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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = \"traffic-signs-data/train.p\"\n",
    "validation_file=\"traffic-signs-data/valid.p\"\n",
    "testing_file = \"traffic-signs-data/test.p\"\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "X_train, y_train = shuffle(X_train, y_train)"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 34799\n",
      "Number of testing examples = 12630\n",
      "Number of validation examples = 4410\n",
      "Image data shape = (32, 32, 3)\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",
    "import numpy as np\n",
    "\n",
    "# TODO: Number of training examples\n",
    "n_train = len(X_train)\n",
    "\n",
    "# TODO: Number of validation examples\n",
    "n_validation = len(X_valid)\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = len(X_test)\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = len(np.unique(y_train))\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Number of validation examples =\", n_validation)\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9684e3e400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "import random\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "# show image of 10 random data points in X_train dataset\n",
    "fig, axs = plt.subplots(2,5, figsize=(15, 6))\n",
    "fig.subplots_adjust(hspace = .2, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for i in range(10):\n",
    "    index = random.randint(0, len(X_train))\n",
    "    image = X_train[index]\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image)\n",
    "    axs[i].set_title(y_train[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e6f4b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show image of 10 random data points in X_valid dataset\n",
    "fig, axs = plt.subplots(2,5, figsize=(15, 6))\n",
    "fig.subplots_adjust(hspace = .2, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for i in range(10):\n",
    "    index = random.randint(0, len(X_valid))\n",
    "    image = X_valid[index]\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e3fa9e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show image of 10 random data points in X_test dataset\n",
    "fig, axs = plt.subplots(2,5, figsize=(15, 6))\n",
    "fig.subplots_adjust(hspace = .2, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for i in range(10):\n",
    "    index = random.randint(0, len(X_test))\n",
    "    image = X_test[index]\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e817c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e882cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e8caa58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the count of each sign\n",
    "#distribution of classes in training,validation and test sets\n",
    "hist, bins = np.histogram(y_train, bins=n_classes)\n",
    "width = 0.6 * (bins[1] - bins[0])\n",
    "center = (bins[:-1] + bins[1:]) / 2\n",
    "plt.bar(center, hist, align='center', width=width, color='b')\n",
    "plt.show()\n",
    "\n",
    "hist, bins = np.histogram(y_valid, bins=n_classes)\n",
    "width = 0.6 * (bins[1] - bins[0])\n",
    "center = (bins[:-1] + bins[1:]) / 2\n",
    "plt.bar(center, hist, align='center', width=width, color = 'g')\n",
    "plt.show()\n",
    "\n",
    "hist, bins = np.histogram(y_test, bins=n_classes)\n",
    "width = 0.6 * (bins[1] - bins[0])\n",
    "center = (bins[:-1] + bins[1:]) / 2\n",
    "plt.bar(center, hist, align='center', width=width, color = 'r')\n",
    "plt.show()"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape in/out: (32, 32, 3) (32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e79dd30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rotate image     \n",
    "from skimage.transform import rotate\n",
    "def rotate_image(image):\n",
    "    img = rotate(image, 30.0)\n",
    "    return img  \n",
    "\n",
    "test_img = X_train[1000]\n",
    "test_dst = rotate_image(test_img)\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n",
    "\n",
    "axs[0].axis('off')\n",
    "axs[0].imshow(test_img.squeeze(), cmap='gray')\n",
    "axs[0].set_title('original')\n",
    "\n",
    "axs[1].axis('off')\n",
    "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n",
    "axs[1].set_title('rotated')\n",
    "\n",
    "print('shape in/out:', test_img.shape, test_dst.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape in/out: (32, 32, 3) (32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9668d976d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to increase brightness of images\n",
    "# will choose images randomly for this function \n",
    "import cv2 \n",
    "\n",
    "def increase_brightness(img,value=20):\n",
    "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)#convert it to hsv\n",
    "    h, s, v = cv2.split(hsv)\n",
    "    v += 255\n",
    "    final_hsv = cv2.merge((h, s, v))\n",
    "    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)\n",
    "    return img\n",
    "\n",
    "    \n",
    "test_img = X_train[1000]\n",
    "test_dst = increase_brightness(test_img)\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n",
    "\n",
    "axs[0].axis('off')\n",
    "axs[0].imshow(test_img.squeeze(), cmap='gray')\n",
    "axs[0].set_title('original')\n",
    "\n",
    "axs[1].axis('off')\n",
    "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n",
    "axs[1].set_title('brightened')\n",
    "\n",
    "print('shape in/out:', test_img.shape, test_dst.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape in/out: (32, 32, 3) (32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e87d3c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to blur image\n",
    "# will choose images randomly to blur\n",
    "\n",
    "def blur_image(img):\n",
    "    \n",
    "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "    lower_red = np.array([30,150,50])\n",
    "    upper_red = np.array([255,255,180])\n",
    "    mask = cv2.inRange(hsv, lower_red, upper_red)\n",
    "    res = cv2.bitwise_and(img,img, mask= mask)\n",
    "    median = cv2.medianBlur(res,15)\n",
    "    return img\n",
    "\n",
    "    \n",
    "test_img = X_train[1000]\n",
    "test_dst = blur_image(test_img)\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n",
    "\n",
    "axs[0].axis('off')\n",
    "axs[0].imshow(test_img.squeeze(), cmap='gray')\n",
    "axs[0].set_title('original')\n",
    "\n",
    "axs[1].axis('off')\n",
    "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n",
    "axs[1].set_title('blurred')\n",
    "\n",
    "print('shape in/out:', test_img.shape, test_dst.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train, y_train shapes before adding new images: (34799, 32, 32, 3) (34799,)\n",
      "X_train, y_train shapes after adding new augmentated images: (46892, 32, 32, 3) (46892,)\n"
     ]
    }
   ],
   "source": [
    "# Augment images randomly with above methods as new added images\n",
    "# which will be concatanate with existing X_train to form new training dataset\n",
    "\n",
    "print('X_train, y_train shapes before adding new images:', X_train.shape, y_train.shape)\n",
    "\n",
    "for class_n in range(n_classes):\n",
    "    X_train_new= []\n",
    "    y_train_new = []\n",
    "    class_indices = np.where(y_train == class_n)\n",
    "    n_samples = int(len(class_indices[0]) * 0.3)\n",
    "    for i in range(n_samples):\n",
    "        index = random.randint(0, len(class_indices[0]))\n",
    "        new_img = X_train[index]\n",
    "        new_img_label = y_train[index]\n",
    "        new_img =  rotate_image(increase_brightness(blur_image(image),value=20))\n",
    "        X_train_new.append(new_img)\n",
    "        y_train_new.append(new_img_label)\n",
    "    X_train = np.concatenate((X_train, X_train_new), axis=0)\n",
    "    y_train = np.concatenate((y_train, y_train_new), axis=0)\n",
    "print('X_train, y_train shapes after adding new augmentated images:', X_train.shape, y_train.shape)        \n",
    "# #         new_X.append(class_indices[0][i%n_samples])\n",
    "# #         new_y.append(X_train.shape[0])\n",
    "#             new_img = X_train[class_indices[0][i % n_samples]]\n",
    "#             new_img =  rotate_image(increase_brightness(blur_image(image),value=20))\n",
    "#             X_train_new = np.concatenate((X_train, [new_img]), axis=0)\n",
    "#             y_train = np.concatenate((y_train, [class_n]), axis=0)\n",
    "#     print(class_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e7e8588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize new X_train labels\n",
    "hist, bins = np.histogram(y_train, bins=n_classes)\n",
    "width = 0.6 * (bins[1] - bins[0])\n",
    "center = (bins[:-1] + bins[1:]) / 2\n",
    "plt.bar(center, hist, align='center', width=width, color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implement Min-Max scaling for grayscale image data\n",
    "# tested it is better performance than pixel-128/128\n",
    "def normalize_grayscale(image_data):\n",
    "    \"\"\"\n",
    "    Normalize the image data with Min-Max scaling to a range of [0.0, 1.0]\n",
    "    :param image_data: The image data to be normalized\n",
    "    :return: Normalized image data\n",
    "    \"\"\"\n",
    "    a = 0.0\n",
    "    b = 1.0\n",
