CarND-Traffic-Sign-Classifier-P2/Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "## Deep Learning\n",
    "\n",
    "## Project: Build a Traffic Sign Recognition Classifier\n",
    "\n",
    "In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary. \n",
    "\n",
    "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission. \n",
    "\n",
    "In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) that can be used to guide the writing process. Completing the code template and writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/481/view) for this project.\n",
    "\n",
    "The [rubric](https://review.udacity.com/#!/rubrics/481/view) contains \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the \"stand out suggestions\", you can include the code in this Ipython notebook and also discuss the results in the writeup file.\n",
    "\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "matplotlib.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_IMAGE_DIR = 'GTSRB\\\\Final_Training\\\\Images'\n",
    "\n",
    "dfs = []\n",
    "#open csv file\n",
    "for train_file in glob.glob(os.path.join(TRAIN_IMAGE_DIR, '*/GT-*.csv')):\n",
    "    folder = train_file.split('\\\\')[3]\n",
    "    df = pd.read_csv(train_file, sep=';')\n",
    "    df['Filename'] = df['Filename'].apply(lambda x: os.path.join(TRAIN_IMAGE_DIR, folder, x))\n",
    "    dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>Roi.X1</th>\n",
       "      <th>Roi.Y1</th>\n",
       "      <th>Roi.X2</th>\n",
       "      <th>Roi.Y2</th>\n",
       "      <th>ClassId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GTSRB\\Final_Training\\Images\\00000\\00000_00000.ppm</td>\n",
       "      <td>29</td>\n",
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       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GTSRB\\Final_Training\\Images\\00000\\00000_00001.ppm</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GTSRB\\Final_Training\\Images\\00000\\00000_00002.ppm</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GTSRB\\Final_Training\\Images\\00000\\00000_00003.ppm</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GTSRB\\Final_Training\\Images\\00000\\00000_00004.ppm</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Filename  Width  Height  Roi.X1  \\\n",
       "0  GTSRB\\Final_Training\\Images\\00000\\00000_00000.ppm     29      30       5   \n",
       "1  GTSRB\\Final_Training\\Images\\00000\\00000_00001.ppm     30      30       5   \n",
       "2  GTSRB\\Final_Training\\Images\\00000\\00000_00002.ppm     30      30       5   \n",
       "3  GTSRB\\Final_Training\\Images\\00000\\00000_00003.ppm     31      31       5   \n",
       "4  GTSRB\\Final_Training\\Images\\00000\\00000_00004.ppm     30      32       5   \n",
       "\n",
       "   Roi.Y1  Roi.X2  Roi.Y2  ClassId  \n",
       "0       6      24      25        0  \n",
       "1       5      25      25        0  \n",
       "2       5      25      25        0  \n",
       "3       5      26      26        0  \n",
       "4       6      25      26        0  "
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.concat(dfs, ignore_index=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training images : 39209\n",
      "Number of classes         :    43\n"
     ]
    }
   ],
   "source": [
    "N_CLASSES = np.unique(train_df['ClassId']).size  # keep this for later\n",
    "\n",
    "print(\"Number of training images : {:>5}\".format(train_df.shape[0]))\n",
    "print(\"Number of classes         : {:>5}\".format(N_CLASSES))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_class_distribution(classIDs, title):\n",
    "    \"\"\"\n",
    "    Plot the traffic sign class distribution\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    plt.title('Class ID distribution for {}'.format(title))\n",
    "    plt.hist(classIDs, bins=N_CLASSES)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 319,
       "width": 885
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_class_distribution(train_df['ClassId'], 'Train Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>Roi.X1</th>\n",
       "      <th>Roi.Y1</th>\n",
       "      <th>Roi.X2</th>\n",
       "      <th>Roi.Y2</th>\n",
       "      <th>ClassId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GTSRB\\Final_Test\\Images\\00000.ppm</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GTSRB\\Final_Test\\Images\\00001.ppm</td>\n",
       "      <td>42</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GTSRB\\Final_Test\\Images\\00002.ppm</td>\n",
       "      <td>48</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GTSRB\\Final_Test\\Images\\00003.ppm</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GTSRB\\Final_Test\\Images\\00004.ppm</td>\n",
       "      <td>60</td>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>52</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Filename  Width  Height  Roi.X1  Roi.Y1  Roi.X2  \\\n",
       "0  GTSRB\\Final_Test\\Images\\00000.ppm     53      54       6       5      48   \n",
       "1  GTSRB\\Final_Test\\Images\\00001.ppm     42      45       5       5      36   \n",
       "2  GTSRB\\Final_Test\\Images\\00002.ppm     48      52       6       6      43   \n",
       "3  GTSRB\\Final_Test\\Images\\00003.ppm     27      29       5       5      22   \n",
       "4  GTSRB\\Final_Test\\Images\\00004.ppm     60      57       5       5      55   \n",
       "\n",
       "   Roi.Y2  ClassId  \n",
       "0      49       16  \n",
       "1      40        1  \n",
       "2      47       38  \n",
       "3      24       33  \n",
       "4      52       11  "
      ]
     },
     "execution_count": 755,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TEST_IMAGE_DIR = 'GTSRB\\\\Final_Test\\\\Images'\n",
    "\n",
    "test_df = pd.read_csv(os.path.join(TEST_IMAGE_DIR, 'GT-final_test.csv'), sep=';')\n",
    "test_df['Filename'] = test_df['Filename'].apply(lambda x: os.path.join(TEST_IMAGE_DIR, x))\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 319,
       "width": 879
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_class_distribution(test_df['ClassId'], 'Test Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 396
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_distribution, test_distribution = np.zeros(N_CLASSES), np.zeros(N_CLASSES)\n",
    "for c in range(N_CLASSES):\n",
    "    train_distribution[c] = np.sum(train_df['ClassId'] == c) / train_df.shape[0]\n",
    "    test_distribution[c] = np.sum(test_df['ClassId'] == c) / test_df.shape[0]\n",
    "fig, ax = plt.subplots()\n",
    "col_width = 0.5\n",
    "bar_train = ax.bar(np.arange(N_CLASSES), train_distribution, width=col_width, color='r')\n",
    "bar_test = ax.bar(np.arange(N_CLASSES)+col_width, test_distribution, width=col_width, color='b')\n",
    "ax.set_ylabel('PERCENTAGE OF PRESENCE')\n",
    "ax.set_xlabel('CLASS LABEL')\n",
    "ax.set_title('Classes distribution in traffic-sign dataset')\n",
    "ax.set_xticks(np.arange(0, N_CLASSES, 5)+col_width)\n",
    "ax.set_xticklabels(['{:02d}'.format(c) for c in range(0, N_CLASSES, 5)])\n",
    "ax.legend((bar_train[0], bar_test[0]), ('train set', 'test set'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 1: Dataset Summary & Exploration\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).\n",
    "- `'labels'` is a 1D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id.\n",
    "- `'sizes'` is a list containing tuples, (width, height) representing the original width and height the image.\n",
    "- `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. **THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**\n",
    "\n",
    "Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the [pandas shape method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html) might be useful for calculating some of the summary results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 39209\n",
      "Number of val examples = 7841\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (12630, 8)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### Replace each question mark with the appropriate value. \n",
    "### Use python, pandas or numpy methods rather than hard coding the results\n",
    "\n",
    "# TODO: Number of training examples\n",
    "n_train = train_df.shape[0]\n",
    "\n",
    "# TODO: Number of validation examples\n",
    "n_validation = int(train_df.shape[0] * 0.2)\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = test_df.shape[0]\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = test_df.shape\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = N_CLASSES\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of val examples =\", n_validation)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include an exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc. \n",
    "\n",
    "The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python.\n",
    "\n",
    "**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import matplotlib.pyplot as plt\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed limit (50km/h)\n"
     ]
    }
   ],
   "source": [
    "def get_sign_names():\n",
    "    filename = 'C:\\\\Users\\\\petrush\\\\Documents\\\\Python Scripts\\\\Project 2\\\\CarND-Traffic-Sign-Classifier-Project-master\\\\signnames.csv'\n",
    "    \n",
    "    signnames_df = pd.read_csv(filename, index_col='ClassId')\n",
    "    sign_names = []\n",
    "    #print(signnames_df.columns)\n",
    "    for name in signnames_df['SignName']:\n",
    "        sign_names.append(name)\n",
    "    \n",
    "    return sign_names\n",
    "    \n",
    "sign_names = get_sign_names()\n",
    "print(sign_names[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_samples(image_data, num_samples, class_id=None):\n",
    "    \"\"\"\n",
    "    Randomly select image filenames and their class IDs\n",
    "    \"\"\"\n",
    "    if class_id is not None:\n",
    "        image_data = image_data[image_data['ClassId']==class_id]\n",
    "    indices = np.random.choice(image_data.shape[0], size=num_samples, replace=False)\n",
    "    return image_data.iloc[indices][['Filename', 'ClassId']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_images(image_data, cols=5, sign_names=None, show_shape=False, func=None):\n",
    "    \"\"\"\n",
    "    Given a list of image file paths, load images and show them.\n",
    "    \"\"\"\n",
    "    num_images = len(image_data)\n",
    "    print(num_images)\n",
    "    rows = num_images//cols\n",
    "    plt.figure(figsize=(cols*3,rows*2.5))\n",
    "    for i, (image_file, label) in enumerate(image_data):\n",
    "        image = plt.imread(image_file)\n",
    "        if func is not None:\n",
    "            image = func(image)\n",
    "        plt.subplot(rows, cols, i+1)\n",
    "        plt.imshow(image)\n",
    "        if sign_names is not None:\n",
    "            plt.text(0, 0, '{}: {}'.format(label, sign_names[label]), color='k',backgroundcolor='c', fontsize=8)        \n",
    "        if show_shape:\n",
    "            plt.text(0, image.shape[0], '{}'.format(image.shape), color='k',backgroundcolor='y', fontsize=8)        \n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X.shape =  (10, 2) [['GTSRB\\\\Final_Training\\\\Images\\\\00010\\\\00019_00002.ppm' 10]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00011\\\\00040_00005.ppm' 11]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00013\\\\00039_00001.ppm' 13]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00004\\\\00055_00025.ppm' 4]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00040\\\\00008_00022.ppm' 40]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00020\\\\00001_00009.ppm' 20]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00017\\\\00028_00029.ppm' 17]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00013\\\\00061_00006.ppm' 13]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00031\\\\00013_00010.ppm' 31]\n",
