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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "from pandas.io.parsers import read_csv\n",
    "import os\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = 'train.p'\n",
    "validation_file= 'valid.p'\n",
    "testing_file = '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",
    "signnames = read_csv(\"signnames.csv\").values[:, 1]    \n",
    "X_train_original, y_train_original = train['features'], train['labels']\n",
    "X_valid_original, y_valid_original = valid['features'], valid['labels']\n",
    "X_test_original, y_test_original = test['features'], test['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34799,)\n",
      "(34799, 32, 32, 3)\n",
      "41\n"
     ]
    }
   ],
   "source": [
    "print(y_train_original.shape)\n",
    "print(X_train_original.shape)\n",
    "print(y_train_original[0])"
   ]
  },
  {
   "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",
      "Image data shape = 32\n",
      "Number of classes = 43\n",
      "(34799, 32, 32, 3)\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 = np.size(X_train_original, 0)\n",
    "\n",
    "# TODO: Number of validation examples\n",
    "n_validation = np.size(X_valid_original, 0)\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = np.size(X_test_original, 0)\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = np.size(X_train_original, 1)\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = np.size(np.unique(y_train_original))\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)\n",
    "print(np.shape(X_train_original))"
   ]
  },
  {
   "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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import matplotlib.pyplot as plt\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "def show_images(class_indices, x, y):\n",
    "    fig = plt.figure(figsize=(6*4, 8*4))\n",
    "    for idx in range(0, n_classes):\n",
    "        plt.subplot(8, 6, idx + 1)\n",
    "        plt.title(signnames[y[class_indices[idx]]])\n",
    "        plt.imshow(x[class_indices[idx]])\n",
    "    fig.tight_layout()\n",
    "    #plt.savefig(\"traffic_signs_original.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9960  2220 31439  5370  6810 12360 21450 23730 15870 11040 17130  8580\n",
      " 27329 21810 29219 29909  5010 30449 20370  6630 25950 25680  4500  1770\n",
      " 10800 33449  1230 10350 26849 10560 25020   210 10140 26250 20010 18930\n",
      "   900  4830 14010 25410  4200     0  9750]\n"
     ]
    }
   ],
   "source": [
    "train_classes, train_indices, train_counts = np.unique(y_train_original, return_index = True, return_counts = True)\n",
    "valid_classes, valid_indices, valid_counts = np.unique(y_valid_original, return_index = True, return_counts = True)\n",
    "test_classes, test_indices, test_counts = np.unique(y_test_original, return_index = True, return_counts = True)\n",
    "print(train_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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7Jp/z7v7IFpp9B4B3uvtwK9sSmRLlmi3mmvGbaL4ewD9B+xsI/xTAN4xfE6DaRoRRvtl6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\nbeBwNKRGbIKjyfIDy10AYMH7rr0Jrot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f/VtOtaS0+P3o/83cVIyRfTu192Arje3XsTpmuRbp5H2/wTpJvfH7H0Op3o1TS9E7snAYw1N8F3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\nIiIiIkOlG1AiIiIiIiIiIiIiIiIyVLoBJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5503eb400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_images(train_indices, X_train_original, y_train_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_stats_image(class_counts, title):\n",
    "    fig = plt.figure(figsize=(8, 6))\n",
    "    plt.bar(np.arange(n_classes), class_counts, align='center')\n",
    "    plt.xlabel('Class')\n",
    "    plt.ylabel('Number of ' + title + ' examples')\n",
    "    plt.xlim([-1, 43])\n",
    "    #plt.savefig(\"numClasses.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4e4d50908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_stats_image(train_counts, 'training')"
   ]
  },
  {
   "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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "from skimage import exposure\n",
    "\n",
    "def process_images(images, labels, isShuffle=True):\n",
    "    images = 0.299 * images[:,:,:,0] + 0.587 * images[:,:,:,1] + 0.114 * images[:,:,:,2]\n",
    "    images = (images / 255.0).astype(np.float32)\n",
    "    for i in range(len(images)):\n",
    "        images[i]=exposure.equalize_adapthist(images[i])\n",
    "    images = images.reshape(images.shape+(1,))\n",
    "    if isShuffle:\n",
    "        images, labels = shuffle(images, labels)\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wengyao04/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float32 to uint16\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train = process_images(X_train_original, y_train_original)\n",
    "X_valid, y_valid = process_images(X_valid_original, y_valid_original)\n",
    "X_test, y_test = process_images(X_test_original, y_test_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wengyao04/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float32 to uint16\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    }
   ],
   "source": [
    "X_train_, y_train_ = process_images(X_train_original, y_train_original, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_processed_images(image_index_list):\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "    idx = 0\n",
    "    for image_idx in image_index_list:\n",
    "        idx = idx + 1\n",
    "        plt.subplot(2, 4, idx)\n",
    "        original = X_train_original[image_idx]\n",
    "        plt.title(signnames[y_train_[image_idx]])\n",
    "        plt.imshow(original)\n",
    "        \n",
    "        idx = idx + 1\n",
    "        plt.subplot(2, 4, idx)\n",
    "        gray = X_train_[image_idx]\n",
    "        plt.title(signnames[y_train_[image_idx]])\n",
    "        plt.imshow(gray.reshape(32,32), cmap = plt.get_cmap('gray'))\n",
    "    fig.tight_layout()\n",
    "    plt.savefig('preprocessed_sign.png')\n",
    "#print(signnames[y_train[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+Ny2/c+dOpx06dMhpLLEV4G9zZ0nI7JgAbu7AkojZeQaSG1awa9LT00PrZOVZ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4e49082e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_index_list = [29909, 9960, 30449, 20370]\n",
    "#plt.imshow(X_train_original[21810])\n",
    "#plt.imshow(X_train_[21810].reshape(32,32), cmap = plt.get_cmap('gray'))\n",
    "plot_processed_images(image_index_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "data_generator = ImageDataGenerator(rotation_range=20, width_shift_range=0.2, height_shift_range=0.2,\n",
    "                                    zoom_range=0.2, shear_range=0.5, fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the .flow() command below generates batches of randomly transformed images\n",
    "# and saves the results to the `preview/` directory\n",
    "example_image = X_train_[20370]\n",
    "x = example_image.reshape((1,) + example_image.shape)  # this is a Numpy array with shape (1, 3, 150, 150)\n",
    "i = 0\n",
    "for batch in data_generator.flow(x, batch_size=1,\n",
    "                                 save_to_dir='.', save_prefix='train_general_caution', save_format='jpg'):\n",
    "    i += 1\n",
    "    if i > 8:\n",
    "        break  # otherwise the generator would loop indefinitely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['train_general_caution_0_6703.jpg', 'train_general_caution_0_4602.jpg', 'train_general_caution_0_3343.jpg', 'train_general_caution_0_4980.jpg', 'train_general_caution_0_5339.jpg', 'train_general_caution_0_3178.jpg', 'train_general_caution_0_1701.jpg', 'train_general_caution_0_7278.jpg']\n"
     ]
    },
    {
     "data": {
      "image/png": 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XxjJHRYCPM8w1V7muFYIC5VROTg4qVaoU6UwrXbo0PQabD9S9Vv2TrR+uu+46\nGjt8+HCqszWkculla962bdvSWOUAyMYYNe506NCB6sxtW81rzZo1izS1VmTrBwC4/vrrI+3FF1+k\nsYr+/ftHGnMQBnieqLlq+fLlVM/NzY00dp1DCPT1+1PQb7CGAhiQ//cBAIYU8DjGmP/DeWVMujin\njEkX55QxGZCJTftLAD4F0CqEsDyEcB2A3wDoG0KYD6Bv/r+NMRnivDImXZxTxqSLc8qYgnPQRwST\nJLlK/K/41wSNMRnhvDImXZxTxqSLc8qYgnPITS6MMcYYY4wx5lihoCYXBWLPnj3YsGFDpLNi7F27\ndtFjTJ48OdJUIbwq1G/UqFGksXYBQNeuXanODByaNm1KY1kRpjJqUEV5c+bMiTRWiAgAd9xxB9UZ\nqs0LFy6MNHU9VWHmX//610hj5iAAMHDgQKr369cv0rp06UJjWZGpMh5hxcAAv/6sMDrTIsdDTYkS\nJWgxPjOJUW1mxg7r1q2jsatWraI6688333wzjR0zZgzVO3fuHGnK7IH1AeVSpXKKmYOo81bj0cqV\nKyOtTp06NPbyyy+PNGVEoXL7oYceijRmDgLw4nGAF+krAxtmPKEKo3/xi19QPVP3WJXXxUFOTg41\nKGD9gBlfqFg1ViqdjVO9evWisW+++SbVH3zwwUhTxkTsHvTp04fGXnLJJVR/5JFHIu3JJ5+ksXfd\ndRfVmZGHGnuYQZKK/frrr6nevn37SFNrAmUawfKQrTUAbpyk5iRWeA/weYkZGWzevJm+vqjZtWsX\nXaux+7dt2zZ6DBb7wAMP0Fg1d9x4442RpsYeZZQxZEhcdvbRRx/RWDY/n3vuuTRWmZwxA5R69erR\nWDXfsbWQmjvYmrV169Y0tkmTJlR/+OGHI00ZQFWvXp3qp556aqQpM5i33nor0tj8DHDjEYDPu2p8\nzwR/g2WMMcYYY4wxKeENljHGGGOMMcakhDdYxhhjjDHGGJMS3mAZY4wxxhhjTEp4g2WMMcYYY4wx\nKVGkLoLly5fHSSedFOkrVqyINOYWCHD3nebNm9PYxYsXU71KlSqR1qFDBxqrHFIeffTRSHv11Vdp\nbIMGDTJu23/9139RnbkRfetb36KxZcuWpTpzVipZkncBFnvrrbfSWOa2BXAHnrPPPpvGMqcdgDso\nqb7BnKfUfR02bFjG7WDOMsp1rajZsWMH5s6dG+ktWrSINOYeB3DXL4Vyv1y/fn2kvfvuuzSWuekB\n3FGPuXUKxLJHAAAgAElEQVQCvC+qPpSXl0f1ihUrRhpzTwK069CiRYsiTbkIMrp160Z15VTWv3//\nSPvzn/9MY++9916q//GPf4w05lIK8ByuUKECjd2yZQvVmVsjcyfcvn07fX1xoNw5zzgj/vkf1W7m\nQsfc6gDg7rvvpjpzzFLjrXKeZM5dgwcPprHvv/9+pP3sZz+jscpxs2/fvhm1AdBz5vnnnx9pyhWT\ntUM5jZUuXZrqbA2inOz27t1L