    "    grayscale_min = 0\n",
    "    grayscale_max = 255\n",
    "    return a + ( ( (image_data - grayscale_min)*(b - a) )/( grayscale_max - grayscale_min ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46892 4410 12630\n"
     ]
    }
   ],
   "source": [
    "# Preprocess the data here. It is required to normalize the data. \n",
    "\n",
    "# grayscale and normalize the data\n",
    "# ensoure data is 3D (32,32,1)\n",
    "X_train2 = []\n",
    "for i in range(len(X_train)):\n",
    "    img = np.uint8(X_train[i])\n",
    "    gray= cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n",
    "#     gray = (gray - 128)/128\n",
    "    gray = normalize_grayscale(gray)\n",
    "    X_train2.append(gray)\n",
    "    \n",
    "X_valid2 = []\n",
    "for i in range(len(X_valid)):\n",
    "    img = np.uint8(X_valid[i])\n",
    "    gray= cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n",
    "#     gray = (gray - 128)/128\n",
    "    gray = normalize_grayscale(gray)\n",
    "    X_valid2.append(gray)\n",
    "\n",
    "X_test2 = []\n",
    "for i in range(len(X_test)):\n",
    "    img = np.uint8(X_test[i])\n",
    "    gray= cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n",
    "#     gray =(gray - 128)/128\n",
    "    gray = normalize_grayscale(gray)\n",
    "    X_test2.append(gray)\n",
    "    \n",
    "print(len(X_train2),len(X_valid2),len(X_test2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32) (32, 32) (32, 32)\n"
     ]
    }
   ],
   "source": [
    "print(X_train2[900].shape, X_valid2[300].shape, X_test2[700].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f966e27d780>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f966e7cc940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting traffic sign images\n",
    "index = random.randint(0,len(X_train2))\n",
    "image = X_train2[index].squeeze()\n",
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(image,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.4677550589\n",
      "83.5564273756\n",
      "82.1484603612\n",
      "0.238398287211\n",
      "0.324523083539\n",
      "0.318735230199\n"
     ]
    }
   ],
   "source": [
    "# mean before normalization\n",
    "print(np.mean(X_train))\n",
    "print(np.mean(X_valid))\n",
    "print(np.mean(X_test))\n",
    "# mean after normalization\n",
    "print(np.mean(X_train2))\n",
    "print(np.mean(X_valid2))\n",
    "print(np.mean(X_test2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32, 1) (32, 32, 1) (32, 32, 1)\n"
     ]
    }
   ],
   "source": [
    "#turn the input data from (32,32) to 3D (32,32,1)\n",
    "X_train=[]\n",
    "for i in range(len(X_train2)):\n",
    "    img_expanded_train = X_train2[i][:,:,np.newaxis]\n",
    "    X_train.append(img_expanded_train)\n",
    "\n",
    "X_valid=[]\n",
    "for i in range(len(X_valid2)):\n",
    "    img_expanded_valid = X_valid2[i][:,:,np.newaxis]\n",
    "    X_valid.append(img_expanded_valid)    \n",
    "\n",
    "X_test=[]\n",
    "for i in range(len(X_test2)):\n",
    "    img_expanded_test = X_test2[i][:,:,np.newaxis]\n",
    "    X_test.append(img_expanded_test)\n",
    "    \n",
    "print(X_train[5290].shape,X_valid[200].shape,X_test[2890].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture\n",
    "Input\n",
    "The LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case.\n",
    "\n",
    "Architecture\n",
    "Layer 1: Convolutional. The output shape should be 28x28x6.\n",
    "\n",
    "Activation. Your choice of activation function.\n",
    "\n",
    "Pooling. The output shape should be 14x14x6.\n",
    "\n",
    "Layer 2: Convolutional. The output shape should be 10x10x16.\n",
    "\n",
    "Activation. Your choice of activation function.\n",
    "\n",
    "Pooling. The output shape should be 5x5x16.\n",
    "\n",
    "Flatten. Flatten the output shape of the final pooling layer such that it's 1D instead of 3D. The easiest way to do is by using tf.contrib.layers.flatten, which is already imported for you.\n",
    "\n",
    "Layer 3: Fully Connected. This should have 120 outputs.\n",
    "\n",
    "Activation. Your choice of activation function.\n",
    "\n",
    "Layer 4: Fully Connected. This should have 84 outputs.\n",
    "\n",
    "Activation. Your choice of activation function.\n",
    "\n",
    "Layer 5: Fully Connected (Logits). This should have 10 outputs.\n",
    "\n",
    "Output\n",
    "Return the result of the 2nd fully connected layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "def LeNet(x):    \n",
    "    # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "    \n",
    "    # SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.\n",
    "    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))\n",
    "    conv1_b = tf.Variable(tf.zeros(6))\n",
    "    conv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\n",
    "\n",
    "    # SOLUTION: Activation.\n",
    "    conv1 = tf.nn.relu(conv1)\n",
    "\n",
    "    # SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.\n",
    "    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "\n",
    "    # SOLUTION: Layer 2: Convolutional. Output = 10x10x16.\n",
    "    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n",
    "    conv2_b = tf.Variable(tf.zeros(16))\n",
    "    conv2   = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\n",
    "    \n",
    "    # SOLUTION: Activation.\n",
    "    conv2 = tf.nn.relu(conv2)\n",
    "\n",
    "    # SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.\n",
    "    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "\n",
    "    # SOLUTION: Flatten. Input = 5x5x16. Output = 400.\n",
    "    fc0   = flatten(conv2)\n",
    "    \n",
    "    # SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.\n",
    "    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))\n",
    "    fc1_b = tf.Variable(tf.zeros(120))\n",
    "    fc1   = tf.matmul(fc0, fc1_W) + fc1_b\n",
    "    \n",
    "    # SOLUTION: Activation.\n",
    "    fc1    = tf.nn.relu(fc1)\n",
    "\n",
    "    # SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.\n",
    "    fc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n",
    "    fc2_b  = tf.Variable(tf.zeros(84))\n",
    "    fc2    = tf.matmul(fc1, fc2_W) + fc2_b\n",
    "    \n",
    "    # SOLUTION: Activation.\n",
    "    fc2    = tf.nn.relu(fc2)\n",
    "       \n",
    "    # Add dropout before feed to last fully connected layer to prevent overfitting\n",
    "    fc2 = tf.nn.dropout(fc2, keep_prob)\n",
    "\n",
    "    # SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 43 (n_classes).\n",
    "    fc3_W  = tf.Variable(tf.truncated_normal(shape=(84, n_classes), mean = mu, stddev = sigma))\n",
    "    fc3_b  = tf.Variable(tf.zeros(n_classes))\n",
    "    logits = tf.matmul(fc2, fc3_W) + fc3_b\n",
    "    \n",
    "    return logits"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "##### Features and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.reset_default_graph() \n",
    "\n",
    "# x is a placeholder for a batch of input images. \n",
    "# y is a placeholder for a batch of output labels.\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "one_hot_y = tf.one_hot(y, n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rate = 0.0005\n",
    "\n",
    "logits = LeNet(x)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "training_operation = optimizer.minimize(loss_operation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model Evaluation Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "def evaluate(X_data, y_data):\n",
    "    num_examples = len(X_data)\n",
    "    total_accuracy = 0\n",
    "    sess = tf.get_default_session()\n",
    "    for offset in range(0, num_examples, BATCH_SIZE):\n",
    "        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
    "        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Train the Model\n",
    "Calculate and report the accuracy on the training and validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "\n",
      "EPOCH 1 ...\n",
      "Training Accuracy = 0.400\n",
      "Validation Accuracy = 0.443\n",
      "\n",
      "EPOCH 2 ...\n",
      "Training Accuracy = 0.572\n",
      "Validation Accuracy = 0.695\n",
      "\n",
      "EPOCH 3 ...\n",
      "Training Accuracy = 0.626\n",
      "Validation Accuracy = 0.762\n",
      "\n",
      "EPOCH 4 ...\n",
      "Training Accuracy = 0.668\n",
      "Validation Accuracy = 0.825\n",
      "\n",
      "EPOCH 5 ...\n",
      "Training Accuracy = 0.689\n",
      "Validation Accuracy = 0.843\n",
      "\n",
      "EPOCH 6 ...\n",
      "Training Accuracy = 0.695\n",
      "Validation Accuracy = 0.866\n",
      "\n",
      "EPOCH 7 ...\n",
      "Training Accuracy = 0.710\n",
      "Validation Accuracy = 0.880\n",
      "\n",
      "EPOCH 8 ...\n",
      "Training Accuracy = 0.714\n",
      "Validation Accuracy = 0.880\n",
      "\n",
      "EPOCH 9 ...\n",
      "Training Accuracy = 0.725\n",
      "Validation Accuracy = 0.891\n",
      "\n",
      "EPOCH 10 ...\n",
      "Training Accuracy = 0.724\n",
      "Validation Accuracy = 0.892\n",
      "\n",
      "EPOCH 11 ...\n",
      "Training Accuracy = 0.729\n",
      "Validation Accuracy = 0.907\n",
      "\n",
      "EPOCH 12 ...\n",
      "Training Accuracy = 0.733\n",