      " ['GTSRB\\\\Final_Training\\\\Images\\\\00005\\\\00059_00026.ppm' 5]]\n",
      "10\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 294,
       "width": 841
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = get_samples(train_df, 10)\n",
    "print(\"X.shape = \", X.shape, X)\n",
    "show_images(X, sign_names=sign_names, show_shape=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39209 GTSRB\\Final_Training\\Images\\00000\\00000_00000.ppm 0\n"
     ]
    }
   ],
   "source": [
    "X = train_df['Filename'].values\n",
    "y = train_df['ClassId'].values\n",
    "\n",
    "print(len(X), X[0], y[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train  (31367, 32, 32, 3)\n",
      "X_val  (7842, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "INPUT_SHAPE = (32, 32, 3)\n",
    "\n",
    "X_tr_names, y_train = shuffle(X, y)\n",
    "\n",
    "X_tr_names, X_val_names, y_train, y_val = train_test_split(X_tr_names, y_train, test_size=0.2, random_state=0)\n",
    "X_train = []\n",
    "X_val = []\n",
    "for img in X_tr_names:\n",
    "    X_train.append(cv2.resize(plt.imread(img), INPUT_SHAPE[:2]))\n",
    "for img in X_val_names:\n",
    "    X_val.append(cv2.resize(plt.imread(img), INPUT_SHAPE[:2]))\n",
    "\n",
    "X_train = np.asarray(X_train)\n",
    "X_val = np.asarray(X_val)\n",
    "print(\"X_train \", (X_train.shape))\n",
    "print(\"X_val \", (X_val.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12630 GTSRB\\Final_Test\\Images\\00000.ppm 16\n",
      "X_test  (12630, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "X_t = test_df['Filename'].values\n",
    "y_t = test_df['ClassId'].values\n",
    "\n",
    "print(len(X_t), X_t[0], y_t[0])\n",
    "\n",
    "X_test = []\n",
    "for img in X_t:\n",
    "    X_test.append(cv2.resize(plt.imread(img), INPUT_SHAPE[:2]))\n",
    "\n",
    "X_test = np.asarray(X_test)\n",
    "print(\"X_test \", (X_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 767,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Step 2: Design and Test a Model Architecture\n",
    "\n",
    "Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "\n",
    "The LeNet-5 implementation shown in the [classroom](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play! \n",
    "\n",
    "With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission. \n",
    "\n",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture (is the network over or underfitting?)\n",
    "- Play around preprocessing techniques (normalization, rgb to grayscale, etc)\n",
    "- Number of examples per label (some have more than others).\n",
    "- Generate fake data.\n",
    "\n",
    "Here is an example of a [published baseline model on this problem](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf). It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data Set (normalization, grayscale, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, `(pixel - 128)/ 128` is a quick way to approximately normalize the data and can be used in this project. \n",
    "\n",
    "Other pre-processing steps are optional. You can try different techniques to see if it improves performance. \n",
    "\n",
    "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(X, hist=False, normalize=False):\n",
    "    X = np.array([np.expand_dims(cv2.cvtColor(rgb_img, cv2.COLOR_RGB2YUV)[:,:,0], axis=2) for rgb_img in X])\n",
    "    if hist == True:\n",
    "        X = np.array([np.expand_dims(cv2.equalizeHist(np.uint8(img)), axis=2) for img in X])\n",
    "    if normalize == True:\n",
    "        X = (X - np.mean(X)) / np.std(X)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_gray.shape =  (31367, 32, 32, 1)\n",
      "X_val_gray.shape =  (7842, 32, 32, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_gray = preprocess(X_train, True, True)\n",
    "X_val_gray = preprocess(X_val, True, True)\n",
    "print(\"X_train_gray.shape = \", X_train_gray.shape)\n",
    "print(\"X_val_gray.shape = \", X_val_gray.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31367, 32, 32, 1) -1.12131038913e-16\n",
      "(7842, 32, 32, 1) -7.18859498267e-17\n",
      "(12630, 32, 32, 1) 3.30232014866e-17\n"
     ]
    }
   ],
   "source": [
    "X_test_gray = preprocess(X_test, True, True)\n",
    "\n",
    "print(X_train_gray.shape, np.mean(X_train_gray))\n",
    "print(X_val_gray.shape, np.mean(X_val_gray))\n",
    "print(X_test_gray.shape, np.mean(X_test_gray))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d69d6b9278>"
      ]
     },
     "execution_count": 771,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train_gray[10].squeeze(), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# define our input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 1])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "# define model\n",
    "def complex_model(X, classes):\n",
    "    \n",
    "    Wconv1 = tf.Variable(tf.truncated_normal(shape=(5,5,1,6), mean=0, stddev=0.1), name='Wconv1')\n",
    "    bconv1 = tf.Variable(tf.constant(0.1, shape=(6,)), name='bconv1')\n",
    "    Wconv2 = tf.Variable(tf.truncated_normal(shape=(5,5,6,16), mean=0, stddev=0.1), name='Wconv2')\n",
    "    bconv2 = tf.Variable(tf.constant(0.1, shape=(16,)))\n",
    "    \n",
    "    W1 = tf.Variable(tf.truncated_normal(shape=(5,5,16,400), mean=0, stddev=0.1), name='W1')\n",
    "    b1 = tf.Variable(tf.constant(0.1, shape=(400,)), name='b1')\n",
    "    W2 = tf.Variable(tf.truncated_normal(shape=(800, classes), mean=0, stddev=0.1))\n",
    "    b2 = tf.Variable(tf.constant(0.1, shape=(classes,)))\n",
    "    \n",
    "    \n",
    "    print(W1.shape)\n",
    "\n",
    "    # define our graph (e.g. two_layer_convnet)\n",
    "    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,1,1,1], padding='VALID') + bconv1\n",
    "    h1 = tf.nn.relu(a1)\n",
    "    print(\"h1.shape: \", h1.shape)\n",
    "    pool1 = tf.nn.max_pool(h1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
    "    print(\"pool1.shape: \", pool1.shape)\n",
    "    drop1 = tf.nn.dropout(pool1, keep_prob=keep_prob)\n",
    "\n",
    "    print(drop1.shape, Wconv2.shape, bconv2.shape)\n",
    "    a2 = tf.nn.conv2d(drop1, Wconv2, strides=[1,1,1,1], padding='VALID') + bconv2\n",
    "    h2 = tf.nn.relu(a2)\n",
    "    pool2 = tf.nn.max_pool(h2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
    "    print(\"pool2.shape: \", pool2.shape)\n",
    "    drop2 = tf.nn.dropout(pool2, keep_prob=keep_prob)\n",
    "    \n",
    "    a3 = tf.nn.conv2d(drop2, W1, strides=[1,1,1,1], padding='VALID') + b1\n",
    "    h3 = tf.nn.relu(a3)\n",
    "    \n",
    "    fc0 = tf.concat([flatten(h3), flatten(drop2)], 1)\n",
    "    print(\"fc0.shape: \", fc0.shape)\n",
    "    \n",
    "    y_out = tf.matmul(fc0, W2) + b2\n",
    "    print(\"Logits.shape = \", y_out.shape)\n",
    "    return y_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_one_hot.shape =  (?, 43)\n",
      "(5, 5, 16, 400)\n",
      "h1.shape:  (?, 28, 28, 6)\n",
      "pool1.shape:  (?, 14, 14, 6)\n",
      "(?, 14, 14, 6) (5, 5, 6, 16) (16,)\n",
      "pool2.shape:  (?, 5, 5, 16)\n",
      "fc0.shape:  (?, 800)\n",
      "Logits.shape =  (?, 43)\n"
     ]
    }
   ],
   "source": [
    "#lr = 0.0015\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "global_step = tf.Variable(0, trainable=False)\n",
    "start_lr = 0.0015\n",
    "lr = tf.train.exponential_decay(start_lr, global_step, 10000, 0.6, staircase=False)\n",
    "\n",
    "y_one_hot = tf.one_hot(y, 43)\n",
    "print(\"y_one_hot.shape = \", y_one_hot.shape)\n",
    "logits = complex_model(X, 43)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_one_hot)\n",
    "loss = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=lr)\n",
    "train_step = optimizer.minimize(loss=loss, global_step=global_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y_one_hot, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(X_data, y_data, batch_size=64):\n",
    "    m = X_data.shape[0]\n",
    "    \n",
    "    print(\"Evaluate on \", m, \" samples\")\n",
    "\n",
    "    total_acc = 0\n",
    "    \n",
    "    sess = tf.get_default_session()\n",
    "    for offset in range(0, m, batch_size):\n",
    "        batch_x, batch_y = X_data[offset: offset+batch_size], y_data[offset: offset+batch_size]\n",
    "        acc = sess.run(accuracy, feed_dict={X: batch_x, y: batch_y, keep_prob: 1.0})\n",
    "        total_acc += acc * len(batch_x)\n",
    "        \n",
    "    return total_acc / m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epoch = 50\n",
    "batch_size = 128\n",
    "\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples =  31367\n",
      "Epoch 1 \n",
      "Iter = 0\n",
      "0.0015\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.709      Validation accuracy = 0.710\n",
      "Epoch 2 \n",
      "Iter = 246\n",
      "0.00148127\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.845      Validation accuracy = 0.840\n",
      "Epoch 3 \n",
      "Iter = 492\n",
      "0.00146277\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.905      Validation accuracy = 0.893\n",
      "Epoch 4 \n",
      "Iter = 738\n",
      "0.0014445\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.925      Validation accuracy = 0.918\n",
      "Epoch 5 \n",
      "Iter = 984\n",
      "0.00142647\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.942      Validation accuracy = 0.928\n",
      "Epoch 6 \n",
      "Iter = 1230\n",
      "0.00140865\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.948      Validation accuracy = 0.939\n",
      "Epoch 7 \n",
      "Iter = 1476\n",
      "0.00139106\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.956      Validation accuracy = 0.946\n",
      "Epoch 8 \n",
      "Iter = 1722\n",
      "0.00137369\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.958      Validation accuracy = 0.945\n",
      "Epoch 9 \n",
      "Iter = 1968\n",
      "0.00135654\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.966      Validation accuracy = 0.957\n",
      "Epoch 10 \n",
      "Iter = 2214\n",
      "0.0013396\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.968      Validation accuracy = 0.954\n",
      "Epoch 11 \n",
      "Iter = 2460\n",
      "0.00132287\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.972      Validation accuracy = 0.960\n",
      "Epoch 12 \n",
      "Iter = 2706\n",
      "0.00130635\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.976      Validation accuracy = 0.967\n",
      "Epoch 13 \n",
      "Iter = 2952\n",
      "0.00129004\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.975      Validation accuracy = 0.964\n",
      "Epoch 14 \n",
      "Iter = 3198\n",
      "0.00127393\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.975      Validation accuracy = 0.964\n",
      "Epoch 15 \n",
      "Iter = 3444\n",
      "0.00125802\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.975      Validation accuracy = 0.961\n",
      "Epoch 16 \n",
      "Iter = 3690\n",
      "0.00124231\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.978      Validation accuracy = 0.965\n",
      "Epoch 17 \n",
      "Iter = 3936\n",
      "0.00122679\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.985      Validation accuracy = 0.972\n",
      "Epoch 18 \n",
      "Iter = 4182\n",
      "0.00121147\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.983      Validation accuracy = 0.972\n",
      "Epoch 19 \n",
      "Iter = 4428\n",
      "0.00119635\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.980      Validation accuracy = 0.968\n",