9aVLl0aamufZsdXYU6pUKaozV1/m2Knm+KKmVKlSaNiwYaSz/Fmy\nZAk9Bhuz1fpPXc/du3dHWk5ODo1V890555wTaWpdwvrLM888Q2OVYyRzs7799ttprFqzMtQcwdZN\nynFaOeyy/qncS9X1Z9eUzScAX/fWqlWLxvbu3Zvq7FxmzJgRacqNdH/8DZYxxhhjjDHGpIQ3WMYY\nY4wxxhiTEt5gGWOMMcYYY0xKeINljDHGGGOMMSnhDZYxxhhjjDHGpESR2svs2bMHa9eujfTq1atH\nmnLiYq5dzK3kQMdgLjIbN26kscxxEACqVq0aaddccw2NZQ5k48ePp7HMLRDgrjoqVjFgwIBIU843\nzJGlXr16NHbKlClUZw487F4DwIIFC6jOXBUnTZpEY48//vhIY256gHYeYo6P3bt3j7SHH36Yvr6o\nqVKlCi655JJInzdvXqQppx7mlqbcr5ijDsBduLJ18nrllVciTbk4tWrVKtKYQxXAHesA3hdbtmxJ\nY9mYAQDf//73I23lypU0ljlgMQ3Q/Za5HCmXxKeeeorqM2fOjLQ+ffrQWDbOsf4GAI899hjVmSMi\nc3dS7lDFRYkS8eeP77zzTqQxRzEAOPXUUyNN3SvlRMjGoy5dutDYadOmUZ3loXIAZGOEytfTTjuN\n6qzfXXTRRTSWufECQOPGjSPtwgsvpLEdO3aMtM8++4zGqjxmc+n8+fNprHL1Y6hxlLn0Kvdf1Wbm\nRMjGE+VIWtSEEKijIXOHVH2cOeHOnj2bxqo+/sEHH0SaymFF7dq1I03Nd+XKlYu0NWvW0FjmPApw\nJ0I1r6n5h40P9913H41lTqDf/va3aaxyj2Wupq+//jqNHT16NNWzcWtmawXloMnuH8DHEuY4qBy1\n98ffYBljjDHGGGNMSniDZYwxxhhjjDEp4Q2WMcYYY4wxxqSEN1jGGGOMMcYYkxJFanKxbds2jB07\nNtKzKW7Ozc2NNFXspwqHWVGlKuBj5hIAUKNGjUhbvXo1jWXGHh9//DGNHTRoENVZwSArVge0mQGj\nc+fOVP/73/+e8XGVcQUzPggh0NgzzzyT6syAQV1nVpSuCl1Z4SLAzSFYf8nmGh9KSpQoQYuuu3bt\nGmmsQBgApk6dGmnKuEQVvLNi0goVKtBYZtQAAIMHD440ZiIB8HNRhauqDzADG2WMo4pwWVFyr169\naCzrtyoflLkH03v06EFjVfHxqFGjIu3kk0+msRdffHGkqevMchXg95uNf6xov7jYtGkT3njjjUi/\n8sorI42NGQCf184++2waO3z4cKqzeUkZMnXq1Inqv//97yPtnnvuobHLly+PtPXr19NYZjQDABdc\ncEGkMXMQgBfTA99c//255ZZbaOy5554baWz8A/Scye5V/fr1aezmzZup3r59+0hTxlcsJz799FMa\nq4xRqlWrFmlsrTFr1iz6+qJm7969+PrrryOdzdvMGAzgY8yJJ55IY//5z39SnZl1KWMIZTCyY8eO\nSGP3Q+nXX389jZ0+fTrVmYGXej/WBwDePz/88EMay3JbzSdqPGJzqTKN27NnD9XZvVJtZut9dY3U\nnoGZj7C+we4/46DfYIUQng4hrA0hzNxH+0UIYUUIYWr+n/MyejdjDADnlTFp45wyJl2cU8YUnEwe\nEXwWAPOw/H2SJJ3y/7ydbrOMOep5Fs4rY9LkWTinjEmTZ+GcMqZAHHSDlSTJRwD492nGmALhvDIm\nXZxTxqSLc8qYglMYk4ubQgjT879Cjn+NMp8QwsAQwsQQwkT1DKcx5t8cNK/2zSn1/LMx5t9kPVep\nmgBjDIAC5NTh8oPHxhQVBd1gPQqgOYBOAFYBeFAFJknyeJIkXZIk6cLMAowx/yajvNo3p6pWlXOb\nMaaAcxUzbTHGAChgTpUvX76o2mfMYUGBZpEkSf5ttRFCeALAsExet3PnTixatCjS2aeFzIEO4I4e\nq1atUu2ketOmTSMtW2esL774ItKUu8nEiRMjTS2MlfMhc7Np3Lgxje3QoQPVmUuXcq9i90Q56iiY\nux27/wBQq1YtqrN45QzDnA8HDhxIY1kfALgj2IIFCyJNOfIVhoLmVaaLwbJly1KdOSIy9x5A3z/m\n2sXcvQBg9uzZVGf9Rbns1a1bN9LUN+RNmjShOjuXbBxNAT7GKIdQtsBYuHAhjVXObT//+c8jTTk7\nqjGU9Rc2RgG8zcwJDOBulADwu9/9jur7c9556dfKFzSncnNzqQvWW2+9FWlq7Lr00ksj7ZNPPqGx\n8+fPp3rbtm0jbdy4cTT2pJNOojq7h2rMYDn4+eef01jmbAYA5cqVizQ13ykHTNYflQPZ0KFDI61d\nu3Y0Vo37r732WqQpN7aOHTtSnY0dbJ0AcFdgNRcrl1627mHXKO1vjgqaU3v37qXzJluXqPUYGxfV\nOk+tj9j9U3NE5cqVqc7G7QkTJtDYAQMGRJpyq1UOlcz1TrnxHnfccVRnjthqLr799tsjTeXUJZdc\nQnU27vTp04fGqvGIrffV/MNcetVace7cuVRn95W5eGaaUwX6BiuEsO/q5hIA3PvUGJMxzitj0sU5\nZUy6OKeMyYyDfvQdQngJwOkAaoQQlgMYBOD0EEInAAmAxQBuOIRtNOaow3llTLo4p4xJF+eUMQXn\noBusJEmuIvJTh6AtxhwzOK+MSRfnlDHp4pwypuAUxkXQGGOMMcYYY8w+eINljDHGGGOMMSlRpF60\nIQTqUjR58uRIY44gADBt2rRIU64pyt2JOaQpFxPlxHXBBRdE2l133UVjzzrrrEhTjmfKyp656lSo\nUIHGKgcydj1KlOB7bKar2E2bNlF90KBBkbZu3Toaq1ymmFOVcjMcMWJEpCkXGeUCw/on64vZOioe\nKkIIyM3NjXTWn9U5M5cq5gQGgL4XADRq1CjSzj77bBp79913U531AeW2xNydlKuS0keNGhVpnTp1\norGtWrWi+oYNGyLtnXfeobHM/WjWrFk0VjkDsvuqcoeNOwBw8cUXR5pyO2NjZYsWLWjsnXfeSXV2\nr5Rb1uFC3bp1qWMjc5tT7lrMmUw5Kvbr14/q/+///b9Ie/rpp2mscrxlzoUqJ5hrl3K2VXN0gwYN\nIk2NBarfMde0NWvWkEg+l3700Uc0lrmPAnzeVXP/k08+SXV2v++//34ay+al119/ncaq68/GaHYt\nevXqRV9f1JQvXx7dunWLdDYu7tixgx6DOVqq36zLxhZezY3q2K1bt440tjYFuLuqcuDs3Lkz1ffu\n3RtpJ554Io1Va1nWF9V817Nnz0hTjooffPAB1Zlb46mnnkpjlUMoG6fYnAtwtz/lDsncWQHg7bff\njjTmNqxycn/8DZYxxhhjjDHGpIQ3WMYYY4wxxhiTEt5gGWOMMcYYY0xKeINljDHGGGOMMSlRpCYX\nJUuWpMWdDRs2jDRmNgBwY4cQAo3t2rUr1Rs3bhxpqrhQFfAxQwVlOtG/f/9IU+enDCpY0S8zvgB0\nMWJOTk6kMYMDAHj44YcjbcmSJTRWXX/WZmXuoc67efPmkTZ+/Hga+z//8z+R9tRT/Cc7rrjiCqoz\nUwVWgKlMCIoDVgDLWLx4MdU//PDDSFMF7Mz0AOBFuzNmzKCxH3/8MdUffPDBSFMmF8xsI0kSGvun\nP/2J6qxYdsyYMTRWnQsrKN62bRuNZePDr3/9axqrcqpZs2aRps5bGbGwdqicWrVqVaSdf/75NHbs\n2LFUZ+MOKypXY0BxsGfPHjp+MTMEVVTOrrPqi+eeey7Vr7/++khTffGEE06gOitkV/eqRo0akcZM\nqABtAnHLLbdkdFxAG1Qx44O5c+fSWGacNHjwYBqrjFi+973vRZoy9Hn00UepzowPVF5Nnz490ti9\nBrR5ydKlSyONrSsynR8ONTk5OTQnsjHfYnOxMhxghhgA8O6770aaMqhgRg0AMGfOnEg7/fTTaSwz\nMlJmX+pef+c734k0ZUTFzHkAbkbG1sIAH4uXLVtGY5XJEuvPL7/8Mo295pprqM7WLPPnz6exzNBC\nXaM2bdpQnY07zOiEmQEx/A2WMcYYY4wxxqSEN1jGGGOMMcYYkxLeYBljjDHGGGNMSniDZYwxxhhj\njDEp4Q2WMcYYY4wxxqREkbsIMjch5mS3aNEieoxevXrR4zJatmxJdeamohzWhgwZQvXly5dHGnO4\nAYC8vLxIY65rgHbS2rlzZ0bHBbSry5o1ayJNOY21bds20j799FMa26pVK6rPnDkz0h566CEaq1x1\nmOsk0wBgypQpkaYct5RrDXPzeuuttyKNOcsUByEE6hy1a9euSGP3FOAuTCofmMsOwB18Zs+eTWNr\n1qxJdebQplyVmANg3759aSxzsQO4k51y3FLtYGOMcvccNGhQpDGnOgCYNGkS1V955ZVIU31ROT6y\n93z77bdpbO/evSNN9YGePXtSnfXPrVu3Rpq6xsUF6wvMPap9+/b09QsXLoy0zZs301jl0MocYZU7\nrjo26x9ffPEFjWXj8NSpU2ns5ZdfTnXm/KncvJRbXP369SOtT58+NLZFixaR9oc//IHGKoc1Noc9\n88wzNPaGG26g+rBhwyJNuQUzh0I15lasWJHq7Jree++9kaYcYYuanJwc6XC8P2ptw/rFjh07aGz5\n8uWpztYEyiFPOcKytR6bAwGge/fukfarX/2KxiqHPNbHO3ToQGNHjRpFdTbvqr7Frv/AgQNpLDs/\ngLuaPv/88zR2+PDhVGdrFrVWZP1AuXiqe9W0adNIY9dejYn742+wjDHGGGOMMSYlvMEyxhhjjDHG\nmJTwBssYY4wxxhhjUsIbLGOMMcYYY4xJiSI1udi5cyct5Jw3b16kTZ8+nR6jS5cukRZCoLELFiyg\nOiuUbNiwIY1VsOJAZsABAK+++mqkNWvWjMayImqAF9OzQnFAF+p/8MEHkaYMB6ZNmxZprAAQ0IXK\nJ598cqQxsw5AXw92Lh07dqSxI0eOjLRTTz2VxirDjjPPPDPS2rRpE2lly5alry9qSpYsSU1iWN9Q\nBa3MDIEV6APAiSeeSPXWrVtHmipcnTx5MtVZnjAjCgCoW7dupF122WU0Vp33Z599FmknnXQSjZ04\ncSLVBwwYEGnMFAUAJkyYEGmqQPj000+nOivQVtdIjQPvvPNOpG3ZsoXGMpRhgSo2ZznP7okaR4qD\nxYsX47vf/W6ks3NRY8mDDz4YaU888QSN7dGjB9Vr1aoVaer6M/MmgBfk33///TT2ueeeizSVr+p+\nsX6u5saNGzdSvUGDBpGmzFXY3MGMewB9jT755JNI+8c//kFj27VrR/XOnTtHGluvANwY5Te/+Q2N\nVfnNzHQOF/MlRZIkkcYMFdQ5V6hQIdKUmQW7HwDw+eefR5oyI2H9AuDjtjJfYIYmyrBFzVVsvTFn\nzhwaq/o4M4FiaxuAn4syDGO5CgDXXXddpDETMUCfyxVXXBFpbO4H+DjF7jXA+yHADfOY0UamhkwH\nndFCCA1DCKNDCHNCCLNCCD/O16uFEN4LIczP/y+3rjPG/AfOKWPSx3llTLo4p4wpOJl8ZLgHwE+S\nJGkD4GQAN4YQ2gK4HcCoJElaAhiV/29jzMFxThmTPs4rY9LFOWVMATnoBitJklVJkkzO//tWAHMA\n1AdwEYB/PU/wHICLD1UjjTmacE4Zkz7OK2PSxTllTMHJ6qH3EEITACcAGA+gdpIkq4BvkhBA/LD4\nN68ZGEKYGEKYqH60zZhjlcLmFPvBXWOOdQqbV6p+zZhjlcLmlKq3M+ZoJeMNVgihAoDBAG5OkiTj\niugkSR5PkqRLkiRdWIGiMccqaeQUM7gw5lgmjbw6nAw3jClu0sipqlVdpmWOLTJyEQwh5OKb5Hox\nSZLX8+U1IYS6SZKsCiHUBbD2YMf56quvqOMScwdSC8f58+dHWunSpWmscuRjLoLKNW3YsGFUZ86F\nF110EY1dtWpVRq8HtPPhrbfeGmm7du2isTk5OVRnzjxLly6lseyeKEeWG264gersvCtVqkRjlSsL\nc8SpVq0aje3atWukjRkzhsbOmjWL6uzYzKWKuc1kQ1o5lZubS53GatSoEWnKCY/1iy+++ILGKqc+\nlmv//Oc/aaxyYGS59uKLL9JY5palHJhUH2djzOrVq2ms6p/MbUk5A2bj7Lh582aqM/exN998k8ay\ncQ7gbWZOcwDAFkU/+tGPaOx7771HdTaWHIqcAtLLq5IlS1KXNjamtWjRgh6D5ZUau5Rr5HHHHRdp\natyvU6cO1VmuKJc9dgzW5wBg/fr1VGcfpCr3WDUPMpcvde2Yq6LKQeWExjbUKgfVeMLarOZi5tp5\n77330tj77ruP6qwfMKdf5ZCXKWnlVF5eHh0D2dijvkFm11ONzT179qQ6c/VbtGgRjWX5DgDPP/98\npKk2/+Uvf4m0H//4xzRW5TAby++8804aqz4cuvDCCyNNuemx+ZyNRQCoMzjAr0f//v1p7PLly6nO\n1m9qXmMOx+q46n6z67F79+6M4hiZuAgGAE8BmJMkyUP7/K+hAP7lUTwAwJCM3tGYYxznlDHp47wy\nJl2cU8YUnEw+MjwFwLUAZoQQpuZrdwL4DYBXQgjXAVgKgG9NjTH745wyJn2cV8aki3PKmAJy0A1W\nkiQfA+Df4wNnpNscY45+nFPGpI/zyph0cU4ZU3BcyWuMMcYYY4wxKeENljHGGGOMMcakROFtm7Ig\nLy9POvPsj3K1Yu5mzEUI0A5FI0eOjLTf/OY3NFY5+DAXkUceeSTj2D//+c80Vjnf3HzzzZF2zz33\n0Nhly5ZRnbmhMA0AmjRpEmmTJ0+mseraMXdH5UKnzpu5mCmnnYYNG0YaOw9AO/6wY3/55ZeRxpy5\nioMkSei5MGefefPm0WPMnj070vLy8mgsc3UEuHvVU089RWOVYydzkXvttddobKdOnSKtfPnyNJa5\nLAK8zynXoeHDh1OdOfJNnz6dxvbt2zfSlOOT6l/MZbRXr140dsWKFVRnOajcS1n+KAclln8Ad+xk\nfW7Hjh309cVBXl4e7eus37H7CnCXN+VKpuYq1h/V3Kgc61q3bh1p//jHP2jskiVLIo05/wLaRfA7\n3/lOpCnnw7VrufkcOzbLNQBgv6+k3q9evXpUb9myZaRNmjSJxrZr147qrH3KtZC56Y0fP57GqmOw\ncZ+NG4fL74/m5ubS+ZW5+qn7x9wolSOm+lkgNs8wV0dAu7wyh1Dl1sjy9bPPPqOxrVq1ovro0aMj\njTlqA3qNxdxcVQ6zNbWaR5Wr5qWXXhppr776Ko397ne/S/Xbbrst0pRb7ZVXXhlpygFVOR82btw4\n0tj4kun6z99gGWOMMcYYY0xKeINljDHGGGOMMSnhDZYxxhhjjDHGpIQ3WMYYY4wxxhiTEkVqchFC\noAWNrJBXFX+zgkFWvAfoovcNGzZEWrNmzWjsHXfcQfWVK1dG2mOPPUZj27RpE2k//elPaeyIESOo\nft5550XaT37yExqrCjM7dOgQaeranXXWWZF2xhn8Zy8+//xzqv/v//5vpLHrBugiU6Y3aNCAxrJ7\nqAw/lAED64u/+tWvIo0ZeBQHu3btogWbrGieFegDwJQpUyJN3WtVMP3KK69E2gsvvEBjlfkCM8Cp\nVKkSjS1btmykXXzxxTS2TJkyVH/++ecjTY0DSq9Zs2ak3XjjjTSWnYsyLFBmCKxIX5mXnH766VRn\nRgaqaLdatWqRVr16dRo7c+ZMqjOjGpbXyjyjOChdujSaN28e6SeddFKkKSOdOXPmRJoydVD9nBkn\nqD6zZs0aqrP5QI23rB2qbTVq1KA6G3PVuMHyBwAeffTRSFNF/cxwQL2fmu/YmoCZIQDadOL888+P\ntMqVK9PYNAyg2BqJmVade+659PVFjVr/MXMbZdjCxh5lvKSMMtgaRBkvKeMd9p6NGjWisazN/fvz\n32WuX78+1dl6RRnjdO3aleqszb1796axb775ZqRdddVVNHbo0KFUZ+uKadOm0Vg1v95www2Rduqp\np9LYunXrRpq6zizfAX5N2bhqkwtjjDHGGGOMKWK8wTLGGGOMMcaYlPAGyxhjjDHGGGNSwhssY4wx\nxhhjjEkJb7CMMcYYY4wxJiWK1EUwSRLk5eVFOnPUUQ4wzDGoYcOGNFY50TBXpC+//DLjWIC7iCjH\nwYsuuijSTjnlFBrbp08fqu/cuTPS2LUEtBsXc3VT12j79u2R1rhxYxqr3NtYm5X7XjaufKpvHHfc\ncZE2YcIEGqtcHB944IFI++STTyJNuVQVB+weMpc9pgHcWUu5PTKnP4C7nan3Uy6QzIWpRYsWNLZ1\n69aRNnfuXBrL8g8AbrvttkgbMmQIjVWufuy833vvPRrL3Nw6duxIY7t06UJ1llPKqUy5TLH8UfnH\nnKeyzeGtW7dGWqlSpSJNtbc4yMvLo057zBFR9XM2bpxwwgk09rnnnqP69773vUhT7rjlypWj+pgx\nYyJNuQg2bdo00rKZAwHuUqnyRzkDsnNkDnkAH/9UTihnQOa4yRw0AeDss8+mOrsee/fupbFsjmZ5\nAui8YMc+nPMqNzeXOr2xa6HuNVv/VahQgcYqd0Hm1FerVi0au3r1aqqznFDrMda36tSpQ2NVm6dO\nnRppzKUZ0ONRu3btIo05EAPcRfjdd9+lsWosYfOBcpq98MILqc6cMu+++24a+4tf/CLSWH8DgOOP\nP57qLIeZk6TK6/3xN1jGGGOMMcYYkxLeYBljjDHGGGNMSniDZYwxxhhjjDEp4Q2WMcYYY4wxxqSE\nN1jGGGOMMcYYkxIHdREMITQE8DcAdQDsBfB4kiR/DCH8AsD1AP5lo3VnkiRvH+x4mbpvKBcZ5oij\nYnNzczN6L0C7Cyl3GebWoxxgPv3000grU6YMjVWufsz5pl+/fjT2+eefp/oVV1wRaSNHjqSxzN1J\nOecxtywA+NnPfhZp7FoA2lmuevXqkaacb9j9PvXUU2nsiBEjqM5cwpijYqb9mJFmTuXm5tI+ytqn\nnAE3btwYaew6AMDy5cupzu7f+vXraSzLHYC7VLZt25bGMhcn5Wak7hVzKFJjhspL5sCYjVtjNg5h\n5sCkmVclS5ZEjRo1Ip05vTFnMwC48sorI+3nP/85jX3ooYeozpy4mNMfwB0tla4czyZNmhRpLE8A\n7q4FAKNHj440NRawnAC4i6By+mXuaPfddx+NVbmZjQOgcnFctmxZpA0bNozGMpc2dh6AdgV+9tln\nI42thVS/yIQ0c2rv3r30fis3Sga79jVr1qSxzHEV4H1ROUYqB002H6ixnM136v3U2o2NA2rdq+Z5\n1p9PO+00GsvcE7O5FgAfY1SblVsju6Ynn3wyjWX6iy++SGOV4zdzYm3evHmkZep6nYlN+x4AP0mS\nZHIIoSKASSGEf/kQ/z5JktjX2hhzIJxTxqSP