajNlpHApCIVLt+iDLcLfqPoo\nSfjyVcuc89efR+RLVdliJDnuVGApOtzlLsfzHxdfjLGKn191DW/7q78ky4ryvLIVTRTsimh8XxOU\nsK2cDjwcCvvKAP29ayW8XouprqXmWUvCuT6pr7xUIfC3QtYFsHppM8F9WfXb27JQ2s777e/2xYo/\nqXyr6Ub3R7xec3/vb9fFl2esc1780nPYsTBAxn0uvfRndLvd6sibiVsvm3GvJ70TZmKMnG7UccKD\nGzq//ZFEFBy6NOQ/3vUknvyQe2HkQSuMidW1SfL/yHvveNuK8v7/PbPaLqefe26h9yoCimJXYgVE\nBQOIDQMqFuwx8WtMMTH6M2rsYlA0iqJGQQmoMWAvICqiIlKVcum3nbLLKjPz+2PWrD17n33uPbeA\nV/O8XvM656yzyqxZM898nk673a764X8jh8acm9suz/93bsl3J1ZtcmFQQb9xa9h33RoJxk+W7mhw\ngx/0sHggaSlU/T/XL/D+i28A7Ps5FK61plarURQFJ576At7+rvdAkHD0448hjkNbp2wHk1Ot/F+n\nnYKxDqLVStdq+pHCsMXoT3ybxLkfAQwzgPklX4ZVGjDG+pIiFDZ9YPnT9HxhpZSEMgLjrP29Se8v\nPh9pgce4hEbKGCkCmx1KSjKVorN+hm+MQRBhjEAZU4azBoAEUeASUV/27e+yca7LxvkFLvza13ny\nU49epIN2Y+EQ4bd/tUC29ySyPlmVjR4cB6mxzQik6nNu7DVtEAgEpmwWSdcbo9SjBg8fSbniUy8h\nNKrMIZpijMAlzXY+qs4DYGRkpAoGcagINGnWQmcdrrkp5bC3XoJpriYTgkwFiMJ6T7gSL8N0ptU3\n8XRxvrQy2Abnni8pGKVs1Vwp0SxGae6791mgvVDXyivA6D6r96Ce0CFXV5qn7xlS2pZIPnfV7Vx7\nT5exolttSGEY0ikyRBwSJyFvfNPrefe/fYRumrOp3SXLnFub1ZO6cPEtGd2MGOaRY/1Tl2ewk31t\nW5CnkxB9b5Gl79NLJON/zy2pf2yOvW3Tv+40SVj8hCibQ6GOnJ5yUDE9aCX3yV80LrmIKuOepQy3\nOCkG+6eX+DCb27F90X7QG2JLVuphSCyOYw466CAUcPd9G1CqyQnPOo4wsiKjzxQcucKLZ3/4RzQm\n9qDL+iWfuT2UL6zjoy95GscdEdEuCuI4XuTp4BCfMaYsamgRTxAEjI6OlgvfMqfpiXFe+k9f4get\nOiO6SSfURGUp68oY6RktMc5ytuOpQmbeBr+cOeuu3RZa0sBjrF769HddwtWfPAuz6X6ixGaMqtVq\n5HmOMXbDOv30F3Ddb2+hKAraWc7ESHOb+tKbt/bvQQDxQJOd1w/a47aadgrEOgyJGmdl9XaYQeTh\no09dKLR2CK5HdjLb6CcfnarcXdNDsdVzjU3FJwihDHVzi0ZKWSE+d8zXBfq+cT7CdRQYm1AlwqKN\nIAiIZERAmd0q0BQICkTlQeAWsdYapTO0yVE6BSSt+Tne98GPcv/sPGNTk/zP5d8mqWu0WtxfN7aj\nYzEnve1S5Kgi1ff0659Lb4C+7+DPYIdSoQ94GDzU38nRc7P8/jMv5rhH78WCaZQFINPFOkV06ePr\nNpuw+l9RFNWG06wnHHDSB/ifWzuk7YJukIHS1sPCmKowYYVaVOkBUOooKxSqDcb7Vj6zc2PkkKb0\nqstWPpyl56Yp0aojhxL95ktZdrhKBFTdi0XVYJciLcSSoEGgUEKSR3WOOvPDjDQjRKEJNBWACAKb\nj3Zq1Qw33nwTIyNjrN8wzyOOfqT/lLIN+mkPPM8MBw/ba13fmuuH9Wtp7wINLOYfD6QKZ6dgrI4G\nDQSOfIboJqpjqFk3tTWzSuZqlF50rv+7UwG4/AEmLxBqiPjtFiUFgYzALEbSlahXMiODQukc9GJD\nmFPUu2AIEYfgMhyV14fGVnN1AQKVusNkSK1QOgNdgC4IrTzG3gcfRAFsmtvEPffOc/wJf0GnXQy4\nqlkXNSklURTz1nOv5o5cY0SE9oQWZ7ASBAijkCzWLQplm9RUTNaG4AoINJGAZ+6puflTL2F+XYu5\nhfXV4vaZTm8jgiCI+p6hdUEYSpokJKMBN982y76v/CR61W7U6iOYUFLoAoFGlBVJhXZVXMF4uT3d\nPZ3rFwBS9i0uibBuUGU130BYNZRSGl0U6KJXN8lISeFJPRVz1gZMaNsSNJgBa3ChD57ri9xgAy61\nJ/JW1+MwfU4aN9j37AuImk0ykfatKScVPv1Zz+S1b/471nU0X/yvb5AkSfk9+l2nlhKt/XVQje9W\nMamljVVbomEBA75L5VL9HWZLWVIC2AG0UzHWYQxx0B908JzBY4PnDhOfB40P/vUPNg2bkG4RuNaH\nWEt07EI+P/uZC5hvdzEq5qc/vYookoyM1Ctm1qd/ikKmxkf4/PdupNFYXhTO8kiASElywbmnP5yP\nvO0vyXW77wzXbx/FV+qYkkE5ZO8Wybo45x/f+01O/vDl1Md3rRZIkGtiEfYxJf+b+8l0HG2Ny90w\nZleNf5YTCrnonD/G3FmKjA55/39eSj3sSUqu/1Fk0yu++c1v5rLLLkNHmjvuuOMBS/j8YJCvCtpZ\naKdirIMGqUGR0e1yPqOxBiaF0PkixjoMEQwTbwbFUyEEgYGgzBRVoSsERvTispGGQIeV246ry+Vo\nkRP5wM4ZmH4do0NBRmhkKAjKighChiiTUaRZVWAxVxnjo1NkRcrs/CwbFnKe8MSjqYUBQhXlQrGJ\nLwJZAxNRH4l5yye+g6xLOt2ih7T98THWhUgjUb4rlTHWWONTCPUiQ4kOTRHyuZccyWMO35VuBgG1\nvvd2v/vhww5B+YmbLTXIZcC/fOx7fP+ODlJClq2nXijGgjo1qZFCEAlJrGNEGEMIOgIVFJii64Vh\nCoSRFRjTQnro1WBzqzqx34aUup9+sKaPzJw+NQzDKtTTGAMmt62cD4uSmJTnuuafs4g8dYG/NoDK\noDVILjmNjCPO+cUcGzrT6O4CQvb7tsZxTHOkTqs9T5EHzLUFBxy4N2BzTCwVRurmvRGLVQFbsk8M\np61HrtsSZjoImvxN+IGincJ45esloTeJnIjoztla96ilyDce7Ugads/BY4PqBK1tzlQ/8YirKODG\noVC6Sn/ozqtFMd/4zmVsXOiSjKzg1p9dwaEP2bfvvm4nL7Qt/9HNx/jUd2/dYe8bpDlFPMpfzBRc\n8K8vYK69iaLIEUSVftR/Z6c3dQzVRxpusyyKgkK0aeRNzj37L9Bhg5YpuOOejfzh5o3ctvY+fnbj\nPdzw+zuYDeukIqJWG0MZjQICHZMHGqEVBkNW6kmlcuOfo8QyHfELXRVjrPSknghcFEWf1dh483KY\np8Cg8Wp7Ue7m7pPkBcf8yxe58t9fRDJ7BzqoLbr2uOOO42/f+g+88x1/w3d/8CtmpmqMT0zQbrcX\n3e/PhbZGctke2ikYq0NqTjwMhAQ5nIEuOmYC24K4z1Hf3rcX/upfd9A++3Hz7bdWlQSUsPlXh/UL\n/FDQojRmBUAwVGTsMe3hx93vPSt2RpYJqyM23RL5SAIZYXTPJSzPc4oyUklrzbq5edpZyuzsLDff\ntp6DD9mHOAkRFdTobVZJXFCP6zzupe8l12NVus1Kz0h1wBplsElToIfZND2Ry2iNrEfUkilOeWid\nf3jxY9m0sA5BUpU5GRwb931dZJVjpE4tYIwiDK37VUyI1gXruiCl1ZeuGotZc9QaHnPUGl5oDicI\nAtpZShzHdGVIninWbdL85JqbuOF3d/Dr2+5lfSpRRUCnHlAENYIwxpgOKCiEQhQaYSQ6BJ13iMKa\n3YgKjRI9jwJnyLJzyFQDIgC8sQzKgAQtLJM1WOOUFGLoJrIcGsY05aJDZT9dQI2GNE6QaZennPlu\nvveB52Hz9Npqwr6UdPbrX8EdazeQTqZsnM9YNV1Dyrh8x166TidlVLXBBnK2PtAIsHpT0T+/l3dN\ngNF+Up8HXm2wUzBWH7HaCKNS3I6Wl4Rkcy5Ww+jpxzyZ35//6a0e4G0JZfV1nL4oDPa9u90uOrQZ\n8m0C76zPOKeUIs2se1SapuR5ThiGvOud76HdtsaJ2267jd12n7F9KmPqHRMrioLWgmYhivl9p0YY\nD6/ltFXjEASo++/lgnc+nyNWQ7ujgYghgU7VGMRxXLpTeVm7PHe5HuPq15n5v7tz81CiVUYtisiy\nDJRB1iPWTGme9/QDMU87gDC0uVoboaKeTGG0YlNH8ZOf3s1Vf7iHm39/N7/fMM/6bgeVAs0ZAmOz