      "Epoch 20 \n",
      "Iter = 4674\n",
      "0.00118141\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.982      Validation accuracy = 0.970\n",
      "Epoch 21 \n",
      "Iter = 4920\n",
      "0.00116665\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.983      Validation accuracy = 0.972\n",
      "Epoch 22 \n",
      "Iter = 5166\n",
      "0.00115208\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.981      Validation accuracy = 0.970\n",
      "Epoch 23 \n",
      "Iter = 5412\n",
      "0.0011377\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.984      Validation accuracy = 0.972\n",
      "Epoch 24 \n",
      "Iter = 5658\n",
      "0.00112349\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.985      Validation accuracy = 0.973\n",
      "Epoch 25 \n",
      "Iter = 5904\n",
      "0.00110946\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.987      Validation accuracy = 0.973\n",
      "Epoch 26 \n",
      "Iter = 6150\n",
      "0.00109561\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.987      Validation accuracy = 0.977\n",
      "Epoch 27 \n",
      "Iter = 6396\n",
      "0.00108192\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.988      Validation accuracy = 0.975\n",
      "Epoch 28 \n",
      "Iter = 6642\n",
      "0.00106841\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.989      Validation accuracy = 0.977\n",
      "Epoch 29 \n",
      "Iter = 6888\n",
      "0.00105507\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.989      Validation accuracy = 0.977\n",
      "Epoch 30 \n",
      "Iter = 7134\n",
      "0.0010419\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.988      Validation accuracy = 0.976\n",
      "Epoch 31 \n",
      "Iter = 7380\n",
      "0.00102889\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.992      Validation accuracy = 0.980\n",
      "Epoch 32 \n",
      "Iter = 7626\n",
      "0.00101604\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.986      Validation accuracy = 0.971\n",
      "Epoch 33 \n",
      "Iter = 7872\n",
      "0.00100335\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.990      Validation accuracy = 0.977\n",
      "Epoch 34 \n",
      "Iter = 8118\n",
      "0.000990819\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.990      Validation accuracy = 0.976\n",
      "Epoch 35 \n",
      "Iter = 8364\n",
      "0.000978446\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.992      Validation accuracy = 0.979\n",
      "Epoch 36 \n",
      "Iter = 8610\n",
      "0.000966228\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.992      Validation accuracy = 0.981\n",
      "Epoch 37 \n",
      "Iter = 8856\n",
      "0.000954162\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.992      Validation accuracy = 0.980\n",
      "Epoch 38 \n",
      "Iter = 9102\n",
      "0.000942247\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.993      Validation accuracy = 0.983\n",
      "Epoch 39 \n",
      "Iter = 9348\n",
      "0.00093048\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.993      Validation accuracy = 0.980\n",
      "Epoch 40 \n",
      "Iter = 9594\n",
      "0.000918861\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.992      Validation accuracy = 0.979\n",
      "Epoch 41 \n",
      "Iter = 9840\n",
      "0.000907386\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.994      Validation accuracy = 0.982\n",
      "Epoch 42 \n",
      "Iter = 10086\n",
      "0.000896055\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.993      Validation accuracy = 0.981\n",
      "Epoch 43 \n",
      "Iter = 10332\n",
      "0.000884865\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.990      Validation accuracy = 0.977\n",
      "Epoch 44 \n",
      "Iter = 10578\n",
      "0.000873815\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.993      Validation accuracy = 0.981\n",
      "Epoch 45 \n",
      "Iter = 10824\n",
      "0.000862903\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.993      Validation accuracy = 0.980\n",
      "Epoch 46 \n",
      "Iter = 11070\n",
      "0.000852128\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.995      Validation accuracy = 0.983\n",
      "Epoch 47 \n",
      "Iter = 11316\n",
      "0.000841487\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.994      Validation accuracy = 0.980\n",
      "Epoch 48 \n",
      "Iter = 11562\n",
      "0.000830978\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.995      Validation accuracy = 0.982\n",
      "Epoch 49 \n",
      "Iter = 11808\n",
      "0.000820601\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.994      Validation accuracy = 0.980\n",
      "Epoch 50 \n",
      "Iter = 12054\n",
      "0.000810354\n",
      "Evaluate on  31367  samples\n",
      "Evaluate on  7842  samples\n",
      "Train accuracy = 0.995      Validation accuracy = 0.983\n"
     ]
    }
   ],
   "source": [
    "train_acc = []\n",
    "val_acc = []\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    m = X_train_gray.shape[0]\n",
    "    print(\"Train samples = \", m)\n",
    "    iteration = 0\n",
    "    for epoch in range(num_epoch):\n",
    "        print(\"Epoch {} \\nIter = {}\".format(epoch + 1, iteration))\n",
    "        print(sess.run(lr))\n",
    "        \n",
    "        X_train_gray, y_train = shuffle(X_train_gray, y_train)\n",
    "        \n",
    "        for offset in range(0, m, batch_size):\n",
    "            batch_x, batch_y = X_train_gray[offset: offset+batch_size], y_train[offset: offset+batch_size]\n",
    "            \n",
    "            sess.run(train_step, feed_dict={X: batch_x, y: batch_y, keep_prob: 0.5})\n",
    "            \n",
    "            iteration = iteration + 1\n",
    "            \n",
    "        train_acc.append(evaluate(X_train_gray, y_train))\n",
    "        val_acc.append(evaluate(X_val_gray, y_val))\n",
    "        \n",
    "        print(\"Train accuracy = {:.3f}      Validation accuracy = {:.3f}\".format(train_acc[-1], val_acc[-1]))\n",
    "        \n",
    "        saver.save(sess, save_path='C:\\\\Users\\\\petrush\\\\Documents\\\\Python Scripts\\\\Project 2\\\\CarND-Traffic-Sign-Classifier-Project-master\\\\model\\\\model.ckpt', global_step=epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 882
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(train_acc, '-o', label='train')\n",
    "plt.plot(val_acc, '-o', label='val')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n",
    "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from C:\\Users\\petrush\\Documents\\Python Scripts\\Project 2\\CarND-Traffic-Sign-Classifier-Project-master\\model\\model.ckpt-45\n",
      "Test samples =  12630\n",
      "Evaluate on  12630  samples\n",
      "Test accuracy = 0.929\n"
     ]
    }
   ],
   "source": [
    "### Train your model here.\n",
    "### Calculate and report the accuracy on the training and validation set.\n",
    "### Once a final model architecture is selected, \n",
    "### the accuracy on the test set should be calculated and reported as well.\n",
    "### Feel free to use as many code cells as needed.\n",
    "test_acc = 0\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    #sess.run(tf.global_variables_initializer())\n",
    "    saver.restore(sess, 'C:\\\\Users\\\\petrush\\\\Documents\\\\Python Scripts\\\\Project 2\\\\CarND-Traffic-Sign-Classifier-Project-master\\\\model\\\\model.ckpt-45')\n",
    "    m = X_test_gray.shape[0]\n",
    "    print(\"Test samples = \", m)\n",
    "    test_acc = evaluate(X_test_gray, y_test)\n",
    "    \n",
    "    print(\"Test accuracy = {:.3f}\".format(test_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Test a Model on New Images\n",
    "\n",
    "To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.\n",
    "\n",
    "You may find `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Output the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_img = []\n",
    "test_img_rgb = []\n",
    "for img in glob.glob(os.path.join('C:\\\\Users\\\\petrush\\\\Documents\\\\Python Scripts\\\\Project 2\\\\CarND-Traffic-Sign-Classifier-Project-master\\\\examples\\\\new', '*.*')):\n",
    "    test_img_rgb.append(cv2.resize(plt.imread(img, 0), (32, 32)))\n",
    "    #print(test_img[-1].shape)\n",
    "\n",
    "test_img = np.asarray(test_img_rgb)\n",
    "test_img = preprocess(test_img_rgb, True, True)\n",
    "\n",
    "test_img = np.asarray(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [35, 9, 0, 27, 30, 34, 40, 7, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 427,
       "width": 492
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = len(test_img)\n",
    "rows = n//3\n",
    "plt.figure(figsize=(3*3,rows*2.5))\n",
    "for i, image in enumerate(test_img):\n",
    "    plt.subplot(rows, 3, i+1)\n",
    "    plt.imshow(image[:,:,0], cmap='gray')\n",
    "    plt.text(0, 0, '{}: {}'.format(labels[i], sign_names[labels[i]]), color='k',backgroundcolor='c', fontsize=8)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Run the predictions here and use the model to output the prediction for each image.\n",
    "### Make sure to pre-process the images with the same pre-processing pipeline used earlier.\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from C:\\Users\\petrush\\Documents\\Python Scripts\\Project 2\\CarND-Traffic-Sign-Classifier-Project-master\\model\\model.ckpt-45\n",
      "Predictions =  [35  9  3 11 28 34 40  7  2]\n"
     ]
    }
   ],
   "source": [
    "prob = []\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, 'C:\\\\Users\\\\petrush\\\\Documents\\\\Python Scripts\\\\Project 2\\\\CarND-Traffic-Sign-Classifier-Project-master\\\\model\\\\model.ckpt-45')\n",
    "    score = sess.run(logits, feed_dict={X: test_img, keep_prob: 1.0})\n",
    "    prob = sess.run(tf.nn.softmax(score))\n",
    "    predict = np.argmax(np.array(prob), axis=1)\n",
    "    \n",
    "print(\"Predictions = \", predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 852,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 427,
       "width": 492
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = len(test_img)\n",
    "rows = n//3\n",
    "plt.figure(figsize=(3*3,rows*2.5))\n",
    "for i, image in enumerate(test_img):\n",
    "    plt.subplot(rows, 3, i+1)\n",
    "    plt.imshow(image[:,:,0], cmap='gray')\n",
    "    plt.text(0, 0, '{}: {}'.format(predict[i], sign_names[predict[i]]), color='k',backgroundcolor='c', fontsize=8)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 853,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.5555555556 %\n"
     ]
    }
   ],
   "source": [
    "print(100 * sum(predict == labels) / len(labels), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. \n",
    "\n",
    "The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tf.nn.top_k` is used to choose the three classes with the highest probability:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 854,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    top5 = sess.run(tf.nn.top_k(tf.constant(prob), k=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 855,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ahead only  =  99.9324202538 %\n",
      "Dangerous curve to the left  =  0.0289083487587 %\n",
      "Go straight or right  =  0.0158331880812 %\n",
      "Turn right ahead  =  0.0116099086881 %\n",
      "Right-of-way at the next intersection  =  0.00920008678804 %\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[0])\n",
    "for (i, prob) in zip(top5[1][0], top5[0][0]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 856,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No passing  =  90.1623606682 %\n",
      "End of no passing  =  9.53091531992 %\n",
