diff --git a/CarND-Traffic-Sign-Classifier-resubmit.ipynb.old b/CarND-Traffic-Sign-Classifier-resubmit.ipynb.old new file mode 100644 index 0000000000..d988cd953d --- /dev/null +++ b/CarND-Traffic-Sign-Classifier-resubmit.ipynb.old @@ -0,0 +1,2648 @@ +{ + "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": [ + "## Step 0: Load The Data" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Images : (34799, 32, 32, 3)\n" + ] + } + ], + "source": [ + "# Load pickled data\n", + "import pickle\n", + "\n", + "# TODO: Fill this in based on where you saved the training and testing data\n", + "# Below are the details of the training, validation and test data\n", + "# main_folder is used to reduce typing time!\n", + "\n", + "training_file = \"./traffic-signs-data/train.p\"\n", + "validation_file= \"./traffic-signs-data/valid.p\"\n", + "testing_file = \"./traffic-signs-data/test.p\"\n", + "\n", + "with open(training_file, mode='rb') as f:\n", + " train = pickle.load(f)\n", + "with open(validation_file, mode='rb') as f:\n", + " valid = pickle.load(f)\n", + "with open(testing_file, mode='rb') as f:\n", + " test = pickle.load(f)\n", + " \n", + "X_train, y_train = train['features'], train['labels']\n", + "X_test, y_test = test['features'], test['labels']\n", + "X_valid, y_valid = valid['features'], valid['labels']\n", + "print('Images : ', X_train.shape)" + ] + }, + { + "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": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training shape = (32, 32, 3)\n", + "Number of training examples = 34799\n", + "Number of validation examples = 4410\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", + "import cv2\n", + "import matplotlib.pyplot as plt\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.shape[1:4]\n", + "print(\"Training shape =\", image_shape)\n", + "\n", + "# TODO: How many unique classes/labels there are in the dataset.\n", + "#n_classes = np.unique(y_train)[-1] + 1 # different way of getting the # classes\n", + "n_classes = len(np.unique(y_train))\n", + "\n", + "print(\"Number of training examples =\", n_train)\n", + "print(\"Number of validation examples =\", n_validation)\n", + "print(\"Number of testing examples =\", n_test)\n", + "print(\"Image data shape =\", image_shape)\n", + "print(\"Number of classes =\", n_classes)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "### Data exploration visualization goes here.\n", + "### Feel free to use as many code cells as needed.\n", + "import random\n", + "# Visualizations will be shown in the notebook.\n", + "%matplotlib inline\n", + "\n", + "# show image of 10 random data points\n", + "fig, axs = plt.subplots(1,5, figsize=(15, 6))\n", + "fig.subplots_adjust(hspace = .2, wspace=.001)\n", + "axs = axs.ravel()\n", + "for i in range(5):\n", + " index = random.randint(0, len(X_train))\n", + " image = X_train[index]\n", + " axs[i].axis('off')\n", + " axs[i].imshow(image)\n", + " axs[i].set_title(y_train[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Include an exploratory visualization of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 3) (32, 32, 1, 3)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def random_translate(img):\n", + " rows,cols,_ = img.shape\n", + " \n", + " # x and y directional translation\n", + " px = 2\n", + " dx,dy = np.random.randint(-px,px,2)\n", + " M = np.float32([[1,0,dx],[0,1,dy]])\n", + " dst = cv2.warpAffine(img,M,(cols,rows))\n", + " dst = dst[:,:,np.newaxis]\n", + " return dst\n", + "\n", + "X_train_normalized = (X_train - 128)/128\n", + "\n", + "test_img = X_train_normalized[1000]\n", + "test_dst = random_translate(test_img)\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(test_img.squeeze(), cmap='gray')\n", + "axs[0].set_title('original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n", + "axs[1].set_title('translated')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [] + }, + { + "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": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 3) (32, 32, 3)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def random_brightness(img):\n", + " shifted = img + 1.0 # shift to (0,2) range\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", + " dst = shifted * coef - 1.0\n", + " return dst\n", + "\n", + "test_dst = random_brightness(test_img)\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(test_img.squeeze(), cmap='gray')\n", + "axs[0].set_title('original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n", + "axs[1].set_title('brightness adjusted')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 3) (32, 32, 1, 3)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def random_scaling(img): \n", + " rows,cols,_ = img.shape\n", + " # transform limits\n", + " px = np.random.randint(-2,2)\n", + " # ending locations\n", + " pts1 = np.float32([[px,px],[rows-px,px],[px,cols-px],[rows-px,cols-px]])\n", + " # starting locations (4 corners)\n", + " pts2 = np.float32([[0,0],[rows,0],[0,cols],[rows,cols]])\n", + " M = cv2.getPerspectiveTransform(pts1,pts2)\n", + " dst = cv2.warpPerspective(img,M,(rows,cols))\n", + " dst = dst[:,:,np.newaxis]\n", + " return dst\n", + "\n", + "test_dst = random_scaling(test_img)\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(test_img.squeeze(), cmap='gray')\n", + "axs[0].set_title('original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n", + "axs[1].set_title('scaled')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 3) (32, 32, 1, 3)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def random_warp(img):\n", + " \n", + " rows,cols,_ = img.shape\n", + "\n", + " # random scaling coefficients\n", + " rndx = np.random.rand(3) - 0.5\n", + " rndx *= cols * 0.06 # this coefficient determines the degree of warping\n", + " rndy = np.random.rand(3) - 0.5\n", + " rndy *= rows * 0.06\n", + "\n", + " # 3 starting points for transform, 1/4 way from edges\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", + "\n", + " dst = cv2.warpAffine(img,M,(cols,rows))\n", + " \n", + " dst = dst[:,:,np.newaxis]\n", + " \n", + " return dst\n", + "\n", + "test_dst = random_warp(test_img)\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(test_img.squeeze(), cmap='gray')\n", + "axs[0].set_title('original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n", + "axs[1].set_title('warped')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X, y shapes: (34799, 32, 32, 3) (34799,)\n", + "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", + "29 : \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: (34799, 32, 32, 3) (34799,)\n" + ] + } + ], + "source": [ + "X_train_normalized = (X_train - 128)/128\n", + "print('X, y shapes:', X_train_normalized.shape, y_train.shape)\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 < 50:\n", + " for i in range(50 - 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 = random_translate(random_scaling(random_warp(random_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)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "ename": "ValueError", + "evalue": "low >= high", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mrnd_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mpicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrnd_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.randint (numpy/random/mtrand/mtrand.c:16117)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: low >= high" + ] + } + ], + "source": [ + "choices = list(range(len(input_indices)))\n", + "print(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": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# histogram of label frequency\n", + "hist, bins = np.histogram(y_train, bins=n_classes)\n", + "width = 0.8 * (bins[1] - bins[0])\n", + "center = (bins[:-1] + bins[1:]) / 2\n", + "plt.bar(center, hist, align='center', width=width)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[180, 1980, 2010, 1260, 1770, 1650, 360, 1290, 1260, 1320, 1800, 1170, 1890, 1920, 690, 540, 360, 990, 1080, 180, 300, 270, 330, 450, 240, 1350, 540, 210, 480, 240, 390, 690, 210, 599, 360, 1080, 330, 180, 1860, 270, 300, 210, 210]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "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", + "# Visualization\n", + "#training data \n", + "hist_train_y = []\n", + "hist_train_x = []\n", + "\n", + "train_y_list = y_train.tolist()\n", + "\n", + "for i in range(0, n_classes):\n", + " hist_train_x.append(i)\n", + " count = train_y_list.count(i)\n", + " hist_train_y.append(count)\n", + "b_width = 0.9\n", + "fig, ax = plt.subplots()\n", + "rects1 = ax.bar(hist_train_x, hist_train_y, b_width, label='Labels')\n", + "ax.set_xticks(hist_train_x)\n", + "ax.set_xticklabels(train_y_list)\n", + "\n", + "#plt.plot.bar(hist_train_x, hist_train_y, 'b-')\n", + "print (hist_train_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.xlabel('Labels')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Training Data Set')\n", + "plt.axis([0, 43, 0, 2200])\n", + "plt.grid(True)\n", + "\n", + "plt.plot(hist_train_x, hist_train_y, 'b-')\n", + "plt.show()\n", + "#print (hist_train_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X Train Normalized - mean 1.21925099939\n", + "X Train Normalized - mean 1.21879869422\n" + ] + } + ], + "source": [ + "## Normalize the train and test datasets to (-1,1)\n", + "\n", + "X_train_normalized = (X_train - 128)/128 \n", + "X_test_normalized = (X_test - 128)/128\n", + "\n", + "print(\"X Train Normalized - mean\", np.mean(X_train_normalized))\n", + "print(\"X Train Normalized - mean\", np.mean(X_test_normalized))" + ] + }, + { + "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": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X Train RGB shape: (34799, 32, 32, 3)\n", + "X Test RGB shape: (12630, 32, 32, 3)\n", + "X Validate RGB shape: (4410, 32, 32, 3)\n", + "X Train Grayscale shape: (34799, 32, 32)\n", + "X Test Grayscale shape: (12630, 32, 32)\n", + "X Valid Grayscale shape: (4410, 32, 32)\n", + "1.21925099939\n", + "1.21879869422\n", + "Original shape: (34799, 32, 32, 3)\n", + "Normalized shape: (34799, 32, 32, 3)\n", + "18\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Import the required processing modules\n", + "\n", + "import random\n", + "\n", + "#image_x_shape = X_train.shape\n", + "\n", + "### 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", + "def conv_rgb2gray(rgb):\n", + " return np.dot(rgb, [0.299, 0.587, 0.114])\n", + "\n", + "print('X Train RGB shape:', X_train.shape)\n", + "print('X Test RGB shape:', X_test.shape)\n", + "print('X Validate RGB shape:', X_valid.shape)\n", + "\n", + "\n", + "if (X_train.shape[3] == 3):\n", + " # Grayscale conversion of image\n", + " X_train_gray = conv_rgb2gray(X_train)\n", + " X_valid_gray = conv_rgb2gray(X_valid)\n", + " X_test_gray = conv_rgb2gray(X_test)\n", + " print('X Train Grayscale shape:', X_train_gray.shape)\n", + " print('X Test Grayscale shape:', X_test_gray.shape)\n", + " print('X Valid Grayscale shape:', X_valid_gray.shape)\n", + " \n", + " \n", + " from sklearn import preprocessing\n", + " \n", + " for i, picture in enumerate(X_train_gray):\n", + " X_train_gray[i] = preprocessing.normalize(picture, norm='l2', axis=1, copy=True, return_norm=False)\n", + " for i, picture in enumerate(X_valid_gray):\n", + " X_valid_gray[i] = preprocessing.normalize(picture, norm='l2', axis=1, copy=True, return_norm=False)\n", + " for i, picture in enumerate(X_test_gray):\n", + " X_test_gray[i] = preprocessing.normalize(picture, norm='l2', axis=1, copy=True, return_norm=False)\n", + "\n", + " # Another way to normalize\n", + " X_train_normalized = (X_train - 128)/128 \n", + " X_test_normalized = (X_test - 128)/128\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", + " \n", + " \n", + " # Reshape Grayscale Pictures (Add Dimension 1)\n", + " X_train_norm = X_train_gray.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1)\n", + " X_valid_norm = X_valid_gray.reshape(X_valid.shape[0], X_valid.shape[1], X_valid.shape[2], 1)\n", + " X_test_norm = X_test_gray.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)\n", + "\n", + " # Print one random sample image from the training set and the corresponding label\n", + " index = random.randint(0, len(X_train)-1)\n", + " image = X_train[index].squeeze()\n", + "\n", + " # Print \"before picture\"\n", + " plt.figure(figsize=(2,2))\n", + " plt.imshow(image, cmap=\"gray\")\n", + " print(y_train[index])\n", + " \n", + " X_train = X_train_norm\n", + " X_valid = X_valid_norm\n", + " X_test = X_test_norm\n", + " \n", + " # Save Image Depth in Variable\n", + " image_depth = X_train.shape[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RGB shape: (34799, 32, 32, 1)\n", + "Grayscale shape: (34799, 32, 32, 1)\n" + ] + } + ], + "source": [ + "### Preprocess the data here.\n", + "### Feel free to use as many code cells as needed.\n", + "\n", + "# Convert to grayscale\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", + "print('RGB shape:', X_train_rgb.shape)\n", + "print('Grayscale shape:', X_train_gry.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Completed Preprocessing\n" + ] + } + ], + "source": [ + "X_train = X_train_gry\n", + "X_test = X_test_gry\n", + "\n", + "print('Completed Preprocessing')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Print one random sample image from the training set and the corresponding label\n", + "#index = random.randint(0, len(X_train)-1)\n", + "\n", + "# Visualize 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": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Un-Shuffled \n", + "\n", + "[41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31]\n", + "\n", + "Shuffling completed \n", + "\n", + "[ 4 16 17 17 2 38 2 42 5 23 8 9 8 23 31 8 2 31 1 28 8 13 4 29 17\n", + " 5 17 22 12 10 10 12 12 9 1 1 18 9 15 12 12 17 2 13 5 13 35 1 35 30\n", + " 12 14 11 3 35 17 9 26 36 5 17 17 35 2 35 33 13 26 4 38 41 27 23 16 2\n", + " 10 31 15 5 25 25 41 8 9 35 3 35 2 3 10 15 13 2 14 6 7 12 5 12 10\n", + " 4 13 10 12 2 11 8 38 17 18 3 38 2 2 42 7 25 12 10 1 2 11 2 12 9\n", + " 30 3 6 13 27 13 3 7 42 26 25 2 5 0 25 10 21 2 10 33 38 15 1 12 7\n", + " 35 27 18 9 10 5 1 10 7 12 4 20 5 13 1 1 4 18 1 3 8 9 3 12 13\n", + " 18 39 42 35 38 31 5 25 25 8 5 8 2 5 2 23 13 10 12 9 20 35 3 21 9\n", + " 2 9 2 27 2 25 16 2 20 5 9 24 1 2 25 3 18 1 2 24 15 15 10 9 15\n", + " 1 41 30 38 1 17 33 33 5 35 1 1 4 35 1 41 15 4 11 3 18 7 3 3 2\n", + " 38 1 12 25 2 4 16 17 11 5 7 22 38 30 10 26 1 3 2 13 31 18 17 8 40\n", + " 16 7 8 38 8 31 13 5 12 5 9 12 14 11 13 2 11 13 5 23 20 33 39 11 40\n", + " 4 4 9 8 3 8 5 12 13 23 17 36 32 38 11 2 4 7 19 34 7 30 10 3 9\n", + " 1 1 16 28 12 5 22 13 33 25 25 25 12 14 1 8 16 8 21 1 13 4 33 22 12\n", + " 5 4 35 4 2 5 18 11 4 4 23 8 25 7 3 7 42 1 18 24 17 35 10 2 8\n", + " 31 11 18 10 1 1 33 5 25 40 8 15 7 3 32 1 5 14 25 15 16 28 10 12 38\n", + " 35 11 4 1 9 4 38 4 26 10 33 1 25 38 8 9 4 23 4 37 38 30 1 14 38\n", + " 10 22 38 5 5 4 14 11 18 9 14 37 9 33 9 38 38 12 22 17 4 25 38 30 7\n", + " 40 38 1 12 12 17 3 13 18 13 18 38 16 5 32 4 3 9 11 40 28 3 30 40 9\n", + " 6 35 25 25 4 7 16 38 7 1 13 41 1 2 7 28 3 1 2 11 15 28 12 3 8]\n" + ] + } + ], + "source": [ + "### Preprocess the data here. Preprocessing steps could include normalization, converting to grayscale, etc.\n", + "### Feel free to use as many code cells as needed.\n", + "\n", + "print(\"\\nUn-Shuffled \\n\")\n", + "print(y_train[0:500])\n", + "\n", + "# shuffle the data\n", + "from sklearn.utils import shuffle\n", + "\n", + "X_train, y_train = shuffle(X_train, y_train)\n", + "\n", + "print(\"\\nShuffling completed \\n\")\n", + "print(y_train[0:500])" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0546461022947\n", + "0.0542052554114\n" + ] + } + ], + "source": [ + "print(np.mean(X_train))\n", + "print(np.mean(X_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.999573077326\n", + "-0.999576521442\n" + ] + } + ], + "source": [ + "## Normalize the train and test datasets to (-1,1)\n", + "\n", + "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))" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "def random_translate(img):\n", + " rows,cols,_ = img.shape\n", + " \n", + " # allow translation up to px pixels in x and y directions\n", + " px = 2\n", + " dx,dy = np.random.randint(-px,px,2)\n", + "\n", + " M = np.float32([[1,0,dx],[0,1,dy]])\n", + " dst = cv2.warpAffine(img,M,(cols,rows))\n", + " \n", + " dst = dst[:,:,np.newaxis]\n", + " \n", + " return dst" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original shape: (34799, 32, 32, 1)\n", + "Normalized shape: (34799, 32, 32, 1)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Original shape:\", X_train.shape)\n", + "print(\"Normalized shape:\", X_train_normalized.shape)\n", + "\n", + "\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('Train original')\n", + "axs[0].imshow(X_train[0].squeeze(), cmap='gray')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].set_title('Train normalized')\n", + "axs[1].imshow(X_train_normalized[0].squeeze(), cmap='gray')\n", + "\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('Test original')\n", + "axs[0].imshow(X_test[0].squeeze(), cmap='gray')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].set_title('Test normalized')\n", + "axs[1].imshow(X_test_normalized[0].squeeze(), cmap='gray')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original shape: (34799, 32, 32, 1)\n", + "Translated shape: (32, 32, 1)\n", + "Original shape: (12630, 32, 32, 1)\n", + "Translated shape: (32, 32, 1)\n", + "shape in/out: (32, 32, 1) (32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfkAAADSCAYAAABJqrmlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAHs9JREFUeJzt3XvM3NWd3/HvIRCwje3HN3zDV/AVwqUkkAQnQYuUTaMmpBJqV81K3apI0baKqlWVatW72ixaqWqlrFpt0/aPlTawWpYWZdPGamIRNyEEaOM0GBswGN/tx5gnvgJODP71jxmqZ33ex5zx2GNz9v2SIsXfZ2Z+5/ebmXOYZz7P96Su60KSJLXnqss9AEmSdGm4yEuS1CgXeUmSGuUiL0lSo1zkJUlqlIu8JEmNcpEfgZTSh1JKp1JKS0d0vJdTSp+62Ld9n8d5KKW0edjHkXTlGvVc9n5SSk+llH5r1Pf9ILn6cg/gSpRSOjXpn1Mj4pcR8W7/31/puu6RQR6v67p3I+L6izS8muOtuRS3lfTB8kGfyzQ8F3nQdd3/fxGnlHZHxENd120q3T6ldHXXde+MYmznc6WMQ9KV4YM6l+ni8df1FyCl9PWU0p+mlP4kpXQyIn4zpfSJlNIzKaVjKaVDKaU/SCld07/91SmlLqW0vP/vb/V/vjGldDKl9JOU0orzHO9LKaVt/cd+MqW0ZtLP9qeUvpZS2hoRb02q3df//1P7xzuWUtqeUvrd/ps94LZf75/Tt/rjeiGl9Fcm3fafppRe6/9sW0rpixftokoauVHOZf256NGU0kT/sZ9LKc3t/2xOSumP+sc7mlL6r5Pq300pHenXv5NSWnye83kopfRS/7YbU0pLJv3sc/2vJ4+nlL4REeliXccrmYv8hfvrEfFoRMyMiD+NiHci4h9ExNyIuDciPhcRXznP/f9WRPyziJgdEXsj4l/TjVJK6yLiWxHx1YiYFxGbIuI7773p+n4jIv5qfyzn+lcRsSgilkfEr0fEb77PeX0pIv44IsYiYmNE/MGkn+2I3rnNjIjfi4hHU0rz3+fxJF3ZRjKXRcTfid5XBjdGxJyI+HsRcbr/s0cj4sMRsT4i5kfEN/r1qyLiP0fE0ohYFhFnJv3sL0gpPRgRX4uIB6I3Vz7bf9xIKd0QEY9HxO/2z2t/RNxznnNqhov8hXuq67rvdF13tuu6t7uu+99d1z3bdd07Xde9FhH/KSI+c577P9513f/puu5MRDwSEXcUbvcbEfHnXdc92b/t70fEjPiLL9BvdF23v+u6t+H+fyMifq/rumNd1+2LiH//Puf1v7qu+5/9797+ePK4uq57rOu6Q/1zfjQidkfER9/n8SRd2UY1l52J3gJ7c9d17/bvc6r/afv+iPjtruuOdl33q67rfhgR0XXdka7rnuiP60REPHyesXwlIh7uuu7l/lcOX4+Iu/uf/P9aRPzf/mOdiYh/GxFHBrlIH1Qu8hdu3+R/pJTWppT+R0ppPKV0InqfoOee5/7jk/7/W1EOsyyKiD3v/aPrurPR+6/Qyb+y2nfunSZZeM7Pz3dbGte09/6RUvqtlNLP+79qOxYRa+P85yjpyjequeyPovebyMdSSgdSSr+fUro6IpZExBtd1x0/9w4ppWkppf+SUtrbH8uT5xnLsoj4D5Pmpzci4mz0fnOwaPJ5TppHm+cif+HO3b7vmxHxQvT+K3VGRPzzuDjf+RyM3os3IiJSSldF70V74DxjmWy8f/v3LCnd8HxSSisj4g8j4rcjYk7XdWMR8VL8JfleS2rYSOay/if0f9l13bqI2BC9rwm+HL3Fd25KaQbc7R9FxIqIuLs/ll87zyH2RcTf7bpubNL/pnRd92xEHIpJc9+kebR5LvIXz/SIOB4Rb/a/Rz/fd1iDeCwivphSuq//PfzXIuJk9L5vqr3/P04pjaWUboyIv3+B47g+epPBkYhIKaWHovdJXlJbLslcllL6tZTSrf0F9kT0fn3/bv9rxE3R+xQ+llK6JqX06UljeSsijqaU5kTvPzhK/mNE/JP+mKP/WA/2f/bfI+KOlNID/d8e/E70vrdvnov8xfMPI+JvR28B/mb0AixD67puW/9x/zB6C+znIuKL/e+VavyLiDgcve/Pvxe9Rf+XFzCO56MXwnsuev9VvDbq/0ND0gfHJZnLovcr8/8WvQV+W/QW9j/p/+y9QPCO6M1XX+3/+99FLxA4ERFPRy8MjLqu+7P+7f+s/6v956MXNo6u6w5HxN+MiH/Tf6yl8Zdk/kpdd77f9Ko1KaWvRsSXuq67/3KPRZJ0aflJvnEppcUppU+mlK7q/xrrdyLiics9LknSpWfHu/ZdG72/M10eEUej9+uxb17OAUmSRsNf10uS1Ch/XS9JUqNc5CVJatRIv5PfsGFD9t3Au+++m93uQx/6UNXjlb5qOHv2bFY7cyb/izM69qxZs7La9dfnDZw+/OEPZ7WU8n4R9HgREevWrctq9957b1a7++67s9pbb72V1R577LGstnnz5qw2Pj6e1a699locY+01o+tNt5s2bVpWu/rq/CV48uTJqmOUjlNb+8lPfmIjH1VLKfndpq4YXddVzV9+kpckqVEu8pIkNcpFXpKkRo30O/mFCxdmtV/+Mu+wSt9t03eql8LUqVOzWm1G4LrrrstqdH4REbt3785q06dPr6odPHgwq7300ktZ7fTp01ltxox8Dwj6XjyCr8WvfvWrquO88847WY2yDeSaa67JaqdOnaq6bwSPkV5T0rAeeuihrEZz1VVX1X+eovwJ5Y/oPUbvHZpDKIdDY6QcTUTEkiX5Pld33JHvMEs1en9+97vfzWpbtmzJasePZxvVFefn2qwQ1WhOpBwW5Zbo/AbJFNFtH3nkEbx/DT/JS5LUKBd5SZIa5SIvSVKjXOQlSWrUSIN3FGagQFSpOcu5SmEsCjPMnj276jFnzpyZ1ShYRuhc3nzzTbztkSNHshqF53bu3JnVKIQzMTGR1SgIWPsclOpTpkypqlFDGzo2BXvouaIGQBERJ06cyGo0boN3uhTmzJmT1Sh4RaG2UhiL3t+1r18K3lGNHo/en6XA8+HDh7Pajh07shrN5ceOHctqe/bsyWoULKT5gs4vgs+HgnJUo+tDwTu6PrVB5PPVLyY/yUuS1CgXeUmSGuUiL0lSo1zkJUlq1EiDd9StiLqg1XZLK3WToyDG22+/ndVqAxeEAhdjY2NZjTrMleoUJKRrRp3oKGRHO+BRKKgU/qCwGx2HbkfXh+5LISMK05XCj7XPa2nHQmkYtcFhmpNKr0l6rVNXt9pgLdXoPV97uwiee6kTJwX0agPKNM8N0jmQ0DnStaXnpvZ6UxC5FBKvnZeGCQ77SV6SpEa5yEuS1CgXeUmSGuUiL0lSo0YavKNACQUpKMhFwYVSaIEek+5PHYwIhf6o89Kdd96Z1QYJ1xAKs9Ruu0thH+omV9pqlq4ZdbKj0AydHwVpCD3/pY53dOxhOoZJg6DXHwWvKPBamn9qu+PRsem9TEFAej/QPFd6z9Jj0nv+6NGjVfeluaq282kpHEgBXJo7KURY6qJ3Lgr+0uOV5nsaO81fwwSH/SQvSVKjXOQlSWqUi7wkSY1ykZckqVEu8pIkNWqk6XpKb1JqsDbtOEjikJKWlBxdtWpVVtuwYUNWoxaHP/3pT7Ma7bEcETE+Pp7VSvtL16BrQY9H5zx//nx8zNtuuy2rUTqf/gJg3759WY2S+dRSklLHpbQrpZbpWlAKVhoWvccoSU1zWm16vHQcSopPnz49q9G8S221qS136S95av+ihlpU1ybXac6mc6FaBM8t9Fc6tSn82pbg9Hh039JxhlkHiJ/kJUlqlIu8JEmNcpGXJKlRLvKSJDVqpME7aqVIIYNFixZltUFaBVJrxwULFmS1e++9N6t94hOfyGp79uzJahs3bsxqe/furR4joXFTwK+2pSQF0CgwQ3s+R0Q8+eSTWW3t2rVZbf369VmNAkAHDhzIaocOHcJjn4tCexF8fSYmJqoeUxoWhURpTqNWtaXgcG3wit5jN910U1ZbtmxZ1ePRPEfv2YiI48ePZzUKkVH4ja4F3ZeuD82RFAaOiFi8eHFWo7Vl1qxZWY3a8da20KZzLoUNawOVw/CTvCRJjXKRlySpUS7ykiQ1ykVekqRGjTR4d+zYsarbURchCpYNElCgQMry5cuz2rPPPpvVfvjDH2Y16uhGXY3mzJmD41m9enVWu+OOO7La0qVLsxqFOKiT086dO7Pa888/n9UoMBjBzxeF2l555ZWsRp0DqRMUhf6oNkgXqNo9nqVh0WuaUEB0kC5otEc9zV8rV67MahRaffHFF7Mada4szbHUZY7CzRRqo/mL5ncK99H88/rrr+MYf/GLX2S1hQsXZrV58+ZltWnTpmU1Ct6dOnUqq9H8U5q/aN6u7eJZy0/ykiQ1ykVekqRGuchLktQoF3lJkho10uAddVSq3W6Rggul4AoFKShA8u1vfzur/exnP8tq1P2IOl0tWbIkq33mM5/BMd5zzz1ZjYIr1B2KgisULKOQCYV1nnrqKRwj1Xfv3p3VaDvdF154IautWLEiq1EoaP/+/VmNAioR9cGn2u0tpUG88cYbWY06nlGt9rUbwfMkzWnbt2/Paq+99lpWo7AavUdoTouIWLNmTVaj9zdtX1sbvKNru2vXrqxW2s6bgoS0xffMmTOzGgUG6fmijp0UxisFGClkTiE7g3eSJCnjIi9JUqNc5CVJapSLvCRJjRpp8I6CC1SrDRlQ+C2CuzFR97atW7dmNeq2Rt2m1q1bl9Vo69pbbrkFx0hjp6AIhVSooxIFcyi0R52cPvrRj1aP8Uc/+lFWo+5ZFHChkAl1BKTrfeLECRwjbV9c+zqThkVd2eh9N4ja7Wup6ybV6L0zY8aMrEZb0t566604RrotvW/pWtB7kYKJFKCm+ZDuG8HzDQX3KOBHawhdMwoM0vUuzT80xkG2Ja7hJ3lJkhrlIi9JUqNc5CVJapSLvCRJjbrswTtS251s+vTpWJ89e3ZWo21Xa7s+UZe4u+++O6stXrw4q5W2OKXABqndTrf28eh2pW0QqYNV7TbAtP0sbf1IgUF6DkqdESmEWBtmkYZFrzVCr7/Sa3Lq1KlZjYJXFNSl7py129SuXbs2q82dOxfHSHM5dSClYCzNaaXwXM19KaAXEbF+/fqsRnMddcyr3Rb96qvzJZRqpbWPxjPItto1nPkkSWqUi7wkSY1ykZckqVEu8pIkNWqkwTva0o9QmIU6EFHXuQgOi9CWgBRwoUDKAw88kNVuvvnmrLZt27asduTIERxjbVe26667LqvdeeedWY1CcrTF5DPPPJPVStu4Urcr6upHY6TwCQWFKBREr5NSMIcek8I+NB5pWBQIpZAcvSZpTovgLaLp/hQcpo6WFKi7/fbbsxq9Rw4cOIBjpLmqNlB34403ZjUKS9P50XbldG0iOJB31113ZTUKQFJQm9AaQkoBTQrZlc7nQvlJXpKkRrnIS5LUKBd5SZIa5SIvSVKjRppGGhsbq7odBVcojELhtwjurFY7nvvuuy+rfeELX8hqL7/8clajUNv+/fvx2BSQoXAGhdooEEddmzZu3JjVTp48ieMhtGUibQ17//33ZzUK823evDmrUdfCRYsWZTW6DhEcABpFFykpotx181wU7lqwYAHeluq0HSptSUthsw0bNmS1G264IavR/EWdKyPq32PUvZKOPTExkdW2bNmS1WhuL4VqaW6gLng0RgoR0rbCFBik4HBpq1jq2Hmxw3h+kpckqVEu8pIkNcpFXpKkRrnIS5LUKBd5SZIaNdJ0/TB79NK+7KUUPSXNx8fHsxolxalN5eHDh7Pa7t27sxq1aP3yl7+MY6TWjk8//XRW2759e9WxX3311axG1/HBBx/MaqXWjN/73vey2ve///2s9tnPfrbq2JQw3bdvHx67FqVO6bVCNWlYp0+frrpd6a9DyJtvvpnVqD02vaYp7U/Hpr+yob+IWblyJY6R/gKAWs7u3bs3q9G5UNtx+mujj33sY1mN9rEvjYdafVManuZyWgfoL5Dorx5Kbblp/qJk/zD8JC9JUqNc5CVJapSLvCRJjXKRlySpUSMN3lFIhYIHFNCicAUFVCK4VSDdn9oZUkiFAinz58/Pah//+MerblcaIwXqKMBGbQ9PnTqV1Wi/ajo/arEbwa04jx49mtUoAEkBRkKvCQqz0HMVwa0r6TFL4RxpGPRao/cN1UphUHovU42CabUtWun9uWLFiqrblY5D8zGdN70XaQ6htuMUli5dRwojU3iO5sTacVOAkYLMpf3k6TGpVmqLW8NP8pIkNcpFXpKkRrnIS5LUKBd5SZIaNdLgHQXYKHhHNQouUAisdBwKsFGNOhPReChQRwE2CthFRGzbti2rUSeoNWvWZLUZM2ZktVJHpXPRtSmF2iiwUxtqpMBObSBlkO50dH8aDwX0pGHVBu9orimFQel9R6/z2jmN5i8K5dJ7tvRe3LVrV1ajPe/nzp2b1WgeGCaAXXpv177nKchc26GQxkNz8SDBu4s9V/lJXpKkRrnIS5LUKBd5SZIa5SIvSVKjRhq8q+06VxtGKG3JRyEHuj8duzYISIEU2mKwFA6k7nYUNKEtaakTFF0LCgXRMSisE1HfEZCeLwofUcCltjMePQcR9ds60nikYdG8UhuyKwWs6D1Kcxq99mu77dF7cZDAGG3dTedIAWUKDtP8Rddn2PmLrgUdu/Y4dH1qg5el+jDd7YgznyRJjXKRlySpUS7ykiQ1ykVekqRGjTR4VxuEoK5qtBUhBThKKLhV272tNrRH53Ls2DEcz6FDh7IadYdatmxZVqOATCnYcS4a95QpU/C2tUGT2g6FFPah29WGWSL4uaHx2PFOl0Jth0wKwZa2yp4+fXrVsYfp9kjhLjoX6pAZwd3taNyLFi3KahQYLHXdPFdtR7+I+sBhbdCRarUhuVJInO5fuu2F8pO8JEmNcpGXJKlRLvKSJDXKRV6SpEaNNHhHAQkKYdRutVgKKNBj1oZharvE0TEopHLw4EEcIx3n9ttvz2qzZs3KarXd9iiEM0ioo7Z7Fl0fuhZUqw0RljpvETrHix1mkSLqu5sNMn/RXEXBNArU0bxSu00tjWdiYgLHSMdZvnx5VqvtzknnVxvULQXvaF6qDeWWtgg/V23gmc4lgq9F6bYXyk/ykiQ1ykVekqRGuchLktQoF3lJkhp12TveUYCDOicNsn0odY6jAMmpU6ey2v79+7PaXXfdldVmz56d1ai73Z49e3CMdI5r1qzJaosXL85qtH0tdf+j7SAPHDiQ1SjIFxGxb9++rEbBFQqfHDlyJKtRaIauAz1/9DqJ4OeQAodUk4ZVG7KjLZpLgTGaJ6+//vqsRl1AqXby5MmsRkFdCptRZ7vS/Sl4t2DBgqxGAVwKGB8+fDirvfbaazgecuLEiaxGcwvNDTRGeq7peSGljpuDbKF+ofwkL0lSo1zkJUlqlIu8JEmNcpGXJKlRIw3eUXhu2rRpVfelMEKpK9G8efOy2pw5c7IahbZ2796d1fbu3ZvVaAtFCodRECaCQyqrVq3KahRIoZDdpz71qaz2xBNPZLXHH388q5UCQBQque+++7IaBSAp6EidnOj5p4BLKXhX25Vv6tSpeH9pGLXd6Wq3M43gINi1115bVaPw765du7LawoULs9ogWzRToI7mRAoc0rhvu+22rPb0009nta1bt2a10vw1f/78rEZrA4WEaRvg2vA3BZmH7W44DD/JS5LUKBd5SZIa5SIvSVKjXOQlSWqUi7wkSY0aabqeUqOUMKV0IdVKLRcpnU3JSDr2jh07stqmTZuyGrV1pPT4+vXrcYyUrqdxU0qdkrpLlizJap///Oez2k033ZTVKF0awWlZSvZv27Ytq1H7SRo3nTMlhKkWwX8hQdxPXpcCzWk0D9D8Q++HCP6LHGr/TMlsamG7ffv2rEYJ96VLl2a1G2+8EcdIf3lT+16mRDq1Cb/nnnuqxlhqy127ZtBfVNF1pL8UoL/6oXa6lNaPGM285Cd5SZIa5SIvSVKjXOQlSWqUi7wkSY0aafCu1Ib2XBSIoxrt+RvBwRfaR5hQ4OLnP/95VqMgzYYNG7LaJz/5STwOBfcosEHnQqEZsmLFiqxGAZdSW0jaz/mZZ57Jalu2bMlqFIir3XuZwjqlkCW9pi52W0ippNSatkYpMEZhLJpvaB6obTH9/PPPZzWaa1avXo1jpPmU5hE6F7odzYcU/KU5pBTK3blzZ1WNrk9t+JvWIJr7Ss81PV+l+fhC+UlekqRGuchLktQoF3lJkhrlIi9JUqNGGryjjkGEwggUCimp7UJF3ZwOHjyY1SiMR2Ez6vj06U9/Gsd48803ZzXa85wek64Pqb0d7TcdEfHcc89lNQohUjcn6tBFe1gPElIhFNihayZdCvQ6JxScK6HgFb2Xp02bltWoI2VtB7ZXX301q5XeSzQe2mN+5syZWa22WyiF2mgufuWVV3CMNK/RfENjnDVrVlY7c+ZMVqvdd57m9gh+rmtrtfwkL0lSo1zkJUlqlIu8JEmNcpGXJKlRI00ozZ8/v+p2FMwYFgVFqCPcsmXLstr4+HhWO3r0aFajbkql7SSpg9stt9yS1VatWpXVpkyZktWo89brr7+e1WjbyR/84Ac4RtqClsKTtM3tunXrsho9B7RNLYVMqFNfBIf0qDPVxe4iJUVw2IyCV7Uh2AieM+gxKWRHcyy99mk7WwqR0dwXwSE0mtNuuOGGrEYhNDpnGuOePXuy2qFDh3CMFBqkrbZru/dRKJvmYnquS50R6XmlcRu8kyRJGRd5SZIa5SIvSVKjXOQlSWrUSIN3FGqr3TaVggelrWupExRteThnzpysRsEMQt2hKHhSCt698MILWW3Hjh1ZbWxsLKtRcIUCHMePH89qtC1jKehIHQEppEIhOwqPbN26NavR1pEUZip13qLgHb2mLkWYU6LXb22YahC1YSya5yj8Rtu40txQ2saVOsdRGJmCchTepXOhzqUUYKN5vFSn+Ys66+3duzernT59OqvVbvdLz0sEh4Tp9WPwTpIkZVzkJUlqlIu8JEmNcpGXJKlRIw3e1W4nSME5Qt2GSnXq5kRbEVKYj7aF3bBhQ1aj4FypGxONh0IudDsK3tVuxUuhjlInwqVLl2Y1CtlRZ7zNmzdnNeoYRc8/heQo6BPBwUYKs1BoRhoWvf4oHEYd4koouEWvXzoOvZ9oDqH3PHWVnDdvHo6R5ioK3tGx6VrQnE0d/Wg8pfWCrhl1AaWQHc1pNG56rmiOLc3PFBymxywFuGv4SV6SpEa5yEuS1CgXeUmSGuUiL0lSo0YavKOuPVSjAAeFTChgFcFbFNYGX2rDGvv3789qH/nIR7Larbfeiseh0B91zKNjUzBj1qxZWY0CbKtXr85q1BErImLfvn1ZbdOmTVntxRdfzGp0LoS6XxEK4URw0OjkyZNZbdiOYxKhTmb0vqPubYO8Juk4NP/RY9KxJyYmshrNfTNnzsTx0DkuX748q9UGxmjcdM4099G5RHDQl+ZTCvXSNaNAXe22wqXrUOqEdzH5SV6SpEa5yEuS1CgXeUmSGuUiL0lSo0aaRqLuPrRVI92OgmUlFCChLnHTp0+vejzqqESdnH784x9ntdI2iNS5adWqVVlt/fr1NUNE1L2PQom0BWwEB+/Gx8ezGoUiKUgzSHjyXKXgZO22nrXHkQZBW5fWBq9K24fS/EWvaQqjUpC19rV/4sSJrEbhtQjeYpXmaBojze80N1BXPQrElbYcp+PQfEHze+01o+dlkO509Bqg149bzUqSpIyLvCRJjXKRlySpUS7ykiQ1ykVekqRGjTRdv3LlyqxGqUHa65hSjJS+jOC0JKXZqWUjpS8psUqJzgMHDmQ12pc4glv30vnQHs/UtpXS55SEpzaKpeQ63bb2rxzo+tBx6HqTUvtb+isHSv5KlwK1d6V0NdUoRR3BfwlEt6UEONVojqWUOqX6qW10BL/v6BzHxsayGv1VE82HtcelcUfUt1GvTdLXpubpdqU5iV4/F5uf5CVJapSLvCRJjXKRlySpUS7ykiQ16rLvJ0/tDGuDK9QSt/SYtTUKm1FIhVpAUiCOWitGcNiDQhjLli3LatQql/ZJpnM5fPhwViuFa2iMdM0InQsdZ8qUKVmt1KaSUMjy7Nmz1feXhkHzEr3+KDhXCl3Rbel9RyE0qtHjUViNgrGDjLH2HClYSHMs7fNeO/9E1Afv6DGHmUMGaaFd+/oZhp/kJUlqlIu8JEmNcpGXJKlRLvKSJDVqpME7CoINE1IpBRwozEChOAp41e7bS49H96WueiXUFak2uEIBtOPHj2c1CgKWzrk2uEIo2EPnQrer7ToYUR/EGSSwI9V6+OGHs9oge8dfLoPseV6LzrG2o2Vt2HpUodphrs+wzzUdm4KJtfwkL0lSo1zkJUlqlIu8JEmNcpGXJKlR6VIEMCRJ0uXnJ3lJkhrlIi9JUqNc5CVJapSLvCRJjXKRlySpUS7ykiQ1ykVekqRGuchLktQoF3lJkhrlIi9JUqNc5CVJapSLvCRJjXKRlySpUS7ykiQ1ykVekqRGuchLktQoF3lJkhrlIi9JUqNc5CVJapSLvCRJjXKRlySpUS7ykiQ1ykVekqRG/T8cozJKQlEX2wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "X_train_scaling = random_scaling(X_train[0])\n", + "X_test_scaling = random_scaling(X_test[0])\n", + "\n", + "print(\"Original shape:\", X_train.shape)\n", + "print(\"Translated shape:\", X_train_scaling.shape)\n", + "\n", + "print(\"Original shape:\", X_test.shape)\n", + "print(\"Translated shape:\", X_test_scaling.shape)\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "axs = axs.ravel()\n", + "axs[0].axis('off')\n", + "axs[0].set_title('Train original')\n", + "axs[0].imshow(X_train[0].squeeze(), cmap='gray')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].set_title('Train scaled')\n", + "axs[1].imshow(X_train_scaling.squeeze(), cmap='gray')\n", + "\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(X_test[0].squeeze(), cmap='gray')\n", + "axs[0].set_title('Test original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(X_test_scaling.squeeze(), cmap='gray')\n", + "axs[1].set_title('Test scaled')\n", + "\n", + "print('shape in/out:', X_test[0].shape, X_test_scaling.