      "Validation Accuracy = 0.909\n",
      "\n",
      "EPOCH 13 ...\n",
      "Training Accuracy = 0.735\n",
      "Validation Accuracy = 0.912\n",
      "\n",
      "EPOCH 14 ...\n",
      "Training Accuracy = 0.738\n",
      "Validation Accuracy = 0.916\n",
      "\n",
      "EPOCH 15 ...\n",
      "Training Accuracy = 0.741\n",
      "Validation Accuracy = 0.926\n",
      "\n",
      "EPOCH 16 ...\n",
      "Training Accuracy = 0.740\n",
      "Validation Accuracy = 0.921\n",
      "\n",
      "EPOCH 17 ...\n",
      "Training Accuracy = 0.743\n",
      "Validation Accuracy = 0.928\n",
      "\n",
      "EPOCH 18 ...\n",
      "Training Accuracy = 0.744\n",
      "Validation Accuracy = 0.929\n",
      "\n",
      "EPOCH 19 ...\n",
      "Training Accuracy = 0.744\n",
      "Validation Accuracy = 0.927\n",
      "\n",
      "EPOCH 20 ...\n",
      "Training Accuracy = 0.746\n",
      "Validation Accuracy = 0.928\n",
      "\n",
      "EPOCH 21 ...\n",
      "Training Accuracy = 0.748\n",
      "Validation Accuracy = 0.933\n",
      "\n",
      "EPOCH 22 ...\n",
      "Training Accuracy = 0.748\n",
      "Validation Accuracy = 0.940\n",
      "\n",
      "EPOCH 23 ...\n",
      "Training Accuracy = 0.748\n",
      "Validation Accuracy = 0.934\n",
      "\n",
      "EPOCH 24 ...\n",
      "Training Accuracy = 0.750\n",
      "Validation Accuracy = 0.934\n",
      "\n",
      "EPOCH 25 ...\n",
      "Training Accuracy = 0.750\n",
      "Validation Accuracy = 0.940\n",
      "\n",
      "EPOCH 26 ...\n",
      "Training Accuracy = 0.750\n",
      "Validation Accuracy = 0.940\n",
      "\n",
      "EPOCH 27 ...\n",
      "Training Accuracy = 0.751\n",
      "Validation Accuracy = 0.941\n",
      "\n",
      "EPOCH 28 ...\n",
      "Training Accuracy = 0.752\n",
      "Validation Accuracy = 0.939\n",
      "\n",
      "EPOCH 29 ...\n",
      "Training Accuracy = 0.752\n",
      "Validation Accuracy = 0.939\n",
      "\n",
      "EPOCH 30 ...\n",
      "Training Accuracy = 0.752\n",
      "Validation Accuracy = 0.939\n",
      "\n",
      "EPOCH 31 ...\n",
      "Training Accuracy = 0.753\n",
      "Validation Accuracy = 0.941\n",
      "\n",
      "EPOCH 32 ...\n",
      "Training Accuracy = 0.754\n",
      "Validation Accuracy = 0.944\n",
      "\n",
      "EPOCH 33 ...\n",
      "Training Accuracy = 0.752\n",
      "Validation Accuracy = 0.938\n",
      "\n",
      "EPOCH 34 ...\n",
      "Training Accuracy = 0.753\n",
      "Validation Accuracy = 0.942\n",
      "\n",
      "EPOCH 35 ...\n",
      "Training Accuracy = 0.753\n",
      "Validation Accuracy = 0.941\n",
      "\n",
      "EPOCH 36 ...\n",
      "Training Accuracy = 0.754\n",
      "Validation Accuracy = 0.948\n",
      "\n",
      "EPOCH 37 ...\n",
      "Training Accuracy = 0.754\n",
      "Validation Accuracy = 0.943\n",
      "\n",
      "EPOCH 38 ...\n",
      "Training Accuracy = 0.754\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 39 ...\n",
      "Training Accuracy = 0.754\n",
      "Validation Accuracy = 0.948\n",
      "\n",
      "EPOCH 40 ...\n",
      "Training Accuracy = 0.754\n",
      "Validation Accuracy = 0.950\n",
      "\n",
      "EPOCH 41 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.942\n",
      "\n",
      "EPOCH 42 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 43 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 44 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.944\n",
      "\n",
      "EPOCH 45 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.947\n",
      "\n",
      "EPOCH 46 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.945\n",
      "\n",
      "EPOCH 47 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.943\n",
      "\n",
      "EPOCH 48 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 49 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 50 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.947\n",
      "\n",
      "EPOCH 51 ...\n",
      "Training Accuracy = 0.755\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 52 ...\n",
      "Training Accuracy = 0.754\n",
      "Validation Accuracy = 0.943\n",
      "\n",
      "EPOCH 53 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 54 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 55 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 56 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 57 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 58 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.949\n",
      "\n",
      "EPOCH 59 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 60 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.950\n",
      "\n",
      "EPOCH 61 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 62 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 63 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.949\n",
      "\n",
      "EPOCH 64 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.944\n",
      "\n",
      "EPOCH 65 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 66 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 67 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 68 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.946\n",
      "\n",
      "EPOCH 69 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.948\n",
      "\n",
      "EPOCH 70 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 71 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 72 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 73 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.950\n",
      "\n",
      "EPOCH 74 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 75 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 76 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 77 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 78 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 79 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 80 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 81 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.961\n",
      "\n",
      "EPOCH 82 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 83 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.960\n",
      "\n",
      "EPOCH 84 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 85 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 86 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.949\n",
      "\n",
      "EPOCH 87 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 88 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 89 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 90 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 91 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 92 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 93 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 94 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.950\n",
      "\n",
      "EPOCH 95 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 96 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 97 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 98 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 99 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 100 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 101 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 102 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 103 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 104 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.950\n",
      "\n",
      "EPOCH 105 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 106 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 107 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.950\n",
      "\n",
      "EPOCH 108 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 109 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 110 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 111 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 112 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 113 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 114 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 115 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 116 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 117 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 118 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 119 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.950\n",
      "\n",
      "EPOCH 120 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 121 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 122 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.948\n",
      "\n",
      "EPOCH 123 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 124 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.948\n",
      "\n",
      "EPOCH 125 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 126 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 127 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 128 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 129 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 130 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 131 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 132 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 133 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 134 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 135 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.960\n",