\nz+gApDAIHXrRZFtGl9W4lihXbOW83KFUqq82yhl+fW/A4bvKvo3MIbc4jvnQOZ/mXe/4e+bn562K\nI4rYY489uPnGmyoJw68s8X+Vtmbd7BSMFcqdWRuMASXL6olaYYxdiMNCTqUsq7OGQR8DG0QERkDR\nTcmNJkkSNrTnGRkZodPpbJYpC2HTjTiG6PRqSxmcKkQibYJB4R0P4wgRSOJaggwDtBFkRQfmBd3A\nMtJu2zLWPM/tT1XQ7XbpdDqkaUq73SWdb2GigOee9nz+cOtatKlz1JEHExhDoCUG1a+DBqZizT5n\nvh/RnKkWl30HH48CXsnxwRGp9KtS0uim/PqLryXtdmgP6B6dW49dvM79LagWZs9DoD8RiFLKlgDX\num8s/fnh+hZqm+xGGVdZVZHPt6k3m+jUum7pQhCKmDQ15HlZo0trHnvUDE84ejVF8RAkZWG+tEu9\nNkERGHIT885PfI1LvnkL6S570pUbCVSMFjXapjcHHWpUWoEQSCN6GlmlESxG7UZrhFyiSKEDwsOm\noun3x+3ds389+KoIjEIjCKKcF73jQn75qVfTnbuNpD7SNwdqtQZvfO2r+Od/ejdvfetrmW+n7L3n\nHvzhD7dw2mmn8ZnPfGYIytMV8vUPCyEq1PxgkpNKxRIqnmqNl8pZ34i6FGn67SXL8VPue+ayz9xJ\naZij/SCNJnX+9o1/zXi9SU2GfOc73+EpT3kKSbL8+kf+swb1pI7844Po1ulN/QxbRVHQ6c4yNzfH\n/Px8VXam2+3S7XZZWFhgYWGBdrtNq9Wy/1c5J51yMhs3zRHIhKuvvpqkLPcyaKSrVBir90BO7rmo\nv65SwXKosurLgvl8jvnOhqpksS9i5XneF98/iDx9H2B/DB1DHvRnHjb+w9DT2NiYRa8l+QvH9dM9\nz4X4FoGgaxRFs8a6Yp5ukdIu5nj9ix7PTy9+OXdecCqvf9x+TKXzxN02cU0QaVtBdofQg+BJIMJR\ngpEaj3zxhxmfXNFnvHXSQ60heNrTnsFdd97P9Tf8nv33359Ou8vq1aup1+sPeB//GLQ1qoutZaqw\nkzHWYb57PvmeApVlnvL8YLF4HghJnmbMzs9z2WWX8aqzXsELnncaa2+/gwN23wud9RzTXViqa7r0\nMrAP0LhKJP4g20TQPZ/JCjET9FlW3flhGGK0QBWWqaZpSqvVYX5+3taxas/T7izQ6bZotefpdFq0\n2wsVYu2kOZmS/P3b30EURcRRk3ptrAwlVEhpLcPW6CfRIqcwXf7fBy+jm7X6dHzG2AQTto/WZN5z\nDV9MFSPMDbIxwSve/hWagUDHPcOUzzAteo0QIuj7bkrl+GGIxthUiVpbxOGMdv0GTWn1ZGXIsvsO\nFmHbv7vdLkII0jQnTfPqO1XZozz9diXaFhCagKyVI5XdFMgyhIHWQsbtd6/j9OMO5jsfO5Mf/ccp\n7NMqECMxIypAYWwpHeMSlPsJ1JcI+Br0SLHuB2BMldC6SrVYBjYI3fOprtZJdQ8bfWRKNF7+s0S4\nAYiAomijdEBek8zpGBmoai5WBk5TY489V3HLLbewevVqvnrRJUxOjS2J6twa7Tv2AG0Sgx4KS9k1\nfM+eQRoM/vG9VBY9r2S4vjfHthi5dyrG6mhr9JjDznUM9yWnvYA3vOps/vo1r+OaG67jvR/+INfd\nchNves3r0FrTbDa3yzvALd5hze/foGju4uAXFhaYnZ1ldna2QqdOl5qmaZ9awKGxXXbZhVarTWsh\n54brf89DHrovWSqqoAdf5N4412ZsagXnX34z4WKLx3bR1XMNjnvFv5Go4bv+MF9SoNLx+X8Pjtug\nD7OjYf7I7jofCcdxXOmnfTc2dw83RkVRVDrrYSSlRMkGaaAp9Dzf+9SLufbfjuaRq2qsCAoCvQXv\nEsfkdjBVG9nWIKk84PjXf4JGspJaLKoxyLKMKIqYnJxk5R4rueqX17Ewn/Lba2/iuuuu44wzztjh\n/f+/QDsNY/WNO0ibMtD3X3U7jfs5aBRyO4wQApUXrJ5awU2/v4WPfuoTrN+0kde88lWIQPLDq67g\nw+edSyEMz3jyU6tJWpEUyCX8B3uiaIZSGVIIcjQyiAijhCiJkWFQif2D+lutNWnWsYi0Nc/s7EZb\n2bU1T7fTqtypqoQkgPTGpdvt8l8XXwgiQMuMO+9dSxBIRsdqJEmdIIhQKqcoMqSEFaMzfO1//0DQ\nsEhv0AfQ6qQkRghbHsQvKLeU1IBEI0kzze8mH86pb7oAmW5CqX7DkxPrB1UAQgQUhf8cG2nlM82e\nb6v1EvD164Pfw/92YRiSJEl5fUSa5n3zpScCRyhlCAJBFAUe2u9X2YBEKIVZ6BK2IzZunEV3mnzk\nbY/nyveczD5iA6IQmDglsFrVsgnbnL+qYWizaLRsA6G3w76Df44QojKsuWQsGANSDs3rKho15k2D\nt3/uBzSjBK1zsqxDLYQNs/cRB5qHHfxQzvvM55jXBTfdfjuHHHIISZIsqj82TJe54wIM+n1bt8Zv\ndak5a3W/OaroDyza1vsth3Yaxgo95rqUDnNLFAQBY2NjvOrlZ3HCscdx5c9/hkwiPvP5zyELzapV\nqwiCgLAW8773vY999tlnWX1yNBi4MFjqZLC20eC1eZ7T7XZptVoVQnW6U2fxdwYgdx9fLzs+Pk6z\n2WTTpk1s2thm//33rhCYY0r1er1Ku4dYx9+++8JF77SUL9/WegvUN67j6o0Zt2ysEdWGV/EcRKN+\ndNUgsl/qmzsr9mDC62FJP/yxCMOwMqT51Ol0+qK+HDldrB/l5r5DHMfV/bNuyKwR/PADJ/Dix+7O\nxPp86ALckb7STtw3W/mNBulLP7iKjrAlfGZnZ1m9ciWEMbPtLkWry4tOOY3b7r6XWnOc73//+xWi\nfaBE/Z2NlusBsiXaqRirI98taUs7lkMiKs+JjeA5xx7Pxz/5CX505RWc/bKz0J0MEUg++ulP8pxn\nnsA+q3bFKIgbdbRXFM5HNT5jFEIQyMi2IEHpnCzv0um2KIqMPPN0iyJEEFRo2zFfJ6Z3Oh3a7Tbz\n8/OVwcqJ/FprQhlUpTriMCJJEtuimDiMaLVaxGGMLgzIcVasmfEWtCYIRJXRqCg0190ZEu82CkWG\nkXGlr+rpQQu0LirU5DOBSh9VNnes0sHKkDSuIeoTHPf2r3Ph/15fvaePUP3UjE5FYb9bQJ73mFqP\nmZlSLwtaF/jRdu5eg8ZKnxn6or5DnfPzrUrtkmUZYRhSq9VwqQudftc1pyP2DZG5sQlfpIiRgaFR\npGxIx/iH5x/CLz97MseuCKgZRUCBS/SthSkbA80eN9LmHjCb8TJx9xJS9JDpANnPIyqdrdbaU0OU\nCVWwJX6KPOA5b/5P1oxPsHrchjWHMqIwsL7ocvjDDuXrF32NWmOEr37zW6zbsJ7Vq1cPXaP+uvH7\nvT2byaA0uqXjyyYTVomCfHKAZ1Bi2l7aaRjr4Ifydw7f8uyoUmqXBoSZmRledOpp/O7GG9Bac8Mt\nN3PjLTfz7Kc+wy6UQHDRJZdy/S23gBC89PQzufo314IMylQetvnki4V2sDVFkVVRUFnWrcRYpRRZ\nkZOroqwR1J/js4oqcnlgiwJKcT+OY6Ioqp7jWhQmhIH9n5SSf3nHu9mwoUXXdLj6l1fYBVdNNOkZ\ngCLMxi5f/MZVjE6PcOzDD0B0N5fg2c9qtDSJUmVgKuMXNqCgpvj7L1/DFTdsQMY2wscxQPfNHGN1\nm2YP+QsGUzb21D7OuNWb8IOLy7/v4P8d0hwfH6cWRgQGxkdHF+Xhdf3szTtRqgt65wUGIuHnjbX9\namUBs/OGD7z1mTx+JiUsEurkGJNCoaBQCK0RWoNSSGNcJHF53PQZpvo2C6Wo1Ap2ZIa2oYymtKAJ\nKUHY6r/aaE465lAO2GsFd6ZdCpFw173riIMMZEGrtcDt997NG974Wn5zzbWs27CBl535Uu66666+\n/tmMZP3qGaM1wQBD2h7k5wf3DN7TVBuGo6VMrsPu259z5PDDD2f33Xdnenolhx760KGlgbaFdhrG\nOowGkcgwppC1u6h2l7mFBS65/Fsc/pDDqgVx0X9fzG577sEh+x1AaATr169HlhbhL174ZX581ZXV\nAh+8v1ukvljuwk+dW9Sge5QzPPluSP67OBcrZ8CJoogoiqpkK77YP6w94xnPoNGMqdfGGB0dXXLi\nGmMIGpKvXnE3d966kbVr1z6wolweIpM6L//Yd1g1MV6N36D47sbahbu65utlN4cWfIbmq2TcxuaL\n9b5OtdPp0EaRhaLKCeuT74UwjKSUi4rwDaoyjDGc94/P5T0vOIxcQBQ2lkSYm6MHKuRS6Bx0zm9v\nuosDdtuVT3zpahqqg9aamckpsnaHMAwZGxvjdzf+gUu+8b9kRrLH3gf0SRp/bvTrX/+atWvXsnbt\nWhqNhs149+eqCnDkI7600yXrptUHjuOYF5x8Kq951Vmc+oJTOP4ZT+W2u+/ksu98m5ef/ld24ocB\nH//Mp5hrd8jKWuhxHCOEoNVuk5UigK+X81UBlUoiCkGKSkc6Pz/L3Nwm5ufnK32p/zPPc/JcYSUy\nw6DeL4o8MT+uU08aJFGNMIwJgogqmYYQhIE1hrXzlMZ4k43rM1SesP8+u1MLo0UidhW1FcPxj96L\nA/aYQDZG0Y2lkyw7l6fliFi+y0pPHy7QWoKJ2O+0D2PCCWqjyZL3G2RIvsN6nudoFVAUVrenkohC\nSXIjyLQmV4U1LnqZm5AGHdjm+uQkgwJDLYaZ5hhRrllYWOjLTzuMabj/uWAQIQRJkixSF/nnp6qg\nnUU8/eg9uf7f/4q60QhhRXCnG+0xe9/ItRh9VsaqLTJlh2bL63VBrDQUedVv0+2y33jB2c96JCc/\n7kBuuHuB/77iRvbcZ3fy+t4QRtyx9h4O2m//MlgrYGbVrpz16pfRSTWbOjknnnwKgfRyeZjFHRcS\nXgAAIABJREFUahhEmfJyB9GWVALbQr5UAnatHHbYERxwwMGsWLGCbrcL/JmpArZELzrlebzlDW+q\nXIrGGiNMNEf54MfO5YsXXszU+ASPOvxhrL3rThqNBieddBJCWGZ46623AsNds5ZCrD65WH3HWP1o\nqDRNK91pt9u1Mf1FMRQBO6uzY66u+eqGYU1rzcknnwzYDeXmm2/2cmha8hd8EATcdG/Oxdfczo3r\nWjapxmYmvWMi20tGpiyEExzy8k9i4mY1dluDwOr1Opls8ca3n88XL72KG363lmhEMj0WMllvMIIm\nHGlgullvkWlDpAIi1asm4FpRVi7oFjlRvdYn6g0u0sFvNujaBfQly/HJoV5jDDLZxMde/AgS1SIJ\no21CrttEmea0px0CLtAiz5kIZnn2MY/gvAv/lzUTdZ76sN25PW2SdQUXf/9nBEZjooTrf38rcRxX\n79lsNvned3/M2HiND3/oY5ihIWF/+hSGITfeeCMbNmzYZoY99L477E7bQZW+tPw5zDL3hYu+wivP\neCmPOPJh/Pr661g9s5pzP/VpDHD2mS/nwq9+hec+5yRuuf1W3v+hD3P22WdXuSfDOELqXonjQpYL\nQfQQpe/k7i8wK2LmZey+1a8a3Yu5tox0cXJtx7wrw1ZZL16IgCiMeoaxIBhw8ekteF9v+KznnkKa\nafJigfn5eYJgt6EIyomrb33fRRxzxP4ERpN3Bbfecy3dYLJPN+n30R0bnFxVUpbyb5ecxSGxQMoK\nqUhRQ5ERxwn7Pf/j3HzeqczOQz3sic5ZlvWV4x5kblmWUUvqvP+dr+Pxr/sPZr99OyazjCwyOQBB\nK0Mzzz4rRthzxThPeNRBPPrwg1gzM0U9bBFIKNKMJA5prZsjDEYIZc7U9AypMRR5ComhJgwiCDF5\nf1iu++5OYnH+rv6mXCE1IJTS6krLb5G2BY86Ylfe97yjeetXryfXiiJMEHqQIS/Wj/bmva9XHU5C\nCLRSCJcQJQ4ZCwWnPfkQLrnqdhY6Lc543tP40Fd+QC6ayCDi8H1X8bs7buC9F32bt5/6BJJQIKME\nY2KkLNdLBEpJ7r3nfma7XVqtFFFrIIq00rFKGS7qi79m/bHcNnpgNiI/WMCqeFL222/vEqhk1UY8\nbPPcGhI7gxtFsGaVcQwkDEOSKLaoLrAfzxjDfNZBdzLe8vo3cvHXL+U3t9yMUoq3vPYNfPb881FC\n8ornPx8TSs6/8Cslw8tKhrfYlciFcirZQ5NW3ympBxHNJKIWRtRCG/PujFWOsTodqTUyWD1emmel\nnjWvFmWjltBsNqklFp0iev6tlapB9nKSQj/6KYqChYWccz97IY2xkE47Zf2mLhMTSd9C91UkIyMj\n7PfCz9IRbaQuKLRBhE1MFVs/GKbn5QhYgrG6cibuGreQ/AXkxGoZB2gVsKJYxzWffhlzrbxiUL6h\nyH3bQVIqJ4wjpiam2f+Fn6Alyg3PuYdohQgShFBoA5EU5GEN8haoGLCZpqIoIN+0DtXpUI8F46OC\nkx59BI9/7IHssmKaVZNjjMiMrlqwARimP8TZVyu4v32Dqvtba11FMjkKwxCV5dxxf5uT/v1y2iIY\nivoG3ad6yHY5jFX2MdbQaHIFr37avnzz53/g93MhYb4RoQrOPvGxvP+rV1KjzWue8wQ+9s2r6KQh\nj9g94ONvfE75Xj2jon3vjO9/70qe8sRH8IPLv8Vfv+7sUkdd2FzBwhV6pJIOjerfqLedlsNYezku\nNkdurdWaIZPjY6xcuZKVK1cyNjZGvV7vm4tRFPUyeA2hj5/z6WXB2p1CFeBKYVR6IQ81FlqhjGYk\nqtFsNvnsBZ/nhGOPYySu2Sz4Rc6G7hxhYFCNGh897zyybopRNqTSNkE9CEhEr8koRHh6OufWFfp5\nLErGlqUd8qxLnmUUpV4W6CW4xjGcMvRSBH0Myk5U6/ojhaic/n0m5dOgDm//gw+lOR7YxS8TGg1Z\nIWY3iZ2zuwoEv7t9gVR30BoKQhAJwuhevSUcoqDUA1sH+eH611J6kBKk7PMKqL4Vdoord15hkKZg\nUzLD4c9/LyP1xX6rvq520MgXBBFGwX1zG/nAWU+AAOK4hpSRbWV6PwiRIiQnQOY5khgRS0QcYKQh\nUwVmdIJg1S6kEyu5L5jhnJ+u5fQPfI+nvu1CDn3dl9ntjPM44IzzOfXvvsL1d8wzVotRIiDVssow\n5ksG/vfxv5Pvt+yyisX1GvvtOc0jxiK0EbaMjC6W6Y/q9K7ekeo6K95VblhlX5IwQArNRy+9hhMf\nsy+j+SYKLXnUQ/fjnvXzoCMacY371s+x76pVaB3w09/eX86f/mTjVpIruOqqK2iMjHPs8c9iYmJi\nEdN0TGtwzrox2XoabuEfJsVu/Z17vMVHrk5iDMOwb8PcvmftBLQ5hbRJc55+zJN59UtfzunPez73\nz27kl7/9Da97xauoyZC77r6bt7zmDbzyjJdy0UUXUavViOOYOI6pB5FFn1FCM0oYSeqMJHUaUUKC\nJDaCmgypyV6klBtgR376Pljs9O7rTQcTVMNivZ1/nZuUW6JnnvQcgkaNLMu4/fbbK72sc2B3VBQF\nzcDwr+f+F0W3s+g+D5TFeSkKTc6m5h4893Xn9Rmplq3L0vCYQ6Z406NW003zZV2yJNMq61FJEZMV\nkBWgFu6zRrD6CL/pjHLKh77HHq+8kFe++79o1MerjWtznim+0c2RS4/oIukueN+p7NG6e/g83wF6\nPV3ukJHpsFKvQyQRv7h5nlefeAQIwaZNm9h3l2lW1rq8/FlH8d8/v4vr7u1AGMKqldVccvPRzeNm\ns8kLX/hC7rzzThYWFrbotfHnRNvLWHcKVUBzzz2si3OJ9MKwDG0tFGe/9Cx+fOUVXH/LTZx64nPZ\nNDvL5d//Lg8/8mEcvP8BXHTJpdSSxOq4SgQqXU5SUWapl4ZQWWQVGEhVQa6sgj8vkbIOelbgMAyJ\ngUgYhCrI8nblh+oYYhRFRIE9v1CmdPQvaLVatLudKuKnUUtoNBrU63Wbhaq8v/QQgqAf8fguRXme\n8zf/+gH23WsNndTwy1/cwEMeuhdRlJRIRVULu1arkesuhzzvC4ipmjc5ZBX+6FOVmBm3Sy+9yAd1\nsz57qQIIBvSxWlu0F0QJR9bv4fN/dyJFfboPvfp+pMPmopQSkyl+8vv7OPODP6LRjOnk/fqvQWYt\npdyssUWXHZSm9ATxVE4AkS7QAuINba694AzSTXeTTKyxiXOUfXZges8a7IuL6NKliB6jkNEIj3r1\nf7K+iCCJqmsrRi3E5vMKCNGTfAciZgIMr3zGYdy1bgM6bLDXqgk+8NVf8Jg9A5766MN5+wU/Yo9x\nzVF77cVXf3EzUa1BocscFihWzN/HN9/3QoJG025+5X2LMgvYOeecx9/+9VnUDBxx4D50VF7VYvNf\nXymFyvUiVLst5ObiUjpop+u1tDxVQFwLmJ4aZ3p6mtWrVzMxMVFlhhu2YfiGUEd/WqqAgVBQR2Nj\nY6RFzq9v+B1PeMITmJiYYK9dduOgfffnp7/4OWsmpjl0/wOJ6jVWrFjB9Mg40yPjTI6OMTk6xnhz\nxLbGCJOj9vjUyBgrxibs72Pj1TkrRieYbIxWrV6vE8dxn8V+