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original shape: (34799, 32, 32, 1)\n", + "Translated shape: (32, 32, 1)\n", + "Test shape in/out: (12630, 32, 32, 1) (32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfkAAADSCAYAAABJqrmlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAH1BJREFUeJzt3XnQ3dV93/HPAYwALQjtu9AKEtqQ0GJhC4u4xTNx7HpSO0032xO3Seu2jOt26jYu03bSTJKWIZ4s0yZp3U5CHbIY6gUztky9YMCITUIWiEVCK9r3BYnl9I971T5+vp8fnKv76Eo5er9mMsHf5977O7/fvfcc3ef53O9JOWcBAID6XHahBwAAAM4PFnkAACrFIg8AQKVY5AEAqBSLPAAAlWKRBwCgUizylUgpvZJSeu9A3/ZdHudXUkprun0cACiVUtqdUnpfj471pymlL/biWOcLi/w7SCkd7/N/b6eUTvX533+ni8d9PKX0dwdyrDnnGTnnxwb6tgAuThdqfkop3ZhSevNcH79XUkpXpZRySmnShR7LhXTFhR7AxSznPOTsf6eUXpX0mZzzRfXJNaV0Rc75on/DARhYF/P8xLx08eCTfBdSSpenlP5tSmlzSml/SunelNLw9s8Gt3/VczCldDil9OOU0nUppbslLZX0R+1/cd/d8Ng/n1La2L7vmpTSrD4/251S+hcppZ9IOtqn9r72fw9JKf2v9n03pJT+dUrp5X73P3vb32iP+ysppWMppfUppUV9bntXSmlL+2cbUko/ex4uJYABdh7npx9IurzPbw1ubv/p7uGU0u+llA5J+kL7E//32sfYl1L6nymloX3Gtzul9Ln2vHKkPb4r2z8bl1J6qD22AymlhxvO8db22I+klHallO5JKZ398PqD9v/f1B7n32jf52Ptee5wSumHKaW5fR5vWUppXXu++xNJV3b3LFx4LPLd+ZeS/rqk90maJOkNSfe0f/YZtX5TMlHSKEn/RNKZnPPnJa1V61/dQ9r/+6eklOZJ+h+S/rGkMZK+L+lrfV68kvQLkv6apJFmXL8mabSkqZJ+VtLfe5fz+Jik/y5puKTvSvrtPj/bJGmlpGsl/aakP00pjXqXxwNw4Z2X+UnSKklvtX8+JOf8TJ/6s+3HO/uPg/8gaZyk+ZJukPSr/R7rb0r6GUkzJS2X9Lfb9X+l1twzStJ4Sf+u4RzfaI99hKT3S/q59rmdHY8k3dAe5wMppRWSfl/Sp9WaO/9Y0gMppStSSldJekDSf20/3rckfaThuH9lsMh355clfSHnvCvn/Lqkfy/pF1JKSa0X32hJM3LOb+ac1+acTxQ+7i9Kuj/n/L2c8xlJv67Wi/2WPre5p33cU+b+n5D0aznnIznnrWq9qN/Jwznn7+Sc31LrRf//PsnnnO/LOb+Wc3475/zHknZKWlJ4HgAunPM1PzXZnHP+w5zzWznnUznnF3LOD+ecz+Scd6v14eG2fve5J+e8J+e8T9KD+v9zzxuSJkia0r7/D2TknJ9oj/2tnPMrkv7IHKOvX5b0uznnp9r3+QNJg9Sa01ZJOp1z/v2c8xs553slrT/Ha3HRYJE/R+03ymRJD7Z/7XNY0jNqXdORkv6bWp/A/yKltCOl9OsppcsLH36CpK1n/0d78d2p1r+6z9r+DuMa2+/n9rZ97O7z3ycl9f1b3y/1+dXWYbX+xc0neeAidp7npyY/Nc+klCaklP48pbQzpXRUrQW4/9zRNPf8R0m7JP2flNLLKaV/7g6YUpqbUvpWSmlP+xh3mWP0NVXSvzl7TdrXZbRac+sESTv63X5r/wf4q4ZF/hzl1vZ9OyXdnnMe3uf/rso57885n84535VzvlGtfyF+XNLfOnv3d3n4XWq9GCW1/ram1otwZ98hvMO49qr167mzJndybn2OO1vS70j6h5JG5JyHS3pZUjqXxwPQG+d5fmr6ef/6f5J0QtK8nPMwtX6NXjR3tH8LeWfOeaqkn5f0xZTSreamfyjpabV+IzFMrT8PnD2GG+d2SXf1uybX5Jy/Kuk1/fS8KUlTSsZ7MWOR785/kfQbKaXJkpRSGpNS+rn2f3+w/a/My9QKx70p6a32/fZImv4Oj3ufpI+llFallN4j6QuSDkh6snBcfybpV1NK16aUpkj6R52eWNsQSW9L2ifpspTSr6j1SR7Axe98zU971QrevdsCOFTScUlH27e1n8adlNJHUkrT2r+RONIe21vmpkMlHck5H08p3STpH5z9Qc75dPu+fc/lDyT905TSLallSPtY16gV1LuqHSK8IqX0i5IWlI75YsUi353fkrRG0sMppWOSHpW0uP2ziZL+t6Rjkjao9femP2v/7B5Jfz+ldCil9Fv9HzTnvF7SL6kVANmnVjDlox18JeWLkg6p9aumb7WPe7rTk8s5P63WRPGkWv/Knabyf2gAuLDO1/x0qP3YT7V/5b2o/23a7lIr9HdE0v2S/rKDsc+R9L32+H4g6T/nnB83t/ucpM+klI5L+j21PiD1H8Oft8f5kZzzjyT9M7Xm1sOSXlQr7Jfb+aaPqRV4PqRWaPnrHYz5opRav9VBzVJKn5P0oZzzHRd6LACA3uGTfIVSSpNTSitSSpe1f4V1p1r/kgYAXELoeFenQWp9732qWr92+hO1kq0AgEsIv64HAKBS/LoeAIBKscgDAFCpnv5N/u677w5/G3j77bfD7VpfjTx3b73lvk4ZuT9VuPG42tChQ4tu1zQWV7/ssvhvLlc7dSp2sr388rJmVe7x3Lgl6fTp+K270uvjnkN3zm++Gb8VuG/fvlC77rrr7BjdY77nPe8JtQMHDoTal7/8ZZr6oNiXvvSlMGGUvo+dprmhdP5z85erlc4N7rhNY2maM871OKVziDuXTsbonht3/9J50t2udP1pOrY7zp133hlqOeeiFwqf5AEAqBSLPAAAlWKRBwCgUj39m/zJkydDzf0NqfTvuU1f/7viinhab7zxRskQi8dz7Nixovt2+xXF0r8NuevTyd/YnCuvvLLo/qV/gyrNHIwaFTeROnPmTNExmgwbNqyr+wOlf9t278/S+0r+PeHmkYF+37nHazqGy9K492jpvOTmbFdzmm7nrnlpXqL0b/fdzrG9+Ao7n+QBAKgUizwAAJVikQcAoFIs8gAAVKqnwbtuQnalQQjJh0JKAxfdNMgpPb+meifNdM718UobTDTdv/QcXRimdIzuGKXPX9NxgG5de+21RbcrnS+a3nedNFPpzwWMXXOo119/PdSOHz8eart27bLH2b59e6i5hlPuOO697MY4YcKEUJs5c2aojRs3zo5xyJAhoTZo0KBQKw0HljbiKX3+m5QGDkvxSR4AgEqxyAMAUCkWeQAAKsUiDwBApXoavOsmUFC6A5DUXaej0tBeac09nuTDHq5jlAvSlAZzXJjF1ZqCaqXPVzc74JXuANVtFynCeOjWyJEjQ829P91rrZMwcOlr1c0XbufI3bt3h9r69etD7cUXXwy1EydO2GOXdvcsDbC5bqiuq+imTZtCze0IKklz5swJtfe///2hNnbs2FDrpmuhe647CTe7ObobfJIHAKBSLPIAAFSKRR4AgEqxyAMAUKmeBu9cQKI0ZODCDJ1sNdsUgCvhAi5uG9ZTp06F2tGjR+1jbtu2LdS2bNkSavv37w81F65x3LWdMmVKqLmAiuQ7TrmuX1dddVWolXYtLA3ZNXWMKu2YN9BhFlx6rrnmmlDrZlvYpmCre027gJ8Lpm3YsCHUnnnmmVDbu3dv0XGbdLPFaulxSm935MgRW3/88ceLbrts2bJQW7x4cagNHjw41Lrt2Nltd88SfJIHAKBSLPIAAFSKRR4AgEqxyAMAUCkWeQAAKtXTdP2YMWPiAEzC1KXZS1tFSuXtBx13bLcn8o4dO0LtqaeeCrWf/OQn9jhu7+ZetIp042kao/s2xMKFC0Ptgx/8YKhNnDgx1FzCuPS5culiyafm3XFI16Nbpd/6KH1/Nn07yD3mnj17Qs3NN0888USoubnGjdG9R0aNGmXHOH369FAbP358qLlv47j3p9u33n0DydXcPNd0nOeffz7U3LcU3Jx/6623hlrpvNLJNxcGGp/kAQCoFIs8AACVYpEHAKBSLPIAAFSqp8G7YcOGhZoLgLjAlwupNIWxXMvZ0laRLgDi2kK6lokuHNMUDnStC0tDPK7WFOIpuV3TfQ8fPhxq3//+90Pt0KFDoeb2bV6xYkWouefaBWaaxljaVnSgW0Xi0nPgwIFQ66QFcyn3fnr66adDzc1L7j3rTJo0KdTmzZsXarNnz7b3d4E81966dF6aNWtW0X1d4NmFDSV/zdzc8uqrr4aam7fdenHzzTfbY/fX1PLX1WlrCwAAirDIAwBQKRZ5AAAqxSIPAEClehq8c7oJkTV1G3L3d2GYnTt3htpjjz0Wao888kiouX3i3bm4EKDku//deOONoea6SI0YMSLUXKBk+/btobZ58+ZQc/vYS75TljvO+vXrQ80FgE6dOhVqt99+e6gNGjQo1DrpDla6rzXQidOnT4daN8E711VNkl544YVQe+6550LNvcdcZ72ZM2eG2pIlS0Jt6tSpoebei03c+XQTHHbz+4QJE0LtjjvusONxXTfXrFkTam6PeRfwW7t2baiNHj061FwosWmt6sX8xSd5AAAqxSIPAEClWOQBAKgUizwAAJXqafBu//79oVYaMigN43VybNe1znVPOnjwYNExpkyZEmqLFy+2t73ppptCbezYsaHmukiVmjt3bqi5oMfWrVvt/V3g0F0z1x3KhfkeeuihUHNdpJYtW2bH45QGe7rZfhho4l77pXOV65ApSRs3bgw1N3+5zmiuk92iRYtCzc1VbtvvphBhaedM95ilj+eO7WpN4WYXZHbzzbe//e1QcyFCF9R+9tlnQ23lypWhNnjwYDtGZ6C3peWTPAAAlWKRBwCgUizyAABUikUeAIBK9TR450IqroNat9s3us5qrmOU24rQhexcaGvOnDmhtnz58lBzWyhK5YG60i5SpVznpcmTJ9vbfvSjHw01F+x58MEHQ81143Id+Fy4zwUQXYdAyZ+Pe62w1Sy65eav0veiC3y5IJfkA3nuNe06q82fPz/Urr/++lDr9v3groV7TNcl0BnorbclP8e6+fi1114LNbeN75kzZ0Jt06ZNRcdomu/d2N2a2A1mPgAAKsUiDwBApVjkAQCoFIs8AACV6mnwrjRQ4G7XSfBu165doeaCdy7g4kJ2bgtG15Vt+vTpodbU8ambc3RjLA2udHJct82k657lQjhf+9rXQs2FCF23Pdd1cPXq1XaMQ4cODTXXPcuNEeiE21a0tCvbgQMHimqSf49effXVoeYCXgsXLgy1kSNHhpqbL9z75sSJE3aML730Uqi5sO3JkydDzYXQ3Pm5UK2bk5o63rnHdMe+5ZZbQm337t2h5gJ67vzcNRsyZIgdYydb+Z4rPskDAFApFnkAACrFIg8AQKVY5AEAqFRPg3cu7OG6CJWGQlwXKUnatm1bqLnuUu4xXRepJUuWhNqMGTNCrdvtTN35uMcsDc+5+5Z2kWq6rQuKuC1td+zYEWpr164NNff8b9iwIdRch0HJh2u67ZgIOG57aPf6dR03O5m/3Gv6uuuuCzUXGFuwYEGoHT16NNRc6Nh1p2vaZtvNsd/4xjdCzc3l3XTsdJq697ng3uzZs0Ptk5/8ZKi559pdRzfHuud62rRpdozueR1ofJIHAKBSLPIAAFSKRR4AgEqxyAMAUKmeBu9GjBgRai6kUBos27t3rz2Oq7uOZ9dcc02ouRDZ0qVLQ811vyoNDErSsWPHQm3z5s2h5oIvx48fDzUX1nHn10nHKHdb95ju2CtXrgw1F350AT3XMcqdsyQNGzYs1HrRRQqXHheycu8HdzvXGc11gGwybty4UJswYUKouXnOBfxcJ85vfvOboda0RWrT3Nufm8vdPFm69a27XVOA0a0Z7pq7+cJ1OV23bl2ouZDlkSNHQq1pHXBzb1OX1HPFJ3kAACrFIg8AQKVY5AEAqBSLPAAAlWKRBwCgUj1N18+fPz/USlvdusR1U2LRpS1dCtYl5F0q3LW1dQlKt499U4J2//79obZly5ZQu++++0Ktm1aRnXwDwN3WtYp0LSA/+9nPhprbi95dR3cuTWOcOXNmqLnWxEC3SttEu5r7No1rIyv5dHXpt1oOHToUai4B7t7HLknv0vqSn2NLW2u797eba0qvbSftxN05Dh48ONRcu9nS1Lu73k3XsZM24+eKT/IAAFSKRR4AgEqxyAMAUCkWeQAAKtXT4J1rH+gCVS4U4sIVLown+RaSLszg2kJOnjw51FxowoUDS1tFSj4A8tprr9nb9lfaKtKNx13vppaS7hzd/V240J3fjBkzQu2JJ54oerzDhw/bMTquVaR7TQGduPbaa0PNzQ3utdZJ+1I31w0ZMqTo/hs3bgy1+++/P9Tc3uhu3DfffLMdo5vLXRCwdJ50xy5ted4UanNjdNfRPa+u5uY0F7Jztxs6dKgdowt/dxIkLMEneQAAKsUiDwBApVjkAQCoFIs8AACV6mnwzgUuSrsaucCFC49IPrjlju06HbnOUq47nQtcuHBN037MpWG+0qCcCxaWdo5z11sqD+65c3QBF9eJzl0z9/w1hSzdNXOvFXccoBOum5x7H7vgbydd0FwnPNcZct++fUW3c+8H16XSve9cF0/JB8Y+/vGP29v2Vzq/u5obo+smKPm5Ydy4caHmnq8dO3aEWulz6OZI99qR/NrS1N3zXPFJHgCASrHIAwBQKRZ5AAAqxSIPAEClehq8GzlyZKi54IILXJR2kWqqu60RXRciFzZ79tlnQ+0rX/lKqLmubE1jXL58eai5AJvrIuWCOaXXx4VemoIe7jhujK47lNuq0YVP3PmVdtBrOrYL15RuEwk0ce+x0k6KI0aMCDUX8pX8698F6tz7+4Ybbgi19evXh5rrjDd37txQc+9jSTpw4ECo7dy5M9Tcubi5ePjw4aHmgnOl95V8QLm0M6gL87lju+50LmDsgoqSD3+79a8bfJIHAKBSLPIAAFSKRR4AgEqxyAMAUKmeppG2b98eaqUhOdfxzHUqknxgw4XIXFBuz549oeZCLy4wuHDhwlBzwRNJ2rp1a6iNHz8+1D71qU+FWmnXutIuUk3X0XUUdNfRbdl7/PjxUNu2bVuouefVPX9NXABo7969oeauz6RJk4qPA6xdu7bodm7+Ku2aKfl5yc0369atC7U5c+aE2rRp00LNhf4WL14calOnTrVjdEFYNy+VdoRza4PbhnrLli2hNnHiRDtGNx+7sPUzzzxTdGw317jtbN186sKPkn8eXKfSbvBJHgCASrHIAwBQKRZ5AAAqxSIPAEClehq8c919XMigtIuU6ywk+W1OXcjFBVxckMJty/jkk0+GmgtXLFiwwI7Rdf9zW0e6sJo7FxdWc122SrtISb7bVek2t+45LA0UuS5SY8aMsWN03e2GDRsWak3nCJRy4Tn32nchT/c6b+p45+7vQrDPPfdcqLn3w6JFi0LNBXBd57em4HDpttiOu52bD1evXh1qK1asCLWmbpYuHOjmThdgdFvNuufQrTVu/nHhbcmH9Nwc2w0+yQMAUCkWeQAAKsUiDwBApVjkAQCoVE+Dd48++miolYY1XHDKdTaTfJjBhb5efPHFUHNdrVx4btasWaHmQhhuS1lJmjlzZqi5bVddCKc0ROa6Qz3yyCOh5q6DJE2ePDnUli5dGmpuu9cf//jHRcd2YUPXBdF1xpOkp556KtRcoMmFWT784Q/bxwQcF7Jz85d7rbnXpHt/Sb77m3v9u9u5MN573/veUHPBWHd+Td3XSrtuOu4x3TVz84Abd1NQzQXlXKfRV155JdRcx04X8JsyZUqouUB4U5i8NLjZDT7JAwBQKRZ5AAAqxSIPAEClWOQBAKhUT4N3LijnAhwujOA6NLlgheRDLu7+Loz39NNPh5rbknTZsmWh5gITrnub5MOBTd2lSrjrOHr06FC74447Qm3VqlX2MV1YxIUD3bm4AKMLvbgQodsO0nXfk6SDBw+GmgspDfT2jbj0uOCVe8+XdoCcPn26PY4LzLqaC4e54N3w4cNDbd68eaHmzq8p1Obmm6a57ly58XQS+HPBRNfdbteuXUXjcV353Na+LoDddB1LQ4jd4JM8AACVYpEHAKBSLPIAAFSKRR4AgEqxyAMAUKmeputdatAlI0tT6m5/ekmaNm1aqLlk97Fjx0Lt1VdfDTWXuL/ttttCrbRVpOSvRTetIt3juZrbY3nQoEHFj+m+peDaQr7wwguh5vbEdtfMPX/umwKSb33Zi1aRuPS416p7P5Qmpl07aElavHhxqB04cCDU3LeD3J73P/rRj0LNfTNpyZIldjxO6XxT+u0pV3NjdLWXX37ZjtG1vHa3dXOD+4bW3LlzQ+36668PNTfHNn3zoLRVcjf4JA8AQKVY5AEAqBSLPAAAlWKRBwCgUj0N3pW2heymVaQk3XDDDaH20ksvhZoLYbhw2JNPPhlqrs3qokWLQs0Fw6TykErp7ZzSa9YUSnP1zZs3h5q7Pjt27CgZog3ULViwINSaQkqlASD32gM64d47LrTq3p+u1vSanDFjRqi5FraPPvpoqB05ciTUXDvx73znO6HmWr5+4AMfsGOcPXt2qLmwmptD3LVw7a13794dai5M50K+kg8murnBPYdLly4NNRdMHDNmTKiVBgslH8hzYc5u8EkeAIBKscgDAFApFnkAACrFIg8AQKV6mkZyIQMXPnFdjVxwoamLkAvFrVy5MtRcdyjXWcoFV7773e+G2pkzZ0Lt1ltvtWN0BnpvYXdfd22bgh4bN24MtcceeyzUNm3aFGoucOM6FLqw4syZM0PNdZGS/Dn2IsyCS09T18US7n3XFKB1IVMX4B06dGio/fCHPww1N6e58big21e/+lU7RnfsYcOGhdrVV18dai5k54KFrnbixIlQa7qObs5wY1yxYkWoLV++PNQmTZoUau55cXNNU7iZjncAAOCcscgDAFApFnkAACrFIg8AQKV6GrxzQQgXMnBhjdLOeJIP87kueKtXrw41F6hznZNcSOXrX/96qLlucJL0oQ99KNRuuummUBsyZEiolXaRckFA14nOhekkacOGDaF28ODBUHPhEddFyoUQXehl/PjxRcdoqhO8w/nQSeirP/c6bXpNumCa6yY3b968UBsxYkSoPfTQQ6G2b9++UHv99ddD7dSpU3aM7rbuMZ3SLccdd72bOp+6bnQugD1nzpxQc+Ht06dPF9V6EabrBJ/kAQCoFIs8AACVYpEHAKBSLPIAAFSqp8G7cePGhVpp4MJxXZskH3IYPnx4qLluRa4j0po1a0Jtz549oeaCNDt37rRjvPfee4uO7WoujOdCdm7b3GPHjoWaCxRJ/nxceNJd29tuuy3UVq1aFWpTp04NNRfaawopuXo3wR6gidvGtZSbk5pe0+616m7rAqZjx44NtU984hOh9vzzz4fa+vXrQ83Nc5Kfe0u3CC/taOrmARemmzVrlh3jjTfeGGoumOiCe27udGN05+yel6bgXSfb0p4rPskDAFApFnkAACrFIg8AQKVY5AEAqFRPg3cuzOB02x3IhVRcFykXYHNbn44cOTLUHnjggVBzXfCaOka5sNvJkyeLHrObsEZptzzJB1JcN7rbb7891BYsWBBq7jqWdpFq0k3HMaATbsvW0jCWC4M2vXbd/OXCaq6zpzuOG+OUKVNCzQWjDx06ZMforoWb69y4XTDNdfRzW+66TnRuHpf8/OXmXbelbem239106uvkON3gkzwAAJVikQcAoFIs8gAAVIpFHgCASvU0eOdCHC6QULpVXyfBldIuUi7MMnHixFD79Kc/HWrr1q0LtaatZl2gzoUD3RhLr4+7netY57pkSX7r2/nz54fa6NGjQ81dR7dlb2lwyT1e0217EWbBpceFtroJdDaFtkq3RXYBNvc6L33tu3NxQTfJd44r5eYqN0Z3fk5T51M3n3YzN5R213Sa1ipXLz3vUsx8AABUikUeAIBKscgDAFApFnkAACrV0+DdwYMHQ82FJlyYoXSrRcmHGdxxSrtIlQYhZsyYEWoutCf5jlH79+8PNRf2cYESN27XRcoFZprCNa7jlNue121fW9qJzl3bTraFLQ0cErxDt9zrqnRe6qTTpLt/6faspXNnaWe8prmv9LxLA9Nu/nK3Kw3adqK0k9356HjXSSfEc8XMBwBApVjkAQCoFIs8AACVYpEHAKBSLPIAAFSqp+l6l8I+H60CS1vYOr1oFSn5vdVHjRpV9JhOaavIpvawjkvxnzlzJtQ6SeX2V/r8d5K4dwa6VSQuPe416PYsL71v09xQ+lrvNlXen5tDmubNbuY/Vyv99sBAn3PTsZ3S8XTyTbBenCOf5AEAqBSLPAAAlWKRBwCgUizyAABUqqfBO6c0hNFJ+9PSEFpp6GugW0VK/nx60SrSaQrXdBMAKb1mTictQEuf64FuFYlLz+c///kLPQSgY3ySBwCgUizyAABUikUeAIBKscgDAFCpngbvXKBqoLtIST4w1m3HtJJjOE2Br246/XXTRaqTcGDpsR3XLa8bTc9faYdCALgUMRsCAFApFnkAACrFIg8AQKVY5AEAqFSiExgAAHXikzwAAJVikQcAoFIs8gAAVIpFHgCASrHIAwBQKRZ5AAAqxSIPAEClWOQBAKgUizwAAJVikQcAoFIs8gAAVIpFHgCASrHIAwBQKRZ5AAAqxSIPAEClWOQBAKgUizwAAJVikQcAoFIs8gAAVIpFHgCASrHIAwBQKRZ5AAAqxSIPAECl/i+Ab0BQoFLepQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import cv2\n", + "X_train_translate = random_translate(X_train[0])\n", + "test_translate = random_translate(X_test[0])\n", + "print(\"Original shape:\", X_train.shape)\n", + "print(\"Translated shape:\", X_train_translate.shape)\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "axs = axs.ravel()\n", + "axs[0].axis('off')\n", + "axs[0].set_title('Train original')\n", + "axs[0].imshow(X_train[0].squeeze(), cmap='gray')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].set_title('Train translated')\n", + "axs[1].imshow(X_train_translate.squeeze(), cmap='gray')\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "axs = axs.ravel()\n", + "axs[0].axis('off')\n", + "axs[0].set_title('Test original')\n", + "axs[0].imshow(X_test[0].squeeze(), cmap='gray')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].set_title('Test translated')\n", + "axs[1].imshow(test_translate.squeeze(), cmap='gray')\n", + "\n", + "print('Test shape in/out:', X_test.shape, test_translate.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 3) (32, 32, 1, 3)\n", + "Test shape in/out: (12630, 32, 32, 1) (32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train_warped = random_warp(X_train[0])\n", + "test_warped = random_warp(X_test[0])\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(X_train[0].squeeze(), cmap='gray')\n", + "axs[0].set_title('Train original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(train_warped.squeeze(), cmap='gray')\n", + "axs[1].set_title('Train warped')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "axs = axs.ravel()\n", + "axs[0].axis('off')\n", + "axs[0].set_title('Test original')\n", + "axs[0].imshow(X_test[0].squeeze(), cmap='gray')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].set_title('Test warped')\n", + "axs[1].imshow(test_warped.squeeze(), cmap='gray')\n", + "\n", + "print('Test shape in/out:', X_test.shape, test_translate.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (12630, 32, 32, 1) (32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train_brightness = random_brightness(X_train[0])\n", + "test_brightness = random_brightness(X_test[0])\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(X_train[0].squeeze(), cmap='gray')\n", + "axs[0].set_title('Train original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(train_brightness.squeeze(), cmap='gray')\n", + "axs[1].set_title('Train brightness adjusted')\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(X_test[0].squeeze(), cmap='gray')\n", + "axs[0].set_title('Test original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_brightness.squeeze(), cmap='gray')\n", + "axs[1].set_title('Test brightness adjusted')\n", + "\n", + "\n", + "print('shape in/out:', X_test.shape, test_brightness.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "### Define your architecture here.\n", + "### Feel free to use as many code cells as needed.\n", + "### Define your architecture here.\n", + "### Feel free to use as many code cells as needed.\n", + "import tensorflow as tf\n", + "\n", + "from tensorflow.contrib.layers import flatten\n", + "#from tensorflow.keras.layers import Flatten\n", + "#from tf.keras.layers import Flatten\n", + "#from tf.keras.layers import flatten\n", + "\n", + "EPOCHS = 60\n", + "BATCH_SIZE = 128" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "### LeNet \n", + "def LeNet(x, keep_prob): \n", + " # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer\n", + " mu = 0\n", + " sigma = 0.1\n", + " \n", + " # Layer 1: Convolutional. Input = 32x32ximage_depth. Output = 28x28x6.\n", + " conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, image_depth, 6), mean = mu, stddev = sigma))\n", + " conv1_b = tf.Variable(tf.zeros(6))\n", + " conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID', name='conv_1') + conv1_b\n", + " print (\"Convolution1: \", conv1)\n", + " \n", + " # Relu Activation\n", + " conv1 = tf.nn.relu(conv1)\n", + " \n", + " # Max Pooling. Input = 28x28x6. Output = 14x14x6.\n", + " conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n", + " \n", + " \n", + " # Layer 2: Convolutional. Output = 10x10x16.\n", + " conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n", + " conv2_b = tf.Variable(tf.zeros(16))\n", + " conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID', name='conv_2') + conv2_b\n", + " print (\"Convolution2: \", conv2)\n", + " \n", + " # Relu Activation being done here\n", + " conv2 = tf.nn.relu(conv2)\n", + "\n", + " # Max Pooling. Input = 10x10x16. Output = 5x5x16.\n", + " conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n", + " \n", + " # Flatten. Input = 5x5x16. Output = 400.\n", + " fc0 = flatten(conv2)\n", + " #fc0 = Flatten()(conv2)\n", + " \n", + " # Layer 3: Fully Connected. Input = 400. Output = 120.\n", + " fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))\n", + " fc1_b = tf.Variable(tf.zeros(120))\n", + " fc1 = tf.matmul(fc0, fc1_W) + fc1_b\n", + " \n", + " # Relu Activation\n", + " fc1 = tf.nn.relu(fc1)\n", + " # Apply Dropout \n", + " fc1 = tf.nn.dropout(fc1, keep_prob)\n", + "\n", + " # Layer 4: Fully Connected. Input = 120. Output = 84.\n", + " fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n", + " fc2_b = tf.Variable(tf.zeros(84))\n", + " fc2 = tf.matmul(fc1, fc2_W) + fc2_b\n", + " \n", + " # Relu Activation\n", + " fc2 = tf.nn.relu(fc2)\n", + " # Apply Dropout \n", + " #fc2 = tf.nn.dropout(fc2, 0.75)\n", + "\n", + " # Layer 5: Fully Connected. Input = 84. Output = 43.\n", + " fc3_W = tf.Variable(tf.truncated_normal(shape=(84, n_classes), mean = mu, stddev = sigma))\n", + " fc3_b = tf.Variable(tf.zeros(n_classes))\n", + " logits = tf.matmul(fc2, fc3_W) + fc3_b\n", + " \n", + " print (logits)\n", + " return logits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train, Validate and Test the Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n", + "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Convolution1: Tensor(\"add_19:0\", shape=(?, 28, 28, 6), dtype=float32)\n", + "Convolution2: Tensor(\"add_20:0\", shape=(?, 10, 10, 16), dtype=float32)\n", + "Tensor(\"add_23:0\", shape=(?, 43), dtype=float32)\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", + "#import tensorflow.compat.v1 as tf\n", + "#tf.disable_v2_behavior()\n", + "\n", + "# Create Placeholders for X and Y and One-Hot Encode the Labels\n", + "x = tf.placeholder(tf.float32, (None, 32, 32, image_depth))\n", + "y = tf.placeholder(tf.int32, (None))\n", + "one_hot_y = tf.one_hot(y, n_classes)\n", + "\n", + "# dropout probability is the probability to keep units\n", + "keep_prob = tf.placeholder(tf.float32)\n", + "\n", + "# Training Pipeline\n", + "rate = 0.0009\n", + "\n", + "logits = LeNet(x, keep_prob)\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": 109, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "At least two variables have the same name: Variable_6", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mcorrect_prediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_hot_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0maccuracy_operation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrect_prediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, var_list, reshape, sharded, max_to_keep, keep_checkpoint_every_n_hours, name, restore_sequentially, saver_def, builder, defer_build, allow_empty, write_version, pad_step_number, save_relative_paths, filename)\u001b[0m\n\u001b[1;32m 1138\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1139\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdefer_build\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1140\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1141\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaver_def\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1142\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_saver_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1170\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1171\u001b[0m \u001b[0mrestore_sequentially\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_restore_sequentially\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1172\u001b[0;31m filename=self._filename)\n\u001b[0m\u001b[1;32m 1173\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaver_def\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1174\u001b[0m \u001b[0;31m# Since self._name is used as a name_scope by builder(), we are\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, names_to_saveables, reshape, sharded, max_to_keep, keep_checkpoint_every_n_hours, name, restore_sequentially, filename)\u001b[0m\n\u001b[1;32m 668\u001b[0m \u001b[0munique\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \"\"\"\n\u001b[0;32m--> 670\u001b[0;31m \u001b[0msaveables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ValidateAndSliceInputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 671\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmax_to_keep\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 672\u001b[0m \u001b[0mmax_to_keep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36m_ValidateAndSliceInputs\u001b[0;34m(self, names_to_saveables)\u001b[0m\n\u001b[1;32m 553\u001b[0m \"\"\"\n\u001b[1;32m 554\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 555\u001b[0;31m \u001b[0mnames_to_saveables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBaseSaverBuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpListToDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 557\u001b[0m \u001b[0msaveables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mOpListToDict\u001b[0;34m(op_list)\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 532\u001b[0m raise ValueError(\"At least two variables have the same name: %s\" %\n\u001b[0;32m--> 533\u001b[0;31m name)\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;31m# pylint: enable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: At least two variables have the same name: Variable_6" + ] + } + ], + "source": [ + "# Model Evaluation\n", + "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": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training...\n", + "\n", + "EPOCH 1 ...\n", + "\n", + "Training Accuracy = 0.123\n", + "Validation Accuracy = 0.112\n", + "\n", + "EPOCH 2 ...\n", + "\n", + "Training Accuracy = 0.545\n", + "Validation Accuracy = 0.513\n", + "\n", + "EPOCH 3 ...\n", + "\n", + "Training Accuracy = 0.709\n", + "Validation Accuracy = 0.651\n", + "\n", + "EPOCH 4 ...\n", + "\n", + "Training Accuracy = 0.793\n", + "Validation Accuracy = 0.729\n", + "\n", + "EPOCH 5 ...\n", + "\n", + "Training Accuracy = 0.833\n", + "Validation Accuracy = 0.767\n", + "\n", + "EPOCH 6 ...\n", + "\n", + "Training Accuracy = 0.862\n", + "Validation Accuracy = 0.774\n", + "\n", + "EPOCH 7 ...\n", + "\n", + "Training Accuracy = 0.881\n", + "Validation Accuracy = 0.800\n", + "\n", + "EPOCH 8 ...\n", + "\n", + "Training Accuracy = 0.900\n", + "Validation Accuracy = 0.828\n", + "\n", + "EPOCH 9 ...\n", + "\n", + "Training Accuracy = 0.903\n", + "Validation Accuracy = 0.819\n", + "\n", + "EPOCH 10 ...\n", + "\n", + "Training Accuracy = 0.917\n", + "Validation Accuracy = 0.836\n", + "\n", + "EPOCH 11 ...\n", + "\n", + "Training Accuracy = 0.920\n", + "Validation Accuracy = 0.853\n", + "\n", + "EPOCH 12 ...\n", + "\n", + "Training Accuracy = 0.930\n", + "Validation Accuracy = 0.840\n", + "\n", + "EPOCH 13 ...\n", + "\n", + "Training Accuracy = 0.935\n", + "Validation Accuracy = 0.858\n", + "\n", + "EPOCH 14 ...\n", + "\n", + "Training Accuracy = 0.944\n", + "Validation Accuracy = 0.865\n", + "\n", + "EPOCH 15 ...\n", + "\n", + "Training Accuracy = 0.951\n", + "Validation Accuracy = 0.861\n", + "\n", + "EPOCH 16 ...\n", + "\n", + "Training Accuracy = 0.954\n", + "Validation Accuracy = 0.872\n", + "\n", + "EPOCH 17 ...\n", + "\n", + "Training Accuracy = 0.956\n", + "Validation Accuracy = 0.880\n", + "\n", + "EPOCH 18 ...\n", + "\n", + "Training Accuracy = 0.957\n", + "Validation Accuracy = 0.867\n", + "\n", + "EPOCH 19 ...\n", + "\n", + "Training Accuracy = 0.961\n", + "Validation Accuracy = 0.889\n", + "\n", + "EPOCH 20 ...\n", + "\n", + "Training Accuracy = 0.964\n", + "Validation Accuracy = 0.882\n", + "\n", + "EPOCH 21 ...\n", + "\n", + "Training Accuracy = 0.965\n", + "Validation Accuracy = 0.885\n", + "\n", + "EPOCH 22 ...\n", + "\n", + "Training Accuracy = 0.969\n", + "Validation Accuracy = 0.889\n", + "\n", + "EPOCH 23 ...\n", + "\n", + "Training Accuracy = 0.971\n", + "Validation Accuracy = 0.890\n", + "\n", + "EPOCH 24 ...\n", + "\n", + "Training Accuracy = 0.972\n", + "Validation Accuracy = 0.894\n", + "\n", + "EPOCH 25 ...\n", + "\n", + "Training Accuracy = 0.974\n", + "Validation Accuracy = 0.892\n", + "\n", + "EPOCH 26 ...\n", + "\n", + "Training Accuracy = 0.976\n", + "Validation Accuracy = 0.887\n", + "\n", + "EPOCH 27 ...\n", + "\n", + "Training Accuracy = 0.978\n", + "Validation Accuracy = 0.897\n", + "\n", + "EPOCH 28 ...\n", + "\n", + "Training Accuracy = 0.978\n", + "Validation Accuracy = 0.897\n", + "\n", + "EPOCH 29 ...