88qYdHFOGVNADrrBSpJkFYBV+X/fGkKYAyD+OsUY\nkxHOKWPSx3llTLo4p4wpOFnVYIUQmgA4AcD4fOmmEML0EMLTIQT6TFEIYWAIYWIIYaL6ytaYY5XC\n5pR69MaYY5nC5pV6lMWYY5XC5tSGDRuKqKXGHB5kvMEKIVQAMBjAzUmSbAHwKIDmADrhm084HmSv\nS5Lk8SRJuiRJ0iXT5xaNORZII6dYnYgxxzJp5FU29bvGHO2kkVOsntqYo5lMarAQQsjFN8n1YpIk\nrwNAkiRr9vn/TwDg1ZyZNIIYTKjCR7ZJ27VrF42tUKEC1dP4Jo0VvilDjFWrVkVa7dq1aaz65JQV\n8Kuiwy5dulB9zZo1kXb77bfT2OnTp0faa6+9RmM3b95MdWZysXbtWhqrCnFZP7jhhhtobMuWLSNN\nFZ4qvXPnzpHWrVu3SFPFy5mSVk6VKFFC9vP9UQYjrOB26dKlNLZKlSpUZ4Wu6gMVda9ZMbc6Biu8\nZwWqgB4fWK5lY66j4lWbmVmKQhUO2/ziwKSVV7t27aI5cPXVV0eauq/s22XVR5WpDDOPeeedd2gs\nG98BPt+NGzeOxrI5ZdSoUTRWmTqxMbt37940tk6dOlRnpk4nnngijWXnp74t+eijj6jO7osyolKm\nHy+99FKkKRMPdt4dOnSgseq+sj7DzqOwH2ynlVN5eXm0z7DrqUzH2PinxkRlDsKMMlh/A4AZM2ZQ\nnc2Dqr+wHFZzIOtDADdFGTp0KI3t378/1Tt27BhpyjCMnZ8yYWndujXVmVGaukbM4ArgJmdLliyh\nsWxsVvO5MhP54Q9/GGks/zI1ZjnoN1jhm5XFUwDmJEny0D563X3CLgEwM6N3NOYYxzllTPo4r4xJ\nF+eUMQUnk2+wTgFwLYAZIYSp+dqdAK4KIXQCkABYDIB/rWCM2R/nlDHp47wyJl2cU8YUkExcBD8G\nwJ6POehvXhljYpxTxqSP88qYdHFOGVNwsnIRNMYYY4wxxhij8QbLGGOMMcYYY1IiIxfBtEiSBHl5\neZHOHLqUM0zZsmXpcRnK+WvHjh2RptxGlFsIc0Np0aIFjWUuMso1Rb1fs2bNMmrDgY7Rtm3bSPvr\nX/9KY5mrzvHHH09jb7rpJqo/+uijkfbll1/S2DfffJPq/fr1i7Q77riDxl577bWRphyfunfvTvVt\n27ZF2sMPPxxpyg2xOGD9f+PGjZG2fPly+nrmPKVySjlSsT7Hch3Q/farr76KNOUAyNzOWJ4B2qGN\nodyrFOw6qfxj1t9qnFPXiI1T6l6ZghNCoPPH3//+90i77rrr6DHYPMPcWQHg8ccfpzrLN+WipXQ2\nBq2l2vQAACAASURBVLK2Adx9j+UlAPz0pz+lOnNeu/zyy2lsp06dqM7OReU3c6abOnUqidTzPDtH\ndd7KeY3Fq/xm88e9995LY9u0aUP1ypUrRxpzIlSOpIcLbJ5QYyi7ntk4vALA6tWrI03N5+rYbB3T\no0cPGnvJJZdE2ogRI2jsfffdR/Vnnnkm0m677TYay9aKAHf169mzJ41lztAvvPACjVVOmey+sj4L\nAJMnT6Y6a5+y+2/SpEmkMQdoQF9n1o6GDRtGWqY/4+FvsIwxxhhjjDEmJbzBMsYYY4wxxpiU8AbL\nGGOMMcYYY1LCGyxjjDHGGGOMSYkiNbnYu3cvtm/fHumqGJFRqlSpjGOVwQErCs/GVEMdQxVmsiJV\nVTypCmjr1q0baevWraOxy5Yto/qsWbMi7dxzz6Wxzz77bKSp4smdO3dSfeDAgZFWq1YtGtu5c2eq\ns0JOVSj52GOPRZq6r9dccw3VmRHI66+/nvFxiwPWFydMmBBp1apVo69fsGBBpLHCToAXlAO8AF31\nT2WUwXJbFWgz84uaNWvSWGVcwe5hNsXVAB9jVHF8xYoVMz6uMghhuWaTi/RRhkzMHEcVit95552R\nxgxlAGDIkCFU79ixY6R16dKFxrZr147qq1atijRlHsP6aNeuXWksM7MAeH/8xz/+QWNV4Xzz5s0j\nTZkIsPap/GHmPwAwZsyYSKtTpw6NnTJlCtXZWMXMeADgpZdeijRmhgDwewLw+fhwNsEpWbIkNSjY\nunVrpCnjBGYuUL58eRqrxmFm5DJt2jQay+ZGANiyZUukqdx+++34J8NUX37kkUeozswXqlSpQmPV\nPM+My9QczdaKTZs2pbFjx46lOsuHX//61zT2+eefpzqbX1V/ZutCNo4AfG2q3o9dI7VOiI6XUZQx\nxhhjjDHGmIPiDZYxxhhjjDHGpIQ3WMYYY4wxxhiTEt5gGWOMMcYYY0xKeINljDHGGGOMMSlR5C6C\nzB2GOd9k4wCo3FuU24hyJmOUKVOG6syZTDmC1atXL9LWrl1LY5nLIsCvkXKAUW5/n332WaTNmzeP\nxt50002Rptr8wAMPUJ1do5/97Gc0Vp33o48+GmnKweXJJ5+MNOagBeg2P/XUU5HWsmXLSFu6dCl9\nfXHAcmXNmjWRlo1j3Y4dO2isygd2DOX4uX79eqoz1y41DixZsiTSjjvuOBqr8l05fDHUtWPHTiNW\nOYFm6l5kCkcIgfZfNtewvggAn3zySaRl6z56zz33RJoaKytUqEB15qamHDeZe+zy5ctprHKEZfOE\nGk8uvPBCqq9YsSKj4wLcTY2Nf4DOeXbt1NzBHFMB7lCo8phdO+X+ppx+v/jii0hjjnzKUbGoKVGi\nBL3OLM+UiyAb/9T4ruYO1geYwx7A3ZsBvqZ79dVXaSzrt2rMGDZsGNVr1KgRaU888QSNVa65bNy4\n7LLLaOycOXMibfbs2TS2U6dOVGf3mrl1Avr6M3fHxo0b01h23szBG9Br5FGjRkXaM888E2nMRZLh\nb7CMMcYYY4wxJiW8wTLGGGOMMcaYlPAGyxhjjDHGGGNSwhssY4wxxhhjjEkJb7CMMcYYY4wxJiUO\n6iIYQigD4CMApfPjX0uSZFAIoSmAlwFUAzAZwLVJkuw6yLEQQoh05nKTm5tLj8FcZFTszp07D9Sc\n/0A5DpYtW5bq5cuXjzTllMTciDZs2EBjlRMU048//ngaq1wVmTOTclX661//mnHbvv3tb1N96tSp\nkfbII4/Q2FtuuYXqLVq0iLRt27bR2Ntvvz3SfvGLX9BYdb+vu+66SGPuO4Vxc0szpxSlS5eONOU+\nVr169UhT7lfKVYldD+XipNwFma6OwXJb9XvlqsTeT+UDG7dU+5TTIhsf1HEnT55MdXad1fU81kgz\nr0qUKEH7E7vfFStWpMcYPHhwpKkxVI0nCxcujDTluDV69GiqM9ct5koLANdee22ksbHkQMc477zz\nIk05ACoX28WLF0da5cqVaSwbk5gLGqAdTNk1Zdce4HMSAHz66aeRxhwOAe5u1rZtWxpbu3ZtqrPx\nnDkLZ+OavD9FMVex8UuNaWwMVfOa6rfMbbZPnz40dtGiRVSfNGlSpPXr14/Gsr6v2qzWRyyHlVPm\nH/7wB6qzef6+++6jscyNkmmAXmerNRZj7ty5VB87dmyk3XHHHTSWXVPlBNqjRw+qMyfP733ve5Gm\nrvH+ZPIN1k4AfZIkOR5AJwDnhBBOBvBbAL9PkqQlgI0A4pWpMYbhnDImfZxXxqSLc8qYAnLQDVby\nDf/6yiA3/08CoA+A1/L15wBcfEhaaMxRhnPKmPRxXhmTLs4pYwpORjVYIYScEMJUAGsBvAdgIYBN\nSZL867mG5QDocwshhIEhhIkhhInqkQJjjjXSyin1Y9PGHIuklVf+QWdjvsFzlTEFI6MNVpIkeUmS\ndALQAEA3AOznkekDl0mSPJ4kSZckSbqoWiljjjXSyqmaNWseymYac0SRVl6puj1jjjU8VxlTMLKa\nRZIk2RRC+ADAyQCqhBBK5n+K0QDAyoO9PoRAC8BZ4aLajLFvwVSh+Ndff031vXv3Rpr6xFIVzrPC\nWlZECAC7dsW1n6owUJlqsELJOnXq0Fh1LgMGDIi0IUOG0FhWBLhgwQIa+/7771O9ffv2kVarVi0a\nO3/+fKqzYuDp06fTWFbkqIo4Vf8aOXJkpLFCyffee4++PlsKm1MHOG6kqQL7LVu2RJoye1DmLI0a\nNYq01atX01hVrM7yVRXWsnOpWrUqjVUFxZs3b86oDYA222Bjj8rhTZs2ZfR6QBf9ZtqGY53C5lWZ\nMmXQsmXLSGdzx7hx4+gxWA41adKExjKDBIAXXQ8bNozGdu/ePeN21KtXj8Z++OGHkabmKpWbzDCC\nmVYAwFdffUV1ZiKljA9Y+9Q8o0wu2JzZtGlTGqu45pprIm348OE0lm3gN27cSGPVvXr77bczOm5a\n38YWNqeSJKFtyebDDLZ+VKZC2aDMED7//HOqM4MJNp8AQI0aNSLt448/prH3338/1bNZb/z4xz+m\nOssTZkgDABMnToy02bNn01g1drF5V83FgwYNojozKnn55Zdp7BVXXBFpzGgNAH76059Snc2lLP8y\n/bLooN9ghRBqhhCq5P+9LIAzAcwBMBrA5flhAwDwlbox5j9wThmTPs4rY9LFOWVMwcnko4O6AJ4L\nIeTgmw3ZK0mSDAshzAbwcgjhbgBTADx1CNtpzNGEc8qY9HFeGZMuziljCshBN1hJkkwHcALRF+Gb\n53GNMVngnDImfZxXxqSLc8qYgpORyYUxxhhjjDHGmIPjDZYxxhhjjDHGpERgbmOH7M1CWAdgSf4/\nawDglj5HBz6/I5uDnV/jJEmK3XfWOXVUcayf32GRU8B/5NWxfk+OdHx+h0leea46qjjWzy+jnCrS\nDdZ/vHEIE5Mk6VIsb14E+PyObI7E8zsS25wNPr8jmyPx/I7ENmeDz+/I5kg9vyO13Zni8zuySev8\n/IigMcYYY4wxxqSEN1jGGGOMMcYYkxLFucF6vBjfuyjw+R3ZHInndyS2ORt8fkc2R+L5HYltzgaf\n35HNkXp+R2q7M8Xnd2STyvkVWw2WMcYYY4wxxhxt+BFBY4wxxhhjjEkJb7CMMcYYY4wxJiWKfIMV\nQjgnhDA3hLAghHB7Ub//oSCE8HQIYW0IYeY+WrUQwnshhPn5/61anG0sKCGEhiGE0SGEOSGEWSGE\nH+frR8v5lQkhfBZCmJZ/fr/M15uGEMbnn98/QgilirutB+Joy6ujOacA59WRkFfOqSML55Rzqqhx\nTh3Z53ioc6pIN1ghhBwADwM4F0BbAFeFENoWZRsOEc8COGc/7XYAo5IkaQlgVP6/j0T2APhJkiRt\nAJwM4Mb8e3a0nN9OAH2SJDkeQCcA54QQTgbwWwC/zz+/jQCuK8Y2HpCjNK+exdGbU4Dz6rDOK+fU\nEYlzyjlV1DwL59SRfI6HNKeK+husbgAWJEmyKEmSXQBeBnBREbchdZIk+QjAl/vJFwF4Lv/vzwG4\nuEgblRJJkqxKkmRy/t+3ApgDoD6OnvNLkiTZlv/P3Pw/CYA+AF7L1w/38zvq8upozinAeZWvH87n\n55w6wnBOATi8z885dYThnAJQiPMr6g1WfQDL9vn38nztaKR2kiSrgG86KYBaxdyeQhNCaALgBADj\ncRSdXwghJ4QwFcBaAO8BWAhgU5Ike/JDDvd+eqzk1VHT5/bFeXVY4pw6gnFOHZY4p45gnFPZU9Qb\nrEA0+8QfAYQQKgAYDODmJEm2FHd70iRJkrwkSToBaIBvPmVrw8KKtlVZ4bw6QnFeHbb91Dl1hOKc\nOmz7qXPqCMU5VbB+WtQbrOUAGu7z7wYAVhZxG4qKNSGEugCQ/9+1xdyeAhNCyMU3yfVikiSv58tH\nzfn9iyRJNgH4AN88a1wlhFAy/38d7v30WMmro6rPOa8O637qnDoCcU4d1v3UOXUE4pwqeD8t6g3W\nBAAt8x06SgG4EsDQIm5DUTEUwID8vw8AMKQY21JgQggBwFMA5iRJ8tA+/+toOb+aIYQq+X8vC+BM\nfPOc8WgAl+eHHe7nd6zk1VHR5wDnVX7Y4Xx+zqkjDOcUgMP7/JxTRxjOKQCFOb8kSYr0D4DzAMzD\nN8853lXU73+IzuklAKsA7MY3n9JcB6A6vnFXmZ//32rF3c4CnltPfPP16HQAU/P/nHcUnV9HAFPy\nz28mgP/N15sB+AzAAgCvAihd3G09yHkcVXl1NOdU/vk5rw7zvHJOHVl/nFPOqWI4H+fUEXyOhzqn\nQv7BjDHGGGOMMcYUkiL/oWFjjDHGGGOMOVrxBssYY4wxxhhjUsIbLGOMMcYYY4xJCW+wjDHGGGOM\nMSYlvMEyxhhjjDHGmJTwBssYY4wxxhhjUsIbLGOMMcYYY4xJCW+wjDHGGGOMMSYlvMEyxhhjjDHG\nmJTwBssYY4wxxhhjUsIbLGOMMcYYY4xJCW+wjDHGGGOMMSYlvMEyxhhjjDHGmJTwBssYY4wxxhhj\nUqJQG6wQwjkhhLkhhAUhhNvTapQxxzLOK2PSxTllTLo4p4w5MCFJkoK9MIQcAPMA9AWwHMAEAFcl\nSTJbvaZEiRJJiRLxni6EEGl5eXnqfdlxaaw6RsmSJSMtNzeXxlasWJHq7Lrt3buXxpYtWzbSdu/e\nTWP37NlDdXaOZcqUobHqvFn72LUAgFKlSkWaarPS2b3aunUrja1duzbV2bXbuHEjjf3yyy8zej2g\nrx2LZ9dt3bp12LJlS3yChSTbvKpcuXJSp06dSFd9kbFr1y7WDhqrxovNmzdHmupbOTk5VGfvqc6D\nHWPnzp00tlKlSlRn7VNtY/kAACtWrIg01bfYMdT7qTxh12PLli00tnTp0hnrrA8AwPbt2yNNtbll\ny5ZUZ8dm92Tx4sVYv359secUAOTk5CSq/+6P6qNsHFb5o+Ywdq3VMTJtL6DHW9Zm1Z8rVKhAddbm\nr7/+msaqa8f6tDrGV199FWnqWqj5nJ2LapuaoytXrhxpa9asobFsvaHuieobCxYsiLTmzZtH2pIl\nS1LPq4LkVJkyZRJ2/dn5qTVMrVq1Ik3N8WpcZOsHNd6qXGPtU/2FzWvZ5Gq2x6hZsybV2TpN9Tn2\nftu2baOxmzZtono2a2R1v9ncocYBNr+WK1eOxqq1Qqbj7Y4dO7B79+6D5lR2d/k/6QZgQZIkiwAg\nhPAygIsAHGiDhSpVqkQ6G2w2bNhAj8EGe3URVYKxNtSvX5/G9u7dm+qss7LFCAC0a9cu0tauXUtj\n169fT3U2iLRq1YrGqg7PJiKVjA0bNoy0devW0dhly5ZRnd3XDz74gMbecsstVO/UqVOkvfbaazT2\nhRdeiLTjjz+exqrFYIcOHSKN3dc77riDvj4FssqrOnXq4LHHHot0NQgx2P3LdoP11ltvRVr16tVp\nrFqYsYW/Og+2QP/iiy9obJ8+fajOJhe2SAL0+HDnnXdGmsrLZs2aRZpa8I0aNYrqrC+OHDmSxqo+\nztqxfPlyGjt58uRIK1++PI0dPHgw1ZcuXRppffv2jbQuXbrQ16dA1nNVyZIlUa9evUhnCwS1OWUL\nOTWxq5xg/VF9oMXmNYAvGn784x/TWPZBierPPXv2pDrLzTlz5tBYld/vvPNOpM2YMYPGjh8/PtLY\nQhwAevXqRfUePXpEmprP2Qd5ANCvX79Iu//++2ks61s333wzjVV944ILLoi0119/PdK6d+9OX19I\nss6pihUr4qKLLqL6/qgPUdk1atOmDY199913qf7GG29E2ujRo2msym22tlQbELbwr1atGo1Vmya2\nlqpatSqNvfHGG6nOPgz8yU9+QmPZmPHJJ5/Q2H/+859U37FjR6SpnFJrdTZ3zJo1i8Y2btw40th6\nDtBrBXZf2JjN5kVGYR4RrA9g35XZ8nztPwghDAwhTAwhTCzot2XGHEMcNK/2zSm2IDLG/AdZz1Xq\nE1VjDIAC5FQ2H/oZczRQmA0W+4g72kElSfJ4kiRdkiTpoj4VN8b8m4Pm1b45pb5xMcb8m6znKvUY\npDEGQAFySj3KZ8zRSmE2WMsB7PscWQMAKwvXHGOOeZxXxqSLc8qYdHFOGXMQClODNQFAyxBCUwAr\nAFwJ4OoDvaB27dr0+VD2jLF6VpM9i6oMKtgzoAB/jl49anXVVVdRff78+ZHGngUHgEWLFkWa+rpc\nPRvKas9UwWDdunWpzp5zVTUBLFbVoahngVn8cccdR2ObNm1KdVaTpurX2DFUjUuTJk2ozj65Zv3r\nEH4bm1Ve7dixA7Nnx4+9s/NWbWaPQ6nn15XZA6sDYs/bq/cDeN1lo0aNaCyrQ1F9S+UDe96dPccN\n6Bqljz/+ONJUwTRrX7aPov3tb3+LNFUjw+oXAV6zoOoHWL3VD3/4QxqrxuEPP/ww0h599NFIW7hw\nIX19CmQ9V+Xk5NCaJlbfqsZydm/VNVJ1bSzfVF2Imu8aNGgQaapOl52z6vsTJ06kOqv9Y3V/ALBq\n1Sqqs7rQKVOm0Fg2ljOzBwA444wzqD5u3LhIU3OHqutlNc7vv/8+jb3vvvsi7bLLLqOxqpb1zTff\njLQlS5ZEmuovhSTrnAoh0P7P1iBq/ccMRlRNorp/rEZQGbmomii29lLrMZbD6riqjCab81b3m11n\nNn8BwEknnRRpzEsAAMaMGUN1ti5RtWdq/c32BtnUmqr5VV1ntkZiY7MyntmfAm+wkiTZE0K4CcAI\nADkAnk6ShFefGWMywnllTLo4p4xJF+eUMQenMN9gIUmStwG8nVJbjDFwXhmTNs4pY9LFOWXMgSnU\nDw0bY4wxxhhjjPk/vMEyxhhjjDHGmJQo1COC2VKlShX6Y3zsh4Kz+VVsFasKipmBgyrUU8XHbdu2\njTRVDMxMLtSP86qiX/aDwMoAYPXq1VRnqELJjz76KNLOOeccGqt+1LFjx46RpkwE1C/esx+xe++9\n9zJuhzIcUDbMrHCbGQAoU4CiZsuWLbSQmuWEKsZnfVxZ6rJ+AQD9+/ePtPPPP5/GjhgxguqsIFX9\neK0yOmEoswf2Y6usMBfgP+Cpjq1ym5k9sB/0BoCZM2dSnRnHqB9pVAXF7Acj336bP+nD2qeKx59/\n/nmqM9OD2267LdLUj8AWB3l5efQHZdl4qXKFjTFq3FD3kOWxMqtR+c3mpeHDh9NY9uO1zGgI0GM5\nM9VQRf3MGALgRejsh8hVO3r37k1jlQEAM61Sc6P6oXs2BqpY9sPsalxU6wr2I6zMCEQZRhQ1e/fu\npUYs7KdGatSoQY/BDD9U7mSjqzVkNqYTKv+U+QwjGxMctm4GdB+vXbt2pCmzBnZ+as5V4wM7troW\naj3GclAZYrAxRpmXqJ+3YUYgTMvUnMrfYBljjDHGGGNMSniDZYwxxhhjjDEp4Q2WMcYYY4wxxqSE\nN1jGGGOMMcYYkxLeYBljjDHGGGNMShSpi2BeXh42btwY6Rs2bIg05ZTEnEmUe0ulSpWozlx1Fi5c\nSGOVi0yVKlUijTmvANxlSrWtTp06VGfXTTkOMqcd4P+39+ZRVlXn1v5cRWsEaaXvEZBGBAFFJBo6\nFRuQSGxj1Gv0S2I0yc+h8XpzE0luRtAY4xc1dtGADSIqiqJeRQMCaoCiB+kRpBNQQLAJ0qzfH5Tf\nMKw55ZxiW1QV8xnDAUzfOrW7d62965z1FDcRtm3bltYyS4qyBSqjynvvvZdkq1evprX/+7//S3Nm\nU1OWRGaiydewxmxSzBSn7DslTUFBAbUJMSsjM9AB/LpV5iplZWQWpq5du9JaZcti9r2GDRvm/P1U\n7/zmN7+heatWrZLsD3/4A629/vrrad6zZ88ke/3112ktG+fU/s2cOZPmrB9atGhBa08++WSaV6tW\nLcneeecdWsvMbayvAaBHjx40P/XUU5OMmaeUte1QUKFCBTrGs7lDGcjYGKEMWMr8xeZBZXFUVjE2\nPzJDIsDnNXVelD2RmcLGjx9Pa/v160fzu+++O8nuvPNOWsv6bdSoUbQ2H3sYM8oC+p6A9RV7XYDb\nOdW8ps73mDFjkuzqq69OsuHDh9OvL2lCCNSSx0ySyi7JcnXdKxMhM8upexi1HaznlXGabR+7VgDd\na2yOVnOxuj7ZPaTab3YvpcY5Zepj95ZLly6ltfmM/aqH2diljrOyNbLtYNeRsqLuT+m4SzTGGGOM\nMcaYcoAfsIwxxhhjjDEmI/yAZYwxxhhjjDEZ4QcsY4wxxhhjjMmIEpVc7Ny5ky6QZiICtYiMLahT\nIgO16JAt7Fu1ahWtveeee2jeoUOHJFOLVNlixGOOOYbWKvlF69atk6xTp060tlatWjRnx6NRo0a0\nlh3TOXPm0Fq1kLdz5840Z6hF3myBL5N1AHybldhh3bp1NJ8+fXqSsYWPavFrSVO5cmV6Dps0aZJk\najEwW/Devn17WqtkMIMHD06yV155hdayxagA0L179yRTC2uZUIEtHAd47wBcqsHkC4AWdrDFx4sX\nL6a1y5cvT7Lvfve7tJaJIQAuELjwwgtprZIQMLp160ZzJgJRi8fVtVG/fv0kY+ektIhjgH29wo7J\nwoULk4xdRwAfF9XiarXvbB5UAih2bQB8DFVzIxtDVe1bb71F89mzZyfZa6+9RmvbtGlDczbmvvTS\nS7SWHaNt27bRWjUWsPPKhCaAFiSx76nkMWzMXbJkCa1l/QMAP/nJT5JMCQ5KAwUFBfRaYtczk+sA\n/N5NiRrUfQm711B9qeYq1ieq/5hkRvW76m22feoYKSkKuz5VLXttNb8quRsTbVWuXJnWKth9+Sef\nfEJrmdBC9YOaG1k9u75y7bPSM6MZY4wxxhhjTBnHD1jGGGOMMcYYkxF+wDLGGGOMMcaYjPADljHG\nGGOMMcZkhB+wjDHGGGOMMSYjDsoiGEJYBWAHgD0AdscYUw3YV9i+fTsmTJiQ5MwKomwjzBSiLIIV\nKlSgOTMUTZo0idYqg9Ipp5ySZPXq1aO1ixYtSrITTjiB1jLTmKpXJhRlImRGvfXr19Paxx9/PMny\nORYAcPbZZyeZOieFhYU037x5c5ItXbqU1jIGDhxI8xYtWtCcWS7ZMWLXUFbk01cVKlSgpiNmI2I2\nI4Cb4pQZTZm8GjRokGRjx46ltcxwCADLli1LMnVtvf/++0mmzJzq+z311FNJdtttt9HafEylzIYI\nAH//+9+TrG/fvrRWWdCYsWny5Mm0Vm3zhg0bkkzZq5h5UpkPN23aRPOPPvooyaZMmZJkyg6VBfnO\nVTFG2uPMLKfGAjaHKfuU2nd2vvN9DTbmqjF03LhxScbGcUAbN9m89P3vf5/WKlvp8OHDk0xZeplh\nTY3vKmfHWW3bm2++SfPRo0cnmRq/brjhhiRTVjg1n7PjwQx5ytB7sOTbU7t376ZmOWaGVvd/W7du\nTbLmzZur7aM561fVU6q32VyqrJPsvNapU4fW1qhRg+bMDN2wYUNaq8ZhZjlk4zDADcDf/va3aa26\nvsaMGZNkysDJrgGAX+PqGLVs2TLJlMla3ROwnPXf+eefT79+f7LQtPeJMfKR1hhTXNxXxmSLe8qY\nbHFPGSPwRwSNMcYYY4wxJiMO9gErAngthDAzhHANKwghXBNCKAwhFKpfxGaM+Te+tq++2lOl5Rce\nG1PKyWuuUr+k1Bjz//D9nzFfw8F+RPCUGOP6EEI9ABNCCItjjP+2ICDG+CCABwGgTp06pffXjBtT\nevjavvpqTzVp0sQ9ZcyB8VxlTLa4p4z5Gg7qHawY4/qiPzcBeA7AiVlslDGHM+4rY7LFPWVMtrin\njPl6iv0OVgjhSAAFMcYdRX8/HcBvv+5r9u7dS00mBQXpc97evXvpa7CvV7UVK/LdY8YglgHAcccd\nR/P69evn/Bps/5QlRxlxjj76aJoz1MfGGjVqlGTM6gNwO5oywCjTDrPvqf3OJ1fGGZYrO5AyDDFD\nDbNlqWvuYClOX7Hri12fH3/8Mf16ZgGaP38+rZ0xYwbNr7zyypxeF+CmRoBbLtVHtU48MZ3LTz75\nZFq7ZMkSmjProDpG119/Pc3Z9/zpT39Ka5lpcc+ePbSWGZEAbjBTljHV2+z4K4tnx44dk0zZS7t1\n60bzXMf322+/nX79wVKcnqpQoQI11jKrorKrMrudGjfUGMrq1fFXdi1mMWPjA8AtvSwDgOOPP57m\n1atXT7KhQ4fS2p/85Cc0Z5bDk046idY+88wzSaYMmn369KH5gAEDkkwZfdu2bUtzdq6aNm1Ka9k5\nVGOdymfNmpVkzGT3TXyMvDg99dlnn9F5ZceOHUmWz8cJlRlS9QmzPavzxOx9AO9XZUhmZjq1zeqe\nld3HsLEI4OMOwLeZ2XgBYPXq1UmmjLfMiAkA06ZNSzI2FwB8Pgf4ONWlSxday+6/1X2lGv+YdZAd\nT3UPuj8H8xHB+gCeK7pRrQhgVIzxfw/i9Ywx7itjssY9ZUy2uKeMOQDFfsCKMa4EwH+EZYwpXVNw\nkwAAIABJREFUFu4rY7LFPWVMtrinjDkw1rQbY4wxxhhjTEb4AcsYY4wxxhhjMuJgNe15EUKgcgG2\nYEwt+s1n8aNa5MgWo6rFcGqxOVsMxxadAnyb7733XlqrFqSyhXZqkbFakL9s2bIkmzt3Lq1t1apV\nkrVv357Wqv2eNGlSkvXv35/W1qxZk+bsOshH4qGugWbNmtG8TZs2ScYWgipJRkmze/duKuFYvHhx\nkqlFsawfJk+eTCr58QGABQsWJNmWLVtorXrtc889N8lU/7F+ffLJJ2ktW2wLALfeemuSqW1etGgR\nzf/2t78lGVsgDHAJxLhx42jt1VdfTfPTTz89ydQ1Pm/ePJqvW7cuyVRPXXjhhTnX5tMTbEGyGvMP\nBTVr1sT555+f5ExKMmXKFPoabNxXC+GZiALgvamOE5NLAEDdunWT7Jpr6K8toov6t27dSmvVYnom\nNxo/fjyt7d27N80ZSrzDtpnJHgAtJGGCnZUrV9JaJZz65JNPkkyJftauXZtkHTp0oLXPP/88zZnw\nhomo1LEoab744gt6TNk5Udf4xIkTk6xhw4a0dvPmzTRn0hAlcFCylA8++CCnDODXEbtWAD2Ws15T\nAg51b8Neu1+/frSWjfFq7FLyMzYede/endaqeZ59TyXK2LZtW5Kpe546derQfObMmUnG7lfUud4f\nv4NljDHGGGOMMRnhByxjjDHGGGOMyQg/YBljjDHGGGNMRvgByxhjjDHGGGMywg9YxhhjjDHGGJMR\nJWoRrFKlirSQ7Y8yhTAqVuS7wWwxALddKUPRwIEDad6pU6ck27FjB61lZjJlkckHZuICgKOOOorm\nzGr11ltv0do77rgjyZS1ZunSpTQfPnx4kp122mm0tkWLFjRv0qRJkr377ru0Np/XVcYzZrNhJq8Y\nY87b8E1SqVIlalFiRkW2HwDvE3bcAX5OAeD+++9PMnWMN27cSHNmDBo0aBCtZdf4bbfdRmtbt25N\nc2Y5uuCCC2ht8+bNac5sRMcddxytnT17dpItXLiQ1qqx69hjj00yZgcFtImLmZzYeAYA//jHP5JM\nWarUmMb2pUaNGklWWmxnXwcbc9X8w2qVeU+db3Z9dezYMa/XGDx4cJKx8wpwIxizAgLAWWedRXPW\nV2puVMdDzW0MZupT9xlqDGTX3ueff05rq1WrRnNmBrznnnto7ccff5x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UIur58+fTvG3b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\n3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4wxprziByxjjDHGGGOMyQg/YBljjDHGGGNMRvgByxhjjDHGGGMywg9YxhhjjDHGGJMR\nfsAyxhhjjDHGmIzwA5YxxhhjjDHGZMT/D4onT+pGovH+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe46c18e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.image as mpimg\n",
    "general_image_list = [x for x in os.listdir() if x.startswith(\"train_general_\")]\n",
    "print (general_image_list)\n",
    "def plot_augumented_images(image_index_list):\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "    idx = 0\n",
    "    for image in image_index_list:\n",
    "        idx = idx + 1\n",
    "        plt.subplot(2, 4, idx)\n",
    "        image_ =  mpimg.imread(image)\n",
    "        plt.imshow(image_.reshape(32,32), cmap = plt.get_cmap('gray'))\n",
    "    fig.tight_layout()\n",
    "    plt.savefig('augumented_images.png')\n",
    "plot_augumented_images(general_image_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import tensorflow as tf\n",
    "dtype = tf.float32\n",
    "def variable_with_weight_decay(name, shape, stddev, wd):\n",
    "    var = tf.get_variable(name, shape=shape, dtype=dtype, initializer=tf.truncated_normal_initializer(stddev=stddev))\n",
    "    if wd is not None:\n",
    "        weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')\n",
    "        tf.add_to_collection('losses', weight_decay)\n",
    "    return var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE=512\n",
    "def inference(image, is_training):\n",
    "    with tf.variable_scope('conv1') as scope:\n",
    "        weights = variable_with_weight_decay('weights', shape=[5,5,1,64], stddev=0.01, wd=0.0)\n",
    "        biases = tf.get_variable('biases', shape=[64], dtype=dtype, initializer=tf.constant_initializer(0.01))\n",
    "        conv = tf.nn.conv2d(image, weights, strides=[1,1,1,1], padding='SAME')\n",
    "        pre_activation = tf.nn.bias_add(conv, biases)\n",
    "        conv1 = tf.nn.relu(pre_activation, name=scope.name)\n",
    "    with tf.variable_scope('pooling1') as scope:\n",
    "        pool1 = tf.nn.max_pool(conv1, ksize=[1,3,3,1], strides=[1,2,2,1], padding='SAME', name='pooling1')\n",
    "        pool1 = tf.cond(is_training, lambda: tf.nn.dropout(pool1, keep_prob = 0.7), lambda: pool1)\n",
    "        \n",
    "    with tf.variable_scope('conv2') as scope:\n",
    "        weights = variable_with_weight_decay('weights', shape=[3,3,64,128], stddev=0.01, wd=0.0)\n",
    "        biases = tf.get_variable('biases', shape=[128], initializer=tf.constant_initializer(0.01))\n",
    "        conv = tf.nn.conv2d(pool1, weights, strides=[1,1,1,1], padding='SAME')\n",
    "        pre_activation = tf.nn.bias_add(conv, biases)\n",
    "        conv2 = tf.nn.relu(pre_activation, name=scope.name)\n",
    "    with tf.variable_scope('pooling2') as scope:\n",
    "        pool2 = tf.nn.max_pool(conv2, ksize=[1,3,3,1], strides=[1,1,1,1], padding='SAME', name='pooling2')\n",
    "        pool2 = tf.cond(is_training, lambda: tf.nn.dropout(pool2, keep_prob=0.7), lambda: pool2)\n",
    "        \n",
    "    with tf.variable_scope('conv3') as scope:\n",
    "        weights = variable_with_weight_decay('weights', shape=[3,3,128,256], stddev=0.01, wd=0.0)\n",
    "        biases = tf.get_variable('biases', shape=[256], initializer=tf.constant_initializer(0.01))\n",
    "        conv = tf.nn.conv2d(pool2, weights, strides=[1,1,1,1], padding='SAME')\n",
    "        pre_activation = tf.nn.bias_add(conv, biases)\n",
    "        conv3 = tf.nn.relu(pre_activation, name=scope.name)\n",
    "    with tf.variable_scope('pooling3') as scope:\n",
    "        pool3 = tf.nn.max_pool(conv3, ksize=[1,3,3,1], strides=[1,1,1,1], padding='SAME', name='pooling3')\n",
    "        pool3 = tf.cond(is_training, lambda: tf.nn.dropout(pool3, keep_prob=0.7), lambda: pool3)\n",
    "        \n",
    "\n",
    "    with tf.variable_scope('conv4') as scope:\n",
    "        weights = variable_with_weight_decay('weights', shape=[3,3,256,512], stddev=0.01, wd=0.0)\n",
    "        biases = tf.get_variable('biases', shape=[512], initializer=tf.constant_initializer(0.01))\n",
    "        conv = tf.nn.conv2d(pool3, weights, strides=[1,1,1,1], padding='SAME')\n",
    "        pre_activation = tf.nn.bias_add(conv, biases)\n",
    "        conv4 = tf.nn.relu(pre_activation, name=scope.name)\n",
    "    with tf.variable_scope('pooling4') as scope:\n",
    "        pool4 = tf.nn.max_pool(conv4, ksize=[1,3,3,1], strides=[1,1,1,1], padding='SAME', name='pooling3')\n",
    "        pool4 = tf.cond(is_training, lambda: tf.nn.dropout(pool4, keep_prob=0.7), lambda: pool4)\n",
    "        \n",
    "    \n",
    "    with tf.variable_scope('local5') as scope:\n",
    "        tensor_shape = pool4.get_shape().as_list()\n",
    "        dim = tensor_shape[1] * tensor_shape[2] * tensor_shape[3]\n",
    "        reshape = tf.reshape(pool4, [tf.shape(pool4)[0], dim])\n",
    "        weights = variable_with_weight_decay('weights', shape=[dim, 256], stddev=0.01, wd=0.0)\n",
    "        biases = tf.get_variable('biases', shape=[256], initializer=tf.constant_initializer(0.01))\n",
    "        local5 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)\n",