      "\n",
      "EPOCH 136 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 137 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 138 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.948\n",
      "\n",
      "EPOCH 139 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 140 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 141 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 142 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 143 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 144 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 145 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 146 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 147 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 148 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 149 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 150 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 151 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 152 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 153 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 154 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.961\n",
      "\n",
      "EPOCH 155 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 156 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 157 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.945\n",
      "\n",
      "EPOCH 158 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 159 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 160 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 161 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 162 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 163 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.963\n",
      "\n",
      "EPOCH 164 ...\n",
      "Training Accuracy = 0.756\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 165 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 166 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.960\n",
      "\n",
      "EPOCH 167 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 168 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 169 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 170 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 171 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 172 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.958\n",
      "\n",
      "EPOCH 173 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.960\n",
      "\n",
      "EPOCH 174 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 175 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 176 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 177 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 178 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 179 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 180 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.960\n",
      "\n",
      "EPOCH 181 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 182 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.949\n",
      "\n",
      "EPOCH 183 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 184 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.960\n",
      "\n",
      "EPOCH 185 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.961\n",
      "\n",
      "EPOCH 186 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.954\n",
      "\n",
      "EPOCH 187 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 188 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.956\n",
      "\n",
      "EPOCH 189 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.952\n",
      "\n",
      "EPOCH 190 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 191 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.957\n",
      "\n",
      "EPOCH 192 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 193 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.961\n",
      "\n",
      "EPOCH 194 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.955\n",
      "\n",
      "EPOCH 195 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.961\n",
      "\n",
      "EPOCH 196 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.949\n",
      "\n",
      "EPOCH 197 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.945\n",
      "\n",
      "EPOCH 198 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.951\n",
      "\n",
      "EPOCH 199 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.960\n",
      "\n",
      "EPOCH 200 ...\n",
      "Training Accuracy = 0.757\n",
      "Validation Accuracy = 0.960\n",
      "\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 200\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples = len(X_train)\n",
    "    \n",
    "    print(\"Training...\")\n",
    "    print()\n",
    "    for i in range(EPOCHS):\n",
    "        X_train, y_train = shuffle(X_train, y_train)\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})\n",
    "            \n",
    "        training_accuracy = evaluate(X_train, y_train)\n",
    "        print(\"EPOCH {} ...\".format(i+1))\n",
    "        print(\"Training Accuracy = {:.3f}\".format(training_accuracy))\n",
    "        validation_accuracy = evaluate(X_valid, y_valid)\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        print()\n",
    "        \n",
    "    saver.save(sess, './lenet')\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Test Accuracy = 0.933\n"
     ]
    }
   ],
   "source": [
    "# Once a final model architecture is selected, \n",
    "# the accuracy on the test set should be calculated and reported as well.\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
    "    test_accuracy = evaluate(X_test, y_test)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9648d675f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the images found from web and plot them here\n",
    "import glob\n",
    "web_images = []\n",
    "fig,axs = plt.subplots(2,4,figsize=(8,6))\n",
    "fig.subplots_adjust(hspace=.2,wspace=.002)\n",
    "axs = axs.ravel()\n",
    "for i, img in enumerate(glob.glob('examples/*.png')):\n",
    "    image = cv2.imread(img)\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image)\n",
    "    web_images.append(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y label for web images\n",
    "y_web = [11, 12, 3, 38, 18,1,34, 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grayscale and normalize the web images first\n",
    "X_web = []\n",
    "for i in range(len(web_images)):\n",
    "    gray= cv2.cvtColor(web_images[i],cv2.COLOR_RGB2GRAY)\n",
    "    gray = normalize_grayscale(gray)\n",
    "    img_expanded = gray[:,:,np.newaxis]\n",
    "    X_web.append(img_expanded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAy0AAAFpCAYAAACRRsdVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3We4XVW59vHHox4ghEB6JYRACiGU0EIvgjQVpEmToqiIHBEVsJ3j5VGQo4BIU7oogiBVmkqTFjohCRASShJCGimkEbD7fvDTuJ9b93iXO9tJ9v/3bYxr7LXmmmuWNZNxj+c9f/vb3wIAAAAAmuo//t0bAAAAAAD/DA8tAAAAABqNhxYAAAAAjcZDCwAAAIBG46EFAAAAQKPx0AIAAACg0XhoAQAAANBoPLQAAAAAaDQeWgAAAAA0Gg8tAAAAABrtfR35ZjfddNPftO8//qN8blpjjTXS33Xr1i31vf/97/+nrxMR8Z73vCf1/eUvfynaf/7zn9OYP/7xj232/e1v6aNYug3/+Z//WbWd6n3vy1+V/p3uk4i6/bL66qunMWuttVbq+8Mf/lC0//rXv6YxXbt2TX013D5fvnx50f7973+fxmy11VZt77xVS92BB9TrbOdQXH/99ek8mjZtWtF21/hNN9009Y0cObJou2vZihUrUp9eT/XeFOGvefp3y5YtS2P+9Kc/pT69Xuv1NSLi7bff/qd/84+2U+897n7l7k+6r9xru/tjzf3X/d173/veNt/P3aP79u1btNddd900ZpdddulU59GiRYu4Fxl6bNb8vsPf9ezZs82dxf+0AAAAAGg0HloAAAAANBoPLQAAAAAarUMzLTWZC5fBcPNOdW6q4+bj6nxD99qOvp/Lwri5i9pXMxe3Np+j49wYNz9XufnHbk60cvmjLl26pD6dt6xzst2YiPzd1OaIgKVLl6Y+l9Ny5xpWfQsWLEh9en9y16SZM2emvtmzZxdtd+2sua/VXhe1r+ZeGJHvh+56WjPG3TN12919Z7XVVmvztdx+qrnu19zrI/J9222Ty2UOGDCgaNfuczST/lZyx0+rv+fWXHPNou2OaZdxq3ltfgPxPy0AAAAAGo6HFgAAAACNxkMLAAAAgEbr0EyLy0DUzNFzc4Rbndun8wZdNqXV13Z/p3Nf3fzGmnn1rc5vdGN0Hq/LtDg6ztV3aVVNvZyanA1WfW7u/6WXXlq0J06cmMb06NEj9X3lK18p2j179vwXtw7vBjX5EZcnWbRoUeqrqf1Rk51stR5J7fW75rV1br/Lobq8Ss02uM+nvwlq369mO11epeZe62rjvPLKK0Xb1bjZcccd23xtNJM7LlzORc919xvI/c5V7hhzOZeabVKrek5z1f50AAAAAN71eGgBAAAA0Gg8tAAAAABoNB5aAAAAADRahwbxawoyuUC261M1hYAi6opLurBTq+Em3Yaa13HhrppCWa1ukwsxuj7dLredLryqIU0XgnXfQ00BKHQ+bkGGJUuWFG0XxO/du3fqmzNnTtEmiN85uLCsBrfdNdAFzvW+5u5zrs+9fg29LrrrsHs/PW9cUF1D7+5+5YLxrS5GoNf0t99+O4155513Ut/cuXOLtrvvuPvM4sWLi7a7r7qQtG7nBhtskMbg3UO/z5ri3RH5OHPXg379+hXtT3/602mMu4fp77na60htgfRVBf/TAgAAAKDReGgBAAAA0Gg8tAAAAABoNB5aAAAAADRahwbxa9QG6mvCRzXBf6emUnDtdtYE73U7p0yZksYMHDgw9bkK3zXbpAFQF+Rcc8012/w799lcn4bO3AIC7v009FZTHRqrvttuuy31nXPOOUV73333TWOmTp2a+k444YSifc0116Qx66233v/vJqLhunXrlvr02uWuUy6krePcgiEueKt9NcHfiBxMd+/nrqd9+vQp2u641gUK3DX3rbfeanOb3BgXstfPV7vP9Z7pttOFpAcMGJD6lFsgQfeL+17w7qHnmjt+ao4pd85+7WtfK9oHHHBAGlOz2JN7bfe7t+ZcWJXwPy0AAAAAGo2HFgAAAACNxkMLAAAAgEbr0ExLq0VwanIhbi6sm0esVvb8P/3MXbt2TWPGjRtXtM8666w0Zvfdd099X/nKV9p8f5dp0XmZLvvj9qe+lpsz7Oj35/aBy9WsWLGiaLt50li1uLm+V111VdF2xbpOOumkon3YYYelMcuXL099X/ziF4v2oYcemsbccMMNqW/ddddNfXj3mDlzZuqrKbrrjs+ae0hNtrBmvnpExNChQ4v2Ouusk8a4vKOOc++n2RR3D3XFJXW/uGu8ozkX937uuu8KR7a1TRF5n7tsSk2GttW8LFrnzrOa78H9XU1+ZN68ealPc2Djx49PY/T8uOOOO9KYESNGpL6ddtqpaE+fPj2N6d69e+qrKezqzgV3nL8b8D8tAAAAABqNhxYAAAAAjcZDCwAAAIBG46EFAAAAQKP924P4rYbslQtb1QTxa94/ovUCPhr8W7BgQRpz9dVXF20XSn/wwQdT31ZbbVW0DzzwwDTGBRZrgluOfuaaAkkRdUUp3TZocHTJkiVV24l3B/edX3755alPC0CecsopacyHP/zhov3GG2+kMS5A/IMf/KBoa6A/wof6b7rppqLdr1+/NAbNNWjQoNRXUzjO0eug+zu3aIkWTXT3K1cMUY81Dc9H+EKOixYtKtru/NO/c4UdXXhd94H7vKuttlrqU27xA1coU+9hbpENd763qtV7JurobwsXEq/5jeeOH3cs6jl6+OGHpzFPPfVU6tNj0Z3rup1usQD3+fSzuN9uffv2TX2PPvpo0Xa/k2p+l7ljuomFKvmfFgAAAACNxkMLAAAAgEbjoQUAAABAo/HQAgAAAKDROjSIXxOorw0/ttfftaqm0nxExBprrFG0XdB44cKFRdsFD92+u+aaa4r21ltvncZstNFGqU/DVTXfS0T+zC6I70Jn+t3UhNAicii0iaEw1NPv+JJLLkljTj755NT37W9/u2gPGzYsjXn22WeL9qhRo9IYd5w/88wzRfvMM89MY77xjW+kPg3nX3/99WmMC02iGVzotKZCtLvPaDC9tnK33i9GjhyZxnTp0iX1LV26tGjr/eMf0XuRCy1rWN7d01xAeMWKFUXbnWtuwQC9JrhFaHr06JH6NGTvQvdvvvlm6tPP7L7zjv4tgcydQ+73hn7vLoT+ve99L/XdeeedRdsdd2uttVbqq1mQSbfTHWM1vx/dcbhs2bLUN2LEiKK96aabpjHnnntu6tPzyn2WmgURav+uvXB2AgAAAGg0HloAAAAANBoPLQAAAAAa7d+eaVmZ80fb87V1jp6b66vFECMiHn744aL90EMPpTE9e/Ys2m7OsHttLVR52WWXpTFnn3126tN5y7XZFP3M7vt0c8Xfeuutou0+n87Tjshzkino9e7hvuNLL720aJ966qlpzLe+9a3Ut8ceexTtefPmpTE6r9fNbXZz+IcPH1603dz48847L/WdeOKJRdsVoPzlL3+Z+nr37p360PHcnGu95tXeP/SYcYUd3fV08ODBRdtd32bMmJH6dJx7bXe/6Nq1a9F2eRn9LO4+57ZTMy0uZ+MKvmqmRe8VbkxEno/v8gc19yLN+fyjv6spGIjW6fnozk9XnFSLn2677bZpTLdu3VKfngu1uV5VU2DbZT5qfsvUZkU0jzNp0qQ0ZuzYsalvypQpRdsd0+7zrcy8Sg3+pwUAAABAo/HQAgAAAKDReGgBAAAA0Gg8tAAAAABotA4N4q/MwpFuTG1Rn5oxGlJyIUYXPrz66quLtgtXad9pp52Wxlx55ZWpb/HixUX7vvvuS2NuvfXW1HfkkUcW7ZoCQhE5PObCZK6gl4blXLBSC7RF5BAYxSWbyRWNu/jii1Pf//zP/xRtLRoZEbH99tunvpriq7ooxU9+8pM0ZsiQIanvoIMOKtru2HQhzQsvvLBof+5zn0tjDj/88NT3i1/8omgTzP/3cPcLvd7U3q/03uBC4eutt16br/P666+3uU0ROXg7aNCgNMZtgwaZa4pgunCuW6xCg82usOrAgQNTn4aBp02blsa486/mXtCvX7/UN3/+/KLtznen1aA2spqFf9xx5+4zWkRYFzX6R+/n+traplo1r11z/an9XabHpiuU6V5LF6FxAX63UIVuQ20wv71+v/E/LQAAAAAajYcWAAAAAI3GQwsAAACARuOhBQAAAECj/duD+K2GczS05EJTNeF8N6amz1XX1pBtRK7e7UJL++23X9Heeuut0xgN3UdE/OAHP0h96vLLL099W221VdHecMMN0xgNz0fUfVcunK8hexdqdH+n+9xVZ0bH06rSP/rRj9KY7373u6nv9NNPL9pbbLFFGuMWctBQsQssX3fddUXbBdxnzZqV+m688caifcghh6QxNWHdCy64IPWdcMIJqe+oo44q2j//+c/TmF69erX5fvjXuLBszfXF/Z1ez0aOHFn12no8unuDW3RCg/cufKzB+Ii66vOqNoivn0UXxojwC2HovcjdV6dOnZr6dNvddq6zzjqpr3v37kXbXRPc52MRmNbULDwUke//f/jDH9IYt0iLXudrF23QxV169OiRxjh6nNVUtndqwvqtcvvA/aZde+21i/Ymm2ySxjzxxBOpT/f5yvwsDv/TAgAAAKDReGgBAAAA0Gg8tAAAAABotA4NCdQU+Wk191KTiYjIcxJri1Lq3OJx48alMQ888ECb2zBmzJg0Zvfddy/amoOJiBg7dmzq22mnndp8/xkzZqS+Sy65pGi7In9OTXHJmoJMtTkiff3aIkZoP8uWLUt9F110UdH+/ve/n8ZofiUiYvPNNy/arhirK4qn58O1116bxuicZFf81fXNnDmzaN98881pjMu5aObLHfe6nyIiTjzxxKJ99NFHpzEu51I75xp1agqw1RSAi8jfjcuTuKKJem5169Ytjdlggw1Sn85FdxkMzXhF5LzIvvvum8a4Ir/qvPPOS31azNVdN/T8j4j4zne+U7RHjx6dxrj7oRaJXLFiRZtjInLBSbfvXK6m1cKjnV3t7yu9fi5fvjyNcbkwzWm542727NltbufQoUOr3k8LtNZeI9pLTcHJVn8Lu3vvkiVLUl+fPn3a3CaXA22vTDJnHgAAAIBG46EFAAAAQKPx0AIAAACg0XhoAQAAANBoHRrEd0VvlAsCuiC+BuhqChZF5ACmCxG5IJ6Ghl0hSUfDlUcccUQaowElF+RywSYNCE+ePDmNcUGq3/zmN0V7xx13TGP23nvv1Kfh45pAXUTen7UhrZrjBe3HHSsXXnhh6jvnnHOK9v/93/+lMRtvvHHq03PIBZbnzp2b+jR470LpGrKvKQgZkQv1ucKVLpx/wAEHFG1XjNVdR7QIpStAedxxx6W+q666qmhrGBv/f2qv+8rdU/r371+0XYFUVxxYuYKobvEIPW+effbZNOakk05KfWeccUbRdsFbLep39dVXpzGukOqpp55atF0hSQ3du74rr7wyjRk2bFjqW7p0adF29wpXPFO/d7f4gfuu9PdGq0UF4c89vfe4hYdcsVD9vXHyySenMe63oR4bL730UhqjhYAjIp555pmi3Wq4vNVipe7vahaFqFkcwP1222uvvVLf008/XbTd97Iyi7HyPy0AAAAAGo2HFgAAAACNxkMLAAAAgEbjoQUAAABAo3VoEL8mDFRT9dT11Qb4dZwLX7pg0U033VS0Z82alca4INN+++1XtDfccMM0RsOP77zzThrjgrcaIj7ooIPSmMsuuyz1qSuuuCL1uSC1BqBdGNH1aYix1QqqK7PSbGe0aNGion3JJZekMT/84Q9T39lnn120N9poozTGVbvXY9idQ26Bi169ehVtrUockYO47hhz9JgaOHBgGjN9+vTU96tf/apo77///mmMq9Kt58KPfvSjNMaF87/whS8UbbdAglvYAJ6rhF4TaK3Zx3pe/SMavO/Zs2ca4+5hemy78Lq7fh9++OFFW8PsEfke9utf/zqN2W233VLfaaedVrTdvnz11VdT349//OOivWDBgjRG73MRES+//HLRdouIuPuo9q255pppjKuqrvtlZQaNVyXu94D7zaXXeHee1fw2dL93brnlltQ3bty4or18+fI0Rhc/icj3J7e4hLs/qZqFh9xvIrfv9Ddt7eIAeu9zv6/cQiB63ar9jtsL/9MCAAAAoNF4aAEAAADQaDy0AAAAAGi0f3umRfvcfEDXpzkQN6+uJgPh5rROmjQp9d13331F280bHDlyZOrbd999i7ab63fnnXcWbTcv88tf/nLq23333f9pOyIXAoqImDhxYtHW+cEREddff33q07n2tQX8lJvvXDMPs2bOOTxX8E6LRF566aVpzA9+8IPUN3z48DZf2xWu08KN1113XRrjCuxpQViXX1PuWHHz0LXPFalzc+qnTZtWtDXjEuFzLjqn3mUrLr744tT3uc997p+2I3wmaY011kh9qOOub64YoV67XKFRl7HSefvuPqfHfkQuJvnb3/42jTnrrLNSn+a1XCHVmvPBZT41E1BbeFjv2+793DGsr+WuCe6eop/P3f9bLcSHzO0nd23Wgrq1xab1OHPni8tJ6bngihq7bNOhhx5atF2+a9dddy3a7vh1v1f12HTH4dSpU1OfFgefM2dOGuPuM/r67px1f6f5Spd7XZn5Y34FAgAAAGg0HloAAAAANBoPLQAAAAAajYcWAAAAAI3WoUF8R8NHGrCPqAvUOS4EpmEjF+C78cYbU58Gi1yA78gjj0x9Gkh24eOLLrqoaLvA2be//e3U99ZbbxXtj3zkI2nMYYcdlvo0eO8Cp7feemvqGzt2bNHebLPN0hhX0Kum0J9boED7aoo2wQcPzz333NSnx915552XxowYMSL1LV68uGi778UVjtRj34XuXWhRz1F3Xuv52Z7F39z5OGDAgKL9yiuvpDF33XVX6ttrr72Ktrv+uEUMNOx44oknpjFf+tKXUp8GsilA+Y/VhKtdEF+vee566oKpeqzXLlBy9dVXF213rM+YMSP1/fznPy/aLkSs7+fObRci1s/s9mXN/q0JDEfke0ptoWN3Lit3L6KYZPtx36cudOQWe3C/I/R4dd+vO4b1Ox48eHAa4+5hGs539zC9FwwdOjSNcfe5ms/iPPDAA0Vbg/kRfrEcvd64/et+j2uhdbfoR21x51bwPy0AAAAAGo2HFgAAAACNxkMLAAAAgEbr0EzL2muvnfp0LqzmNNyYiLpCPG6OsM6VvP3229MYLRzn7Lnnnqlv8803T30vvfRS0XYF/HT+n5t37vbLNddcU7R33nnnNGajjTZKfZp9+cUvfpHGuPmUV155ZdF2OZuaolC1GaWVOS9yVeYKip5//vmpT4/FDTbYII1ZsmRJ6tP5wC7H9LOf/Sz1aQ6kS5cuaYw77lopKuqOnVaPMfd3up1DhgxJY9z3oOf2Bz7wgTTG7U/N0Lnv89Of/nTq0wzNRz/60TSmM3JzymtyCy5zoddmdwy719broLvuu6yU3rO6d++exui884iIyZMnF+1TTjkljfna176W+pTLYelndudsTSbR3etr9qfLr7RajNhlWrgXrVy6f2vyKxF1Ba7db0PNV7ljUwtQRuSci/tdpn0zZ85MY/r165f69Ldp7f1qxYoVRXvLLbdMYzQzFFGXmXHnkPYtXbo0jXHXpPbKhfE/LQAAAAAajYcWAAAAAI3GQwsAAACARuOhBQAAAECjdWgQ3xVN05CUCwe5YKqGgVyQygUbp0+fXrTvueeeNMYVwdKg7YEHHpjG1KgpsOWCYy6EpuFct9CBCxUedNBBRXv8+PFpjCtONmnSpKJ95513pjFuv+j3VxuaJPzYGhcydsfBxIkTi7ZbtKHV99tll11S36OPPlq0XUEvdxzUFI7Uv3PHWG04v+bv9HycP39+GtOrV6/UN3r06KLtrndu2/Vcf+qpp6r+joKsnrsOu/2naq5J7nVqrvvuHNWCkBF5MYCf/OQnacywYcNS32c+85mi/ctf/rLNMY77fLpNtfcw3Z+umJ0L/msA2x3n7rtaffXVi3ZtUUr9fNyb6q6nNfsyom7RhJrQvVOzkIo7P91CMeutt17R1kLd7v2WL1+exsydOzf16f3C/V6uuV+5wpw1ixG0+rusb9++aYw7j9sL/9MCAAAAoNF4aAEAAADQaDy0AAAAAGg0HloAAAAANFqHBvFdkEqDPi78VBNidKF7F3LV8KELDLn3O/LII4v2ggUL0pgXX3wx9c2ZM6dou7CThqRqQ+laCfW2225LY9znGzNmTNE++uij05jvfOc7qU8D1zfeeGMas8kmm6S+/v37F+3f//73aUzNZ261wnFns9VWW6W+c845J/Udd9xxRXvZsmVpjAvmLlmypGi7Y9pVetcg7oMPPpjG1ITzXahQt8GdL62G/F2AeNGiRalPHXPMMalPw5XumuiqCeuCIe78/P73v5/6dt1117Y2s1Ny36lyx4K7N+h3WHud0mPWXRd1sYyIiG233bZou/Pdhfr1uv/444+3uU3uPFq8eHHq0+Pa7Tu3WIXeo9334oLMeu9z+9wFvjWIr6/zj7ah5jdIZ1MTCnfHofs7d01v5f1q6fFS+9r6eTbccMM05vrrry/auvhKhL/X6vnRp0+fNMZVn9dtHzVqVBrjfgsrd67XLLbgPoueZ+7vWsWvQAAAAACNxkMLAAAAgEbjoQUAAABAo3VopsXN2dU5gm4unJvvqH0u03LttdemPlcMSO21116pb5tttinaJ5xwQhrj5ghrUbhWCyu5ebYLFy4s2uedd14a4+bsfv7zny/axx9/fBqz2267pT6dV++Kfv7iF79IfV/4wheKtisWVpNBINNSx50LRx11VJvjPv7xj6cxb7/9duo7+eSTi7ZmXCL8PPSdd965aLv5zvfdd1/qGzhwYOpTNcdGTUE4d36++eabbf7dJz7xiarX0vyDy6+4oq1nnHFG0T733HPTmP/6r/9KfczF99wc71aLrdUUVnTzufUcccdnjx49Up9mJ6dNm5bGuKzUr371q6K98cYbpzGaP9xyyy3TmLPOOiv1nXLKKUXbHdcXXHBB6vvoRz9atF2mzRU/rsm0uOJ8bb1OhL8uaV+rhQ5XJe6Y1mO4Nsegvwlq/64mC9Pq69TcU9zv1cMOO6xof+pTn0pjvvKVr6Q+zYbMnj07jXFFv/WcHTp0aBpTc72rLQir1ztXXLo980eKX4EAAAAAGo2HFgAAAACNxkMLAAAAgEbjoQUAAABAo3VoEL9fv36pT4NwLuDmAooaBpo8eXIa8+tf/zr1abDIbdMBBxyQ+jRs/OlPfzqNcX2vvfZa0XYB2prAuVvEYMSIEUX7W9/6Vhrj9t1qq61WtF3RLw1IRkRMmjSpaOtCABERjz32WOrTomY77bRTGuOC2xoUqwlSw3PhUv2Ob7rppjTm8MMPT30aGPzmN7+Zxrhwvv7ddtttl8a44Pjdd99dtF0wX89rF6J2dL/UhO4jIj75yU8WbRfIdOesFgy79dZb0xhXJPJHP/pR0XbhTkL39dy1pKbQqAtu9+rVq2jrNT/CL2ihBUpdWFaPs4i86INbNMUd/3rdd8eZBqK1AG2EL3DnFmBRH/vYx1KfBpLnzZuXxrjwsd4j9bNF+EUMas5vFz4meJ/V3I9duNvtSz2mb7nlljSmPRfiaTXAX/N3eixefvnlaYwrrHz77bcXbbefevfu3eb7DxkyJPW5+38NV5z8e9/7XtF227ky70X8TwsAAACARuOhBQAAAECj8dACAAAAoNF4aAEAAADQaB0axO/WrVvq0/ChC2TXhPOvu+66NMYFYfXvDj300DRm7bXXTn26Xa7qqKvAW1PpVUOTNdsdkRcjcCEt9366Ta6yfc+ePVPfwQcfXLRdhWMX+LrhhhuK9vDhw9OYLl26pD4NgbmAJFqnx8++++6bxmg4MCLikEMOKdpf+9rX0hgN60XkEKwLNdeE8++66640ZtCgQUXbhTbdOaRhaBdgdtXutQqwCyz27ds39WnA9Oyzz05jLr300tR37LHHFu32DKV2RmuuuWbqq7lWuwUmRo4cWbRdhWgXsl+wYEHR1oVOIiJ23HHH1KcLZtx4441pjAumH3TQQUXb3cM0ZK/XiAi/8MZnPvOZ1Kfc/Unfb/z48WmMLuDhtkurgkf4fa7fn/u7GiwK469Bes6466k7pnRRiOuvvz6Nccd0q4sB6Da4Me53mF4j3DGm9zC9N0X4xWR04ahdd901jXF0n6+zzjppjPse9PeU2weu77DDDiva7t7nfrO31z2LOx8AAACARuOhBQAAAECj8dACAAAAoNE6NCTg5v/p3Dc3P65r166p75577inaL7zwQtU26BzhbbbZJo156623Up/OnXTb6TIXWlTMFRnTebUbbLBBGvPAAw+kPp3LqPMtI/xcRjdXW7ls0bbbblu0n3zyyTTGFZfUgmE333xzGuNyA5p5cJ8P7ccVhNp5551Tn35/Osc1IuKLX/xi6jvnnHOKtitS5467rbfeumi7ecx33nln0R48eHAa4/IIen64oo1uLrWe/26+/m233Zb6NMNyxRVXpDFHHXVU6kP7qskIOi7/p/cwd+xNmDAh9ekxNG3atDTG5SRHjRpVtE8//fQ0xs1F1/dzn0WvAbVz/WuK3rnCkXrfri3uqhlI9/5Tp05Nfeuvv37Rdvdsd8/U44VMmae/Ldx5VvM7pabYtOtz7+f63O835Y59PUfdmJpcj6PHlPudVlMU1xU6dhkTPfbd+7nzY/HixW2OWZnnB2ceAAAAgEbjoQUAAABAo/HQAgAAAKDReGgBAAAA0GgdGsR3IXQNRLnQ65w5c1KfFr1zIS1XJFIL47lQWE0AqibMHpEDZa6I4le/+tWircHjiIj//d//TX2vvfZam9vkQmAa3KoNTWmYS4tNRvgFETRQ/7vf/S6N2XTTTVPfxhtvXLRdOA8rlzuvdDGLW2+9NY1xRVtPPfXUon3mmWemMa7gpAb2t9pqqzRGj2EtaBoR0b1799SnC0C4woAueKzFV7UwWEQumBYRceWVVxbtj3/842kMVj4OHC3TAAAgAElEQVS3KIyqDevPmDGjaA8ZMiSN6dGjR+qbP39+0XbX6ilTpqQ+PR9c8N/dR13hX6Xb4P7GhY91m+bOnZvGuM+i13R3L3L7XAsUu9d2xaz1N4E7tx3dLopL+vuxHtNuUSFHz0ct+hvhzys9Pl3gvCbA764H7hwaMWJE0XaLCumCRe68dse5nkOu2KzbL1qQ1X0v7hyqWQTr9ddfT33ud3yN2t/MbeF/WgAAAAA0Gg8tAAAAABqNhxYAAAAAjcZDCwAAAIBGe097hWNqPPvss+nNtPq8C1JdeOGF7rXafL/jjjsu9e2+++5F24WWXMhO95MLUrngVs12jh07tmi7cKALP2pF+i222CKNcdVKaz6Lo/vFVWu+6667Up9W/XbBuH79+qW+U045pWi7fXDggQd2tkRkx52wldw1RMOBEbnavFso47vf/W7q04CgCzbqAhfPP/98GuOC+H379i3arkq4287777+/aLvQ/QUXXJD6GlrtvrOdQ/HYY4+lg7a2crXSxVxcdXa3AIueI1ppOsJfm/X6uc4666Qx7tqsx3FN5WwXutV7dkQO3rvK9m7RGw0I14SfIyJefPHFou3u2WPGjEl9eg9x1y53n9HvwY3ZbbfdOtV5tGjRorTzdH+6/ev69Jh2x8qxxx6b+iZPntzma7vfQPr9uePH/V3NAh56DtUuZqH7oNVj04Xu3X1Nr3fnnXdeGtO/f//Ut8kmm/zT16ndTqdnz55tnkP8TwsAAACARuOhBQAAAECj8dACAAAAoNE6NNPy1FNPpTfTuYRaNDIi4ic/+Unq0/l/2223XRrzxS9+MfVpXsTNvaudh1kzRj+fy3PofEP3Om4+oM6VdIX5Wv1+a+ZTum1