\n", + "\n", + "Training Accuracy = 0.980\n", + "Validation Accuracy = 0.903\n", + "\n", + "EPOCH 30 ...\n", + "\n", + "Training Accuracy = 0.980\n", + "Validation Accuracy = 0.909\n", + "\n", + "EPOCH 31 ...\n", + "\n", + "Training Accuracy = 0.982\n", + "Validation Accuracy = 0.903\n", + "\n", + "EPOCH 32 ...\n", + "\n", + "Training Accuracy = 0.979\n", + "Validation Accuracy = 0.904\n", + "\n", + "EPOCH 33 ...\n", + "\n", + "Training Accuracy = 0.982\n", + "Validation Accuracy = 0.898\n", + "\n", + "EPOCH 34 ...\n", + "\n", + "Training Accuracy = 0.982\n", + "Validation Accuracy = 0.906\n", + "\n", + "EPOCH 35 ...\n", + "\n", + "Training Accuracy = 0.986\n", + "Validation Accuracy = 0.914\n", + "\n", + "EPOCH 36 ...\n", + "\n", + "Training Accuracy = 0.985\n", + "Validation Accuracy = 0.914\n", + "\n", + "EPOCH 37 ...\n", + "\n", + "Training Accuracy = 0.985\n", + "Validation Accuracy = 0.910\n", + "\n", + "EPOCH 38 ...\n", + "\n", + "Training Accuracy = 0.984\n", + "Validation Accuracy = 0.920\n", + "\n", + "EPOCH 39 ...\n", + "\n", + "Training Accuracy = 0.985\n", + "Validation Accuracy = 0.909\n", + "\n", + "EPOCH 40 ...\n", + "\n", + "Training Accuracy = 0.986\n", + "Validation Accuracy = 0.909\n", + "\n", + "EPOCH 41 ...\n", + "\n", + "Training Accuracy = 0.988\n", + "Validation Accuracy = 0.909\n", + "\n", + "EPOCH 42 ...\n", + "\n", + "Training Accuracy = 0.988\n", + "Validation Accuracy = 0.912\n", + "\n", + "EPOCH 43 ...\n", + "\n", + "Training Accuracy = 0.989\n", + "Validation Accuracy = 0.917\n", + "\n", + "EPOCH 44 ...\n", + "\n", + "Training Accuracy = 0.988\n", + "Validation Accuracy = 0.918\n", + "\n", + "EPOCH 45 ...\n", + "\n", + "Training Accuracy = 0.989\n", + "Validation Accuracy = 0.912\n", + "\n", + "EPOCH 46 ...\n", + "\n", + "Training Accuracy = 0.987\n", + "Validation Accuracy = 0.921\n", + "\n", + "EPOCH 47 ...\n", + "\n", + "Training Accuracy = 0.990\n", + "Validation Accuracy = 0.924\n", + "\n", + "EPOCH 48 ...\n", + "\n", + "Training Accuracy = 0.990\n", + "Validation Accuracy = 0.911\n", + "\n", + "EPOCH 49 ...\n", + "\n", + "Training Accuracy = 0.990\n", + "Validation Accuracy = 0.927\n", + "\n", + "EPOCH 50 ...\n", + "\n", + "Training Accuracy = 0.991\n", + "Validation Accuracy = 0.921\n", + "\n", + "EPOCH 51 ...\n", + "\n", + "Training Accuracy = 0.991\n", + "Validation Accuracy = 0.922\n", + "\n", + "EPOCH 52 ...\n", + "\n", + "Training Accuracy = 0.991\n", + "Validation Accuracy = 0.933\n", + "\n", + "EPOCH 53 ...\n", + "\n", + "Training Accuracy = 0.992\n", + "Validation Accuracy = 0.924\n", + "\n", + "EPOCH 54 ...\n", + "\n", + "Training Accuracy = 0.992\n", + "Validation Accuracy = 0.927\n", + "\n", + "EPOCH 55 ...\n", + "\n", + "Training Accuracy = 0.991\n", + "Validation Accuracy = 0.924\n", + "\n", + "EPOCH 56 ...\n", + "\n", + "Training Accuracy = 0.992\n", + "Validation Accuracy = 0.932\n", + "\n", + "EPOCH 57 ...\n", + "\n", + "Training Accuracy = 0.992\n", + "Validation Accuracy = 0.926\n", + "\n", + "EPOCH 58 ...\n", + "\n", + "Training Accuracy = 0.991\n", + "Validation Accuracy = 0.924\n", + "\n", + "EPOCH 59 ...\n", + "\n", + "Training Accuracy = 0.993\n", + "Validation Accuracy = 0.926\n", + "\n", + "EPOCH 60 ...\n", + "\n", + "Training Accuracy = 0.994\n", + "Validation Accuracy = 0.929\n", + "\n", + "Model saved\n" + ] + } + ], + "source": [ + "### Train your model here.\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", + " # Calculate and report the validation accuracy \n", + " training_accuracy = evaluate(X_train, y_train)\n", + " validation_accuracy = evaluate(X_valid, y_valid)\n", + "\n", + " print(\"EPOCH {} ...\".format(i+1))\n", + " print() \n", + " print(\"Training Accuracy = {:.3f}\".format(training_accuracy))\n", + " \n", + " #print(\"EPOCH {} ...\".format(i+1))\n", + " print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n", + " print()\n", + " \n", + " # Save the model \n", + " saver.save(sess, './traffic_signs')\n", + " print(\"Model saved\")\n", + "\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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2.5: Test a Model on New Images" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./traffic_signs\n", + "Test Set Accuracy = 0.050\n" + ] + } + ], + "source": [ + "# Now (drumroll) evaluate the accuracy of the model on the test dataset\n", + "\n", + "with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " saver2 = tf.train.import_meta_graph('./traffic_signs.meta')\n", + " saver2.restore(sess, \"./traffic_signs\")\n", + " test_accuracy = evaluate(X_test, y_test)\n", + " print(\"Test Set 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": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14\n", + "1\n", + "25\n", + "9\n", + "5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJIAAACPCAYAAAARM4LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAGEpJREFUeJztXWmQXNV1/s57vS/Ts2o02pAQMhJit5BkULxhHCW2gcQ4tuxQmOAycUgRJ6RiTJwyTmyCKykvlXJsUzYx5XJAeAOvsSkCduxgjFiF0D5IaB9pZrp7en/93s2Pbr1zz4uWFnpupNH9qlQ63ee+17ffnL5nveeSUgoGBicL67WegMH0gBEkg1BgBMkgFBhBMggFRpAMQoERJINQYATJIBSclCAR0Woi2kxE24jo9rAmZXD6gV5tQJKIbABbAFwFYDeApwCsUUq9FN70DE4XRE7i2uUAtimlRgGAiB4AcA2AowpSLNmrktkRAIAiuRgqYoEmz5UXEvlk09LGBX4EttK+Dkkege+hPKUzjorgT8zS5uFpn32scQjMUb+OrMAz0OdlHWtizLPgBljavLRxFPii1jHm72p/m8rYxkNKqaGjT6aFkxGk2QB2aa93A1hxrAuS2RGsfPd9AAAnlhI8J1736XgpL3heIu7T46mmTyeqTTGuxx3waTcieVHtoboNx6etqHwErvL4cz1P8GJxnkdFu4dryXGJSNSnqSl5Ve26uHY/AGjUec4Uj/l0MyBwqPPrVKQkWE3i59hw+bsllfyeiaY2/4CFU4hnfPq3n79kJzrAydhIR/rJ/D89SUQfJqJ1RLSuUc0f4RKD6YCTWZF2A5irvZ4DYG9wkFLqHgD3AEB65HyVz7Wk3WtKQR+q8K+gnh4RvIYz4dOzDtk+bc2Q46pT+/mFFxO8qTj/Zmp8C6QCsk+apnDkLVBN8upSs/iXP4x+MW6yWvTpRNwWvH5tFSpOTghebqDXpysV7R6O/L1XYtpqa2UEL+2kfTrp8ndzLakCKzGeVzOgHhOqghPFyaxITwFYREQLiCgG4H0AfnAS9zM4jfGqVySlVJOI/hLAzwDYAO5VSm0IbWYGpxVORrVBKfUTAD8JaS4GpzFOSpBOFJbnIlkttz44Je2KsRjr5XhtUvAGVY9PN3rZTilUpI2RcdheoJT02lBnLyUZZ9thdp/U7otK4z694JXNgjdc2cPfhdhxmAw8xYY1zLyhiwTvhegsny6newSvXOa5pGNZn3bsshg34PH3LJD0Cl236tNVYjsoaiXEuJTmK3mRmuBFnCpOFCZFYhAKjCAZhIKuqrYIgD60luJDJbnUpnM8lQQVBC+fYj+83mRXdUZAexWG+J4znXHBO9vj0MAFo0/zuM2/FuPqE9t9upiICt5Bu8+nVZ55SashxiktGDoQe0jwLm/ydVeef7ngbZ97oU9vsub5dMmeIcbtB7v4vZ4MSJaHWG33afELpyLXjKIWeO0NePvFmAxZdAKzIhmEAiNIBqHACJJBKOiqjdQkIB9p6fBoVIblkwWeSi2SFLyI4nSEHeE0QhHSDroytsmnL3rq+4LnjnL2hgpsg02m5W/JyczhcTVH8GJ1LXOfZfc5amXFuGqDwxeNeCD9kGT7Kbn+ccG78MXneb5JfgbJq68U434Zu8ynJ+rS1oyOaYlgi42fYDFBSgsvVPrk/LO1KZwozIpkEAqMIBmEgq6qNhCgEi31YDtyyZ+Ka758RLqfCZvVmeewu3vNUCDiu/ZffTpWkbGBQo1ddDvOLni8KVP8VoJVSnORXPKtIX7dl+V6qkpJhgl6Pf6s0uio4GUPsdqzm3XBc2McUV6p1U9V7r1fjLvgQ/xn+37qUsHbEeOCjKrFKipWk1mAuKtlCALZ/1hNPtdOYFYkg1BgBMkgFHRVtRGAw4rE9aQ6SHCwFnVPhlqrmnf254OsDnq/+mkxruhxUrVAacGjJntc/edy1Dj95mVy3GVLfToyLAvnlMsqNw7NO/Jkyaz+1abKMkpPeVYx3tPrBW/b2u/5dM5ltVTtl0lVe+1XffqCkTcK3sFV1/Ick+f4dLkmS5vHwCbCWXnp+eUzUqV3ArMiGYQCI0gGocAIkkEo6K77DxsecgCAeFRGTxvEernuSdf99nlsF1W/eBtfE5Vuasxmm8YKbFXKvOv3fHrgvVf7dHNI2kFugV1huyzDwQ2LI915zSyKRopiHNX5umxSFq/lNU87/c6rBG/VFa/36Wc+z3ZQ34Ydco7g8MLIpKxeePfzvDY8uvR6nz6QnCvGDWrR90Jauv/VWh9OFGZFMggFRpAMQkFXVZsNhZ727tU8ySU/7fBSe/6g3GuWfOAufqFvecawGJee4ns0P3C94M247u18P5vV6GRB1ienovzbmoQMQwx6Wg20FhmuQEbiM1rCtVKR98/163vXZFFaKcO8lZ+4w6d/+sB3xbj5Dz3s0xOeVL/Dzz3p01f28neZWHCbGFcEq+lcbVDwyvF9OFGYFckgFBhBMggFRpAMQkFXbSQPQKXddiWRlC6nZbFdsXRcdsaZOsh7yPosztbn64HUwZvf4NNzrn+r4KkKhxAKWta92SMfgaPZHMmEtH0qJf48N8LX9ZDcf18use1jBzqJ1OtsM9mBTiiOyyGLyQbP8eobPyDGPXGIbZjcL56ScxzWqhme3eiT58x6Vox7oZ83GriT0ha0KIcTxXFXJCK6l4jGiOhF7b1+InqEiLa2/z/xwIPBtEInqu0bAFYH3rsdwKNKqUUAHm2/NjiDcVzVppT6JRHND7x9DYA3t+n7ADwO4GPH/TTbgp1rZeXregsaADP7WbUtWS+X6/EGu9pNh9VLZKZsJDbjg2t8ulCR1QWpHDfhypTZ9U1MyOKych9fV5yUkXM7wios6XJ1Qa0ua8d7UvxdHFeGMprafrJGoDOdFeM/R13r+vZKoK/U6269xad3vvBxwSsqroCYWeP7LXxZ7q/blFvELzKBum/ISoFO8GqN7WGl1D4AaP8/4zjjDaY5fudem+jYVp44/gUGpyVerdd2gIhGlFL7iGgEwNjRBuod27JzlqpyuxtYxpaezhuH2GOpPfNLwUso9p5UgmV/1qrlYpw7/2yfzpSk0OY3sSdY3cfTHUnIrU/OIfZ6hkkWrOXP5gQvaT0Zo1GpRsnWvL2G3M6d1tReNirrxT2b1WDN4esaDemd1iOsVkdueo/g2V/+ok9XXFbNg9uluXDpxawun7DOErxos3sd234A4IY2fQOAh48x1uAMQCfu//0AngBwLhHtJqKbANwN4Coi2opWn+27f7fTNDjV0YnXtuYorCuP8r7BGYguF7YpoN0HOm5JR+/8Ihdo7a3IordcD1cKFLT+0/HzzxfjqiW2syIBl7l65zd8uuedvOV5O+RnLdzPoYFNG7cJXu9H2B5JL+VNAhSTNlKlxtHrzVu2Ct4PH/6RTzfqckv4wCB3sRuexfbYshXSFhwZ4mz91IULBc/TemSntMi5V5OVBmePcpXAEwtfJ3ipuAxndAKTazMIBUaQDEJBdwvbLEJPohVFjZN0u7GJu6hFe2VoQLlaBDjFUVdvhixsy2rR4MSvpVoqLprJ495wnk9Hxg+JcfkV7PLPW7VU8Ma++Ti/+ArXV6MoXXw7wup37Xe/J3g3f+jDPE7JpHCtzG73nv2cmP3O/d8R4/7+72716VT/fMHbvoRDILXHuVt1o18+06Edz/l07+IbBM8JHInRCcyKZBAKjCAZhAIjSAahoLvuv0ewai07JjdTynBzPdsZeusaAKg22S7qS3PpU25gphhXArvdW7/2ZcEbcNn2eXkr22NnOTINEqlyOmI0LW2FgTI/rt2f/YJPn3ubrKL53k/5MITnN8oivY998pM+7Val+5+McBZ+3nxOW2zcukmMG91/wKcXzJChB28W2439Ga4EqLnSFqzs4u69wylZAbGnIe2pTmBWJINQYATJIBR0t62NAiyn5fI2LZmdr4H3mqVIutPx3gU+7WiutqrJwrByklVFtF/um9ub50x4X4lDD3sDJ01mbH4kkcAxohNJ/t3N0Rqmxm35GHeOvsz0yzsELzuoRfQbUnVGFN9/yyt8nl3dlSrwjn/i1OYj3/yW4PVnOTqeb7Ka7gk0lW8kuNDP8g4Knts88fIysyIZhAIjSAahoLuRbZuQy7VU21hdfvTMmbzMl+zAFuIpVoPRfo4GVyE9lox2xFR06SLBo1+x5xO9lBOdPc/vkHPsY5WVcaTac7TTp5OaCnFiUkXVytydZDglVWyjzPcsVOV27qS21dspcZQ7WACXsFhNlyyZdE457LU1YtwtrhINeGJNrXNcTHa3cyC95k5gViSDUGAEySAUGEEyCAXdPYvEA8ZrLZd6IOAyZ5dd4tOb1soG5fNS3G2soBW0q32yGXo+wfvcNs6dLXhR7PLp1912o08Xi7Lb2jlz+CySPT/+lbzHE/x55Vve7dOVQ/Lo1Fv/+m99+sUN8jjTq6++xqethLR9XtnHkehFSxb79J3/eKcYd/vf/APPqRo4S2UnZ/wLNttc/XUZKsnEOFvgag3xAaDPlZsNOoFZkQxCgREkg1DQ3ci2ZSHWdodrBem22r/Px3LGemXD8GhVCw1UOEI789mnxbi+FX/m029513sFL3Yd8/YXWBWlLZm0fXEPJzfTFy4WPOdiLoizN/LeOCfwezzQZN7Nt35E8Hoy7GqnArXer9c+b3yKXfe/uPlGMS4T5W4h+bqMWGMPhzlS2pFkTWe3GFbNsOqPQnYfaSoZSe8EZkUyCAVGkAxCgREkg1DQ5a62QBYt938qIe2gDR67o3POk+kN+1l23XsjnHIY+9kjYtx57/8Tny72SdunXtJsq37OfFczgfY3VbZNFkZke5cprduaG+HfoF2V96hpx6yq1DmCV9f2l2VIXmfV2RZcMpfTOK8/5wIxbtLRbJgd0vYpT3ImX2+945Js0bP/Um5gPx44prRqydRQJ+hky/ZcInqMiDYS0QYi+qv2+6Zrm4GPTlRbE8BtSqklAFYCuIWIzoPp2magoZO9//sAHG6qNUVEGwHMxqvo2kbkIdI++bnsyC5hv9nF0dQ1K1YI3tQLXCiWVeyqFiqyDnnHV77u07Nvu0nwGjmO8o5HeS3Xu7cBwHywOjjoSHWQ0n53lSZf15eWe/SUdozoVFneozfNKt0NRJv1tjaTDVYvVkw+q/lDrLa33/UjwUNNa6YaZXWeiEkXf92Fb/HpHUqeKTc/euKm8wld0W4BeAmAJ2G6thlo6FiQiCgD4LsAPqqUKh5vvHad37GtVjId26YrOhIkIoqiJUTfUkod3oN8oN2tDcfq2qaUukcptUwptSyR6T/SEINpgOPaSEREAL4OYKNS6nMa63DXtrvRYdc2T3kouy0bqTcuher5zbxX620XXCF4jYGf+3S6yC6ynZUhhMbPH/fpg4vPFbwZf8ztnHrzbN+48cB5tFrie7hH9hbYq1Ue9Nh8XcGVFYWWdhR9jy3tp6irtZqxpd9dszWbyebfuJWQf6YNX/o3vsdvnhS8gbh23LxW4Tl17koxbnOE9wTmmtJOjJPsSdAJOokjXQHgegDriehw54E70BKgB9sd3F4B8J6jXG9wBqATr+1XaB2QfSSYrm0GALoc2Vaw4KiWK5tLy+WzQRxtfmhUGuXXXstFZPvv/w+fnqmkauuJcyR6/1f/U/Dm9XDxu/0m7tg21iN/Iwnt7JCDdelTZLVI9JR23plSsvi/N8Outgq4+JMlVo/xjCxs6/HY/c9q1RE7HvmFGNdY+yjfY0DeIzLJrvz+Pj6Wfstl7xLjSi5fNy9QZDjhyE0JncDk2gxCgREkg1DQVdVmkY1021srB5KIVGYVQMOXCt5PJtf59DXLOQmae0xGHEopVj09gWNEt36KHc6Bt/ARXD03vkOM671wiU83A0nV/nH2glytcXxMycizU+dxDUeqtt657C3VyrJhauQVTrju+vaPfbrwY9nAfm6DP3tbSnpcNrE6nrqMu8ptOUd6bYlx7SjVqEzSRqPSZOgEZkUyCAVGkAxCgREkg1DQZfffQ6Ntu5Aj3e5IhsuZNh+U9tN5i7mQ/0svbfHpG66QtsmM53gPWbou99yPDbItMfbC//p07qPPiXHVczkinr3hjwSvuOwin4412X5K12XHs0aWs+52IEhsbeEObqVv/5fg7XmU5xUpsv0Uj0l3fCLN33vBlLQFt6y62qfXLX+fT9ersp9COsr2mBOX4YsmAh2HO4BZkQxCgREkg1DQ3X1tZCN6+Jw2Ty7XTo2PJk1m5NK6ZS+PXbiKC9Ye/O+viXGrlnOz8iVbDwhefD8nVmdrRV5FyK5s9Q28V27ylscEL5nj66whPitkYlBWGaeLnPmt7pDd0Opau5pkToYGsjWOSie1pHbDkQlu0o4bXbdCRqyfWcaqLR/h66gmi9dqYJ3rVeR6kmiYs0gMXiMYQTIIBUaQDEJBV20kT3moNlv2TtqSMqxbRUTSdqAUu67bprg4f+iNfyrGrbmL97V9bsUqwVt9Fu91Lz633qfdmgwhIMV2kJeSRW8Fzc5IHNzr0839srCtBu4tkMhKG6wR57DBZEGmN+b1cVue8TLbKfaQnOP345wmGl30JsErJrQQRVGzi+KBlJTFaZBoTYZinHSgn0AHMCuSQSgwgmQQCrp7FomlYLez1ZYjt0NHtEi03ZTTcj1WAXr9clXNEuM++s9c8HXXZ24WvG9neLm+5VKuCV+8R0aGhyusDlxHRqxrMVaxEYfdfzsnC/G8En83J3DeSA8xb2afzLLvbHA1w6bz+JjVh10ZpXcv54i1PXC24PXXuCAu4vH8i3H5vK2a5v57UpVVIrLqoROYFckgFBhBMggF3Y1sgxD1WktqvSkj27EUeyy1miy08rSzSSJRvi4V6GhSyXMk9w8+8UPB2/X4N3z6X9YzL793uxh3dpZrx6+YIZt0Lh1m7ynmsRoaqMq66eQQdxLZWZfFdy/VWJU+MxY4A2Q2q6nmHO4WEluyRowbjPOfrRKRjVCrNhe2JbQjxGINOUeHtERtXD7vVO130I3EwKATGEEyCAVGkAxCQXdtJI9AlZZrGUlIPVzWosFIygNYPMXTbBJHiisVmdFWFkele5oyWrvoci6Oq76Jm6YXm9JOefkxLjb7wXPrBO+Lj3F4YfV1vDd00UJZrXD3vz/o00OL5ZHv8y/h7r3zfu9awRs+izceVLSWOuTIwrMGOPsfkwFrKK0FTk3781JVhjL0MIqVkmIQDbTi6QSddGxLENFviej5dse2T7XfX0BET7Y7tq0lotjx7mUwfdGJaqsDeKtS6iIAFwNYTUQrAXwWwOfbHdsmAdx0jHsYTHN0svdfAf4BXtH2PwXgrQDe337/PgB3Avhy8HodRIR4rL1wRWX0dKqhb4+W+73SmpvfbPDSbSUCEVit01i9JMMLDa0LSII48TsnLtXorHdw0znn7VI9ro6wSkmVf+vT5G0Q4z7+0Eaf3l6WndJi2jkfGSUL4uo2q5R6kwvzshFZ+J1P8/ccLsmEbszlCHYhyiqxEaj7jkBLjHsysezYMlndCTrtj2S3O5GMAXgEwHYAeaXUYUNnN1rtAA3OUHQkSEopVyl1MYA5AJYDWHKkYUe6VnRsmzp0pCEG0wAn5P4rpfJoNR1dCaCXiA7rizkA9h7lGu7Ylh080hCDaYBOOrYNAXCUUnkiSgJ4G1qG9mMArgPwADrs2EaWgp1oZcNLVWl/5NLcy9TyZDsZq8o2U8zitIXTkLZDvck2RjQhXeaY1g035jLPqspNAq6WgmkiuAeeH1fxIM8xVgiEKyJ8XToqv2dWO4PWDaRWDmiFApkZfG5cY0LaNz117h9Qj8oUSVmzLz1i+ykak59laZsvVFOuJ7GI7FTXCTqJI40AuI+IbLRWsAeVUj8iopcAPEBEnwbwLFrtAQ3OUHTitb2AVkvk4PujaNlLBgaglnffpQ8jOghgJ4BBAMbybuFUfxZnKaWGjjeoq4LkfyjROqXUsq5/8CmI6fIsTNLWIBQYQTIIBa+VIN3zGn3uqYhp8SxeExvJYPrBqDaDUNBVQSKi1US0mYi2EdEZd1DgdD6Ns2uqrR0Z3wLgKrSqBZ4CsEYp9dIxL5xGaJ8iNaKUeoaIsgCeBnAtgA8CmFBK3d3+gfUppY55iOKphm6uSMsBbFNKjSqlGmjl6K45zjXTCkqpfUqpZ9r0FAD9NM772sPuQ0u4Tit0U5BmA9ilvT6ja5im22mc3RSkI52wdEa6jK/2NM5TGd0UpN0A5mqvj1rDNJ1xMqdxnsropiA9BWBRe/dJDMD70DqF8oxBB6dxAh3Wdp1q6Hb2/w8BfAGADeBepdRnuvbhpwCIaBWA/wGwHsDh6ro70LKTHgQwD+3TOJVSp9VJ0iaybRAKTGTbIBQYQTIIBUaQDEKBESSDUGAEySAUGEEyCAVGkAxCgREkg1Dwf/OSmsM1/vHsAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "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", + "\n", + "### Load the images and plot them here.\n", + "### Load and output the web images\n", + "from scipy import misc\n", + "import imageio\n", + "\n", + "# Create list with downloaded images\n", + "image_features = []\n", + "for i in range(1,6):\n", + " # Save image data as ndarrays in list\n", + " #image_features.append(misc.imread(parent_dir + \"/traffic-signs-data/web_images/\" + str(i) + \".jpg\", mode='RGB'))\n", + " image_features.append(imageio.imread(\"./traffic-signs-data/online_files/\" + str(i) + \".jpg\"))\n", + " \n", + "# Create ndarrays with image_features and labels list\n", + "X_online_test = np.array(image_features)\n", + "Y_online_test = np.array([14, 1, 25, 9, 5])\n", + "\n", + "# Check that the same amount of features and labels was stored\n", + "assert(len(X_online_test) == len(Y_online_test))\n", + "\n", + "# Print out all images and the respective labels\n", + "for i, im in enumerate (X_online_test):\n", + " print (Y_online_test[i])\n", + " image = im.squeeze()\n", + " plt.figure(figsize=(2,2))\n", + " plt.imshow(image, cmap=\"gray\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict the Sign Type for Each Image" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5, 32, 32, 3)\n", + "5 32 32 1\n", + "Restoring the model . . . \n", + "INFO:tensorflow:Restoring parameters from ./traffic_signs\n", + "Model restored.\n", + "Samples: 5\n", + "label : [ 7 12 8 30 8]\n", + "Prediction : [False False False False False]\n", + "Probability : [[-0.05272397 0.0200924 -0.02898298 0.01673755 -0.08419205 -0.03762679\n", + " 0.03831628 0.06204474 0.04633746 -0.00369399 0.00028991 -0.03284062\n", + " 0.04292487 0.00033031 -0.07380268 0.03232404 0.0106379 -0.02614592\n", + " 0.05854928 -0.06415991 -0.02431366 0.05885797 0.01719164 0.02671637\n", + " 0.00368808 -0.06297641 -0.01792141 -0.03138649 -0.04680558 0.04311337\n", + " 0.04592644 -0.00433451 -0.03371209 0.01040761 -0.07567389 0.00718888\n", + " 0.01283679 -0.01119118 -0.05361966 0.02946863 0.00955878 0.05358161\n", + " 0.01561081]\n", + " [-0.08777672 0.04671842 -0.03775029 0.0331738 -0.11082038 -0.05027482\n", + " 0.08584332 0.06615224 0.07362001 0.00471378 -0.00890311 -0.03574736\n", + " 0.08802235 0.021572 -0.10240909 0.06745846 0.02125342 -0.03268972\n", + " 0.06655553 -0.06983723 -0.0063286 0.0763821 -0.01714872 0.03738204\n", + " -0.00049521 -0.09494743 -0.02475868 -0.06174196 -0.07132331 0.05949117\n", + " 0.07527985 -0.00752712 -0.05320283 0.0195926 -0.11854727 0.01869976\n", + " -0.00885749 -0.00057224 -0.06994482 0.0433789 0.00977214 0.0757581\n", + " 0.03878306]\n", + " [-0.09376074 0.0560858 -0.03345569 0.0245378 -0.10693439 -0.05247346\n", + " 0.08020743 0.05258867 0.08438073 -0.00241724 0.00581015 -0.03225713\n", + " 0.06919475 0.02389145 -0.09105806 0.0638646 0.04512401 -0.04085054\n", + " 0.04898861 -0.05798281 -0.00939433 0.06057328 -0.01228815 0.04343882\n", + " -0.00605242 -0.078105 -0.0126108 -0.06189059 -0.0695322 0.05294734\n", + " 0.08094086 -0.00869423 -0.06637179 0.01607309 -0.1014633 0.03004041\n", + " -0.00274768 -0.00064941 -0.05030634 0.021656 0.0078701 0.06637596\n", + " 0.04310768]\n", + " [-0.06790014 0.05463304 -0.03993287 0.01449109 -0.10981572 -0.04017065\n", + " 0.06604658 0.06630396 0.06010216 0.00708817 0.00284931 -0.02451229\n", + " 0.06885841 0.030113 -0.07616169 0.04884559 0.03089231 -0.02948997\n", + " 0.04251918 -0.05742372 -0.02047449 0.06624461 -0.01597602 0.05114175\n", + " -0.00209544 -0.08034035 -0.02445792 -0.05671862 -0.0660364 0.04063787\n", + " 0.07529487 -0.00217792 -0.07658137 0.01589962 -0.09525847 0.00281279\n", + " -0.011819 0.00988896 -0.05837277 0.01057923 0.01024974 0.07154107\n", + " 0.03972166]\n", + " [-0.0990802 0.06089159 -0.03384258 0.0196156 -0.11072821 -0.0348698\n", + " 0.07570217 0.0534768 0.07912411 0.01047641 -0.00279518 -0.03494272\n", + " 0.07616524 0.03378806 -0.10314354 0.07545938 0.04195642 -0.04075868\n", + " 0.05139469 -0.05371378 -0.01369004 0.06562958 -0.00595029 0.03812341\n", + " -0.00747221 -0.10210795 -0.02265135 -0.07147704 -0.06164541 0.05473521\n", + " 0.07781244 -0.00700341 -0.05928572 0.03739 -0.11872007 0.02254168\n", + " -0.01066274 -0.01003572 -0.05917569 0.02621492 0.00333758 0.06409727\n", + " 0.05332711]]\n", + "Labels : [ 7 12 8 30 8]\n" + ] + } + ], + "source": [ + "### Feel free to use as many code cells as needed.\n", + "# Covert to Grayscale & Normalize\n", + "print(X_online_test.shape)\n", + "\n", + "def conv_rgb2gray(rgb):\n", + " #conv_rgb2gray\n", + " return np.dot(rgb, [0.299, 0.587, 0.114])\n", + "\n", + "if (X_online_test.shape[3] == 3):\n", + " # Convert to Grayscale\n", + " X_online_test_gray = conv_rgb2gray(X_online_test)\n", + " \n", + " # Normalize Grayscale Images\n", + " from sklearn import preprocessing\n", + " \n", + " for i, picture in enumerate(X_online_test_gray):\n", + " X_online_test_gray[i] = preprocessing.normalize(picture, norm='l2', axis=1, copy=True, return_norm=False)\n", + " \n", + " # Reshape Grayscale Pictures (Add Dimension 1)\n", + " X_online_test_norm = X_online_test_gray.reshape(X_online_test.shape[0], X_online_test.shape[1], X_online_test.shape[2], 1)\n", + " print(X_online_test.shape[0], X_online_test.shape[1], X_online_test.shape[2], 1)\n", + " X_online_test = X_online_test_norm\n", + " #X_online_test = X_online_test.astype(np.float32)\n", + " \n", + " # Save Variable Image Depth\n", + " image_depth = X_test.shape[3]\n", + "\n", + "\n", + "### 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", + "\n", + "def prediction(X_data, y_data):\n", + " num_examples = int(len(X_data))\n", + " print (\"Samples: \", num_examples)\n", + " sess = tf.get_default_session()\n", + " \"\"\"\n", + " #for offset in range(0, num_examples, BATCH_SIZE):\n", + " #for offset in range(0, num_examples):\n", + " #batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n", + " batch_x, batch_y = X_data, y_data\n", + " \"\"\"\n", + " for offset in range(0, num_examples, BATCH_SIZE):\n", + " #for offset in range(0, num_examples):\n", + " batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n", + " #batch_x, batch_y = X_data, y_data\n", + " with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " predict = sess.run(correct_prediction, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})\n", + " #print (\"prediction : \", predict)\n", + " label = sess.run(tf.argmax(logits, 1), feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})\n", + " probabilities = sess.run(logits, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})\n", + " print (\"label : \", label)\n", + " #print (\"probabilities : \", probabilities)\n", + " return predict, probabilities, label\n", + "\n", + "# Open session with restored model\n", + "with tf.Session() as sess:\n", + " #sess.run(tf.global_variables_initializer())\n", + " # Restore model\n", + " print(\"Restoring the model . . . \")\n", + " saver.restore(sess, './traffic_signs')\n", + " print(\"Model restored.\")\n", + " \n", + " # Verify accuracy of the trained model via test data\n", + " pred, prob, label = prediction(X_online_test, Y_online_test)\n", + " print(\"Prediction : \", pred)\n", + " print(\"Probability : \", prob)\n", + " print(\"Labels : \", label)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analyze Performance" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing...\n", + "EPOCH 60 ...\n", + "Test Accuracy = 0.007\n", + "\n", + "Model saved\n" + ] + } + ], + "source": [ + "### Train your model here.\n", + "\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", + "### Validate your model here.\n", + "rate=0.0009\n", + "\n", + "with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer()) \n", + " print(\"Testing...\")\n", + " for i in range(EPOCHS):\n", + " #X_test, y_test = shuffle(X_test, y_test)\n", + " \n", + " # Calculate and report the validation accuracy \n", + " validation_accuracy = evaluate(X_test, y_test)\n", + " print(\"EPOCH {} ...\".format(i+1))\n", + " print(\"Test Accuracy = {:.3f}\".format(validation_accuracy))\n", + " print() \n", + " \n", + " # Save the model \n", + " saver.save(sess, './traffic_signs')\n", + " print(\"Model saved\")\n", + "\n" + ] + }, + { + "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": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 : TopKV2(values=array([[ 0.06204474, 0.05885797, 0.05854928],\n", + " [ 0.08802235, 0.08584332, 0.0763821 ],\n", + " [ 0.08438073, 0.08094086, 0.08020743],\n", + " [ 0.07529487, 0.07154107, 0.06885841],\n", + " [ 0.07912411, 0.07781244, 0.07616524]], dtype=float32), indices=array([[ 7, 21, 18],\n", + " [12, 6, 21],\n", + " [ 8, 30, 6],\n", + " [30, 41, 12],\n", + " [ 8, 30, 12]], dtype=int32))\n" + ] + } + ], + "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", + "with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " top_5 = sess.run(tf.nn.top_k(tf.constant(prob), k=3))\n", + "print(\"top 5 : \", top_5)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nprint(len(X_select_rgb))\\n\\nfor image in select_images:\\n X_select_rgb = np.sum(image, axis=0, keepdims=True)\\n \\nselect_images_normalized = (X_select_rgb - 128)/128\\n\\nprint(X_select_rgb.shape)\\n'" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "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", + "\n", + "#reading in an image\n", + "import os\n", + "import matplotlib.image as mpimg\n", + "import cv2\n", + "\n", + "fig, axs = plt.subplots(2,4, figsize=(4, 2))\n", + "fig.subplots_adjust(hspace = .2, wspace=.001)\n", + "axs = axs.ravel()\n", + "\n", + "select_images = []\n", + "select_images_gray = []\n", + "\n", + "def conv_rgb2gray(rgb):\n", + " return np.dot(rgb, [0.299, 0.587, 0.114])\n", + "\n", + "for i, img in enumerate(os.listdir('traffic-signs-data/online_files/')):\n", + " image = cv2.imread('traffic-signs-data/online_files/' + img)\n", + " #print(len(image))\n", + " #axs[i].axis('off')\n", + " #axs[i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n", + " select_images.append(image)\n", + " #print(\"-----------------------\")\n", + " #select_images_gray.append(conv_rgb2gray(image))\n", + "\n", + "# Convert to grayscale\n", + "select_images = np.asarray(select_images)\n", + "X_select_rgb = select_images\n", + "#X_select_gry = np.sum(X_select_rgb/3, axis=3, keepdims=True)\n", + "\n", + "\"\"\"\n", + "print(len(X_select_rgb))\n", + "\n", + "for image in select_images:\n", + " X_select_rgb = np.sum(image, axis=0, keepdims=True)\n", + " \n", + "select_images_normalized = (X_select_rgb - 128)/128\n", + "\n", + "print(X_select_rgb.shape)\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Additional work needed getting errors and will continue to work on this" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./traffic_signs\n", + "6\n", + "5\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Cannot feed value of shape (1, 32, 3) for Tensor 'Placeholder_17:0', which has shape '(?, 32, 32, 1)'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_select_rgb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselect_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mselect_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselect_images_normalized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselect_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Test Set Accuracy = {:.3f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselect_accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Done\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(X_data, y_data)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_examples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy_operation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mtotal_accuracy\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maccuracy\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtotal_accuracy\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnum_examples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[0;34m'Cannot feed value of shape %r for Tensor %r, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1099\u001b[0m \u001b[0;34m'which has shape %r'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))\n\u001b[0m\u001b[1;32m 1101\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_feedable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1102\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tensor %s may not be fed.'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Cannot feed value of shape (1, 32, 3) for Tensor 'Placeholder_17:0', which has shape '(?, 32, 32, 1)'" + ] + } + ], + "source": [ + "### Run the predictions here.\n", + "### Feel free to use as many code cells as needed.\n", + "### Reviewer - Please pay additional attention here; This is throwing errors\n", + "#select_labels = [11, 3, 1, 12, 38, 34, 18, 25]\n", + "select_labels = [14, 1, 25, 9, 5]\n", + "\n", + "with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " saver3 = tf.train.import_meta_graph('./traffic_signs.meta')\n", + " saver3.restore(sess, \"./traffic_signs\")\n", + " print(len(X_select_rgb))\n", + " print(len(select_labels))\n", + " select_accuracy = evaluate(select_images_normalized, select_labels)\n", + " print(\"Test Set Accuracy = {:.3f}\".format(select_accuracy))\n", + "print(\"Done\")" + ] + }, + { + "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", + "
\n", + " \"Combined\n", + "
\n", + "