0KI/6Ge6XAS6tVSr1RgdHSVNU1at\nWtVn2Q7DkE7HolNjDFFtHOord3gftoUqEVF3+fmGSZ70ivdV/xuMyPL9WR1VkVSJ5smH7MrDpjI6\neXeb+7PUQh9EubmIkDqiM55y8Ou+yes+cBHdbrfPC8NP/+juOyitOMqShETC6084nKi2REx96bK0\nmc4vObee++h9yRbmuOhXc1x57U2MmHl2Gcn4yS1tFgrN0Yfsyj3zo1z027WE9VEy5ampBNwfH8z4\n2Ngijxk3/x9y2MGkHUh1wUK27eP/p0bboxLYKRhriCBEEAgQRtuE10rTabX5+HmfoC5DHrLPPnz0\nnA/zjf/9Ok98/GPYe/XufPmSS+h0UvaY2YWpsSkmJyeZGZtg5cQUM+OTrJiYZOXUNCsnVjA1tYJV\n0yuYnlnBzOQUKyZsWzM9w8zMDNOTk6yYsvdoNpuM1EdoJA2bni9MyhYRhxaphtJjpmVzJa4lgkDI\nHiIcEnjgaNhS8i3TRgqSULBx40aUyjGhgjB2Kk+63W7lEwswNx8RyPvthChTxzGgx4QSFWNTB/q6\n10V9oac/dTrUHnDqhbv6f/tRVUIE5EZiSFm34lEc9eRXI4Jun47Suaf5yNVHsKKAjsq55N9eyC6m\njckVCf1uUxUZiVYsuk+fPlRTlu+WYGT1t1SmlHY0udRIsQLJLD+a3Z1DT38Xo0FPJ+4ij7QUKGHR\nuWO8Lt9rhczTgoUi4wUn7MUanQCaSA4EJSwDsWqjBrPogDFcfeNdnHvZtRw2lXHmkw7mPV/4Hmc8\n7Uim6or3XfAjVo+NkgkFJibXwkYbColCIJQgjtfytavvqlwKw8CmlHHf8ZgnPon5TpfZds4jHnE0\n6N7798bdlGtg+WVnNkdLqQeHH+8latocI5RBf5WKwXkx9JqBOblc2ikYK6YAUyCLrK+pvEsQwF88\n+YlceMnFKKU4/vjjOf/88+mmbSYmVjA9OcXKFTOsmVnJrtMzrJqcZtXYJKvHp5gZm2TVxDS7TM+w\ny/QMq6ZWMjOxglUrVrHbzBp2m1nDLqvXsNuKVewys4bV06tYNTbNxOgEo7UGcRxTq9UqpJokSR9a\nHdS1+oYM+6FVZWSSwiC8OlR+oOIg0nGTp5HErF83y8T0BLVglNHRCXZZNYHJU8IwJE3TSh/mVBWf\n+/pP0ImNfhqs9Onf37mMgUUtIhg+FQZNCVU2JmMqQ1alf4ShRfSENpgoIS1y1KGP4lEn/QsmFdTj\nRp8Bz3dx8n1rXS7W2VTx3Y+cybG7auJkzhZFTdO+GlCDxrDBhbicxeEYrTGGNJPWcXxyd/Z6xeeY\nF+OkLU1uNAU9tCpEUCFw1+9YBrbar7HgoZs2+fgbDmUkgHzd3fRlcRocM8do/XcZ9Ic1BoTg5vVd\nqI/xpIfvx3suvg4zuT+5SnnDKU+BWpOLf35bFeJaNS+LVG5qvPmDl6GynJpsVL7ivpHuu9/9LoFS\nfOWi/0blvUKRFWMStoLCAyW19V7Zz3rXT0szY1OtRZ9B+gUvdzTtFH6sokwjpz0rqAuHDMOQyy+/\nnL96wYtot9vceeedZFnGRBk/X6/XqdfrjMgIoRUiVJhSB6dlGR0U9AxFbqeqEId7YJnJOlUFgcpJ\nBSAUWqVeRqaey9GWPojvi7qtH09KyUFHPoowDOkudCmErkqYtNvtKuOSH0t/+U+vxeQphMuvjtBD\nGNu2ICp96TLOzeoxxYqH8rUrr+U5jzsAGdp38K2xTrweloquKArOe9tp7PrCjxDOjKA6D5aNYBQV\ndHj2W77Mzz50AvOtdNEZm2MqWZaRqoL999+f1e2fcfNMEzYTJWqMqfTiy/UJjUyGylrsOxFw9/qc\nL37jiuW9WgrJiimSqd3JNtyHjO07uIQ+WVpw5513IuMaa+9dv0OCSf7caSdBrMq2EmEJY10jhBAI\nZQ04X7rwK3z7+9/jkksvJU4SpAiJIkEU1qgFNWJPPA/j2LYyW1QU9HxjnceAQ6D1JKEWx9STGvWk\nRiNOqAcRURwQhpZ5Iw0yFJX7lq9A90WKQTccX2fVQ2Y2D6jPeB0N7rid7gLHPfsEuq0F7l1/P3Oz\n3Sr7UxQlVJmLyvyYQRCw9m6NcGXDXbaPweEukQ5VP0O2NBWGOcFU6NW9yxJ3