    "        local5 = tf.cond(is_training, lambda: tf.nn.dropout(local5, keep_prob=0.7), lambda: local5)\n",
    "        \n",
    "    with tf.variable_scope('local6') as scope:\n",
    "        weights = variable_with_weight_decay('weights', shape=[256, 128], stddev=0.01, wd=0.0)\n",
    "        biases = tf.get_variable('biases', shape=[128], initializer=tf.constant_initializer(0.01))\n",
    "        local6 = tf.nn.relu(tf.matmul(local5, weights) + biases, name=scope.name)\n",
    "        local6 = tf.cond(is_training, lambda: tf.nn.dropout(local6, keep_prob=0.5), lambda: local6)\n",
    "    \n",
    "    with tf.variable_scope('softmax_linear') as scope:\n",
    "        weights = variable_with_weight_decay('softmax_linear', shape=[128, n_classes], stddev=0.01, wd=0.0)\n",
    "        biases = tf.get_variable('biases', shape=[n_classes], initializer=tf.constant_initializer(0.01))\n",
    "        logits = tf.add(tf.matmul(local6, weights), biases, name='logits')\n",
    "    return {'logits': logits,\n",
    "            'pooling1': pool1,\n",
    "            'pooling2': pool2,\n",
    "            'pooling3': pool3,\n",
    "            'pooling4' : pool4,\n",
    "            'local5': local5,\n",
    "            'local6': local6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predictions(logits):\n",
    "    return tf.nn.softmax(logits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def losses(logits, labels):\n",
    "    with tf.variable_scope('softmax_linear', reuse = True):\n",
    "        l2_loss = tf.nn.l2_loss(tf.get_variable('softmax_linear'))\n",
    "    with tf.variable_scope('losses') as scope:\n",
    "        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)\n",
    "        losses = tf.reduce_mean(cross_entropy, name='cross_entropy_mean') + 0.0001 * l2_loss\n",
    "    return losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluation(predictions, labels):\n",
    "    with tf.name_scope('accuracy'):\n",
    "        with tf.name_scope('correct_prediction'):\n",
    "            correct_prediction = tf.equal(tf.argmax(predictions, 1), tf.argmax(labels, 1))\n",
    "        with tf.name_scope('accuracy'):\n",
    "            accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    return accuracy_operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n",
    "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class EarlyStopping(object):\n",
    "    def __init__(self, saver, session, patience=100, minimize=True):\n",
    "        self.minimize = minimize\n",
    "        self.patience = patience\n",
    "        self.saver = saver\n",
    "        self.session = session\n",
    "        self.best_monitored_value = np.inf if minimize else 0.\n",
    "        self.best_monitored_epoch = 0\n",
    "        self.restore_path = None\n",
    "\n",
    "    def __call__(self, value, epoch):\n",
    "        if (self.minimize and value < self.best_monitored_value) or (not self.minimize and value > self.best_monitored_value):\n",
    "            self.best_monitored_value = value\n",
    "            self.best_monitored_epoch = epoch\n",
    "            self.restore_path = self.saver.save(self.session, os.getcwd() + \"/early_stopping_checkpoint\")\n",
    "        elif self.best_monitored_epoch + self.patience < epoch:\n",
    "            if self.restore_path != None:\n",
    "                self.saver.restore(self.session, self.restore_path)\n",
    "            else:\n",
    "                print(\"ERROR: Failed to restore session\")\n",
    "            return True\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_curve(axis, train_values, valid_values, linewidth = 2, train_linestyle = \"b-\", valid_linestyle = \"g-\"):\n",
    "    epochs = train_values.shape[0]\n",
    "    x_axis = np.arange(epochs)\n",
    "    axis.plot(x_axis[train_values > 0], train_values[train_values > 0], train_linestyle, linewidth=linewidth, label=\"train\")\n",
    "    axis.plot(x_axis[valid_values > 0], valid_values[valid_values > 0], valid_linestyle, linewidth=linewidth, label=\"valid\")\n",
    "    return epochs\n",
    "\n",
    "def plot_learning_curves(train_loss_history, train_accuracy_history, valid_loss_history, valid_accuracy_history):\n",
    "    curves_figure = plt.figure(figsize = (12, 6))\n",
    "    axis = curves_figure.add_subplot(1, 2, 1)\n",
    "    epochs_plotted = plot_curve(axis, train_accuracy_history,  valid_accuracy_history)\n",
    "\n",
    "    plt.grid()\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"epoch\")\n",
    "    plt.ylabel(\"accuracy\")\n",
    "    plt.ylim(0, 1.15)\n",
    "    plt.xlim(0, epochs_plotted)\n",
    "\n",
    "    axis = curves_figure.add_subplot(1, 2, 2)\n",
    "    epochs_plotted = plot_curve(axis, train_loss_history, valid_loss_history)\n",
    "\n",
    "    plt.grid()\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"epoch\")\n",
    "    plt.ylabel(\"loss\")\n",
    "    plt.ylim(0.0001, 10.)\n",
    "    plt.xlim(0, epochs_plotted)\n",
    "    plt.yscale(\"log\")\n",
    "    plt.savefig(\"loss_accuracy.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "### Train your model here.\n",
    "### Calculate and report the accuracy on the training and validation set.\n",
    "### Once a final model architecture is selected, \n",
    "### the accuracy on the test set should be calculated and reported as well.\n",
    "### Feel free to use as many code cells as needed.\n",
    "from sklearn.utils import shuffle\n",
    "EPOCHS = 100\n",
    "early_stopping=None\n",
    "rate = 0.0005\n",
    "def train():\n",
    "    \n",
    "    image = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
    "    y = tf.placeholder(tf.int32, (None))\n",
    "    y_true = tf.one_hot(y, n_classes)  \n",
    "    is_training = tf.placeholder(tf.bool)\n",
    "    \n",
    "    with tf.variable_scope(\"model_variable\"):\n",
    "        train_op = inference(image, is_training)\n",
    "        logits = train_op['logits']\n",
    "        predictions_op = predictions(logits)\n",
    "        losses_op = losses(logits, y_true)\n",
    "        evaluation_op = evaluation(predictions_op, y_true)\n",
    "    \n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "    train_op = optimizer.minimize(losses_op)\n",
    "    \n",
    "    saver = tf.train.Saver(tf.global_variables())\n",
    "    \n",
    "    datagen = ImageDataGenerator(rotation_range=20,\n",
    "                                 width_shift_range=0.2,\n",
    "                                 height_shift_range=0.2,\n",
    "                                 zoom_range=0.2,\n",
    "                                 shear_range=0.5,\n",
    "                                 fill_mode='nearest')\n",
    "    \n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    early_stopping = EarlyStopping(tf.train.Saver(), sess, patience = 30, minimize=True)\n",
    "    \n",
    "    train_loss_history = np.empty([0], dtype = np.float32)\n",
    "    train_accuracy_history = np.empty([0], dtype = np.float32)\n",
    "    valid_loss_history = np.empty([0], dtype = np.float32)\n",
    "    valid_accuracy_history = np.empty([0], dtype = np.float32)\n",
    "    \n",
    "    def get_accuracy_loss_in_batches(X_data, y_data):\n",
    "        num_examples_ = len(X_data)\n",
    "        total_accuracy = 0\n",
    "        total_loss = 0\n",
    "        for offset in range(0, num_examples_, BATCH_SIZE):\n",
    "            batch_x_data, batch_y_data = X_data[offset:offset + BATCH_SIZE], y_data[offset:offset + BATCH_SIZE]\n",
    "            accuracy_, loss_ = sess.run([evaluation_op, losses_op], feed_dict={image: batch_x_data, y: batch_y_data,\n",
    "                                                                               is_training: False})\n",
    "            total_accuracy += (accuracy_ * len(batch_x_data))\n",
    "            total_loss += (loss_ * len(batch_x_data))\n",
    "        return total_accuracy / num_examples_, total_loss / num_examples_\n",
    "  \n",
    "    print(\"Training...\")\n",
    "    for epoch in range(EPOCHS):\n",
    "        batch_ct = 0\n",
    "        for x_batch, y_batch in datagen.flow(X_train, y_train, batch_size = BATCH_SIZE):\n",
    "            batch_ct += 1\n",
    "            sess.run(train_op, feed_dict={image: x_batch, y: y_batch, is_training: True})\n",
    "            if batch_ct > (X_train.shape[0] / BATCH_SIZE):\n",
    "                break\n",
    "        \n",
    "        train_acc_, train_loss_ = get_accuracy_loss_in_batches(X_train, y_train)\n",
    "        val_acc_, val_loss_ = get_accuracy_loss_in_batches(X_valid, y_valid)\n",
    "        \n",
    "        train_loss_history = np.append(train_loss_history, [train_loss_])\n",
    "        train_accuracy_history = np.append(train_accuracy_history, [train_acc_])\n",
    "        valid_loss_history = np.append(valid_loss_history, [val_loss_])\n",
    "        valid_accuracy_history = np.append(valid_accuracy_history, [val_acc_])\n",
    "        \n",
    "        if early_stopping(val_loss_, epoch):\n",
    "            print(\"Early stopping.\\nBest monitored loss was {:.8f} at epoch {}.\".format(\n",
    "                early_stopping.best_monitored_value, early_stopping.best_monitored_epoch))\n",
    "            break\n",
    "        \n",
    "        msg = \"Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}, Training Loss: {3:.3f} Validation Loss: {4:.3f}\"\n",
    "        print(msg.format(epoch + 1, train_acc_, val_acc_, train_loss_, val_loss_))\n",
    "    \n",