y+/Oiiy4q2tOnT09j3BzWffbZp2gfdNBBacyee+7ZqeYRRwMzLY47fp577rmifcIJJ6Qxbg79xRdfXLRnz56dxuixv/7666cxLi83bdq0ou0KmD322GOpT+f/uvnARxxxROprqM52DsXjjz+eDtCa+eqOzn1388ddnkrvDa+++moas3DhwtSn55abj1+TuXRz7fUccZ/FFaHT63ftfHzN3my44YZpjCuyp9/VDjvskMa4e21NMcmaApfufrXLLrt0qvNowYIF6UuuyS2441VzETVjIiJGjhxZtF1xYHcc6Ha2+jtpZRYZdfuyZjvddcztOy1i/qUvfSmN0QLfEX5/qlb3J5kWAAAAAO96PLQAAAAAaDQeWgAAAAA0Gg8tAAAAABqtQ4P4jzzySHqzefPmFe0zzzwz/d2yZctSn4aBXIDPFdiqKVTnAoM6rjZoqONcSKrVglc6rrY4Ws13XvNaNQsPRORtd2FrR//uxBNPTGNOOumkThV+jHdJEL/GlClTUp8rCKvHy2mnnZbGaIB4wIABaYw7XjXoPGHChDTGFbe97LLLiva7KHTvdLZzKJ588sl2Ky6pQXz3Ou6ap8UeXTG7BQsWpL7XX3+9aLuwvCswp33uWq33Jxc4r+lzgehBgwa1+X4zZ85MYwYPHpz6NIBdW8xOv5va71z3lbuWbL755p3qPFq8eHHaCTVFG12QW6/frjipFgKOyMXBP/zhD6cx7hzSRSjcIi1uO2sWI1I1v4ki8vnvfiu6a4See+6Y/uxnP5v6tND65ptvnsa4/eK2XbW6iABBfAAAAADvejy0AAAAAGg0HloAAAAANBoPLQAAAAAarUOD+A899FB6M62W/sQTT6S/c6E+3W5XpdeFiNqrEqpTUx3VBZRqKjG7AFZNFVn3fiuzimsNt00u3KXh1TFjxqQxv/nNbzpV+DFWoSC+O/dmzJiR+j7xiU8UbResvOSSS4q2O/fddeSRRx4p2l/96lfbfO0Iv2DAu1hnO4fimWeeSQdfq9fFmjC3u+ZpgL72mNX7xYoVK9p8f8e9X809ZbXVVkt9ev12i+csWbIk9WmIeOzYsWnMeuutl/pqKts7es1xiwrULoSjNtpoo051Hr355ptpR+lxXhsm10D9mmuumca470Xfb+nSpWnM3LlzU9+ee+5ZtN155hazaOX3Y+1vTH1td8y581EXLdDFZSIifvnLX6a+Aw44oGi739A1ixHULCoQQRAfAAAAQCfBQwsAAACARuOhBQAAAECj5YlnK5Gb56pzELXgVoSfW6i6d+/e0jbVZj50bl+rWRH3+ZSbI921a9fUp3OEtWBShJ+TqH1ujPss2uf+ztG/qynIFJHnQA4bNqzq7/Du4I6x9ddfP/VdeeWVRfv4449PYzRjcsUVV6QxLi936qmnFu3zzz+/zdfGu5+7p7h52DVazbR06dKlzddxRSl1nMumuNyJZmjc++k112UEpk+fnvp0PvyQIUPSmHXXXTf11VzTXYZN92erhUGdmvwKfB5IuWv8K6+8kvq0OLg7F10RVf1N2aNHjzTGnXuvvvpq0XaFjvfZZ5/Up7+x3G8gzcfUZMcick5Lrw8REdOmTUt9mgN1udCPfvSjbb5fTTFWN859V7V5nFbwPy0AAAAAGo2HFgAAAACNxkMLAAAAgEbjoQUAAABAo3Voccm77747vZkWFXKFslxoUkM9tSGfmvB4exZk1G1/6aWX0piZM2cWbRdicsHK0aNHF+3evXunMTWFK2uD8TWFo2pCjDUh/9r322+//TpVQa9YhYpLtsqFEb/2ta8VbRe6d8fPKaecUrRPPvnkf3Hr3pU62zkUzz//fDqP3EImNWoKMtYsiFJLA9AuEO1Cy3PmzCnaN998cxpz4403Fm1XcE7vOxERZ5xxRtEeNWpUGuPotrv7h/uNUnM/rvm72ntfzX1t9OjRneo8csUl9ZiuWUQpIn8vtUUpa37fOLqdL7zwQhpz7733pr4Pf/jDRXvkyJFpjF4P3LHjfudqMP6GG25IY3bYYYfUp+ejez8XqNcFAmp/49aMazWIT3FJAAAAAO96PLQAAAAAaDQeWgAAAAA0Gg8tAAAAABqttRLA7UgrxLuK8e1ZobbVhQf071wAzIXlNcjoKhwvXry4aGsgKyKib9++qa9r165F2wWdXDXW9qpMWluFuKaCqvteakKa6HyGDh2a+g4//PCiPXDgwKrX2n///dtlm/DuUlv9ub2465veQ2oDtPpaeh+I8JXBR4wYUbRd+FgDyuuss04aM2bMmNQ3duzYou2Cxm4f6L3Ajam57tf+nY5r9TvnXuRddNFFRbtbt25pjLv/63HnfrfUnB/ut437jrXP/eZy14j77ruvaLuw/Le//e02t0nPl4iIwYMHF+0333wzjXELY4wbN65ou9+hhx12WOrTfe5+07p90JGLdzn8TwsAAACARuOhBQAAAECj8dACAAAAoNH+7ZmWVuertjLGqZ0Dqdy8zBkzZqS+2267rWhrIUn3fq7o2IQJE1LfpEmTivYRRxyRxvTr1y/1tVqQSbntdN+DFm1zBafcNtXMVwUiIubPn1+0x48fn8a88cYbqe/II48s2uuvv377bhgaqdXCjjVqr1M19yw3p1zvmbXXc82ZuAKUu+yyS9HeZptt0hg91yL8/HtV83lXdn5VX789fzd0Nm7fnXTSSUX77rvvTmO6dOmS+vT3VG3Rz5qilO61dJzL0Lg8jmaSX3755TRGMyWu+OshhxyS+nSc++3mzg+9lrnfrzX7oD2LS65M/E8LAAAAgEbjoQUAAABAo/HQAgAAAKDReGgBAAAA0Gjv+XcXigEAAACAf4b/aQEAAADQaDy0AAAAAGg0HloAAAAANBoPLQAAAAAajYcWAAAAAI3GQwsAAACARuOhBQAAAECj8dACAAAAoNF4aAEAAADQaDy0AAAAAGg0HloAAAAANBoPLQAAAAAajYcWAAAAAI3GQwsAAACARuOhBQAAAECj8dACAAAAoNF4aAEAAADQaDy0AAAAAGg0HloAAAAANBoPLQAAAAAajYcWAAAAAI3GQwsAAACARuOhBQAAAECj8dACAAAAoNF4aAEAAADQaDy0AAAAAGg0HloAAAAANBoPLQAAAAAajYcWAAAAAI32vo58s9dee+1v2rdkyZKiPX/+/PR3L7/8cur761//WrR79OiRxmy33Xapb8WKFUV78uTJacwf/vCH1Kfb9Z73vCeN6dmzZ+obOHBg0Z4zZ04a8/73v79or7/++mmM+7sFCxYU7bXXXjuNeeutt1Lfpz71qaL9xBNPpDGPPvpo6tPPctBBB6Uxr732Wur7y1/+UrSfe+65NGb8+PGpb4899ijad911Vxrz/e9/P38Rq7Czzz47nUNPPvlk0f7zn/+c/m7rrbdOfeuss07R3nHHHdOY//zP/0x9t956a9HeZ5990phZs2alvueff75oP/LII2mMO//1+Jk4cWIa061bt6LdtWvXNMads2+++WbRHjZsWBqzySabpL5FixYV7WnTprX52hER6667btEeNWpUGuP2ue4DN+b1119v8+8mTJiQxsyZM6dTnUMREbNmzUrnURO9973vTX36nf7pT39KY9w1YI011mjz/fS1//a3ut2k59Z//Ef+91C9z9Vy26Db2QSDBg3qVOdR79692zw4ao+fJnL3C6xcCxYsaHOn8z8tAAAAABqNhxYAAAAAjcZDCwAAAIBG69BMy1prrZX6pk+fXrTfeeedNKZ3796pb+bMmUV73rx5aYybT6l9tXMudY7w6quvnsbsuuuuqW/ZsmVF283Z32KLLYq2ZlUicoYnIudz3Gdxc/t1n2smIiLnHSLy9+CyMO97Xz6kdP7xs88+m8a4+f+aeZgxY0Ya09m4/bR48eKi/cc//jGNcRml1VZbrWgfeuihaYzLimlu6cc//nEao9mNiIg33nijaI8ePTqNcXPxly5dWrS33377NGbNNdcs2jVZgIiIKVOmFG2Xn3v66adTn+4Xd55pXs9tp362iIguXbqkvs0226xoP/XUU2mMy73p8aLfOTqGuzbrse6OT9eneSZ3vLj8ih4LNbkQl01xc/3187n7lTu39TxtNatSe76j/dT8dqr5DdYEbpvItDQT/9MCAAAAoNF4aAEAAADQaDy0AAAAAGi0Ds20uPn4ys2hd+vQ67xeNzdcMx8Ree6iy5i4Wg36Wn369Elj3Nxi/TutJxERcc011xRtV2+hX79+qU/Xvdc6KhERs2fPTn2aRRkzZkwa4+YyDxkyJPUply166KGHivbcuXPTGK17EZHn/7vjoLN59dVXU5/mq4YOHZrG6HcQEXHSSScVbXfuue9T54//6le/SmMOOOCA1Ke5DPd9Tp06tc33e/HFF9MYnS/fq1evNMYZOXJk0XbngrtGTJo0qWi7vN7OO++c+vS64fJHbp9rbSN3jdD8XETed1tuuWUag5XPZTw0c+HqmLjspN7D3PfucpGaN3T5w5oxTk2mpSaz4+47juYNyK90vI997GOpT7N9tfWy9Phx18W333479WkdOnf8uGNYf6u513bX4Zo6RjVZGFdnSz9zq9mfjs4M1e6DVvPkiv9pAQAAANBoPLQAAAAAaDQeWgAAAAA0Gg8tAAAAABqtQ4P4LpS14YYbFm0tlBfhQz0a4v3DH/6QxsyaNSv1afB24403rno/3c6a4nkREb///e+LtgsIa6E4LXgXEbH++uunPg07u/DlK6+8kvo01D9o0KA0RoO/EREjRowo2i5M5sLVGgYeMGBAGuMWTdAQqltooLPRwo4RuTipC9S771jD3GeeeWYa4wKK+l0deOCBaYw7P26//fai7Qpe6vkSkYOUrkhdjZrgX9++fVPfDjvskPr233//ou32k1tcQrfdFXF1BSB1QQ1XPNAtDuKuSWgGvc+48LGjgX23uIu7Nqua88EF3N291vUpF4jWfeAC/LXhfHQst0iEHne6mE6Ev75pn7vGu3tDTWFld/xoEN+dQ07Nca7cdtf8LnO/Fd350V4B91rt9foE8QEAAACsknhoAQAAANBoPLQAAAAAaDQeWgAAAAA0WocG8V1V93feeadou3COq/68dOnSor18+fI0xr3WzJkzi7YL0Oo2ReRq3g8//HAaM3r06NSn1ckvvPDCNEZDWS6o5qrIa8jWBR0XLlyY+rSyvQuFuQrfug0u7OmCzBoanjZtWhrjviv9jl3AvLMZNWpU6tOFDlyI0QXxNbToKs0PHz489e25555FW6vDR0Scd955qU+/T7fghQtN6uIONdV2a19bx82fPz+Nufnmm1PfuHHjivbRRx+dxqy33nqp76WXXira7lrjwvO679y57rZ92LBhRVsDqOgYLkCr3DXXLV6jx7E7311fK2F5dy9y26njXIDfLRSj51+ri2yg47mAufa531drr7126tPfCO5a7Y4NvTe89dZbfmOF/l50CwY4elzrvTci31fdvdf95tKFVNw55PpaVXPPdNctHVcbqG+vAD//0wIAAACg0XhoAQAAANBoPLQAAAAAaLQOzbRMnDgx9elcwnnz5qUxbh62Fjp86KGH0hg3h06LNOpc8Qg/z1xzNf37909jXKFKne//4IMPpjE1853dHE+dV7/JJpukMa5AkRaqc/Om3feg2/Dmm2+mMc8880zq03mtbl5m7969U5/OnXTv19m470ULFLoCn24+u869dcehm3+s59o111yTxrj5sTVzYV3RRJ0T7I5pfS0331qP+wifA1Euu6V/5zI8xx13XOrTjMn999+fxrh9rn0u49a9e/fUp0U+a+d8wx+f7riuoQUhI3JxPjc/3t2L5syZU7RfffXVNMblQJctW1a03WfRbIorfuqyqZpldMdizXnr9kFNzqU95/q7bWjP1+9M3Dnk7k+t5iv0vlbzWyoif8fufuGOAz2P3fmpmWHNNUf4wsMbbbRR0dbzNcIXHtdztjY7UnM/rrneud+mK7PAJf/TAgAAAKDReGgBAAAA0Gg8tAAAAABoNB5aAAAAADRahwbxXaBew4i1IWINMrkw0IABA1KfBliXLFmSxrgihrpggHs/59prr23z/XQfON26dUt9WjjShXO1KGZEDm65fT5r1qzUt8ceexRttw9++9vfpr7nn3++aLtg8+DBg1Pf448/XrQJEfvCbnpsuFChK0ao3/HIkSPTGHdMafC+NpysIfvddtstjXF9uuiFK3in26CLDET4c08XB7nzzjvTGC1IG5FDmu4cuvzyy1PfZz7zmaLtCo/NmDEj9em+c5/PFUjTImpu4ZHOyIVs9T7jjjN3L9Jjz722W1xBj5knn3wyjZk8eXLq0+/QhcTdNtScp1oAcvHixWmMK36qAWV3D91uu+1Snwb93TFc8105NYH62oC9vhbBfE9/E7hjzn132lfzm8hxvy1aLdLo7pka9HeLS3zkIx8p2u43kVtwRhd3cb/ddBGOiHy/b3WxEKfmtVzovub3cathff6nBQAAAECj8dACAAAAoNF4aAEAAADQaDy0AAAAAGi0Dg3iuzCihvNHjx6dxrhQj1ZHd6EeF7Zy26BctVKtNv/aa6+lMTfccEPqe+SRR4r2ihUr0hgNc+26665tvk5EDuNOmzYtjdEqqxE59O6C2y58rJWJXbDZVWfW8NqGG26YxrjKshrSrK12uypz54J+f+7YdAFXPaZdpevvfe97qU9Dqe57GThwYOr7whe+ULQ33XTTNMYdixrSrAlRum1yVbr33nvvou3CwrqYRkQOV7rQpqvk/Ytf/KJon3LKKWmMhqEjcoXz2iC+hr1dcBR/pwt9uP3pqlTrdcqFgV34eNy4cUX72WefTWPefvvt1OcWZal5Pz0e3evoeePuq+7Y0/uxWzTF3VeHDx9etEeNGpXGuGNWr4Pu87pzUj+P+yyt/l1nU3M/dmNq9p0b48L5+l2546DmflG7sIIuOHHwwQenMXoP0+tDhD/G9NrirjXbbrtt6rv77ruLtlvwamX+dnJhfXfdaK8FAvifFgAAAACNxkMLAAAAgEbjoQUAAABAo3VopsXN7dO5rzo3NsLPLV5vvfWK9gsvvJDGuPmNvXv3Ltpu7qSb36hF2ubNm5fGXH311W3+nTNmzJiirVmDCJ+Fuf/++4u2+yzjx49PfbrvtEhlhM+YPPfcc0XbFRlzNKfkiue5eeDMG87cvHCdH+vm/q6//vqpb8SIEUX7Zz/7WRrjjjvlCiT+93//d5vv575fN/dW58e6XI/+nRvj+vS1e/bsmcYcf/zxqU/3+R133JHGuDm8en2766670pjtt98+9WlWzJ0v6667burT/JHLSHRG7hqv89XdvnJz0TUr5QqyurzhpEmTirabw+7mhut2uWPB5UD0PuoKQGqBO5dbdLkzPbfcPdvtTy2e6a43rsie3sddfszdw/Ta2LVr1zTGbYN+N65wZWdTk2Vo9R7u7gOuT89Hd17X3C8ct+1aONK9tuaP3f3xox/9aOq76qqrivb06dPTmC222CL1aUbYZVrdNUK/q9rci35m93c1x0arGRf+pwUAAABAo/HQAgAAAKDReGgBAAAA0Gg8tAAAAABotA4N4ruQlAbhXGDIhQG12JoL3bsCiRq2nDNnThozZMiQ1LfWWmsV7SeeeKLNbXLb5QorbrXVVkXbBZs0rB+Rw53u/V0oSwOgQ4cOTWNcyF6/B1dI0gUbNdzpAliu6KeGlt133Nm4IL4G43ShhYiIvn37pj5dgGHChAlpjPuuVltttaL9+c9/Po3ZeOONU9/TTz/9T98/ImLkyJGpT4vguSCwhvzcYgR6HEZETJkypWivscYaaYwrgvnJT36yaLvw9TPPPJP6NMD72GOPpTE77rhj6tNFTNxCIK+88krq04Cyu752Ri4orqFed5zpsR+R9+lLL72Uxug1NyJi6dKlRduFu10ByP79+xftDTbYII1x13S9BrjP9/DDDxdtF2Z3hSNrivO5z6ehd3e/cueyXvNcYVwtrBpRV4zQfT7tcwX8Opv2Khbo1CyaEpF/K9UWOtTXd+fCzjvvnPr0XNd7WkQ+znfaaac0xoXzN99886KtCy1F+PND7xfuXuTovnLnZ02RSPddOe1V4JL/aQEAAADQaDy0AAAAAGg0HloAAAAANBoPLQAAAAAarUOD+C6wo+FuF3DTEGpEDg1pwN6NicjhbhdsdmHgp556qmg/+OCDaYwL/mmwd5dddkljNKT57LPPpjGuSra+1nXXXZfGuKDho48+WrRdcNsF47VCrAZCI3xFcV3EwC0Y4PadcgHJzsYtdKCVpt0CCcOHD099GkJ3oTvngx/8YNF2x+aPf/zj1HfxxRe3+X5uEYyvf/3rRdt9FjVx4sTUd8YZZ6Q+F85Xu+++e+o79dRTi/aRRx6Zxrz88supT49hF050265hy9tuuy2NcRWc9dhwFd07Ixcc14CpG+Oui3ocu0rW7rvR6taDBw9OY1xgVyvZu/Cxq+quIWK3mIMu7uIWZHH3Wg3Lu3uv69P96QLRzz33XOpT7r7jAvz6nbqFBtw1Qe+jel7h30Ovn+43prvG6nFWswBMRMRdd91VtN3vq89+9rNF2y2C437L7LDDDkV7/PjxacyCBQtSn57rW2+9dRrzyCOPpD79re3uxzW/CWp/N9QG9tt8nXZ5FQAAAABYSXhoAQAAANBoPLQAAAAAaLQOneDs5lPr3D43V9T9XU2RNJeT0IJe6667bhrjilneeOONRVuzMRF+3vJ2221XtN38Rn1tV/DS7ZeTTjqpaG+yySZpjJvLqPMwXYG7UaNGpT6XRVFu23V/9unTJ41x8x2ZN5ztscceqW/ZsmVFe/bs2WmMzoOPiLj99tvbfD83L/yQQw4p2i7f5TItmoXRYloREVdeeWXqO//884v2D3/4wzRGz/XzzjsvjXHHmGZTXIHGW265JfVp/kDnMUdEbLvttqnvnnvuKdouj+CKUuprueKBbtv12klxyb9zc8r1u3BztV3xUb2nzJo1K41xeRWdw+7uDY7eZ9y8eten+U23DzR34vKkjl7jdZ9E1GVhnOXLl6e+GTNmFG23nS6noBkaLb4a4a+fmu1xv0n23HPP1Lcqq8kotFdBwX+Fy6ZpMVJXnPTmm29Offqb5KijjkpjNtpoo6Ltclpu3+mx6e71l19+eeq77777ivZee+2Vxrjrj2baWs071hYCbS/8TwsAAACARuOhBQAAAECj8dACAAAAoNF4aAEAAADQaB0axJ87d27q02I5rricC/BNnTq1aLvQtgviazDMFarTQpIRueCbC927gPk222xTtF3wVoNMLpxYE6DV94rI+ykiB7BeeumlNMYVnNTvxi1Y4ML6GoRzRcZ69+