ESKoxG46ITqO+Ncv\n/YrVe66B+ghRvVb136E+ZwDyx9X9b1Zr/vDlNzHV7WBGA7QUi8Z+OaKb76kwrCF0hcqFNIigxmzX\ncPQrv0iUh7TbnUqN5btk+cYb35MgiiLyPOdz7/5LpuWB1opWotKqrx5KXWQEGkCwvfG13/KWDYKz\nnn4gJzzlEXz2J7eSLZGEfBHVBUXR5fyLf0LgPTtJEjqdDkIajjv+GczOr0eIOmmeVc8dNo93DPWL\n9/48WBqo9K4Z9Le2YyQWbdabmyPb4+K1czDWkoaJ1u732dlZ7rrrrj5mVRQFWd6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jQ2t2UsMdoiy9QIBgYGBpYHCY99/UFa8SQt+XSSgabfS4mihLExmwBbKUcOt6GJp01LBloiggQY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c4b47128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Preidcition for randome traffic sign from internet\n",
    "from collections import namedtuple\n",
    "Batch = namedtuple('Batch', ['data'])\n",
    "\n",
    "#load the image , resizes it to 32*32 and converts it to 1*3*32*32 \n",
    "def get_image(url, show=False):\n",
    "    # download and show the image\n",
    "    img =cv2.imread(url)\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    if img is None:\n",
    "         return None\n",
    "    if show:\n",
    "         plt.imshow(img)\n",
    "         plt.axis('off')\n",
    "    # convert into format (batch, RGB, width, height)\n",
    "    img = cv2.resize(img, (32, 32))\n",
    "    img = np.swapaxes(img, 0, 2)\n",
    "    img = np.swapaxes(img, 1, 2) #swaps axis to make it 3*32*32\n",
    "    #plt.imshow(img.transpose(1,2,0))\n",
    "    #plt.axis('off')\n",
    "    img = img[np.newaxis, :] # Add a extra axis to the image so it becomes 1*3*32*32\n",
    "    return img\n",
    "\n",
    "def predict(url):\n",
    "    img = get_image(url, show=True)\n",
    "    # compute the predict probabilities\n",
    "    mod.forward(Batch([mx.nd.array(img)]))\n",
    "    prob = mod.get_outputs()[0].asnumpy()\n",
    "    # print the top-5\n",
    "    prob = np.squeeze(prob)\n",
    "    prob = np.argsort(prob)[::-1]\n",
    "    for i in prob[0:5]:\n",
    "        print('class=%s' %(traffic_labels_dict[i]))\n",
    "\n",
    "predict('traffic-data/turn-left-ahead2.jpg',)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#data_iter = mx.io.NDArrayIter(data=data, label=label, batch_size=30)"
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}