    "    test_accuracy, test_loss = get_accuracy_loss_in_batches(X_test, y_test)\n",
    "    valid_accuracy, valid_loss = get_accuracy_loss_in_batches(X_valid, y_valid)\n",
    "    model_path = os.getcwd() + \"/model\"\n",
    "    saved_model_path = saver.save(sess, model_path)\n",
    "    print(\"Model file: \" + saved_model_path)\n",
    "    plot_learning_curves(train_loss_history, train_accuracy_history, valid_loss_history, valid_accuracy_history)\n",
    "    print(\" Valid loss: %.8f, accuracy = %.2f%%)\" % (valid_loss, valid_accuracy)) \n",
    "    print(\" Test loss: %.8f, accuracy = %.2f%%)\" % (test_loss, test_accuracy)) \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "Epoch 1 --- Training Accuracy:   5.8%, Validation Accuracy:   5.4%, Training Loss: 3.494 Validation Loss: 3.552\n",
      "Epoch 2 --- Training Accuracy:   5.7%, Validation Accuracy:   5.4%, Training Loss: 3.494 Validation Loss: 3.550\n",
      "Epoch 3 --- Training Accuracy:   5.8%, Validation Accuracy:   5.4%, Training Loss: 3.395 Validation Loss: 3.490\n",
      "Epoch 4 --- Training Accuracy:  10.3%, Validation Accuracy:   9.5%, Training Loss: 2.926 Validation Loss: 2.973\n",
      "Epoch 5 --- Training Accuracy:  15.4%, Validation Accuracy:  14.3%, Training Loss: 2.687 Validation Loss: 2.756\n",
      "Epoch 6 --- Training Accuracy:  23.1%, Validation Accuracy:  21.7%, Training Loss: 2.355 Validation Loss: 2.438\n",
      "Epoch 7 --- Training Accuracy:  34.1%, Validation Accuracy:  31.5%, Training Loss: 1.933 Validation Loss: 2.006\n",
      "Epoch 8 --- Training Accuracy:  44.0%, Validation Accuracy:  40.6%, Training Loss: 1.630 Validation Loss: 1.694\n",
      "Epoch 9 --- Training Accuracy:  46.4%, Validation Accuracy:  41.7%, Training Loss: 1.430 Validation Loss: 1.545\n",
      "Epoch 10 --- Training Accuracy:  56.1%, Validation Accuracy:  52.8%, Training Loss: 1.199 Validation Loss: 1.296\n",
      "Epoch 11 --- Training Accuracy:  62.7%, Validation Accuracy:  59.7%, Training Loss: 0.904 Validation Loss: 0.988\n",
      "Epoch 12 --- Training Accuracy:  68.7%, Validation Accuracy:  65.4%, Training Loss: 0.754 Validation Loss: 0.847\n",
      "Epoch 13 --- Training Accuracy:  72.6%, Validation Accuracy:  70.3%, Training Loss: 0.673 Validation Loss: 0.765\n",
      "Epoch 14 --- Training Accuracy:  77.6%, Validation Accuracy:  74.8%, Training Loss: 0.559 Validation Loss: 0.644\n",
      "Epoch 15 --- Training Accuracy:  80.0%, Validation Accuracy:  77.4%, Training Loss: 0.509 Validation Loss: 0.607\n",
      "Epoch 16 --- Training Accuracy:  84.5%, Validation Accuracy:  82.2%, Training Loss: 0.411 Validation Loss: 0.506\n",
      "Epoch 17 --- Training Accuracy:  88.4%, Validation Accuracy:  85.4%, Training Loss: 0.313 Validation Loss: 0.408\n",
      "Epoch 18 --- Training Accuracy:  89.6%, Validation Accuracy:  86.9%, Training Loss: 0.269 Validation Loss: 0.334\n",
      "Epoch 19 --- Training Accuracy:  91.4%, Validation Accuracy:  89.4%, Training Loss: 0.223 Validation Loss: 0.295\n",
      "Epoch 20 --- Training Accuracy:  92.9%, Validation Accuracy:  91.0%, Training Loss: 0.188 Validation Loss: 0.237\n",
      "Epoch 21 --- Training Accuracy:  94.5%, Validation Accuracy:  92.7%, Training Loss: 0.152 Validation Loss: 0.206\n",
      "Epoch 22 --- Training Accuracy:  95.1%, Validation Accuracy:  92.9%, Training Loss: 0.130 Validation Loss: 0.214\n",
      "Epoch 23 --- Training Accuracy:  95.9%, Validation Accuracy:  94.6%, Training Loss: 0.111 Validation Loss: 0.150\n",
      "Epoch 24 --- Training Accuracy:  96.7%, Validation Accuracy:  95.6%, Training Loss: 0.097 Validation Loss: 0.125\n",
      "Epoch 25 --- Training Accuracy:  97.0%, Validation Accuracy:  96.2%, Training Loss: 0.087 Validation Loss: 0.122\n",
      "Epoch 26 --- Training Accuracy:  97.6%, Validation Accuracy:  96.5%, Training Loss: 0.072 Validation Loss: 0.108\n",
      "Epoch 27 --- Training Accuracy:  97.7%, Validation Accuracy:  96.7%, Training Loss: 0.069 Validation Loss: 0.106\n",
      "Epoch 28 --- Training Accuracy:  97.9%, Validation Accuracy:  96.3%, Training Loss: 0.056 Validation Loss: 0.088\n",
      "Epoch 29 --- Training Accuracy:  98.3%, Validation Accuracy:  97.3%, Training Loss: 0.054 Validation Loss: 0.085\n",
      "Epoch 30 --- Training Accuracy:  98.7%, Validation Accuracy:  97.0%, Training Loss: 0.040 Validation Loss: 0.082\n",
      "Epoch 31 --- Training Accuracy:  98.9%, Validation Accuracy:  98.0%, Training Loss: 0.035 Validation Loss: 0.062\n",
      "Epoch 32 --- Training Accuracy:  99.0%, Validation Accuracy:  98.2%, Training Loss: 0.036 Validation Loss: 0.050\n",
      "Epoch 33 --- Training Accuracy:  99.0%, Validation Accuracy:  98.3%, Training Loss: 0.037 Validation Loss: 0.058\n",
      "Epoch 34 --- Training Accuracy:  99.2%, Validation Accuracy:  98.4%, Training Loss: 0.030 Validation Loss: 0.052\n",
      "Epoch 35 --- Training Accuracy:  99.3%, Validation Accuracy:  98.6%, Training Loss: 0.027 Validation Loss: 0.051\n",
      "Epoch 36 --- Training Accuracy:  99.4%, Validation Accuracy:  98.9%, Training Loss: 0.023 Validation Loss: 0.043\n",
      "Epoch 37 --- Training Accuracy:  99.4%, Validation Accuracy:  98.6%, Training Loss: 0.023 Validation Loss: 0.051\n",
      "Epoch 38 --- Training Accuracy:  99.5%, Validation Accuracy:  98.7%, Training Loss: 0.020 Validation Loss: 0.043\n",
      "Epoch 39 --- Training Accuracy:  99.6%, Validation Accuracy:  98.7%, Training Loss: 0.017 Validation Loss: 0.042\n",
      "Epoch 40 --- Training Accuracy:  99.6%, Validation Accuracy:  98.9%, Training Loss: 0.016 Validation Loss: 0.038\n",
      "Epoch 41 --- Training Accuracy:  99.5%, Validation Accuracy:  99.0%, Training Loss: 0.020 Validation Loss: 0.036\n",
      "Epoch 42 --- Training Accuracy:  99.7%, Validation Accuracy:  99.0%, Training Loss: 0.014 Validation Loss: 0.038\n",
      "Epoch 43 --- Training Accuracy:  99.7%, Validation Accuracy:  98.4%, Training Loss: 0.013 Validation Loss: 0.065\n",
      "Epoch 44 --- Training Accuracy:  99.6%, Validation Accuracy:  98.9%, Training Loss: 0.016 Validation Loss: 0.042\n",
      "Epoch 45 --- Training Accuracy:  99.7%, Validation Accuracy:  98.6%, Training Loss: 0.011 Validation Loss: 0.044\n",
      "Epoch 46 --- Training Accuracy:  99.7%, Validation Accuracy:  99.0%, Training Loss: 0.012 Validation Loss: 0.036\n",
      "Epoch 47 --- Training Accuracy:  99.7%, Validation Accuracy:  99.1%, Training Loss: 0.010 Validation Loss: 0.034\n",
      "Epoch 48 --- Training Accuracy:  99.7%, Validation Accuracy:  98.9%, Training Loss: 0.012 Validation Loss: 0.037\n",
      "Epoch 49 --- Training Accuracy:  99.8%, Validation Accuracy:  98.5%, Training Loss: 0.009 Validation Loss: 0.054\n",
      "Epoch 50 --- Training Accuracy:  99.7%, Validation Accuracy:  98.6%, Training Loss: 0.011 Validation Loss: 0.050\n",
      "Epoch 51 --- Training Accuracy:  99.7%, Validation Accuracy:  98.7%, Training Loss: 0.011 Validation Loss: 0.045\n",
      "Epoch 52 --- Training Accuracy:  99.8%, Validation Accuracy:  98.9%, Training Loss: 0.010 Validation Loss: 0.041\n",
      "Epoch 53 --- Training Accuracy:  99.8%, Validation Accuracy:  98.7%, Training Loss: 0.010 Validation Loss: 0.047\n",
      "Epoch 54 --- Training Accuracy:  99.7%, Validation Accuracy:  98.8%, Training Loss: 0.011 Validation Loss: 0.041\n",
      "Epoch 55 --- Training Accuracy:  99.8%, Validation Accuracy:  99.3%, Training Loss: 0.009 Validation Loss: 0.029\n",
      "Epoch 56 --- Training Accuracy:  99.8%, Validation Accuracy:  98.8%, Training Loss: 0.009 Validation Loss: 0.040\n",
      "Epoch 57 --- Training Accuracy:  99.8%, Validation Accuracy:  98.7%, Training Loss: 0.009 Validation Loss: 0.043\n",
      "Epoch 58 --- Training Accuracy:  99.8%, Validation Accuracy:  98.8%, Training Loss: 0.007 Validation Loss: 0.038\n",
      "Epoch 59 --- Training Accuracy:  99.8%, Validation Accuracy:  99.0%, Training Loss: 0.008 Validation Loss: 0.035\n",
      "Epoch 60 --- Training Accuracy:  99.8%, Validation Accuracy:  98.7%, Training Loss: 0.009 Validation Loss: 0.049\n",
      "Epoch 61 --- Training Accuracy:  99.8%, Validation Accuracy:  99.3%, Training Loss: 0.008 Validation Loss: 0.028\n",
      "Epoch 62 --- Training Accuracy:  99.8%, Validation Accuracy:  99.1%, Training Loss: 0.007 Validation Loss: 0.030\n",
      "Epoch 63 --- Training Accuracy:  99.8%, Validation Accuracy:  99.2%, Training Loss: 0.007 Validation Loss: 0.040\n",
      "Epoch 64 --- Training Accuracy:  99.7%, Validation Accuracy:  99.1%, Training Loss: 0.010 Validation Loss: 0.039\n",
      "Epoch 65 --- Training Accuracy:  99.8%, Validation Accuracy:  99.2%, Training Loss: 0.007 Validation Loss: 0.037\n",
      "Epoch 66 --- Training Accuracy:  99.8%, Validation Accuracy:  99.1%, Training Loss: 0.007 Validation Loss: 0.032\n",
      "Epoch 67 --- Training Accuracy:  99.8%, Validation Accuracy:  99.3%, Training Loss: 0.007 Validation Loss: 0.027\n",
      "Epoch 68 --- Training Accuracy:  99.9%, Validation Accuracy:  99.0%, Training Loss: 0.005 Validation Loss: 0.042\n",
      "Epoch 69 --- Training Accuracy:  99.8%, Validation Accuracy:  99.3%, Training Loss: 0.007 Validation Loss: 0.025\n",
      "Epoch 70 --- Training Accuracy:  99.8%, Validation Accuracy:  99.5%, Training Loss: 0.007 Validation Loss: 0.021\n",
      "Epoch 71 --- Training Accuracy:  99.9%, Validation Accuracy:  99.0%, Training Loss: 0.006 Validation Loss: 0.037\n",
      "Epoch 72 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.005 Validation Loss: 0.023\n",