6d+jRMOnbs2DSms9lss81SnxZIW7hwYZtjIvz5qEaMGJH6dPGKCRMmpDH77LNP6jvttNOKtgs/umP/9NNPL9puu/XzaVA3IuIb3/hG6jvwwAOLtltg48UXX0x9eo049thj0xh3vN57772pT7nvT8PIrsihCyNrYVcXMu6MakKn7jrlrsN6zXNB/DXXXDP1aVFhXbQlwhe402JyLmjstl0DwS74v+uuuxbtmkB/RMQLL7xQtN390QWS9bx190d3XGuA3hXi6969e+rTz+x+W7iCmvr6bjs7m5p90F4FBWtf3wX/V1tttdSn9zUXutdCqxERxxxzTNH+0Ic+lMbose8W77jjjjtSn56z++67bxozbty41Ke/Td3vOffbsGYhnpV5nLf62vxPCwAAAIBG46EFAAAAQKPx0AIAAACg0XhoAQAAANBoHRrEf/XVV1OfhnFcoN6FtPXvXMDUhR81gOnCVlph1G2XC5i5IKWGAV2wsWvXrkXbBY3dZ9Gqoy6wuOOOO6Y+DZ1psDMi4rnnnkt922+/fdF2AVBXqVsDZnPmzEljXDVvDf67AGhn44KGugCEC6C6qtI11YrdQhX6d64CrwsoaujWVaOu4c49d14pF2petmxZ0XbhwJ49e6Y+Deu6kLGrQqzXMl2IJMJXE9Zjv3///mmM2wd6vVuZlYrfTfSaG6uDqmoAABv2SURBVJHPEXfNdQFzPa5d8NctrqALWLhzdPTo0alv2LBhRduFyfW4jsjXa/d3AwcOLNpuP7mgulYBd4tXuIBwzcIQPXr0aHOMO/bdAiG6kIn7rtxiQPrbxX0W1KlZjMhx9yt9LfebZMyYManv4YcfLtozZ85MY9zvOf0N9NOf/jSN0WPRHWNuIRU9zt1CUvvtt1/q08VjpkyZksa4hZV0v7gFqNwiAnoPcd/nyrzP8D8tAAAAABqNhxYAAAAAjcZDCwAAAIBG69BMi5t3qvPvtfBhhC84p/PhXS7DzZPUebxPP/10GuMKMup8SjdHcOONN25zO11xMp036OZuun2gn8/t30033TT16ZzHyZMnpzGvvfZa6nvggQeKtpvzuXTp0tSn342bz+kyAU8++WTRdsUsO5vzzz8/9en8UZfBcBmItl4nwudjNKPkjjs3z1XP7WuuuSaNufXWW1PfDjvsULTdnHOdZ+8+i5vvrNvprhnu72ryQF26dEl9mqtxmRZH8w6DBg1KY1zm66233irabt91Ru6Y1Wt1Tf4wIl/TXVbE5WMWL15ctJ9//vk0xhU71b9z9yI3F10LjbpMmWZRXMFkdy/Sa7rLwrnzQffL9OnT0xi3D3Qb3DnqsqF6Hrn95M4R3ce15+2qrNXCka0WFXQFYfUc1bxXhM8tP/LII0V71KhRacwRRxyR+jTv7Io2XnXVVUXb/TY955xzUp/mHV3GzZ3ren+8+uqr0xiX09bcqTv33Dbo9+7utSsz58L/tAAAAABoNB5aAAAAADQaDy0AAAAAGo2HFgAAAACN1qFBfBd20kKDrqCOC/D069evaLtQmAsDaZj7scceS2PcNmhgcKeddkpjXNBQA4IuTKahQheQdIFB/cwujOgCoLrtLoBVE9IcMGBAGuP2uYbzXQFKFxR/4YUXirYr1tfZuCJYWkjOLQih50tEXTDOhYr1uKtZ8CIiByIvvvjiNt8/IuLjH/940XZhaP0steFA7asNiWoQ3wXz3aIbeo1w2+n6dKEKFxJ31y3dV+760xm5fazXYVdI0n3P+l24kLZbfESPD1dY2YWI9Vhw2zly5MjUp+N0kYaIiDvuuKNou4LF7j6uAWgXGHavpQtTuIVcXFFqvb7o4iD/6LX0XufuRTULKbh7fWfjrvvtFbZ212H3m6RXr15FWwu9RkTcdNNNqU/vh8ccc0was+GGG7a5DVqoOyJfI9ziAO46rK/t9q/7fNtuu23R1nM4whcL12NaXyci4re//W3q0+1qdWGFVvE/LQAAAAAajYcWAAAAAI3GQwsAAACARuvQCc7PPPNM6tN5rm5O5MKFC1Ofzil1c7x1vmxEnts3e/bsNMZtgxYRcvMda4rQuTnRq622Wpvv7zItNfPxXdFGLZ611VZbpTFafCkifw+uMKfbLwcddFDR7tu3bxrjCkfefffdRdtlBDobNxdWi1dp8bkIP09buePHzbPX49Md027u7dZbb120b7nlljTmwgsvTH0//elPi/Zuu+2WxtQcGzXzrVstiuXm1Ls52C53UkPnEbuCZe619brR0fOPm8pd9zX/U1s8T+9FNdfqiLpz0p1/WqTVvY67H+r55+6PelzNnz8/jXFFTDV34ubHu/yI5iJdbtEVnNbzrTbTqkWTXTZFs5sR+fNpjrAz6uhribv3rbvuukX7Zz/7WRrjfgPtvffeRXvs2LFpjDumtNCpy1duueWW//RvInKh14h8n6m9Zug2fOxjH0tjZsyYkfo0T+7u4yNGjEh9Wnxdf7/+IzUFmWvwPy0AAAAAGo2HFgAAAACNxkMLAAAAgEbjoQUAAABAo3VoEN+FVTVAq0G5iIgePXqkPg0puVChBo0iIiZMmNDmNrkiWBrEd+FcF7zV8FhNEN8FamuKWdUUz3PbqZ8twocRtRiZGzN8+PDUpwWgXGjavZaGZXUBgc7IFUzTY8yF4N0+16ChO6ZdCFbHuXCgC03q+7nz+ogjjkh99913X9F2x4o7Z9t6/4jWi1LqeVxTEDIif3+1YVYNd7tirN27d099WtSQonh/58KxyoVsXeFIPa4GDRqUxrhAvZ6TWtwywh8fen6//vrraYwLy+sCHa7onZ6Tbh+4EPpLL730T7cxwl8TNthgg6Lt7h/u873xxhtF290z3XVJ/86dM/obIaLu86H9wvnu+3RFkx988MGi7Yovf+ADH0h9hxxySNF2v90ef/zx1KfHhivarL/nXIFvd1/VRQXcwk7PP/986tOFjXbeeec05oEHHkh9jz76aNH+5S9/mcYcffTRqU9/V7vitu440N/arRYi5X9aAAAAADQaDy0AAAAAGo2HFgAAAACNxkMLAAAAgEbr0CC+q9KrAcXa8KOGf1wQ984770x9GiJ0Yb2ddtop9enru5CtCxpqYNe9n4YBXWjaLTSgQa3aqsC67S6c68L5v/nNb4q2C8uNGzcu9fXs2bNoL1y4MI1xQdXevXsXbRdo62zc+aGBNhfCdcedVuV1AduXX3459ekxdccdd6QxGvKLiPj6179etPX7jfDnlXKLZ9RUF3fXH92f7v11AYqIvA9cFfTp06enPnctU+481oC0CwsvX7489el+ceHHo446qs1tWtW4faXHh7unzJo1K/Wtv/76RVurvEdEPPnkk6lv4MCBRdvdP1yfBn1dENb1PfbYY0Vbq7xH5GPd7QN3v9A+d5y782Hw4MFF2y22ogHliPw9uCC1u1/oNc6dazXBYhfgR+v0t8R6662XxsydOzf1PfXUU0XbfS+f+cxnUp/+JtFFfyL878eTTz65aLt7w9133120TzjhhDTmlVdeSX26uIN+toiIW265JfXpOfPNb34zjfnkJz+Z+saPH1+03YIXDz/8cOrbeuuti7YL+btFRdoL/9MCAAAAoNF4aAEAAADQaDy0AAAAAGi0Ds20uDm0Oh/PzV91hZx0buqkSZPSGDf/WDMmOh85ImKTTTZJfTUF9dx8cZ0zWzMf341xc+Y1++KKEbk+nZ/rxmy00Uapb8qUKUX7hRdeSGNccdCJEycWbbfP3RxPnadck3dY1WmxwIh83LnMkMtzjBw5smi7effTpk1LfXquufnH3/72t1Of2n333VPfxRdfnPo0+6LbHZGPaS24FRHx85//PPVp/sBleFxBrw996ENF283zf+aZZ1Kfzpd3c+pdbkmvETfddFMas2LFitSn+6W9ir+92z300EOpT/Mj7l6kxQkj8vHpija6e59ez9x12N1TdDtdBtLR89RdE7TPZUUczce5XJ3bd3ofd4U5Xa5G94G7H7v7qP6W0O2OiOjTp0/q06Kw7rztbGoztModU7rP3fX0+uuvT32aYdHMSYQvWKoZ2ssvvzyN2XbbbVOfntuaC4nI57G7Z2suLSLiyiuvLNrumuHMmDGjaF944YVpzOmnn576DjzwwKJ97rnnpjEur6K/j4cOHZrGuN+Bep2iuCQAAACAVRIPLQAAAAAajYcWAAAAAI3GQwsAAACARuvQIL4rXrP99tsX7f79+6cxLuz05ptvtjnG0SKNrpBkTci+NkSkwTRXRFGDTcccc0wa44puaWjSbZMuPOD6XADUBf91X02dOjWNcQUK9btxwTgt9hSRA+UuwN/ZuKCjfleuwJZblEKPuyeeeKJqG6699tqifdZZZ6Uxp512WurToN+9996bxrjQrRbLcsFGddJJJ7X5OhERxx13XJuv5QLZxx57bNF2hc9cgc2aoKpbaECLmLlzXQtQOgSI/86FwjWk7RY2cMHtyZMnF+0NN9wwjdlss81Sn1vgQdUE4V3g3PVpON6NcUWMlQsR1xSJdp9FzwdXrM8tNKBBbf09EFH3+dy1UgteRkTMnDmzaLugcWfjrmXa535/uO9TF4m47LLL0hgNnEdEHH/88UV7v/32S2PcseEKnaqDDz449elvLvdbsV+/fkVbFzCKiDjzzDNTX81iT+780CKqbgGYCy64IPXpPcwtxONe65577inaH/vYx9IYdz9Urd6LuIMBAAAAaDQeWgAAAAA0Gg8tAAAAABqNhxYAAAAAjdahQXxXpVqrAmsYMsIHt37zm98UbVeB14XAxowZU7RdBd4//elPqa8mYFYTLHIBWg1kvvjii2nM6quvnvpGjx5dtF3Q0b2fBu/dZ3HhfA0ku3Dp7373u9SnIXCtqB4R0atXr9SnYS63TZ2NOw60zy1q4ILH22yzTdHWAOE/+rvHH3+8aN96661pzKc//enUt/XWWxft2bNnpzGuuq5ulwtIq9122y31XXLJJalv3LhxRdvt3z322CP16bngqhC7hUf0GuHOva222ir16TXBfceuirS+H0H8v3PV4BcvXly0//jHP6Yxbr/37t27aLsFEVzgW49/F+5276ch9+XLl6cxjlaR72ju/TXU784/d45ohXptR/jrhF6DaulvAhfyR+a+O7fQgYbA3UIObtEkDYHPnz8/jdHfmO713eJHc+bMSX36edxvU71+L1iwII05/PDDU597LeV+A+lvPHeNd79p9e80mB/hFz/QhQUmTpyYxmyxxRap77HHHivabrGnGtzBAAAAADQaDy0AAAAAGo2HFgAAAACN1qGTXHUOfUSed+qyKRMmTEh9WtjQzfVzReh0Tqubm+qyIdpXW9BL5/G64os33XRT0XbzMt38eC22WFMUMyLvKzcH0v2d7oMddtghjXF5HC3upHMbIyIOOeSQ1Kf5BjfHtLNxc9w1b+WKv9Xklj74wQ+mMVdffXWb73fFFVekMX369El9u+yyS9F2Rfhc1kDnJLvzTD+fm8e80UYbpT4tsFk77/+2224r2nfccUcaU7Odbh+4An+vvPJK0XZz/922k2nxXFZLr28uh+Kusfp38+bNS2Nc0dRRo0YV7WnTplVtp37Prsifu4fp8dieGcGa19IieBH5s7jrlLsm6Odzr+0ySVrE0O1zLSQZkYtnuiKjnY27luj357KqrtiiFqAeMGBAGuNyGZqddLkQl4/R7XSv7Whezd1ndBtcVtTdx/W64c5hd29XLhfu8o733Xdf0XafxX3HAwcOLNq33357GvOFL3wh9el36op+1uAOBgAAAKDReGgBAAAA0Gg8tAAAAABoNB5aAAAAADRahwbxa4reuGI2WgAuIoeGXADLFSPSYJgL67lAq/a5z+L6NPzoQrYaBn722WfbHBORw1U1ofsIHyatoa/vAmYuzH3dddcVbVf0y4Xz9TPXFF9a1bmAu4ayXSjVBfH0mNagfIT/XjQUvmTJkjTmjDPOSH1vvfVW0XYFIN2xqaFJd0zruddqONmFfl3Q8PLLL29zm9w2qEMPPTT1ufNDw5W1RfgI3nuuGKEeC+566r6b5557rmi7cLdbFEbvT66o6Msvv5z6dEESV6TN9bVXgUS3X/TYc2Pc++k4d/67z1JzXO+4446pT+8p11xzTRrzwgsvpD4tkNzqPXRVp/cUd8/+1a9+lfr0euYKMrqirVpY1R1juohCRF2R35qFBtwYDf5r0dp/1Kf3Hrfdbjt1H9Re8/WeVbswhxZWdt/xvffem/p23XXXor1w4cKq91Pc0QAAAAA0Gg8tAAAAABqNhxYAAAAAjcZDCwAAAIBG69AgvquWroF6DbxF+EroGirUSrcRuWJ8RMSiRYuKtgvL1gRo3RgXAtNxbsy+++5btLfffvs0pnfv3qlPg80uSNVq1WNXIVbDh+6zuArfQ4YMKdpusQV3bGjlVRdC72xOOOGE1KehPg3mRURssMEGqU8D9S7o+NWvfjX1ffnLXy7aLtS8bNmy1Ped73ynaD/xxBNpzN577536hg0bVrTdQgMasnfnp9svzzzzTNF2le0fffTR1Kdc+NGde/vss0/RPuigg9KYa6+9NvW5AGYNDTq3+jqrGned0mPWXQPdMaT71FXgfu2111KfHrNuIQwX4NeFWtxCGC4cq+eEC8vXqFnwxR37Llz96quvFu3+/funMe570MrgLhiv142I/D24MPAbb7yR+moC2J1NTVDd/UY47rjj2nytmteOyPcC93fu2NC+2t9JGpZ395mhQ4cWbfeb1i3uss466xTtAw44oGqbdL+4hSvcNULPY3eeuYWq9NxzCwbomIj8O6XVc4gzDwAAAECj8dACAAAAoNF4aAEAAADQaB2aaXFz33TO3KRJk9IYVxROixFpViUi4uc//3nqqymC1epcXzfnsqZ4Vk1BppoCSW5eZqvzj2uKLdUWJ9OCbDXFySIinnzyyaLt8g6djSt+qOeH27/z5s1rs8+9tsuPnHLKKUX73HPPTWPc+ajHzz333JPG3H///alP8zgDBgxIY/R6oMUYI/zcYs31uH3n5i3rOHcOfehDH0p9mknSvFeEn1us51rtfGC93rjCuZ2Ry2/pvch9p+640u/G7WM3N1zvda5w5c4775z6tt1226LtClC68123obZAsnLX6poCsO780/uam4/vzkmdM7/NNtukMW6f//SnPy3abq6/y+iRaamj+8Xlg1zmqyZT4vZ5zW8LR8fVXONrTZw4sWjrPSYiYvz48alv+PDhRXv+/PlV26TbXpPJdq9V+3f6O8EVOnb3sPYq0MqZBwAAAKDReGgBAAAA0Gg8tAAAAABoNB5aAAAAADRah6YyXWBHiwrOnTs3jakJfLsQowviafjIhfxd0FC3oSbg7tSEj9w2uffTsFNNQNK9fqtB/NrwWs1CAzXh8eeeey6N6Wyef/751KfflTvutYhrRD4WZ86cmca4QJ0Wbf3ud7+bxlx44YWpT78/d/y44+6ll14q2lOmTEljVG2osObv3DZpmPTggw9OY/r165f69Dh34VJ3vdNzqLb4Ws251xm5YrZ6Xezbt28aowVvI/J1yhWSdPc+LRw5e/bsNMYVodthhx2K9mabbZbGuGLLGvx31wS9drhjseY+5/7OXeP1mtO9e/c0ZvPNN0992223XdHu1atXGjNt2rTUp9+pKzKq15uIXBhPCzt3Ru5aqdcld01yiwrVaHWRiI5eNEHfr0uXLmmMK1Kri0K4e6/ra88isarmN17t4jXt9T3wPy0AAAAAGo2HFgAAAACNxkMLAAAAgEbjoQUAAABAo3VoEL8mxOiCcS5cVcMFBrWvNryuwaLa8JP+nav4qyFNF9Jylcm1cu+yZcvSmJqFBmrC+v9oXM2Ymqrcrhq7hs7cPuhsXKVrDbi5494dr/p3bjELZ/LkyUW7d+/eaYxWfo+ImDBhQtG+++6705gZM2akPg31thqyrzlnXWB67NixqW/XXXct2m4RgyuvvDL1jRkzpmi7z1ITfq4NnGpgmEref1dTtd6FrV0QVse5809D9xE5dL5o0aI0xoXC9drsrtUDBgxIffvvv3/RdvcLrd79+uuvpzGuYrzuT7fgjFaxj8iLCAwePDiNceeDnjfu2uX2gb6+e+2nn3469ekCKAsXLkxjOpuaxRZqFlH6V9QsbLIyuet3zYIeZ555ZurThTjceb2y96dq9bd3zb221QUEuIMBAAAAaDQeWgAAAAA0Gg8tAAAAABqtQzMtLquhGY/hw4enMT169Eh9Oh9OC95F+Pm477zzTtF2xdbWWWed1Ddy5Mii7ebQuszF9OnTi7ab6zt16tSi7YovrbvuuqlP50S74otujrAWMaotlKXzst131adPn9T36KOPFm1X2M0V4tMCafrddUZuHnpNsSdHjzM3H9jN/de5tgsWLGhzmyLyd/zZz342jXGZHT1eZ82alcboMdytW7c0xtGc3ZAhQ9IYdz4+8cQTRdvNg3eF8nScy7i5ong1Rf8cnXPtvs/OqKY4sMuvuP2n10V3H1hrrbVSn96f3Px4Nx9ej8ff/va3aYw7rvR6usEGG6Qxep9zeS537Onx6a4l/fv3T316PXPn0UMPPZT61MYbb5z63D2zZ8+eRdvtJy0cG5GvJ+Qr/b6rUVMYt7Z4bhPVfJYPfvCDqU9/J7lC6zXFyWt19P5sNcOi+J8WAAAAAI3GQwsAAACARuOhBQAAAECj8dACAAAAoNE6NJXpQrYaLHJhaxd60yD8yy+/nMbUFKqrDXxp0S1X+Mf1aUDYLSqgIcY333wzjXGh0N///vdF2xW4mzNnTpvb6bbb7RctDvbaa6+lMV26dEl9+l25bXLFuvTz1RY/XJW5Am2qJmQckQOntYW59LVqirhG5O/dLZShQdmIXPDRjdECly4w7cLQen7ec889aYwL/ut1yy144bbz/vvvL9qPP/54GuMWB9l0002Ltp4bEX6fa9FW93edkbtO6X3G3Xfc+afFFt0+dvcUPUbdNd7dC5S7fuviFRG5eKU7rh9++OGirfemCL9QjS5g4e5zrnimbuf8+fPTGLfQhwb4H3jggTRm2LBhqW/LLbcs2m5xAHce6bHgFkjobPRYiahbFMb1tVowuIlqAufut5MG791v4Y4untle4fn2fC3+pwUAAABAo/HQAgAAAKDReGgBAAAA0Gg8tAAAAABotA4N4g8YMCD1uTCuctXS1e233576Ro0alfoGDRpUtF340YUmNZBYGyrS4JT7u9mzZxdtV6nYVZ/VbXdVgVutIu/CiLpf3Gd5/vnnU5/uAxeCfeSRR1KfBlwJP/ogsAZx3fHrKulqn6t07f6uJgyoAfCIXMnbjXHHqy4wMWHChDRG94sL3bs+XdzBha+32Wab1Fezz905NHDgwKLtzoWaavfuO3ABfj1H3fWuM3ILLmjVc3e/clXW9brk7ldrr7126tNj6I033khjpk6dmvr0OHahd7fYierVq1fq02PIhefddV/PW3ceu32n561bbKVmG3Sxjgh/TurfLV68OI1xoXC32EFn5xY/qPmNAH/9qVkkCvxPCwAAAICG46EFAAAAQKPx0AIAAACg0To00+LmpmpRODd3VIvuRESst956RVvnI0f4ecQ6T9DNA3dzWnXurZurOX369Da3wRXmmjhxYtHeZZdd0hg3T1rnp7ttcvtT59q3Ou+0Zg59RMS4ceOKtpu77eb2k2HJXCZB58K6wopufmxN1sjNFdfzw33n7v30uHP5HM18ROTPrFmniDw/XwtnRvisiOZlXJHIHXfcsc1tckX4XGZH94Gbd+/+Trn3c0UN9btZlYq4/Svc/UKPWXedWr58eerT+5ob465lffr0Kdp6L4zwuRo9tt2x4I5/LaSq7+/GuHuvO840i+IKSbtjT+9rel+P8N+VFqF0+TF37dJroytqXFMstyZnu6pzx1irvyXIvnQ+rd6L+J8WAAAAAI3GQwsAAACARuOhBQAAAECj8dACAAAAoNHeQwAKAAAAQJPxPy0AAAAAGo2HFgAAAACNxkMLAAAAgEbjoQUAAABAo/HQAgAAAKDReGgBAAAA0Gg8tAAAAABoNB5aAAAAADQaDy0AAAAAGo2HFgAAAACNxkMLAAAAgEbjoQUAAABAo/HQAgAAAKDReGgBAAAA0Gg8tAAAAABoNB5aAAAAADQaDy0AAAAAGo2HFgAAAACNxkMLAAAAgEbjoQUAAABAo/HQAgAAAKDReGgBAAAA0Gg8tAAAAABotP8HjA68XkqO3IAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96483a6550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show images\n",