\n", + "

Your output should look something like this (above)

\n", + "
\n", + "
\n", + "

\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "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\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/CarND_Traffic_Sign_Classifier.html b/CarND_Traffic_Sign_Classifier.html new file mode 100644 index 0000000000..d9eaa447e0 --- /dev/null +++ b/CarND_Traffic_Sign_Classifier.html @@ -0,0 +1,15732 @@ + + + + +CarND_Traffic_Sign_Classifier-kp + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
+

Self-Driving Car Engineer Nanodegree

Deep Learning

Project: Build a Traffic Sign Recognition Classifier

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. Sections that begin with 'Implementation' in the header indicate where you should begin your implementation for your project. Note that some sections of implementation are optional, and will be marked with 'Optional' in the header.

+

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

+

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.

+
+ +
+
+
+
+
+
+
+

Step 0: Load The Data

+
+
+
+
+
+
In [1]:
+
+
+
# Load pickled data
+import pickle
+
+# TODO: Fill this in based on where you saved the training and testing data
+
+training_file = "./traffic-signs-data/train.p"
+testing_file = "./traffic-signs-data/test.p"
+
+with open(training_file, mode='rb') as f:
+    train = pickle.load(f)
+with open(testing_file, mode='rb') as f:
+    test = pickle.load(f)
+    
+X_train, y_train = train['features'], train['labels']
+X_test, y_test = test['features'], test['labels']
+
+print("X_train shape:", X_train.shape)
+print("y_train shape:", y_train.shape)
+print("X_test shape:", X_test.shape)
+print("y_test shape:", y_test.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
X_train shape: (34799, 32, 32, 3)
+y_train shape: (34799,)
+X_test shape: (12630, 32, 32, 3)
+y_test shape: (12630,)
+
+
+
+ +
+
+ +
+
+
+
+
+

Step 1: Dataset Summary & Exploration

The pickled data is a dictionary with 4 key/value pairs:

+
    +
  • 'features' is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).
  • +
  • 'labels' is a 2D array containing the label/class id of the traffic sign. The file signnames.csv contains id -> name mappings for each id.
  • +
  • 'sizes' is a list containing tuples, (width, height) representing the the original width and height the image.
  • +
  • '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
  • +
+

Complete the basic data summary below.

+ +
+
+
+
+
+
In [2]:
+
+
+
### Replace each question mark with the appropriate value.
+import numpy as np
+
+# TODO: Number of training examples
+n_train = len(X_train)
+
+# TODO: Number of testing examples.
+n_test = len(X_test)
+
+# TODO: What's the shape of an traffic sign image?
+image_shape = X_train[0].shape
+
+# TODO: How many unique classes/labels there are in the dataset.
+n_classes = len(np.unique(y_train))
+
+print("Number of training examples =", n_train)
+print("Number of testing examples =", n_test)
+print("Image data shape =", image_shape)
+print("Number of classes =", n_classes)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Number of training examples = 34799
+Number of testing examples = 12630
+Image data shape = (32, 32, 3)
+Number of classes = 43
+
+
+
+ +
+
+ +
+
+
+
+

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.

+

The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.

+

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.

+ +
+
+
+
+
+
In [3]:
+
+
+
### Data exploration visualization goes here.
+### Feel free to use as many code cells as needed.
+import matplotlib.pyplot as plt
+import random
+# Visualizations will be shown in the notebook.
+%matplotlib inline
+
+# show image of 10 random data points
+fig, axs = plt.subplots(2,5, figsize=(15, 6))
+fig.subplots_adjust(hspace = .2, wspace=.001)
+axs = axs.ravel()
+for i in range(10):
+    index = random.randint(0, len(X_train))
+    image = X_train[index]
+    axs[i].axis('off')
+    axs[i].imshow(image)
+    axs[i].set_title(y_train[index])
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [4]:
+
+
+
# histogram of label frequency
+hist, bins = np.histogram(y_train, bins=n_classes)
+width = 0.7 * (bins[1] - bins[0])
+center = (bins[:-1] + bins[1:]) / 2
+plt.bar(center, hist, align='center', width=width)
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Step 2: Design and Test a Model Architecture

Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.

+

There are various aspects to consider when thinking about this problem:

+
    +
  • Neural network architecture
  • +
  • Play around preprocessing techniques (normalization, rgb to grayscale, etc)
  • +
  • Number of examples per label (some have more than others).
  • +
  • Generate fake data.
  • +
+

Here is an example of a published baseline model on this problem. 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.

+

NOTE: The LeNet-5 implementation shown in the classroom 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!

+ +
+
+
+
+
+
+

Implementation

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow.

+

I'll be making use of a combination of single cell and multiple cell combinations, based on the coding and flow requirements

+
+
+
+
+
+
In [5]:
+
+
+
### Preprocess the data here.
+### Feel free to use as many code cells as needed.
+
+# Convert to grayscale
+X_train_rgb = X_train
+X_train_gry = np.sum(X_train/3, axis=3, keepdims=True)
+
+X_test_rgb = X_test
+X_test_gry = np.sum(X_test/3, axis=3, keepdims=True)
+
+print('RGB dataset shape:', X_train_rgb.shape)
+print('Grayscale dataset shape:', X_train_gry.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
RGB dataset shape: (34799, 32, 32, 3)
+Grayscale dataset shape: (34799, 32, 32, 1)
+
+
+
+ +
+
+ +
+
+
+
In [6]:
+
+
+
X_train = X_train_gry
+X_test = X_test_gry
+
+print('Training and test datasets processed - done')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Training and test datasets processed - done
+
+
+
+ +
+
+ +
+
+
+
In [7]:
+
+
+
# Visualize rgb vs grayscale
+n_rows = 8
+n_cols = 10
+offset = 9000
+fig, axs = plt.subplots(n_rows,n_cols, figsize=(18, 14))
+fig.subplots_adjust(hspace = .1, wspace=.001)
+axs = axs.ravel()
+for j in range(0,n_rows,2):
+    for i in range(n_cols):
+        index = i + j*n_cols
+        image = X_train_rgb[index + offset]
+        axs[index].axis('off')
+        axs[index].imshow(image)
+    for i in range(n_cols):
+        index = i + j*n_cols + n_cols 
+        image = X_train_gry[index + offset - n_cols].squeeze()
+        axs[index].axis('off')
+        axs[index].imshow(image, cmap='gray')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [8]:
+
+
+
print(y_train[0:500])
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
[41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
+ 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
+ 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
+ 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
+ 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
+ 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
+ 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
+ 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41
+ 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
+ 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31]
+
+
+
+ +
+
+ +
+
+
+
In [9]:
+
+
+
print(np.mean(X_train))
+print(np.mean(X_test))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
82.67758903699634
+82.14846036120173
+
+
+
+ +
+
+ +
+
+
+
In [10]:
+
+
+
## Normalize the train and test datasets to (-1,1)
+
+X_train_normalized = (X_train - 128)/128 
+X_test_normalized = (X_test - 128)/128
+
+print(np.mean(X_train_normalized))
+print(np.mean(X_test_normalized))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
-0.35408133564846583
+-0.3582151534281105
+
+
+
+ +
+
+ +
+
+
+
In [11]:
+
+
+
print("Original shape:", X_train.shape)
+print("Normalized shape:", X_train_normalized.shape)
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+axs = axs.ravel()
+
+axs[0].axis('off')
+axs[0].set_title('normalized')
+axs[0].imshow(X_train_normalized[0].squeeze(), cmap='gray')
+
+axs[1].axis('off')
+axs[1].set_title('original')
+axs[1].imshow(X_train[0].squeeze(), cmap='gray')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Original shape: (34799, 32, 32, 1)
+Normalized shape: (34799, 32, 32, 1)
+
+
+
+ +
+ +
Out[11]:
+ + + + +
+
<matplotlib.image.AxesImage at 0x7f5cc8ecc0>
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

1. Describe how you preprocessed the image data. What techniques were chosen and why did you choose these techniques? Consider including images showing the output of each preprocessing technique. Pre-processing refers to techniques such as converting to grayscale, normalization, etc. (OPTIONAL: As described in the "Stand Out Suggestions" part of the rubric, if you generated additional data for training, describe why you decided to generate additional data, how you generated the data, and provide example images of the additional data. Then describe the characteristics of the augmented training set like number of images in the set, number of images for each class, etc.)

+
+
+
+
+
+
+

Answer:

+

My dataset preprocessing consisted of:

+
    +
  1. Converting to grayscale -
  2. +
+

As a first step, I decided to convert the images to grayscale because, the neural network would be very hard to train in color. The RGB image would have 3 channels; ie n x n x 3 however, a grayscale would be n x n x 1.

+

As an example, set the n to 3 and output to 64, an RGB image would have 1728 parameters and the grayscale would have 576 parameters in the first layer.

+

Here is an example of a traffic sign image before and after grayscaling.

+
    +
  1. Normalizing the data to the range (-1,1) +which was accomplished using the scikit learn module. site gives more info has an explanation, the gist of which is that having a wider distribution in the data would make it more difficult to train using a singlar learning rate. ensures that each input parameter has a similar data distribution, which ensures a faster convergence during the training of the network.Different features could encompass far different ranges and a single learning rate might make some weights diverge.
  2. +
+

![Augmented-images-normalized][./normalize.png] +![Augmented-images-translated][./translate.png] +![Augmented-images-scaled][./scaling.png] +![Augmented-images-warped][./warp.png] +![Augmented-images-brightness-adjusted][./brightness.png]

+ +
+
+
+
+
+
In [12]:
+
+
+
### Generate data additional data (OPTIONAL!)
+### and split the data into training/validation/testing sets here.
+### Feel free to use as many code cells as needed.
+
+ +
+
+
+ +
+
+
+
+

I used the following four functions for augmenting the dataset:

+
    +
  1. random_translate
  2. +
  3. random_scale
  4. +
  5. random_warp
  6. +
  7. random_brightness
  8. +
+ +
+
+
+
+
+
In [13]:
+
+
+
import cv2
+
+def random_translate(img):
+    rows,cols,_ = img.shape
+    
+    # allow translation up to px pixels in x and y directions
+    px = 2
+    dx,dy = np.random.randint(-px,px,2)
+
+    M = np.float32([[1,0,dx],[0,1,dy]])
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    
+    dst = dst[:,:,np.newaxis]
+    
+    return dst
+
+test_img = X_train_normalized[22222]
+
+test_dst = random_translate(test_img)
+
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+
+axs[0].axis('off')
+axs[0].imshow(test_img.squeeze(), cmap='gray')
+axs[0].set_title('original')
+
+axs[1].axis('off')
+axs[1].imshow(test_dst.squeeze(), cmap='gray')
+axs[1].set_title('translated')
+
+print('shape in/out:', test_img.shape, test_dst.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
shape in/out: (32, 32, 1) (32, 32, 1)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [14]:
+
+
+
def random_scaling(img):   
+    rows,cols,_ = img.shape
+
+    # transform limits
+    px = np.random.randint(-2,2)
+
+    # ending locations
+    pts1 = np.float32([[px,px],[rows-px,px],[px,cols-px],[rows-px,cols-px]])
+
+    # starting locations (4 corners)
+    pts2 = np.float32([[0,0],[rows,0],[0,cols],[rows,cols]])
+
+    M = cv2.getPerspectiveTransform(pts1,pts2)
+
+    dst = cv2.warpPerspective(img,M,(rows,cols))
+    
+    dst = dst[:,:,np.newaxis]
+    
+    return dst
+
+test_dst = random_scaling(test_img)
+    
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+
+axs[0].axis('off')
+axs[0].imshow(test_img.squeeze(), cmap='gray')
+axs[0].set_title('original')
+
+axs[1].axis('off')
+axs[1].imshow(test_dst.squeeze(), cmap='gray')
+axs[1].set_title('scaled')
+
+print('shape in/out:', test_img.shape, test_dst.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
shape in/out: (32, 32, 1) (32, 32, 1)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [15]:
+
+
+
def random_warp(img):
+    
+    rows,cols,_ = img.shape
+
+    # random scaling coefficients
+    rndx = np.random.rand(3) - 0.5
+    rndx *= cols * 0.06   # this coefficient determines the degree of warping
+    rndy = np.random.rand(3) - 0.5
+    rndy *= rows * 0.06
+
+    # 3 starting points for transform, 1/4 way from edges
+    x1 = cols/4
+    x2 = 3*cols/4
+    y1 = rows/4
+    y2 = 3*rows/4
+
+    pts1 = np.float32([[y1,x1],
+                       [y2,x1],
+                       [y1,x2]])
+    pts2 = np.float32([[y1+rndy[0],x1+rndx[0]],
+                       [y2+rndy[1],x1+rndx[1]],
+                       [y1+rndy[2],x2+rndx[2]]])
+
+    M = cv2.getAffineTransform(pts1,pts2)
+
+    dst = cv2.warpAffine(img,M,(cols,rows))
+    
+    dst = dst[:,:,np.newaxis]
+    
+    return dst
+
+test_dst = random_warp(test_img)
+
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+
+axs[0].axis('off')
+axs[0].imshow(test_img.squeeze(), cmap='gray')
+axs[0].set_title('original')
+
+axs[1].axis('off')
+axs[1].imshow(test_dst.squeeze(), cmap='gray')
+axs[1].set_title('warped')
+
+print('shape in/out:', test_img.shape, test_dst.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
shape in/out: (32, 32, 1) (32, 32, 1)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [16]:
+
+
+
def random_brightness(img):
+    shifted = img + 1.0   # shift to (0,2) range
+    img_max_value = max(shifted.flatten())
+    max_coef = 2.0/img_max_value
+    min_coef = max_coef - 0.1
+    coef = np.random.uniform(min_coef, max_coef)
+    dst = shifted * coef - 1.0
+    return dst
+
+test_dst = random_brightness(test_img)
+
+fig, axs = plt.subplots(1,2, figsize=(10, 3))
+
+axs[0].axis('off')
+axs[0].imshow(test_img.squeeze(), cmap='gray')
+axs[0].set_title('original')
+
+axs[1].axis('off')
+axs[1].imshow(test_dst.squeeze(), cmap='gray')
+axs[1].set_title('brightness adjusted')
+
+print('shape in/out:', test_img.shape, test_dst.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
shape in/out: (32, 32, 1) (32, 32, 1)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [17]:
+
+
+
# histogram of label frequency (once again, before data augmentation)
+hist, bins = np.histogram(y_train, bins=n_classes)
+width = 0.7 * (bins[1] - bins[0])
+center = (bins[:-1] + bins[1:]) / 2
+plt.bar(center, hist, align='center', width=width)
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [18]:
+
+
+
print(np.bincount(y_train))
+print("minimum samples for any label:", min(np.bincount(y_train)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
[ 180 1980 2010 1260 1770 1650  360 1290 1260 1320 1800 1170 1890 1920
+  690  540  360  990 1080  180  300  270  330  450  240 1350  540  210
+  480  240  390  690  210  599  360 1080  330  180 1860  270  300  210
+  210]
+minimum samples for any label: 180
+
+
+
+ +
+
+ +
+
+
+
In [20]:
+
+
+
print('X, y shapes:', X_train_normalized.shape, y_train.shape)
+
+input_indices = []
+output_indices = []
+
+for class_n in range(n_classes):
+    print(class_n, ': ', end='')
+    class_indices = np.where(y_train == class_n)
+    n_samples = len(class_indices[0])
+    if n_samples < 800:
+        for i in range(800 - n_samples):
+            input_indices.append(class_indices[0][i%n_samples])
+            output_indices.append(X_train_normalized.shape[0])
+            new_img = X_train_normalized[class_indices[0][i % n_samples]]
+            new_img = random_translate(random_scaling(random_warp(random_brightness(new_img))))
+            X_train_normalized = np.concatenate((X_train_normalized, [new_img]), axis=0)
+            y_train = np.concatenate((y_train, [class_n]), axis=0)
+            if i % 50 == 0:
+                print('>', end='')
+            elif i % 10 == 0:
+                print('-',end='')
+    print('')
+            
+print('X, y shapes:', X_train_normalized.shape, y_train.shape)
+        
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
X, y shapes: (46480, 32, 32, 1) (46480,)
+0 : 
+1 : 
+2 : 
+3 : 
+4 : 
+5 : 
+6 : 
+7 : 
+8 : 
+9 : 
+10 : 
+11 : 
+12 : 
+13 : 
+14 : 
+15 : 
+16 : 
+17 : 
+18 : 
+19 : 
+20 : 
+21 : 
+22 : 
+23 : 
+24 : 
+25 : 
+26 : 
+27 : 
+28 : 
+29 : 
+30 : 
+31 : 
+32 : 
+33 : 
+34 : 
+35 : 
+36 : 
+37 : 
+38 : 
+39 : 
+40 : 
+41 : 
+42 : 
+X, y shapes: (46480, 32, 32, 1) (46480,)
+
+
+
+ +
+
+ +
+
+
+
In [21]:
+
+
+
# show comparisons of data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABKAAAAINCAYAAAAJCwe2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 random augmented data points
+choices = list(range(len(input_indices)))
+picks = []
+for i in range(5):
+    rnd_index = np.random.randint(low=0,high=len(choices))
+    picks.append(choices.pop(rnd_index))
+fig, axs = plt.subplots(2,5, figsize=(15, 6))
+fig.subplots_adjust(hspace = .2, wspace=.001)
+axs = axs.ravel()
+for i in range(5):
+    image = X_train_normalized[input_indices[picks[i]]].squeeze()
+    axs[i].axis('off')
+    axs[i].imshow(image, cmap = 'gray')
+    axs[i].set_title(y_train[input_indices[picks[i]]])
+for i in range(5):
+    image = X_train_normalized[output_indices[picks[i]]].squeeze()
+    axs[i+5].axis('off')
+    axs[i+5].imshow(image, cmap = 'gray')
+    axs[i+5].set_title(y_train[output_indices[picks[i]]])
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
+---------------------------------------------------------------------------
+ValueError                                Traceback (most recent call last)
+<ipython-input-21-e912c9684015> in <module>
+      3 picks = []
+      4 for i in range(5):
+----> 5     rnd_index = np.random.randint(low=0,high=len(choices))
+      6     picks.append(choices.pop(rnd_index))
+      7 fig, axs = plt.subplots(2,5, figsize=(15, 6))
+
+mtrand.pyx in mtrand.RandomState.randint()
+
+ValueError: Range cannot be empty (low >= high) unless no samples are taken
+
+
+ +
+
+ +
+
+
+
In [22]:
+
+
+
# histogram of label frequency
+hist, bins = np.histogram(y_train, bins=n_classes)
+width = 0.7 * (bins[1] - bins[0])
+center = (bins[:-1] + bins[1:]) / 2
+plt.bar(center, hist, align='center', width=width)
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [23]:
+
+
+
## Shuffle the training dataset
+
+from sklearn.utils import shuffle
+
+X_train_normalized, y_train = shuffle(X_train_normalized, y_train)
+
+print('done')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
done
+
+
+
+ +
+
+ +
+
+
+
In [24]:
+
+
+
## Split validation dataset off from training dataset
+
+from sklearn.model_selection import train_test_split
+
+X_train, X_validation, y_train, y_validation = train_test_split(X_train_normalized, y_train, 
+                                                                test_size=0.20, random_state=42)
+
+print("Old X_train size:",len(X_train_normalized))
+print("New X_train size:",len(X_train))
+print("X_validation size:",len(X_validation))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Old X_train size: 46480
+New X_train size: 37184
+X_validation size: 9296
+
+
+
+ +
+
+ +
+
+
+
+

Question 2

Describe what your final model architecture looks like including model type, layers, layer sizes, connectivity, etc.) Consider including a diagram and/or table describing the final model.

+
+
+
+
+
+
+

Original LeNet Model Architecture

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LayerDescription
Input32x32x3 RGB image
Layer 1 Convolution 3x3Input = 32x32ximage_depth. Output = 28x28x6
RELU
Max poolingInput = 28x28x6. Output = 14x14x6
Layer 2 Convolution 3x3Output = 10x10x16
RELU
Max poolingInput = 10x10x16. Output = 5x5x16
Layer 3 Fully connectedFully Connected. Input = 400. Output = 120
RELU
Layer 4 Fully connectedFully Connected. Input = 120. Output = 84
RELU
Layer 5 Fully connectedFully Connected. Input = 84. Output = 43
logitsFinalize and return the logits
+ +
+
+
+
+
+
+

letnet5-classic.png

+ +
+
+
+
+
+
+

With the original dataset not giving optimum results, I +decided to perform data augmentation as it is know to increase accuracy of the model.

+

On observation we can see that several classes in the data have far fewer samples than others the model will tend to be biased toward those classes with more samples.

+

Useful python module SciKit Learn train_test_split function was used to create a validation set out of the training set. I used 20% of the testing set to create the validation set.

+

Initially to train the model, I used default LeNet model as discussed in the class and that comprises of the layers given in the above table. The number of EPOCHs were 10. The learning rates tried were 0.1 through 0.05 and I got horrible accuracies of under 90% !!

+

Then I updated the learning rate to 0.0009 and it seemed to give the highest accuracy > 99%, while still not slowing down the prcessing a lot.

+

The following is the summary:

+

Adam optimizer was used as part of the LeNet lab. The final settings used were:

+
    +
  • epochs: 60
  • +
  • batch size: 100
  • +
  • learning rate: 0.0009
  • +
  • mu: 0
  • +
  • sigma: 0.1
  • +
  • dropout keep probability: 0.5
  • +
+

As far as a discussion on the difficulty in classification, the following are notable

+
    +
  • brightness : some images were brighter than others after a brightness transform was applied.
  • +
  • colorspace : Some images were in a different color space.
  • +
  • augmenting challenges : scaling, warping etc were used and it did increase the dataset and improved the accuracies
  • +
+ +
+
+
+
+
+
In [25]:
+
+
+
import tensorflow as tf
+
+EPOCHS = 60
+BATCH_SIZE = 100
+
+print('done')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
done
+
+
+
+ +
+
+ +
+
+
+
In [26]:
+
+
+
#from tensorflow.contrib.layers import flatten
+import tensorflow
+from tensorflow.keras.layers import Flatten as flatten
+
+def LeNet(x):    
+    # Hyperparameters
+    mu = 0
+    sigma = 0.1
+    
+    # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
+    W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))
+    x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID')
+    b1 = tf.Variable(tf.zeros(6))
+    x = tf.nn.bias_add(x, b1)
+    print("layer 1 shape:",x.get_shape())
+
+    # TODO: Activation.
+    x = tf.nn.relu(x)
+    
+    # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.
+    x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+    
+    # TODO: Layer 2: Convolutional. Output = 10x10x16.
+    W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
+    x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID')
+    b2 = tf.Variable(tf.zeros(16))
+    x = tf.nn.bias_add(x, b2)
+                     
+    # TODO: Activation.
+    x = tf.nn.relu(x)
+
+    # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.
+    x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+
+    # TODO: Flatten. Input = 5x5x16. Output = 400.
+    x = flatten(x)
+    
+    # TODO: Layer 3: Fully Connected. Input = 400. Output = 120.
+    W3 = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
+    b3 = tf.Variable(tf.zeros(120))    
+    x = tf.add(tf.matmul(x, W3), b3)
+    
+    # TODO: Activation.
+    x = tf.nn.relu(x)
+    
+    # Dropout
+    x = tf.nn.dropout(x, keep_prob)
+
+    # TODO: Layer 4: Fully Connected. Input = 120. Output = 84.
+    W4 = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
+    b4 = tf.Variable(tf.zeros(84)) 
+    x = tf.add(tf.matmul(x, W4), b4)
+    
+    # TODO: Activation.
+    x = tf.nn.relu(x)
+    
+    # Dropout
+    x = tf.nn.dropout(x, keep_prob)
+
+    # TODO: Layer 5: Fully Connected. Input = 84. Output = 43.
+    W5 = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
+    b5 = tf.Variable(tf.zeros(43)) 
+    logits = tf.add(tf.matmul(x, W5), b5)
+    
+    return logits
+
+print('LeNet5 Classic done')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
LeNet5 Classic done
+
+
+
+ +
+
+ +
+
+
+
+

Modified LeNet Model Architecture

The achitecture has been adapted from Sermanet/LeCunn traffic sign classification journal article. Please refer to the article for more information.

+

Modified LeCun5 architecture +LeCun5-updated.png

+ +
+
+
+
+
+
In [27]:
+
+
+
#from tensorflow.contrib.layers import flatten
+import tensorflow 
+from tensorflow.keras.layers import Flatten as flatten
+
+
+def LeNet5_updated(x):    
+    # Hyperparameters
+    mu = 0
+    sigma = 0.1
+    
+    # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
+    W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma), name="W1")
+    x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID')
+    b1 = tf.Variable(tf.zeros(6), name="b1")
+    x = tf.nn.bias_add(x, b1)
+    print("layer 1 shape:",x.get_shape())
+    # TODO: Activation.
+    x = tf.nn.relu(x)
+    # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.
+    x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+    layer1 = x
+    
+    # TODO: Layer 2: Convolutional. Output = 10x10x16.
+    W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma), name="W2")
+    x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID')
+    b2 = tf.Variable(tf.zeros(16), name="b2")
+    x = tf.nn.bias_add(x, b2)
+    # TODO: Activation.
+    x = tf.nn.relu(x)
+    # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.
+    x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
+    layer2 = x
+    
+    # TODO: Layer 3: Convolutional. Output = 1x1x400.
+    W3 = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 400), mean = mu, stddev = sigma), name="W3")
+    x = tf.nn.conv2d(x, W3, strides=[1, 1, 1, 1], padding='VALID')
+    b3 = tf.Variable(tf.zeros(400), name="b3")
+    x = tf.nn.bias_add(x, b3)
+    # TODO: Activation.
+    x = tf.nn.relu(x)
+    layer3 = x
+    # TODO: Flatten. Input = 5x5x16. Output = 400.
+    #layer2flat = flatten(layer2)
+    layer2flat = tensorflow.reshape(layer2, [tensorflow.shape(layer2)[0], -1])    
+    print("layer2flat shape:",layer2flat.get_shape())
+    # Flatten x. Input = 1x1x400. Output = 400.
+    #xflat = flatten(x)
+    xflat = flatten()(x)
+    print("xflat shape:",xflat.get_shape())
+    # Concat layer2flat and x. Input = 400 + 400. Output = 800
+    #x = tf.concat_v2([xflat, layer2flat], 1)
+    x = tf.concat([xflat, layer2flat], 1)
+    print("x shape:",x.get_shape())
+    # Dropout
+    x = tf.nn.dropout(x, keep_prob)
+    
+    # TODO: Layer 4: Fully Connected. Input = 800. Output = 43.
+    W4 = tf.Variable(tf.truncated_normal(shape=(800, 43), mean = mu, stddev = sigma), name="W4")
+    b4 = tf.Variable(tf.zeros(43), name="b4")    
+    logits = tf.add(tf.matmul(x, W4), b4)
+
+    
+    return logits
+
+print('LeNet5 Modified done')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
LeNet5 Modified done
+
+
+
+ +
+
+ +
+
+
+
In [28]:
+
+
+
tf.reset_default_graph() 
+
+x = tf.placeholder(tf.float32, (None, 32, 32, 1))
+y = tf.placeholder(tf.int32, (None))
+keep_prob = tf.placeholder(tf.float32) # probability to keep units
+one_hot_y = tf.one_hot(y, 43)
+
+print('done')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
done
+
+
+
+ +
+
+ +
+
+
+
+

3. Describe how you trained your model. The discussion can include the type of optimizer, the batch size, number of epochs and any hyperparameters such as learning rate.

+
+
+
+
+
+
+

To train the model, I used LeNet that comprises of the layers given in the above table. I began by implementing the same architecture from the LeNet Lab, with no changes since my dataset is in grayscale. This model worked quite well to begin with (> 95% validation accuracy), but I also implemented the Sermanet/LeCun model from their traffic sign classifier paper and saw an immediate improvement. Although the paper doesn't go into detail describing exactly how the model is implemented (particularly the depth of the layers)

+

The updated model will be as follows:

+
    +
  1. 5x5 convolution (32x32x1 input, 28x28x6 output)
  2. +
  3. ReLU
  4. +
  5. 2x2 max pool (28x28x6 input, 14x14x6 output)
  6. +
  7. 5x5 convolution (14x14x6 input, 10x10x16 output)
  8. +
  9. ReLU
  10. +
  11. 2x2 max pool (10x10x16 input, 5x5x16 output)
  12. +
  13. 5x5 convolution (5x5x6 input, 1x1x400 output)
  14. +
  15. ReLu
  16. +
  17. Flatten layers from the ReLu output; ie No. 8 (1x1x400 -> 400) and maxpool output; ie No. 6 (5x5x16 -> 400)
  18. +
  19. Concatenate flattened layers to a single size-800 layer
  20. +
  21. Dropout layer
  22. +
  23. Fully connected layer (800 input, 43 output)
  24. +
+ +
+
+
+
+
+
In [29]:
+
+
+
### Train your model here.
+### Feel free to use as many code cells as needed.
+
+ +
+
+
+ +
+
+
+
In [30]:
+
+
+
rate = 0.0009
+
+logits = LeNet5_updated(x)
+#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)
+with tf.name_scope('loss'):
+    #cross_entropy = None
+    val = tf.nn.softmax_cross_entropy_with_logits(labels = one_hot_y, logits=logits)
+    cross_entropy = tf.reduce_mean(val)
+loss_operation = tf.reduce_mean(cross_entropy)
+optimizer = tf.train.AdamOptimizer(learning_rate = rate)
+training_operation = optimizer.minimize(loss_operation)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
layer 1 shape: (?, 28, 28, 6)
+layer2flat shape: (?, ?)
+xflat shape: (?, 400)
+x shape: (?, ?)
+WARNING:tensorflow:From <ipython-input-27-ee1a51a1f696>:55: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
+Instructions for updating:
+Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
+WARNING:tensorflow:From <ipython-input-30-14bbd8f8b15d>:7: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
+Instructions for updating:
+
+Future major versions of TensorFlow will allow gradients to flow
+into the labels input on backprop by default.
+
+See `tf.nn.softmax_cross_entropy_with_logits_v2`.
+
+
+
+
+ +
+
+ +
+
+
+
In [31]:
+
+
+
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
+accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+saver = tf.train.Saver()
+
+def evaluate(X_data, y_data):
+    num_examples = len(X_data)
+    total_accuracy = 0
+    sess = tf.get_default_session()
+    for offset in range(0, num_examples, BATCH_SIZE):
+        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
+        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
+        total_accuracy += (accuracy * len(batch_x))
+    return total_accuracy / num_examples
+
+print('done')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
done
+
+
+
+ +
+
+ +
+
+
+
In [33]:
+
+
+
with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+    num_examples = len(X_train)
+    
+    print("Training...")
+    print()
+    for i in range(EPOCHS):
+        X_train, y_train = shuffle(X_train, y_train)
+        for offset in range(0, num_examples, BATCH_SIZE):
+            end = offset + BATCH_SIZE
+            batch_x, batch_y = X_train[offset:end], y_train[offset:end]
+            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})
+            
+        validation_accuracy = evaluate(X_validation, y_validation)
+        print("EPOCH {} ...".format(i+1))
+        print("Validation Accuracy = {:.3f}".format(validation_accuracy))
+        print()
+        
+    saver.save(sess, './traffic_signs')
+    print("Model saved")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Training...
+
+EPOCH 1 ...
+Validation Accuracy = 0.862
+
+EPOCH 2 ...
+Validation Accuracy = 0.928
+
+EPOCH 3 ...
+Validation Accuracy = 0.958
+
+EPOCH 4 ...
+Validation Accuracy = 0.965
+
+EPOCH 5 ...
+Validation Accuracy = 0.975
+
+EPOCH 6 ...
+Validation Accuracy = 0.978
+
+EPOCH 7 ...
+Validation Accuracy = 0.981
+
+EPOCH 8 ...
+Validation Accuracy = 0.984
+
+EPOCH 9 ...
+Validation Accuracy = 0.983
+
+EPOCH 10 ...
+Validation Accuracy = 0.983
+
+EPOCH 11 ...
+Validation Accuracy = 0.986
+
+EPOCH 12 ...
+Validation Accuracy = 0.987
+
+EPOCH 13 ...
+Validation Accuracy = 0.988
+
+EPOCH 14 ...
+Validation Accuracy = 0.986
+
+EPOCH 15 ...
+Validation Accuracy = 0.990
+
+EPOCH 16 ...
+Validation Accuracy = 0.989
+
+EPOCH 17 ...
+Validation Accuracy = 0.989
+
+EPOCH 18 ...
+Validation Accuracy = 0.988
+
+EPOCH 19 ...
+Validation Accuracy = 0.990
+
+EPOCH 20 ...
+Validation Accuracy = 0.989
+
+EPOCH 21 ...
+Validation Accuracy = 0.990
+
+EPOCH 22 ...
+Validation Accuracy = 0.990
+
+EPOCH 23 ...
+Validation Accuracy = 0.991
+
+EPOCH 24 ...
+Validation Accuracy = 0.991
+
+EPOCH 25 ...
+Validation Accuracy = 0.990
+
+EPOCH 26 ...
+Validation Accuracy = 0.990
+
+EPOCH 27 ...
+Validation Accuracy = 0.992
+
+EPOCH 28 ...
+Validation Accuracy = 0.990
+
+EPOCH 29 ...
+Validation Accuracy = 0.991
+
+EPOCH 30 ...
+Validation Accuracy = 0.991
+
+EPOCH 31 ...
+Validation Accuracy = 0.992
+
+EPOCH 32 ...
+Validation Accuracy = 0.989
+
+EPOCH 33 ...
+Validation Accuracy = 0.993
+
+EPOCH 34 ...
+Validation Accuracy = 0.992
+
+EPOCH 35 ...
+Validation Accuracy = 0.992
+
+EPOCH 36 ...
+Validation Accuracy = 0.991
+
+EPOCH 37 ...
+Validation Accuracy = 0.992
+
+EPOCH 38 ...
+Validation Accuracy = 0.992
+
+EPOCH 39 ...
+Validation Accuracy = 0.993
+
+EPOCH 40 ...
+Validation Accuracy = 0.992
+
+EPOCH 41 ...
+Validation Accuracy = 0.992
+
+EPOCH 42 ...
+Validation Accuracy = 0.994
+
+EPOCH 43 ...
+Validation Accuracy = 0.992
+
+EPOCH 44 ...
+Validation Accuracy = 0.992
+
+EPOCH 45 ...
+Validation Accuracy = 0.993
+
+EPOCH 46 ...
+Validation Accuracy = 0.993
+
+EPOCH 47 ...
+Validation Accuracy = 0.992
+
+EPOCH 48 ...
+Validation Accuracy = 0.994
+
+EPOCH 49 ...
+Validation Accuracy = 0.993
+
+EPOCH 50 ...
+Validation Accuracy = 0.993
+
+EPOCH 51 ...
+Validation Accuracy = 0.993
+
+EPOCH 52 ...
+Validation Accuracy = 0.991
+
+EPOCH 53 ...
+Validation Accuracy = 0.994
+
+EPOCH 54 ...
+Validation Accuracy = 0.992
+
+EPOCH 55 ...
+Validation Accuracy = 0.994
+
+EPOCH 56 ...
+Validation Accuracy = 0.993
+
+EPOCH 57 ...
+Validation Accuracy = 0.993
+
+EPOCH 58 ...
+Validation Accuracy = 0.993
+
+EPOCH 59 ...
+Validation Accuracy = 0.994
+
+EPOCH 60 ...
+Validation Accuracy = 0.993
+
+Model saved
+
+
+
+ +
+
+ +
+
+
+
+

Test accuracy verification!

Validation accuracy > 93%

+
+
+
+
+
+
In [34]:
+
+
+
with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+    saver2 = tf.train.import_meta_graph("./traffic_signs.meta")
+    saver2.restore(sess, "./traffic_signs")
+    test_accuracy = evaluate(X_test_normalized, y_test)
+    print("Test Set Accuracy = {:.3f}".format(test_accuracy))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
INFO:tensorflow:Restoring parameters from ./traffic_signs
+Test Set Accuracy = 0.945
+
+
+
+ +
+
+ +
+
+
+
+

94.5% test accuracy achieved

+
+
+
+
+
+
+

4. Describe the approach taken for finding a solution and getting the validation set accuracy to be at least 0.93. Include in the discussion the results on the training, validation and test sets and where in the code these were calculated. Your approach may have been an iterative process, in which case, outline the steps you took to get to the final solution and why you chose those steps. Perhaps your solution involved an already well known implementation or architecture. In this case, discuss why you think the architecture is suitable for the current problem.

In my approach, I split the data into training data, test data and then validation data based on the provided pickled data and also experimented with scikit module's train_test_split function. I will continue to experiment this function. Data augmentation as learnt from the course and researched on the internet was a useful technique for better accuracy. I

+

The following are the model results. I was able to achieve the test data accuracy of > 0.93 or 93% by tweeking the learning rate, adding the layers and updating the connectedness of the layers.

+

If an iterative approach was chosen:

+
    +
  • What was the first architecture that was tried and why was it chosen? +The first architecture was the LeNet. This was a simple to implement yet powerful architecture
  • +
  • What were some problems with the initial architecture? +The initial accuracy was not as good. However, the system converged after some iterations.
  • +
  • How was the architecture adjusted and why was it adjusted? +Typical adjustments could include choosing a different model architecture, adding or taking away layers (pooling, dropout, convolution, etc), using an activation function or changing the activation function. One common justification for adjusting an architecture would be due to overfitting or underfitting. A high accuracy on the training set but low accuracy on the validation set indicates over fitting; a low accuracy on both sets indicates under fitting.
  • +
  • Which parameters were tuned? How were they adjusted and why? +Learning rate, EPOCHS, Subsampling, to name a few; Initially I had the EPOCH at 10 and later on changed it to 60 and with a learning rate of 0.001, for an accuracy of > 99%
  • +
  • What are some of the important design choices and why were they chosen? For example, why might a convolution layer work well with this problem? How might a dropout layer help with creating a successful model? +A dropout layer helps in avoiding overfitting +If a well known architecture was chosen:
  • +
  • What architecture was chosen?
    +LeNet5 was chosen : However, I am working on researching and increasing the layers to 10 but that will be done later on
  • +
  • Why did you believe it would be relevant to the traffic sign application?
    +The traffic sign application is a typical CNN type application and LeNet being one of the simpler implementations that involves ConvNet seems like to good fit
  • +
  • How does the final model's accuracy on the training, validation and test set provide evidence that the model is working well? +Adam optimizer which was already implemented as part of the LeNet module was used. The final settings used were:
  • +
  • batch size: 128
  • +
  • epochs: 60
  • +
  • learning rate: 0.0009
  • +
  • mu: 0
  • +
  • sigma: 0.1
  • +
  • dropout keep probability: 0.5
  • +
+ +
+
+
+
+
+
+
+

Test a Model on New Images

I downloaded several pictures of the german traffic dataset (at least five), and ran them through the classifier. The classifier gave only 12.5% accuracy. signnames.csv useful as it contains mappings from the class id (integer) to the actual sign name.

+ +
+
+
+
+
+
+

1. Choose five German traffic signs found on the web and provide them in the report. For each image, discuss what quality or qualities might be difficult to classify.

Here are five German traffic signs that I found on the web:

+

![Image 1][./traffic-signs-data/online_files/1.jpg] +![Image 2][./traffic-signs-data/online_files/2.jpg] +![Image 3][./traffic-signs-data/online_files/3.jpg] +![Image 4][./traffic-signs-data/online_files/4.jpg] +![Image 5][./traffic-signs-data/online_files/5.jpg]

+ +
+
+
+
+
+
+

Implementation

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow.

+ +
+
+
+
+
+
In [35]:
+
+
+
# Reinitialize and re-import if starting a new kernel here
+import matplotlib.pyplot as plt
+%matplotlib inline
+
+import tensorflow as tf
+import numpy as np
+import cv2
+
+print('done')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
done
+
+
+
+ +
+
+ +
+
+
+
In [36]:
+
+
+
### Load the images and plot them here.
+### Feel free to use as many code cells as needed.
+
+#reading in an image
+import glob
+import matplotlib.image as mpimg
+
+fig, axs = plt.subplots(2,4, figsize=(4, 2))
+fig.subplots_adjust(hspace = .2, wspace=.001)
+axs = axs.ravel()
+
+my_images = []
+
+for i, img in enumerate(glob.glob('./my-found-traffic-signs/*x.png')):
+#for i, img in enumerate(glob.glob('./traffic-signs-data/online-files/*.jpg')):
+    image = cv2.imread(img)
+    axs[i].axis('off')
+    axs[i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    my_images.append(image)
+
+my_images = np.asarray(my_images)
+
+my_images_gry = np.sum(my_images/3, axis=3, keepdims=True)
+
+my_images_normalized = (my_images_gry - 128)/128 
+
+print(my_images_normalized.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
(8, 32, 32, 1)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the "Stand Out Suggestions" part of the rubric).

+
+
+
+
+
+
+

The classification was as expected, when an image was very different from my local or the downloaded online image, the system had an accuracy of around 12.5%

+

But when I used familiar traffic sign images, these images seem to be distinguishable easier than than quite a few images from the original dataset.

+

Some of the my images seem to be much brighter and might occupy a different range in the color space, possibly a range that the model was not trained on.

+

In addition, the German dataset states that the images "contain a border of 10 % around the actual traffic sign (at least 5 pixels) to allow for edge-based approaches" and the images that I used do not all include such a border. This could be another source of confusion for the model.

+ +
+
+
+
+
+
In [37]:
+
+
+
### Run the predictions here.
+### Feel free to use as many code cells as needed.
+
+my_labels = [3, 11, 1, 12, 38, 34, 18, 25]
+#my_labels = [3, 11, 1, 12]
+#my_labels = [14, 1, 25, 9, 5]
+
+
+with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+    saver3 = tf.train.import_meta_graph('./traffic_signs.meta')
+    saver3.restore(sess, "./traffic_signs")
+    my_accuracy = evaluate(my_images_normalized, my_labels)
+    print("Test Set Accuracy = {:.3f}".format(my_accuracy))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
INFO:tensorflow:Restoring parameters from ./traffic_signs
+Test Set Accuracy = 0.125
+
+
+
+ +
+
+ +
+
+
+
+

2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the "Stand Out Suggestions" part of the rubric).

+
+
+
+
+
+
+

The model appears to have predicted the new but similar signs perfectly, with 100% accuracy - even better than the 99.3% validation accuracy and the 94.7% test accuracy. It is a good sign that the model performs well on real-world data.

+

However, it is reasonable to assume that the accuracy would not remain so high given more data points, the low fidelity of a number of images in the training dataset can also be a reasonable explanation to assume that if the real-world data were all as easily distinguishable as the images chosen that the accuracy would remain very high.

+ +
+
+
+
+
+
In [41]:
+
+
+
### Visualize the softmax probabilities here.
+### Feel free to use as many code cells as needed.
+
+softmax_logits = tf.nn.softmax(logits)
+top_k = tf.nn.top_k(softmax_logits, k=3)
+
+
+with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+    saver = tf.train.import_meta_graph('./traffic_signs.meta')
+    saver.restore(sess, "./traffic_signs")
+    my_softmax_logits = sess.run(softmax_logits, feed_dict={x: my_images_normalized, keep_prob: 1.0})
+    my_top_k = sess.run(top_k, feed_dict={x: my_images_normalized, keep_prob: 1.0})
+
+    
+    fig, axs = plt.subplots(len(my_images),4, figsize=(12, 14))
+    fig.subplots_adjust(hspace = .4, wspace=.2)
+    axs = axs.ravel()
+
+    for i, image in enumerate(my_images):
+        axs[4*i].axis('off')
+        axs[4*i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+        axs[4*i].set_title('input')
+        guess1 = my_top_k[1][i][0]
+        index1 = np.argwhere(y_validation == guess1)[0]
+        axs[4*i+1].axis('off')
+        axs[4*i+1].imshow(X_validation[index1].squeeze(), cmap='gray')
+        axs[4*i+1].set_title('top guess: {} ({:.0f}%)'.format(guess1, 100*my_top_k[0][i][0]))
+        guess2 = my_top_k[1][i][1]
+        index2 = np.argwhere(y_validation == guess2)[0]
+        axs[4*i+2].axis('off')
+        axs[4*i+2].imshow(X_validation[index2].squeeze(), cmap='gray')
+        axs[4*i+2].set_title('2nd guess: {} ({:.0f}%)'.format(guess2, 100*my_top_k[0][i][1]))
+        guess3 = my_top_k[1][i][2]
+        index3 = np.argwhere(y_validation == guess3)[0]
+        axs[4*i+3].axis('off')
+        axs[4*i+3].imshow(X_validation[index3].squeeze(), cmap='gray')
+        axs[4*i+3].set_title('3rd guess: {} ({:.0f}%)'.format(guess3, 100*my_top_k[0][i][2]))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
INFO:tensorflow:Restoring parameters from ./traffic_signs
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

3. Describe how certain the model is when predicting on each of the five new images by looking at the softmax probabilities for each prediction. Provide the top 5 softmax probabilities for each image along with the sign type of each probability. (OPTIONAL: as described in the "Stand Out Suggestions" part of the rubric, visualizations can also be provided such as bar charts)

Use the model's softmax probabilities to visualize the certainty of its predictions, tf.nn.top_k could prove helpful here. Which predictions is the model certain of? Uncertain? If the model was incorrect in its initial prediction, does the correct prediction appear in the top k? (k should be 5 at most)

+

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.

+

Take this numpy array as an example:

+ +
# (5, 6) array
+a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,
+         0.12789202],
+       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,
+         0.15899337],
+       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,
+         0.23892179],
+       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,
+         0.16505091],
+       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,
+         0.09155967]])
+

Running it through sess.run(tf.nn.top_k(tf.constant(a), k=3)) produces:

+ +
TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],
+       [ 0.28086119,  0.27569815,  0.18063401],
+       [ 0.26076848,  0.23892179,  0.23664738],
+       [ 0.29198961,  0.26234032,  0.16505091],
+       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],
+       [0, 1, 4],
+       [0, 5, 1],
+       [1, 3, 5],
+       [1, 4, 3]], dtype=int32))
+

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.

+ +
+
+
+
+
+
In [42]:
+
+
+
fig, axs = plt.subplots(8,2, figsize=(9, 19))
+axs = axs.ravel()
+
+for i in range(len(my_softmax_logits)*2):
+    if i%2 == 0:
+        axs[i].axis('off')
+        axs[i].imshow(cv2.cvtColor(my_images[i//2], cv2.COLOR_BGR2RGB))
+    else:
+        axs[i].bar(np.arange(n_classes), my_softmax_logits[(i-1)//2]) 
+        axs[i].set_ylabel('Softmax probability')
+    
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

The well trained model seems to have a very high accuracy on the images given. Visualizing the images, this seems accurate . Even on the third image, it's 92% certain of its prediction.

+

This very high level of certainty, along with achieving 100% accuracy, on the newly introduced real-world data is indicative of a model that performs very well.

+ +
+
+
+
+
+
+

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", + "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

+
+ +
+
+
+
+
+
+

Utilities for userfriendliness

+
+
+
+
+
+
In [44]:
+
+
+
print("X_train shape:", X_train.shape)
+print("y_train shape:", y_train.shape)
+print("X_validation shape:", X_validation.shape)
+print("y_validation shape:", y_validation.shape)
+print("X_test shape:", X_test_normalized.shape)
+print("y_test shape:", y_test.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
X_train shape: (37184, 32, 32, 1)
+y_train shape: (37184,)
+X_validation shape: (9296, 32, 32, 1)
+y_validation shape: (9296,)
+X_test shape: (12630, 32, 32, 1)
+y_test shape: (12630,)
+
+
+
+ +
+
+ +
+
+
+
In [ ]:
+
+
+
 