      "No passing for vehicles over 3.5 metric tons  =  0.2798672067 %\n",
      "Vehicles over 3.5 metric tons prohibited  =  0.0154292632942 %\n",
      "Ahead only  =  0.00280636395473 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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qA95tqqVaUF9TBtwVsgq1Wj3url43UPCvsIeXnzVld2W4ai47XlTYWCzlsiopjqwacJ42/bmwc7u75rpTxcxichzu2jQdf+M7jw3q68YlFwa1G+7r9W7z24svDutrS/f42GaH/SRJV//o+6nYBRee693PJT+6x7uNS7PK1J6hJMGvkfSkpB1GurMx5s2SrpDUL+kyScslHS7pK5L2k3RMk/YLAIBJrVlf3X9Y0naSuiW9f6Q7GmO6JX1HUkXSImvte621H1XybcCtko42xoR9FAQAAC/RlERvrb3OWvuAtXZ9vrs6WtImki611v5prcfoV/LNgDTKhwUAALB+xmMy3kH126sdsRsl9Ura1xjj/wM2AAB4iWb9Ru9j+/rt/esGrLXDxphHJO0oaYGkEWciGGNud2231kqSfnLJNanY1lttmxlLFEbq0qmr6t9Gkgp594SOkZRy/m0kqVpzt5u51XxJ0nsu+y9nfOr8zb37mlcJm0uZsYsjtwmcANWYdLeuzs4OSdJ++y10xkMmQI2lwOGRqbOjTZJ0wF47p4Nhhz5ck/+2kWSNxa725HgctEf6eITuXmi7kMOfrwS+f2TMAe5sbZUkLdrhlalYyxYLgvqaud+eQe007P++M1gKez3XMubkbrrZFpKkfz3zgnRfVfcE4LEwHu9ajWntL2TEG9unjsG+AAAQtfE4ox9N4yPnqB9YrbV7ZIRqknTk2w9JBRpn8q6YJOXVtR67+FKL+sI+k0xt9z/8mwUu7+oZdLdrnMl/720fcMbffMFXvPsKXV43VPT/RF6tVYP66hxyf7punMnffPOfnPGJvrwuF3hKn9WucSZ/w//emQ5Wx/aUPp8fu/OSwYxlm40z+WtvTx+P8OV1Qc2Cvl1qeSFseW7PXPff1jiTv/7eu1Ox7f+a+tJ2vYQur8styzp3zPb4nM6gvrKW1zXO5M//7Emp2GOrlnr306zldeNxRt94NrIWrHevcz8AABBoPBL9ffXb7dYNGGOKkraSNCzp4bHcKQAAYjQeif7a+u2hjtj+ktol3WKt9b9MGgAAeInxSPSXS1om6VhjzItTm40xrZI+V//nN8dhvwAAiE6zrnX/Fklvqf9zdv12H2PMRfX/X2atPU2SrLWrjDEnKkn41xtjLlVyCdwjlCy9u1zJZXEBAMAGatYZ/a6Sjq//9/r6tgVrbTt67Ttba69UUgTnRklHSfqgpCFJp0o6dj2vsAcAAEbRlDN6a+1iSYs929ws6Y3N6D9Lf1t6qVw1X8iMSdJ/f+Gr3v387tSzvdtIUnfO//NMJXA52fzDDnBuL3d3jRh/pNrv3Ve1K2xY1Qb9/7aWobC1Sb2t7gsvVnP5EeNDA/5LAPOFsH2sVf376uoKWy7U09/j3F6tLxkbKKQrrLWN8erckNV1ocsNWzMuvpKvNeLpsTqWy/8kqRCwnG9oetjUp6mr3a+HQrURTx/npTtsG9TXYClsuXLXLP/j/4qB7tHv5LDbBz/o3D5j0zmSpH/+4KdTsdt/nr6IzliZ2Jf5AgAAG4REDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMTGtirFGGt1fI7JjxCTpK6Amg+dLQX/RpLa/GuWaLjcGtTXqxbt796HehGUrPij/cPefbVU0gVQ1kfhuRe82/T1+xfdkSRl1Dqp7fxKSVLlkX8449NaO/z7KoYVVim2+r88a0uXBfXVUXCP4VI1KZyy6ZrBVGxNeymor1AhRWNCC81UNXKBJdfjhhbQCW0XJqwoVjXvLqBTGynu/9YhSVr41rBaZ49f/WvvNtU1Ye9Vj//hLuf2uXN3zIzP33r7oL6agTN6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiFnX1uu5yW2pbIZfPjEnS8jsf8O6nI7A6WWfA4V8WUj1N0nMZVbwW5EaOlwtl775qxYCyfJJ++eMfe7c54qTjg/oaqLhLa+VKSRW38mbTnPFiwb96YLUcNj4qGfs4kpaAaoOSNKXsfp4L+WTfp7Sm/+6eMa26Jq1Zs8a7zfz584P6qlXdx7FQTMbHtGnp8dEfWEmxVnNXhhvNcMX/dfbZzywO6uuM088aeV9q6ap4+cGwSnltu78iqN3QL67xbtPeGlZ59M6fXOfcvuuBb8yMv/5jrw/qqxk4owcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACIWdVGbmd1TU9uKhUJmTJLuvvmP3v1Ma3cXyBlNa69/MYvBXNhns+EO9z7W6sVssuL5Qf+iD8XBsKI2r9tjb+82pXLYEC60uIvT5AvJ8Wif2umMV6sl774qYXUz1J5RaGgkncN9QX2teeI55/bK7pV6fHkqVtxmTlBfoc4991zvNmedNXIxliyFjENfrSSFWlatWpWKlUr+Y0OSKgHFaSQpH1BU6BP//tGgvoaGh5zbGwV5XPGp5bACXCta3X2NJpfzP/612kBQX/NbZzq3l/OFejxd9Kh3MKzgVDNwRg8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMSaUr3OGHO0pAMk7SppF0ldkn5orT3Ocd8tJT0ywsNdZq09thn7dc+D/0ht6x8YyoxJ0kFzp3v30xZQ4U2S+jr8q0/lAitdlfLu6nS5+me9rHhPoce7rxn3PuXdRpLu+/kPvdsMX+2uQjiavjb3czb0jS9Kkp5efI4znh/0f84qpap3G0kaGF7j3aa14l8RUZJaq+6/a8fXvEKS9NSS81Kx+f/5n0F9rQrcx6HaC95tPnHiiUF91aa5K69d8v0lkqSPnnZKKtaojOkrH1ClMGnoPxbndrqrro3mne99h3N7tZK8n/YvfzYV65u/TVBf6vf/uyRpqOR/HDsU1lep/2nn9nw1OR7tjvgTDzzm3c8+h3g3cWpWmdozlCT4NZKelLTDerT5q6QrHdvvatI+AQAw6TUr0X9YSYJ/UMmZ/XXr0eYOa+3iJvUPAAAcmpLorbUvJnZjTDMeEgAANEGzzuhDzDHGnCRphqTnJd1qrb1zHPcHAIDo5Gq1sIkxWYwxi5R8dR8yGe96Scdbax9fz75ud2231u4uSX/7e/rn/m0WbC1JevDhh5yPuUnJPSltJOXAY1gNmIMzVAubPNIxZ457e1uLJKmnb8AZr9b8J5KV+t2PNZqe55/zblPMhU2AqmZMZJq6+TxJ0sonnnTGcwHz6gKfMtXkP/EyF/hyzhqK3VvMlySteiz9kizPnRfUVyXw9fLUP57wbzQUNhFSBfcR2WrLLSVJjzz6aCqWywU+0aECuivnw87tps90T1Lu6uqSJK1evToVK5RbgvpS4BgefGapd5uiAsdHxvti5+abS5LWPJEeq9Up/rll+qYvvsY2aHCNx/K6XkmflbSHpGn1/xq/6y+S9DtjjHvKKwAA8DLmX91ba5+V9Kl1Nt9ojDlE0u8l7SXpBElfW4/H2iMjVJOkw446MhW46oqfKCsmSSfP3Wm0blO2HBzybiNJfW3+H9KeqoQ9ZXt97nPO7a9+1QJJ0h/+9rAz3jPkv7xuzt8f9G4jSX/47295t5lZau7yujfVl9f94uTTnPHJtrzukAv/S5J0zXs/kIqN9fK6T3zyX73b1J72H7/S6Mvr3v7P707FJuPyuoNee6Ak6drfpudfTwldXlcJe708efaXvNtMVfqbiPWRHxp0bt//60nauvFD/5aK9b/JP7eYk8NeY+uaMBfMsdYOS/pu/Z/7j+e+AAAQiwmT6OsaP9Ly1T0AAE0w0RL93vVb9/fIAADAy5gnemPMXsaYsmP7QUouvCNJPxjbvQIAIE7Nutb9WyS9pf7P2fXbfYwxF9X/f5m1tjG76RxJOxpjrldyNT1J2lnSQfX/P9Nae0sz9gsAgMmuWbPud5V0/DrbFtT/k6THJDUS/cWSjpS0p6Q3SCpJWirJSjrfWntTk/ZJ3d3dqW35fCEzJkm5kv8hqQ2HzbovhayDDVxOWai6Z7LmRonnAxa1rh5wz0gdzdRiq3ebwcGwNfvFjIsY5Oozfos9/e54W8m7r9aQCyZI6sx1ebcZbgksGJPxZ9Xqr5eh7s5UrLqmL6ivzvb0Y62PcsH/tdk5Z1ZQX8U2d1+lYnI8Zk9Pr/YoBM66r2a89kYTNFs/8EILS5Zc6Ny+y647Z8ZP/vhZQX2VQ1chBLwPF1vD+splrFJpXEuhUEjHc6EXCGiCZl0Cd7Gkxet53wsluUcNAABoqok2GQ8AADQRiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICINat63YS0ZtXq1LZqtZIZk6T5r97Ku5/8Pfd5t5GkWsW/yltnwb/CmyS1l9zlyfL1aktZ8aD6ZFPbQ1pJw/5Neqd0BHVVqbgrhlXqlbNWtbY444WA56xQLXu3kbIr7I2klPF3jaY1o1m+XnCrdSBdjatSC+urVA2r4pULaRdYCS2feQ6Uy4zXKmF/Vz4Xto+lgGp+lcBKec8tW+rcPlyvGOeKl4thlTbLYbuozqJ/Zcl8PqzSZm6UceWqLNjRGfi+2ASc0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAELGoq9cN9qZrr9Xq1ZtcMUk64HUHe/dz0wNh1evyFf82LdWwilDDvb3O7Y3jkRWv5ANKym2zqX8bSc91uSvGjWTBu44N6mvKLHeVwrbZsyVJO37ydGe8VHMfp5H0FcOq17UEVKJ75te/Currqd/f4Nw+WE2e/3/0LUvFZs3sDupruC+sytv999/v3ea/v/XfQX1N33S2c/tmm82RJJ3x6c+lYjmFvTZDFUoF7zannvKBoL7e9Z7jndtnzJyRGS8H7J8kdQSef7aEdJcLLJU3WjtHfOrUsNdLM3BGDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARCzqojbDpcHUtlqulhmTpNrmm3j30zIUVhihp+xf1WbaQE9QX+1DK5zb87XhEePPdE3x7qs7F1bEZc2223i3yW0yLaivwbJ76NdyuRHjvWr17ywXVuykGlAkpW/r7YP6Gsi5CwrVOpNCHAP/dEg6NhBQ8EjS0NBAULujj3y7d5vBWlgBncb7hE+8psC+AvexOuzfbo9ddwnqa+WQe3wM13KZ8bJ//SdJUq71haB2nXn3e/pIXigFVBaT1Fdxv+9UckllnZWFdHxK5emgvpqBM3oAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACK2wdXrjDEzJB0p6TBJr5I0V9KgpL9JWiJpibU2Vd7NGLOvpDMk7S2pVdKDkr4n6TxrbVhJoXVUHMW/aiPEJOm7l/3Qu59dCmGHsVjzr/41WArqSrddeIlz+7yttxkxvu0pH/Du67lVq7zbSNI/HXmkd5tVhbDKgdWqu12jAllmfJSqZi75alh1MgW0m7Ng66Cu5m65wLm9vatLkrTwoINSscrQUFBfuXxYNb+jjz7Gu01Li7vq2mhCx8fY8t+HNx391qCeWjs2dW7v7OiUJO23176pWE81bHxM7+kPavfUqpXebYphw0PLWt2V8obr42OZo5LevlNmhnXWBM04oz9G0nck7SXpfyV9VdIVknaS9F1J1hjzkle2MebNkm6UtL+kn0j6hqSypK9IurQJ+wQAANScevT3SzpC0lVrn7kbYz4p6Q+SjpL0ViXJX8aYbiUfDCqSFllr/1TffqakayUdbYw51lpLwgcAYANt8Bm9tfZaa+3P1/163lr7jKTKgkNtAAAfCElEQVRv1f+5aK3Q0ZI2kXRpI8nX79+v5Kt8SXr/hu4XAADY+JPxGj/SrP1jdOPHvqsd979RUq+kfY0xgb+eAACAhlytFjhRaBTGmKKkvyj5rf5Qa+2v69v/KGmhpIXW2tsd7e6StKOkV1pr7xmlj1R7SbLW7i5Jd9z111Rsu623lSTd/9ADzsfcdOr0kbp0au91T8wYTTUXMqEnbCJTpeCexTd17mxJ0sqnnnHGWzbdxL+vathcypDfkUJnbeZy7s+4ne2tkqQ1vVkTgjbO68UlYN5fuIy+OjrbJEk9a/rSwbFesxNwPEIn/mVpb0vGR29f2ISx8Varhb1icnn3+0d7a3I+1ts/4OgrbACXq/6TlCWp/5ml/o0Cx/BgruDcPm3eXEnSiiefSsWmbNLu3U9L9+zG/27QQN6YL9WzlST5XzaSfN2U+u0LGe0a26durB0DAGCyaMZkvBRjzIckfUTSvZLe6dm88cll1I+D1to9MkI1STr4LYekAr+78hplxSTp3446bj128aV2+fPD3m0kaSDvf1ZQq4V9Nls+fTPn9qO/cKYk6fJPftYZD1let3J12PK6TQI+d4YurysXupzb9959B0nSbX++1xmv5PyXDOUDz8wLlYClfAFtJCmXsZRv4d67SJL+dFv62zG1us9qRlML/FakMux/Nhq6vC6Xc59A7bbLKyRJf/nriF82jomsfRxJ30DW+dXIspbX7bZjspzzL39/KBUbDlxet3nvsqB29/3HF/0bBf5A/EjrFOf2t51ztiTpso99PBU74v27e/ezxcHpxwnR9DN6Y8zJkr4m6W5JB1prl69zl8ZIcx8pqXud+wEAgEBNTfTGmFMknS/pLiVJ3vXD73312+0c7YuStlIyeS/sNBkAALyoaYneGPMxJRe8uUNJkn82467X1m8PdcT2l9Qu6RZrbXp2BwAA8NKURF+/2M3Zkm6XdLC1dqQfWS6XtEzSscaYhWs9Rqukz9X/+c1m7BcAAJNdM651f7ykzyhZ6XSTpA8ZY9a926PW2oskyVq7yhhzopKEf70x5lJJy5VcXW/7+vbLNnS/AABAc2bdb1W/LUg6JeM+N0i6qPEPa+2VxpgDJJ2u5BK5jaI2p0r6urW2KSuIWx0zbvP19dOumCR96Qff9e7noqPf491Gkqbc6T8NYXU5bM3+jBfccxsLlcqI8S36Vnv31Rv4RVFvS9m7TTEfNoSrvRkzgutrf6sD7niuHDA0A0fz4KD/c10OLLBULrvXSTfWoRcc8b7BsLXkbW1tQe0Kef9Z/k0vPlN/LiuVptTdkiTl82N3QYLh9rBp5qWq+5fUXK2aGV9T7A3q68avf2v0Ozls0+n/t+V6w16ciz7g+uVZ6pqZzCVf9J50/OqfuAuHjeSkJs263+BEb61dLGlxQLubJb1xQ/sHAADZqEcPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABErBnV6yas3udWpLZVh4czY5LU1t3h3c+CI1/v3UaS/n7fBd5tNhkMq8a1tK3Hub2WS6pwDebd8Ru++GXvvnY79TTvNpK0tOT/uTM/EHY8csWM6nX18mT5jHiu4l9BLVSu6P/yDK3VNlBzV2Or1av5ueLFgP2TpKGhrGM/slwuF9SumWr18eGqihdaha5xjMdCt8Kq1xXkHh+5EeKzn3wuqK81Pf4VMyWpr+Rf7XFFoTWoL618zLl5uJLswwpH/MT/OCesrybgjB4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIhFXb1u3tQZqW3lQjEzJknPD/Z69/OOE07wbiNJ3zzzdO82qy+6IqivjoK78lf+xbi77tn0VQPefS3969+820hSbY8dvdsUVArqq5rLqHSVq1cSy4jnq20BfXk3kdSoo+fZJrCvrMJwjcdz/Q2cJbz8zMz7j19J6i30Obfn6oOgWE6PhnuWhL1XzeoK20dV/avXaau5QV39014L3YGO9sz46jb/yoHd3i3ceK0CABAxEj0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARi7qozYq+/tS24Wo1MyZJPat7vPvpaQ0opiDpnP+5xLvNG1s6gvracvmQc3u+kpROaVk+7IyvaKv4d/abK/3bSNq07C6sM5KOV+0R1NdAeZpzez6XvCTKLe74wLB/0SNVwirN5Kv+ZW3ytcDP7hV3X40aP0VHPF8IGBuSimHNpIDDOFwKOx7D+YzO6psrRcfxqPqPX0kq5wtB7Wol/wPSq+eD+rr5s2c7t8/74tfq8U+lYltVVwf1NTDo/x4sSdUV7vewkXTstG9QX1f9aIlz+2ve9UpJ0u8d8YV77ODdT/frvuDdxoUzegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIrbB1euMMTMkHSnpMEmvkjRX0qCkv0laImmJtba61v23lPTICA95mbX22A3dL0kqFNJVoXK5XGZMkmo1/4phUwfdleFG88eHHvRu86Uf/Cyor78vdldBqtYrZ/V3djvjm7as8u5r6VBAhTdJwz/7rXebxx54PKivTY84xLm9Vk0qYFX7ljvj0+RfaSxsdEj9ef/P4YOBH92rOXfDar1AWn85HSsHvFYkqRJaYC+gel3VUWVufbRmVqJLHq+odAm+oZawKnS5alg5v9m9/gfyjkvODepr78qAc3tHfQy44j19YVU9e4ZKQe1mveco7za7HDg7qK9yfjt3YNoUSdJhxxyRCq0cuDeor2ZoRpnaYyR9U9LTkq6T9LikWZLeKum7kt5gjDnGWrvuK+6vklz1TO9qwj4BAAA1J9HfL+kISVetc+b+SUl/kHSUkqR/xTrt7rDWLm5C/wAAIMMGJ3pr7bUZ258xxnxL0uclLVI60QMAgI2sGWf0I2n8PDnsiM0xxpwkaYak5yXdaq29cyPvDwAAk0ouZPLZ+jDGFCX9RdJOkg611v66vn1LZU/Gu17S8dba9ZphZYy53bXdWru7JP3tb+nPDdtss40k6cEH3RPhhiv+E2NCPy1Vi/6Td7bfcuugvvqefsa5vWvzeZKk1U886YwXcv7HYyhwTBUDjmSltSWsrynuyYcdHR2SpJ6eHme8IP8ZYaGvsGrA5LNawP4l7dw629skSWt6+1KxfPBfFiaot1zY8chl9NbR1i5J6ulzTDgN7SvwMJZq/v31Ln86qK+WIffkxNZ58yVJ/U+m37KrmRMaR1YNPB7F6VO827R1hU38y3yqS1OT26GVqVCl2u/dT7Ft3otdejdey8ZcXne2kiT/y0aSr+uV9FlJe0iaVv/vACUT+RZJ+p0xpmMj7hcAAJPGRjmjN8Z8SNLXJN0raT9rrXut0kvbFCX9XtJekk6x1n5tA3ahJklbbDk3FfjFz38lSXrT4W9wNlyxYoV3Z1MLYR+2BgI+gd7Y5OV1B56XLLe57oMfdca7gpbX+X9ylaQZwzO926x8RcYyl1FkLa/bc89XS5L++Mc/OOOdE355XdgSr6zlda/ZbWdJ0u//kv52rFxz/SI3utBTk7DldWHnMq0Zp5ULd91VkvSnO+5IxYZLYce+HPLVjaTZA/5no3dcclZQX1s/7V4y+8ovf1OSdPep70/FenrXBPXVE/iC2eQd6SVtowlfXpfxXM97U3L75C9SoZDlddNeeXbjfyfWGb0x5mQlSf5uSQeuT5KXJGvtsJLleJK0f7P3CwCAyaipid4Yc4qk85WshT/QWuv+YTjbc/VbvroHAKAJmpbojTEfk/QVSXcoSfLPBjzM3vXbh5u1XwAATGZNSfTGmDOVTL67XdLB1tplI9x3L2NM6oKaxpiDJH24/s8fNGO/AACY7JpxrfvjJX1GUkXSTZI+ZIxZ926PWmsvqv//OZJ2NMZcL6mxpmtnSQfV//9Ma+0tG7pfAACgORfM2ap+W5B0SsZ9bpB0Uf3/L1ZSBGdPSW+QVJK0VJKVdL619qYm7JMkKT/CrOWsWMjaz1VdYbNtOwb9+9r1MPdqgdE8ev3Vzu2d85JZp/ude7ozftOpH/Pua2ZP2Gzs2nR34YyRtN/516C+HrjfPQN2569/OYl/8evO+A5nu4/TSFY8m15Tuz6mlN1r/UfS0hc2Obel4KhaI6lYS14nU4daHcGwcR/6PWIl4BoXld6wKdyN6ymsq1B/y+woTEvFVvcsDeqr9nBYeY+7rrrGu80W5bACOr2djudfUrWQz4xXh8LG4vQjFwW1e/XR/kVtlt9zcVBf5c1f4Q40FtiX0ql12pwTgvpqhmZcAnexpMUe979Q0oUb2i8AABgd9egBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICINaN63YQ1NJSuXFWr1TJjUnbVqpHkutu920jS0PJV3m2mtYV9NtvhkNc5t1/362slSQe+3h3/8VeXePf1+P9817uNJHX2rvFu06qw6mRz+wad20v16oVz+1Y74ytP8q/mt6oU9py1L9rPu82MhbsF9ZWfNsW9PZdUImwvrUjFcuWwt49cLux4FAf9K6/1Pvd8UF8PX3mVc/u2//4pSdIjP/xyKtb2dFhf04bdY3E07dUe7zYD1VJQXytr7tdDpZKMjxVr0hUat3/LPkF9bXHYgqB21a4HvNvc/eefBvXV9ay74uC2i/aWJD1w+49SsQWvcR/DEftp28a7jQtn9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARCzq6nXDQ8OpbfXidc6YlF3VbiS1pcu920hSe4d/1bt8PuyzWUvN/XflGvGMhz3uoyd493XO+V/1biNJQ0vcFcNGMndZ2LF/sv9J5/ZaNTlOAz3POeOrS/4vmY7WTu82kvTCTdd6t1lz041BfeUzPvNvdcEFkqTHPvW5VGyoWAjqq1QLaqZCzb96XVFhnc0vuKu8lfuTSnPz73siFRuo9gX1VekMO46t7S3ebZ6aEla97og3n+rc3r7JZpKkg0/6ZCr2zAunB/VVmrYwqN3q4Z282+x53PuD+ip0u499IT9VkrTjEYenYsXarKC+moEzegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGJRF7WpOQta1EaISbWafxGM7qlTvNtIUjGgKMiqVauC+srlciPGq9Wqc3tIEZ2PvON93m0kaWi6f8GN2668Iqivn77VvY/7DCfP/+3L3AVKDtxstndf5UrYy2xAA95tqgX/NpKkvHvc53PJuGgt9aZixcACS7lcWKGZWsY+jtwmbB+Xa9C5fTifHI/lLT2pWDFfDuqrr60jqN0OBx/g3eb+318Y1NfNv/64c/s+O18mSbrVEd/v8E2D+nrmjluD2m2+36HebWql7YP6yhX2yogkhcqKhbelIsuX3+Ddz/Tp3k2cOKMHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiTaleZ4w5R9JCSdtJmimpT9Jjkq6UdL619nlHm30lnSFpb0mtkh6U9D1J51lrK83Yr0I+XR2uUcXNFVs77mP16rCKcoWCf/W6rCpzo8msQlcvCJZVtS/keGiTbv82ksqDw95tDj7s