    "fig, axs = plt.subplots(2,4, figsize=(15, 6))\n",
    "fig.subplots_adjust(hspace = .2, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for i in range(len(X_web)):\n",
    "    image = X_web[i].squeeze()\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image,cmap='gray')\n",
    "#     plt.imshow(image,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 8\n"
     ]
    }
   ],
   "source": [
    "print(len(X_web), len(y_web))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Web Image Accuracy = 0.750\n"
     ]
    }
   ],
   "source": [
    "# Once a final model architecture is selected, \n",
    "# the accuracy on the test set should be calculated and reported as well.\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver = tf.train.import_meta_graph('./lenet.meta')\n",
    "    saver.restore(sess, \"./lenet\")\n",
    "    web_image_accuracy = evaluate(X_web, y_web)\n",
    "    print(\"Web Image Accuracy = {:.3f}\".format(web_image_accuracy))"
   ]
  },
  {
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "[[  1.00000000e+00   1.34324388e-12   6.91444218e-16   4.15413867e-18\n",
      "    2.07773729e-26]\n",
      " [  1.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00]\n",
      " [  9.99971390e-01   2.85842380e-05   1.20358412e-09   2.07426472e-11\n",
      "    1.62081145e-12]\n",
      " [  1.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00]\n",
      " [  1.00000000e+00   1.30338902e-32   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00]\n",
      " [  6.39133573e-01   3.60866398e-01   7.63609947e-32   5.15263255e-32\n",
      "    4.48792206e-38]\n",
      " [  1.00000000e+00   1.55883969e-25   5.20872653e-26   2.49145769e-31\n",
      "    6.21531015e-36]\n",
      " [  6.17030442e-01   3.35868508e-01   2.55320650e-02   2.01739427e-02\n",
      "    1.39464159e-03]] [[11 30 27 21 40]\n",
      " [12  0  1  2  3]\n",
      " [ 3  1  2 28  5]\n",
      " [38  0  1  2  3]\n",
      " [18 27  0  1  2]\n",
      " [ 2  1  5  3 38]\n",
      " [34  3 38 35 36]\n",
      " [29 39 22 25 24]]\n"
     ]
    }
   ],
   "source": [
    "# Print out the top five softmax probabilities for the predictions \n",
    "# on the German traffic sign images found on the web. \n",
    "\n",
    "softmax_logits = tf.nn.softmax(logits)\n",
    "top_k = tf.nn.top_k(softmax_logits, k=5)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
    "    my_softmax_logits = sess.run(softmax_logits, feed_dict={x: X_web, keep_prob: 1.0})\n",
    "    values,indices = sess.run(top_k,feed_dict={x: X_web, keep_prob: 1.0})\n",
    "print(values, indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-capture the top_k and Top 5 Softmax Probabilities For Each Image Found on the Web\n",
    "import numpy as np\n",
    "values = [[  1.00000000e+00,   1.34324388e-12,   6.91444218e-16,   4.15413867e-18,  2.07773729e-26],\n",
    "     [  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,   0.00000000e+00,  0.00000000e+00],\n",
    "     [  9.99971390e-01,   2.85842380e-05,   1.20358412e-09,   2.07426472e-11,  1.62081145e-12],\n",
    "     [  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,   0.00000000e+00,  0.00000000e+00],\n",
    "     [  1.00000000e+00,   1.30338902e-32,   0.00000000e+00,   0.00000000e+00,  0.00000000e+00],\n",
    "     [  6.39133573e-01,   3.60866398e-01,   7.63609947e-32,   5.15263255e-32,  4.48792206e-38],\n",
    "     [  1.00000000e+00,   1.55883969e-25,   5.20872653e-26,   2.49145769e-31,  6.21531015e-36],\n",
    "     [  6.17030442e-01,   3.35868508e-01,   2.55320650e-02,   2.01739427e-02,  1.39464159e-03]]\n",
    "    \n",
    "indices = [[11, 30, 27, 21, 40],\n",
    "     [12,  0,  1,  2,  3],\n",
    "     [ 3,  1,  2, 28,  5],\n",
    "     [38,  0,  1,  2,  3],\n",
    "     [18, 27,  0,  1,  2],\n",
    "     [ 2,  1,  5,  3, 38],\n",
    "     [34,  3, 38, 35, 36],\n",
    "     [29, 39, 22, 25, 24]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The same grayscale and normalization function with all the other datasets\n",
    "# Implement Min-Max scaling for grayscale image data\n",
    "def normalize_grayscale(image_data):\n",
    "    \"\"\"\n",
    "    Normalize the image data with Min-Max scaling to a range of [0.0, 1.0]\n",
    "    :param image_data: The image data to be normalized\n",
    "    :return: Normalized image data\n",
    "    \"\"\"\n",
    "    a = 0.0\n",
    "    b = 1.0\n",
    "    grayscale_min = 0\n",
    "    grayscale_max = 255\n",
    "    return a + ( ( (image_data - grayscale_min)*(b - a) )/( grayscale_max - grayscale_min ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the images found from web and plot them here\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "web_images = []\n",
    "fig,axs = plt.subplots(2,4,figsize=(8,6))\n",
    "fig.subplots_adjust(hspace=.2,wspace=.002)\n",
    "axs = axs.ravel()\n",
    "for i, img in enumerate(glob.glob('examples/*.png')):\n",
    "    image = cv2.imread(img)\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image)\n",
    "    web_images.append(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y label for web images\n",
    "y_web = [11, 12, 3, 38, 18,1,34, 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grayscale and normalize the web images first\n",
    "X_web = []\n",
    "for i in range(len(web_images)):\n",
    "    gray= cv2.cvtColor(web_images[i],cv2.COLOR_RGB2GRAY)\n",
    "    gray = normalize_grayscale(gray)\n",
    "    img_expanded = gray[:,:,np.newaxis]\n",
    "    X_web.append(img_expanded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fabee7c9860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show images\n",
    "fig, axs = plt.subplots(2,4, figsize=(15, 6))\n",
    "fig.subplots_adjust(hspace = .2, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for i in range(len(X_web)):\n",
    "    image = X_web[i].squeeze()\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fabee138d68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import division\n",
    "fig, ax = plt.subplots(len(web_images),3,figsize=(20,30))\n",
    "fig.subplots_adjust(hspace = .20, wspace=.005)\n",
    "ax = ax.ravel()\n",
    "width = 0.4 # width of the bars\n",
    "x = [1,2,3,4,5]        \n",
    "for i in range(8):\n",
    "    val = values[i]\n",
    "    top_guesses = indices[i]\n",
    "    \n",
    "    ax[3*i].axis('off')\n",
    "    ax[3*i].imshow(web_images[i])\n",
    "    ax[3*i].set_title('input is {}'.format(y_web[i])) \n",
    "    \n",
    "    ax[3*i+1].axis('off')\n",
    "    ax[3*i+1].imshow(X_web[i].reshape(32,32),cmap = 'gray')\n",
    "    ax[3*i+1].set_title('normalized is {}'.format(y_web[i])) \n",
    "\n",
    "    rects = ax[3*i+2].bar(x, val, width, color='r')\n",
    "    ax[3*i+2].set_ylim(0,1.4)\n",
    "    ax[3*i+2].set_title('Top 5 Guesses')\n",
    "    ax[3*i+2].set_xticks(np.add(x,(width/2))) # set the position of the x ticks\n",
    "    ax[3*i+2].set_xticklabels(('1st', '2nd', '3rd', '4th', '5th'))\n",
    "    # attach top 5 possibility labels on the top of each bar\n",
    "    for j,rect in enumerate(rects):\n",
    "        height = rect.get_height()\n",
    "        ax[3*i+2].text(rect.get_x() + rect.get_width()/2., 1.2*height, str(top_guesses[j]),ha='center', va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two incorrect predictions over 6 correct predictions to the 8 new images found on the internet, so the model shows 75% accuracy on these new images predictions. The two wrongly predicted images are: \n",
    "1. \"Speed limit of 30km/h\" was wrongly predicted as \"Speed limit 50km/h\", the model was close to predict correctly as its second highest probability is correct as itself.\n",
    "2. \"Roadwork\" was wrongly predicted as \"Bicyble crossing\" as first guess, \"Keep left\" as second guess. The correct label was in its 4th guess.\n",
    "\n",
    "The images among the correct label and wrongly predicted labels contain pariticial simalities. Such as, the model could not see the difference between 30 and 50 to some images; pedestrian are shown on both roadwork sign and bicycles corssing signs, which confused the model.\n",
    "\n",
    "Further work can be focusing on improving the model on these relatively difficult classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}