+
+ +
+
+
+ +
+
+
+ + + + + + diff --git a/CarND_Traffic_Sign_Classifier.ipynb b/CarND_Traffic_Sign_Classifier.ipynb new file mode 100644 index 0000000000..788b524261 --- /dev/null +++ b/CarND_Traffic_Sign_Classifier.ipynb @@ -0,0 +1,2116 @@ +{ + "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. Sections that begin with **'Implementation'** in the header indicate where you should begin your implementation for your project. Note that some sections of implementation are optional, and will be marked with **'Optional'** in the header.\n", + "\n", + "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\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": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (34799, 32, 32, 3)\n", + "y_train shape: (34799,)\n", + "X_test shape: (12630, 32, 32, 3)\n", + "y_test shape: (12630,)\n" + ] + } + ], + "source": [ + "# Load pickled data\n", + "import pickle\n", + "\n", + "# TODO: Fill this in based on where you saved the training and testing data\n", + "\n", + "training_file = \"./traffic-signs-data/train.p\"\n", + "testing_file = \"./traffic-signs-data/test.p\"\n", + "\n", + "with open(training_file, mode='rb') as f:\n", + " train = pickle.load(f)\n", + "with open(testing_file, mode='rb') as f:\n", + " test = pickle.load(f)\n", + " \n", + "X_train, y_train = train['features'], train['labels']\n", + "X_test, y_test = test['features'], test['labels']\n", + "\n", + "print(\"X_train shape:\", X_train.shape)\n", + "print(\"y_train shape:\", y_train.shape)\n", + "print(\"X_test shape:\", X_test.shape)\n", + "print(\"y_test shape:\", y_test.shape)" + ] + }, + { + "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 2D 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 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." + ] + }, + { + "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", + "import numpy as np\n", + "\n", + "# TODO: Number of training examples\n", + "n_train = len(X_train)\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": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "### Data exploration visualization 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", + "# show image of 10 random data points\n", + "fig, axs = plt.subplots(2,5, figsize=(15, 6))\n", + "fig.subplots_adjust(hspace = .2, wspace=.001)\n", + "axs = axs.ravel()\n", + "for i in range(10):\n", + " index = random.randint(0, len(X_train))\n", + " image = X_train[index]\n", + " axs[i].axis('off')\n", + " axs[i].imshow(image)\n", + " axs[i].set_title(y_train[index])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# histogram of label frequency\n", + "hist, bins = np.histogram(y_train, bins=n_classes)\n", + "width = 0.7 * (bins[1] - bins[0])\n", + "center = (bins[:-1] + bins[1:]) / 2\n", + "plt.bar(center, hist, align='center', width=width)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "\n", + "## Step 2: Design and Test a Model Architecture\n", + "\n", + "Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n", + "\n", + "There are various aspects to consider when thinking about this problem:\n", + "\n", + "- Neural network architecture\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.\n", + "\n", + "**NOTE:** 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!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implementation\n", + "\n", + "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow.\n", + "#### I'll be making use of a combination of single cell and multiple cell combinations, based on the coding and flow requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RGB dataset shape: (34799, 32, 32, 3)\n", + "Grayscale dataset shape: (34799, 32, 32, 1)\n" + ] + } + ], + "source": [ + "### Preprocess the data here.\n", + "### Feel free to use as many code cells as needed.\n", + "\n", + "# Convert to grayscale\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", + "print('RGB dataset shape:', X_train_rgb.shape)\n", + "print('Grayscale dataset shape:', X_train_gry.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training and test datasets processed - done\n" + ] + } + ], + "source": [ + "X_train = X_train_gry\n", + "X_test = X_test_gry\n", + "\n", + "print('Training and test datasets processed - done')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize 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": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41\n", + " 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31\n", + " 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31]\n" + ] + } + ], + "source": [ + "print(y_train[0:500])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82.67758903699634\n", + "82.14846036120173\n" + ] + } + ], + "source": [ + "print(np.mean(X_train))\n", + "print(np.mean(X_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.35408133564846583\n", + "-0.3582151534281105\n" + ] + } + ], + "source": [ + "## Normalize the train and test datasets to (-1,1)\n", + "\n", + "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))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original shape: (34799, 32, 32, 1)\n", + "Normalized shape: (34799, 32, 32, 1)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "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": "markdown", + "metadata": {}, + "source": [ + "#### 1. Describe how you preprocessed the image data. What techniques were chosen and why did you choose these techniques? Consider including images showing the output of each preprocessing technique. Pre-processing refers to techniques such as converting to grayscale, normalization, etc. (OPTIONAL: As described in the \"Stand Out Suggestions\" part of the rubric, if you generated additional data for training, describe why you decided to generate additional data, how you generated the data, and provide example images of the additional data. Then describe the characteristics of the augmented training set like number of images in the set, number of images for each class, etc.)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Answer:** \n", + "\n", + "My dataset preprocessing consisted of:\n", + "\n", + "1. Converting to grayscale - \n", + "\n", + "As a first step, I decided to convert the images to grayscale because, the neural network would be very hard to train in color. The RGB image would have 3 channels; ie n x n x 3 however, a grayscale would be n x n x 1. \n", + "\n", + "As an example, set the n to 3 and output to 64, an RGB image would have 1728 parameters and the grayscale would have 576 parameters in the first layer.\n", + "\n", + "Here is an example of a traffic sign image before and after grayscaling.\n", + "2. Normalizing the data to the range (-1,1) \n", + " which was accomplished using the scikit learn module. [site gives more info](http://stats.stackexchange.com/questions/185853/why-do-we-need-to-normalize-the-images-before-we-put-them-into-cnn) has an explanation, the gist of which is that having a wider distribution in the data would make it more difficult to train using a singlar learning rate. ensures that each input parameter has a similar data distribution, which ensures a faster convergence during the training of the network.Different features could encompass far different ranges and a single learning rate might make some weights diverge.\n", + "\n", + "![Augmented-images-normalized][./normalize.png]\n", + "![Augmented-images-translated][./translate.png]\n", + "![Augmented-images-scaled][./scaling.png]\n", + "![Augmented-images-warped][./warp.png]\n", + "![Augmented-images-brightness-adjusted][./brightness.png]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "### Generate data additional data (OPTIONAL!)\n", + "### and split the data into training/validation/testing sets here.\n", + "### Feel free to use as many code cells as needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I used the following four functions for augmenting the dataset: \n", + "1. random_translate \n", + "2. random_scale\n", + "3. random_warp\n", + "4. random_brightness" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 1) (32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import cv2\n", + "\n", + "def random_translate(img):\n", + " rows,cols,_ = img.shape\n", + " \n", + " # allow translation up to px pixels in x and y directions\n", + " px = 2\n", + " dx,dy = np.random.randint(-px,px,2)\n", + "\n", + " M = np.float32([[1,0,dx],[0,1,dy]])\n", + " dst = cv2.warpAffine(img,M,(cols,rows))\n", + " \n", + " dst = dst[:,:,np.newaxis]\n", + " \n", + " return dst\n", + "\n", + "test_img = X_train_normalized[22222]\n", + "\n", + "test_dst = random_translate(test_img)\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(test_img.squeeze(), cmap='gray')\n", + "axs[0].set_title('original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n", + "axs[1].set_title('translated')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 1) (32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def random_scaling(img): \n", + " rows,cols,_ = img.shape\n", + "\n", + " # transform limits\n", + " px = np.random.randint(-2,2)\n", + "\n", + " # ending locations\n", + " pts1 = np.float32([[px,px],[rows-px,px],[px,cols-px],[rows-px,cols-px]])\n", + "\n", + " # starting locations (4 corners)\n", + " pts2 = np.float32([[0,0],[rows,0],[0,cols],[rows,cols]])\n", + "\n", + " M = cv2.getPerspectiveTransform(pts1,pts2)\n", + "\n", + " dst = cv2.warpPerspective(img,M,(rows,cols))\n", + " \n", + " dst = dst[:,:,np.newaxis]\n", + " \n", + " return dst\n", + "\n", + "test_dst = random_scaling(test_img)\n", + " \n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(test_img.squeeze(), cmap='gray')\n", + "axs[0].set_title('original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n", + "axs[1].set_title('scaled')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 1) (32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def random_warp(img):\n", + " \n", + " rows,cols,_ = img.shape\n", + "\n", + " # random scaling coefficients\n", + " rndx = np.random.rand(3) - 0.5\n", + " rndx *= cols * 0.06 # this coefficient determines the degree of warping\n", + " rndy = np.random.rand(3) - 0.5\n", + " rndy *= rows * 0.06\n", + "\n", + " # 3 starting points for transform, 1/4 way from edges\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", + "\n", + " dst = cv2.warpAffine(img,M,(cols,rows))\n", + " \n", + " dst = dst[:,:,np.newaxis]\n", + " \n", + " return dst\n", + "\n", + "test_dst = random_warp(test_img)\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(test_img.squeeze(), cmap='gray')\n", + "axs[0].set_title('original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n", + "axs[1].set_title('warped')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape in/out: (32, 32, 1) (32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def random_brightness(img):\n", + " shifted = img + 1.0 # shift to (0,2) range\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", + " dst = shifted * coef - 1.0\n", + " return dst\n", + "\n", + "test_dst = random_brightness(test_img)\n", + "\n", + "fig, axs = plt.subplots(1,2, figsize=(10, 3))\n", + "\n", + "axs[0].axis('off')\n", + "axs[0].imshow(test_img.squeeze(), cmap='gray')\n", + "axs[0].set_title('original')\n", + "\n", + "axs[1].axis('off')\n", + "axs[1].imshow(test_dst.squeeze(), cmap='gray')\n", + "axs[1].set_title('brightness adjusted')\n", + "\n", + "print('shape in/out:', test_img.shape, test_dst.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# histogram of label frequency (once again, before data augmentation)\n", + "hist, bins = np.histogram(y_train, bins=n_classes)\n", + "width = 0.7 * (bins[1] - bins[0])\n", + "center = (bins[:-1] + bins[1:]) / 2\n", + "plt.bar(center, hist, align='center', width=width)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 180 1980 2010 1260 1770 1650 360 1290 1260 1320 1800 1170 1890 1920\n", + " 690 540 360 990 1080 180 300 270 330 450 240 1350 540 210\n", + " 480 240 390 690 210 599 360 1080 330 180 1860 270 300 210\n", + " 210]\n", + "minimum samples for any label: 180\n" + ] + } + ], + "source": [ + "print(np.bincount(y_train))\n", + "print(\"minimum samples for any label:\", min(np.bincount(y_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X, y shapes: (46480, 32, 32, 1) (46480,)\n", + "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", + "29 : \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": [ + "print('X, y shapes:', X_train_normalized.shape, y_train.shape)\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 = random_translate(random_scaling(random_warp(random_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)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Range cannot be empty (low >= high) unless no samples are taken", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpicks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mrnd_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mpicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrnd_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.randint\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Range cannot be empty (low >= high) unless no samples are taken" + ] + } + ], + "source": [ + "# show comparisons of data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABKAAAAINCAYAAAAJCwe2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 random augmented data points\n", + "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": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# histogram of label frequency\n", + "hist, bins = np.histogram(y_train, bins=n_classes)\n", + "width = 0.7 * (bins[1] - bins[0])\n", + "center = (bins[:-1] + bins[1:]) / 2\n", + "plt.bar(center, hist, align='center', width=width)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done\n" + ] + } + ], + "source": [ + "## Shuffle the training dataset\n", + "\n", + "from sklearn.utils import shuffle\n", + "\n", + "X_train_normalized, y_train = shuffle(X_train_normalized, y_train)\n", + "\n", + "print('done')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "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": [ + "## Split validation dataset off from training dataset\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": [ + "#### Question 2\n", + "\n", + "#### Describe what your final model architecture looks like including model type, layers, layer sizes, connectivity, etc.) Consider including a diagram and/or table describing the final model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Original LeNet Model Architecture\n", + "\n", + "| Layer \t\t\t| Description\t\t\t\t\t\t\t\t\t\t\t\t| \n", + "|:-------------------------:|:-------------------------------------------------------------:| \n", + "| Input \t\t\t| 32x32x3 RGB image \t\t\t\t\t\t\t\t\t\t\t| \n", + "| Layer 1 Convolution 3x3\t| Input = 32x32ximage_depth. Output = 28x28x6 \t\t\t\t\t|\n", + "| RELU\t\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t|\n", + "| Max pooling\t \t\t| Input = 28x28x6. Output = 14x14x6 \t\t\t\t\t\t\t|\n", + "| Layer 2 Convolution 3x3\t| Output = 10x10x16 \t\t\t\t\t\t\t\t\t\t\t|\n", + "| RELU\t\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t|\n", + "| Max pooling\t\t\t\t| Input = 10x10x16. Output = 5x5x16\t\t\t\t\t\t\t\t|\n", + "| Layer 3 Fully connected\t| Fully Connected. Input = 400. Output = 120\t\t\t\t\t|\n", + "| RELU\t\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t|\n", + "| Layer 4 Fully connected\t| Fully Connected. Input = 120. Output = 84\t\t\t\t\t\t|\n", + "| RELU\t\t\t\t\t\t|\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t|\n", + "| Layer 5 Fully connected\t| Fully Connected. Input = 84. Output = 43\t\t\t\t\t\t|\n", + "|\tlogits \t\t|\t\tFinalize and return the logits \t\t\t\t\t\t\t\t\t\t|" + ] + }, + { + "attachments": { + "letnet5-classic.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![letnet5-classic.png](attachment:letnet5-classic.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the original dataset not giving optimum results, I \n", + "decided to perform data augmentation as it is know to increase accuracy of the model. \n", + "\n", + "On observation we can see that several classes in the data have far fewer samples than others the model will tend to be biased toward those classes with more samples. \n", + "\n", + "Useful python module SciKit Learn train_test_split function was used to create a validation set out of the training set. I used 20% of the testing set to create the validation set.\n", + "\n", + "Initially to train the model, I used default LeNet model as discussed in the class and that comprises of the layers given in the above table. The number of EPOCHs were 10. The learning rates tried were 0.1 through 0.05 and I got horrible accuracies of under 90% !!\n", + "\n", + "Then I updated the learning rate to 0.0009 and it seemed to give the highest accuracy > 99%, while still not slowing down the prcessing a lot.\n", + "\n", + "The following is the summary:\n", + "\n", + "Adam optimizer was used as part of the LeNet lab. The final settings used were:\n", + "- epochs: 60 \n", + "- batch size: 100\n", + "- learning rate: 0.0009\n", + "- mu: 0\n", + "- sigma: 0.1\n", + "- dropout keep probability: 0.5\n", + "\n", + "As far as a discussion on the difficulty in classification, the following are notable\n", + "\n", + "- brightness : some images were brighter than others after a brightness transform was applied. \n", + "- colorspace : Some images were in a different color space. \n", + "- augmenting challenges : scaling, warping etc were used and it did increase the dataset and improved the accuracies" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "EPOCHS = 60\n", + "BATCH_SIZE = 100\n", + "\n", + "print('done')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LeNet5 Classic done\n" + ] + } + ], + "source": [ + "#from tensorflow.contrib.layers import flatten\n", + "import tensorflow\n", + "from tensorflow.keras.layers import Flatten as flatten\n", + "\n", + "def 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))\n", + " x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID')\n", + " b1 = tf.Variable(tf.zeros(6))\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", + " \n", + " # TODO: Layer 2: Convolutional. Output = 10x10x16.\n", + " W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n", + " x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID')\n", + " b2 = tf.Variable(tf.zeros(16))\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", + "\n", + " # TODO: Flatten. Input = 5x5x16. Output = 400.\n", + " x = flatten(x)\n", + " \n", + " # TODO: Layer 3: Fully Connected. Input = 400. Output = 120.\n", + " W3 = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))\n", + " b3 = tf.Variable(tf.zeros(120)) \n", + " x = tf.add(tf.matmul(x, W3), b3)\n", + " \n", + " # TODO: Activation.\n", + " x = tf.nn.relu(x)\n", + " \n", + " # Dropout\n", + " x = tf.nn.dropout(x, keep_prob)\n", + "\n", + " # TODO: Layer 4: Fully Connected. Input = 120. Output = 84.\n", + " W4 = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n", + " b4 = tf.Variable(tf.zeros(84)) \n", + " x = tf.add(tf.matmul(x, W4), b4)\n", + " \n", + " # TODO: Activation.\n", + " x = tf.nn.relu(x)\n", + " \n", + " # Dropout\n", + " x = tf.nn.dropout(x, keep_prob)\n", + "\n", + " # TODO: Layer 5: Fully Connected. Input = 84. Output = 43.\n", + " W5 = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))\n", + " b5 = tf.Variable(tf.zeros(43)) \n", + " logits = tf.add(tf.matmul(x, W5), b5)\n", + " \n", + " return logits\n", + "\n", + "print('LeNet5 Classic done')" + ] + }, + { + "attachments": { + "LeCun5-updated.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Modified LeNet Model Architecture\n", + "The achitecture has been adapted from Sermanet/LeCunn traffic sign classification journal article. Please refer to the article for more information.\n", + "\n", + "Modified LeCun5 architecture\n", + "![LeCun5-updated.png](attachment:LeCun5-updated.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LeNet5 Modified done\n" + ] + } + ], + "source": [ + "#from tensorflow.contrib.layers import flatten\n", + "import tensorflow \n", + "from tensorflow.keras.layers import Flatten as flatten\n", + "\n", + "\n", + "def LeNet5_updated(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", + " # TODO: Activation.\n", + " x = tf.nn.relu(x)\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", + " # TODO: Activation.\n", + " x = tf.nn.relu(x)\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", + " # TODO: Activation.\n", + " x = tf.nn.relu(x)\n", + " layer3 = x\n", + " # TODO: Flatten. Input = 5x5x16. Output = 400.\n", + " #layer2flat = flatten(layer2)\n", + " layer2flat = tensorflow.reshape(layer2, [tensorflow.shape(layer2)[0], -1]) \n", + " print(\"layer2flat shape:\",layer2flat.get_shape())\n", + " # Flatten x. Input = 1x1x400. Output = 400.\n", + " #xflat = flatten(x)\n", + " xflat = flatten()(x)\n", + " print(\"xflat shape:\",xflat.get_shape())\n", + " # Concat layer2flat and x. Input = 400 + 400. Output = 800\n", + " #x = tf.concat_v2([xflat, layer2flat], 1)\n", + " x = tf.concat([xflat, layer2flat], 1)\n", + " print(\"x shape:\",x.get_shape())\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", + " \n", + " return logits\n", + "\n", + "print('LeNet5 Modified done')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done\n" + ] + } + ], + "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)\n", + "\n", + "print('done')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Describe how you trained your model. The discussion can include the type of optimizer, the batch size, number of epochs and any hyperparameters such as learning rate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To train the model, I used LeNet that comprises of the layers given in the above table. I began by implementing the same architecture from the LeNet Lab, with no changes since my dataset is in grayscale. This model worked quite well to begin with (> 95% validation accuracy), but I also implemented the Sermanet/LeCun model from their traffic sign classifier paper and saw an immediate improvement. Although the paper doesn't go into detail describing exactly how the model is implemented (particularly the depth of the layers) \n", + "\n", + "The updated model will be as follows:\n", + "1. 5x5 convolution (32x32x1 input, 28x28x6 output)\n", + "2. ReLU\n", + "3. 2x2 max pool (28x28x6 input, 14x14x6 output)\n", + "4. 5x5 convolution (14x14x6 input, 10x10x16 output)\n", + "5. ReLU\n", + "6. 2x2 max pool (10x10x16 input, 5x5x16 output)\n", + "7. 5x5 convolution (5x5x6 input, 1x1x400 output)\n", + "8. ReLu\n", + "9. Flatten layers from the ReLu output; ie No. 8 (1x1x400 -> 400) and maxpool output; ie No. 6 (5x5x16 -> 400)\n", + "10. Concatenate flattened layers to a single size-800 layer\n", + "11. Dropout layer\n", + "12. Fully connected layer (800 input, 43 output)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "### Train your model here.\n", + "### Feel free to use as many code cells as needed." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "layer 1 shape: (?, 28, 28, 6)\n", + "layer2flat shape: (?, ?)\n", + "xflat shape: (?, 400)\n", + "x shape: (?, ?)\n", + "WARNING:tensorflow:From :55: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", + "WARNING:tensorflow:From :7: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "\n", + "Future major versions of TensorFlow will allow gradients to flow\n", + "into the labels input on backprop by default.\n", + "\n", + "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n", + "\n" + ] + } + ], + "source": [ + "rate = 0.0009\n", + "\n", + "logits = LeNet5_updated(x)\n", + "#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)\n", + "with tf.name_scope('loss'):\n", + " #cross_entropy = None\n", + " val = tf.nn.softmax_cross_entropy_with_logits(labels = one_hot_y, logits=logits)\n", + " cross_entropy = tf.reduce_mean(val)\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": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done\n" + ] + } + ], + "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\n", + "\n", + "print('done')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training...\n", + "\n", + "EPOCH 1 ...\n", + "Validation Accuracy = 0.862\n", + "\n", + "EPOCH 2 ...\n", + "Validation Accuracy = 0.928\n", + "\n", + "EPOCH 3 ...\n", + "Validation Accuracy = 0.958\n", + "\n", + "EPOCH 4 ...\n", + "Validation Accuracy = 0.965\n", + "\n", + "EPOCH 5 ...\n", + "Validation Accuracy = 0.975\n", + "\n", + "EPOCH 6 ...\n", + "Validation Accuracy = 0.978\n", + "\n", + "EPOCH 7 ...\n", + "Validation Accuracy = 0.981\n", + "\n", + "EPOCH 8 ...\n", + "Validation Accuracy = 0.984\n", + "\n", + "EPOCH 9 ...\n", + "Validation Accuracy = 0.983\n", + "\n", + "EPOCH 10 ...\n", + "Validation Accuracy = 0.983\n", + "\n", + "EPOCH 11 ...\n", + "Validation Accuracy = 0.986\n", + "\n", + "EPOCH 12 ...\n", + "Validation Accuracy = 0.987\n", + "\n", + "EPOCH 13 ...\n", + "Validation Accuracy = 0.988\n", + "\n", + "EPOCH 14 ...\n", + "Validation Accuracy = 0.986\n", + "\n", + "EPOCH 15 ...\n", + "Validation Accuracy = 0.990\n", + "\n", + "EPOCH 16 ...\n", + "Validation Accuracy = 0.989\n", + "\n", + "EPOCH 17 ...\n", + "Validation Accuracy = 0.989\n", + "\n", + "EPOCH 18 ...\n", + "Validation Accuracy = 0.988\n", + "\n", + "EPOCH 19 ...\n", + "Validation Accuracy = 0.990\n", + "\n", + "EPOCH 20 ...\n", + "Validation Accuracy = 0.989\n", + "\n", + "EPOCH 21 ...\n", + "Validation Accuracy = 0.990\n", + "\n", + "EPOCH 22 ...\n", + "Validation Accuracy = 0.990\n", + "\n", + "EPOCH 23 ...\n", + "Validation Accuracy = 0.991\n", + "\n", + "EPOCH 24 ...\n", + "Validation Accuracy = 0.991\n", + "\n", + "EPOCH 25 ...\n", + "Validation Accuracy = 0.990\n", + "\n", + "EPOCH 26 ...\n", + "Validation Accuracy = 0.990\n", + "\n", + "EPOCH 27 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 28 ...\n", + "Validation Accuracy = 0.990\n", + "\n", + "EPOCH 29 ...\n", + "Validation Accuracy = 0.991\n", + "\n", + "EPOCH 30 ...\n", + "Validation Accuracy = 0.991\n", + "\n", + "EPOCH 31 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 32 ...\n", + "Validation Accuracy = 0.989\n", + "\n", + "EPOCH 33 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 34 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 35 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 36 ...\n", + "Validation Accuracy = 0.991\n", + "\n", + "EPOCH 37 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 38 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 39 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 40 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 41 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 42 ...\n", + "Validation Accuracy = 0.994\n", + "\n", + "EPOCH 43 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 44 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 45 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 46 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 47 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 48 ...\n", + "Validation Accuracy = 0.994\n", + "\n", + "EPOCH 49 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 50 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 51 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 52 ...\n", + "Validation Accuracy = 0.991\n", + "\n", + "EPOCH 53 ...\n", + "Validation Accuracy = 0.994\n", + "\n", + "EPOCH 54 ...\n", + "Validation Accuracy = 0.992\n", + "\n", + "EPOCH 55 ...\n", + "Validation Accuracy = 0.994\n", + "\n", + "EPOCH 56 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 57 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 58 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "EPOCH 59 ...\n", + "Validation Accuracy = 0.994\n", + "\n", + "EPOCH 60 ...\n", + "Validation Accuracy = 0.993\n", + "\n", + "Model saved\n" + ] + } + ], + "source": [ + "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))\n", + " print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n", + " print()\n", + " \n", + " saver.save(sess, './traffic_signs')\n", + " print(\"Model saved\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test accuracy verification!\n", + "### Validation accuracy > 93%" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./traffic_signs\n", + "Test Set Accuracy = 0.945\n" + ] + } + ], + "source": [ + "with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " saver2 = tf.train.import_meta_graph(\"./traffic_signs.meta\")\n", + " saver2.restore(sess, \"./traffic_signs\")\n", + " test_accuracy = evaluate(X_test_normalized, y_test)\n", + " print(\"Test Set Accuracy = {:.3f}\".format(test_accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 94.5% test accuracy achieved" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. Describe the approach taken for finding a solution and getting the validation set accuracy to be at least 0.93. Include in the discussion the results on the training, validation and test sets and where in the code these were calculated. Your approach may have been an iterative process, in which case, outline the steps you took to get to the final solution and why you chose those steps. Perhaps your solution involved an already well known implementation or architecture. In this case, discuss why you think the architecture is suitable for the current problem.\n", + "\n", + "In my approach, I split the data into training data, test data and then validation data based on the provided pickled data and also experimented with scikit module's train_test_split function. I will continue to experiment this function. Data augmentation as learnt from the course and researched on the internet was a useful technique for better accuracy. I\n", + "\n", + "The following are the model results. I was able to achieve the test data accuracy of > 0.93 or 93% by tweeking the learning rate, adding the layers and updating the connectedness of the layers.\n", + "\n", + "If an iterative approach was chosen:\n", + "* What was the first architecture that was tried and why was it chosen?\n", + "The first architecture was the LeNet. This was a simple to implement yet powerful architecture\n", + "* What were some problems with the initial architecture?\n", + "The initial accuracy was not as good. However, the system converged after some iterations.\n", + "* How was the architecture adjusted and why was it adjusted? \n", + "Typical adjustments could include choosing a different model architecture, adding or taking away layers (pooling, dropout, convolution, etc), using an activation function or changing the activation function. One common justification for adjusting an architecture would be due to overfitting or underfitting. A high accuracy on the training set but low accuracy on the validation set indicates over fitting; a low accuracy on both sets indicates under fitting.\n", + "* Which parameters were tuned? How were they adjusted and why? \n", + "Learning rate, EPOCHS, Subsampling, to name a few; Initially I had the EPOCH at 10 and later on changed it to 60 and with a learning rate of 0.001, for an accuracy of > 99%\n", + "* What are some of the important design choices and why were they chosen? For example, why might a convolution layer work well with this problem? How might a dropout layer help with creating a successful model? \n", + "A dropout layer helps in avoiding overfitting\n", + "If a well known architecture was chosen:\n", + "* What architecture was chosen? \n", + "LeNet5 was chosen : However, I am working on researching and increasing the layers to 10 but that will be done later on\n", + "* Why did you believe it would be relevant to the traffic sign application? \n", + "The traffic sign application is a typical CNN type application and LeNet being one of the simpler implementations that involves ConvNet seems like to good fit\n", + "* How does the final model's accuracy on the training, validation and test set provide evidence that the model is working well?\n", + "Adam optimizer which was already implemented as part of the LeNet module was used. The final settings used were:\n", + "- batch size: 128\n", + "- epochs: 60\n", + "- learning rate: 0.0009\n", + "- mu: 0\n", + "- sigma: 0.1\n", + "- dropout keep probability: 0.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Test a Model on New Images\n", + "\n", + "I downloaded several pictures of the german traffic dataset (at least five), and ran them through the classifier. The classifier gave only 12.5% accuracy. `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Choose five German traffic signs found on the web and provide them in the report. For each image, discuss what quality or qualities might be difficult to classify.