Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "## Deep Learning\n",
    "\n",
    "## Project: Build a Traffic Sign Recognition Classifier\n",
    "\n",
    "In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary. \n",
    "\n",
    "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission. \n",
    "\n",
    "In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) that can be used to guide the writing process. Completing the code template and writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/481/view) for this project.\n",
    "\n",
    "The [rubric](https://review.udacity.com/#!/rubrics/481/view) contains \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the \"stand out suggestions\", you can include the code in this Ipython notebook and also discuss the results in the writeup file.\n",
    "\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file=\"../data/train.p\"\n",
    "validation_file=\"../data/valid.p\"\n",
    "testing_file=\"../data/test.p\"\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']"
   ]
  },
  {
   "cell_type": "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 34799\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### Replace each question mark with the appropriate value. \n",
    "### Use python, pandas or numpy methods rather than hard coding the results\n",
    "import numpy as np\n",
    "\n",
    "# TODO: Number of training examples\n",
    "n_train = len(X_train)\n",
    "\n",
    "# TODO: Number of validation examples\n",
    "n_validation = len(X_valid)\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = len(X_test)\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = len(np.unique(y_train))\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include an exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc. \n",
    "\n",
    "The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python.\n",
    "\n",
    "**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f164c6c58d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "f, axes = plt.subplots(1,5, figsize=(15, 6))\n",
    "f.subplots_adjust(hspace = .2, wspace=.001)\n",
    "axes = axes.ravel()\n",
    "for i in range(5):\n",
    "    item = random.randint(0, len(X_train))\n",
    "    image = X_train[item]\n",
    "    axes[i].axis('off')\n",
    "    axes[i].imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16284fbef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes=[]\n",
    "item, count = np.unique(y_train, return_counts=True)\n",
    "for i in item:\n",
    "    classes.append(np.sum(y_train == i))\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.bar(item, count, alpha=0.6)\n",
    "plt.title('Bar Chart Distribution of Traffic Sign Original Data')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('../CarND-Traffic-Sign-Classifier-Project/test_folder/bar_chart.jpg')"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1628370c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include \n",
    "### converting to grayscale, etc.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "X_train_rgb = X_train\n",
    "X_train_gry = np.sum(X_train/3, axis=3, keepdims=True)\n",
    "\n",
    "X_test_rgb = X_test\n",
    "X_test_gry = np.sum(X_test/3, axis=3, keepdims=True)\n",
    "\n",
    "\n",
    "X_train = X_train_gry\n",
    "X_test = X_test_gry\n",
    "\n",
    "# rgb vs grayscale\n",
    "n_rows = 8\n",
    "n_cols = 10\n",
    "offset = 9000\n",
    "fig, axs = plt.subplots(n_rows,n_cols, figsize=(18, 14))\n",
    "fig.subplots_adjust(hspace = .1, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for j in range(0,n_rows,2):\n",
    "    for i in range(n_cols):\n",
    "        index = i + j*n_cols\n",
    "        image = X_train_rgb[index + offset]\n",
    "        axs[index].axis('off')\n",
    "        axs[index].imshow(image)\n",
    "    for i in range(n_cols):\n",
    "        index = i + j*n_cols + n_cols \n",
    "        image = X_train_gry[index + offset - n_cols].squeeze()\n",
    "        axs[index].axis('off')\n",
    "        axs[index].imshow(image, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f161c107b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(2,1, figsize=(10, 8))\n",
    "f.subplots_adjust(hspace = .1, wspace=.001)\n",
    "axes = axes.ravel()\n",
    "\n",
    "rgb_image=X_train_rgb[2342]\n",
    "greyscale_image=X_train_gry[2342].squeeze()\n",
    "\n",
    "axes[0].axis('off')\n",
    "axes[0].imshow(rgb_image)\n",
    "axes[0].set_title('RGB Image')\n",
    "\n",
    "axes[1].axis('off')\n",
    "axes[1].imshow(greyscale_image)\n",
    "axes[1].set_title('Greyscale Image')\n",
    "f.savefig('../CarND-Traffic-Sign-Classifier-Project/test_folder/greyscale_image.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.354081335648\n",
      "-0.358215153428\n",
      "Original shape: (34799, 32, 32, 1)\n",
      "Normalized shape: (34799, 32, 32, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f161a7c62b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f161c0db908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_normalized = (X_train - 128)/128 \n",
    "X_test_normalized = (X_test - 128)/128\n",
    "\n",
    "print(np.mean(X_train_normalized))\n",
    "print(np.mean(X_test_normalized))\n",
    "\n",
    "print(\"Original shape:\", X_train.shape)\n",
    "print(\"Normalized shape:\", X_train_normalized.shape)\n",
    "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n",
    "axs = axs.ravel()\n",
    "\n",
    "axs[0].axis('off')\n",
    "axs[0].set_title('normalized')\n",
    "axs[0].imshow(X_train_normalized[0].squeeze(), cmap='gray')\n",
    "\n",
    "axs[1].axis('off')\n",
    "axs[1].set_title('original')\n",
    "axs[1].imshow(X_train[0].squeeze(), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f161a7dee10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "def image_translate(img):\n",
    "    rows,cols,_ = img.shape\n",
    "    px = 2\n",
    "    dx,dy = np.random.randint(-px,px,2)\n",
    "    M = np.float32([[1,0,dx],[0,1,dy]])\n",
    "    img_trns = cv2.warpAffine(img,M,(cols,rows))\n",
    "    img_trns = img_trns[:,:,np.newaxis]\n",
    "    return img_trns\n",
    "\n",
    "img_test = X_train_normalized[23750]\n",
    "img_trans = image_translate(img_test)\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n",
    "\n",
    "axs[0].axis('off')\n",
    "axs[0].imshow(img_test.squeeze(), cmap='gray')\n",
    "axs[0].set_title('original')\n",
    "\n",
    "axs[1].axis('off')\n",
    "axs[1].imshow(img_trans.squeeze(), cmap='gray')\n",
    "axs[1].set_title('translated')\n",
    "\n",
    "fig.savefig('../CarND-Traffic-Sign-Classifier-Project/test_folder/translated.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'scaled')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16528f0c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def image_scaling(img):   \n",
    "    rows,cols,_ = img.shape\n",
    "    px = np.random.randint(-2,2)\n",
    "    pts1 = np.float32([[px,px],[rows-px,px],[px,cols-px],[rows-px,cols-px]])\n",
    "    pts2 = np.float32([[0,0],[rows,0],[0,cols],[rows,cols]])\n",
    "    M = cv2.getPerspectiveTransform(pts1,pts2)\n",
    "    img_scale = cv2.warpPerspective(img,M,(rows,cols))\n",
    "    img_scale = img_scale[:,:,np.newaxis]\n",
    "    return img_scale\n",
    "\n",
    "img_scaled = image_scaling(img_test)\n",
    "    \n",
    "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n",
    "\n",
    "axs[0].axis('off')\n",
    "axs[0].imshow(img_test.squeeze(), cmap='gray')\n",
    "axs[0].set_title('original')\n",
    "\n",
    "axs[1].axis('off')\n",
    "axs[1].imshow(img_scaled.squeeze(), cmap='gray')\n",
    "axs[1].set_title('scaled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'warped')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f160ab4a9e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def image_warp(img):\n",
    "    rows,cols,_ = img.shape\n",
    "    rndx = np.random.rand(3) - 0.5\n",
    "    rndx *= cols * 0.06 \n",
    "    rndy = np.random.rand(3) - 0.5\n",
    "    rndy *= rows * 0.06\n",
    "\n",
    "    x1 = cols/4\n",
    "    x2 = 3*cols/4\n",
    "    y1 = rows/4\n",
    "    y2 = 3*rows/4\n",
    "\n",
    "    pts1 = np.float32([[y1,x1],\n",
    "                       [y2,x1],\n",
    "                       [y1,x2]])\n",
    "    pts2 = np.float32([[y1+rndy[0],x1+rndx[0]],\n",
    "                       [y2+rndy[1],x1+rndx[1]],\n",
    "                       [y1+rndy[2],x2+rndx[2]]])\n",
    "\n",
    "    M = cv2.getAffineTransform(pts1,pts2)\n",
    "    img_warp = cv2.warpAffine(img,M,(cols,rows))\n",
    "    img_warp = img_warp[:,:,np.newaxis]\n",
    "    return img_warp\n",
    "\n",
    "img_warped = image_warp(img_test)\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n",
    "\n",
    "axs[0].axis('off')\n",
    "axs[0].imshow(img_test.squeeze(), cmap='gray')\n",
    "axs[0].set_title('original')\n",
    "\n",
    "axs[1].axis('off')\n",
    "axs[1].imshow(img_warped.squeeze(), cmap='gray')\n",
    "axs[1].set_title('warped')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'brightness adjusted')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15fc5b2588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def image_brightness(img):\n",
    "    shifted = img + 1.0   \n",
    "    img_max_value = max(shifted.flatten())\n",
    "    max_coef = 2.0/img_max_value\n",
    "    min_coef = max_coef - 0.1\n",
    "    coef = np.random.uniform(min_coef, max_coef)\n",
    "    img_bright = shifted * coef - 1.0\n",
    "    return img_bright\n",
    "\n",
    "img_brightness = image_brightness(img_test)\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n",
    "\n",
    "axs[0].axis('off')\n",
    "axs[0].imshow(img_test.squeeze(), cmap='gray')\n",