      "Epoch 73 --- Training Accuracy:  99.9%, Validation Accuracy:  99.4%, Training Loss: 0.006 Validation Loss: 0.023\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 74 --- Training Accuracy:  99.9%, Validation Accuracy:  99.6%, Training Loss: 0.005 Validation Loss: 0.022\n",
      "Epoch 75 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.005 Validation Loss: 0.023\n",
      "Epoch 76 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.005 Validation Loss: 0.025\n",
      "Epoch 77 --- Training Accuracy:  99.8%, Validation Accuracy:  99.2%, Training Loss: 0.007 Validation Loss: 0.035\n",
      "Epoch 78 --- Training Accuracy:  99.9%, Validation Accuracy:  99.3%, Training Loss: 0.006 Validation Loss: 0.024\n",
      "Epoch 79 --- Training Accuracy:  99.9%, Validation Accuracy:  99.0%, Training Loss: 0.005 Validation Loss: 0.038\n",
      "Epoch 80 --- Training Accuracy:  99.8%, Validation Accuracy:  99.1%, Training Loss: 0.006 Validation Loss: 0.032\n",
      "Epoch 81 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.005 Validation Loss: 0.025\n",
      "Epoch 82 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.005 Validation Loss: 0.020\n",
      "Epoch 83 --- Training Accuracy:  99.9%, Validation Accuracy:  99.3%, Training Loss: 0.005 Validation Loss: 0.028\n",
      "Epoch 84 --- Training Accuracy:  99.9%, Validation Accuracy:  99.3%, Training Loss: 0.006 Validation Loss: 0.036\n",
      "Epoch 85 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.005 Validation Loss: 0.020\n",
      "Epoch 86 --- Training Accuracy:  99.9%, Validation Accuracy:  99.3%, Training Loss: 0.005 Validation Loss: 0.031\n",
      "Epoch 87 --- Training Accuracy:  99.9%, Validation Accuracy:  99.4%, Training Loss: 0.004 Validation Loss: 0.026\n",
      "Epoch 88 --- Training Accuracy:  99.9%, Validation Accuracy:  99.2%, Training Loss: 0.004 Validation Loss: 0.027\n",
      "Epoch 89 --- Training Accuracy:  99.9%, Validation Accuracy:  99.2%, Training Loss: 0.004 Validation Loss: 0.036\n",
      "Epoch 90 --- Training Accuracy:  99.9%, Validation Accuracy:  99.0%, Training Loss: 0.004 Validation Loss: 0.039\n",
      "Epoch 91 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.004 Validation Loss: 0.022\n",
      "Epoch 92 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.004 Validation Loss: 0.021\n",
      "Epoch 93 --- Training Accuracy:  99.9%, Validation Accuracy:  99.4%, Training Loss: 0.004 Validation Loss: 0.023\n",
      "Epoch 94 --- Training Accuracy:  99.9%, Validation Accuracy:  99.1%, Training Loss: 0.004 Validation Loss: 0.032\n",
      "Epoch 95 --- Training Accuracy:  99.9%, Validation Accuracy:  99.4%, Training Loss: 0.004 Validation Loss: 0.022\n",
      "Epoch 96 --- Training Accuracy:  99.9%, Validation Accuracy:  99.2%, Training Loss: 0.004 Validation Loss: 0.033\n",
      "Epoch 97 --- Training Accuracy:  99.9%, Validation Accuracy:  99.4%, Training Loss: 0.004 Validation Loss: 0.031\n",
      "Epoch 98 --- Training Accuracy:  99.9%, Validation Accuracy:  99.3%, Training Loss: 0.004 Validation Loss: 0.029\n",
      "Epoch 99 --- Training Accuracy:  99.9%, Validation Accuracy:  99.4%, Training Loss: 0.004 Validation Loss: 0.028\n",
      "Epoch 100 --- Training Accuracy:  99.9%, Validation Accuracy:  99.5%, Training Loss: 0.004 Validation Loss: 0.028\n",
      "Model file: /home/wengyao04/machine_learning/udacity_selfdrivingcar_nanodegree/CarND-Traffic-Sign-Classifier-Project/model\n",
      " Valid loss: 0.02809283, accuracy = 0.99%)\n",
      " Test loss: 0.09074351, accuracy = 0.98%)\n"
     ]
    },
    {
     "data": {
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4uPmzxe9NjlpXXfnDD19e9HKtqGpvQ+XMdthtiaTbDpJjTaKW42801HdTW99OSkYmR88e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\nDBWxzTdKCu1S4K+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sCCdOePGvf51fXsOvBu1C25GVm8V3W74zL6AQwuWk0L4Ba9YY9x4N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\n8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe4e4aabd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /home/wengyao04/machine_learning/udacity_selfdrivingcar_nanodegree/CarND-Traffic-Sign-Classifier-Project/model\n"
     ]
    }
   ],
   "source": [
    "def model_on_test():\n",
    "    cnt = 0\n",
    "    with tf.variable_scope(\"model_variable\", reuse=True):\n",
    "        image = tf.placeholder(tf.float32, shape = (None, 32, 32, 1))\n",
    "        is_training = tf.placeholder(tf.bool)\n",
    "        y = tf.placeholder(tf.int32, (None))\n",
    "        y_true = tf.one_hot(y, n_classes) \n",
    "        \n",
    "        train_op = inference(image, is_training)\n",
    "        logits = train_op['logits']\n",
    "        predictions_op = predictions(logits)\n",
    "        bi = BatchIterator(batch_size=1)\n",
    "        false_predictions_op = tf.equal(tf.argmax(predictions_op, 1), tf.argmax(y_true, 1))\n",
    "        \n",
    "        y_true_idx_op = tf.argmax(y_true, 1)\n",
    "        y_pred_idx_op = tf.argmax(predictions_op, 1)\n",
    "\n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    tf.train.Saver().restore(sess, tf.train.latest_checkpoint('.'))\n",
    "    false_pred_list = []\n",
    "    for x_batch, y_batch in bi(X_test, y_test):\n",
    "        false_pred, y_true_idx, y_pred_idx = sess.run([false_predictions_op, y_true_idx_op, y_pred_idx_op], feed_dict = {image : x_batch, y: y_batch, is_training: False})\n",
    "        if false_pred[0] == False:\n",
    "            false_pred_=[]\n",
    "            false_pred_.append(x_batch[0])\n",
    "            false_pred_.append(y_true_idx[0])\n",
    "            false_pred_.append(y_pred_idx[0])\n",
    "            cnt = cnt + 1\n",
    "            false_pred_list.append(false_pred_)\n",
    "        if cnt > 7:\n",
    "            break\n",
    "    return false_pred_list \n",
    "false_pred_list = model_on_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0aNEitGxZWjbGn7dq1SqyV66MQ5HXWy99ZuRtbdu2jeyamhowvA/XI0sFUPfn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L/TTjuV0px99tmZ9hMLrLvuupn+7W9/W8rjwgsvzLQPHuzbi6qgqz54sJ+Q4O677y6l8fbhJ8nw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\nnPdlxowZDbZTNEgicVhTUxPkg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe36d03fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_false_pred_images(image_index_list):\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "    idx = 0\n",
    "    for image, y_true_idx, y_pred_idx in image_index_list:\n",
    "        idx = idx + 1\n",
    "        plt.subplot(2, 4, idx)\n",
    "        plt.title(\"True: \" + signnames[y_true_idx] + \"\\n Pred: \" + signnames[y_pred_idx])\n",
    "        plt.imshow(image.reshape(32,32), cmap = plt.get_cmap('gray'))\n",
    "    fig.tight_layout()\n",
    "    plt.savefig('false_predict_images.png')\n",
    "plot_false_pred_images(false_pred_list)"
   ]
  },
  {
   "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": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import PIL\n",
    "from PIL import Image\n",
    "\n",
    "def resize_image(numpy_array_image, new_width, new_height):\n",
    "    # convert nympy array image to PIL.Image\n",
    "    image = Image.fromarray(np.uint8(numpy_array_image))\n",
    "    image = image.resize((new_width, new_height), PIL.Image.ANTIALIAS)\n",
    "    # convert PIL.Image into nympy array back again\n",
    "    return np.array(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "from scipy import ndimage, misc\n",
    "import re\n",
    "new_images = []\n",
    "for root, dirnames, filenames in os.walk(\"traffic_signs\"):\n",
    "    for filename in filenames:\n",
    "        if re.search(\"\\.(jpg|jpeg|png|bmp|tiff)$\", filename):\n",
    "            filepath = os.path.join(root, filename)\n",
    "            image = ndimage.imread(filepath, mode=\"RGB\")\n",
    "            image_resized = resize_image(image, 32, 32)\n",
    "            new_images.append(image_resized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wengyao04/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float32 to uint16\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    }
   ],
   "source": [
    "#X_new, y_new = process_images(np.array(new_images), [])\n",
    "new_images_ = np.array(new_images)\n",
    "#print(new_images_[0])\n",
    "X_new, y_new = process_images(new_images_, ['Roundabout mandatory', 'Road work', 'Yield', 'Children crossing', 'Speed limit (50km/h)', 'Stop'], False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zXoK/L1gs87qTYR02zw5Y9hy3QcDXreSpiSVfVfVMP/Dsd75zkq8/A/P105O3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\nIiIiIjKh6AaXiIiIiIiIiIiIiIiITCi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe36ebdab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_new_images(image_index_list, label_list):\n",
    "    fig = plt.figure(figsize=(24,4))\n",
    "    for idx in range(0, 6):\n",
    "        plt.subplot(1, 6, idx + 1)\n",
    "        plt.imshow(image_index_list[idx], cmap = plt.get_cmap('gray'))\n",
    "        plt.title(label_list[idx])\n",
    "    plt.savefig(\"new_traffic_signs.png\")\n",
    "    fig.tight_layout()\n",
    "plot_new_images(new_images_, y_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nolearn.lasagne import BatchIterator\n",
    "def get_top_k_predictions(X, batch_size, k = 5):\n",
    "    \n",
    "    with tf.variable_scope(\"model_variable\", reuse=True):\n",
    "        image = tf.placeholder(tf.float32, shape = (None, 32, 32, 1))\n",
    "        is_training = tf.placeholder(tf.bool)\n",