7UF9tbe2Ore/o35Afpwbcsb3f5d/f6XAz9OP/O467zbl51cG9TWzpd0dyCdvEfmWdNW+9oBKj5LUP9wf1G5g0F1RbiRV/5eYJKlr+12c23MtSaW54oI9U7GZB+4a1NdXLjw3qN0Jqzb3blMbCquU11ZY49yeV7UeT1dNXP73sLFY3ukvQe2GVv/Nu83q6rKgvqZPGe01nY63Tds/qK9maNYZ/YcldUj6jaSvSfqhpGFJiyXdaYx5yYg0xrxZ0o2S9pf0E0nfkFSW9BVJlzZpnwAAmPSaVY++21qb+phujPm8pE9K+oSkD9S3dUv6jqSKpEXW2j/Vt58p6VpJRxtjjrXWkvABANhATTmjdyX5Rqh+u+1a246WtImkSxtJfq3HOKP+z/c3Y78AAJjsNvZkvMPrt3eute2g+u3VjvvfKKlX0r7GmJaNuWMAAEwGuaxJWCGMMadJ6pQ0RcnkvNcoSfKvtdY+V7/PH+uxhdba2x2PcZekHSW90lp7zyj9pdpLkrV2d0m6886/pmLbbJN8ufDggw84HzNoslvAfLWkmX/DmsKer6y+tt12O0nSAw/c726XD9nHwAMSMBYDe1I+5/6Mu/U220iSHnrwQWd889mzvPsKnA+m/tXuCVAjyQ37T2iUpGLGZM3OefMkSWuefDLdV1BP2RM/R1Otjd1rM9/qnpzYOSt5/tcsXZqKFTvbgvp69vn0Y62PmV2d3m36e3uD+motuudHt8/aWpLUu/ShVKxcDhv5+baw889ixxzvNhW5J92O2ldhSkakse/psVoNGPb53IvHMPTlJql5v9E3nCZp7XfCqyW9q5Hk6xpH6IWMx2hsn9rkfQMAYNJpaqK31s6WJGPMLEn7Sjpb0l+MMW+y1v55PR+m8cll1M8/1to9MkI1STr00NelAldf/RtlxSSpfyBg6U8+bMnbRFhe99tf3yBJeu3rD3DGy+Wyd1/DxbBhlQtYXleqhn3QzVped8VPr5IkHfXmw5zxr37837376g78hez+sVxel3EGu/dXvyRJuu2Uj6RixTFeXtdfGf/ldft89DRJ0q3nfjEVG/PldQf6L9e69/Y/BPX1ylnub5d2P/XHkqQ/f/mtqdiWW3QF9VXeKWwJ4My9Pu/dZnX1qaC+pk85PCPSeF9Jj/EB+a8ab1PYUuV1bZTf6K21S621P5F0iKQZkr6/Vrhxxp713Uf3OvcDAACBNupkPGvtY5LulrSjMWZmffN99dvt1r2/MaYoaSsla/Af3pj7BgDAZDAWl8BtzJBofG9xbf32UMd995fULukWa236UksAAMDLBid6Y8wOxpjU9TGNMfn6BXM2VZK4V9RDl0taJulYY8zCte7fKulz9X9+c0P3CwAANGcy3qGSzjXG3CjpIUnPK5l5f4CkBZKekXRi487W2lXGmBOVJPzrjTGXSlou6QhJ29e3X9aE/QIAYNJrRqL/raRvS9pP0i5KlsX1SLpf0sWSvm6tXb52A2vtlcaYAySdLuko/V9Rm1Pr92/K4v5iKf3nNYq0uGKS1CL/6/QMBcwGDhUyU1/Knj3fWCff0uL+uwcDConkh8N+dSmVA66RFHg8Vlfd62er9cUeq2vu+Bnnfce7r0ceecS7jSRVh/u82+y9V7rYyvrYfx/3Apad25JZxLfv9opUbKvtt01tWx+lUthz9se//NG7zQ9+8IOgvgoPuK+zcdWJ75Ukve8Xl6dic373y6C+tuwOe7v784N3ebe5757Hg/rarDNd1EiSKvVVLyt60u8vLQ+vSG1bH3sePC2o3WOPXuXdZsF2bwzqS5Xb3Nvz9ddf1TFW12zi38+Unf3bOGxworfW3iXp5IB2N0sKPMoAAGB9UI8eAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiOVqtabUj5loovyjAACTUm5DGnNGDwBAxJpRpnYiyvz0Y4y5XZKste66nJMMx+OlOB4vxfF4KY7HS3E8XmqiHg/O6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIhYrOvoAQCAOKMHACBqJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBisVavSzHGzJP0GUmHSpoh6WlJV0o6y1q7Yjz3bawZYx6VtEVGeKm1dvYY7s6YMMYcLekASbtK2kVSl6QfWmuPG6HNvpLOkLS3pFZJD0r6nqTzrLWVjb7TG5HP8TDGbCnpkREe7jJr7bEbYz/HgjFmhqQjJR0m6VWS5koalPQ3SUskLbHWVh3tohwfvscj9vEhScaYcyQtlLSdpJmS+iQ9piSHnG+tfd7RZsKMj0mR6I0xW0u6RdKmkn4q6V5Jr5b0b5IONcbs53qiIveCpK86tq8Z6x0ZI2coSWhrJD0paYeR7myMebOkKyT1S7pM0nJJh0v6iqT9JB2zMXd2DHgdj7q/KnljW9ddTdyv8XCMpG8q+fB/naTHJc2S9FZJ35X0BmPMMdbaF68uFvn48D4edbGOD0n6sKQ/S/qNpGcldShJ4Islvc8Ys7e19onGnSfa+JgUiV7SfylJ8h+y1p7X2GiM+bKSJ/Dzkv5lnPZtvKy01i4e750YQx9WktAeVHIme13WHY0x3ZK+I6kiaZG19k/17WdKulbS0caYY621l270vd541vt4rOWOSMfM/ZKOkHTVOmeqn5T0B0lHKUlyV9S3xz4+vI7HWmIdH5LUba3tX3ejMebzkj4p6ROSPlDfNuHGR/S/0RtjFkg6RNKjkr6xTvjTknokvdMY0zHGu4YxZK29zlr7gOMsxOVoSZtIurTxIq0/Rr+SM2FJev9G2M0x43k8omatvdZa+/N1v5631j4j6Vv1fy5aKxT1+Ag4HtFzJflGqH677VrbJtz4mAxn9AfVb69xDNzVxpiblXwQ2FvS78Z658ZRizHmOEnzlXzYuVPSjS/n3xabqDFmrnbEbpTUK2lfY0yLtXZg7HZr3M0xxpykZI7L85JutdbeOc77tLEN1W+H19o2mceH63g0TMbxcXj9du2/c8KNj8mQ6Lev396fEX9ASaLfTpMr0c+WdPE62x4xxrzbWnvDeOzQBJI5Zqy1w8aYRyTtKGmBpHvGcsfG2evq/73IGHO9pOOttY+Pyx5tRMaYoqR/rv9z7TftSTk+RjgeDdGPD2PMaZI6JU1RMjnvNUqS/Nlr3W3CjY/ov7pX8oRIyeQzl8b2qWOwLxPFEkkHK0n2HUpm1l4gaUtJvzLG7DJ+uzYhMGZeqlfSZyXtIWla/b/G7/qLJP0u0p++zpa0k6RfWmt/vdb2yTo+so7HZBofpyn5yfcUJUn+akmHWGufW+s+E258TIYz+tHk6reT5rdKa+1Z62y6S9K/GGPWSPqIkpmkR471fr2MTKoxY619VtKn1tl8ozHmEEm/l7SXpBMkfW2s921jMcZ8SMlr4V5J7/RsHt34GOl4TKbx0Vh6bIyZJWlfJR9+/mKMeZO19s/r+TBjPj4mwxl949PTlIx49zr3m8waE232H9e9GH+MmfVgrR1WstxKimjMGGNOVpKU7pZ0oLV2+Tp3mVTjYz2Oh1Os40OSrLVLrbU/UfKz7wxJ318rPOHGx2RI9PfVb7fLiDdmS2b9hj+ZPFu/jeVrtlCZY6b+O+VWSiYjPTyWOzVBNb6yjGLMGGNOkXS+km+5DqzPNF/XpBkf63k8RhLV+FiXtfYxJR+AdjTGzKxvnnDjYzIk+sb64EOMMS/5e40xXUouXtAn6bax3rEJaJ/67cv+DWoDXVu/PdQR219Su6RbIpxRHWLv+u3LfswYYz6m5IImdyhJas9m3HVSjA+P4zGSaMbHCObUbxsrlibc+Ig+0VtrH5J0jZKJZievEz5LySfN71tre8Z418aFMWZHY8x0x/YtlHxyl6QfjO1eTTiXS1om6VhjzMLGRmNMq6TP1f/5zfHYsfFgjNnLGFN2bD9IyYV3pJf5mKlfzORsSbdLOthau2yEu0c/PnyOR+zjwxizgzEmdVlwY0y+fsGcTZUk7sal1Cfc+MjVatHMF8nkuATuPUomiByo5Cv7fSfLJXCNMYslfVzJNx2PSFotaWsl17VulfRLSUdaawfHax83BmPMWyS9pf7P2ZJer+Qs46b6tmXW2tPWuf/lSi5heamSS1geoWTpzOWSzMv5YjM+x6O+RGpHSdcruZqeJO2s/1svfKa1tvEG9rJjjDle0kVKzsjOk/u300ettRet1Sba8eF7PCbB+DhF0rlK1sA/pOQaAbOUrCxYIOkZJR+G7l6rzYQaH5Ni1r219qH6J6tGUZs3KrmO89eVFLVZr8klkbhOyWDbTclX9R2SViqZHXuxpItfrm9Qo9hV0vHrbFtQ/09KClS8mOittVcaYw6QdLqSS342ilKcKunrERwjn+NxsZJVGHtKeoOkkqSlSq4Kdr619ia9vG1Vvy0oWTblcoOS5Ccp+vHhezxiHx+/lfRtJT/z7qJkWVyPkpPEi5U83y/JIRNtfEyKM3oAACar6H+jBwBgMiPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABH7/wIQOyY47YG2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[1])\n",
    "for (i, prob) in zip(top5[1][1], top5[0][1]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 857,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed limit (60km/h)  =  49.5362013578 %\n",
      "Speed limit (20km/h)  =  23.5199466348 %\n",
      "Roundabout mandatory  =  7.32517167926 %\n",
      "No passing for vehicles over 3.5 metric tons  =  3.27312983572 %\n",
      "Priority road  =  2.9486656189 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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NvzCQJC3+6D+4f17f/HH78/nAgiyYFoeX5gTFbbv+WnfHfgcnj47+yp++I2isP3yqOyjuC+WA8467Ns2Ejj/pOGd7V3dXZv9v/l9YwanqSNj5Y/4C/7jSYFgKnL/UXbCnUCxk9m9avzForFbgih4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIhFXb2uPjKabqzVsvskbalUvcfZf9Fc7xhJKm2te8f0bysGjbW5NOJsr1ST/bF5h7u/f2CB91ilE9w/ayLD3f3eMUP/9I2gsRYV3BXD8rnks29vRn973r9a3prnNnjHSNKcOf6V19oDP7q3FXLj9tfr6WO1UqkEjVWthx3D+YJ/tbZtE/xeWUp9+zvbewvJKbO00NFfmxc01p9uWR0Ud90B/qfvwtaw8nUrfv6Us700PJrZf8xJLwoaa6gcdv7YvnbQO6bUVg4aq7fgfqPVc8n7pFxI55dDTzggaKxW4IoeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIWNRFbc7fujHV1tcoxOHqk6SvFGd7j1Po3OodI0n1nP/un9UZNJQOnuv+TNfTqNFy8gHu/uXH/tp7rL5b/YuPSNIB2/zj+hf4F93ZE2ede653zODA9qCxOor+xV++e9t/Bo01q9ddxGc85513XtBYo/0DQXGdnT3eMXfe89OgsToG8872/OykSM5sR3/5xo8FjbXjTe8Pilv3q194x/R19AaN1dPhLsDVpqS9J58uBjY65l+kSpJW/+K5oLiOef4nx3wt7Fy1bbO7GE51LNkP29akj/HhjcNBY7UCV/QAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESsJdXrjDEXSTpL0omSTpA0S9I3rLVvHifmdEkflLRcUqeklZK+Julaa226FFKAk3Ppilw9jc82rj5JuqfPv3LS0PA87xhJevPqp71jHntJ2K45YvGhzvZ53Un1qT86vuTsPyp3sPdYi39yt3eMJI10z/GOKRTCDuH29nZney6XG7d/644d3mNd8/GrvGMk6fOf/7x3zOsvNkFj3f697zjb6/Xk+Kg5qnz194dVJ/vA+/4kKO6L/3Kzd8xZZywPGuv2+x5wthdzyfljpHNWqq9rx9KgsUrFsOqGbzvtCO+Y763dFDRWvtrhbM/lkyp+Hb3pqnjbNvi/VySp7l+0UZJUa3dXHBxPbjSsel1Xl/v80NaWy+yv19wxU6FVZWo/qCTBD0paK+no8Z5sjLlQ0rcklSTdLGmrpNdI+rSkMyRd3KLtAgBgRmvVV/dXSDpSUp+kPx/vicaYPklfllSVdLa19k+stX+p5NuAeyVdZIy5pEXbBQDAjNaSRG+tvdNa+4S1tj6Jp18kaaGkm6y19+/0M0pKvhmQJviwAAAAJmc6JuOd23j8vqPvbknDkk43xrj/KAQAACYt15xs0yrGmLMl3amMyXjGmF9IOkXSKdba1IwXY8wKScdKepG19pEJxnLOmLHWniRJpXVrU33FBftJksY2b3T+zI2FynhDOtXkPwlEkuaNjXnHlLrDXq/OovtzU0/n/pKkodIGZ39HwGfB4ppB7xhJqjUmsnhZtixorOaku6z2rPfFw4886j3WQYsP8I6RpE2b/CdOlathx8cRh7v3Y74x2apaTU8CffRR/30hSfvvNz8obvOmbf5BAYeUJB1xlHuaUb5xjFZr6f3c5piwOBm1VU8Exa3r8H+tB8b8z28J93ngsCXJcfPU2ifTnbmw/VEph8W15QOuW1uc/w4/OJkguXJ1+jWtBxyMxx95fPN/A4/kxHRc0c9uPGZNNW22+0/BBgAAu2jVrPtWan5ymfCjlrX25IyuuiStuuGaVMfS/3GZsvok6dr5/ldRQ7kX7vK65S++UpJ034p/dPYflevxHmvx5WHL60a7/f9aU/2PbweNlbV8rqMj2YbR0VFn/+vNH3iPdc0/fNg7RgpbXrdhIOyKLWt5XW9j2dTgYPpbmte97nVBY03l8jrlwt4vWcvrZncmp8ztpfR+7toR9k1W6bI/Cor7zFL/q9FWL6+z19wqSTKXXZjqq7f7f2MpSVvXhy3LK871P1dptBw0Vtb19fc+/wNJ0gV//spUX8jyujV3Pusd4zIdV/TNK/bZGf19uz0PAAAEmo5E/1jj8cjdO4wxBUmHSqpIemoqNwoAgBhNR6K/o/F4vqPvTEndku6x1rq/OwUAAJM2HYn+m5I2S7rEGHNKs9EY0ynpY41/+v9xEgAApLTqXvevk9ScmbOo8XiaMeaGxv9vttZeKUnW2gFjzDuUJPy7jDE3KbkF7mslHdVoD5h1AwAAdteqK/oTJV3a+O/3Gm2H7dR20c5PttbeoqQIzt2S3ijpPZLKkt4n6ZJJ3mEPAABMoCVX9NbaqyRd5RnzM0mvbsX4WdYf1ZdqW9yZz+yTpJf0+N+U44TtYTfMKdyXroA1keMPPDZorJEhd7U+Hd5YNvP44c7uBT8PuJlHLuzz4zOnnOAdc3Bb2FilkrtaX3PZXVZ/MeCmKCe/9EzvGEn6zLEneseELnmrVMZflufqnzcvbFnpG0zY8rrBEf/lUz+89T+CxhprG3K213OzMvtrfZ1BY3WWFgTF/cGY/7F/ZyVs6drg9mFne72SvB/K/SOpvkrgPV6WHhF2Q6WnHtniHTM2N+zmPLMySuw1r1DrjpsFlQbc55SpQD16AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYi0parOvum9HusjB0bVKZp8kPbd2g/c4c+f2esdI0o4lA94xR9y/Imis/U86xtleqFaT/gF3sYsH5/sXLjmhGFYo4rA/uNA7ZvxSLNk6O90FSNoaRXKy+v/+6r/1HyujntBEPva/PuMdUyiEvaXz+fELM7n6t23zLwAlSbWAwkCS1NbmXzyqP3Abuyru/Zgr5jL7Byvpwi6T0a7tQXFLNs3xjtmwKmx/dCxx7496PinjUumppvoq+bGgsR5+YjAobk6X/7G/sNdd3Gwiw5VRZ3uznlehy3GslilqAwAA9gISPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAELGoq9e9ZGm6wlBXsZ7ZJ0mvmO2u8jaeH33nN94xklQ7xL8yXGepGDTW0oefdW9DqSxJGsno31zzH69WD6taVTloSVBc0FgVd927YrE4bv+5Z7/Ke6xqOaxa2+233+4dk8uFfXYfG3O/ZvV6PbO/2ecrNG779n7vmGpgpbxcOaO9nt0/r5YLGmtrx6yguEXD/r9b55KwSpujY+7Ka/XGJrgOn3pH2P4olv2rFEpSbqG74uR4RgbdeWAi5R3pan2SVG+cNsqb0v2lcth5sRW4ogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACIWdVGbgw5OF3AoFtsy+yQp39PhPU5e271jJOmh+x/3juk9wr/ojiRt73MXwFiWTwot/KrvGWf//IJ/wY3usJolGsq1e8e0BRZICZXL+b9lTl1+QtBYCxbO9o751je/EzRWe4e7eFEul8vsX7Tf/kFj5QP2oSQNju7wjhmSuzjRREYL7iIu3bnOzP56vSdorJzCjuHRtozKO+Noy7l/r4m87OxTne09s3oy++9/4K6gsXIF/3OwJO23eIF3zPo17mJeEynLve/rjdfS1T93oX8Rs1bhih4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIi1pHqdMeYiSWdJOlHSCZJmSfqGtfbNjuculfT0OD/uZmvtJa3Yrl89eV+qbdH8t2f2SdIxB5/uPc7m9WGflxYfuNQ7ZqTHXWVsIsOlEWd7tVE4q7/srqD1i80bvcf6vWLYNuZrAZW1GtXVfLW1uV+zZrW2fD7v7K9X/SuG9fR0esdI0nve8x7vmK6urqCxqmMZVd4a1QFrjv7amP++kKRqtRoU19/f7x0zWgqr1pavuY+rXD2X2V/KjQWN1VkKi6sGHFdjW8PeLyvuecDZPvJHw5n9i5YdFDTWhpEtQXH9z27yjhleF1Y58OzTz3S293YnVVF/55R0/8/X3h80Viu0qkztB5Uk+EFJayUdPYmYX0m6xdG+okXbBADAjNeqRH+FkgS/UsmV/Z2TiHnQWntVi8YHAAAOLUn01trnE7sxphU/EgAAtECrruhDHGiMeaek+ZK2SLrXWvvQNG4PAADRydXrYZMRshhjzlby1X3IZLy7JF1qrV09ybGcM0SstSdJ0taBx1J9fT2HSJIGhp5x/szOYs9kht7F1o1D3jGSpLr/xJhCwT1JbOKx3K/z/P2WSJK2bFzr7B+t1ryHWjIaeEwdeoh/TFt70FC5jEl8zfas90W95v+7Pfb4o94xkrRo0f7eMX19c4LGyjo+8oXkWqBaSU/GW7lyZdBQyw4/PCjuuXXPecfsGBgIGuvoo49xtjcnabomFAa8nSVJuSefDItr8x/w4Yp7Uu6EY+XdYy07KHktn1yTPhbyHWHnqkopY2LoBEL2R6Xsf36TpFk9vc72JYuS8+na9enz6VB52Huc45cd3/zfwKMrMR3L64Yl/b2kkyXNbfzX/Lv+2ZJ+ZIzxz7YAACBlyr+6t9ZulPTh3ZrvNsacJ+mnkk6V9HZJ10ziZ52c0VWXpP/6+dtTHb/3sq8oq08KW15302fu9Y6RpFqlwztmwX6zg8aqZyyvu/TyZDf/62cuc/Y/sd1/edKnV416x0hS7t+/5h/UdWDQWFnL6zo7kyVLpYxlWZWAJWVv+sOLvWMk6QN/daV3zKvOf23QWNWK+4p+ztzkG4L+bemlbX/8lkuDxvr2d78TFPexf/gH75i7bv+voLHue+AXzvbZvcn+2D6Y3h/lXNjptP2d7nPRhHEBy+t+f+uvg8bq6HVfnf/H5/5TkvSGd70q1TdnWdi3SxueCFteV+zyv27d+mzYuers09154pMf+LQk6QOfvCLVF7K8bs0t67xjXPaZG+ZYayuSvtL4p3uRIgAA8LLPJPqG5h0P+OoeAIAW2NcS/fLG41PTuhUAAERiyhO9MeZUY0zqHqnGmHOV3HhHkv5tarcKAIA4tepe96+T9LrGPxc1Hk8zxtzQ+P/N1trmzKJPSDrWGHOXkrvpSdLxks5t/P+HrLX3tGK7AACY6Vo16/5ESbtPwT2s8Z8kPSOpmei/Lun1kl4q6VWS2iVtkGQlXWet/UmLtkkbtqdnb5arlcw+SXryf09qCf8u1mzp9o6RpM5O/zXgG1aFFRLJj7iXYZYak04fezpjXXn7Af6D1dz3KJjIpvvchYbGs/CcNwSNVS6792NHR8e4/S95yXHeY730ZS/xjpGks88+xzsmazXBRIod7lNBrvHzujrSK0RchW4mI3QbSyX/GdK10PuEZG1jLru/txa2JntUYXHr8/6/W19/2H0nKnPdcc8XgWpPHx9DJf9145LUMyesCFTv7HneMeXymqCxRkbd906p1WuZ/f0rdwSN1QqtugXuVZKumuRzvyrpq60YFwAAjG9fm4wHAABaiEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxFpVvW6f1N7Vk2prVuNy9UnSgBZ4j7P0mPneMZKkgOpf61evnfhJDs/lB53t5VxVkrQuo/+4nv28xyqud/+sidS/8CX/oLMuDhurNuZub1Q7q9eqzv6ebv9KhVd/9OPeMZLU0dHnHZMvFIPGqpQzjsXG/qg4KrPVKmHV6wpdYds4sGW7d8zRhy8NGquFTRWYAAAgAElEQVS74D4/tOXaMvsH5K4AOZHOXNh+vHnsCf+gpekqc5MxlnFNWG/8zmOOVJJf736PTWRgY9j5Y2BByTtm8fxDgsbaNLbV2V6pVzL7u/vcx9RU4IoeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIRV297t7/m65E9+oXFzL7JGls4CnvcebM7vKOkaTaqH91p3JG1bWJHLJokbO92N4+bv+PH3zAe6yto7O9YyRp/4DiX7V6OWiskRF3hayent5x+0cDXrOLLr7IO0aS1NbuHVIo5IOGuuvOH7o7Gq9JvlBPdbW1pSvaTYYxfxQUt+apNd4xJx4XVp2sUtrhbC8W52T2zx7eFDRW+3BY9bpfzPevRDf4qLvq2kRqGYXX6pWkymNlS/rnjh3grgA5kZdc+OKguG2P+lev27B5Q9BY1U738VGpJOeHDdueSfXNWRh2XmwFrugBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIRV3UZu78dKGFfL6a2SdJm4b8d0n3LP/iEpK0ev1q75iSu9bKhEY3r3O3l5JCEE88+htnf2+h6D3Wq+b0e8dI0oMDc71jttfDCoLMm+8uMJFvFIXJ6v+rd7/De6xVq1Z5x0jShi3bvGOefvrpoLHyBfdn/lwul9n/iU9+PGisd1/23qC4eV193jHXXXdd0FgDOXfxog7VMvuf/eSng8Y6dr+MijETOLLTv0jKTzetDRqrvNU9VmUsOT62rkkXYJozElClStJj254Iiusoz/GOWXiy/zlHkvIVd1G09mKSCxYvOTTV9/iKVUFjtQJX9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARGyPq9cZY+ZLer2kCyQdJ2mxpDFJv5Z0vaTrrbU1R9zpkj4oabmkTkkrJX1N0rXW2uqebpckXXDO/qm22bMKmX2SlM/1eo9T31H3jpGkwZOWescs7tgvaKz+0SFn+/7zuyRJH3j78c7+7eXt3mMNlsOqVpW/9Kx3TO5Nfxg0VvuN/9f98+Ykn33bd3Q7+y++9G3eY7XlwvZHpZyuCDaRei31VpuUcsVdBbBYrGf2v3j5S4PG+sm9dwXF5fN5/5h6WGW4qvvlV64tOT4K3eknHLPiwaCx+pccEBQ3lvMvZXnMWUcEjfXgzx9zd+SS46JWTFdaLI+F7fveQf8qdJJUXVD2jln7g+GgsfIF9/l07N2jyc/9yTOpviNPCnudW6EVV/QXS/qypFMl/T9Jn5H0LUkvlvQVSdYYs8uZzhhzoaS7JZ0p6duSPiepKOnTkm5qwTYBAAC1ph7945JeK+l7O1+5G2P+RtLPJb1R0huUJH8ZY/qUfDCoSjrbWnt/o/1Dku6QdJEx5hJrLQkfAIA9tMdX9NbaO6y1393963lr7XpJX2j88+ydui6StFDSTc0k33h+SclX+ZL053u6XQAAYO9Pxmv+0WTnP/Cd23j8vuP5d0salnS6MaZjb24YAAAzQSu+uncyxhQkvbXxz52T+lGNx8d3j7HWVowxT0s6VtJhkh6ZYIwHXO3WWknSaaf8c6qvt2dJZp8k5XL+E35UDZuMV635xxXbwl6ySsYkrd6+AyVJy1/xMWd/te4/LzLg15IkFZb7T6bpyYftj9ycjLj8+P09mh80Xoh6PWwSX4hcxkf+trZkh/R0z0711euBL7TC4kL2Ri7wWqY9I6zQOD/Ma09P2m37wr8GjdXR7j/pUpL+Muc/8XKo5p50OZGRoZKz/ajDj5Yk/fQ796T62trC9n0h8BxXDwirjQUewzn3efHwQ4+UJH3v5h+l+ordYa9zK+zNK/qPK5mQd5u19r92am+eMbKmczfbw6ZeAgCA5+2VK3pjzHslvV/So5Le4hne/OA+4Ucta+3JGV11Sbr3/velOppX8q4+aYqX15VGvGNavbyueSV/3w8/6OyfyuV1bwlYXjcyZ2HQWL2Zy+uSt0S9333lM5Tf4j3WC2F5Xb7bvY3NK/mh4fRxUC6HXR3ma6NhcVO4vG5HRljzSn5rOb20rfvPLg0aayBwed2nApbXPVDaHDRW1vK65pX877z29FTfrM6wfT+3Z15QXHWB/7E/+Iz/t4hS9vK65pX8BX/w8lTfkpMWeY9z77/+xjvGpeVX9MaYd0m6RtLDks6x1m7d7SnNM0b6u8BE327PAwAAgVqa6I0xl0u6TtIKJUl+veNpzY+GRzriC5IOVTJ576lWbhsAADNRyxK9MeavlNzw5kElSX5jxlPvaDye7+g7U1K3pHustWHf7wEAgOe1JNE3bnbzcUkPSHq5tXa8PwR9U9JmSZcYY07Z6Wd0SmpO/f58K7YLAICZrhX3ur9U0keV3OnuJ5Lea4zZ/WmrrLU3SJK1dsAY8w4lCf8uY8xNkrYqubveUY32m/d0uwAAQGtm3R/aeMxLujzjOT+WdEPzH9baW4wxZ0n6WyW3yG0WtXmfpM9aa0MX6O5i4YHpWZ+FYltmnyRVA9aZ7pgfOHOz5F6bOp7No2uCxtpRcf8lpNqebPu2heuc/bNKc73H2rRt9/mXk/Pj0/xn6Z5836qgsUaVLjohSR1a0uhf6w7ML/Aeqxq43rytcywoLki9K6OjMRu/nl4B0F4IWxdcU2dQXEilq+F82DbO+9DfOdsL770s6f/sNam+gY6wvzZeufHhoLgtAfdZWL11Q9BYx591tLO9a1ZnZv+m9f4rVCRpeGhHUNyOtf7n0+6OsGNxdI57BUg9n7wmo/PT/c9uCNv3rbDHid5ae5WkqwLifibp1Xs6PgAAyEY9egAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGKtqF63zxqrpqs71RsVn1x9kpTPd3iPM6fTv4qUJPX1+ldOKubDXrKhoSH3zyskVZYO3n+Os3/z4KD3WMPlzd4xkvSz44reMWf+ZFbQWDve9IfO9vbP3ShJGn7XW5399Vt+7D1WR2m7d4wkFeq93jHD/rtQkpSr15ztzbp7lXy6Al+7O2RCA51h1xfza/7HYm5LRhXCCZRW3Ols7x7548z+sfqioLG2DYZVNdua968O15PzrxApSZue6Xe2V8aqmf197VkVEcdXO8T/uJek2fv5n4fzPSE1EaWBTdvcP69xaM9y/OqVbf65pVW4ogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIkaiBwAgYiR6AAAiRqIHACBiJHoAACJGogcAIGJRV6/LtQ87GmvZfZIqNf+SXKVSurLXZNTb894xo2FDqVh0lzXL5ZKKT4Wi+1A4YM4877EOWHCgd4wkVcsV75hfHRtWQu2UP13t7ignn33nbXJXFhx+9ZneY5V/8EPvGEmqDI96x+RHwypk1TJe/99K9w+3Bw2luaNjQXEldwHGcfW+6d1BY421Z/xyjeqXKqf7/3jW00FjjZTDKqjVx/xP37XBsBPI4DZ35cDqWPL+G1yb7t/eGTbWgt6FQXH9j7kryo1n4YlhlfKGK+78UWtUgXT3+1crbRWu6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIhFXdSmrHKqra56Zp8k5fI573FyRf8YSark/Iu45BQ21ljdXSClrtr4/RX/8Xa0hRUt6a76F8FYEFAYSJI2v73P2T5nQfLz+jP6u/53QNGj81/hHSNJHcf8rndM99VhRVxyJXd7vmOJJGlWaX06pj+gyoykwcs/GhRX27HCO2a4vSdorNccMuBs/2pH8p79k0M2p/pGngk7nVbaw663euRfDGdL4HuzkHOfB3Lj9Je2hRXr2bxiXVDccCHjIB5HcWPQUNkVnWq5zP7R/sCKZC3AFT0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABHb4+p1xpj5kl4v6QJJx0laLGlM0q8lXS/pemttbafnL5X09Dg/8mZr7SV7ul2StKOUrqhUq2X3SVKh4L9LymM7vGMkqRhQWWuk4l+hSZLyeXeVt2o9qag0OOquatVW8a+4VPUvyidJGpB/Za1SfnbQWE8td7/+ZzZeknsz+hcfON97rBf/Y1g1rsEVP/WO2X7xXUFjtdc6ne29/3K9JGnoL96W6uscC6tOVuoMi1s03OUdY5asDRpLm7rd7eXG+2FT+iAvj40EDTU4HHYa3m++/7HYO8d/H0rSQNld5i3X2PS2uenzS3Eo7EQwtj3s+HjRKw/3jln3y21BYxU6M86LjebCSLq/sn0waKxWaEWZ2oslfV7SOkl3SlotaX9Jb5D0FUmvMsZcbK3d/Tf/laRbHD/PvxYlAABwakWif1zSayV9b7cr97+R9HNJb1SS9L+1W9yD1tqrWjA+AADIsMeJ3lp7R0b7emPMFyRdLelspRM9AADYy1pxRT+ecuPR9ceaA40x75Q0X9IWSfdaax/ay9sDAMCMkqvX/SdbTYYxpiDpvyW9WNL51tr/arQvVfZkvLskXWqtXT3JMR5wtVtrT5Kk7cMrU329nUskSYMl9ySdXM49CWs89XrY5JFczn/RQ/DrlfFr9XYcJEkaHF3jDgsYrjbxUzL4D5bLuScZTjhSzT3WRMdHu/98QXVtKE/8JIea/6GoelvY8ZGru4/F/MFLJUnV1atSfW2Bx2I1cK1Pe8DE0KeKYfu+nLE/DlmSTPh6Zm363FKtBu6PkBdaUkfIxOGM434itbp7Px6+9AhJ0spVT6RjxsLGCj3HdfQVvWPKw2HnbrW5z3KHLz1SkrRy1eOpvnrZ/8x4wvEnNf837CBp2JvL6z6uJMnf1kzyDcOS/l7SyZLmNv47S8lEvrMl/cgY4z8dHQAApOyVK3pjzHslXSPpUUlnWGu3TiKmIOmnkk6VdLm19po92IS6JN320O+nOs44/DOSpJ+tvNwZuK8vrxtr8fK605Z9VpJ075PvdfaHLK8bDfz4WA9YXlfMhS2vGxtzX6GceXRyfNz9qPv4WLza/5cLXl7X7h9TK4YtaZpoed1gC5fX9Ycur+v3vyIyS54LGuu5qnt53Vf/6TuSpD95/2tTfQP9Yd8ehC6vWxawvG7dYNg2Zi2v+96/JtdwF1z6e6m+0trRoLHGKmHbeMQrD/GOCV1el+scdrZ/7xt3SZIu+KOzU32ldf7LLzete34f7ltX9MaYdylJ8g9LOmcySV6SrLUVJcvxJOnMVm8XAAAzUUsTvTHmcknXKVkLf461dr3nj9jUeOSrewAAWqBlid4Y81eSPi3pQSVJ3v1dz/iWNx6fatV2AQAwk7Uk0RtjPqRk8t0Dkl5urd08znNPNcakpkcaY86VdEXjn//Wiu0CAGCma8W97i+V9FFJVUk/kfReY8zuT1tlrb2h8f+fkHSsMeYuSc01TMdLOrfx/x+y1t6zp9sFAABac8OcQxuPeUnuqcrSjyXd0Pj/ryspgvNSSa+S1C5pgyQr6Tpr7U9asE2SpGI+Peszl6tl9klSW85/xmctHzYhcmzMfwZ9NfAVG6u4Z7TXGqsuRjL6Q5ap56ph+6PuvK/S+EZzYYVEKhlj1Rt3ASjL/dpsOsi/KMidX1jiHSNJ874/5B1zzG0BC/0l7ci4QUB346Ucbku/pht7A5YFSFpz2WFBcU/N9V8B8pd9/oVOJKlUcc/wP3BxMhv/H64+KdU36ChkMhmr1g8ExT37sP+s9u9/J73efTJGSu4vf3ONewC0O/o754atiMkf4l4BMpHBbZOa972Ltq7tQWMVi+7fra1xb5SOjvR5YrjL//3cKq24Be5Vkq7yeP5XJX11T8cFAAATox49AAARI9EDABAxEj0AABEj0QMAEDESPQAAESPRAwAQMRI9AAARI9EDABAxEj0AABEj0QMAEDESPQAAESPRAwAQsVZUr9tnFQsdqbZco7qQq0/6bTU3H/lC1TsmVLEeUE5OknLuz3R5JdWnenPuSmSD1eGQwQJiwqrX1dr8K3hJktozXudco73dvS076v7jddb9fy9Javvdpd4xV34rrMLzhhH3a/ZP1aSK2/sHdqT6RgqrgsZ655yw084GR0WwieRLYe/NQtkdV6kl7RtHtqX6im1h1fwOOch9LprIkkX+54JTXnF00FgVuX+3xY1Kc5/42rGpvu2bw84Da9alj7XJeHiF/3XrL7f3B42lwhZ3e67xXu9I989bsihsrBbgih4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIhFXb1uoJyuNFat1zL7JKmQVdVsHPla2TtGkmpV/+pTHd3+2ydJtVrN3dGW/Ly2DvfvMLvNvwJVvRZ2WFXK/nHVXOD+qLurkzV/2w6591e+zX8bayp5x0jS1uJm75hDNBI01nCt19neODw0q5a+JpjXOztoLHVkHIsT6Ax4n9WzjvuJ4goZ10C5Zn/6fVEqh73OxXxnUNxYwO+WDyy02dHuPg/kGjukw1GxcsHcsKqN+82bFxT3OyfM9Y6pXbI0aKyhjCqni5fMkiR96lMvT/Vt3RZ2rmoFrugBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIRV3Upief/hyTbxRfcPVJUi2gcEau3uEdI0ndXfO9Y8rVjUFj9XZ1OdvzuWQ/9Ha6+6v1Me+xhktbvGMkqbvbfz9Wq+7tnki+rS+jPSk0NLvb3V+p+BcSKRYWeMdIUmev//7o6vM/piRp9qD7/dA8PmYX0vu51hFWpKM98KzTnvGeHU//gLt41UQ6O9qd7c3SLYVc+jiYM/eEoLEGBx8Kiusu+h8fbW1h75dKNaNYUnM/5NLniTm9c4LGGh4ZDoqrBly3lkbDrnXndrsL6BRyycE9t5Dub+vdGjRWK3BFDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxEj0AABEjEQPAEDESPQAAESMRA8AQMRI9AAARIxEDwBAxFpSvc4Y8wlJp0g6UtICSSOSnpF0i6TrrLWpcmbGmNMlfVDSckmdklZK+pqka6211VZsV2c+n2rL5bL7JCkfsEfy9QP8gyRVatu8YwqFsEp55ZK7ile9Xhu3v+iowjSRvvYe7xhJGqut9o7Jt3UGjZXPuV/oXOMAyeqfM/tA77F2DK/zjpGk0SH/yoHlem7iJzm0ZVVra8tl9pd6wt6mswOqNkpSTu5tHM+C+WEV1CrD7uOq0NiGRW1LUn0jw48GjdXTMS8orlb1v06rqBQ0VnvefVw13y/txXR/rupf6VGSivmw42o04Lr1oKVHBY21YeMjzvaaKpKkEaUr1eW7pu+6ulUjXyGpR9IPJF0j6RuSKpKukvSQMeagnZ9sjLlQ0t2SzpT0bUmfk1SU9GlJN7VomwAAmPFaVY++z1qb+qhojLla0t9I+mtJf9Fo65P0ZUlVSWdba+9vtH9I0h2SLjLGXGKtJeEDALCHWnJF70ryza7G4xE7tV0kaaGkm5pJfqef8cHGP/+8FdsFAMBMt7f/aPCaxuNDO7Wd23j8vuP5d0salnS6MSbsj9EAAOB5uXq93rIfZoy5UlKvpNlKJuf9jpIk/wpr7abGc37R6DvFWvuA42eskHSspBdZa90zHn773FS8JFlrT5KkwdITqb6uYjJdYGRsjfNn5oLmMvlPEpIk1Sv+Mbmw1yvrde4qHixJGhlzT4TLBf11J3Ab5T/5rC73pMqJ5HLuuK72ZJLVSHmtsz/f5v9aV2tl7xhJUsBcpsE1YWOV5T7w9z/4EEnShtXPpPpq7WETu+Yd2BsUp4xtHE9b4OmtXnOP1dWZTMYcKT2XjlHA+1mScmHXWyG/Wj3wvZl1MPZ2JOePwdH0+aMt8DqyOUHYOy7g+GgvhE3mLVfcx35vZ5JfBkvp/BKy5+d0P/9leNgs24ZW/Y2+6UpJ++/07+9L+h/NJN8wu/G4PeNnNNvDpssCAIDntTTRW2sXSZIxZn9Jp0v6uKT/Nsb8vrX2l5P8Mc1PLhN+ALLWnpzRVZekX6++LNVx3MHXKKtPkvIF/6UdU7m8Tm2BV2xld9yJSz8nSXpw1buc/SHL66SwbRyr+y+vq+dmT/wkh3yuz9l+/JJ/kiQ9tPb9zv5Z3VO3vE4j/tcAP70ibKy1laKz/S+/+BVJ0qfe+fZU3/AB6W/MJuOtf788KC5keV1X4AVs1vK6Fx/+UUnSipUfTvWN1DeEDdYe9g3HVC6vq+eGnO2nLfuiJOneJ9+Z6uuuzwoaq1x1jzWR0YDjY9HCY4LGylped/pR10qS7nnsPam+asA3HL//ktu8Y1z2yt/orbUbrLXflnSepPmSbtypu3nFnnWG7tvteQAAINBenYxnrX1G0sOSjjXGLGg0P9Z4PHL35xtjCpIOVbIG/6m9uW0AAMwEU3GrnuZ3nc3vxO9oPJ7veO6Zkrol3WOtdd+qDQAATNoeJ3pjzNHGmEWO9rbGDXP2U5K4m3+Q/qakzZIuMcacstPzOyV9rPHPz+/pdgEAgNZMxjtf0qeMMXdLelLSFiUz78+SdJik9ZLe0XyytXbAGPMOJQn/LmPMTZK2SnqtpKMa7Te3YLsAAJjxWpHofyjpS5LOkHSCkmVxQ5Iel/R1SZ+11u5yh39r7S3GmLMk/a2kN+q3RW3e13h+Sxb3D1XTP6ZWz+6TpO6cf8GNWiVslnmh03/dbbUedh+hfEfGTNbGuvx8h3sNe1vd/y8odYUVpejKL/aOKVXC/sLTUXTPdM411jRn9ZdG/X+3asj9EiR1h4R1hxWMqY25Z+vXc8nvW+scTPXNmbUwaKyOXNh9J0rq944ZLIetye7syijM1NZ4/bvSc4X76mHFnIbqYe+Xtpx/XHfgKX+o7F6FkGsUUSrU0v3VatjS78GqewXIRBb0HDTxk3azfdNw0Fjz5h7ubC/kOzL7Vz/nvO3LlNjjRG+tXSHJvTZr/LifSXr1no4PAACyUY8eAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiOXq9ZbUj9nXRPlLAQBmpLAKQQ1c0QMAELFWlKndF2V++jHGPCBJ1tqTp25z9l3sj12xP3bF/tgV+2NX7I9d7av7gyt6AAAiRqIHACBiJHoAACJGogcAIGIkegAAIhbrOnoAACCu6AEAiBqJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiFis1etSjDFLJH1U0vmS5ktaJ+kWSR+x1m6bzm2basaYVZIOyejeYK1dNIWbMyWMMRdJOkvSiZJOkDRL0jestW8eJ+Z0SR+UtFxSp6SVkr4m6VprbXWvb/Re5LM/jDFLJT09zo+72Vp7yd7YzqlgjJkv6fWSLpB0nKTFksYk/VrS9ZKut9bWHHFRHh+++yP240OSjDGfkHSKpCMlLZA0IukZJTnkOmvtFkfMPnN8zIhEb4xZJukeSftJulXSo5JeJukySecbY85wvVCR2y7pM472wanekCnyQSUJbVDSWklHj/dkY8yFkr4lqSTpZklbJb1G0qclnSHp4r25sVPAa380/ErJiW13K1q4XdPhYkmfV/Lh/05JqyXtL+kNkr4i6VXGmIuttc/fXSzy48N7fzTEenxI0hWSfinpB5I2SupRksCvkvSnxpjl1to1zSfva8fHjEj0kv5FSZJ/r7X22majMeaflbyAV0v6s2natunSb629aro3YgpdoSShrVRyJXtn1hONMX2SviypKulsa+39jfYPSbpD0kXGmEustTft9a3eeya9P3byYKTHzOOSXivpe7tdqf6NpJ9LeqOSJPetRnvsx4fX/thJrMeHJPVZa0u7Nxpjrpb0N5L+WtJfNNr2ueMj+r/RG2MOk3SepFWSPrdb999JGpL0FmNMzxRvGqaQtfZOa+0TjqsQl4skLZR0U/NN2vgZJSVXwpL053thM6eM5/6ImrX2Dmvtd3f/et5au17SFxr/PHunrqiPj4D9ET1Xkm92NR6P2Kltnzs+ZsIV/bmNx9sdB+4OY8zPlHwQWC7pR1O9cdOowxjzZkkHK/mw85Cku1/If1tsoeYx831H392ShiWdbozpsNaOTt1mTbsDjTHvVDLHZYuke621D03zNu1t5cZjZae2mXx8uPZH00w8Pl7TeNz599znjo+ZkOiPajw+ntH/hJJEf6RmVqJfJOnru7U9bYx5m7X2x9OxQfuQzGPGWlsxxjwt6VhJh0l6ZCo3bJq9svHf84wxd0m61Fq7elq2aC8yxhQkvbXxz51P2jPy+BhnfzRFf3wYY66U1CtptpLJeb+jJMl/fKen7XPHR/Rf3St5QaRk8plLs33OFGzLvuJ6SS9Xkux7lMys/aKkpZL+0xhzwvRt2j6BY2ZXw5L+XtLJkuY2/mv+Xf9sST+K9E9fH5f0Ykm3WWv/a6f2mXp8ZO2PmXR8XKnkT76XK0ny35d0nrV2007P2eeOj5lwRT+RXONxxvyt0lr7kd2aVkj6M2PMoKT3K5lJ+vqp3q4XkBl1zFhrN0r68G7NdxtjzpP0U0mnSnq7pGumetv2FmPMe5W8Fx6V9BbP8OiOj/H2x0w6PppLj40x+3rSei4AAAN7SURBVEs6XcmHn/82xvy+tfaXk/wxU358zIQr+uanp9kZ/X27PW8ma060OXNat2L6ccxMgrW2omS5lRTRMWOMeZeSpPSwpHOstVt3e8qMOj4msT+cYj0+JMlau8Fa+20lf/adL+nGnbr3ueNjJiT6xxqPR2b0N2dLZv0NfybZ2HiM5Wu2UJnHTOPvlIcqmYz01FRu1D6q+ZVlFMeMMeZySdcp+ZbrnMZM893NmONjkvtjPFEdH7uz1j6j5APQscaYBY3mfe74mAmJvrk++DxjzC6/rzFmlpKbF4xIum+qN2wfdFrj8QV/gtpDdzQez3f0nSmpW9I9Ec6oDrG88fiCP2aMMX+l5IYmDypJahsznjojjg+P/TGeaI6PcRzYeGyuWNrnjo/oE7219klJtyuZaPau3bo/ouST5o3W2qEp3rRpYYw51hgzz9F+iJJP7pL0b1O7Vfucb0raLOkSY8wpzUZjTKekjzX++fnp2LDpYIw51RhTdLSfq+TGO9IL/Jhp3Mzk45IekPRya+3mcZ4e/fHhsz9iPz6MMUcbY1K3BTfGtDVumLOfksTdvJX6Pnd85Or1aOaLZHLcAvcRJRNEzlHylf3pM+UWuMaYqyT9TyXfdDwtaYekZUrua90p6TZJr7fWjk3XNu4NxpjXSXpd45+LJP2ekquMnzTaNltrr9zt+d9UcgvLm5TcwvK1SpbOfFOSeSHfbMZnfzSWSB0r6S4ld9OTpOP12/XCH7LWNk9gLzjGmEsl3aDkiuxauf92uspae8NOMdEeH777YwYcH5dL+pSSNfBPKrlHwP5KVhYcJmm9kg9DD+8Us08dHzNi1r219snGJ6tmUZtXK7mP82eVFLWZ1OSSSNyp5GB7iZKv6nsk9SuZHft1SV9/oZ6gJnCipEt3azus8Z+UFKh4PtFba28xxpwl6W+V3PKzWZTifZI+G8E+8tkfX1eyCuOlkl4lqV3SBiV3BbvOWvsTvbAd2njMK1k25fJjJclPUvTHh+/+iP34+KGkLyn5M+8JSpbFDSm5SPy6ktd7lxyyrx0fM+KKHgCAmSr6v9EDADCTkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiJHoAQCIGIkeAICIkegBAIgYiR4AgIiR6AEAiBiJHgCAiP1/++OTxQPHneYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[2])\n",
    "for (i, prob) in zip(top5[1][2], top5[0][2]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 858,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Right-of-way at the next intersection  =  27.180147171 %\n",
      "Road narrows on the right  =  24.9597206712 %\n",
      "Beware of ice/snow  =  18.668320775 %\n",
      "Road work  =  10.3640198708 %\n",
      "Children crossing  =  9.70191508532 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[3])\n",
    "for (i, prob) in zip(top5[1][3], top5[0][3]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 859,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Children crossing  =  42.5620228052 %\n",
      "Speed limit (50km/h)  =  15.2649283409 %\n",