    "axs[0].set_title('original')\n",
    "\n",
    "axs[1].axis('off')\n",
    "axs[1].imshow(img_brightness.squeeze(), cmap='gray')\n",
    "axs[1].set_title('brightness adjusted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 180 1980 2010 1260 1770 1650  360 1290 1260 1320 1800 1170 1890 1920  690\n",
      "  540  360  990 1080  180  300  270  330  450  240 1350  540  210  480  240\n",
      "  390  690  210  599  360 1080  330  180 1860  270  300  210  210]\n",
      "minimum samples for any label: 180\n",
      "X, y shapes: (34799, 32, 32, 1) (34799,)\n"
     ]
    }
   ],
   "source": [
    "print(np.bincount(y_train))\n",
    "print(\"minimum samples for any label:\", min(np.bincount(y_train)))\n",
    "\n",
    "print('X, y shapes:', X_train_normalized.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : |----|----|----|----|----|----|----|----|----|----|----|----|-\n",
      "1 : \n",
      "2 : \n",
      "3 : \n",
      "4 : \n",
      "5 : \n",
      "6 : |----|----|----|----|----|----|----|----|---\n",
      "7 : \n",
      "8 : \n",
      "9 : \n",
      "10 : \n",
      "11 : \n",
      "12 : \n",
      "13 : \n",
      "14 : |----|----|\n",
      "15 : |----|----|----|----|----|\n",
      "16 : |----|----|----|----|----|----|----|----|---\n",
      "17 : \n",
      "18 : \n",
      "19 : |----|----|----|----|----|----|----|----|----|----|----|----|-\n",
      "20 : |----|----|----|----|----|----|----|----|----|----\n",
      "21 : |----|----|----|----|----|----|----|----|----|----|--\n",
      "22 : |----|----|----|----|----|----|----|----|----|-\n",
      "23 : |----|----|----|----|----|----|----\n",
      "24 : |----|----|----|----|----|----|----|----|----|----|----|\n",
      "25 : \n",
      "26 : |----|----|----|----|----|\n",
      "27 : |----|----|----|----|----|----|----|----|----|----|----|---\n",
      "28 : |----|----|----|----|----|----|----|----|----|----|----|----|----|----|\n",
      "30 : |----|----|----|----|----|----|----|----|\n",
      "31 : |----|----|\n",
      "32 : |----|----|----|----|----|----|----|----|----|----|----|---\n",
      "33 : |----|----|----|----|\n",
      "34 : |----|----|----|----|----|----|----|----|---\n",
      "35 : \n",
      "36 : |----|----|----|----|----|----|----|----|----|-\n",
      "37 : |----|----|----|----|----|----|----|----|----|----|----|----|-\n",
      "38 : \n",
      "39 : |----|----|----|----|----|----|----|----|----|----|--\n",
      "40 : |----|----|----|----|----|----|----|----|----|----\n",
      "41 : |----|----|----|----|----|----|----|----|----|----|----|---\n",
      "42 : |----|----|----|----|----|----|----|----|----|----|----|---\n",
      "X, y shapes: (46480, 32, 32, 1) (46480,)\n"
     ]
    }
   ],
   "source": [
    "# adding image data for label having less training samples\n",
    "\n",
    "input_indices = []\n",
    "output_indices = []\n",
    "\n",
    "for class_n in range(n_classes):\n",
    "    print(class_n, ': ', end='')\n",
    "    class_indices = np.where(y_train == class_n)\n",
    "    n_samples = len(class_indices[0])\n",
    "    if n_samples < 800:\n",
    "        for i in range(800 - n_samples):\n",
    "            input_indices.append(class_indices[0][i%n_samples])\n",
    "            output_indices.append(X_train_normalized.shape[0])\n",
    "            new_img = X_train_normalized[class_indices[0][i % n_samples]]\n",
    "            new_img = image_translate(image_scaling(image_warp(image_brightness(new_img))))\n",
    "            X_train_normalized = np.concatenate((X_train_normalized, [new_img]), axis=0)\n",
    "            y_train = np.concatenate((y_train, [class_n]), axis=0)\n",
    "            if i % 50 == 0:\n",
    "                print('|', end='')\n",
    "            elif i % 10 == 0:\n",
    "                print('-',end='')\n",
    "    print('')\n",
    "            \n",
    "print('X, y shapes:', X_train_normalized.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15fc576b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "choices = list(range(len(input_indices)))\n",
    "picks = []\n",
    "for i in range(5):\n",
    "    rnd_index = np.random.randint(low=0,high=len(choices))\n",
    "    picks.append(choices.pop(rnd_index))\n",
    "fig, axs = plt.subplots(2,5, figsize=(15, 6))\n",
    "fig.subplots_adjust(hspace = .2, wspace=.001)\n",
    "axs = axs.ravel()\n",
    "for i in range(5):\n",
    "    image = X_train_normalized[input_indices[picks[i]]].squeeze()\n",
    "    axs[i].axis('off')\n",
    "    axs[i].imshow(image, cmap = 'gray')\n",
    "    axs[i].set_title(y_train[input_indices[picks[i]]])\n",
    "for i in range(5):\n",
    "    image = X_train_normalized[output_indices[picks[i]]].squeeze()\n",
    "    axs[i+5].axis('off')\n",
    "    axs[i+5].imshow(image, cmap = 'gray')\n",
    "    axs[i+5].set_title(y_train[output_indices[picks[i]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old X_train size: 46480\n",
      "New X_train size: 37184\n",
      "X_validation size: 9296\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "X_train_normalized, y_train = shuffle(X_train_normalized, y_train)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_validation, y_train, y_validation = train_test_split(X_train_normalized, y_train, \n",
    "                                                                test_size=0.20, random_state=42)\n",
    "\n",
    "print(\"Old X_train size:\",len(X_train_normalized))\n",
    "print(\"New X_train size:\",len(X_train))\n",
    "print(\"X_validation size:\",len(X_validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import tensorflow as tf\n",
    "\n",
    "EPOCHS = 15\n",
    "BATCH_SIZE = 64\n",
    "\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "def sermanet_lenet(x):    \n",
    "    # Hyperparameters\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "    \n",
    "    # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.\n",
    "    W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma), name=\"W1\")\n",
    "    x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID')\n",
    "    b1 = tf.Variable(tf.zeros(6), name=\"b1\")\n",
    "    x = tf.nn.bias_add(x, b1)\n",
    "    print(\"layer 1 shape:\",x.get_shape())\n",
    "\n",
    "    # TODO: Activation.\n",
    "    x = tf.nn.relu(x)\n",
    "    \n",
    "    # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.\n",
    "    x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "    layer1 = x\n",
    "    \n",
    "    # TODO: Layer 2: Convolutional. Output = 10x10x16.\n",
    "    W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma), name=\"W2\")\n",
    "    x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID')\n",
    "    b2 = tf.Variable(tf.zeros(16), name=\"b2\")\n",
    "    x = tf.nn.bias_add(x, b2)\n",
    "                     \n",
    "    # TODO: Activation.\n",
    "    x = tf.nn.relu(x)\n",
    "\n",
    "    # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.\n",
    "    x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "    layer2 = x\n",
    "    \n",
    "    # TODO: Layer 3: Convolutional. Output = 1x1x400.\n",
    "    W3 = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 400), mean = mu, stddev = sigma), name=\"W3\")\n",
    "    x = tf.nn.conv2d(x, W3, strides=[1, 1, 1, 1], padding='VALID')\n",
    "    b3 = tf.Variable(tf.zeros(400), name=\"b3\")\n",
    "    x = tf.nn.bias_add(x, b3)\n",
    "                     \n",
    "    # TODO: Activation.\n",
    "    x = tf.nn.relu(x)\n",
    "    layer3 = x\n",
    "\n",
    "    # TODO: Flatten. Input = 5x5x16. Output = 400.\n",
    "    layer2flat = flatten(layer2)\n",
    "    print(\"layer2flat shape:\",layer2flat.get_shape())\n",
    "    \n",
    "    # Flatten x. Input = 1x1x400. Output = 400.\n",
    "    xflat = flatten(x)\n",
    "    print(\"xflat shape:\",xflat.get_shape())\n",
    "    \n",
    "    # Concat layer2flat and x. Input = 400 + 400. Output = 800\n",
    "    x = tf.concat([xflat, layer2flat], 1)\n",
    "    print(\"x shape:\",x.get_shape())\n",
    "    \n",
    "    # Dropout\n",
    "    x = tf.nn.dropout(x, keep_prob)\n",
    "    \n",
    "    # TODO: Layer 4: Fully Connected. Input = 800. Output = 43.\n",
    "    W4 = tf.Variable(tf.truncated_normal(shape=(800, 43), mean = mu, stddev = sigma), name=\"W4\")\n",
    "    b4 = tf.Variable(tf.zeros(43), name=\"b4\")    \n",
    "    logits = tf.add(tf.matmul(x, W4), b4)\n",
    "    \n",
    "    # TODO: Activation.\n",
    "    #x = tf.nn.relu(x)\n",
    "\n",
    "    # TODO: Layer 5: Fully Connected. Input = 120. Output = 84.\n",
    "    #W5 = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n",
    "    #b5 = tf.Variable(tf.zeros(84)) \n",
    "    #x = tf.add(tf.matmul(x, W5), b5)\n",
    "    \n",
    "    # TODO: Activation.\n",
    "    #x = tf.nn.relu(x)\n",
    "\n",
    "    # TODO: Layer 6: Fully Connected. Input = 84. Output = 43.\n",
    "    #W6 = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))\n",
    "    #b6 = tf.Variable(tf.zeros(43)) \n",
    "    #logits = tf.add(tf.matmul(x, W6), b6)\n",
    "    \n",
    "    return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() \n",
    "\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "keep_prob = tf.placeholder(tf.float32) # probability to keep units\n",
    "one_hot_y = tf.one_hot(y, 43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer 1 shape: (?, 28, 28, 6)\n",
      "layer2flat shape: (?, 400)\n",
      "xflat shape: (?, 400)\n",
      "x shape: (?, 800)\n"
     ]
    }
   ],
   "source": [
    "rate = 0.0009\n",
    "\n",
    "logits = sermanet_lenet(x)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=one_hot_y)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "training_operation = optimizer.minimize(loss_operation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "def evaluate(X_data, y_data):\n",