    "        \n",
    "        train_op = inference(image, is_training)\n",
    "        logits = train_op['logits']\n",
    "        predictions_op = predictions(logits)\n",
    "        top_k_predictions_op = tf.nn.top_k(predictions_op, k)\n",
    "    \n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    tf.train.Saver().restore(sess, tf.train.latest_checkpoint('.'))\n",
    "    p = []\n",
    "    bi = BatchIterator(batch_size=batch_size)\n",
    "    for x_batch in bi(X):\n",
    "        [p_batch] = sess.run([top_k_predictions_op], feed_dict = {image : X, is_training: False})\n",
    "        p.extend(p_batch)\n",
    "       \n",
    "    return np.array(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "from skimage import io\n",
    "\n",
    "def plot_image_statistics(predictions, labels, original, preprocessed, index):  \n",
    "    # Prepare the grid\n",
    "    fig = plt.figure(figsize = (8, 3))\n",
    "    gridspec.GridSpec(2, 2)\n",
    "    \n",
    "    # Plot original image\n",
    "    plt.subplot2grid((2, 2), (0, 0))\n",
    "    plt.imshow(original[index])\n",
    "    plt.axis('off')\n",
    "\n",
    "    # Plot preprocessed image\n",
    "    plt.subplot2grid((2, 2), (1, 0))\n",
    "    plt.imshow(preprocessed[index].reshape([32,32]), cmap=plt.get_cmap('gray'))\n",
    "    plt.axis('off')\n",
    "\n",
    "    #Plot predictions\n",
    "    plt.subplot2grid((2, 2), (0, 1), colspan=2, rowspan=2)\n",
    "    plt.barh(np.arange(5)+.5, predictions[0][index], align='center')\n",
    "    plt.yticks(np.arange(5)+.5, signnames[predictions[1][index].astype(int)])\n",
    "    plt.tick_params(axis='both', which='both', labelleft='off', labelright='on', labeltop='off', labelbottom='off')\n",
    "    fig.tight_layout()\n",
    "    plt.savefig(labels[index] + \".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /home/wengyao04/machine_learning/udacity_selfdrivingcar_nanodegree/CarND-Traffic-Sign-Classifier-Project/model\n"
     ]
    }
   ],
   "source": [
    "### Run the predictions here and use the model to output the prediction for each image.\n",
    "### Make sure to pre-process the images with the same pre-processing pipeline used earlier.\n",
    "### Feel free to use as many code cells as needed.\n",
    "top_k_predictions = get_top_k_predictions(X_new, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Roundabout mandatory', 'Caution Road', 'Yield', 'Children crossing', 'Speed limit (50km/h)', 'Stop']\n"
     ]
    }
   ],
   "source": [
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.\n",
    "print(y_new)"
   ]
  },
  {
   "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": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe36c8dae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LkJmdaWYzzWzm8uXLu7s4ItK16cAuZvasmV1sZoduwzV7AJe6+z7AGuAruXNr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\na2sD2rf4o5jWd+XKlQBMmjQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3745e0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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w3333AdW0R6imLkYPP/IInnnmmWaZ6PGPGDECqKZElsswR35FXCei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\njRoHIiIiUqPGgYiIiNT8P58y3Zm0yLPZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3743fd5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c5xwze8rMHjGzjWO7GoHp6b7fDT3kpYyZjTOzCWY2YdasWd3dHBHpXHcCm5vZ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\niEiF6tJMfvDgwUA+zl7cECb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe36d714278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe36ca87ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "### Feel free to use as many code cells as needed.\n",
    "#print (top_k_predictions[0])\n",
    "filenames = ['RoundaboutMandatory', 'RoadWork', 'Yield', 'ChildrenCrossing', 'SpeedLimit50', 'Stop']\n",
    "for i in range(0, 5):\n",
    "    plot_image_statistics(top_k_predictions, filenames, new_images_, X_new, i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Writeup\n",
    "\n",
    "Once you have completed the code implementation, document your results in a project writeup using this [template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) as a guide. The writeup can be in a markdown or pdf file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RoundaboutMandatory\n",
      "Priority road: 0.989575088024\n",
      "Yield: 0.00529247801751\n",
      "Stop: 0.00103384349495\n",
      "Ahead only: 0.00102106039412\n",
      "Roundabout mandatory: 0.000658783596009\n",
      "RoadWork\n",
      "Road work: 1.0\n",
      "Bicycles crossing: 2.66110934964e-14\n",
      "Slippery road: 6.43749910353e-16\n",
      "Bumpy road: 5.41721721362e-16\n",
      "Dangerous curve to the right: 2.68919394991e-17\n",
      "Yield\n",
      "Yield: 1.0\n",
      "Priority road: 3.73760499957e-19\n",
      "End of all speed and passing limits: 1.00851527998e-24\n",
      "End of no passing: 9.3484531167e-29\n",
      "General caution: 9.06802387418e-29\n",
      "ChildrenCrossing\n",
      "Children crossing: 1.0\n",
      "Bicycles crossing: 1.64836304328e-11\n",
      "Speed limit (120km/h): 1.59387172243e-14\n",
      "Roundabout mandatory: 8.3265854403e-15\n",
      "Stop: 1.98137692912e-15\n",
      "SpeedLimit50\n",
      "Speed limit (50km/h): 0.999192535877\n",
      "Speed limit (30km/h): 0.000553100195248\n",
      "Speed limit (80km/h): 0.00023143949511\n",
      "Speed limit (60km/h): 2.27998207265e-05\n",
      "Speed limit (100km/h): 4.26735091708e-08\n",
      "Stop\n",
      "Stop: 1.0\n",
      "Keep left: 2.70686010118e-21\n",
      "Priority road: 3.03382878477e-22\n",
      "End of speed limit (80km/h): 3.20343129193e-23\n",
      "Speed limit (120km/h): 6.89647525779e-24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wengyao04/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/ipykernel_launcher.py:4: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "for idx in range(0, 6):\n",
    "    print (filenames[idx])\n",
    "    for k in range(0, 5):\n",
    "        print(signnames[top_k_predictions[1][idx][k]] + \": \" + str(top_k_predictions[0][idx][k]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 4 (Optional): Visualize the Neural Network's State with Test Images\n",
    "\n",
    " This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.\n",
    "\n",
    " Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the [LeNet lab's](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.\n",
    "\n",
    "For an example of what feature map outputs look like, check out NVIDIA's results in their paper [End-to-End Deep Learning for Self-Driving Cars](https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/) in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.\n",
    "\n",
    "<figure>\n",
    " <img src=\"visualize_cnn.png\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above)</p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /home/wengyao04/machine_learning/udacity_selfdrivingcar_nanodegree/CarND-Traffic-Sign-Classifier-Project/model\n",
      "(1, 32, 32, 1)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe32e1989b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualize your network's feature maps here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# image_input: the test image being fed into the network to produce the feature maps\n",
    "# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer\n",
    "# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output\n",
    "# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry\n",
    "\n",
    "def outputFeatureMap(X):\n",
    "    with tf.variable_scope(\"model_variable\", reuse=True):\n",
    "        image = tf.placeholder(tf.float32, shape = (None, 32, 32, 1))\n",
    "        is_training = tf.placeholder(tf.bool)\n",
    "        train_op = inference(image, is_training)\n",
    "        conv1 = train_op['pooling1']\n",
    "    \n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    tf.train.Saver().restore(sess, tf.train.latest_checkpoint('.'))\n",
    "    #conv1_layer = tf.get_default_graph().get_tensor_by_name('model_variable/conv1/model_variable/conv1:0')\n",
    "    #outputFeatureMap(X_new, conv1_layer)\n",
    "    #print (tf.get_default_graph())\n",
    "    for offset in range(0, 1):\n",
    "        x_batch = X_new[offset:offset+1]\n",
    "        print (x_batch.shape)\n",
    "        activation = sess.run(conv1, feed_dict = {image : x_batch, is_training: False})\n",
    "        #print (conv1_)\n",
    "        #print (conv1)\n",
    "        featuremaps = activation.shape[3]\n",
    "        fig = plt.figure(1, figsize=(30,30))\n",
    "        for featuremap in range(featuremaps):\n",
    "            plt.subplot(8,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "            plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")\n",
    "        plt.savefig('roundabout_conv1.png')\n",
    "    #for x_batch in batch_iterator(X):\n",
    "        #tf.train.Saver().restore(sess, tf.train.latest_checkpoint('.'))\n",
    "        #print (conv1)\n",
    "    #    activation = sess.run([conv1], feed_dict = {image : X, is_training: False})\n",
    "        #print (activation[0].shape)\n",
    "        #featuremaps = activation.shape[3]\n",
    "outputFeatureMap(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#[op.name for op in tf.get_default_graph().get_operations()]\n",
    "#test(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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