      "Speed limit (60km/h)  =  9.23648476601 %\n",
      "Road work  =  6.42005354166 %\n",
      "Speed limit (80km/h)  =  4.65737767518 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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f/6q0/7D57imbA3nq8b8Exc3d/xDvmKYG94VvBtLT655mWyi+brj62zs2BOWqhN1xMB4AAKgQCj0AABGj0AMAEDEKPQAAEaPQAwAQMQo9AAARo9ADABAxCj0AABGj0AMAEDEKPQAAEaPQAwAQMQo9AAARG6rV6wZF+6a2krZ8Lle2TwpcaOZ/bvGOkaSrvn2dd8z3vnNNUK6GhgZnezqd7re/kPZf9KEnlfWOkaSR6WbvmGxd2KI2YzZ1O9trCsmqNhN73Y/bdtMvvHNN7ez1jpGkroz/4kDH/+K7QbmeKdQ520cXzwU2OfrzuVRQrqOvujwobvGJZ3rHFLrci48M5DeX/7uz/aQr/1OSdJej/+P/+eWgXL//9b1BcbVl/mb92bquIyjXqJaxzvbqqpqy/SuXvxSU66QT3xoUl0/5l7N0Puy16tlnlzvbDzlwXrH/+ZK+sePdC4cNBs7oAQCIGIUeAICIUegBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCIGIUeAICIUegBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCIWNSr163dsKGkrTeXLdsnSRd8/DzvPB87/VTvGEmac8zbvGN6e7qCcmWzBWd7Pp+0b+10P24+7Y7rT6on7P1jKu3/u6Va80G5Wh563Nledcqp/fZXb97onas+VeUdI0mrjzncO+bpsRODcjV2u1fYSxdX86vLlfa3N9YG5ertHREUt3qy/+qGI1/1/3tJUmt7p7O9Jpccb5Mc/b+95gdBudaN9F+lUJKOPeYI75jf33lnUK5ZM6c42zOZmrL97WvWB+Xae/a+QXH33P9n75jpe44PyjVtyhxneyZTW7Z/3Ya1QbkqgTN6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYlEvajNtTGtJW6a6qmyfJN14/be98+QdC37siMndPd4xDdmwRVwK6TK5CsnjFbLu/lzefxubq8MOq83NKe+YvZ9bFZQr8+gfne2prVv67a/JNnjnWlQTthDRqZ+/wDtmRU/YQjPVmayzPV88FejOlJ4TZHr8jw1JaguKkk698QbvmHve9TdBuaa2uRe9qs7lJEkjHf2TlrwSlGvT0YcGxT32xGPeMRd948tBuS4+/1+d7fni/mjfWLp40MSpYYsX3XH/3UFxU2fv4x3z0BMPBeWaMsG9EFFvzwGSpFdfKl0UK9dbF5SrEjijBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhVZvc4Yc5akYyUdKGmepGZJ/2WtfV8/MUdIukjSAkl1kpZI+qGkq621uUpsV6G309FYKN8nafS4sf55At8vdXZu9Y555ZUVQbkKVTXO9t7eZOW9VatWOvtr6xu9c/XUu1d2Gkiz/FdDS/32/qBco9LulfKqB+jvTHV75zrnl//jHSNJS2tavGNG9nQE5epNu1e9SynZD+l06TFeKD6XBssrOf9jcf2I0UG5Wjeudbbnir9ze2/pipVjXlsTlGt8yn/VRkl6pdZ/NbRvXXN9UK4vXvF1Z3vLyBFl+88//yNBudI164PiNvX6r+w5a86coFydbe5VM/ueErlc6XNj3rz9g3JVQqWWqb1ISYHfImmlpLn93dkYc5qkX0jqknSzpA2S3iXpm5KOlHR2hbYLAIBhrVIf3X9a0p6SWiR9rL87GmNaJH1fUk7ScdbaD1lrP6vk04A/STrLGHNOhbYLAIBhrSKF3lp7r7V2sbV2Rz7LO0vSWEk3WWsf3eYxupR8MiAN8GYBAADsmKEYjHdC8fZOR9/9krZKOsIY4/7SEAAA7LBUpQfUGGOOk3SvygzGM8Y8Imm+pPnW2scc/Ysk7StpH2vtswPkKomXJGvtwZL07NNPlfTN2CMZfLF8yWLnY9bWVGrYwsBqAga6KWzcTtnA8RMmSJLWrF7tjnIMwhqIa+DWjqgqMwCuP7Ub24NyZfLu8Z51s2ZJkrqWLnX25wOeLg2zZ/sHSequqvKOSef9ByRJUiHl/ptlis+Hnt5saYwGdzBeOiDdluXLg3LV5kp/X0mqnzVTktS5dFlJXyHwuO8aMSIorncQ9/+4MWOc7VXVyfGRy5bur5dXhg0cTpU5FgdSU5Pxjslkws4n87nSwZiSNGp0sp82rF9X0lcf8Hrf0vr6gNzgV35paM7oW4u3m8r097WHHf0AAOB1g3f6uuP63rkM+HbVWntIma6CJL3/nSeWdNx4+90q1ydJsycP3vS6iQfM944JPVsuN73uM5/9vCTpP75+ubM/ZHpdfej0ugb/M9i9br4rKNekro3O9rk/v0WS9NzZZzn7O3v89//Bt93mHSNJSwPO9Oo6AqfXlTmzmTEpOUNZvqr0DGWwp9dlcv6zbh/6wMeDcs0qM71u3i9ukiQ9dWbpeOGepuagXC+c+a6guFfKnFX2pzrt/xyTpE9+2D1Vrm96XfvGtpK+L37+M0G50jUBn3RKmjRlpnfMtOl7BOUqN73ub//+A5Kkn/3kRyV98/Z/i3eeE055q3eMy1Cc0fedsbeW6W/Z7n4AACDQUBT654u3e27fYYypljRTUlaS+0tSAACww4ai0N9TvD3Z0XeMpAZJD1lr/S9BBgAA3mAoCv0tktZJOscY8/qX1MaYOkmXFX+8dgi2CwCA6FTqWvenSzq9+OOE4u3hxpgbiv9eZ629QJKste3GmI8oKfj3GWNuUnIJ3HdL2qvYfnMltgsAgOGuUmf0B0o6t/jf24tts7Zpe8MQZmvtbUoWwblf0pmS/klSr6R/lnTODl5hDwAADKAiZ/TW2kskXeIZ86Ckd1Qifzm16dKLh6SKs/ZcfZLUsdl/sP+YiVO8YyQpX/CfHpNO+V8UQpLKzqpJ9d+fy/tvY0N32PCK8QufH/hO25leHXaBmOqUe+WvvtXa6sv0L9xzgrO9P9PGhq2gVuW+Zku/Ug2Bx0e+//f8odM6XUKn5aXS5SbqlHfatd8MyvX435zrbC8Uj4/eVOlLZ1Nn2NTG0U/+NSju1QP8L8SUzoUdH9+67jpn+0c//GFJ0nU/+EFJ35cuc694N5DLL3NP9R3IXxcu8o6ZNc1/Sp4k1da5nw+p4kW/XP2/ut3/g+o38/Q6AAAwSCj0AABEjEIPAEDEKPQAAESMQg8AQMQo9AAARIxCDwBAxCj0AABEjEIPAEDEKPQAAESMQg8AQMQo9AAARKwii9rsrvbda05JW31dXdk+SWrI+O+Sw48PW3jgnoX+i1mMGjUqKFdX1r34SyqVLMJQU+P+vbt6/Beo2Zjf7B0jSe9atNI7pqM2bAGdlwvuVXwmFW+XFdz76z1Xf9c712shq9NIau71fx++uda9GM9AMr3uhWb6jo/q6sq9VOTzYQsRjciVW5mpvJcn1QflUnWZ/VjcH67+bNp/AShJGrd4RVDcxMP28Y7ZsCHs+VJozLnbU8lxU6gpPcZv+vF/B+XK5MOOtTNPfqd3zMLHnwrKla9f72zv7u6UJC1Z+nRJ37qNa4NyVQJn9AAARIxCDwBAxCj0AABEjEIPAEDEKPQAAESMQg8AQMQo9AAARIxCDwBAxCj0AABEjEIPAEDEKPQAAESMQg8AQMQo9AAARCzq1es2t7eVtOVy2bJ9kjRpxnTvPHfd93vvGElqnr6Xd0xnwGpyktRVZjW2QrG9M+d+3JYe//eC7354sXeMJHXXdXrH9GxKBeU65Q+3uTsmJevXnfJrd//K+ibvXJmwxeu0at1q75gJEyYE5Zo1c7Kz/c8PPyRJOmzBESV9Gza1B+Va+WrYKl6ba2q8YzLZsNUeD770Imd7w8SJZfv/+i//EpSrrta9MtxADm5u9Y75zZpXgnKps8y+71v0sLP0eVjT3BKU6qKvfCEo7nOfvdg7Zq899wvK9eNb3a8PHzpviyTpjvseKOn7h789OyhXJXBGDwBAxCj0AABEjEIPAEDEKPQAAESMQg8AQMQo9AAARIxCDwBAxCj0AABEjEIPAEDEKPQAAESMQg8AQMQo9AAARCzqRW22bNlS0pbP58v2SVIm479wxsF7HuQdI0mvtK3zjunMNgblqilkyvQk7/Vqsu7+GUuf985V2+vetwPpKFR5x7SfcFhQrjUN7sVpRqeT/bG+TH+6UHC27woXXfg575iHH344KFddrfu4T6VSZfvP+8hHgnJddc23g+Laev0XPWrqDjuX6Z7vXnCqvrGubH9bTUNQrtFV7gWnBrLm2pu9Y6ree3RQrqpsrbM9/frxUdrfmw1brOe6664LivvYJ9/vHXPXnXcG5RpR715QqCpdVbb/xef8X0srhTN6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiVpHV64wxZ0k6VtKBkuZJapb0X9ba9znuO0PSsn4e7mZr7TmV2K6ZM2eWtNVmasv2SdKKFS975xlZ7b/inSSlGsZ4xzTm/Fd4k6RM90Zne1U+K0lq3eruP+rlTd65VvWErV6XyUzyjtnr658PyvXKS68625ubktUBX3nZ3b9ggf9qedOnT/eOkaRFixZ5x7S2ulfVGkg63etsLxRX68v2dpf0/eq2XwblOmT+/KC4vz/3vd4xtdV1Qbk2llklsiWVnBt11Jf2z/nkeUG51n3n2qC42YUu75iZ02YH5bptyZoyPcnqdcqnSnp68/6rDUpSdXXYCp0b1vu/7owaNSoo14lHuVcsbW6qL9t/3313BeWqhEotU3uRkgK/RdJKSXN3IOYpSbc52v1f3QAAgFOlCv2nlRT4JUrO7O/dgZgnrbWXVCg/AABwqEiht9a+XtiNMZV4SAAAUAGVOqMPMckY84+SRktaL+lP1tqFQ7g9AABEJ9U32KZSjDHHKfnoPmQw3n2SzrXWrtjBXI+52q21B0vSy0ueL+kbPyUZGLVm5UvOx8znczuS+g2qa2u9YyRJ6YD3WYF/rlTB/XuNmTBZkrRu9SvO/lFbSgdhDaSnkPWOkaR0KuMdU7PnjKBc2R73/qirSwZvdXW5Bzo9/9xz3rkygcfHPvvs4x3z5JNPBuUqd2DNnZsMt3nO8XunUqUDsHbExEmTg+JGjx7pHZMOnFiULeSd7TXVyXO2N1t6jBc2+g9claTsa68FxVWF7P8J/gOAJamt2/2cHjNmtCRp3br1jt7AF6vAsIaGeu+Y7u6wAYO9ve7Xh7HjkgHFr61dVdK3eXO7d565e8/r+2fYk61oKKbXbZX0ZUmHSBpZ/K/ve/3jJN1tjAkbdgkAAN5g0D+6t9aulfSF7ZrvN8acJOmPkg6T9GFJV+7AYx1SpqsgSV//zIdKOj77H9erXJ8k9QS86xo5a5Z3jBQ2vS4dPL3Ofbbx4Yu/Ikn6wZcvdPabhxZ751rV456qN5CQ6XWT/3BjUK4NL212ts/dew9J0nPPLnH2L1hwpHeuwZxed/iCo4JylZte9+BDD0uSjjxiQWlMddgnFZde9pWguKDpdQo7Z9iYdR8fE0ckZ7CvtpWewWZ/fntQrtDpda0BnwjmL/xoUK5y0+s++OF/kCT98Ac3lOZS2Cd7KoSdvM4/eF/vmKUvPhOUa9VK9yd7H/9/l0qSvnPlF0v6QqbXPfjIau8Yl93mgjnW2qykHxR/PGYotwUAgFjsNoW+qO/LKj66BwCgAna3Qt/3+eDSId0KAAAiMeiF3hhzmDGmZHi1MeYEJRfekaSfDu5WAQAQp0pd6/50SacXf5xQvD3cGHND8d/rrLUXFP/9NUn7GmPuU3I1PUk6QNIJxX9fbK19qBLbBQDAcFepUfcHSjp3u7ZZxf8k6SVJfYX+RklnSDpU0imSaiStkWQlXWOtfaBC26QXX3yxpK27u7tsnySNH+G/KMjmze4RugMZmZ7jHfNyS9ic27//o3sUeVNHsj+OesTd/4q2eucaWx+2UMS+t3/POyabHhGUa+/j3CN0H3rgQUnSkce5R9fX1fovYHTgvP29YyRpc3ubd0xHR9hc7lGt7mExfVM3Aq9MAAAgAElEQVS1a9KlI6FXrXYv/DOQ3qx7jvpAZo7yf26+7zMXDHwnh0s+456FUlWcztyUK51x0HL2qUG5Nl79naC4zmr/5+aL1/4kKFf2hLc6219f9CjnuC5Fyj2TYyCpXHNQ3CMP+4+gHz3Kf+69JFXLPf8+VbwIgKt/772mBOWqhEpdAvcSSZfs4H2vl3R9JfICAID+7W6D8QAAQAVR6AEAiBiFHgCAiFHoAQCIGIUeAICIUegBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCIGIUeAICIUegBAIhYpVav2y3td9gRJW11jY1l+yRpyfP+KyDNmeq/Cp0ktfW0e8ccv/CVoFwjC93O9uriakvl+tvT/odI97EnesdI0saREwa+03ZaqluCcuV7u9wdhXz//Rn3Km/9+d3dd3nHSNKIgNXannnW//iVpG7H4mOSVFyczNnf1NQUlCtUb32Dd8yPr7suKNe3v/EtZ/ujj/5ZkjR//mElfUtfXBaUa8YnPxgUt+76G7xjZq3dGJRrwWH7OdvHNCarv33Q0f/9Pz4ZlKupuRAUt6XHf7W88z56flCur37xr2V6qor/L33u9uT9V76sFM7oAQCIGIUeAICIUegBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCIGIUeAICIUegBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCIWNSr123auKGkLZfLle2TpLq0/4pcS1et8Y6RpIMa/Hf/3qvbgnJVb3EvT5bKFfrtfymX98515hc+5x0jSVtSdd4xvVvLrDI3UFzWvUJW32pt5frXrF7hnWvMmDHeMZI0atQo75hsNhuUq76+3tmeTqfL9qdSqaBcI0aMCIpraPBfva6rK+z4uPv3dzvb58yZU7Z/4sSJQblyp58RFNf7vZ97x2wqhL1WdX7xCmf7yB/vJUl6ydGfPeGooFzKh62KWNflvzrcF77i/r0GcvNNNzvbz3j/JyRJNzj6Z+05KShXJXBGDwBAxCj0AABEjEIPAEDEKPQAAESMQg8AQMQo9AAARIxCDwBAxCj0AABEjEIPAEDEKPQAAESMQg8AQMQo9AAARCzqRW2eeeqvJW1dnZ1l+yRp9h57eOeZ1rPVO0aS3rJwnXdMVa4nKNfmEc3O9lx1Vb/9f/fL//bOtSLV6x0jSbVb/ON6G/wXwpGk3m73Ij5VVal++9euXe2da9OmTd4xkvSJT3zCO8ZaG5Sru7vb2Z7P58v2V1eHvXw8+eSTQXGtra3eMe95z3uCct16663O9j3mzCzbP3fu3kG58qnaoLinav3P02YXwp4vEze5FweqKS56NdnRf9qM6UG5/ndD2POlIeW/AFc+5X6eD6SjzNpR+X76//r84qBclcAZPQAAEaPQAwAQMQo9AAARo9ADABAxCj0AABGj0AMAEDEKPQAAEaPQAwAQMQo9AAARo9ADABAxCj0AABGj0AMAEDEKPQAAEdvp1euMMaMlnSHpVEn7S5osqUfS05J+JOlH1tqSZYWMMUdIukjSAkl1kpZI+qGkq621YUsKbeekE04saWtpbi7bJ0nZOv/3PpmHH/OOkaRUtsY7pktllk0aQNPhhzjb040N/fZvbB7tnaslcDW/tPxXnzrkyKODcq3861Jn+58efViSdPj8Bc7+39x9p3euE090H2sDaWho8I7J5cKeOlVVVd4xdXVhK6FNnx62qlk263/s/+pXvwrK1bHFvZrfqNHJCnoXXXRRSV9tbdj+2LQ57Pnyvpu/6x3z3NvOCcq1od69suS4VLG/pvRvs+nhPwfl6pg7NSguk/ZfBbA6FfZ6etAhhzvbGxqayvYvWfpIUK5KqMQZ/dmSvi/pMEl/lvQtSb+QtJ+kH0iyxpjUtgHGmNMk3S/pGEm3Svq2pIykb0q6qQLbBAAAVJn16F+Q9G5Jv972zN0Yc6Gkv0g6U9J7lBR/GWNalLwxyEk6zlr7aLH9Ykn3SDrLGHOOtZaCDwDATtrpM3pr7T3W2v/d/uN5a+1qSdcVfzxum66zJI2VdFNfkS/ev0vJR/mS9LGd3S4AALDrB+P1fbGz7RchJxRvXV923i9pq6QjjDH+X7gAAIA3SBUKhV3ywMaYaklPKPmu/mRr7W+L7Y9Imi9pvrW2ZBSbMWaRpH0l7WOtfXaAHM5RcNbagyXp1ZdKB1yNmTBZkrRu9SvOxyykUs72/qQ7wgbTNOf9cxUU9vdKFwchbq9uwgRJUtfq1c7+zKTJ/skK/oPqioHeEc+9sDgoU0+Xe7DV3Llzk8d97jln/x5z9vDOtXhx2Dam0/7vwyv9fO5vf6QCniuSdNBBBwXFhfxu+XzYsZjPu3NVVyeDFrPZ0kGPofsjF7iNmYL/wMuuxcuCcuVT7v1RN2tm8rhLSx8311gflGtTXSYorioX8HqaCtv3W7d2OtunTkkGEr688uWSvu7uDu88++17YN8/ww6uol15Rn+FkiJ/R1+RL2ot3m4qE9fXPmJXbRgAAMNFJQbjlTDGfErSZyQ9J+n9nuF971wGfPturXXPCSvGXn/Z50s6PnTR5SrXJ4VNr6sPnF731s3+0+t6AqfXNZ54vLN9j3/9N0nSkq9d4eyf9KWveudKDeL0utPee2ZQrlin1/X2uqdBDaTc9LoHH3xQknTkkUeW9FVXh718bN68OSguZOpgV1dXUK6BptdtWF96njLY0+umZTd6xyw/7e+Ccm0tM71u7s03S5Kee+97S/rWzz+wpG1H/CZwet3IDv9ve7tTYfv+scefcLZf/a1vS5L+6fxPlPSFTK9b8oz/39il4mf0xphPSLpS0jOSjrfWbtjuLn3PkFa5tWx3PwAAEKiihd4Yc76kayQtUlLkXV/8Pl+83dMRXy1pppLBe+5TLgAAsMMqVuiNMf+q5II3Tyop8mvL3PWe4u3Jjr5jJDVIesha6/7sDAAA7LCKFPrixW6ukPSYpBOttev6ufstktZJOscYM3+bx6iTdFnxx2srsV0AAAx3lbjW/bmSvqTkSncPSPqUMWb7uy231t4gSdbadmPMR5QU/PuMMTdJ2qDk6np7Fdtv3tntAgAAlRl1P7N4WyXp/DL3+YOkG/p+sNbeZow5VtLnlVwit29Rm3+WdJW1tiKTgVd0tpW09eRzZfskqeHZl7zznLLFf/S8JHU0+I8i3tDtPxJbko6/8EJne+3YcZKk6WX6e3r8/xQ9hbApn1VV/vNnL/23LwTlOu+885ztqXSy7VX17m0544wzvHONGjXKO0aSXnvtNe+YTCZsDnLffPnt9S1cM2fOnJK+ZcvC5mSHLobT09MzaLlWvuy+rkTfXPmamtLnfOhsgprA2Qsrq/1nID+bDhtlPrvaPXY6lUo+FK6uLn1dan36haBcM2bPCIpbGzAnPtcT9lp18tvf7mxvaWkp2//1bwzdojY7XeittZdIuiQg7kFJ79jZ/AAAoDzWowcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJWidXrdlvVPV0lbalCoWyfJB29yn9FuY35bu8YScqk6r1j3nnbjUG5lhbcq97VFt/rrSnTX9+7wTtXKh22YljfymA+brrpv4NyHXTQQc72+vqGfvufeuoJ71yrVq3yjpGk9vZ275jGxsagXNOnT3e2962G5+p/6KGHgnJdcMEFQXE/+9nPvGNCVryTpGw262wvFF8/XP2hKweme/1XXZOkbJV/zBm//HlQrkfe+wFne9/+6O4pfd3MZ8J+r2PXhq2wd2PAIpF1mZagXPMPOdTZ3tjQULa/rS3s96oEzugBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCIGIUeAICIUegBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCIGIUeAICIUegBAIhY1KvXzX5mZUlbbWdP2T5Jqulyr2rXn/VVtd4xkrTv5z7nHbOoO2ylvHHV7tW40sXVp5p63P09Gf/fLfSgyuf9V7u6/vofVjTXmDHJEli33HKLs3/q1MneuboD/2bGGO+Yjo6OoFybNm1ytudyubL95VZ4G8jPfx62glp1tf+RtXVr2Iph5XL1rbDo6u8K/DvnUwHL0ElK5Xu9Y1aNnxqUa2HXRmf7rHyubP8Bcq+IOZDND/whKO7o8/7WO+bBl93H/UBamlud7emqqrL9rS3NQbkqgTN6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYlEvajN1Y3tJW01xkQ5XnyRV5TLeeZ4dMco7RpIOO/It3jFVqbD3Zr2pLc72gpLFF3rT7v6a3pR3rp5M2DbWBLzvLARsnyTVpWqc7alC8nh1efdTo3Ozez/157Of/6x3jCTd/eDd3jFNExuDci18+nFne2fn1rL9e+89NyjX8y8sCYor5P2fmyELJUlSd7d7wZhCvlDsL13QJ58Ly1UduI01Kf+4VKd7cZqBnHrPb5ztrROnle1/6ZSzg3J19fo/xyTp6GVt3jF/aA1bBOrVZa8426dPnVG2v6fL/ZozGDijBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYju9ep0xZrSkMySdKml/SZMl9Uh6WtKPJP3IWpvf5v4zJC3r5yFvttaes7PbJUkdhdqStnzxvY2rT5LaG+u983z6p9d6x0hSu/xzpRS2Wlu5Rd4KxcfrTbn3R0+Vf77GbME7RpJU5X84ZlNh+yNX5X6P21h8uK1l+rvci5r166IvXOEfJOnLF33VO+bA/fYNyvXIq08621syzZKkpa++XNJXKFQF5ero7AqKa8j5x6VzYcdiJu+OS6lJklSX6yztzOaCcr1W7/86IElVIfu/J+xv1tPt+H0ljSgk+6nNsZrfxu6wXE2FsNX8Hv3jPd4xh3/0jKBc0+fOcbbX1tWW7b/9zruCclVCJZapPVvStZJelXSvpBWSxkt6j6QfSDrFGHO2tXb7Z85Tkm5zPN6iCmwTAABQZQr9C5LeLenX2525XyjpL5LOVFL0f7Fd3JPW2ksqkB8AAJSx04XeWuv8vMRau9oYc52kyyUdp9JCDwAAdrFKnNH3p+8bzdIvcKRJxph/lDRa0npJf7LWLtzF2wMAwLCSKhQCB04NwBhTLekJSftJOtla+9ti+wyVH4x3n6RzrbUrdjDHY652a+3BkrRu4dMlfSP22EOS1LZkiftBAwZ3jZg90ztGknJVNUFxIcr9lTM1yXu9nl7XezEpZOxfOvSQChxYV8lc1dXJAKJsmYFVqbT/RJVU4HMsZG88+8wzQbnm7re3s70qleyPXMG1P8L+Xvl82GCrkOMq9PWt3G9WVZ08X3JZx/Ml8LjPpsP2Y1BUmUGGA4e5nw+1mWTwWXdPd0lfz+LlQbnSChvUmK/xfz3NjR0RlKuhsdXZnslkJEk9PT0lfdmAgaFN/zc4fKdeHHfl9LorlBT5O/qKfNFWSV+WdIikkcX/jlUykO84SXcbYxp34XYBADBs7JIzemPMpyRdKek5SUdaazfsQEy1pD9KOkzS+dbaK3diEwqS9KNJ00o63nXnryVJ/3vyqc7AXJ3/VJczbvmZd4wktY8Y7x2TCjzrzZZ5SzdlwlhJ0srVrzn7CwFnG42BZw0h0+vygfsjVebTlLGjk+lkr63f7OxPN/gfHzX5sDOU2oD9GDy9bmn/0+vae0r3x6BPr+vx34+FwOl11WX2ffP45PmyeY3j+TLo0+sC8jnONHcorGuLs33G9OTTzOUvlX5Iu/yU9wXlaiq4cw1k64Rx3jGbAqfXHXD4u53tM6ZNlSQtX1E6HbVts/9xv+CQ/fv+uXud0RtjPqGkyD8j6fgdKfKSZK3NKpmOJ0nHVHq7AAAYjipa6I0x50u6Rslc+OOttas9H6LvbTIf3QMAUAEVK/TGmH+V9E1JTyop8msDHmZB8XZppbYLAIDhrCKF3hhzsZLBd49JOtFau66f+x5mjMk42k+Q9Onijz+txHYBADDcVeJa9+dK+pKknKQHJH3KGLP93ZZba28o/vtrkvY1xtwnaWWx7QBJJxT/fbG19qGd3S4AAFCZC+b0TSKvknR+mfv8QdINxX/fqGQRnEMlnSKpRtIaSVbSNdbaByqwTZKkrGMRjEJxwQRXnySlt5TOBx3IdWee7h0jSV0b/WMOOeKwoFwnvds9yyBz5rskSZN+415wof7Aed65esc0ecdIUi7lP4q7qzrsEN6a7XB3FBqS22ybs7tuXZm4fuSb3QsGDWRDwGUWfr3oT0G56trd+z49MlW2P2z8tlSnuqC4tly7d0xTfdhwn44O9+tAQ3Hw8yY59lfA8StJtZ3+rzmS1JDx34+FgOtASJJq3cdwdSp5/o2uHV3Sl//cBUGpVn7lkqC4yR1bvWP2fm1TUK7aMvPvq4rX4Rjp6C+zbtigqMQlcC+RdInH/a+XdP3O5gUAAANjPXoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBilVi9brdVW1W6mlQ6lSrbJ0lNuZR3nr0KGe8YSeoaGRD03JNBuf68+Gln+/yjDpUkPfofVzj7u3r91yhrbW71jpGkrdmsd0xX4FvV7nzB2X7CLVaS9PBZJUstS5Km5hu8c7Xner1jJGlzyv9YLNQELHkn6ZXDD3C2H3rxxZKkR7785ZK++W89oaRtRzTvMTsobtTU0hXSBpLv2BKUa12n+1hM5ZNjO9O5rqSvfuSooFz5zZ1BcV297hU4+9OR9j+mJEl17te4lnTyPGqrK32dmPjWw4NSLbo8FxTXsmWzd8yrP70tKNe8sz/g7mhokSSl2kuPu/rU2KBclcAZPQAAEaPQAwAQMQo9AAARo9ADABAxCj0AABGj0AMAEDEKPQAAEaPQAwAQMQo9AAARo9ADABAxCj0AABGj0AMAEDEKPQAAEYt69bpnRpeuonZCdVXZPkmana7zzjMiH7ZiWF6bvGPqq8JyVffmne2p4gppdRn3e77WTKN3rt5st3eMJE2sqvWOWV3wX7FKkibXuX/f2lSyGtcsx2pcktTdU++dq77afwVASRoRsCpiXSHs+Nj6yKPO9qqtHZKkZkf/ksfvD8uVClvtsXWLe8XJ/jQ0+R+/ktTZ4169buTNP5YkrX/vB0v6cmVWRBxIIRu2umFXwX+Vt7burUG5mlqbne0Tb/6ZJGnNe/+2pG/LHrOCclVXhR0fG6sDjv3OsNX8uhavdbYXRo4r2/8X+a+kOG3a/t4xLpzRAwAQMQo9AAARo9ADABAxCj0AABGj0AMAEDEKPQAAEaPQAwAQMQo9AAARo9ADABAxCj0AABGj0AMAEDEKPQAAEYt6UZtlk6eWtPXU1JTtk6QN48Z553l55kzvGEnqeHqhd8x5H/toUK7rf/oTZ/vUxmSRllvnz3P217c2eecanwp7/1i/2X+Rjnr/dU4kSYV29wIT2boGSdL6PQ929rc987R3rlSX/0JJktRY7b/gRibdFpSrrqrF2V4ongtkq0oX8yn0hr18jAhc9CjV6p9va0/YIi5pldv3ycI1KZUuRJNxrxs1oO5M2MIqtQFr4UyobwjKlepyPzerir9zs6O/ffHzQbnG14Ttj+5cp3dMNhWWK7t1o7O9kM+W7Z/YGvY6UAmc0QMAEDEKPQAAEaPQAwAQMQo9AAARo9ADABAxCj0AABGj0AMAEDEKPQAAEaPQAwAQMQo9AAARo9ADABAxCj0AABGj0AMAELGKrF5njPmapPmS9pQ0RlKnpJck3SbpGmvtekfMEZIukrRAUp2kJZJ+KOlqa63/MmYOYw6YXdJWXV9btk+ScgELa722IXDFsEmTvGO+f/uvg3Klx01wd1Qnq/mpTH91a6t3rvZc6UpnO2J5yn8/1o4LW42ruW2Ts32/TLI/Hp8+xdlf9ZT/ioN/871vecdI0svdr/gHda0NynXw0ac626tbk/1w6O9vLOlLBayeJknXXX1lUFx6i/+Ts6oQtjrZjHHuv//UUSMlSa+c856SvobqsNXJtjzyYFBc2+p13jH1ClvusWuze7XHaelk/z5XnynpGxu4Mlw6LEzpRv/fbWu2KyjXz++4wdl+7rw9y/ZXj/ZfGfXIY6/yjnGp1Bn9pyU1SvqdpCsl/ZekrKRLJC00xrxhTVhjzGmS7pd0jKRbJX1bUkbSNyXdVKFtAgBg2KvUevQt1tqSt0bGmMslXSjpc5I+XmxrkfR9STlJx1lrHy22XyzpHklnGWPOsdZS8AEA2EkVOaN3Ffm+ruLtnG3azpI0VtJNfUV+m8e4qPjjxyqxXQAADHe7ejDeu4q3236xeULx9k7H/e+XtFXSEcaY2l25YQAADAepQqFQsQczxlwgqUlSq5LBeUcpKfJvtda+VrzPI8W++dbaxxyPsUjSvpL2sdY+O0C+knhJstYeLEkrVr5U0jehOOhs9drVzscM2R2pwAE/KfknSwUOcFGZuHHjxkuS1q5d4+yvqvIf4JIqhL1/zOay/rmqw3JV5dzjPUcX98f6MvsjtandO9fIadO8YySpNx8w2q0QNkKuoanMoMuq4iCrXI8jV1AqvbY2bMCg8gHPl7BMylSXDi6TpKYxYyRJW9aVDoRLBz438x0dQXG5Xv/nS+iZXT6fd7a3zJwhSWpftrykL/R74dC/WbnXuP5kC+7fayDdje6Bl6MnTJYkrV9dOpA21Tfw2cP4ya8PbwveLVLlvqPvc4Gk8dv8fKekf+gr8kV9ryjuYc//1z6iwtsGAMCwU9FCb62dIEnGmPGSjpB0haQnjDHvtNY+voMP0/fOZcC379baQ8p0FSTpa1d9taTjXz/1OZXrk8Km16XyYbuxruD/jjzkDFuS0hn3u8lP/b/PSJKuuvI/nP3NAdPragKn123YOPTT6/7+U+dLkn5ylXtKXNWv7vDO9TfXftc7Rtq9ptdlN60s6QudXme/8+adXrfgQx+WJD18/Q9K+obj9LqTbvqpJOmuc95X0hc8vS7sJFvK+P9ubYHT6144fG9n+7mf+4ok6cdfvbCkL2R63Wcu372m172BtXaNtfZWSSdJGi3pJ9t0973ClqsgLdvdDwAABNqlg/GstS9JekbSvsaYMcXm54u3e25/f2NMtaSZSubgL92V2wYAwHAwGJfA7bv8W9/op3uKtyc77nuMpAZJD1lrAz5EBwAA29rpQm+MmWuMKbl+qjEmXbxgzjglhXtjsesWSesknWOMmb/N/eskXVb88dqd3S4AAFCZwXgnS/q6MeZ+SS9KWq9k5P2xkmZJWi3pI313tta2G2M+oqTg32eMuUnSBknvlrRXsf3mCmwXAADDXiUK/e8lfU/SkZLmKZkW1yHpBUk3SrrKWrth2wBr7W3GmGMlfV7Smfq/RW3+uXj/ikzu71xb+ul/Plso2ydJo0a0ONv7kyuEfcvQ2+w/Yry323+kviQ1lhshXdzT5UZQV230H5XaNTpsTaK6Ef4fMOU7Nw58J4dlD/7R2d7zwQ8X++939u/b4h593J9FX/q4d4wkTRs7fuA7bWfVaxsGvpPDr6/+mbP9uGt/LEm672PnlvR1tb1W0rYjRhz+1qC4JaPHDHyn7XR0bA3K1V7rnkc/rziSfKGjv64ubNR92357BMW9VO//fNm0Luz5sse8A53tRzclM2yeOqq0vzrg7yWFza6QpEsvu9w75vx/+mRQrpbMRGd7Pp1c560jM6ukr7q9ctes8bXThd5au0jSJwLiHpT0jp3NDwAAymM9egAAIkahBwAgYhR6AAAiRqEHACBiFHoAACJGoQcAIGIUegAAIkahBwAgYhR6AAAiRqEHACBiFHoAACKWKhSG7kL7u1CUvxQAYFhK7UwwZ/QAAESsEsvU7o7KvvsxxjwmSdbaQwZvc3Zf7I83Yn+8Efvjjdgfb8T+eKPddX9wRg8AQMQo9AAARIxCDwBAxCj0AABEjEIPAEDEYp1HDwAAxBk9AABRo9ADABAxCj0AABGj0AMAEDEKPQAAEaPQAwAQMQo9AAARi3X1uhLGmCmSviTpZEmjJb0q6TZJl1prNw7ltg02Y8xySdPLdK+x1k4YxM0ZFMaYsyQdK+lASfMkNUv6L2vt+/qJOULSRZIWSKqTtETSDyVdba3N7fKN3oV89ocxZoakZf083M3W2nN2xXYOBmPMaElnSDpV0v6SJkvqkfS0pB9J+pG1Nu+Ii/L48N0fsR8fkmSM+Zqk+ZL2lDRGUqekl5TUkGustesdMbvN8TEsCr0xZrakhySNk/Q/kp6T9BZJ/0/SycaYI11/qMhtkvQtR/uWwd6QQXKRkoK2RdJKSXP7u7Mx5jRJv5DUJelmSRskvUvSNyUdKensXbmxg8BrfxQ9peSFbXuLKrhdQ+FsSdcqefN/r6QVksZLeo+kH0g6xRhztrX29auLRX58eO+PoliPD0n6tKTHJf1O0lpJjUoK+CWSzjPGLLDWvtx3593t+BgWhV7Sd5QU+U9Za6/uazTG/KeSP+Dlkj46RNs2VNqstZcM9UYMok8rKWhLlJzJ3lvujsaYFknfl5STdJy19tFi+8WS7pF0ljHmHGvtTbt8q3edHd4f23gy0mPmBUnvlvTr7c5UL5T0F0lnKilyvyi2x358eO2PbcR6fEhSi7W2a/tGY8zlki6U9DlJHy+27XbHR/Tf0RtjZkk6SdJySd/ervuLkjokvd8Y0zjIm4ZBZK2911q72HEW4nKWpLGSbup7khYfo0vJmbAkfWwXbOag8dwfUbPW3mOt/d/tP5631q6WdF3xx+O26Yr6+AjYH9FzFfm+ruLtnG3adrvjYzic0X/ni5UAAAUoSURBVJ9QvL3LceBuNsY8qOSNwAJJdw/2xg2hWmPM+yRNU/JmZ6Gk+9/M3y1WUN8xc6ej735JWyUdYYyptdZ2D95mDblJxph/VDLGZb2kP1lrFw7xNu1qvcXb7DZtw/n4cO2PPsPx+HhX8Xbb33O3Oz6GQ6Hfq3j7Qpn+xUoK/Z4aXoV+gqQbt2tbZoz5gLX2D0OxQbuRsseMtTZrjFkmaV9JsyQ9O5gbNsTeVvzvdcaY+ySda61dMSRbtAsZY6ol/X3xx21ftIfl8dHP/ugT/fFhjLlAUpOkViWD845SUuSv2OZuu93xEf1H90r+IFIy+Mylr33EIGzL7uJHkk5UUuwblYys/a6kGZJ+Y4yZN3SbtlvgmHmjrZK+LOkQSSOL//V9r3+cpLsj/errCkn7SbrDWvvbbdqH6/FRbn8Mp+PjAiVf+Z6vpMjfKekka+1r29xntzs+hsMZ/UBSxdth812ltfbS7ZoWSfqoMWaLpM8oGUl6xmBv15vIsDpmrLVrJX1hu+b7jTEnSfqjpMMkfVjSlYO9bbuKMeZTSp4Lz0l6v2d4dMdHf/tjOB0ffVOPjTHjJR2h5M3PE8aYd1prH9/Bhxn042M4nNH3vXtqLdPfst39hrO+gTbHDOlWDD2OmR1grc0qmW4lRXTMGGM+oaQoPSPpeGvthu3uMqyOjx3YH06xHh+SZK1dY629VcnXvqMl/WSb7t3u+BgOhf754u2eZfr7RkuW+w5/OFlbvI3lY7ZQZY+Z4veUM5UMRlo6mBu1m+r7yDKKY8YYc76ka5R8ynV8caT59obN8bGD+6M/UR0f27PWvqTkDdC+xpgxxebd7vgYDoW+b37wScaYN/y+xphmJRcv6JT08GBv2G7o8OLtm/4FaifdU7w92dF3jKQGSQ9FOKI6xILi7Zv+mDHG/KuSC5o8qaSorS1z12FxfHjsj/5Ec3z0Y1Lxtm/G0m53fERf6K21L0q6S8lAs09s132pkneaP7HWdgzypg0JY8y+xphRjvbpSt65S9JPB3erdju3SFon6RxjzPy+RmNMnaTLij9eOxQbNhSMMYcZYzKO9hOUXHhHepMfM8WLmVwh6TFJJ1pr1/Vz9+iPD5/9EfvxYYyZa4wpuSy4MSZdvGDOOCWFu+9S6rvd8ZEqFKIZL1KW4xK4zyoZIHK8ko/sjxgul8A1xlwi6d+UfNKxTNJmSbOVXNe6TtIdks6w1vYM1TbuCsaY0yWdXvxxgqS3KznLeKDYts5ae8F2979FySUsb1JyCct3K5k6c4sk82a+2IzP/ihOkdpX0n1KrqYnSQfo/+YLX2yt7XsBe9Mxxpwr6QYlZ2RXy/3d6XJr7Q3bxER7fPjuj2FwfJwv6etK5sC/qOQaAeOVzCyYJWm1kjdDz2wTs1sdH8Ni1L219sXiO6u+RW3eoeQ6zlcpWdRmhwaXROJeJQfbQUo+qm+U1KZkdOyNkm58s75ADeBASedu1zar+J+ULFDxeqG31t5mjDlW0ueVXPKzb1GKf5Z0VQT7yGd/3KhkFsahkk6RVCNpjZKrgl1jrX1Ab24zi7dVSqZNufxBSfGTFP3x4bs/Yj8+fi/pe0q+5p2nZFpch5KTxBuV/L3fUEN2t+NjWJzRAwAwXEX/HT0AAMMZhR4AgIhR6AEAiBiFHgCAiFHoAQCIGIUeAICIUegBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCIGIUeAICIUegBAIgYhR4AgIhR6AEAiBiFHgCAiFHoAQCI2P8HTsbMr6qZMWcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[4])\n",
    "for (i, prob) in zip(top5[1][4], top5[0][4]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Turn left ahead  =  99.4883298874 %\n",
      "Keep right  =  0.510348984972 %\n",
      "Ahead only  =  0.000675602359479 %\n",
      "Priority road  =  0.00011536059219 %\n",
      "Road narrows on the right  =  0.000111861345431 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[5])\n",
    "for (i, prob) in zip(top5[1][5], top5[0][5]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 861,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Roundabout mandatory  =  77.8989195824 %\n",
      "Speed limit (100km/h)  =  19.31617558 %\n",
      "Priority road  =  2.40641180426 %\n",
      "Speed limit (60km/h)  =  0.203468510881 %\n",
      "Speed limit (80km/h)  =  0.131761864759 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[6])\n",
    "for (i, prob) in zip(top5[1][6], top5[0][6]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed limit (100km/h)  =  55.3968071938 %\n",
      "Speed limit (120km/h)  =  13.7195780873 %\n",
      "Vehicles over 3.5 metric tons prohibited  =  11.4429272711 %\n",
      "Speed limit (20km/h)  =  4.32166904211 %\n",
      "Right-of-way at the next intersection  =  3.76123525202 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[7])\n",
    "for (i, prob) in zip(top5[1][7], top5[0][7]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 863,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed limit (50km/h)  =  47.6036190987 %\n",
      "Speed limit (30km/h)  =  27.4452835321 %\n",
      "Speed limit (60km/h)  =  17.6492810249 %\n",
      "Speed limit (80km/h)  =  5.68129532039 %\n",
      "End of speed limit (80km/h)  =  1.32790394127 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img_rgb[8])\n",
    "for (i, prob) in zip(top5[1][8], top5[0][8]):\n",
    "    print(sign_names[i], ' = ', 100*prob, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Writeup\n",
    "\n",
    "Once you have completed the code implementation, document your results in a project writeup using this [template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) as a guide. The writeup can be in a markdown or pdf file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 4 (Optional): Visualize the Neural Network's State with Test Images\n",
    "\n",
    " This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.\n",
    "\n",
    " Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the [LeNet lab's](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.\n",
    "\n",
    "For an example of what feature map outputs look like, check out NVIDIA's results in their paper [End-to-End Deep Learning for Self-Driving Cars](https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/) in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.\n",
    "\n",
    "<figure>\n",
    " <img src=\"visualize_cnn.png\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above)</p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n"
   ]
  },
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    "### Visualize your network's feature maps here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# image_input: the test image being fed into the network to produce the feature maps\n",
    "# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer\n",
    "# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output\n",
    "# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry\n",
    "\n",
    "def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):\n",
    "    # Here make sure to preprocess your image_input in a way your network expects\n",
    "    # with size, normalization, ect if needed\n",
    "    # image_input =\n",
    "    # Note: x should be the same name as your network's tensorflow data placeholder variable\n",
    "    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function\n",
    "    activation = tf_activation.eval(session=sess,feed_dict={x : image_input})\n",
    "    featuremaps = activation.shape[3]\n",
    "    plt.figure(plt_num, figsize=(15,15))\n",
    "    for featuremap in range(featuremaps):\n",
    "        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "        if activation_min != -1 & activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin =activation_min, vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_min !=-1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin=activation_min, cmap=\"gray\")\n",
    "        else:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  }
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