    "    num_examples = len(X_data)\n",
    "    total_accuracy = 0\n",
    "    sess = tf.get_default_session()\n",
    "    for offset in range(0, num_examples, BATCH_SIZE):\n",
    "        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
    "        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, 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": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "\n",
      "EPOCH 1 :Validation Accuracy = 0.903\n",
      "\n",
      "EPOCH 2 :Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 3 :Validation Accuracy = 0.972\n",
      "\n",
      "EPOCH 4 :Validation Accuracy = 0.981\n",
      "\n",
      "EPOCH 5 :Validation Accuracy = 0.981\n",
      "\n",
      "EPOCH 6 :Validation Accuracy = 0.982\n",
      "\n",
      "EPOCH 7 :Validation Accuracy = 0.985\n",
      "\n",
      "EPOCH 8 :Validation Accuracy = 0.988\n",
      "\n",
      "EPOCH 9 :Validation Accuracy = 0.987\n",
      "\n",
      "EPOCH 10 :Validation Accuracy = 0.989\n",
      "\n",
      "EPOCH 11 :Validation Accuracy = 0.989\n",
      "\n",
      "EPOCH 12 :Validation Accuracy = 0.989\n",
      "\n",
      "EPOCH 13 :Validation Accuracy = 0.988\n",
      "\n",
      "EPOCH 14 :Validation Accuracy = 0.990\n",
      "\n",
      "EPOCH 15 :Validation Accuracy = 0.992\n",
      "\n",
      "Model saved\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",
    "\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples = len(X_train)\n",
    "    \n",
    "    print(\"Training\")\n",
    "    print()\n",
    "    for i in range(EPOCHS):\n",
    "        X_train, y_train = shuffle(X_train, y_train)\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})\n",
    "            \n",
    "        validation_accuracy = evaluate(X_validation, y_validation)\n",
    "        print(\"EPOCH {} :\".format(i+1) + \"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        print()\n",
    "        \n",
    "    saver.save(sess, 'lenet')\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Test Accuracy = 0.940\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver2 = tf.train.import_meta_graph('./lenet.meta')\n",
    "    saver2.restore(sess, \"./lenet\")\n",
    "    test_accuracy = evaluate(X_test_normalized, y_test)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Test a Model on New Images\n",
    "\n",
    "To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.\n",
    "\n",
    "You may find `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Output the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c54c5ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import glob\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "fig, axs = plt.subplots(2,4, figsize=(4, 2))\n",
    "fig.subplots_adjust(hspace = .2, wspace=.001)\n",
    "axes = axs.ravel()\n",
    "test_images = []\n",
    "\n",
    "for i, img in enumerate(glob.glob('../CarND-Traffic-Sign-Classifier-Project/test_folder/test_picture*.png')):\n",
    "    image = cv2.imread(img)\n",
    "    axes[i].axis('off')\n",
    "    axes[i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "    test_images.append(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 32, 32, 1)\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",
    "\n",
    "test_images = np.asarray(test_images)\n",
    "test_images_grey = np.sum(test_images/3, axis=3, keepdims=True)\n",
    "test_images_normalized = (test_images_grey - 128)/128 \n",
    "\n",
    "test_labels = [11, 3, 38, 12, 34, 18, 1, 25]\n",
    "print(test_images_normalized.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Test Accuracy = 0.875\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",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver3 = tf.train.import_meta_graph('./lenet.meta')\n",
    "    saver3.restore(sess, \"./lenet\")\n",
    "    test_accuracy = evaluate(test_images_normalized, test_labels)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. \n",
    "\n",
    "The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tf.nn.top_k` is used to choose the three classes with the highest probability:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Test Image 0\n",
      "\n",
      "First Softmax Probability : 100.000\n",
      "Second Softmax Probability : 0.000\n",
      "Third Softmax Probability : 0.000\n",
      "Fourth Softmax Probability : 0.000\n",
      "Fifth Softmax Probability : 0.000\n",
      "\n",
      "Test Image 1\n",
      "\n",
      "First Softmax Probability : 99.938\n",
      "Second Softmax Probability : 0.042\n",
      "Third Softmax Probability : 0.019\n",
      "Fourth Softmax Probability : 0.001\n",
      "Fifth Softmax Probability : 0.000\n",
      "\n",
      "Test Image 2\n",
      "\n",
      "First Softmax Probability : 100.000\n",
      "Second Softmax Probability : 0.000\n",
      "Third Softmax Probability : 0.000\n",
      "Fourth Softmax Probability : 0.000\n",
      "Fifth Softmax Probability : 0.000\n",
      "\n",
      "Test Image 3\n",
      "\n",
      "First Softmax Probability : 100.000\n",
      "Second Softmax Probability : 0.000\n",
      "Third Softmax Probability : 0.000\n",
      "Fourth Softmax Probability : 0.000\n",
      "Fifth Softmax Probability : 0.000\n",
      "\n",
      "Test Image 4\n",
      "\n",
      "First Softmax Probability : 100.000\n",
      "Second Softmax Probability : 0.000\n",
      "Third Softmax Probability : 0.000\n",
      "Fourth Softmax Probability : 0.000\n",
      "Fifth Softmax Probability : 0.000\n",
      "\n",
      "Test Image 5\n",
      "\n",
      "First Softmax Probability : 100.000\n",
      "Second Softmax Probability : 0.000\n",
      "Third Softmax Probability : 0.000\n",
      "Fourth Softmax Probability : 0.000\n",
      "Fifth Softmax Probability : 0.000\n",
      "\n",
      "Test Image 6\n",
      "\n",
      "First Softmax Probability : 100.000\n",
      "Second Softmax Probability : 0.000\n",
      "Third Softmax Probability : 0.000\n",
      "Fourth Softmax Probability : 0.000\n",
      "Fifth Softmax Probability : 0.000\n",
      "\n",
      "Test Image 7\n",
      "\n",
      "First Softmax Probability : 93.983\n",
      "Second Softmax Probability : 3.319\n",
      "Third Softmax Probability : 2.690\n",
      "Fourth Softmax Probability : 0.006\n",
      "Fifth Softmax Probability : 0.002\n",
      "\n"
     ]
    },
    {
     "data": {
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WH8P0ic4GTW/g/L2hp5eocRDjhh77s2JlD+dECrziJMUr93OHgmFHRXacYhhLmDAZEkNd1dyUVg7cKnkVXDr7uExMd1kHeE1Os5cbt+tquOU8qxQnKuw2vVWfnqONU7YrYnkHgBkzZ/LnvHbzZbGdjrzIvEi4MCrlkZsMpUU133HjxiuejEcvV7SHuiia+4TtE2K6+cy3qnE913PMOXnt7Hvv+GlMt89foHjFbAvP31PNYWiuAcMYwoTJkBhMmAyJoc5RA0wPerUGt+TZzsi2aJtp/J/4VHxo6XOKJ++SE1Vts0fr3LJIRBz60fJyK+9vkfOiVE4+r3PSJk6cGNOzhN31yCOP6AeISZa9Iw2ZbxcNa34j5tLP888ee7walXn8sZguPKZdJ9EWjnoY+MXtipd+53tjOt8zpHh+1d5dga1MhsRgwmRIDHVVcz0iIa5H74JRyrCXe8E2fXJf+tUvYjoKtBoKW3l72/Jm3jK7oq5IK3vDDfPuCu3ie9/7hXrJ5XRQ2oUXcmmbRYsWxfS1116rxm3cuJHn77sXRCLCsMLu4lg/ioTroay/W5P43sWlupqvG+L5D3oF7VsP5bRyWrhI8Qp9Wu3tCmxlMiQGEyZDYqirmpM9PkIvBnxuAxdXz976Q8XLD/Iuynn92prfwgVHg4mTeJxQT4A+UPWroEh1Uo60a76lhdvKX3zxxxTvsJexahgS3ur3/v371Li/PsX5b0U/QE16moNRDltly1gveDA9iw+Sm87S3vHem0Q/Fq/PXv+dP4vpxllzFW9wD0TDViZDYjBhMiQGEyZDYqirzTQgmu1kGvSjW37PJXDyTzyleLLhTfY47f1tPPnvYjoSOWjkVY9VeW2jtFkvlvS2e/6C+THte8f7e9nOk+6FwLMHjz2ct+D5IV1upyJdFp5boiLnKejAm3+lj9vP5w7V1YIH5/D8y6JeAQAU13LgX/p3uipe9gwdJLgrsJXJkBhMmAyJoa5qbmmevdVztz+ueKXf8GEucvqgV4q8E0s6AHRf9z8xHRWEioq88nNiyy9dFNX/qOyMrF5L77i3JUdUEuPEB71yQeTkYa5+AEXy9+zx5MPFOIKfpi4D87QqduJ7B16MuctwTuDgA9o73nSITlXfFdjKZEgMJkyGxGDCZEgMdbWZpqa4r5u7Ux+ZlLt4mx00eNt6YTuUvB63TmQmOFFUnqC3+LsOvxMe2z8UaJupIvoDy18lecH4yqbxfr9OlrXxeOppJF0I3gzFPYZ1iVMREl5FXWHbVTyXReF3v+KLL1zm33WnsJXJkBhMmAyJoa5qrmOA+5kM9estfrqN89WCjHYN6NPzkdWQ/Gl4NdKVOvFL08nttL+1loqj2NaqOJkKvz5ZvL3Y0qDH9fJ3Ja/cjqrQ5vEokPMS6jb0FJ34spXhOjAmHbT5IMc6T0G6efOxu7CVyZAYTJgMicGEyZAY6mozPRUdFNMd77xE8cI8l+pzqaziuWDnEYdVZrjTcW6YWSFtIf0bkn3wKt7Gm1Js/2xa86zilbewm4IcuyLyKW1bdczhSIcWv9aAOKLx56ztHdF/2Is2bXQi+N/pmgH6fn4jHsnStmKlaK3oDWMIEyZDYqirmstHvDVdlztE8ZxISYtGXWFHY/Lynxo2TuSnefpEVvetpPW2Pldh9duS71G8hm4OLqMiJzBkxs1Q40qN/OU2VCYoXiRarga+mhPfZ7Qmp80iwK7Ru0co3AhetR2khEovepEU5WE+hheHrUyGxGDCZEgMJkyGxFBXmykQJ9+BHwkp9LefJCkxnCdO7olP59Pe9lm1BPbLBIoqvY2dOgI0fOD6mE5t0OV8ZMp/SiQ4RuUn1bjm57nEzvw3flDx1rRzt4SKl8wgE07V9H3XBrEtWvY6M2TSwmZKeRELIgqzUvDe1x506bWVyZAYTJgMiYFGUykGw+7AViZDYjBhMiQGEyZDYjBhMiQGEyZDYjBhMiSG/UaYiKhf/IuIaEhcn/8S7vsQEV0wCn8hEfnu+n0ORHQMEf2aiLYRUf7FP5E89hthcs417/gHYA2AM8X/3TjW89sHUADwQwAffLGBewv7jTC9GIgoJKLLiGgFEW0lohuJqL3GayKim4moi4i6iehhIhpHRFcBOA7At2sr3FW78JybiejrRPQbIhogonuJaBIRXVu79xIiOlyMv5yIVhJRHxE9TURnCF6KiL5RW02WE9HFchUkog4iuoGINhHRWiL6HJGfxFWFc26Jc+47AJ7dGb8eOGCECcClAE4DcCKAGQBKAL5W412I6qH2dAATAFwEoOic+xcAjwK4sLbC/csuPus8AJ+o3SsF4CEA9wEYD+AuAIvF2GUAXgWgDcBXANxMRDsi5C4C8FoAiwAcD+Bt3nNuBNADYF6N/1YA797FOdYfzrn97h+AVQBO8f5vJYBXi+u5AAZRDUf4MKp/7EU7uddDAC4Y5VkLAZTF9c0ArhbXlwJ4XFwfB2DTKPdbCuD0Gv0ggPcK3pt3PAvAbAADANKC/z4Ad7/Iu1kEID8Wf5e6hqDsLVA13XUmgLuIVOxJgOpqcT2AKQBuI6JmADcAuMw5v7TXLqNT0EM7uW4Wc/sAgI8BmFX7r2ZUVzQAmAZAFpqU9GwAOQBbRDZvAOCFPZzzXscBIUzOOUdE6wGc45x7bIRhlwO4nIjmAfgVgCWoqpG9dtJNRAcDuBrA6wA84pyLiGgpOHhrI6oqeQdmCnotgH4A45zbP07jDySb6ToAVxDRTACoGcVn1uhTiOiwmvHaC6AMrvnXiapNsjfQjGpc2xYAARF9EIBM4r8VwCVENIWIxqNqhwEAnHMrUVXBi4mohYgCIlpARCfu7EFURQ6oFhQgohwR+SWH9yoOJGFaDOC3AH5PRH2o2iPH1HjTAdwOoA/A06gaybfWeF8D8B4i2k5Ei5EgnHN/QVXI/4zqKjS3Ru/ANbV5PoPqRuBOVLf4O/AuAO2o2lldAG4BMHmExx2Cqop9DEC2Rv91hLF7BRbPtA+BiM4GcIVz7pAXHbwP4kBamfY71NTXaTUf2SwAnwXw0xf73L4KW5nGEETUBuAeAAej6ga4A8Alzrn+MZ3YHsKEyZAYTM0ZEoMJkyEx1NVp+buvXhXr1IYtyxSvUdQVap6hmw8PzVwQ01uho0EqRTYvqMJ1ioKyTkYsF0RxCi/hMDvIxSnCkt8mQ5SN9sobRwW+JjGvwHOsp0TxiEygXT9p6Qrynp0S7yQVMo+8yh6RSJj0ynUiK9aLgteGo0e0FekfP1Hxnm09OqYvfv9Zu5SRaSuTITGYMBkSQ31rDfRsi+lxfh0hsXQPdm1QvMHurTHdEOl8/JQ4WpP3QEGrjEj4lV1Jq4mGLKtHIq9jkuim5HeDEg0K4JQK0XMMg7KgdYlFCrkeVNnvKCU7D1RUDwQ1LhL1BYoVfZOiqN2EtPfsHNeNahw3TvPgddbaBdjKZEgMJkyGxGDCZEgMdbWZ2sq9MZ0rFxSvUmE7ozywRfFyYr+b8uo6RcJwkeWNyekCjiTqWDqvcXMllOWfvdpH4nMlrxZmKRI1mYjtj4xXPDKXZjum7N2jQmybBJ5Ng5DdBmGKxxX9hs+iBhN53S5lm4yS9042i3e5orNP8Z7pXoXdha1MhsRgwmRIDHVVc5HwuA5m2jVTNPxLpfVyLDsfEXlTFuX/KiHfv+CVGiyIxoORd4+A2vjCyySKRKwapbT3OgxlvWmmh7xxXVlWJxXneeZlY0bPsz2Y58/li0xv26aDCvL9rEaHKtp8GBjgktLavQB0CV/Edm9dmeCVn94V2MpkSAwmTIbEYMJkSAx1tZlWNnMsfNFpu6JUEs2aS7ruQl4ULh7wjhIKJbY5CuK4o+j9TIZk16XygOKlK+yyaMzpD04cz9v1qeO0ndeUFp0whaujOKjvX+zh0//egrZptgtbqLukn90j3kl/QTRnRpMaVxTuhkqo7a5s69SYTlW0LZpKsZ03LptTvKCw+0GTtjIZEoMJkyEx1FXN/WoLL53dznt01BKTrVGjYpXFKXzBb8Dn2B0guwTIZoJVHquaSeP0kr6wnaMGprXr5X1CK6uQRtLqK1Vg9RiKw/owo9VJWUQ29Ddq9b6FOGpgVV7z1hXiLHNsGODvOZDX9w+l6veiHtKigXXaC46rlEVkgPObbO9+SSpbmQyJwYTJkBhMmAyJoa42k2vlmg050nZRRbTC9HsNk7B3Mp5LwQlbqwA++W5Mr1fjjp7An3t5s+52OdNxh0tX3q54Qa/oROXZYVQULowC84KC/gKh2JKnSEcGNGd4XpM9e3BOlo95nhNHPqvDZjVuQz/fv+y911Ac36QD7XIh4XIphFoUHHa/2pCtTIbEYMJkSAx1VXOVMm9NQ/JTsUSQG+mtryvxkpuq6KU6FMvxxMbumD52tv6dvKyBc9w6ejsVr9y/OaZLXtPDSpZVYiVsUbwS2CNeFrlxYUp7uVMRq99ypOeV7me3RC7SeXkz0uyKaGngz7U16W18NmSP+KoeL+hQqNWiF/jnxDunQLsCyI3adXunsJXJkBhMmAyJoc49ell2vdUegSot6e2GRCw0ol7Fm5LjePHXiniuQ6LNalzQyeOKnid4MGBVRo1tilfKid1iplXximJXVQbfI+20ykiXOO+v2NejeKntIt59SLHQ0M87xEmDrJrDVv1+Us38MssVvZtbNSAOwlN6/qGYZ8ZTa26YGfLisJXJkBhMmAyJwYTJkBjqXAdc5Hd55bflNXneV+kqaGnSUz5iJm99FwRco6B5i65XIAPIujLag9zfMSumXYO2Kyoil23Ai3SogG05neigvfTZRr5/Y5v2omc6uJRN3/rnFa+3c3VMj6+w26N1ULtHZolkiWLHfMWT0RmbS9qlIM2kBqddChHMZjKMIUyYDImhvge96kovo4HwPJPnfI0i3jMfMlOrqINSfDBL29kd0E96Se9Od8T0UMd0xetvm8T3yOlD4JIo4VMo6L17WvQIzIZ8GB0Eeo4bN/Pn8v1bFW/aZHY9jFtwuOINifzBTRufi+mJRZ3K3SrS6ac36gpws1r5+2ze4h30iip2gfPWFXMNGMYSJkyGxGDCZEgMdbWZ0iLPLPLkWJW88ayrKaJm4aJGnWc/bcsavijwKfu6pglq3MCkg/hZmUmKlxKBYX/4w4N6XqJczbHHHqV5RbanwoDtm1/deZca9+vf/pGnWOxWvGlz58T0O9+p+1YfPp2rDhe6umK6tH2TGpcDf+9Mnw4KnNbKNll7WrssCpC1Erz6C3uwzNjKZEgMJkyGxFBnDzirAgr8ym6C53lj509gX8HUwhrFa+3nCr49IkAt36rVXH+OowGaMzrIrXMlb7tv+n/XK9555/+fmG5J68+Vhaf+/nvvj+k77/yNGnf2W87i+U/RVW1/cBs3cbr15jsUb+GH/jGmJ0xkz3apV6t6V2G3RLag1WhHwG6JCY3a7bGml9W0S3nVdSsWA24YQ5gwGRKDCZMhMdQ50pJ1dsnbe5LILeto1G7/6TneCodb1inednFav7lVbPlzuoxeqiiq1TZqe+CX9/2WnzVLH7WcdNKr+XNeqZxA2H2PP/FoTB92xMvUuDe+5dyYbgz0WdHGDXwU8vNf/U7xOgf4qCiczMck5c36uCYjjmhSRf3uGkSzoPHZKYq3LmAXTMX7e4S7n09gK5MhOZgwGRJDXdVcKDzbFQwLDYjJ9kY9rTaxHIdFHVw2JJbnQiPnj5Uz2tubEyfkK1esVLwnnngipt/3nvcoXkcHRxts27ZN8WRjnihi1dncpLfgReGZ90vVUEoUffd66Q2Ke5ZENAN5RfFdmd8J+ZWKxXtuzOntf+hYJYbe3yMILGrAMIYwYTIkBhMmQ2Kos2uAbYB0Sj86FIECbQ2al8mzbs94jQcHxeeKsgOC981aRVLCw/fep+clugRs3qzrENx/Px+TLFqkeweHwnZJp/nZzmsWFIhIURfqiIgoxd/Hr3NA4pRf1ijIwt/Giw4IkXZ7RCJSlLwQ1rToChF6zX2i3TeZbGUyJAcTJkNiqKuay2RZFeT9Hr3C5dqQ8XiiYDuVvVb0IkctEiffqQadc79xDXvRH//zw4rX3MIuhYce0rx169jjftZZZyneOecIXSNTAAAgAElEQVScg52hUtFzVGpP79wRZmTPOq+cjzi5p7LsRew9ULoovESAsrj26+cqNe1NrGJ5c4axhAmTITHUN28ukDHgXgkXcR0Gerl3wmscOb1bCcXvQRYiTQdaV957v9jBeUXfP/KRj8T01KnTFO9b3/pWTD/4oI4PP/XUU7EzRNHIJYEiV1S8lFA1oafmwhKr96zoq0IlL3AtxSnsZb/qnkhvzw9rCytUZ+D3+LPKcYYxhAmTITGYMBkSQ32r7Qo7puLZPjLV3a/8WhTF3J1nT6VFBd9MSWyD8/oeK1aviukZh81TvLnz+Trj2Vpz5syJ6WXLliqetI3kjry/T1fNzWQ5mE0WyAeAnh4uSxh4LoUm8U7SQ5xEUMzrhIsyRDRAShetd2nOjesd1BEX5UC4T7zvDdJBdrsCW5kMicGEyZAY6qrmSuIQ0ovvQhCIQDCv8poj9lBXvANJyHsO8oFqxvPgNjdzzlvnap17t3kTl+KJKnpL/OgjHNs9a8Zsxeto48C5eXM4r+32O25X475/w3diuqVZx2/fdTfn2B3/iuMUb+Z4zrGLXmAVmxvwqryJv+JQk75/d8AqsH9ImxZZ4bLwg+GscpxhTGHCZEgMJkyGxFDf4Djhog+9sjmh6MPbPaRtplSjOOJIrdI3LYqtdh/noKWdznF77etfG9PXX/NNxfv85y6Paf8oJCe22u+7QCcbROJY49TX8dFK/5B+9r0P3hvTYaS/26tPOCmmzzn3DMUr9LItF2zm6IUJXjXigQzPsa+1Q/Ge7+P3Wq5ot0FO+DMiaFdAZMcphrGECZMhMdRVzaWy7I2Nytob68Q2dXu/3voWJvHS3Z/VOWlNZa48mxvgQvJb1y5R4152BFd9+4d/+7jiLfnjQzHd4MWmv+KVr4rpyZMmK15/ntVZIPLVzn7HO9S4E97AKjDnaY8pLbyVD/I6L69n5VMxPaHIKryQ1flv5Qk8r61Frcq29vB6ETRqt0FFmhreqUPgR/HtAmxlMiQGEyZDYjBhMiSG+kZaiijAMKXd9bLC7kBJb1PXlNnWmjNhjuKFfU/GdHuRT+sLG3U9gaEGPk5ZNGOu4h0pq9x6Ns1QiW27vpJXJ0BEJ8oK/66ot+6Tmrm5T6sXLZHaznl6+fXLFK+9jyvstohT/MGmJjWuR3RYWLZW26Kpdi4tVCIvMiDg9+VC/bkgMpvJMIYwYTIkhvpW25Wpy15PWJLRZV7l1yWbeAveNk0HtqWaN8Z0c4kLqk8tapW0fSWrvaEerUbz4zkaoJLJKR6lRMKC30pIBOSnxKl7zkvRbuznvsKVLt07uLhuFc9/UPfvzYp7lkLhEpl+kBr3p3XsHhnMzFK8dI4jD0peIoJsfuS8RITQGvEYxhImTIbEUN/0cFWFw1dzgg70tAYKvMQv1zXTMW7O0THdJQ5pW3p1T7a2Cu9ccl2rFK87z7xyky4cj2b2KDuvGp0r8/MiEXMeDuoY8KLYsVVE0VMAaBIH1U3ebqtPFLGvzOJd2T1rdFH5jWX2gLdOmaN4eVEFJZPS8ecyTa8C7R0f1vRvF2ArkyExmDAZEoMJkyEx1NdmkgFX0ciNXpwXzB4GHDWwsUtvn58J2Y0wfwYH9Zc3rlDjmkTgXM7LCZs0wK6HwpB+JYV+vi6n9G8vEmVukOfvExS8iIiKKAkUaF46zfeM0tqz7aaxp/729VwSaEOhXY2bPffImB6A11AHnGSRDnQ0RiAC9UpOPzty2u7bFdjKZEgMJkyGxFDnovJiSR9lHPk5W6JUmgt0RbhVXayiXAtvkQ+apIPowsbVMZ33XAO5Mm+ZG7wtcbNQx+WS9hIXRDq6zrfTKlw2sC9m2hQvap8Z02vKes6Pr2H3RmrSgpg+YvJCNa5c4s81ePlvMs2QvNapMic/8k4kyl7u4q7AViZDYjBhMiQGEyZDYqirzUSiN1xqd57sSiPziHvxbhjg7WzfgLYBZjTwSXvbhImK11riI46S5zbICfunXNb3LFbkGRDTqZS2N6IyX28e1JEHK7hVHDaU9W978vwTYnrKxKkxXfCC77Kq+Y5XwtHxiy6VPbeBrNLrfS61B6JhK5MhMZgwGRJDffPmUiIG3K+pMwrSokItectxUZy0lzO8Re5Fqxr3TJFP2lMlzRtX4uW/Na1VCJVFbpza5AOBKNM+MMj3X7dpqxrXNcDe5eYJMxUv08FtXKd5J/4Fx+9oUxer+pSXuh047dneVTg3spqLLG/OMJYwYTIkBhMmQ2Kos2uAdkq/OER1fu9jgczFExGajvQ2mESlfqroqMKhMp/C93mt3Pv7OWFh6WO/UbwnH/l9TOdFZMD4GYeqcW8498MxPWGaztkria37QMn7csI0ks13/Hegoyy8Mo2CN6zvriw14H/OWUKBYQxhwmRIDGOm5vwKbaOhJLapgRdRIFuFpiNWUeSd3IeOt/HknZAHaT7Jj3JTFG/GVFZLW9fqgLsU/hDTjTmOZpgy8xA1bs4CPuUf8IrdQ7RLTXt/jVB4qNNizr42LMqK/M73gPN1JfJV4MhwzhIKDGMIEyZDYjBhMiSGOlfb3TPZdcLW8pMNZHt7edRCvu0gPua831BFRCUMVbyTdZG3WCzrJMajX8a20fQFTD/fpYZhoI+PWspOv/KysE2ctx0vi+9QFjagc3r+8vv4DRzkdeQzR0FgtQYMYwkTJkNiILcbS5/BMBpsZTIkBhMmQ2IwYTIkBhMmQ2IwYTIkBhMmQ2LYb4SJiPrFv4iIhsT1+S9+hxHv+xARXTAKfyERlUfi7ysgon8goseJqJeI1hLRl4iorn/f+pZufglwzsXhkUS0CsCFzrnfjt2M9jlkAXwEwKMApgD4BYAtAP67XhPYb1amFwMRhUR0GRGtIKKtRHQjEbXXeE1EdDMRdRFRNxE9TETjiOgqAMcB+HZthbtqF55zMxF9nYh+Q0QDRHQvEU0iomtr915CRIeL8ZcT0Uoi6iOip4noDMFLEdE3iGgbES0noovlKkhEHUR0AxFtqq02nxtptXHOXeOce9A5V3LOrQVwM4BX7/kb3X0cMMIE4FIApwE4EcAMACUAX6vxLkR1FZ4OYAKAiwAUnXP/guov+ULnXHPteldwHoBP1O6VAvAQgPsAjAdwF4DFYuwyAK8C0AbgKwBuJopz2i8C8FoAiwAcD+Bt3nNuBNADYF6N/1YA797FOb4GwJIXHZUknHP73T8AqwCc4v3fSgCvFtdzAQyiGlH/YVT/2It2cq+HAFwwyrMWAiiL65sBXC2uLwXwuLg+DsCmUe63FMDpNfpBAO8VvDfveBaA2QAGAKQF/30A7t6F9/Oh2jtqr+ffZb+xmUYDVeOBZwK4i0hWt0aA6mpxPap2xG1E1AzgBgCXOedGLqw5OjoFPbSTa2nffQDAxwDs6EPRjOqKBgDTAKwVn5X0bAA5AFtEuHMA4IXRJkZE7wDwOQAnO+e6RxubNA4IYXLOOSJaD+Ac59xjIwy7HMDlRDQPwK9QVQE3YnhuUGIgooMBXA3gdQAecc5FRLQUnH+0EVWVvAMyd3wtgH4A45zbtdN4IjoLwDWornxLX+r8dxcHks10HYAriGgmANSM4jNr9ClEdFjNeO0FUAbXCuxE1SbZG2hGNfttC4CAiD4IYL7g3wrgEiKaQkTjUbXDAADOuZWoquDFRNRCRAERLSCiE3f2ICI6HcB3ALzFOff4Xvo+o+JAEqbFAH4L4PdE1IeqPXJMjTcdwO0A+gA8jaqRfGuN9zUA7yGi7US0GAnCOfcXVIX8z6iuQnNr9A5cU5vnM6huBO4EIKtQvAtAO6p2VheAWwDortOMz6Nq5P9G+N9+mtiX2QVYPNM+BCI6G8AVzrlDXnTwPogDaWXa71BTX6fVfGSzAHwWQF1XkyRhK9MYgojaANwD4GBU3QB3ALjEOdc/6gf3UZgwGRKDqTlDYjBhMiSGujotKwNP7uc61Wv4LC51/qRfB0nUl/ISKCGiW/agJFJdEDYduUszs5XJkBhMmAyJ4YA4m9td+CUQZa0onyfrHJSdLmecy4kmgoJVHNQNFoNglBoIo1wNU5f7OGxlMiQGEyZDYjBhMiSGv0mbyYesG+XX2iRRDjqX1V0sm9K6dcUODJQ2qOuosJnv59lBkcx7Ic/1EJrNZPgbhQmTITH8Taq5YaqMZBV//fsqF7lEYRjoJoqzpnXE9Psu/AAzKrr7U6XCnwsCXeYwEJ2uKv68djr7fRe2MhkSgwmTITGYMBkSw9+kzeTbRTJA0A8WTMnO1F5zyE9/5uKYPustb2FG5HXTFI2uA9KvvFLhsWHoz2snk9+HYSuTITGYMBkSQ11jwCsDT4iHjbbxHeX03AsukxX+M6mRfxsV0QmQfO0umkavX9enWFOmxgVN8NAjyxTvr395jqcl3uNhh85W41538tF8//VPKd6EyewqyGX1/EsVETgnMtlDL7KBRGcokHYvjBZwJz/ngpG7OFlwnKHuMGEyJAYTJkNiGEObadhURvukoLX8R6K7o4v4HqlURo2jkK/zhamK97azPhXTf/qD7nbZkJrGT6as4kkPAIWy2bT+LhWRU1mhTsWbOZv9DQ//+ceKly+yjZbLcJfPdEYfyYimVHBeB1BpM0XkVxDidxmMYlyZzWSoO0yYDImhzmpuF/PmvO2/37xZohyJE/lUQ0xv69T3+OxnfhLTP7lVl3rMBZNiOvSaF4bCY+2/q4psiiyC6IYlJYDnSM6rAp1iXjnarlivfDUnLPzPtz8U05Om6218VPGL5Yn5i6HOi3oY5tIfAabmDHWHCZMhMYyZmlNe2+p/jPxBpfa09zrIMa93W0tMz56pKxxn8TKmgybFKxeFd9n7fen4cK0mpAqMlNfe+y7iu1U8NZcKWNUEgdYmhQqrvYJ7PqZXbLpVjWtq2SIe7T1b9gQmPX+K+NnmATfsUzBhMiQGEyZDYqhrcNxop9sSznmB9SJ/LPC23b1d7A6YPpWbO7WkX67GBcJTjoq2W9LiNXjlBOCELRR4eW2qJr2YFgX+OKZD8j34FUHrZ2fSnKfnKgtjetZk3RVj+drvxHRrh456kLl4oXfKIF0YSVjOtjIZEoMJkyEx1Nc10P8Uuwb87bNYaP3tszi/RX5Qb+uPPOyj/Lm+Y2Laeaos08htSfr6NipeU4pVonMNikch672oXNQ8+epIuZrVOHmG6n9vJ78r+fl84p0IFqV0Md68qFP/xHPXKt60yawq5aH47sBcA4a6w4TJkBhMmAyJob6ugWF2kuClBM8/uReBZ91bdVmbaGhuTJdKfFxQJB24v27Lz8XDdBTCnElv5M8N6LYlmcpE/phfKVd8H2U+ef2hR02dUPaUd3fhKwhkxd6oWY3LEB8Vda4Zr3hTp8gSi/r9F8T7yqReuijYymRIDCZMhsQwdunh/vZZ7qy9aW3fwmrjuEXnK16WjorpUGzjL/roRWpcRLytL5XXKd6qztti+ry3/rviPfB7diOEbgI0hOpRD/M84NKH4G//haoMvGC1SJoF4nNR5Ec2NMb0Ka99v+ItWcmugkkTvaDDKNmiPbYyGRKDCZMhMZgwGRLD2B2neI8tiSjAVKiPNEpDc2J6wcSPKl4KnAOXjzi3zIVdatz0g/gI4rGnvqV4xfLqmE4H2i763+sejunPfvIW/ewKPzsQUYvSdgOAcsT2moNOGsi18ryG+vRRUYBmdbUDUeS5Hojb+pYD/b2Xd34/phsaVymeE4kIo9UksOMUQ91hwmRIDPtM3pxMqU5Ro+K98x1Xx/SDd3ufq4iUbVk1N6234Pkyq5dSoEvjdA3+MqYL5RcULxWxx33NSu2yePXR7H5IOS6jEzo9rizUUpm2KN7GwZti+jXHn6N4K5dySrt0SzgvsUE1EkoXFO+Us1kVf/t771U8lPgkwBIKDPsUTJgMicGEyZAY9kmbKajoLfKEDk6ozEXHKV4aHGEgS96UK15pGZF074IBxcsTl9HZ2qeNsnL0bEyTn21Q4oiFmRPOjGlpPwEABTn+SLRN8Tb2/UAM1Nv6Vx75TzG9YcW4mA78t+j4HZQqunFiH56M6S09NytemuQ7MZvJsA/BhMmQGOobNSAjBfwqZmIhrXhhYi4SW2RP/tMicK5Q4gY4FW9hDoU6T0F72CulWTE9vvkNire5j90GDtqlUAZX293Y84uY7mh+rRqXSx3Kz6r4dQiYLJV0VbljXzEvpjetENXnyr4HXLgGAq2KKWLV392tc+omThAumASsHVuZDInBhMmQGOqq5mQsli/FsodIxituGor0cPJyqItlPtyVmeOhF4QWCLXqSvr+WWK1l/Z2YtNbz4rplZtu15/LspqLIi550zX0KzWuteGEmG4M5ymeCov35twxnne1UdQd0/5Bsjz4DQL9J5U70MmTpymeE6cClh5u2KdgwmRIDCZMhsRQV5spGKVqblrYSc7r1xY5Di6LAr0tDsSWX3oDyLcCRPSXV9VGlbyRQW4AkIm4Eu+cyWcp3s/u+nJMH3eC8L4Xl6txvUP3xPTktmMVr3+A3QGpBv29D1vEHvYoYE+8K3sF85X32k9K4AiD3l4dmNfcJBscvXSryVYmQ2IwYTIkhjrnzY2yHAtWinw1Jw4vh3nORfUzoR6DYanc4n6BDiBzJHPXtOs8jPgV5ULdc+XMN14e0x++5PUx/cX/OFuNKxQ5xryz9zeKt3o1p7FPaZikeAsWsJqT/VdCtKpx8pUEaf29cw38fXKNuvcLORmb/tJhK5MhMZgwGRKDCZMhMdTZZhKy69caEG6DSqRtmg/8I7d5v+VbaxUvcHKbLHLLhlXzlc/2WsVj5Er9skaBX1KnIeiI6eu+wUcoD9x/vxr3u3uuEjfUCQVTJ3NXBb8Uz9y5bKNlMmKOBa9Lg3B7FCo6wO7Kb3wypitl/V5T0mxNoOyArUyGxGDCZEgMdY0Bd0OcHu68ci4lkQuWTunKaAGmx/S0pncoXopEOrdQeUGo1VW5LFrWp3OaJ1wKJWzWk05x+Z1KRVfbfeUJXKV3xhyu2DZzdrsad9QxPMdTT9Ue8EhU26WU75bgCIOZHR+I6aA8UY0LxBa/HK5XvPW9P+JnBZoX5UVQ3SgGj8WAG+oOEyZDYjBhMiSGOrsGRAkXr/KuCKZEUSQGAEAA1vVD7jnFaxBlZ1SjGa8UYEokmxVLg4onGw/6LoWVqx/l+zdqmyMI+YhDNjb0qwqn0iLK07NRZQPBctlLlsjyd6uU2LZKpfT9K463/LkWnTc3MCDKBWW86NNAVjW2qAHDPgQTJkNiqKua6+nnvK1tm7cq3kEHzYnp0FtxixX+3PaBvyje1HYuQxM4DpiviEQDAEiJJT3tBeQXRC5bKtDb+tkzz4jpn/7ya4p34mu5AH1UFn3pvF67PT2cDPDkE88o3tYtrJb+8tgS/bkt7ElPB+wpL5d1/luZOMBu3dp7FS8iGag38tqRhIfIViZDYjBhMiSGMauCMvy5I7cXJXEiWRjSKuT8d3CA2uMPiLuVdCBYWuTG+X3X5OFu6OXslYXXm7z480rE1VTSadGW3ru/zGXzPf/lSMaw62dnMnxdHBTFX1P6MPd/rueisQsO0V76RUeyKi5FXr+8soiLD0deV8wDbqg7TJgMicGEyZAY6ltrQAa+h74aZrso5Z2ey3a7uYzm3Xzrl2J63gwOoqPgMDWu5GTRd/83xA9wupCtSkxwkWeHQVwXZLMdfQ/ZUMevBSCd2b5NVhhiT30UsjvgpNfrxIM58/jFHnLofP1s8c5DLzTAKRfGyJXjdhW2MhkSgwmTITHUVc2lU7L/x7D9f0x6xc/gRPDXtm3dijdp8uSYfm4ll7w59e90v7l1K6T+Gqd4KAo1FOrtcyRK0BF5+ksUj5c81V4eQChy2UpFrUczKVaVpYp3wJ3mYqoX/wu3fn3TmVqFH3GM9MTr+TtRbseffkT8opNYVWxlMiQGEyZDYjBhMiSGuh6nRIN8nBKQNoxkBdkwrXn9A2wnNTboPHs5f1mucHuXjhpYu5z3yK/z+thmRcJCSLrVfeBk8oFv5/GzK5EMsPOPU0a2FcMs20m9+aWK1933REzf8fNvxvRZ556hxpUKHBznlyiUZ1MVz5ajkI9raBQ5sOMUQ91hwmRIDHVVc/nuR0d8mNxar1q1WvHmzuf8sXRK57zl8+wlHuhjddjR0aHGDfWItq1prcqefmpDTJ92ynsUL3B8n9Dpni6NWb5PscT3j6B7sxRESnhzm9YYy1c9FtMbNujAuVSG1de0mZyXVyzo7X9KqNFhf0/h5XZ+yTzZp873GwiYmjPUHSZMhsRgwmRIDHW1mcqDf40f5m9hKd0wbDwzhcx7bdjV2YtsUOOX1JHlcMK0Yi17inPx7v/1fYr3hjdxJMLMBUfoexZltVpxPD+gj3xQ4BP/2372Q8U6/DXHx/Qhhx+qeJBbeVFmqFjWRzJ+cyJ9j5FPzEhU4nXDjor4nmHz4WYzGeoLEyZDYqivB3zg6fhh/+9b/1fxxrXySX4q0GooEKrMX+KlR7kictf8rW5FpG8XvaL1hSHZyEbPuamJ3QFbN+tT/S98/sqYlg6Lc487Wo07agZXgHtmcKPiHX4aV+kNc/p7Z3IcUfDOd5wb0+T1VS0L9T4sMUD1+PMD4OR78DznkmOuAUO9YcJkSAwmTIbEUNdISycC9//p4x9RvAfu/l1MNzToY4uUON0OfKMG8riA/7fs9bGVnyuWte1QLLEN0tysoxLyg2wnUXmN4mUz7M4Iirx1P+vc89S4xlJvTB8052TF6xHmj+8ucTIBVLCiitelQb0SbU/pnE/v3anohlGiBkbkjDINg+GlwITJkBjq6howHNiwlcmQGEyYDInBhMmQGEyYDInBhMmQGEyYDIlhvxEmIuoX/yIiGhLX57+E+z5ERBeMwl9IfiO4fRBE9B4ieo6Ieoiok4iuJ6KmF/9kcthvhMk517zjH4A1AM4U/3fjWM9vH8B9AE5wzrUBmA+gFcDn6jmB/UaYXgxEFBLRZUS0goi2EtGNRNRe4zUR0c1E1EVE3UT0MBGNI6KrABwH4Nu1Fe6q0Z8C1O7zdSL6DRENENG9RDSJiK6t3XsJER0uxl9ORCuJqI+IniaiMwQvRUTfIKJtRLSciC6WqyARdRDRDUS0iYjWEtHniPx8pSqcc6udczvKphCq6cfzdzZ2r8E5t9/9A7AKwCne/30KwB8ATEM1Vu27AL5T430MwG0AGlA93D4OQFON9xCAC0Z51kIAZXF9M4BNAI6s3e8BACsAnIfqmeiVAO4W488DMBXVH+67AfQBmFDjfRzAkzX+eAD3e8+6G8DVABprYx4H8N5R5vp6AD2ontr2ATi5rn+XsRaMBIVpJYBXi+u5AAZR/ZV+GFU1sGgn99oTYbpaXF8K4HFxfRyATaPcbymA02v0g1I4ALx5x7MAzAYwACAt+O+TgjrKM2YC+AKAefX8u9S5q9PeAVVjdGcCuItIxVUEqP7irwcwBcBtRNQM4AYAlznndX7edXQKemgn13E7JiL6AKor46zafzUD2NEacxoA2cFa0rNRXWG3iBDkAMALLzY559xaIroHwA8AvOrFxieFA0KYnHOOiNYDOMc599gIwy4HcDkRzQPwKwBLANyIJHpjjQAiOhhVNfU6AI845yIiWgoOwtoIYIb4yExBrwXQD2Ccqy03u4kUgIP24HN7jAPGAAdwHYAriGgmANSM4jNr9ClEdFjNeO1FtbzujlWpE8C8nd0wATSjaghvARAQ0QehjeJbAVxCRFOIaDyAT+xgOOdWoqqCFxNRCxEFRLSAiE7c2YNqroEZNXougC8C+N3Oxu4tHEjCtBjAbwH8noj6ULVHjqnxpgO4HVWj9GkAd6H6hwSArwF4DxFtJ6LFSU7IOfcXVIX8z6iuQnNr9A5cU5vnMwAeBXAngILgvwtAO6p2VheAWwBMxs5xBIBHiGgA1Y3Ik6jainWDxTPtQyCiswFc4Zw75EUH74M4kFam/Q419XVazUc2C8BnAfx0rOe1p7CVaQxBRG0A7gFwMKpugDsAXOKc6x/1g/soTJgMicHUnCExmDAZEkO9nZamU/dPWOEKQ31xQByn7Avo7+cNWGNjo+INT2k/MPG38S0NdYEJkyExmDAZEoPZTLuBUlF3Brj99p/F9AsvcJhRa6suy3P+Be+O6bY23R3hQIKtTIbEYMJkSAym5nYD5YpOn+vv52LxLzy/PKbbO9rVuG1bN8e0qTmDYRdgwmRIDCZMhsRQ73im/fCgl6f821//WnG+cuVXY/pVrzohplevXqvGZUXngX//wuWKN2nytERmuZdhB72G+sKEyZAYTM158JN87/rFXTH9pSt0JtQ73s7NcU4/5ZSYHhjQDXu+9o1rYrqxQTfb+c8v/XtMT5w0FfsoTM0Z6gsTJkNiMGEyJAazmQBEFW5sePvP71S8xYt5+3/++e9UvJNOZHdAXy9HWmbS2i4i0Xzxq//9dcVrbWmJ6S//578rXsf4iS869zrBbCZDfWHCZEgMf5NqrlLRfX5vv/32mP7q165WvAvf//6YnjlTb927t3fF9KzZc/j+ke5nt3z5Mh43U1fv+eZ1347p9nEtivel//hiTI8bNwFjCFNzhvrChMmQGP5m1FypxDW0fvazOxTvmv+5Nqbf//d/r3gvP/bYmN6+favipTPc7rV3gFVnJq1/oxSVdvoZAEil+fqqq76qeBMmjo/p//giq7z2ceNRZ5iaM9QXJkyGxGDCZEgMB6zNVCjk1fXPfsrV/b71f/9X8d7/gffE9BGLDle8/r6BmE5ndP7Flu3Me+a+b8R04/hD1bgjT3hbTFM0qHhhir3j0lMOAFf+19dietpU9oZ/4QufV+Pa2/e6DWU2k6G+MGEyJIYDSs0NDbEK+cmPddHa737v+zF94YXvV7yFCxfEdF9fn+I1NnDLtvWd22aXdFEAACAASURBVBVvxUP/E9MnzOD08M3dngec3hrTLz/pXMULIlaVQeilMYq3tViovFmztCf+85/juPK2tr2i8kzNGeoLEyZDYjBhMiSG/d5mGhzgoLQfCzvp+z+4SY37p3+8MKYXHKwbRPb0dMe0tJEAoHMb3/+FB3SjzONnrorpVIZP/DOBjkpYtaE3ptek36F4Lz+RbaiorO21IBSuAmGGLb5Kz2PevDkx/bnLL1O8lhZd92APYTaTob4wYTIkhv1OzfX396rr2277SUzf9MNbYvrDH/onNW7evLkxLdUaAORyDTG9aatuW7L6YY7ZfsX0FYoXZOKGlyiVWQ+F0K6BTJqv12wqKN7azNtj+uhXnqV4Uu2lUuw2qFR0bt8VV7Lb4DDh5gCAyy77dEw3Ne1xOR9Tc4b6woTJkBhMmAyJYb+wmXp72cb56U9vV7wf3izspA9/MKbnzpmjxkk7KdegOwhs6OyJ6dUPf0PxXjVrVUyH6QbFK4qqhPI1EvlNyfk360dhrt7ANtqG3NsU78hXnBnTlRLbiqmUzsurVNgm+8qV2m3wiuM5UvQT//zPipfz3CCjwGwmQ31hwmRIDPukmuvt7VHXN93E3uzbb/+54n3sYxfF9IwZM8U9tAshm83G9PpO7RpY+TDnyr16tq76FqZzMV0ua/XlHP8WSSmCyBun7qh4aREosHrjgOJtzLF3XKq8qKQ95Wlxk3JFP/vK/+IkhWOOOULxPvXJf43pTFarcA+m5gz1hQmTITGYMBkSwz5jM3V3b4vpW265TfFuFUcm//zxixVv7tzZMd3bu/PjBwDo3MJ20gsPXat4Jwg7KZ3JKZ6ykzzLwTnxH45tFf8XGol37IZx+Todapts1QYuZ9jVel5Mv+zY16txlTIf0TQ2aNunVzRV/K//0kmeJ7/2xJi+6CMfVTzPfWI2k6G+MGEyJIZ9Rs09+KcHY/qzn/2c4v3rv14a0zOmT1e8gQFWbaEIJusf0lvkP/2CK+WeusA7/c9yf7iK12xHqi8fkVBzJL5a4H0mElqi4v9+xVD/T9EkNO6Tz3P5nsJMreqPPvoVMV0s6Eq/oVD3AwPa9fDlL/M7ueyyf1W8k056nbw0NWeoL0yYDIlhn+k3Jz3UgbcTky1L/QNcpRoE3ZjT95h1GBd9f3799xTvsJm8G4rIfyW8wg83CXa+mxumFOQhsHcPR+IQ2Hv01u28E+sKjuf5zjlEjYuEao68qnUNooTPkmefVbxKxLvHTDqLlwpbmQyJwYTJkBhMmAyJYZ+xmQ5buDCmP/7Rjyjel/7zipgeGNAB/2e/9S0x3SciBchrqHPoka+J6SWkg8KeWs0e8SNm63bzpYgD0YZZTMI+CUhwA/LGSReCRloEEXT36O/2RNfRMX3kazlBor1FRx6US2wztbS1Kt4jjzwa09/53+8q3kUXfSimjz76WLxU2MpkSAwmTIbEsM94wCXKZZ1efd9998X057+g+4uceiofer7rnXwY6pfGKZdZFWRyunj70meeiOnKimsU78hZ/LlipGOvR3p35OkyGa+WDjVTqrYnurSqOepkVm2NwhvuPA97SzN/nz8++CfF++73bojpj39Mmw/nnsMx50Gov5sH84Ab6gsTJkNiMGEyJIZ90mbyUS7zdv3hhx9WvMsu/3xMH3/8y2P6A6KBDqBtqFJJ22S5Rt5OL3v2KcUrvcB5dEfM0nUCShCBaOoYQ3/NQBhR3b3e9n/7MTF91MkfUrxmccJRFHMeN26cGnf//X+I6R/84AeK9y+fuCSmzzxT1zIIg1HtJAmzmQz1hQmTITHsF2pOwg9ee+LJx2P6M5/hqmmHHqpP1j/yYd4W9/bonDp5ep5r1G4Dqfbyy3Tq+BFzWO2VItZJ6UBv3bt7WMU+vu3linfkyZzS3tKotUmpyKqtrb0jpu+//z417qYbOa/wkyKQEADe9KYzYppIe853A6bmDPWFCZMhMZgwGRLDfmcz+YgitqGeeoqPRS6/TB+7zJ7DdQgu+uhFijcgcssir3+vPHpZtkxHKg48yzbUcfM4WL+rT9/jL5uPiumj/k7npzUL70KxqCMW2tvZBXD/Hx6I6Rt/8H017jOf4VKDp53+JuwFmM1kqC9MmAyJYb9XcxKR2OI/t+wZxZOe8qnTpineRz7E2/OhIZ1bVhKBZ77b4IXnn4vpbU98JaYLGd1u/mWv+XhMtzXp369Uba2tOrDtoYceienv3fDdmP70pz+txp122l5RbRKm5gz1hQmTITEcUGpOQ3uhnxM7sS9f8RXFa27imPAPflAXo5dqyM9Jy+a4UsgLyznlvKVFq8OJ43lX5rd7bRLPfuyxPyveTTf+MKYv+WdWlaed/mbUGabmDPWFCZMhMZgwGRLDAWwzeQ8WQfgrVzyneFeKQuyFgg6Au/TST8T01q2dilfI89jJU7jUj0xeAICNG9fE9NQpuiTQX59eEtM/uvUWxfv4x9lOOuXUvb79Hw1mMxnqCxMmQ2L4m1FzGnoa69bytv5LX/qy4uWF2nv3Be9UPFk8dfz4SXx375329nLVt+eeW654t93GxWA/9alPKd4YqzYJU3OG+sKEyZAYTJgMieFv1GbywW6DTZvWKc6X/uM/Y7q7Z7viffLST8Z0WfTozWYzatyTf30ypq/75nX6Hp/kBIAz3nz27ky6njCbyVBfmDAZEoOpuRfBxo2rY/rKxYsVb9Mm9oj/279xwNqyZcvUuK9/nXuWXHLJJYp3zrn/J5F57mWYmjPUFyZMhsRgwmRIDGYz7QY2bVylrr/5zW/F9JIlnMAQefUQ3nX+u2L67W+/YK/MbS/DbCZDfWHCZEgM+0xR+f0BU6bOVtennc492SZNGi84WiuceOLJe3FW+w5sZTIkBhMmQ2IwYTIkBrOZdgvaFtq+ndvbL3tuaUxv26Yr6r7+FI6YnDp1L01tH4CtTIbEYMJkSAz19oAbDmDYymRIDCZMhsRgwmRIDCZMhsRgwmRIDCZMhsRgwmRIDPuNMBFRv/gXEdGQuD7/Jdz3ISIaMfyRiBYSUXkk/r4GquKPYzHn/eZszjnXvIMmolUALnTO/XbsZrTP4v0AKi86ai9gv1mZXgxEFBLRZUS0goi2EtGNRNRe4zUR0c1E1EVE3UT0MBGNI6KrABwH4Nu1Fe6q0Z8C1O7zdSL6DRENENG9RDSJiK6t3XsJER0uxl9ORCuJqI+IniaiMwQvRUTfIKJtRLSciC6WKwoRdRDRDUS0iYjWEtHniGjEvxkRjQfwrwA+PdKYvQrn3H73D8AqAKd4//cpAH8AMA1ADsB3AXynxvsYgNsANKC6Gh8HoKnGewjABaM8ayGAsri+GcAmAEfW7vcAgBUAzgMQArgSwN1i/HkApqL6w303gD4AE2q8jwN4ssYfD+B+71l3A7gaQGNtzOMA3jvKXK8H8CF/znX7u4y1YCQoTCsBvFpczwUwiGrcyIcB3Adg0U7utSfCdLW4vhTA4+L6OACbRrnfUgCn1+gHpXAAePOOZwGYDWAAQFrw3ycF1bvvqwD8uSa0YyJM+43NNBqIiADMBHAXEcmT6wDVX/z1AKYAuI2ImgHcAOAy59ye2hayUurQTq6lffcBVFfGWbX/agYwoUZPA7BWfFbSs1FdYbcQdx8PALzgT4aq/VKvBfBB51wkxtcVB4QwOeccEa0HcI5z7rERhl0O4HIimgfgVwCWALgRezGXj4gORlVNvQ7AI7U/9FJwlN1GADPER2YKei2AfgDjXG3pGQUTARwO4Gc1QQoBhES0CcBZzrlHRvtwUjhgDHAA1wG4gohmAkDNKD6zRp9CRIfVjNdeAGXwjqcTwLyd3TABNKNa/GkLgICIPghgvuDfCuASIppSM57jOtHOuZWoquDFRNRCRAERLSCiE3fynM0ApgM4qvbvbFS/31Go2ll1wYEkTIsB/BbA74moD1V75JgabzqA21E1fp8GcBeqf0gA+BqA9xDRdiLSZU5eIpxzf0FVyP+M6io0t0bvwDW1eT4D4FEAdwKQhcjfBaAdVTurC8AtACbv5DmRc27Tjn8Attb+f5NzruSP31uw4Lh9CER0NoArnHOHjPVc9gQH0sq036Gmvk6r+chmAfgsgJ+O9bz2FLYyjSGIqA3APQAORtUNcAeAS5xz/aN+cB+FCZMhMZiaMySGuvqZPnNv8j6d0O2cdt7PpCKuC96BesENxXQ5KipeLpeOaXLaGRiUQ352icelK/q1puTnQsVCJcu07rOpIRWIr0yUm3Y33rD8NjTK5774d1afyVBnmDAZEsP+f5widENU4rW65OmCErH6cll9JNfYzK8hndPdBfr6e/miohURRTw2EJog9LRC6Pj+Xs9ouArP0wV6ziNrHk/ryLO4MdxP2cpkSAwmTIbEYMJkSAz1tZlG23+6PY3BYfsnEHRDRv9Ocinek5dIGy75/sGYHugZUrwwFPZUoPf1WRFBm2XPAFKhtsnCiF0RZe8VFCL+3pG/5VdxSZIO/IEx6fyo3tFe+cisPYKtTIbEYMJkSAx1dg2MsrAqFeirvJF5QcAqJExzKFDZaS+3dBuEKb39b2tsjOl0tkE/WbrSI/1sEtcktvhRUT+7VGG3RNHf1Qc8l+HhtuLZo4biyjXBcy+M8jk1MoFIX1uZDInBhMmQGEyYDIlh37GZFEY7P9eoENtJlMrHdINn++Qa2C7y7YhyiX9TRR00gEKRt/m+WZEWby8rXBGNLdomy+V4LhXvPKVzC4doR15IQSSOhJxwnTin76EiCrxJRmq92EX7aQ9hK5MhMZgwGRLDPqTmBG80T7kHSovT+oZcTEekv1pvgdXJ1s06xHrdus0x3dnZrXgDAwM8Q+9UP5PlZze38bM7JrSocePGxwm+aG3WvI7mjpimSP+2SajESLgeKp4ui4Tr3PnveFcPHRLQc7YyGRKDCZMhMZgwGRJDXW2mIGQlXS7rPXg6LU71SzqjORAiH4Z6+5wT2/C8YzfB0meXqXGrV2yI6YEefdzhSvwaCHpb74jDASJvW981xLYXDTK9pnebGheu4+/d0dyseMcuXBTTLU2al8uxHUbia0dFzzUgbMzAT6Qos2uj4oUlBGJw6EVE6O+6a2uOrUyGxGDCZEgMdVVzsg5XPj+oeA2NvGUeyucVr6mBl/9UWi/H+TKrtkeWcAPBF55fqcZVBnnZbs61Kp4TW/JCXqvYSsT3T4voAgAoSg+1+F0OenVH+gf6Yrp7SDN7uh+N6bkzZijenNmzYrq9rY2f5emyYolNhmyo3QaOhJqreOaDk+9S3zMqSzWnv/dIsJXJkBhMmAyJoa5qzomAtTCll+NCoSB4Wsbb2nhHtWVrr+Kt6uRd2gsrWLX1D2o1mhH54eXCgOLlAs7Rbu/QHupcjpf4bIvmdfbx57aK3VxPSavpktghuoy+R0+eY86fXbVO84ZYfc2ff1BMd7RqNV2KhPoq6vjzXIbfXTbUO9WiMDUK/XrOYSj/BqbmDHWGCZMhMZgwGRJDXW0m6dENU9obWxR2Ribj5aeJ0/n161cr3opVbCcVxRa80dsitzfxs2d0tCne7KnTmDdluv7cOB7bV9RzfuipZ2M6v7aHxxW8CDthxkTe77co7LXBfEHxSp3Ck57hALv5s/R3mzy+PabJO1mQ6YNU0bx8gd9XVPBsTJkIOLwm605hK5MhMZgwGRJDXdVcRaRJ+zliUSRSuxuyiicr2WzctF7fVKjHY8T2efrk8WrY7ImsCia16vvnAn5AeWi74rWKXXG5v0vxiltYzUU9rOZawiY1Lkizik17cd55kc4dNum49YEif7dlK9fENHmRbOPG8eeas/pPGoh3XhrQLpHBni08/5JWsVSQ8zwIuwJbmQyJwYTJkBhMmAyJoa4209AQHx0Eod+di+2AXIOWcRnU37Vts+JNnTQppl+5iLtETO3QgWbNgXA9VHoULx3x/Suk7YrUEI9tLGvexCxfD7XynHudtovWizI9Q/1pxcun2SjLpDWvAj4mKYp3sG5Tpxo3roVtpoNnTVW8piY+Qil5lYS3b+P7DGzfqngZ5Vp5BXYFtjIZEoMJkyEx1FXNyfhtv8BZEPCymvYC4DYKT/DmrXrr3ixSr1ctXxHTExrmq3HpZrFse1EDIfjEvzHlxYcLr/Hs8d6W/6iDeV5DrE7WdmsV/vRyViedJa3K1hdZlZW92O5QBBM2t7DaLnpVf5c+z80xO1pyijelgz36fmBhbze7BtYtf1bxsrT7TUJtZTIkBhMmQ2IwYTIkhrraTA0NbN8MDmm7JSNK3vQX9LT68izzpUAfOaztYpumZ4htgK19+sjhyPm8ZX7ZLB0Z0JLhe0SD+sgkJY4j0l79gtmihsDsLB/XTOvSFXuPmDklpv+6Wrs2nlzHW/K1nj1YEC6GTMu4mK54XYb6RELBk8vXKN4hC/nZUUbPf9q0iTE9tHmp4nVv0NEZuwJbmQyJwYTJkBjqquYisRwj0jlc5SLLdUOz3sKGKZ5mEHrVcDMcvLamV6i8vPbodvcxr7t3guIdMoWD/Cc06OD5jkaROu7lna1Zszamm9qZ15jS92hvY3UYzdbRDI2N7CoYn9Pf+/lNrHK7e7nUTyWtXRRFcXqwaav27m/r5y1+W06/u0lTWM3lp+p5pfrXYndhK5MhMZgwGRKDCZMhMdTVZsqKBoKh1yinWObtdM6LRgxKzBvo0VvrTCMnJLqQjxK6h3RS4Qvr+XNRqU/xXJldBYfOmqR5optBIa+PWu7+41M852aex9TZc9W4KdP5NU+crE/15zeyrZL2aiB0DyyJ6a517DYIUzpSNBuw3TXUr79b5zq2u9pn6ESKVMify2S1nZfOartsV2ArkyExmDAZEkNd1Vz/Nk4GyDXobWpGlInJOM1rE1vmcd72WZ6sO7H8+wXae4q8/K/YqnPEELLn3M+NO3whB9M35PTSv26IX1//dt66L+vW5Xza1rG3/6hDtXvhoGms5qaPH6d4Myfy1n3TFo5sKHu14cvCNVDxeFs7OWJhaoP+bltWc22DTet1wN2AV3tgV2ArkyExmDAZEkN9S+oMsDoJAr1zyTayaiv2aO91a4qDweZM0p9bKWKji8SebMp4nnKx/G8v6kPmpZtYhWzq1cv72l7ewc2YMU3xBhr5upBhT/PmAX3QWxlgFTiQ1weqHakFMT1tmr7/fPG8DZ28m1u3XRfFL4hNZnNKq/eBLv5cT6sOzFvxPAcTbl2zUfGyJS/FfRdgK5MhMZgwGRKDCZMhMdS32m6evbGFivbUhmW2hdZt1kXZx8uSNxN0PtyyzWyHURt7l12ovcSBiDwgz4M8ICrq5gs6kH7lEg42a1+vSyAOCA9DswheG/S8yakse+bXiyB+AOjcuCmmp0/U0QxzJrNrYMUkdiFs3OIF8AmbqdFzXxSETZny+s2VSmyLEnnF+pu0t3xXYCuTITGYMBkSQ13V3PKnuYA6hVqOx0/iWOUtXTrAa4IoGXPwTH0Q+9d1osptkbfkpYpXmU5cpr1ny4C7sldxbvwkLuye93q6DBS6BY+33WWvZX1bA/eU6+/TB9Xbhee85FUIbpzAceXjmll1NnhzlMGDmcArVVTke7Y06py68e2sysJBrWKbQ68y/i7AViZDYjBhMiQGEyZDYqirzTTYw90EikUdaFYpsO2TbdRV/DeveT6mJ0+fpXhHzJ/J457ne+S9YgYFsQ128ErXiIiFSqBfSWcv22GRXyChgW0a9W28SsK9ovnOlJz+biXhlnDe/fNDbO9kRJ2Ahqye/1Afj/Oq+aClkd0grc3abTBpErseBrfofLstW3QUwa7AViZDYjBhMiSGuqq5VIWX4//f3peH2VUWeVfd/Xbf3pd00p10Z18JEAi7goggCI+Cjo6KooIz6KCOjjrjxnz6jfM5zDhufOqoKIOigXFBEVAkEHaSQPZ960466X2/ffflfH/czqmqN3RzE15uEr76PU+ep27q7XPOPV39Vr21jo/LyPdYjrb7cER6X2uryetdXym9y/OZStzdR9dv75ZH8PJKNvI9JD3ggzEeITfmrvnYWFU4UbDkNaNTbo6XnAfkoBxPkJ4zy0aixuIy6yHBSu1TRkbEGYvPdWmfR3r3vUjfKGd0lfN4j//b6s6ksAYVJoU1qDAprKG0BQWso2s+J/V3uIKOrWVGTR0fIDM2JG0hHqA/ZzZF1kd6ZEuY2BjZEkHfTMEr89Jr4BkEAAD5PDtro/m6uF1BNg0a1pXHoe/qQYPHCivSXnl9HhFKsAE7GaNPQzBEtlY2K59/ehOFSfp65HDEgQHuqpG2VsQIvRQD3ZkU1qDCpLCGkqq5ZJyyAfIe6apFNpQtnZRb7uGD+126u0cmvntYRL516UqXvmixLMPewzq0HRmS7WLQT4l5gYCRFMa6tKHxuvj4eQ9Tc1ytFX6OqTnpK4cMU20Jw30dZqXpyGb1gZn1wDrjLl+yWPDmziWV/uzj2wXvEHuv0YEewSuXTvaioDuTwhpUmBTWUFI11zKTVE/UKD/OZ+mEMh6XdWcZdqIKhKQHPJFmOdWss9u85eeKdVXltN07e7oErzNKahUd6R3P5/lcUumhlmqOTmmmmvM6pNocR3qa+0cpr7yjS6qayjr6uUSWrolGo9MgU4fTW6R67+0ls2BsTOaOj41Srj0a6tfvNSLGRUB3JoU1qDAprEGFSWENJbWZytkwmaSRHJdlHvFU2vBCe8gmCPmk/PuzZHsNdOx26VxS2l2ty8htcMGSOfK5WFe2/YMyqZ976j1o2FPI3QasBs0xBupwG8qRtuIAa5WzwXhmP8tuyLGsgbBf/toWL6KBQGCMon/80T/R8xoN8xPj5KoJonyurNpMipMJFSaFNZRUzR04RIHGfFYen6trKCBZ2yg7qHWPkBo6OCxzlavYLJUZzHud6ZcB4b3PP+nSs5aeI3hvnE9tbeZMlyp2ywHylg+lZXl4ggVieR551pEJcHk+MhYkIllS9+m+EcELVJBnfuEc6mDXuHi+WFfN6t/6eqR3v43xqltk4/h4F82pS8VkG6N0QiYvFgPdmRTWoMKksAYVJoU1lLalDmti7qSlzZRkdfbmwJtEitrvZI1jd7iCatfCLFNufEQe8fuOUMu9nCPdEujQkbyhTvYyeNclS12632hUv7+LassO91NoYsRoQ5hnmQE1xpG7kdXAtc6Vc4UbGqa5tJe9Oy/K5w8k6f201sqshwXTlrl0yCvfedfejUQfki2O+sdk659ioDuTwhpUmBTWUOK6OToimx1bgh5SXz6PPFo3Bak2zlMuD9eeMOWO7xYlzfLvJDiNjtnDWXnsXbfhOZf2B2RWQn0DzVWZNl3mjq9soRkpb5m70KW9RnZBhrlB0oaqqWwkNT1mHMf37KKy+O2bKbEN8/K7zW1rc+npRve5umr6Prmg/LlylucdLpPdicMhzQFXnESoMCmsQYVJYQ0ltZlqfKSXyyple5dK1iXWY3TZH46TLRGNyah4MkMugCBLtOedZAEAkmzGrccxCgPY2rHxI4LnGaIQChr9CwY3bnbpfJquUVEmpyg0NVH2Y65G2mRPskzRIaOHwAi7d3SE6FRUuh52baf2jjOny0zLxQvJ3dBkuA3yzEUSNvovVNfKkFYx0J1JYQ0qTAprKKma27WePK6NQdlBbUYtHWkb6uXxdjbbcsvr5fbriVDCXZwloQ0Oywj8kcOUsRA2xs1XsZGog0bDdmR1c9ONdj5+1m4nOkoeZB9IdVXBuvKmjQE3c6bR85eNSc+2L8Vm6UUpm8HjkZ54DFKH3YBHZj342SjbRFx2MfYwF0zWKNd3jAb0xUB3JoU1qDAprEGFSWENJbWZ6kNkH1TkjHZ//aTPR0eN+bBDbJjMuLQ5GqopLjO7gvoOgE9G/3EeReD9RruYVJ5slW6PtItGk+R6qK6W7owo64bbyzoJx42CiHSWMkXzcXkEH+klW6WqTmZCNjZQqCUWpBDN6IiM8OeyZPv4/cZ8Y+ZlGRuV9mCO2Zg5kDZTVZ26BhQnESpMCmtAx0h+VyhOFLozKaxBhUlhDSpMCmtQYVJYgwqTwhpUmBTWcNoIEyKOs395REywz+9/Fdd9ARFvnIK/CNEoVDsFgYi3ImLWeE8XlvIZShpOeTVwHMeNxSBiBwDc4jjOYyfviU5JrHEc54qTdfPTZmd6JSCiFxG/gogHEHEAEe9FxOoJXjkirkLEIUQcQcS1iFiDiN8EgJUA8JOJv+RvFnGfVYj4HUT8CyLGEHENIjYi4vcnrr0dEc9g629HxHZEjCLiNkR8G+P5EPG7iDiIiPsR8ZN8F0TEWkS8BxF7ELETEf8Z0RzHeerglH2wE8DnAOBKALgEAFoAIAMA35rg3QKFXbgZAOoB4DYASDuO8w8AsB4Ku1xk4nMxeA8AfHbiWj4AeAEAngSAOgB4GADuYGt3A8BFAFAFAP8GAKsQ8Wj2320AcCkALAOA8wDgXcZ97gWAUQCYM8F/BwB8YIrnumDiD2k3In6h5ILnOM5p9w8AOgDgCuP/2gHgYvZ5NgDEAQAB4ONQ+GUve5lrvQAAN05xr0UAkGWfVwHA99jnzwHARvZ5JQD0THG9XQBw1QT9HADcxHjXHr0XALQCQAwA/Iz/YQB4ZJLrzgOANihsEGcBwB4A+HQpfy+njc00FRARAWAmADyMiDzY6IHCbnEXADQBwK8RMQIA9wDAVxzHaNhdPHjpcOJlPnP77mYA+BQAHJ1UHYHCjgYAMAMAeHcuTrdCoTdYf+Hrud9nH7wMHMfh/78JEf8VAD4KtDu/5nhdCJPjOA4iHgGAGxzHeWmSZbcDwO2IOAcA/gwA26GgRl6zSDciLgCA7wHA5QCwznGcPCLuAnATrLuhoJKPgtefdwLAOADUOCcWjXfYfUqC15PN9EMA+AYizgQAmDCKr5ugr0DEJRM2xBgUJscf3ZV6tT93VwAAIABJREFUoWCTvBaIQGEoXT8AeBDxViioo6O4HwA+jYhNiFgHBTsMAAAcx2mHggq+AxErENGDiPMR8ZKXuxEiXoOIDRP0MgD4AgD8/jX5VpPg9SRMdwDAYwDwOCJGoWCPrJjgNUPhxUYBYBsUjOT7J3jfAoAPIuIwIt4BFuE4zgYoCPmLUNiFZk/QR3HnxHPugMJB4I8AwNM03wsA1VCws4YA4D4AmAYvj6sBYDsixqDwXe8FgP+w9V2KgeYznUJAxOsB4BuO4yx8xcWnIF5PO9Nphwn1deWEj2wWAHwZAH53sp/rRKE700kEIlYBwBMAsAAKboA/QOE4f/x9k08BqDAprEHVnMIaVJgU1lBSp+Whg12uTh0dlYWEAwPUp6i9vUPwHNauuapK9j4691yaeJlg7Z9379kj1qXTVLw5MDgoeH7mt6yrrRa8xqYml+7u7RU8r49eX+usWS490id7PI0M0XerjMiGHdEYFZjecKPMhNmykfyvm9ZTD6Ym9kwAAFde58aOobNTTvkE1ntq387dgrV56xaXfsMb3yB4q1evdunP3/7VopyfujMprEGFSWENpR1eWE69Bg4dktOHkknqL1BXVyt4R46Q2hjol53z+aDAyWgAAA9TZY7RI8nH6vgvvlAmJw6PkzruH5D3XrHMTVuC2AD1E4gaeZmJcWobmM9JjRGOUP+CHuOdbH5pk0tXVVILwTqjJ8GGlza4tMcn20ZzNbd16zbBGhmm/g7r1smQZuchqaqLge5MCmtQYVJYgwqTwhpKajONjg6zT9LzXsuO5OmMnIURCNBjjkflNMrUJBMbhwZlm+W5bJJk0viZxvoGlw4ZvZv8rD9TQ4V0Szx+P4XRop3kNojMkDZfjF1y3vQZgtfd2+3SLzHbBwBg+bLlLs26UkNLa7NYx99Wb5+caLn2ubUu3d8n38nAEPXaDIUjgme2vi4GujMprEGFSWENJXYNUIu/2XNaBW90lLdalsfnTJaO8tm0PPr2dPe49Jx5lMS4aOEisc5DedQwu2224M2YQV39+wfl8T+dpFy1xmrZmu9PL9GEgtFdHS4dnidV2Vlvp1K2/kGphg50HHTpeqZuAQCmM5W4exdNdWqbL58/GKAW0pms4RJhwxIbp8nWjNEYuWOCQdke0fSyFwPdmRTWoMKksAYVJoU1lNRmmjaNbIJUQg5k9uSphK2rS0a+E2Pk9k+My2wDHwub9HZROCKdlMf/ZJJCGouXLhG8DS/QJMy5S2T6dXMr2XaP/PBuwUvtb3fpaQH6uxzq6hbrMt3UMnksKF/5QB99t7aWNsE71EFDqsvZSI6eXjnKw8cGStc3yHqDQICyFAaHjefKkT01YrTLrqqSI0eKge5MCmtQYVJYQ0nV3O5tW126rExOAuAR+VBQeqHnz6UayXVr1wlejOWwh1qpOHYsKlVBMkVqtbtLRucbm+jIPH+hVHP7tu1w6V1PrxW8MPMSZ/ykpsuz8rUefJY82+EzFwteXS2p/i7m5gAAqKqg4/oA82znUF5/hE2w2sIGQwMAJBLk2sjkpNugto6pMpTumOER+f6Kge5MCmtQYVJYgwqTwhpKajOFymjikN8YMMwzBTweKePDI5Rt4Bip7fU84b+HouJjUTn1saaGshLKymSEfNxLbgMfyGHTL9z3R3quAenOgADZfSM+st1awrIoId5L7ozEERm5n3EOs6FCfsHLpMjG6eqmzMeLLpW9KzxI4ZU1a54SvF17yIbyG1mYPIy0caPMWIjFjr8OVHcmhTWoMCmsoaRqLlJJ3thQWA7ZW7CIovxoHFNHWFJd3qhmd1jyfgVLujfbXA0O0TUWLpYe8AQbBv30bx4SvPZnKNHeG5OFCA5L7F/G6s46n10v1nmYNz5+QCbqNy2hpL3m5hbBwzip/vIeigqkc7JiITpG1+dZCAAAWeYOmDu/TfBSLCOC/24AAHLO8Xer1p1JYQ0qTAprKKmaq6giNRRjZdEAAEPsxGZ6x5tbqNXjrp17BS+TIs9zTT0FZVNGbVwkT9v96Lg8qUS7qVz86V/Jzn04Qie4mKF+W86iHO2KJVRDVx6XQdO+x+mEVRmX+e3dL1Et2+6ZMrd7YJBOpPE0va8dO2SP1L4+ih7kHHkanb9wqUsfOnRY8Hx++vWnjKS6rHP8+4zuTAprUGFSWIMKk8IaSmoz5XIssl5eLniBAHlnx8ZkbVxXFx2nzZq6w0co4cvH6ut6e2UEvmUmJeeXVchj8M71VNMfPyh/zpci2yuyoE3wms4506UHkWyVihXLxLrRg2TjlO2T1x/ooO/Wvn2X4NW1UrZED2sDNDwsbb6OA2QLlZXJ79bfT9F/x5Ee9ooKKpAYGZbvPJ3SrAHFSYQKk8IaSqrmeIlzdExuo2HmEa+ukls1IH32BuTxPOSjz4lByrUeGh4S69rmk8roWLtD8I48RUlvnnRC8MZYy5sVF79R8DbuIrX0/HY64q9ceZ5Yt/RiCszu7/qN4IWTdCTvfl4GWxubKYBbVkFulWRGjnypb6KAbVOlbLczWE7vbl+v9L7HmcmQNyIG4wmp9oqB7kwKa1BhUliDCpPCGkpqM/l8dHw2k68SrI7O55OP5feT28Bs+5rJUOR7dJzsj4ZmWSsfH6EEta2//bO8SB/Zb/GcDMNMX3ER3XuavObj9/yULtHP7JFx6b445+ZbXDqyXPZAiD79gkuH+6UttG8dZR9ULlng0keMwoM8a0802CU7Ao8zN0tommz142fujGRChrcQtaWO4iRChUlhDSVVczy6PWg0dp85k5qym2pu//79Lp2OpwSPn5LjDqmolazWDgDg8It0dO/ZKl0D/hSpiQA7ZgMAtJ1zjkv3+6SSzTK1HfFTTnvEK+v+xtjc5UbWBB8AIL2jw6V9XTI/fHAvZUhUsW5xVfXy+D8wTmq6q1tmBnhy9N0ixvM3NJDa4+YCAMDZZ6+A44XuTAprUGFSWIMKk8IaSmoztbdTsrtZNBCP8wkF0iYAZnP0HpFH32CIwh1eFhQfaJe2w87Hn3FpT0zWvyWY7TP/3AvkvaupHj+dl26DIMsITbIk/7JyWSyRZn+znmr53VovuNil9z3woOB5o3RcH9xKoZsVK1eKdRmg5/LOnil4ZawvQU+ffHcd7WSTzTSyPIcHNWtAcRKhwqSwhpKquVSSPMPV1bKEuq+XWsYEA1JN5DJ0vI0ayfphD3nHw8xTfmS9bC0ztpvXk8maMG8buREql8jENu7MDvvlc4WDpOZ6HfJRYESq8KCHvMnphCz8q1nOGsfvkzPyfDvJneEcIK/3njWytU/LxXSNPUZiYSxOqrKyQpbFp7IUFdiyWWYsjAxLj3gx0J1JYQ0qTAprUGFSWENJbaaFC6jFX4+R8J/Nkh3jONKuaGykVn2ORxYZ5pnbIDNMNsDBF6QNEIrT8Tkdke18Wi6hzMhEuZHlmaZnKQ/Inytntl00T8YVRuQzlrMI/HhGfreharLz6t9wvuB1H6BuvrVJssMGd8oizMBMcl/4jLyKkXGyodrYHGEAgKbpZLfu3rlT8GY2S1dBMdCdSWENKkwKayipmtvERp9X8/Y3AIBs9x8Zkt7XYJii8GNpeawPRcjtHd36okv7Dx8S69Ksdr58uZzDWzabBviY7Wr4AJ+cI5Pewmy2L7LZu2Ve+d3yDq3LemXBgjdN3vjphhoaPZcyFnqeo8b3DUbGxfBa6mIMi+U13nrt1S49xxj008taFT225mnBC/hl5kMx0J1JYQ0qTAprKG3dHJvxwfO6AQA6Wfe2imqpJhwPqZDKCllWnmFj6oc2kdc74JUnqngtJYLNPFsGc9M59hpyMg8b2Okxb4y3D7A5bw5rSRMJGl3Y2N+s45WnOQ/T78msvHfzSkpQG95H3vDMiJwfM7aLArYLF8pZdPUssJzxy/LwnXsp6bCrWybmtc6SAeNioDuTwhpUmBTWoMKksIaS2kwZ1p4mZsybCzNbqHdIHn0rsnQkb66SNtP2hx516cAQXXMkJG2yOlbvj5VylprDWhmCNCuEN97vl6+rkrVV9PnoBysMu87Jk7sBUf795lnt2kjaSL5jM415YUP7o38R6wIpyqQ4+LwcVOStp7Y526Oy/0I2QbWLDXXynYTD8jsUA92ZFNagwqSwhpKquY6ODpduNo6e09gcj0zSqI0bpUSt3q3bBC/dSbPjuBc61DpXrKtZSt1wY0Yn2YCPPudAerm5WkJjpkuAHbV56XsoLAPCfrbOk5Ie9ixzKeSNv+0Y0751CyhpL7hnv1gX30HvBA9Kz38fmz8XNkbY7+mh62Sy8rkSSZmEWAx0Z1JYgwqTwhpUmBTWUFKbiY+YX7FS1txPn0Yj1PdvksUAsQFK8OrZIG0mHzu6j9bSUX3RhReJdY6XEtnyRreYLLJiAOOZeXlfMiHtiIVseNC73/1ul54xSw7USbI6fs8Ug4QcoxdgnvUJSAYpka3lQjlv7sUD1DvBzzIBAAAGNm526QULZDufERZq2T8o3QazZrbC8UJ3JoU1qDAprKGkaq5uGo18j6ekysiwkekVPnm0fvQva+hDn6wLy7A5qzVn0/G5bKZUNQk2otTjldH5HKt5493UAAy1Z2QN7N1DdW579+92aZ9R97dwAbklckmjZo+pNjQSFrirY5wN2Ik0SxXUfAbNzxtd84TgOTyrYvMWwQvPoEyKHtbuCADgjONvHKc7k8IeVJgU1qDCpLCGktpMi5lNM5aQifVDrIXL/q1bBW/kALWT8RsJ/1nWkb/1bIqsJ70y/O8we8eXl+Ea8LC1hs3k89Pf23hMFjo89MgfXHpwkNrVdHXJmsDZLRTG8HnlK+d1f+YAYg8f6YAU5oln5brZ51zq0hu2twtebogyKLs2yKKB2W3vcOlZbTK8dbC3G44XujMprEGFSWENpW0qzyPwaXkOHjpIBQW7n5ej3IOsw246KJPemi4iT3e+gerChmXwH4Issc2PsiaMJ6ghyusD61DrrZCFDrMWLXbp6HZSvzMXLRXrsqzDnFEdDjmmsvLGn7bD3B7cBZIzuu6Fp1Mp95zL3ix423/7a5euHJMvpfP5jS695KzFghcNSfdMMdCdSWENKkwKayipmju0+4BLe+Iy3znTSaeh1BFZwxWO00msbtYMwWtpI093/xB5cStAbtMhpkO8xlwQh6tf4zTH8+G8RlP2Gy8llRJlJ8lqoww7O0Z1bjx4CwCQNev0xIMRmUvTvbNBo7m9n67RNk+Wh4+00CktdvCg4EV30O+jZrp8r5lmo0ltEdCdSWENKkwKa1BhUlhDabvtxuiIX56Sx9RD2yjpzZeRXm5/kKLwGSOJa9uqVS49xowMT0baPkFmmhinc8izev9jx6wRz2fUzXlZIUKA8XqMggiHt+UxbCTumc8bWXt5tjaQoXsljdq+aIh+rhzkNcKjVEvoRflcwSxds/PFTYLX3HA5HC90Z1JYgwqTwhpKquZCLGnsyC7ZQH2kj4KjlSgfa5xrrKQMEOdi9NkBUguelBE0ZYlmKa/8G8oz1eDNG0d1dpm80ZKGKxTRDsfMMWcj3x3HUHPsmbPGSNc8C2o7aXrmtDn3jj1WzHj8LAss50KTuz2SfTI43WvM5CsGujMprEGFSWENKkwKayipzTSd9RPI9snkq/wcSpIPmjYH6w2QM3lsqI6PNXYPGmELD/c2GD0D8iyTP2AUDSD7ewuFywQvGKDsg/FxOoJXGu1oYmOjtC4mCyJyzE7yG/ZUnrXiyTObzPylVfAeCxlpT/nY7OB4QPJyeXrmSFzeuyIkh/YUA92ZFNagwqSwhpKqubEEea8TEXnMxsXUAqe8plbweKS9ZZasGTvMSs4TCVIZQb/MGqiqqnTpuUZrmXHWQc3smNZ5iJL2fEY93/59lG+dZHPw6pubxLrGKuq+u3PXLsFrbaash9ERY1ZcjFoJ+fykwssq5TPObqOZNNNqZcbClvXUaP/Avt2CV9lEdYwzpstsg3jcyC4sArozKaxBhUlhDSpMCmsoqc3U108uewzIW4+x3gN1ZcYod9aHYGeHrAtrP0Q2kwfJpjEzJh2gmvvtBzsEL8u6+fK2gAAAMRauqa6StlyKZT5we2fMqO1LpugaR450Cd7eHrIjc2YWJsue8PvoufwBaW/uOkRtdMpDslgil2DtiPqOCJ4zSnORB1MylBMfl2GrYqA7k8IaVJgU1lBSNTetiY7MPUYJNbAOcNMa5PEWWWObh3h7HQBonU8l59MaqX4sk548Op/Jyob2yMqyvUYnXg+Puhvu954eKoKYzbr7+sulqkmyrIG588+QvCRTL47Zt44N8GEecDOJjrcE2r9Xdt0L+8hECJXJ53ruRRppPxIdFbzG+ulwvNCdSWENKkwKa1BhUlhDSW2m4SE6wtbU1ghemh2D+/pkEWYLKyQsK5PR7PJyCpMgy9D0GVmRHtbmNhA0MvKZDdJ5UB6fKyopFFJVJYcSbttB9smFF9IY+Wn1sj1NMjNFFibrNWDaZMyMBDQbETD4Q/S9HZRuiW0bqY3O6ID8bqEwuWAaGhoFzzGrLoqA7kwKa1BhUlgDOieynykULwPdmRTWoMKksAYVJoU1qDAprEGFSWENKkwKazhthAkRx9m/PCIm2Of3v4rrvoCIN07BX4RouJVPUSDiAkT8EyJGEbEfEf93Ke9f0nDKq4HjOG4cBRE7AOAWx3EeO3lPdGoBEcMA8BgA3AEA10/897xSPsNpszO9EhDRi4hfQcQDiDiAiPciYvUErxwRVyHiECKOIOJaRKxBxG8CwEoA+MnEDvfNIu6zChG/g4h/QcQYIq5BxEZE/P7Etbcj4hls/e2I2D6xW2xDxLcxng8Rv4uIg4i4HxE/yXdBRKxFxHsQsQcROxHxn5GPM5f4KADsdhznTsdxEhP/tk6y9jXB60aYAOBzAHAlAFwCAC0AkAGAb03wboHCLtwMAPUAcBsApB3H+QcAWA+FXS4y8bkYvAcAPjtxLR8AvAAATwJAHQA8DIXd4Sh2A8BFAFAFAP8GAKsQsX6CdxsAXAoAywDgPAB4l3GfewFgFADmTPDfAQAfmOSZLgCAzgkhH0DE1Yi4eJK1rw0cxznt/gFABwBcYfxfOwBczD7PBoA4FOYPfhwKv+xlL3OtFwDgxinutQgAsuzzKgD4Hvv8OQDYyD6vBICeKa63CwCumqCfA4CbGO/ao/cCgFYAiAGAn/E/DACPTHLdpwAgBQBvBoAAAHwZCoLsLdXv5bSxmaYCIiIAzASAhxGRBxs9UNgt7gKAJgD4NSJGAOAeAPiKY3beKh69jE68zGdu390MAJ8CgKMlsxEo7GgAADMAoJP9LKdbASAEAP1IPTE9ALBvkmdKAMDjjuOsnrjv/wGAL0HBbto9yc9YxetCmBzHcRDxCADc4DjOS5Msux0AbkfEOQDwZwDYDgU18ppFuhFxAQB8DwAuB4B1juPkEXEX0LTWbiio5KPgiVCdADAOADVOcdH4LQDAh7Y47D4lwevJZvohAHwDEWcCAEwYxddN0Fcg4pIJ43UMALIAboVBLxRsktcCESikw/UDgAcRbwV5wrofAD6NiE2IWAcFOwwAABzHaYeCCr4DESsQ0YOI8xFRzqEn3AMAlyHipVgoGvxHKKj+yXYy63g9CdMdUDgaP46IUSjYIysmeM0A8HsAiALANigYyfdP8L4FAB9ExGFEvAMswnGcDVAQ8hehsAvNnqCP4s6J59wBhYPAH6Fg9xzFewGgGgp21hAA3AcA0ya511YA+AgA3A0AwwBwBQBc/ypU+XFD85lOISDi9QDwDcdxFr7i4lMQr6ed6bTDhPq6csJHNgsKJ7DfneznOlHoznQSgYhVAPAEACyAghvgDwDwacdxxqf8wVMUKkwKa1A1p7AGFSaFNZTUafnDX/6Vq1OffUaOU8ilqZ/SyvNWCF55kNxAF513jeAFWO/KBx/+hUu/9a/OFuv6+mlq1OHtLwreU09SA4d9XbIv0WCaCi+3PjkgeFWV1S7tZ30mK3LybzTXT8Wnc86XvMUXUfFjtzGxat9WMp2GjtDPzV4oG1CcdT49oy8v+2k6Dn2fgF8WsB7YQ72bsinZlnr9i1Rg2nUgXpTzU3cmhTWoMCmsoaRqbmSMejINDcgeSakknSqffe4ZwfNkqX3yO6/7pODV1pKq6eikgcZ3/3i1WLdgAanDHqOXQetZ1OMpskD2Exg7TNv/tYvqBS9cVuXSuQD1CQhkZM+AHbtosPKGrrWC1/M0PfOcabJ1Yn0FPfOwj9RVZZXslcBbIIb8UgWuOHuRS7/w3E7BO3KE1OpArxxsWFZm9GMoArozKaxBhUlhDSpMCmsoqc20cyv1TSyLyNPmwqVk+zz+qOzL+LFbPuzSdcxGAgDo7T/k0j4fFZHcf79Ma7r+fW9y6TOXv13wqofJLortlPYUjJH907dLZnNkHepVWdnIbC3jIH35QorbXnDWEsHbP77fpbfsfULwIvVkJ119BiULTJteKdalU/S9j3TKSMzWLeSCOXRQfrdolE2UCsh95Zzz5HMWA92ZFNagwqSwhpIGet94dYN7swsullOEauuoLeEzT8ph0OdfcKZLV0TkFp9Ikuo80tvh0gGUKUFnVq506bHVUp10r3nOpbNJ6bLI++iIHMrK4zN3AIiZgcYr5dlplU0tgtd0+YUu7blAqpaBwF6Xjqc30uXzSbGuvIxaXcdjctLA4QP0fgZ65XeLxegb9HQPC955F8536f++c4d6wBWlhQqTwhpUmBTWUFLXwBkrKKLtCUrdnsnTMXj2AvlYngAdn7cfGRS8OXOWu/TVbR9y6dzTB8S6F7/2beJFewWPB1CSxp9XktlJZmY+t5kCzE4KGut4EkGq57Dg7f3l/7h06HHZ8nnprdSPY6CVQjm7B/4i1mXSZBcN9UjXwOgQezBPQPCGRuhdLjhDtm4OV8rQTjHQnUlhDSpMCmsoqZrr6aZtNWEkY1VV0hF83MjFGmcnYcxLZVOVockA3Q9QFfTuu34m1tUwJWX+BcX4h2CV4FW10lG+olFmDXCvSo4NfB453C3WpXup6ttj+A2CIVIn3p5OwXvx699x6SW3fcylly1/k1j37Et3uXQ4KIdZRypItXV1y8GJ9Y2U0NfSJtVcbFy8laKgO5PCGlSYFNagwqSwhpLaTJEIHcIrymX0Px6jI3gAZeL7/g6Kdi9ovUzwZmyjA/r6n/7EpWXQpdD56yhCIXn9RVdeTc94jbRHPDMp7OMJyKM17+GWS9IdMoPyeB5bT6GQgw/+SvBGOijT0ueRtmIwQ+6S7d/9L5de+ZmPiHWRKuplcaRTZlNWBeg9p9PSHVPO7dSo4VIYPf46UN2ZFNagwqSwhtI2+3Io2T0Rk0fkg4fo2FpRKR9r2XJK+F+av1DwdvzoX1x6BrvkGEg4M+nIPO/LnxG88IpzXDoflcUA3gSpr2xa8vLsmO/NkgqMVMuB1b4bSI0uuFLWBB758T0u3fWHhwQv6KXhgpEcHdU3/Ow+se7cr37RpXsHpHe/kxVZZLIZweN6Op2WvPq6Wjhe6M6ksAYVJoU1lFTN9fdFXTrgl4lakTLyBDcvlmpi4ayLXDrx9d8IXjY24tK8uBrb2sS6S/+FmvXjkjMEb4TlvAXTMvEshXSiyvmlmsux2b4OU3ker1zny9N3i0TmCt65H/+8S2/1yZq3A7/9NV2DHfSyAzKX2/klqceV73qz4N23hyIB1WVy75g1q9ml0ZhpHI+bhsIrQ3cmhTWoMCmsQYVJYQ0ltZmGh/vpxo689bxF5JWeOU1GsP37SOb7X5S1+jyxbSxMUfBLPvN5sS60nI7kW9fJmrqBAWqVs2CRLESorKI7JJPSnsox0ygUpJS4XqOXwa5dL9DzBmUvg+XLKblv4d/8nXyuw+Quia6jogczb+3ws2tceuZVsrNzZTnZRWNDuwRv725K1EtkpZ0XKD/+Jr26MymsQYVJYQ0lVXOhcko8G4/JGrRNO6huq2VWjeAl125w6SyYXmjCwqvc6VvQeOkbxLo7f0CB0h/94HvGk9FVZrXKEW1f+iJ5l+fPXyB/jB3XN2/e7NL/+i9fF8v6B2RSGsfll1/l0rd9TqrmlR/+kEs/tZNUlG9cdpgbYWHs7LqNgnfFRfQe/vDYfsFLM49+fESqtXRMvudioDuTwhpUmBTWoMKksIaS2kznnHeLSw8l5PG5f4C64c4pk/Vjoy9RlNwsei/z01F71gf/2qUffEa2MvzRDyg5/4orrha8M886y6V/+tOfCt53v/tdl/7Wt78teJkM2Srf+Q5d32MkuX3+M19w6T37pd3ywO/vd+mGNtl/4RN/+zcu3XTJpS499IiciFHO6KG16wRv7pU3u3Tb3DbB27e33aUD3nLBC3m1DaHiJEKFSWENJVVzM+Zf59KRdFTw+p+m425wWMr48GHq0mtKv2c5eaz9c6jGLb5F5kK/9a3XuvTnPv+Pgtc0jcrWQyEZuf/6v1DyXXdPj+Al4pT50NHR4dJf/NKXxLq333C9Sw8PydY123bTc25eJ5vdR2+6iZ7xDdQSaOBPvxfrAg4d40f7ZeN7Z4hUcUOFzLvvayBVVh2W7pieQzLJrhjozqSwBhUmhTWoMCmsoaQ2U85LtVgz6mYI3v4QhTEyfTLU4s+Sqz8LEv5lLMrPetdcc+Vbxbpr30ahlnRSDtvJ5s2rcrAMSpRHfl6HxjmRSpkZMBilrEU0/nzra8lW6e+Vdko2Qe+hcja5DcINckxvro96GzggCwPS3WSbzpgjWyDGPcw9Y7gC8iBtqGKgO5PCGlSYFNZQUjX3y5992qUDCVmiXcvqtHxVsqCAV9hJBQhQV0lbfspLCWrpvCxY8OdIER3uli1vfn7vz136wQekd/miiy926TajSKGXqSXHIVWZz8mIu5+9ZgdlvWCWtQjKOVLdOkx5YjmpTk//8qpWAAAb0UlEQVS1POJnmJrzGS17nFFSsRV1siVQTZyuOZaWHflmz5dz64qB7kwKa1BhUliDCpPCGkpqMw31bHHpZI88ZvuX0hSCrGkTTHFNPysezHvpmo7XM+m69nbZifcnP/z+pNe/8f0U0ohEpJ0npzuQnYRGbgPPIsg7Rt4Du4RjJDdm2H8EgvSrCpTLFo68+Y35rhIZspnSGWlP5VjWQ1WZzBrwH3/SgO5MCntQYVJYQ0nVXMCho+jQ6IjgDfSzmv45Up1wxWBKf26cebOZ2snnpc5IJGjdynNXCt7//I6i8D+4807Bu/u/73bpy94ku8oFg7x9PFOx5iQevsrwosu18ue8XiqQ4+4GnpQn7/wy9/PQNcZG5DtJjtHvo8YXFjzIGp+LgO5MCmtQYVJYQ2lPc8Pklfb5ZY3zaIYCkk5I8srYRp42VMFoNwUr61npl2McRwJe+qrGQQ/qmPf9ve97n+A9vppGuu7cKRPuqo1T51F4jGiuULnGfD8ePDZVs4d9zCUoqBwfke1uuLI1Q9ahILWKbaiVDefrg9Tep6ZKfpeA0Vy/GOjOpLAGFSaFNagwKayhpDZTdRVp92heJqgNsbY2saCc2BaqpiOsz7AX4rs3uXQ5ktF0z0MPinVbn3napT/7xdsFLzi9zqXHjcQ5Dp9X2nJ+P399ZAtlU7J5e1k52S3R3KjgZWP0fQKO/Nt2yqi4AffS+8n1ysIG/lRZn3QUpBrou21ZL2v24iPkOw/6ZcP8bJ6u87733wzFQHcmhTWoMCmsoaRqrraavKroSC9utJvUSx9K7/hZS5e6dPezzwteeh+NrR/eSK13ZrbJfOf//OpjLu3Jy6994ZXk2b77BzLo21hPSWLzFy0SPF4FXt9IOe2/YMl2AAANzdNdeveBHYK3aetWl77smncIXihCaq77+fUuncrIDnbcVx2okFNj4qzD7h9+KjsVJ9lMOTTd6KLE/ZdQDHRnUliDCpPCGlSYFNZQUpvJixQfqKyQx2yeNHagf4/gXXD2uS6dMGymEAtB7P8JDbV5849l+5tb/4na2tz1H98SvD8/8Sd6rhppc3z5KzTZoLLq5cMnAACf+OTfu/T/+mfZa+Bjt9xkLnfR0tzm0u/5yIcEz9NF3XD7nnrCpc0h8TyE0rxITl/oiZMrIuhIl0V1HblcTJMJPce/z+jOpLAGFSaFNZR2rGo5RfIdR7auqaqg5ud9h6QXeuAtbS4dni67yjndNL59+NmnXLr9V78W6277KEX/LzCS4wY6qe6sZb7s3tbUQC6G5Lh8Lp7Y9sbLaADOnT+U5dvrn3nWpUNBmb998VXklljS0ix4G/7jP1x69DB5r8OGnouxmsBFF8h5fGsPkrukpUFmAoTDzFVjqDWfx1SmrwzdmRTWoMKksAYVJoU1lDicQrZEPivrtHxAw3dyScnrzZKtteC6KwVv74/ucukwq13bced/iXUV0yjcsfxKWRiQa13i0umkDPPEEqxngZGi6bC6tniK7KnFi5aKdWctJtdGKmQcwn0UGtlz328Fq/23D9Dzs1tnjBk59QsozDNYL4sx2lfvpWuEZGaAz0d2kcf4bj6z908R0J1JYQ0qTAprKG1yXJhmvgUDUpWVlZHnucxIZs96yY0QfstVglfzBDVRj++lCHyYzbYDAFj3ZfJKLx+XA2/qWZe5nKEK/El6RWmQ+iXPagN4PVw6K9dlQuR5LjNmALev+h+X3nenzFiI5Ggt/6tPGj7w8278gEtvzspuvpk4XSMYlLVwTp5dFeU1HfWAK04mVJgU1oCOM3kps228tGGLe7PhYbkdj0cpH3nmvDmCt2cfnUgaqmVXuSrWBe6FW2916fyYVHP8W5pqoum6a1x6+jvkXJXIAurO4i2TJ6VMjkKsHhYqzUZlnvr4NspTP/wbmZve89TjLu0/pjyc6DGmOZvf9m6x7m3f/oZL/+m+HwjevXfRKPqA0emEl597DLXmZZ//+NLuqSrQ6RrFLFIoioEKk8IaVJgU1lBS10CK1ZNljeNzjg+TMbrhprvoc29Mqu+eKrJjFn6N5unu+tr/EesSQ9SToNY44icfJDtm8yOPCl5wIc3lrWuWUf0Qq2tLxsgDPnjosFg3spuS/XxGbzeeijcOEuPsMdveSYOEln/yE2LdzDbqIZDJSdeDw+rovFNkAviN3gzcO14sdGdSWIMKk8IaSqrmtm6hcep5o24unSE1cSBpzE7xsWbxnbK56dhBWpud3ebSl31Tjpt/4Tv/7tL9m+RcN56mV26q3+3kVT/EaBNcKZgvlTsUjBgtcAdGMCybvp/3vve49IEldJXxoJzV5/GSSwGNGSgeH32eqmudqeYCJ9AhVXcmhTWoMCmsQYVJYQ0ltZn6WCsYj0eGDvIsNJExh8l4yL7KjcuWNHzwTOeW3S7dO13OS5v3+b9z6WnGuPYjD/7ZpfsPHBS8kIdsB19+8vb2fhZOSRrPz5sLYlhG7me98XyXbrjqCsFb3dvh0n/4BdUE3n2OrI3jjQICeaNLrmjNKJ+L21Dj49Ix4Zmyh+/LQ3cmhTWoMCmsoaRqLjZG4+bzRrICz15AI5MhyxrCgzGvLcC69sZZV7bk0BGxbj/btlua5BF8xWdoDl5rp+zKFh+hZx7plCoQx+iIXsbGvMdAJthFZlLtXfVimfXQgXS/+/fKWr9nX6Rsg+bpVG+3acuTYl2ll7oFd3TI7nDpFEuwMzIDuGvA5Pk9xy8aujMprEGFSWENKkwKayhppuX/+tJXJ71Znj2Hk5NBBydHR3JzugAyGyrHR8ob10izz4OJmOA5zIBrrK4TvBwzf8ZH5BzbcmaHeXy00FshCyIGE5R5ebhzs+QNUIgmUi57GYQj1PKmtpbqCsd7ZGucSJ76L5T5ZA+HTJreSdIIU+WYOyaVkrwUW/vQizs101JRWqgwKayhpK6BEBsNmsvKkTE5pmp8RsQ66Cevrs9nHGEDdE0UZWDSW53MUJl39bhUBbkEPUs6JkfY7z+4z6U3blsveGVBes7yCLkGqiKy+xzEyGVREZFJ/cuXvoWe4xjVzBrVZ0ntVFXLpvthD3XzDRjBfkRSiebxPxAk1WyaO9nMVMNsXx66MymsQYVJYQ0qTAprKKnNxEefe42hNjyzz8wI9LIR82XlMioeDJH9gCwTwRzrjjmyVfJeWSTJRgdDY7O8fgYpZJIabxO82gYKY0TKyR3gRWm4dB485NLNLbLAdOV51N4nkzLsliTZUNksPYdpW2Uy9GvM5uR3Q5azkE4bdmSSFR8YThvUNoSKkwkVJoU1lNQDrnh9Q3cmhTWoMCmsQYVJYQ0qTAprUGFSWIMKk8IaThthQsRx9i+PiAn2+f2v4rovIOKNU/AXIWJ2Mv6pAkS823hHKUTsf+WftIeShlNeDRzHcTs3IGIHANziOM5jk//E/19wHOdDAPCho58RcRUADE22/rXAabMzvRIQ0YuIX0HEA4g4gIj3ImL1BK8cEVch4hAijiDiWkSsQcRvAsBKAPjJxF/zN4u4zypE/A4i/gURY4i4BhEbEfH7E9fejohnsPW3I2I7IkYRcRsivo3xfIj4XUQcRMT9iPhJvgsiYi0i3oOIPYjYiYj/jPjKcygQsQoA3g4A/318b/FVwnGc0+4fAHQAwBXG//0TADwNADOg0CXnbgD42QTvUwDwayhMbfdBQYDKJ3gvAMCNU9xrEQBk2edVANADAGdOXO8ZADgAAO+BQmedfweAR9j69wDAdCj84X4AAKIAUD/B+3sA2DzBrwOAp4x7PQIA3wOAsok1GwHgpiLez98AwM6S/15OtmBYFKZ2ALiYfZ4NAHEojJ/9OAA8CQDLXuZaJyJM32OfPwcAG9nnlQDQM8X1dgHAVRP0c1w4AODao/cCgFYAiAGAn/E/zAV1ins8CwD/VOrfy2ljM00FLOSszASAhxFFya8HCn/xdwFAEwD8GhEjAHAPAHzFcRyz91ax6GV04mU+c/vuZijsjEdHbEYA4GhJ8QwA6GQ/y+lWKOyw/SwlxwMA+2AKIOI8ADgfCjtiSfG6ECbHcRxEPAIANziO89Iky24HgNsRcQ4A/BkAtgPAvXBMJo89IOICKKipywFgneM4eUTcBTSsuxsAWtiP8JmxnVDomVrjTGw3ReImAHjccZzDr7jSMl43BjgA/BAAvoGIMwEAJozi6yboKxBxyYTxOgaF6e1Hd6VeAJjzche0gAgUOur0A4AHEW8FgHmMfz8AfBoRmxCxDgA+e5ThOE47FFTwHYhYgYgeRJyPiJdMdrOJHfoDULAXS47XkzDdAQCPAcDjiBiFgj1ydIxUMwD8HgrG7zYAeBgKv0gAgG8BwAcRcRgR77D5QI7jbICCkL8IhV1o9gR9FHdOPOcOAFgPAH8EAF4N+V4AqIaCnTUEAPcBgJwmLXEZANQAwO+sfIHjhOYznUJAxOsB4BuO4yw82c9yIng97UynHSbU15UTPrJZAPBlOEm7ig3oznQSMeFcfAIAFkDBDfAHAPi04zjmsILTAipMCmtQNaewBhUmhTWU1GnZ1jrL1anHKFeHk8WrXjyBFsPH3p2ugSdyudc5Og4e0v5MitJChUlhDSVVc1OdHHkbQlMLFav2plJ5zhSf+McTVXN5du+88Rz8kxeMJginLHRCgeIkQoVJYQ0qTAprKKnNtOCtf+/SNeWyL2N1hPormnYLT3dLGj2GRuP0uT/KWzzLi4T89HdTGZbjKPg19vbKSEaa16UYNh9/Tj5dqsLoK5nKUA5e1JF9j8RTOuakysk/FY0pXC7idublT+B2ujMprEGFSWENJVVzI15qMZwzbu0PUvu/iqDkhYOkGvxZqeb8SdJDsSC11Utk5D6dZjopF5HXLy8nXjArpxfkkpOniSfZd1iWovTtRb0PiHVj1Stc+k+VFwlewKHWyo6h5szpVu66SZ/o5ZiTu1ymOv1Pdu+poDuTwhpUmBTWoMKksIbSTsJkk4NixhShOPvcWCndBhVhZjMZR/5clmyafI7sj9GY7DXBJ28OS7MIqsrIVeA3elSgQzaax7h3hh35Z7fSuIvp3fIaoWnUNtoTN2wwNonKNGEms1qOsWeYPXhsyIpd9Zjj/1QhrOOH7kwKa1BhUlhDaScUTBGdzzI9FM/IyHqObcdeY+Bzli2Nsob7aXNCAaeNI/gYcy8cM/iYDZQei0v1deZsUm2fbhtx6cojy8S61FwaML0nJicgPLON1HtZWN570uO58fK4ajOnO3B3g6mm+dBGB0/Qw86gO5PCGlSYFNagwqSwhtJ2QWH622+IMdf14ylpm4wzL0JFSD7yZIHvsE9G59PMPsjkJ4/H+42J0uz0D42NcsLlt6+lfhctD/3epYdDZWJd/RHquPMPK+cJ3sZOsqFyxkBmL042WWkK+8awfbgJdUy4htmHx7glNJyiOJlQYVJYQ2kLCph6yRl9PpM52mizGanmHLYJjxtn/uoQqQIPGwady8p1/GPedCCLpfLnvGyo87/fIJuTnBXrc+m+KlKBPo/04CfHaKDgRQOyB9dHLljq0t/+ywHBq2YqnRdcHFs4wb/Q5KkApjvGybP/sFAvqDuTwhpUmBTWUNrTHFNthpMbsmnmjfVMLuNZQ0cNMa90gM39NU9sogTc2NPzTE2Mp+SDffHtC1z6akf2aB98mprAZWZTa8qyv3qjWJf80S/p+vuPCN4t5ze49Oq5DYK3t2PYpcMB+m7mSWuqVG6+X5g/x1/zMae3Eygg1J1JYQ0qTAprUGFSWENpXQNT2EISk+trj8HLMl3vMHPHMVwPzhQZC6OsaOD9l84SvA9hj0tH1+0XvAyzY7yXXUzP1DZdrHPOoSyC1F/WCF7DfnIHfOGSSwXvb7vIpcDdI+brEZkCxxhNk/+csA5Nk0nr5hQnEypMCmsoqZrjR3IzGavoXfWYIytrZSMuYiSaMTd3NCk97CsXU5/22yrkvL+RB9e4dMaoofN98ma62/RGYkRlibn/0vNdOv3iRsEb7yNV9ubRQ4J3w/kzXPrep8ilUFUmf21cvZv6ibPyx3TzmVz3OyfgEdedSWENKkwKa1BhUlhDiUeE8WQvs4bfRnP7ybvmprN0/YamWsH7fBMVA3hXyT4BsRjVzQU//D7B85yxmO6XSNA1PEZSWw1lFHiuepNgJb7/c7rGo08L3ieuu8aln2utcume7qhYF/DyX6M0jPhr8Bxjb07eElHr5hQnFSpMCmsorZrzTJXgZeUGLpV3jNq7IJWA//0iuYnP/dWvXHp0T6fgRT5Kqi14/VWC501SObo/RLncqx/9k1jnY/noF15xpeBlnlrr0vGXtgpey/ZtLn3b+TSz8B9/L10PQeYOcIz3ikxhHTN3nJ3/j/k5dQ0oTiZUmBTWoMKksIZTcnq4WS8/dbY7r7On/x3PyJ9574oal75q9S8EL3qgy6XDl5wveIfffKFLb1n1K8HLpchmCgSoiKC6okKsq6mle2czacHzvYuO/87AqOAN76CMguvnUDbDY+fMEOseXUuhlkjYaPXLsyrMgoIpauNOxKLVnUlhDSpMCmsoqZrjHlhzWI3YcKc6lxosniwXT5FXfflCmZz/4V2rXTq2Zq3g4TJKXhu55d2C9+iz5JW+4KwzBe9Xq1a5dCIWd+mvfu1rYl19PbXUiUdl2zrv7GaXdi47V/DyPya1mvvzky79qXdeL9a9dIDUamwsLng+7o0/phCBuwZefVd53ZkU1qDCpLAGFSaFNZQ205K3cDmOY6mspDc63rLehuWNFFm/Lb5JrAvdd79Lj0+fKXjhW9/v0oEFswXvYtZt96GHHpYPxlI7586h9jper3ytmczkbQ6dOLXR8VyyUvBSDzzi0tFNO116XoV8jg+/key8f38kIXjVzGTKTdWHwPisWQOKkwoVJoU1lFbNsSP/MQHsSWgAcwuWn1JMpdw6i7zLy350n1g3xJqy+a+7QvA8F53j0tXGPLjtw5TwX1YmPdvLly936ZERSrCLROS6qbrhBv3ksc5VRAQvdeM76cO+77hk9MigWPdO56BLr1nUKHgbdtLa8pD562be8WPd43C80J1JYQ0qTAprOGmBXjQytXgSl5mZzDVDNC2346vOIJXy9sd+6NLx9m6xLnjlm+nDjTcInjdL6vG5dfIUuGUrnaIapkmveiAYculwGc1H6evrFeuam8nLncvJb7dzJ7t+g7x+/Zuo5Hz00WeI8fTzYl3l3b926c/8zccF7+YuataaT8pZfbwX7DFaTkfRK04mVJgU1qDCpLCGEvcaYMfuKXq2mBIeZy0KF86uErxPHH6KLvks2Tu5OlkbV3bbh1zaVyt5w13UNufPj64RvI997KMuHRsfE7wnnqB7f+QjH3TpjnbZNfdAkrzSGzdsFrxHHiFvdsvMZsH7xOc/69I1t9L1Y+tkv4J0B9mH569+SPDee+F7Xfpnj8liiUo2+AcNh8yJ7DK6MymsQYVJYQ0nzQN+TAIcU3sZoxVvzTRSbf8Ubhe86rtpZslYHR2tfV+QR+T8Euqa68RkMPTnP6ckt5XnrhC8uXNaXXrzpm2Cl89TMl5FBZWA+5nLAADgV/dSt932joOCxx3Nhztlw/lf/PhnLv13X/oCXf+v3yHWZf/tTpeOrn5O8D604iyXfmZeq+Ad6qDuwcGA3Ffy6gFXnEyoMCmsQYVJYQ0nzWYy3fc5NhQmzfoCAAB8ag4l4c/5v/8leNEOOu6GPn6rSwfefp1Ylx6l6PmeDnlETrMAzjVvv0bw4ikKQWBA1qTVTqMI/c69+1z6P799p1jH+wQ6PvnKk3EqACgLy/m9mzZtcel7f3yXS7/vvX8tr/E8TUrIr5dug/qHqe/BbR+QduRnu+hZzIICM9xVDHRnUliDCpPCGvBExmeeKM742AO8p47AWJbk+io5eRQ+vpuO//ndHYIXYCNMy77+Fbp8lRyB6qTIHZBOyPGl3E0R8ktVlmc55mi0q02nSQUOR6mb29iY9JT7QqS2U478+83mKD88b6iaPMsX52XlZ79JNp8PbN1NP/OFr8t799PxP/Qe2c7ni21vc+kHnpUDgipD9B72/fgdRaUQ6M6ksAYVJoU1qDAprKHEvQYoayBl2B/zptGj/PXO3wlefi0dd3MtLYLnTZAtkfsAuQbAL0MawMIdmJedfp0M2T5Jw4Z0GqgdTiCeFLwQC5s0TKcpBy1GVgJ2UeZlKCunIyBrxZMz/rY9zG3glJPbIPH71WJdntmAZl1evoWGAmX+R9bb3fwF6hb8YnOT4I0OyFaHxUB3JoU1qDAprKGkao5PPs/45bj264OUDTDzN78UvESA1EaoVybr5+vqXNo7Skdyr0d+NYwRDw1VgOz4L7LsAQCZWsoakXWopCO/Mzzg0pmhAbHMxwb/5GJSfTjs3rlRY4CP6IZD9/YYatqbZSaD4blOsZY9McPD3vIoNdC/+s23Ct7P+uC4oTuTwhpUmBTWUOKm8kR6c3KrPughdbX71s8I3qiXVGLAkH+HBVHzrHw77ZGebGRq1es3vnaWTnOO8UpyrNYs65EnvdDcuS7d1ENBWXjkZ2JdtppUTfbamwTPy4LMQUN9BYKklvKjNJY+Y1QWetN0ovVlpXffEyBVjAmpRqNB0qPdw0bj1hPYZnRnUliDCpPCGlSYFNZw0mb0hrxS7/+xm6L/j0YuF7wci6ybI+Z5oN2JsmvmJ112TLuYPMsaMMv5xPhbyYLUCNlhb+mkqHt+xz6xLthW7dJP9smkfu6N9xo2Gc9mQCQPu9l1j78Sc6aclxVn+INGxgKLQgwdlEUWEdMNUgR0Z1JYgwqTwhpOWg64mWMcYDt33qhrw0nownUm4U3dfq5oTJU6GGDfYVOQGqsOx6VbIsxy5bxpeXTPMg+7majoGK1hJ1snYTTr57NTpljp90zVn6846M6ksAYVJoU1qDAprKGkNpOX2RjH1GkxnT1VJ94pm59PdY4vFpOfzo9pj5BnzeLzdRRauenL/yrWvXSIQhU7ZW4cBHkGwwkWd3CXizmbeKoXIW2vKQYjFQndmRTWoMKksIaSqjlfkFrjOB7zGMww9fCUyXlmzbl1GMdndm+fh/TXVZe+SawbfrbDpXe2y4bzPlYJX7RqmWKhByZ/r1O5FEyeqSyLge5MCmtQYVJYgwqTwhpKajNFN9PM2WPq6rnONnU7P5OXrjXCK4I/pgfJyrhrVIZT9nRTH4JktFzwcmamgMVnKmL15D8nzMNLirqa7kwKa1BhUlhDSVvqzGiocm/mGOXhU0W3TzfE0vIbBFl2vn8ql8gpBK7leobGtaWOorRQYVJYgwqTwhpK6hrwl1Fi/fHYaqW062wgUP7Ka051mLOEi4HuTAprUGFSWENJXQOK1zd0Z1JYgwqTwhpUmBTWoMKksAYVJoU1qDAprEGFSWENKkwKa1BhUliDCpPCGlSYFNagwqSwBhUmhTWoMCmsQYVJYQ0qTAprUGFSWIMKk8IaVJgU1qDCpLAGFSaFNagwKaxBhUlhDf8P8j8SCP3POF0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c5916f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "\n",
    "softmax_logits = tf.nn.softmax(logits)\n",
    "top_k = tf.nn.top_k(softmax_logits, k=5)\n",
    "\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver = tf.train.import_meta_graph('./lenet.meta')\n",
    "    saver.restore(sess, \"./lenet\")\n",
    "    test_softmax_logits = sess.run(softmax_logits, feed_dict={x: test_images_normalized, keep_prob: 1.0})\n",
    "    test_top_k = sess.run(top_k, feed_dict={x: test_images_normalized, keep_prob: 1.0})\n",
    "    \n",
    "fig, axs = plt.subplots(8,1, figsize=(9, 19))\n",
    "axes = axs.ravel()\n",
    "\n",
    "for i in range(len(test_softmax_logits)):\n",
    "    print('Test Image {}'.format(i))\n",
    "    print()\n",
    "    axes[i].axis('off')\n",
    "    axes[i].imshow(cv2.cvtColor(test_images[i], cv2.COLOR_BGR2RGB))\n",
    "    axes[i].set_title('Test Image {}'.format(i))\n",
    "    print('First Softmax Probability : {:.3f}'.format(100*test_top_k[0][i][0]))\n",
    "    print('Second Softmax Probability : {:.3f}'.format(100*test_top_k[0][i][1]))\n",
    "    print('Third Softmax Probability : {:.3f}'.format(100*test_top_k[0][i][2]))\n",
    "    print('Fourth Softmax Probability : {:.3f}'.format(100*test_top_k[0][i][3]))\n",
    "    print('Fifth Softmax Probability : {:.3f}'.format(100*test_top_k[0][i][4]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook Traffic_Sign_Classifier.ipynb to html\n",
      "[NbConvertApp] Writing 906008 bytes to Traffic_Sign_Classifier.html\n"
     ]
    }
   ],
   "source": [
    "! jupyter nbconvert --to html Traffic_Sign_Classifier.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Writeup\n",
    "\n",
    "Once you have completed the code implementation, document your results in a project writeup using this [template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) as a guide. The writeup can be in a markdown or pdf file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 4 (Optional): Visualize the Neural Network's State with Test Images\n",
    "\n",
    " This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.\n",
    "\n",
    " Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the [LeNet lab's](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.\n",
    "\n",
    "For an example of what feature map outputs look like, check out NVIDIA's results in their paper [End-to-End Deep Learning for Self-Driving Cars](https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/) in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.\n",
    "\n",
    "<figure>\n",
    " <img src=\"visualize_cnn.png\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above)</p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n"
   ]
  },
  {
   "cell_type": "code",
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   "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(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):\n",
    "    # Here make sure to preprocess your image_input in a way your network expects\n",
    "    # with size, normalization, ect if needed\n",
    "    # image_input =\n",
    "    # Note: x should be the same name as your network's tensorflow data placeholder variable\n",
    "    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function\n",
    "    activation = tf_activation.eval(session=sess,feed_dict={x : image_input})\n",
    "    featuremaps = activation.shape[3]\n",
    "    plt.figure(plt_num, figsize=(15,15))\n",
    "    for featuremap in range(featuremaps):\n",
    "        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "        if activation_min != -1 & activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin =activation_min, vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_min !=-1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin=activation_min, cmap=\"gray\")\n",
    "        else:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  }
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