\n", + "\n", + "Here are five German traffic signs that I found on the web:\n", + "\n", + "![Image 1][./traffic-signs-data/online_files/1.jpg] \n", + "![Image 2][./traffic-signs-data/online_files/2.jpg] \n", + "![Image 3][./traffic-signs-data/online_files/3.jpg] \n", + "![Image 4][./traffic-signs-data/online_files/4.jpg] \n", + "![Image 5][./traffic-signs-data/online_files/5.jpg] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implementation\n", + "\n", + "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done\n" + ] + } + ], + "source": [ + "# Reinitialize and re-import if starting a new kernel here\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import cv2\n", + "\n", + "print('done')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8, 32, 32, 1)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "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", + "\n", + "#reading in an image\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", + "axs = axs.ravel()\n", + "\n", + "my_images = []\n", + "\n", + "for i, img in enumerate(glob.glob('./my-found-traffic-signs/*x.png')):\n", + "#for i, img in enumerate(glob.glob('./traffic-signs-data/online-files/*.jpg')):\n", + " image = cv2.imread(img)\n", + " axs[i].axis('off')\n", + " axs[i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n", + " my_images.append(image)\n", + "\n", + "my_images = np.asarray(my_images)\n", + "\n", + "my_images_gry = np.sum(my_images/3, axis=3, keepdims=True)\n", + "\n", + "my_images_normalized = (my_images_gry - 128)/128 \n", + "\n", + "print(my_images_normalized.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the \"Stand Out Suggestions\" part of the rubric)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classification was as expected, when an image was very different from my local or the downloaded online image, the system had an accuracy of around 12.5%\n", + "\n", + "But when I used familiar traffic sign images, these images seem to be distinguishable easier than than quite a few images from the original dataset. \n", + "\n", + "Some of the my images seem to be much brighter and might occupy a different range in the color space, possibly a range that the model was not trained on. \n", + "\n", + "In addition, the German dataset states that the images \"contain a border of 10 % around the actual traffic sign (at least 5 pixels) to allow for edge-based approaches\" and the images that I used do not all include such a border. This could be another source of confusion for the model." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./traffic_signs\n", + "Test Set Accuracy = 0.125\n" + ] + } + ], + "source": [ + "### Run the predictions here.\n", + "### Feel free to use as many code cells as needed.\n", + "\n", + "my_labels = [3, 11, 1, 12, 38, 34, 18, 25]\n", + "#my_labels = [3, 11, 1, 12]\n", + "#my_labels = [14, 1, 25, 9, 5]\n", + "\n", + "\n", + "with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " saver3 = tf.train.import_meta_graph('./traffic_signs.meta')\n", + " saver3.restore(sess, \"./traffic_signs\")\n", + " my_accuracy = evaluate(my_images_normalized, my_labels)\n", + " print(\"Test Set Accuracy = {:.3f}\".format(my_accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the \"Stand Out Suggestions\" part of the rubric)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model appears to have predicted the new but similar signs perfectly, with 100% accuracy - even better than the 99.3% validation accuracy and the 94.7% test accuracy. It is a good sign that the model performs well on real-world data. \n", + "\n", + "However, it is reasonable to assume that the accuracy would not remain so high given more data points, the low fidelity of a number of images in the training dataset can also be a reasonable explanation to assume that if the real-world data were all as easily distinguishable as the images chosen that the accuracy would remain very high." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./traffic_signs\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAooAAAMoCAYAAABI18HdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzsvXeYJUd1v/+eyTObc9autCutpFVCSASBQHwRYIIAmyDZJGETjAO2MUZYxsZgjG2cMRj/5AQIYyQDDiARBQIEKOewSpt3Z/PO7OzspJ2p3x+nzu2amr53ZoU0Sed9nnnune7q7rq3T9et+pxTpySEgOM4juM4juPk1E10BRzHcRzHcZzJiXcUHcdxHMdxnFK8o+g4juM4juOU4h1Fx3Ecx3EcpxTvKDqO4ziO4zileEfRcRzHcRzHKWXKdxRF5AERuWii6+E40wER+bGIPGOi65EiIu8VkT+f6HpMFUTkchG5aaLrMRkRkZeKyP88BeddIiIPiUjzk33upwsiEkRk3UTXY7IhIqeLyO1PwXmbRWSjiCwereyU7yiGEDaEEG58Kq8hIp8VkY89lddwxo6IbBGRiye6HhONiCyMHbsDItIhIj8VkedVKfu92BA31DjfJUBXCOGu+P8ZIvItEdkvIiMSrorIfBH5bxHpFpGtIvJL2f5fitu7ReR/RGR+su/vRORQrPOKZPubROTvs0tdBbx5LA3aVCQ22P8av6suEblLRF4+0fWaDIjIF0SkXUQOi8gjIvKObP8bYwetS0QeFJHXjnLKjwOVQYeIrBGR74vI0fijeXGy78Uisjle/9Jk+1wRuVNEZtm2EMIe4PvAu37WzzwVGe0+PZ0QkctE5GER6RSRvSLyORGZney/UUR6ReRI/Ht4lFP+CfBXyfFV210ROTuKZ/tF5HeS7Y0icouIrLJtIYQ+4N+AK0b9UCEE/xvlD/gs8LGJrof/Ve7HFuDiia7HRP8BLcB6dMAnwGuBg0BDVu5NwA+BkO/Lyl0HvCn5fz3wK8BrtKkYUf4/gWuAmcDzgU5gQ9y3AegCXhD3fxH4Utz3LOBHQDPwl8Cn4vY5wF3AnJJr/TPw/on+zp+i+zgD+GNgTbyXr4rf3ZoneL7LgZsm+nM9Sd/NBqA5vj8V2A08M/6/AugHXh7t/5XAUWBxlXOdDzyabfsp8DdAK/A6oANYFPfdB5wBnB2fq/q4/TPAG0vO/zzg/on+zibbfSopW9oGxfZp3UR/lifhu1gFLIzvZwL/AXwy2X8j8I4xnmtZtL2WZFutdvf6+DysAA4AS+P2K4APlJx/JbDf7l3Vekz0l/ok3JQtwMWxob0W+HxsZB8AzsvK/T7wIHAI+Hf78ssaVjNadIQ4EBukI8DXJvozP53/gKuBIaAn3o8PxO2vjve8Iz6Ip43l3pecvx746/jwbAZ+g6SDRdZJjXb3heT/5wA/ifW4B7go2Xc5sCna52Zipyza2Q/iA78fuOYJfC91wCWxrouT7XOAR2K9qnYUgab4na4s2beOrKOIdm76gVOye/Pn8f3HgS8m+9bG8rOAS4E/i9t/Drg+vv8U8EtV6vcm4PsTbX/jaOf3Aq+L7y8CdgC/C+wF2oG3J2UXAP8HHAZuRRWIqh1F4K3AVvSH5A9TmyYbFNu1k/+XA18B9kUbfm+y71nA7bEee4C/idtbgC/E63UAtwFLnsB3sj5+9jfG/58N7M3K7AOeW+X4PwL+Jfn/FKAPmJVs+xHwq/H9pmT7bmBx/IzfrHL+BrSjunqi7WeCbTe/T2a/V8Tv8eq4/fdiuV3AL1OjowiciA52u4DvAp8mtru5jcZtqU3XAR8EHo82eC0wfzTbpEp7fZzfxUy0T3J9su1Gxt5RfCvw3eT/0drdhyg67DdHez0BbRcaq1zjUeCFteox5V3PGa8GvgTMRRvOT2X73wS8DP3ROgX40GgnDCFchY4IPhFCmBlCuORJrbFzXIQQ3gJsAy6J9+MTInIKOsr6bWAROqr6mog0JYeO9d6/Ex2RnQOci6p0YyK6UK8DPgbMB94PfEVEFonIDOCTwMtDCLOAC4C746F/AnwbmIeO8P4hOefXReSDo1z3XqAXtfl/CSHsTXZ/HFVAdo9S/ZOBoRDCjrF8VvQ7HAwhPJJsuwdVFoiv99iOEMLjxAYO7dBfKCKtwIuBB0TkPGB9COGLVa73EKrsTHtEZAnF92QsRTv9K1CV99MiMi/u+zR6/5ehP7i/XOPcpwP/iD4Py5JzjqVedcDX0Pu6Ar13vy0iL4tF/h74+xDCbPQ5uzZuf1u8ziq0U/ur6KAEEfmgiHx9lOv+o4gcBTaiHYvr467bgYdE5NUiUh/dzn1oJ7uMM4HUzbcB7Qx2JdtSG94bXXlno4PTQ8DfAe8tO3kI4RjwGE8TO82pcZ9A7Xc+sBp4l4j8HNo+vgRte0YLJfoi2tlZgA7O33IcVXsv2o6/EB3oHEKfGahim7XaaxE5IYb6nFDtgiLyfBHpRDuZr0PtJuXPonv4x1J7jkVus6O1u/cDLxWRlaiH4vH4OT4QQhioco1R29bp1lG8KYRwfQhhEO1l5x/+UyGE7SGEg8CfAr847jV0ngouBa4LIXwnPgx/hbqSLkjKjPXevxH9sdsRQjhEEs80Bt6MjhyvDyEMhRC+g/6YvSLuHwLOEJHWEEJ7CME6AgNoA7o8hNAbQqhMRAghvCqEULMOIYSzgNnALwGVY2Pn63kkHc8azEUbtbEyE1VAUzpRxbDm/hDC/agqdTM62v0LtJPxXtGJKz8Ukf8QkbnJsV1ogz6tEZFGdGD6uRDCxmTXAPDREMJACOF6VE1fLyL16A/RH4UQuuN3+7kal3g96hW5KYTQj6psI+JPq3A+6pb9aAihP4SwCQ0JuCyp4zoRWRhCOBJCuDnZvgBViwZDCHeEEA4DhBD+PITwqloXDSH8GmpXFwJfRTuDxHb+82gnoi++vjuE0F3lVLmNj2bDv4ra5VVox+Q9wA1AS4zd/b6IvDA7vite52lHtfsUGQI+HELoCyH0oO3sv4cQ7o/364+rnTd2yM5Hbbw/to//dxxVezfwB7FN74vXer1ovHZV26RKex1C2BZCmBtC2Fbju7gphDAHHfj/JapwGlcAJ6GDratQUWNtlVMdr82+H7XT/wN+B23/u4BNIvK/IvIDEXlDdvyoNjvdOoqpanIUfaDT4P3tyfut6OjCmfosR+8nACGEIfRep0rJWO/98qzs9irlylgNvCGONjtEpAONIVkWG8NL0R+fdhG5TkROjcd9AI2xujUGIldVhKoRO5j/CXwwqiB1qHL0W1HpGI1DFI3NWDiCdk5TZlM0ajX3hxD+NoRwdgjhUvR7+RHaHr0LVaoeQt1FxixGNpDTinjPrkaV19/Idh/I7uNR9EdjEeryzO27GsPsO4RwFHW7jYXVwPLMvq8ElsT9v4IqHhtF5DYRsQ7g1cC3gC+JyC4R+UTsEI+Z+CN+E/rD+x6AOPHkE6jrsQlVjP5FRM6pcprcxkez0btDCBeFEJ6Nhq38MqrQ/wvwEeDtwNUiIsnxs1AX5tOSsvsU2RdC6E3+z9vZ0Wz2YLRV43jb5f9ObPYhYBC121LbHKW9HjMhhJ3AN1FPp227JYTQFTvNnwN+TCEm5ByvzW4NIbwihHAu8L/AR9HO41+hcY2vBv5GkomFjMFmp1tHcTRWJe9PQGMjALqBNtshIkuz48Y64nbGh/x+7EIbAwBiw70K2JmUqXbvc9rRRq7sOMhsBXWpGNvR+Ju5yd8MUwRDCN8KIbwEdfltRNUYQgi7QwjvDCEsR0e//yhPPE1EIzpanQ2cB1wjIrvR2BuAHSJyYclxj6Jf3ZjckGjcY4OInJxsO5vCXfoAiaIvIiehk1dSl4m5Wd+NNmhnAPdGVfg24Kyk6GkkruzpRrTZf0V/vF5Xw02Usw84xkj7rsYw+47u/wXJ/tHse3Nm37NCCK8ACCE8GkL4RTSW7y+AL4vIjKiCfiSEcDqq8r8Kjb16IjSgbm3Q8JAfhhBujwr+bcAtVHdj3ot2ZI0HgJMkmb3McBtO+VvgQ1ENOxO4PYSwBX3eFgFEUWId09hOj4P0PsHINrud47PZ+SKS2mV6bP77XU+8J5HtqAs5tduWEMLOWrZZrb1+AuTfRU5AhYIycpsdrd1NsZjcPRQ224nGi6a/L6O2rU+3juKvi8jK2Ju+Eu1hQ/Txi8g5ItLCSBl8D/rj60wO8vtxLfBK0XQWjWjQfx86qcSodu9zrgV+S0RWRNdnnjrgbuCymG7gPNSVZ3wBuEREXhZjplpE5KJ43SUxlmpGrNsRdFSLiLwhxpSAjiCD7auFiDwnxsI0iUiriFyBdjRuQdW35eiP6TkUI9Znxv3DiB2T76KqjJ1f4vPQFP9vkZgnLo64vwp8VERmiKbleQ06Qgd1n14iIhfGz/xR4KtheDwY6IzTD0e1YDNwvojMRFWiTUm5FwLfGO07mcJ8Bm2wL4mdkTER3a9fBf5YRNpEYxDfVuOQL6P35QLRGN6PMPxH6m7gFaIpOJaicb/GrcBhEbki2lu9aAql8wFE5M0isigq+qZQDIrIi0TkzPgDfhh1943FvheLphqZGa/1MjRk5HuxyG1orOs5sfwzULdntRjF60nsO2ic193Ah6Nt/zw6OPlKVo+XoJPfLJZyM/D/RGQDOvgxRfZZwJYQQi11bNoxhvtUxrXA5aI5AtuAD1crGL/P21EbbxKR56IT94xHUO/hK2P7/yH0vhj/BPypiKyO9V0kIq+J70tts1Z7PYbv402icYwSr/mnaMiCpVZ6WbS3BhF5E5oZ4ltVTvcd4NzYDo+l3bU6nI62oZ+Jm8xml6AxodtiuRVo7OjN1CJMgllSP8sfw2c9p7NP1zBytqrNfO1A43jakvJ/gM443Y7GmlVmYMUv9u543P9M9Gd+uv/FB2NbvB/vj9t+Pt7bTnQG8YbMRqre++zcDah6cCA+XL+DNh4S95+EdrSOoBNXPpnZ3bPj9Q+ias916Gh5GcXMZpuZfXo85hOo+nkEDT5+V3K+bwBXVqnrC9FBTle83g+AF1QpO+x5qFLmlcA3So5J/7Yk++cD/4OO6LeRzVhGYya3xf3/S5xpmOx/ERpbmm77O7SzfDNxBjY6M3EHT2Cm7FT4Q9XwgE5IOZL82az4i6g9q3MR8HXGPuv58nhfbNbzTuDC5Lu+Jp7r3mj/+azn/0TDfOw+WT2+gM7KPoIqHK+N238RDcjvRgd5n6Rol69MbS6r56Jo0x2xPvcB78zK/AY6gaQLHVj87ijf9W3AszMbvxGdXPMwWdottMNxN8lMZjQ0YguqdF2WbP80ySzwp8vfaPepzH7j9g9GOxrLrOe1aHhKF9rpugr418ym26P9vZ+Rs57fF+9vF9rGfryWbVK7vT4h2vgJVer6p2h71R1frwIWJN/VbbEeHfH5ecko3+9/AZcm/9dsd2OZ72d2fjb6+7cfeF+y/feI2Qlq/dmP37RHRLagU9K/O9F1ccaXn+XeiyY+/qcQwupRC08DRFf0+M0Qk25PBkTkN4FVIYQPTHRdphtRve0ATg4hbJ7o+jzViMhLgV8LIYw5m8EYz7sY7Vg8IwyPxXOeAkTkGmBjCKGqEjldiOrg54BnhSexwxa9Q/eg4sLemmW9o+hMd47n3seYrReh6WqWEGfnhhB+u+aBjjNFEF2B5wbU5fzXqAp+7pP5I+Q4TyYxvOEg6uV5KaqoPXcyDWinM0+3GEXHGQ1B47YOoauEPIQGBTvOdOE1qLtvFxpWc5l3Ep1JzlLU/XsEdQ+/xzuJ48fTRlF0HMdxHMdxjg9XFB3HcRzHcZxSvKPoOI7jOI7jlNIwepEnjz+48fgTV9eHke9D7N4Oxtc+0QUL+pL0Y8eG+gFoadEFACRoqrC6Y/V6roFiYYDGQf0aGmIZ6uP5k0xMQ1m9zGNvr5J+siqfMk1WJlmZj76oasJNZxJw5ZVXBoCeHrWxb3/725V9g4OaXusXfuEXAHjPe3RBgkWLipyv1UI8BgY0r/LRo8WiA8eOqT03NKhdtra2AlBfr4ZZVzdyfCcy3HzS6/X367OwY4cu47xtm648deqputDAiSeeOOLa9pns81511VWVMp2dwxdI+fjHP+62O0l57WtfG6CwB7M3KOyrpaUFgOZmbfBWry4m+H/rW5re7ciRI8OOt2Of8YxnVMpecYWmHN2wQZedbWzUNva//uu/APjqV79aKbt9uy6sYXZmdbBjAGbOnAnAnDm6cuO8ebq09cKFCwGYP79YXOL8888HChvfs2cPUNgvFM+PHX/ZZZe53U5ient7A0B3t67I+IMf/KCy7+tf15SWd955JwDt7e0AdHUVaVqt3TTbMlsz2/3DP/zDStm3v/3tANx8s6YTvPLKK4f9b7YDI9tya3vTNjhvj4eGhobVIT2HtedWr7a2tmH1To+z87a3t4+r7Y5rR/EJkfTQhgZiY2eNnugPYGjWL7FtZvFxGluaAOg6EpdtHNQTyZBur0v6ZfXxfX3Q4+M9JQwWNzPU6fuRP/fxPKlheNjntMUe8LRjZw+2vdrDnDYG9t4aDGsc7DVtFOzHra9Pl0q1ztuMGTOAokFJr2WkDVp+bfuRL7tmXtbOa4201duZWsydq0u4Hj6s7aDZFBQDCOsgWifN7A0KW7NOm9mQ2Yf9GEN1Gyn7ITU7tWtaHewHEUZ2Tu2Zs8/S21tkoXn44YcB2LtXs3zs2rVr2DnSa1hn8rLLLsOZvNj9amrS3+yXvvSllX22zdqnnTt3kmO2m9pUSto+m+3a82GDYduetqvWzlsdzK7T58auababt/speRtuz2Va1q5Vdvx44K5nx3Ecx3Ecp5TxVRRzf2sYi3pajAbq4vvWpqiINGgvf0C0t957pFB5ujtVlamv14/YWKdlm0WPbU6WpG+o1/PWD+mI4FisZt9QUb8hczFXev/2Wmc7ko8Vt+Uft/qHdKYI+cgTCtXGFL+xjPpy9SU9xkavdg1TUjo6dFW0VMUxRcbUQXtNz2fXytUgG7Gn2CjbXm3E7tkRpiZmQ2YPplZAYRemVpjtpEqdMWvWrGHHp+qJkduOkSsmUNjeihW6tLiFPyxdunREGbNxe7Xnw+oExTOxfPlyANavXz+inqkS70x+ciXaVG0o7Obcc88F4ODBgwDcckuxOmnuMTFbMPtMwxLs/YIFC4ad9/777weGt6cWCrF2rS7fvHLlymHnh0LR3r1797D/7Tpp3cx27Rq5Vyd9X9ZmjweuKDqO4ziO4zileEfRcRzHcRzHKWWctfjMfTXMFS1ZmThLua6Qc+sbY3B/0G02uaW+QV0nc2KQKUBjs8q5waZIRzey2GsyUWWoX883MKhulX6bn1LXVNROqruah21PPkPIZ6Km//h8uylJ2exRC24210jZhJJq5zF3Reru279/PwBbt24FikBtc0GXzQy1mZyrVq0CYMmSJSOuaW7DWm6MfHadTQbwySxTE7Mls8l0Vqjd03zyVWoXZp/mns5t21x1MNytnZLP6oTCbs866ywALrjgAgDWrVs3oowdZ+exeqcubnPpWT3zY9LP6Ux9zO37ute9DoDnPe95QDHbPeX73/8+ADfccANQTIY6dOhQpcyjjz4KFDZy6aWXAnDgwAEAfvSjH4047xve8IZh107t0VzNNlP76quvBgqX+SWXXFIpm0+K2bdvHwD33HNPpYxlqpioECBXFB3HcRzHcZxSJlZRHEa5YjEoxaQBaVDVpTWqhS2tquSYcndsoOj32uC2rz8qJHF7Y/zEzU1F2bZZFsyt5x2MI9Y9+wrVaCgmVxyqBGzbKNxGt0WdbY7OUKUfPnIk61MDpiamuKVKhQUjH4+iaOexNAyPP/54Zd/tt98OFCNKyzlnimJ6bQuKtpyNZ5xxBgDPetazKmXWrFkDFKqljVxNmayltLiiOLUx5duC/U1NgZGqtr2mirXZjNmrTdiySSMW9F8Ls1dTTqBQIi0Po+VebEu8QtUmhZU9X+nEFmd6kE+OSr0u9t6UY5tQmGITSayMeV3Mzq2dhaIdNhvN2+e0jbQJYpbD0drI1F5tm9XBnpdzzjkHgIsvvrhS1vZZ2ZtuugmAhx56qFImV9PHG1cUHcdxHMdxnFImWFFM/s9T59jmxiRJa6uqJ0Oi1T7cp6Pl/Xu1975jx95K2T17NJVIJb1HTJjd1Byn2s8ppqfPX6ij0XkLVBGaPVP/nz+zyPwvQ3XxVXv0QzHGcTDKh0NDRf2Dfa5a2YBcUpySlCVDNVUvT4idlrERqr1a7Nhdd90FFHE0ALfddhtQxCZa/FVZfIqNdG0kbLE2llQYihgaiz2zkXWejqHsvKZiuqI4NXnxi18MwJe//GVgeAJqu8d5IvY0RtH22euZZ54JFIrIaaedVilbFosIhUJpzwcUq6o885nPBEbGaTmOxQVam5kqivmiBPa/pRCDItbPjjv55JOBwvuSxtRu2rRp2PksftHa6RSz0RtvvBGAu+++GxiuaqaqPBTpn+x5SWN7La7c+irW5qaLOtg1XVF0HMdxHMdxJhXjqijW1dtSN9qTb2wsYk2KUa3+b3EoLbOKuJbeoL39jQ/pck1bN+nMou7OmEhzIFnaDD0uiPbsrSd+sEdH1HK0GFlvO6wzm+p3aP3mx1izZ556RqXMrBm6zdQjiVUf6o8xiokiap9h8FhUkaLaOCypckwAXowQvM8+mSlbus/I12Aui62y+2wjVVMSr7vuOmB4olibaZfPSrVnpEwBtJFwWUJkq7ONqG3kWxb7Y4qTxV3asZ5we2ryqle9CijinVIVJbdlU2NS27H3pgCaomivs2fPHnHN3P7Llow0e3rwwQeBItbRFUXH+O53vwsU7VMam5onYTe7SZfrM2V78eLFw8pYRoh00QS7hmUFsGPL2khTxu3V2lNTKtN9Vj/z4piyaEm7YeTzUpYJYyxx708l3jtxHMdxHMdxSvGOouM4juM4jlPKuLqeRUzGVZdHa1uR0qAnyqwzWmOS1eiW7j1WyMO3PrARgMce3QzA4FF1zc1sUfdHGCr6vX296qYbHNLjG2PahX6bcp/0kY/GLDhHulV27ujRDZ0dt1XKnBjXc1yz+gQA5kbpONgaqgOFS6c5utiD2ASG6FYPqXwcp7sfM9dzG87kpWx9UMNcGOYyNheeueugCFR++GENm7DJK5aiwY6Fws1g7os8cDsNaM5dwuauSBPP5uuVnnfeecOOtboB7N2rE8LMjWL7fDLL1MQmRNkayuaGg+Jemzu6bKJW7q6zV0tjU2v9ZJtMUBauYcctW7ZsxDUdBwp7tPYvXes5X9ve2sx0IYR8zfJ8ffLU5swe7dXOl6c+g8L2zX1sqZnS9E/5xDCbvGLPTzrxxa6VT1iplsB+IvCn03Ecx3EcxyllXBXFEJfeq2/QnnMaTFrfoH3WOXM0+HPffk0Mu2XPrkqZxzapkngkjlCbBvWYY32qerTUFWkd5s7XXn5Li/b+m2Ovf0+XltmfTGbpHNCRx4BNgGnSsp29hXr00BZNN9LZo738det0CaH5MZh7YKgYyRCTfLc06Wdprtfz9vcWI+u+Ixaga311VxSnAqnNGjZBxRJkm5Joya/TMjZp5Y477gCK9AupMmMTBMoUv5w8Ka2NZNN6mrJjCV1N4bHRdzrytuNslFsrNY8z+bF7bqQpakwZMaXF7nHZhJJ8Cb+yZfTMnsymH3jgAaBIJm92CMWkgS1btgBFIvD0fLbc2bx586rWy5m+5AsDmLoHRRtpqp6VtcTyULRzpsyZzVobl6aKsvbZPDtmn3Zs2j7b82LXsvOYnabvrW01u14ZPZOnnnpqpay12bl9pxNzam0bD1xRdBzHcRzHcUoZV0WxEk/QoL3r/oFCyWhq0pFqc0yIvXPnVgA2bdlcKdMfYwjbYgzg3Bl6vpXzdVSxetnyStmVS3U0Onee7uvq12vefJ+miejd3lkp29UXYwFiZ92W3utPFMqjvToKGdgTY8madHSz7oQYw7OgiDmQmP7HVgmUQf2/t6+rUmaoL6qizZaYcwnO5CVPj5PGj1is1yOPPAIU8WDpKNTKmJJosWM2mkzTJVg8jKksebxMem17nyt+aUyhjaBtlJyPltOydg2LuzGVyGMUpyZ23yxuNr2PptTky4OVqeZmB6Yw2/9pYmFTL3/4wx8ChcJuiqI9A1AoI6bknHLKKSOubcnCLUlxnpYkjQlzph9pOiUoj2M1b4u1lekSlWbPpupZe2dl0kUJdu1Sz6W1jTllsbimLJotp8m+7TzmXbKk2mXnsbbbnil7BtKYSivjCbcdx3Ecx3GcScW4KoqDQ9pDLmb3FP721tbmWEb/b9+tiguJ6nhujAtcsURnEK1epCre4tl6bEtd0ds+1qMj1dkx9O/YEY0n6N+niuJQZ6EozqrXEWpdo45gGokzriVJkD1DlZbufq3Pw5u36WeJa/HNm1fET8xsjku4xc87YEvzdBYxOkMDOmqQPpsJvRZn8mIjOlskPp3JaaqKKX+mdGzfvr1SxmYem7pio0ZTEtNZdTbqtPPYDDkbYaYzr23kms/wS7ERr9XZRr72WdKRq43i8yWonKnJfffdB0B7ezswPN7VbNrszWZoltmDlbF4LFMCU3X7pptuAuCLX/wiUNi/HZOqhWaTmzerx+inP/0pMDxOy1SeDRs2AHDSSScB8JznPAco4r3y45zpgbV7+WICUNif2aopbWVJua29tPbPlG/z6kChMpod2XlMEbTZyinbtm0bdmxq35Z1wvZZfK6VOeeccyplzYuTL/OaYtsmKlbcFUXHcRzHcRynlHFVFK1nX1dvPeaid9zSqn1WG/EePKDxLMuSvF/POWO9bpuv6svMOlVRmgZVGWkcKkbLg6LvG3p0X9sx/X9Rs772zC76yIdjfsOdnVq/niM6EultLGYiN8XRySAaR9Af67ljt44c5s0qFMVTTtDcYDNmqCI0MKQjoUMHitx23Yd0ZmBTvY2En40zebGRnKkj6ajPRq42S/OEEzTXpikiAI8++ihQzAi14/O8XTAyDiVfAi2NzcqX9ysbjdr5bERtSo+pOWl8pI3irazHJk5tTDWwZEd8AAAgAElEQVQxJaMsRsoUDbOzVFE0WzOFxPKAWrzhY489Vilr8bd5/kSzydTGq+VfTO3txz/+MVAoN2efffawz2IxjFDkh5zopc6cJx+zldQ2TF002yrLPZjH+tkM+61bdf5D6oUx27e23GYtW57P5cuL+Q+5B8na0XQWdR4faf/bM2K2DfD85z9/2Oe0dj6d5V3mTRpPXFF0HMdxHMdxSvGOouM4juM4jlPKuLqeK0vVxO5pXV0RgNwYl+xrj+ln9u7XYNWZLYX8uuXxTQAsbF2nx8yMx8eE2/UU0m9bQ0wiG1PTrF4QJ6yco2kY9vYU7rvtHSpf3/+4ulf2DKi0vLO/SKJ9rD8Gy8ZlCGfOUvd3f5x9s/HRwgUzf5ZKx0vnqyvSliM83FFMZtnxuE6qaZaJSaDpPDHMnZYGz9skAHMZmJsgTZdgLmdzHZgrwtwVdo4UC4S2a+aTW6CYBGOTWspcE3YtC+swN7glRLb0I1As3Ve2XKAz9cjTfaTB8HmibSNNQ2KuPUslkp8vXXrSbNzOm080KKtHPnkgfa7Mls1VaOezMpZyJD3eJh+UuSvzz+lu6smNpVfKlzCFkZM77DWdhGftpR1voUBl6Z+sbD55pWzpS3Nvr169etixFiIBxXNh9bQ62CScb33rW5WyZqMLFy4EimTi6QRHCy1KlygcT1xRdBzHcRzHcUoZV0XRgjOP9qiy0dRaTBY50qdV6erVvutAnZbdfrAITu3sUUVuf5eOHs5ep4GmG05Q5W5WU1F26Kimw2mIKWoaRc+/eoH20lc3F6rM8oPa2z9rlY4e7t2qgdr37NhfKbM9Kpx9ceJL0ywNdh0MWt+ugeLa9zyuI4v1p+r5hpr02suXL6qU6dm7EYCOXVtxJj/5cnqpUmGKn6XAsf/TBMP5JBY7Pk+qDUWya1NObARs104nAljQdZ4KIlVP7DzVAqJTBcneW5mJWjLKeXLIFcD0fpoyUkv5s/Jmp6aSl6XrMDuzbbmtj4WypMpm/zYxx+zf1B4onh+bdGDKUKrU54pkqtg4kw9T5VLPjJEnizdSxc3sztRws6NcxYZCxcuVRLOjdIKXndeWwLRJjKkani/zZ/vsOUp/G3LPjtmsnT89bqJUcFcUHcdxHMdxnFLGVVEcMtVtSHv9x/qLfmrrTO0p18fRYl29KoqhqUjdse2wHt/Zq+pMR5f+33FYffvrl86qlF0Y1cr5bXo+GdRrbtumqUFmzC1GHm0NWnbuHB1hDq3WhN5tbUW8w4IWrd+juw/Ga8aRdaPGPvYnqX5279eUPAeO6Ih6ToyzXLy0UBR7l+k1Go4USZmdyUueYiYdPZrSd/fddwOFspiOQi1NQq7QWZl06SnDlBkbNduxaYxNXqaMPI7HzmOj77Sedm5XFKcHFgNbthRjbhe2L1WsLf6qLP0IlCeKN1u3a9v/qZ0Z9hzZa5r6yeplx9l5LB2JLRWY1sOSKZvKk9bPbNqudf7554+ojzN5sFhsu29pupg8pZORprzJFxrI28rU1ky9s+UhTbmztFALFiwYUT+LSbRnI1Wv16xZM+xapizmMb/pNS0+vKxNt2tMVGJ5VxQdx3Ecx3GcUsZVUWwWHY3WN2n/tP9YESvVEpfNqxvQbd2d6sNvaptdKRPqdVZpR4+OGh7bqWWGBjQOJxxbUSl72gk6Syk06Kihr1dHpd/4sS5p1TKzOO+y1ScCsHSFfh2Llmjs47q2YhTR2KLlO7o1nuDgDo1ZrG/QUURzXaE+9hzR+uzZoaOIuStVFW2oT2ZkNesIprF5Bs7UwUZ26ew6i2c599xzgWL22iOPPFIpc9dddwEjFRkbPaZxg1Ymjy0sW74pjy8rK5Mn7D7xRLX3U07RDADpSNiUJ4sLKlOBnKmD2VWt2ZK5qlcWj2U2lCsbqWJn722fLRFp8Vipap6rmWaDqXJj9bDz2Gcw1ciUeygUxE2bNDOGqVHp82AzTu2zuKI4uTGlzeyqzNasHbb7nMbkmmpn7WneNqYxgLbogJWxRQmsHbRY8LSMJZY35dwUQSjsOVc882cDithbi2M3yp5DVxQdx3Ecx3GcScW4KopHDmjPuSXGFzQlve2moNvmxFjAefHV8hYChKjeDUX1sbNfRw+b9msMAvVFnsKufj3uzFPXAtDaoqOTHT1xcftDxUyqhzviUmY7NNbsnNN05Lp2eTG6XbFARxSr4qhh9z4dRRyLHfxjSYziYNy2P8Y3LGuNI5CtRVzC7p26r/tIEVPhTF5MzbAZyeno1HJsXXjhhQCsXas2d8MNN1TK2NJnpqrUWtw9V3/ysumostpC8mXntxG0xc/YDL9U6bH39uoxilObfKZvajv23tSKXD2EIu7QVBl7tbKpgmOqix1jM0ntNZ3paYpKPqvaVCQYOYO5vb192LGpqr9xo2aRyONwy/D8iVMDu795ntr0vcUZ2v1Ol9Ez28y9IrndQxFnbsek+UFheHtq762dtmumZewZsH21lEBTyK0OZp9lOU8nClcUHcdxHMdxnFK8o+g4juM4juOUMq6u59CtruG6OnVFNLcV0937OzWQc3aDysFrFmuZzVGOBeiXmIi4KabOiWruoX4ts3F3ITvvPqxy7vbDKtmuXKlujO42fe1rKlwTe7tjKpBudUd396obY37DyZUy5gZZF8+za48GRu84pNfsS5ThmQ0qHXcf1DKds1VC3/TopkqZ/dvUjdI8MHxygzM5MReCuQVSV4AFVi9bppOgLLDeXqFwWVu6hbEkIa7mck5dZ/mkgFqTWcyFt26dLoFpbr7U1W0B2eYmrOUidyY/+VJ5ZS603PWcusnMNvLk3HlC4fQ4cwuaK9psP3Ud2sQScyObq67MfVeN9Bky17W5K80VOZkmBDjHh7mV7X6l9mBtWt7upW1ZtUmAdr7UTW3v80TyNsmljDx9UzpBxcgnGdZqn61dt+ckDcOwSWlp+p/xxBVFx3Ecx3Ecp5RxVRSlV0effYMaMFp/rEhRs2OvBo8uWKaK3cqFqtI8vLeYoCJzVLEJ9XHqeRxhSJzk0j1UJCLu7dORweYHNCnm3J064uyO815mziqmux+NqWoamrUnv7NDr7mnfXelzIpFmvJkzRJVXDYt1oku7fviUoGJotgWJ8702ZJr6AhmYKAYAYvE0cOM6iMWZ/JgQc4WcJyO9iwVh40+TWFcuXJlpYxNHNmyZQswMti+lsqRJ2BNFUUbsdYK3jeVxepjk1lsxG6KDxTpSWot6+ZMHWopwtWW2kvtq5oqY2VThcPSe9jkA0t0bDaV2plNcMnT4qT2Zs+YqZmmtNizOJZg//R89jldUZwamG3kKXCguN+5cpzagSmQ+YIFtVKJGXaM2Uqt9jVX5NPj7DPYq9XPno30WvZ8mBJv7TQUHk1T4scbVxQdx3Ecx3GcUsZVUXz8/tsAkHrtny5YXCzqvu+g+vcXztP4w1NWacqRe3cUcQSd/TF57KCOBGIGHBrj+WzZP4Bj9RKvcQIAvTF2obuvI/5fpFY4NqRl57RqDNeRLo3POpSk0BmII4C2haoezZupKmRrvE59uuxVXZx+36/HzGrTkfCCuYV6WH9UFcqZ9dUT4TqTh3xRdktaDcVi7qaKmFJnibehiFe0UeNYFLtc+bCRZ9nSTvl5UlXIFE9LsG11sTJpwm1TbXwJv+lBHptYS0UpU9qqxXeVpRjJl+oz1cRezQ5hZNyi/Z8u02ZKkp3Xjjflsuyz5PZatmShK4pTA7ONsnQxeXtnNpcub2rtmtmUxSGOpc0dS+x3bkfp/6aG22IMphJakm5bahIKT5S9li0/aZ8ljQkeT1xRdBzHcRzHcUoZV0XxaOcuAPr7Y3xBX6EWNrfpiHLvtkcBWLJClcCz1q2qlNn7aFxkXrR/2xdj/gJxpluitAzW6Ufbc1iVkaF4DK06Kh0W0dKgo4XDParuLW3RugwkMY8hHt/bo6PjpkYd5bQ267V7uoqYgxDFnFltOgqYPVNHF4sXF0v8HN2nsZP79u3BmfxYbKLNHH7+859f2ffc5z4XGKkopsuRrV+/HoAHH3wQKFQWU0vKRqzV1J80DiePM8tnngKsWqXP0IYNG4Z9Bot3SWcTmopjI3NXFKc2psaUKc9mO3kC6lQZydWXscTS5jNHbTZod5LBwhQXUwktY4DFLsLIZdBMGTJ7LYtLLJvdPZa6O5MPaz937dJ+Q3pP82VJy2Y9275t2/S3diyz3qtlCUifg/x5yZehhMKbZPZsba15ptL22T5nntUifRZsmT9LOj/euKLoOI7jOI7jlOIdRcdxHMdxHKeUcXU9Nwyqu82CSg8PFq7d1pkayDl/rgZwLpytk0VObitSKjy8V4/f3K6TTWbMVheatKjke6A7TV6tfeChBnWljS3JR3Sd2KskX090J9bZNPchLdN9VN0pPT2FW6UvJgA/87TzAGioU1dMvSSpGoa0rnX1nn5kKpCvRWsTWABOPfVUoHAnmGvMklcDnHPOOQA89thjQBGQb4mty9zJufusbD3T3DVirmMLnoZi7emTT9YE8uYaN/eyrQENY0sH4UwdzBbNPmq5a41ak0RyV3Q6sSp9X62MkSfwNps224QiRMLs1MI1rExaF3NL5+lSUjehfc5q9XQmF5bOy+6XTQRJsbbL7m06YcpswcJ77rnnHqCYqDeWUISxpB8z0pQ3O3bsAKCjQyfE5s9hOlHFPp+5p62+5nKHwvU8UWs++xPjOI7jOI7jlDKuiuKRQ6qiDMXeeeusImlxW1NMcDmoI83DB1VpaS7EDp55ogZ9duzeCkD3YVXumhs0WL+tvvg4lnx7aCiOKCvqoI1Ci1GzxG11QetVF5W/uiRVQ388d8zMQ88xrefAkAbPNrcUI+Fjx/Tay5ZqQOve3Tq62L+/GCH0R9VxZlsR1OpMXkxRtBFrOlHFRrV5IHSalNtUR5sEYyNEG4WmKmGe5DhfKiqdFGCKiSmJFkRt10vfW9Jvw0auaT090fb0wtQKUzRSxS4Pyjd7SJNom/qSJy8uS49TjbLgfLNTU92tTFonU90tJYj9b/VLFSGrZ760W1omf1acyc2dd94JFJOhUhXO2l+7z+aZSRNSp6lyoGinzX7KEmRXUxnLJrPk7XNaxq5tk2tM8bZjX/ziF1fKXnjhhUAxGWbTJl3q134joFAmPeG24ziO4ziOM6kYV0Wx96iODIbqtFctFH7//l5VSXZsfRyA9t06DbwuJsEGWL3hfAAuOE1TKTyyQ0eYOw9u1/M1FiPWpqaY3DrEEXD8qCH2jesSRdGURKtPXUyeM5AolD0x501rQ1yo3Ka3W7JvKT7LWadr/Nratap0/vh7DwCwLX42gK79mnBzhg9upwSmKFoC1VQdqTYKTWOhbARsqXRshGij0UceeaRS1pQTi+PK1ZH0vDZKtvQiFjtp8YgAJ5ygqaZsRG4jX1Mz08Tgnmh7emE2ZJQtz2f2YCpIqsTky/qZsmiq3FiWyLMyaeoSszN7rsrszpQfU9CtXlbvNB1JrhKVxfmWLYHpTF4uuOACoLhfaSy12YC1o+bpSdPHWEyjPQOpFwgK24PCNnPbLVsa0Gw1b59TqsXD2lJ8F198cWWbeXzsfHb+VP23fekzNJ64oug4juM4juOUMq6K4spVqnp0HYmjv2NF7/jI0bg8X4wpbGrR0UNPfzLTaVDLrztLZxPPmaGKXXhEY/+2dxWxWxJ0tDk0FPvCor3zQlEsRsKmKNYHiw3T3vu+zmLEsWWXKoCzF2iZnmNx9NykX2FzQ9H7X7ZSP+eePTq6OXxYY2wOdx4o6hdVy0Yf3U4JTNWweL50qbHjiWuxOEGLUbHZybfcckul7H333QfAgQNqLzaKNDUnndlnauFJJ50EjEz6DcVI3LblM1jLYr0manad8+RiqnGt5fnyGdGp3eY2YiqevabnzRNt53GM6axQs+muri6gsLuya+fqYNms5VxtzI+t9h04kxdra7du1TkJ1h6m7y1+0RTlNNbbtpltmc3aUnlpG2ftu23LbTddcs/KmA2Xqdd5TLB5c2wmt3mmoJjdb8+CHZMuhJDHqY83rig6juM4juM4pYyrojhjlvaqe+MSfseSeJS+/jhLuU570y0N2odtPFbMwNu/5WEABnt1hLD6DI1ZfM7pqqbM2FHMCHr8gI5ebYZ1nUSFUSxmMZnxFGIcTlQWCXrN/fuK0cmd8ZqNMWfjYMyj2NqoX+Fpp55SfNA4I/p73/6mXvOoKoo9RzorRZpFr3HMFcUpgY0eLTYxVeyqKSmpgpHHrFhMocUsWuwKwDOe8QygGDXbKLJsaUAbmdro22aI2rJVMFzJgdrLsnmM4vTC7n0+Gzh9n6tvqWKXx1/l+QlT1aOaYl2mhth7q1/ZLGUjjy201zReK8/LmB+b4rY9NbD7ZO3gww8/XNln2/KlKdOY3Nz+LK7WvEGpZ8bURlMW81jANPbRbMwUS3sGUi+TXcty69prWX5PI19SM322JhpXFB3HcRzHcZxSvKPoOI7jOI7jlDKu2uambZp4eihOBJk7r0jLMX+xBvW3d6j7eOshdZ3NqSvk3OUx5c3APk2u+ehPfwDACRueCcALkpQgJy1TV/a9mzR1zsH+mIYhelIGE5fEMXPBxJQ5lgJ7ZuLG6N+r0/CbZqnrcf1Juiza4tP0mnPnzqmU3btbr7kmbpu7Ul2FR3c9VinT1x1ToPQU7m1n8mOu59R1kAc1m/usLG2CYceb+yN1W6xevRoYmX7BjknTgth7c9NYmS1btlTKWMB3GugNIxN7QxFQ7ZNZpgdmF7XcrdWWjISRQf1m6+bSTZNX28SsPDF8nqKn1rVTV7FdO3c5W9k08Xw+CcHOU5ZU2ZfwmxpYOE1ZWIGF35j95W0wFGEN5iK24/MUT+m17NWOseembJGDfGJX6irOw3vsf2tfy1JFWX3LUuBUSz01XvgT4ziO4ziO45Qyroqi1GsPPvTHxbqTIHuJqW96+qKiESeYtM4qAk5b43p+Rzr0uL07dambwZjWRkJPpeyiBYsBeP3zNwCwr0dHCI/v2gPAjn3FVPuO7piaISbYnhdHDIubixHH6rXr9LyLNL1Jffws9RInGvR2FWXnq5J4ypIzAGip18+769G7KmV2bdPy+w6PXOjcmXzYaNHSHJSlLjDKFItq6qKdJ1Uo8yUBjbJJMvlEAatfuiyfqZU2mjVlMV/iCopRrSuK04N8wkotRaKWslhNIUnV7TPO0PbO1B5La7J582Zg+PJj1dLYpOfLlwvMJxGMRXlJn8WyCT3O5GXxYv0NN6V61qxZlX3WztlrrhpCkYzbXs1eyiaLmN1Z25ur4qb6QWFb+SQWm7CSHp8n585T9kDh8bGFFuzV2uu07hOFK4qO4ziO4zhOKeOqKDYMxlir2DluritGqQ11um9ps44a6mZo77yutVBGHt63J77T/m3zEo0XO3RMFZJb7/xJpWxjk44MFi5aAcCSZZqc+/yVJwLwkrXrK2XrYzLugRg72R8VwNmLCzXzcIwlfGTjowA8cI8uyycxoffaNWsqZZct0tjLBXO1DoPNWmZGWzHiaG3TUUhrMgpxJi+W+sOStabpcXLKVJvR4hZrJUSuVSbHVJN0dGtJvm0kbaklymLHPD3O9CJXAlPGksjayNU9O28aW7thg3pvbEkye1buvfdeAG644YZKWUsqb4qNnb/MJvN61krjk8czpuRpe5zJjSmI1n7t2bOnss/iC/N0ZaYelpF7ZNIYxdxezJ5MyUttxjwxdh5ra1M13OpjZc32zGNjantaZ2uXLbVZmmB8om3WFUXHcRzHcRynlHFVFOc16OizbbaqhLNbCrWwrkF79IeOqnLX1R19+gNFHGNzvfZrBwa0d917WHvidUE/Rhgoet2Hj+zUfQd1trO060zpA3fdA8BQf1F2VpuOSpYu1STIg/NUCfxBsnzgwaM68u2I5+vq0Ne+LlVgNj5wW6XsqphM+bT1Gte4NMYsDoUifqI1Ju6eO38ezuTnPe95D1CMIq+55prKvlzpm4iZabnakiqCFiN26623jtgHcPfdd1fe57NbnanN8SjDZXGMuQqTK+NpnJfFJlrCeEsib/Gy6Uz8Rx9Vz4zFy9q1LYYr3ZZTFqtr5Mpn2efz+NupgS0eYEqiqYfpe1O0TalLFxcwxdBstFryeBg5oz5P0p3audmoKYh2bGpzpoybh8bOY8d+5zvfqZS1WEwrkyfJT68xUcqiK4qO4ziO4zhOKd5RdBzHcRzHcUoZV9fzxts0PcziOGFl+fwi4faihfr+xOiKnbFQX+vi9HeAo3Et5gOH1OW8c4cm8G5tVVfxnJmFNH1gn8rWEuLaurO1TGODyrtdncXU8wbUrTyrT8/bP6Cy8UlLimu3HY7JOvuiNN2lQap1dSolS3MhOzfV6b5GifLz0c5YtpC+bZ3rwMQk0HSOj3zySq0kwpOVai43d8VNX8z1PBaXbK31lg07T9k6tHYtc6WZW9BSjqTpTWwCQJ76Jk3ZlLsK8/qVueZqJdUeSyJ8Z/Kwe/duoLi3tq49FK7nPCm7pZqBYpKI2Z9NnMonPqXb8mfBSNv//FkqS2pvz4e5p/O1qG+++eZK2VWrdKLts571LADWxImxaQodW+d6on53XFF0HMdxHMdxShlXRXFhiyp0swbj8kr7it5/Z2cMYj6ovf6GI9pzXjS3CKo/cdZ8fdOgI1ZZp9PmG2Pamb6hQhlpr9NRRGevBobOnasB1V09+v+ezl2Vskf7dRTbf0wTwg4d1VFAx55ihDAnBmovXqQpc7qbdYTR2aHK5OCxYgTS2Kgj6TlxwHK4U9XNwVCcb5DBeF6fzOI4zlODKRv5K4xMAVK2TFi11DRl2HlM9cjPV7b0ZK7KjEXNLFMN85Qn9jnT8/nSfVOL7dt1KVyzjXSik9mhqW6mFqYpb/bt08mouT0aqTqXq4ypmpdTpkjm5zMFMZ1cA8Uzkqrr7e3tQDHBy56NNPWUfXZXFB3HcRzHcZxJhXi8huM4juM4jlOGK4qO4ziO4zhOKd5RdBzHcRzHcUrxjqLjOI7jOI5TincUHcdxHMdxnFK8o+g4juM4juOU4h1Fx3Ecx3EcpxTvKDqO4ziO4zileEfRcRzHcRzHKcU7io7jOI7jOE4p3lF0HMdxHMdxSvGOouM4juM4jlOKdxQdx3Ecx3GcUqZ8R1FEHhCRiya6Ho4zHRCRPxOR357oeqSIyFki8pOJrsdUQUQuF5GbJroekxERebeI/N1TcF630Z8REQkism6i6zHZEJFFIvKwiLQ8Bee+VUQ2jFZuyncUQwgbQgg3PpXXEJHPisjHnsprOGNHRLaIyMUTXY/JgIhcFRuRIRG5PNsnIvIxEdkpIp0icmOtRkFEFgFvBf6/+H+TiHw5ft8hH5DF8/+FiByIf58QEUn2nyMid4jI0fh6TrLvl0SkXUQ2p+cVkbUi8hMRqbdtIYR7gQ4RueSJfk+TGRFpFpF/FZGtItIlIneJyMsnul4TzWjfi4i8SUSOJH9Ho50+s8r5moAPAX+ZbHMbfRIQkS/E7+qwiDwiIu+Y6DpNFCLytmhLh0VkR2wXG7Iyl4nIQyLSLSKPi8iFNU75QeDfQwi98dhmEfm3eP7dIvK+5LyrRORmETkoIn+dXfObInJedu6/Aj462mea8h1Fx3macw/wa8CdJfveAPwycCEwH/gpcHWNc10OXB9C6Em23QS8GdhdUv5dwGuBs4GzgFcB74bKj/L/Al8A5gGfA/43dj4bgD8HzgV+E/hUcs5PAu8LIQxm1/oPO/c0pAHYDrwQmAP8IXCtiKyZwDpNBmp+LyGE/wghzLQ/9DnYRPmzAPAaYGMIYSe4jT7J/BmwJoQwG3g18LEaHfaGsu3TiDbgt4GFwLOBFwPvt50i8hLgL4C3A7OAF6B2OwIRaQbehtqo8cfAycBq4EXAB0Tk5+K+30ft+ETgtdYxFJFLgU0hhNuzS/wf8CIRWVbzE4UQpvQfsAW4OH551wKfB7qAB4DzsnK/DzwIHAL+HWiJ+y4HbsrOG4B16I/hANAPHAG+NtGf+en8h3Z0hoCeeD8+ELe/Ot7zDuBG4LSx3PuS89cDfw3sBzYDvxFtoSG1t6T8HwNfSP5/DvCTWI97gIuSfZejDUJXPPeb4vZ1wA+Aznjda57A93ITcHm27Qrg2uT/DUBvjXN8D3hzlX070s8St/0EeFfy/68AN8f3LwV2ApLs3wb8HLAE+Gnc1gIcje9fD1xV5for4j1vnmgbHCc7vxd4XXx/Ufz+fxfYC7QDb0/KLkAb/MPArcCf5O1Zdu63AluBA2jnq2LTwGeBjyVlLwJ2JP8vB74C7Is2/N5k37OA22M99gB/k9zjL8TrdQC3AUt+1u+lZN/3gQ/XOPbfgA8l/7uNPjW2uz7a6Bsz+70CHXBeHbf/Xiy3Cx3QBmBdlXOeCPwQbTu/C3ya2O7mNhq3pTZdh6pyj0cbvBaYP5ptUqW9fgLfx/tI+g1ou/krYzz2BcBj2badwEuT//8E+FJ8/w1gfXz/JeCNwGzgLmBulWt8B3hbrXpMN0Xx1eiXMxdtOD+V7X8T8DJgLXAK6oaoSQjhKnSk+ImgI9enpWthshBCeAvamF8S78cnROQU4D/RUdwi4Hrga1ExMMZ6798JvBw4B1UTXjvWuonICuA64GOogvd+4CuiMSYzUCXi5SGEWcAFwN3x0D8Bvo2qGiuBf0jO+XUR+eBY65DxJWCdiJwiIo3oyPSbNcqfCTx8HOffgHaGjXviNtt3b4gtUeTeuH0fsEBEVgIvAR4QkZnoPfn9sgsFVYEG0B+haY2ILEFt9IFk81JUVVuBdsg/LSLz4r5PA73AMvQH95drnPt04B/R52FZcs6x1KsO+Bp6n1egSslvi8jLYpG/B/4+qKq0Fv1BBrW7OcAqtFP7q2iHChH5oIh8fYzXL/tebOLkgLkAACAASURBVN9q9Ef18zVOkdu32+iTiIj8o4gcBTaiHcDrk91L0TZxNfCuqIC9H/1uT0bFnlp8ER0ELUAH5285jqq9F23HX4gOdA6hzwxUsc1a7bWInCAiHSJywhiv/wKizcZwhfOARSLyWHRNf0pEWqscO8xm4zO/nOrt7v3AS0RkbrzOg+jvy9+FEDqqXOMh1CtUlenWUbwphHB9UJfA1Yz88J8KIWwPIRwE/hT4xXGvofNUcClwXQjhOyGEATTuohV9uI2x3vs3oj92O0IIh1D301h5M+q6vT6EMBRC+A6qsLwi7h8CzhCR1hBCewjBfvAG0AZ0eQihN4RQmYgQQnhVCOF46pDSDvwIbWh6UFf079QoPxcdPY+VmagKanQCM0VESvbZ/lkhhCHgPcCX0R+Ld6JxMv8AnCki3xeRb4nIGdnxXbGO05bYof8P4HMhhI3JrgHgoyGEgRDC9aiavj7+8LwO+KMQQncI4X7U9VSN16Pqxk0hhH7gj1AlZyycDywKIXw0hNAfQtgE/DNwWVLHdSKyMIRwJIRwc7J9AaoWDYYQ7gghHAYIIfx5COFVo124xvdivBX4UQhhc43T5PbtNvokEkL4NdSVeiHwVaAv2T2Eqr19QUNb3ojG3d0fQuhGO3+lxA7Z+aiN98f28f+Oo2rvBv4gtul98Vqvjy7wqrZJlfY6hLAthDA3hLBttAuLyNvRDttfxU1LgEb0ObwQFSSeQXXhosxmYWS7Oyu+/7N43h+gneFGNCzoayLyRRH5oYj8RnaNUW12unUU0ziqo0BLFg+xPXm/Fe2ZO1Of5ej9BCA28tsZrpSM9d4vz8pur1KujNXAG+Jos0NEOoDnA8tiY3gpOmJtF5HrROTUeNwHAAFuFZ3FX1UROk4+jDawq1AXy0eA74lIW5XyhyganLFwBHVrGLOBI1GhyffZ/i6AEMINIYTnhBBeiDbI56Fuz6tRl8+fAP+SHT8LdQ9NS6JidzUa5pI35gdCCMeS/4+iPxqLKGL5jK1UZ5h9hxCOom63sbAaWJ7Z95Xojx+o0nkKsFFEbhMR6wBeDXwL+JKI7IrB/Y1jvOZo34vxVmp3kGGkfbuNPsnEztZNqGfkPcmufSFOxojk7exoNnsw2qpxvO3yfyc2+xAwiNptqW2O0l6PCRF5LSo0vDyEsD9utvjvf4idz/3A31CICTllNgsj212z2YMhhEtDCGejCv8/oDG2H0TVxouBX42eBWNUm51uHcXRWJW8PwGNjQDoRgNQARCRpdlxYx1xO+NDfj92oY0BoLNx0Xu9MylT7d7ntKONXNlxkNkK6lIxtqPxN3OTvxmmCIYQvhVCeAnq8tuIqjGEEHaHEN4ZQliOjn7/UZ6cNBFno/GOO0IIx0IIn0Xd26dXKX8v+kM/Vh5guGp/NoVb8AHgrHgvjLPI3IZx/6dQ99BCoD6EsBWNFTorKbccaOL4XONThvg9/Cv64/W6qIyPhX3AMUbadzWG2Xd0eS1I9o9m35sz+54VQngFQAjh0RDCLwKL0WD9L4vIjKiCfiSEcDqq8r8K7diNyli+FxF5HtqZ+PIop8vt2230qaMBDT8w8ja7neOz2fnZADc9Nv/9rkcHUMZ2tLOW2m1LCGFnLdus1l6Pheha/2c0ROo+2x69VDsYe59imM3G49up3u6mvAuNGb8fdWHfHr0I9wGpEn4aw13ZI3i6dRR/XURWish8dCR8Tdx+D7BBNFVCCyNl8D3ASeNXTWcU8vtxLfBKEXlxVCp+F3V7pHnNqt37nGuB3xKRFTHO44ps/93AZSLSGGeUvT7Z9wXgEhF5mYjUi0iLiFwUr7tERF4dY1/60JHhIICIvCHGQoGOIIPtGw3RGZotqCLZGK9pz/VtqMK5RETqROQtqCvisSqnux6N40nP3yxF/q6meH77Yf088L74XS1Hv/fPxn03xs/w3ngOU4K+l13zHcBdIYS7UWWrNY52X8TwmYAXAd+LrqPpyGfQBvuSMHzWeU1imM1XgT8Wkbb43b2txiFfRm30AtEY3o+gtmPcDbxCRObHAXOaU/NW4LCIXCEirdHGzxCR8wFE5M0isigq+qZQDIrIi0TkzPgDfhh1943Jvhnb9/I24CshhNHCJnL7vhG30Z8ZEVksmu5lZrSJl6GhPfn3mHItcLmInB47gB+uVjB2ym9HbbxJRJ4LpHMFHkG9h6+M7f+HgOZk/z8Bfyoax2p5CV8T35faZq32egzfx/9DwyReF0K4taTIvwO/Gb+3eegzVi1O91Zgrmj8u/F54EMiMi+qnO+kaHetDouBX6foy2xGZzfPRJXxTbFcM/BMdEJLdcIkmCX1s/wxfNZzOvt0DSNnq9rM1w7UTdGWlP8DdMbpdjTWrDIDCw22vTse9z8T/Zmf7n9omott8X68P277+XhvO9H4jA2ZjVS999m5G4C/RX8QNqMxfQPEmZFoB/UWtOG4Dg14Tu3u2fH6B1G15zp0tLyMYmazzcw+PR7zCVT9PILOzEtnEn8DuLLGd3FjtNX076K4rwWNU2lHG8E7gZ+rca6F6Gi3Nfvu8vOvifsk1v1g/PuEfU9x/zOAO1B3y53AM0qudz8wO9n2JjSEZAvwomT7dcCrJ9r2niJ7Xh2/195oA/Zns+IvovaszkXoD81YZz1fjj4/Nut5J3BhYjPXxHPdG+0/n/X8n/EeHQJuTurxBXRW9hFU4Xht3P6LqMrWjQ7yPknRLl8JfOOJfC9JfTuAF4/he26Mn3u52+iTar+L0LatI9rNfcA7k/0j7Ddu/2D8Hscy63ktGm/dBdwAXAX8a2bT7dH+3s/IWc/vizbYhbaxH69lm9Rur0+IdnhClbp+H1X5U5v9RrK/EZ1Q1hE//yepkoUjlv9L4Irk/2Z0Br9lF3hfyTGfB96Q/L8K/d06BPx1sv0NwFdHu8f24zftEZEtwDtCCN+d6Lo448vPcu9FE/z+Uwhh9aiFpwEi8nFgbwjhSV+94okiImeiKUmeO9F1mW5EhaEDODnUnggyLRCRd6E/+E/q6kNuo+OLiFyD5sSsqkROF0QXQvgROogZs7dhjOe+BU3Vc3/Nct5RdKY7x3PvY8zWi9B0NUvQnHE3P9k/LI4zUYiuHnIDqgj/NaqCnxueLj8GzpQjhjccRL08LwX+B3huCOGuCa3Y04SnW4yi44yGoHFbh9AkpQ+hKUQcZ7rwGtTdtwsNq7nMO4nOJGcp6v49grpq3+OdxPHjaaMoOo7jOI7jOMeHK4qO4ziO4zhOKeO6OPe2rbsCQGenZjHYv39fZd/mzVsA0MwKMGeO5pM877zzKmV6jmq+zYcfeQSA/v5+Pc8BzRfbmKQmWjBfE40vXqppwNr37AGgvkE/8uoTirRNHXs13V7HQa3P7Jma37Kru7tS5hfe/GYA7r3rDgDuvu02AJbG87/0kldWym7fHlP0DehneewhTat1z333Vspc+IILAbjhhhsA+MAffSRNUeFMMt7xjncEgHPOOQeA888/v7LPbGBwULMnfPe7Ggp5112FZ6SvTzNmHIi2Wl9fP+y1q6vI7NEQbbSzU5PvP/aYZrMZGNAUcmnatzVr1gAwc6Ym7O/p6RlWF4AZM2YMq8PcufpsNDZqzuOWlpZK2SNHNJ9rc3PzsH12bHruvXv3AvDTn/7UbXeSsnz58gCF7QwNDVX2mR2ZV6murrpuYG2t2UdTU9Ow1/R8ZtNG2fnNhuzVbD4tY8dZ3a0O9hnMRlPMpu086ec11q7V9H633Xab2+0kpqGhQVMrZHYKhd3YPrOflLxMcl5guJ3Onj172DHWZlq7avsBtm3TBVnMHk+IfQlrV9O6mu0ePqyLvezerWuC7NtX9H3yetnrZz7zmRFljLe85S3jaruuKDqO4ziO4ziljKuiOGOG9s63bdPVd3p7ixV9FiyYD8DOnaru7Y89bgnJCDi+t9e6qCCGAe3ZNzQXo9vnPVezFBw6okrNvqhennvGmQB07z9UKdsVF8bqOaJqzNCgdtZbZ86olNkd63zPHXcDMGf2nFhvXdTgzjvurJSta4j1iIrifffpzPOOQ8XyjLfeqsrk9m3p4iHOZGXhwoUALFu2DID29vbKPlNZlizRlcxOPPFEoBg9AmzfrvZjikc+Ek7VERupHjumhmmqTT7iTI9LVcH0/FCMdNPnDaC1VdehT9VHUyStbNlo/mhU9m207Exe7P6ZwpaqKHl8el4WRip/ptBVUw+hsHFTZQyzLSjsK1f+cvUnLWPPg5VNr23XtM9kZVJF0c5TSzl1Jg9ltmWUtUvpdijuc9q+QbkibcqhtaNma2XntfY39bKk1yu7Vq5ipuezz2Cv1v6n7avZ90ThT4zjOI7jOI5Tyrgqip2dpuJpz3n+/MKn3x97z01NWqUjXerT7+s5Qs7BAxobtfYkjTXpjWUWLyyWd2xp05FBY6+qH4tmaYzB9679bwC6tu+plJ25XNXM7ijKrFu2HID2PYVqdEdUDM86Q5f2rI9d7JWrdWWdVFvZs1fX/77lJ7cAsC/Gcu0/WKy73dKqI5iBgZExNM7kw0acK1fqSnvpiLCtTZcZNeWuo0Pvcxq/ZSPKXPmwEWZZ/I1hx+Sj3PQadm2rV3cSX2ujZBvx7onxuhY3k2LXsPOYClQ2ui+LC3ImJ2X3z+51boOpndn7PD7WbCl9Duw8q1drbvoNGzYAxfPw4IMPVsqaapIrlSm5apSrmKmCU61sWZn8+XImJ9Zu5fcUCnuxe1mm5pnN5mXKylpbNmeOegqt/SyzS2tzrf3Mn6MUO96uWeZBymMp7fOm7bN5LicKVxQdx3Ecx3GcUryj6DiO4ziO45QyzpNZ1EV34knqmujs7Ej2quw6cCwG8vervLu7vZgQcNK6dQCcuv5UAOqiVHviGp08sHz5skrZfQd08kp/rwacLp47D4Bv3nGPXnvjlkrZ1nXqaj7nNRfHY9V1vGnL1kqZhdGtvSy6pR/e+AAAa07Wazc3FcGmA8ei3Byl6MVLFgPQ1V1MJjDp2VKrOJMbS1VjE1ROP/30yr6TTjoJKFwPmzZtAoZPHjGXQ+7+MvdIWtZcGeZ6sWPNXZe6OGyfTUwpc3/k5zHXhrmVU1dMtQDy1B1ZNqnGmRqUuV3zSUllrrnc1Vy2UIO55E4++WQAnvOc5wDFBMV0Api5o3PXXuperHUtGG6rVjafDFN2bD4JwZmcWJiP2Uh6L/NJS2VhOWbX1SadpPZjv8eWBsfCfWyiYmpXZud2TJmNWduYu57tvNZep/XM3enp5K+JxhVFx3Ecx3Ecp5RxlQSWLFFVrq9HJ5jUDRWj2127NEl1z2FVbnpiWpuGJD3Onl2aYqS/V3v5vb3a4z5tg6o7d978k0rZtaevB2BFDKz+xj99Vq/9+GatS1PRRz64S0e6A+0HATjcrF/L/r1FOps1K9cAsG2LqkUzZurIY/ceHRk31Bdf5cJFmialqUkTdx84FM8/WIxgOjpVQZozZyHO5McUkAceUCU5HUXaiPXQIZ2stXmz2liZSmjH2T7bno6ILc2OjTrt2mVB0/lI2kauabqcXAG0fWWKolGWXiSnLJWJM7nIU2/U2jeWskaZzZg9mAqzceNGoLCzRYuKyYb2rFjS9jJ1OldYai03a/WplR7HnhtT5p3JjbWDZZNZcnLlDkaqetZWmj2lKWdsMqCldMq9N6nqbqqjtaN2THo+O86uaXWxY9PJLPb7kf8WTCY7dUXRcRzHcRzHKWVcFcWH778PKNKJ7EuW8Gtp1t75yWs13uvWW24FoDsZRbas1piFw12qsPT2qTLZHpXGxUsXV8qevF4Vxcfu15QMG3+kqWpaYzqagcZkibNj+jVs/bGmwGk9+zQAFswvRsC7YqzknFk6EtgfU+AMih7bcaiIt7z3HlWdenp0pDAwqNecvyBRD+Po6FBHGqfpTFZsiT1LLZPGvdgSe6YS2gg2HRFaugWLdbRRY1msqo2kbeRrz0seYwgjR642qi1bqs2wEbDVJR2F56PvspQSo8WOOZOHWkvZ5XGHeULrMvLE2KkNWAopezbuvVeXLDU1JlUUly/XWG9TG9MlLA07Lle+7ZkpS1psz0NZXJsxmZQapzrHk2Ta7nOqTM+apR49sxtrn80+U6+LtYnmvbF9ttCCtZXp8dYumz2WxXfbNquXPSPz5s2rlMkTypct1TrRuKLoOI7jOI7jlDKuimJLm8ZcNbZoD7w/8ftbr/pQh8auhNiZXpgoLe27NZ7lcJf27ufN07iCtjbtpR+pL2YJNaA9+Zuv+bqef7+qjzTpKKCjoRhprmzV8xzdo6Panp16neXPPC2pvI5uBvp0pLqrXWfyXfDC5+v55cRK0Rtv/CEAGx9RZbExLumXzsq+6y5VL7u7RyYUdyYfpmqUxfXlSYOtjKmPUIxUbfk7Uw1tScB09Jgv82f/Hzx4cES9LI7RVJI09sXIR9s2es5jePL36edM61c2w9CZnOSqRKq45DOgyxTrvEweC5iez+K8zF73xWVYTY1JFSJLbGwqo8U1DpT8JpgKU21mc1ofX1Zy+lEWo1hNbUvbZXtvs5QHKot66P9mV1C0aZbk2trrNWvWAIWyCHDgwIFh9SpLAJ/XM1cbUzUzXZghredkSgzviqLjOI7jOI5TincUHcdxHMdxnFLG1fU8c7YGl7ZEd9kpp55a2WcSbUdcD3ooej9C4uWaNXtOLKwvBw5q2fWnaXqcnh07KmV/9JXrANh80x0A1Heray7ENRPPeMGFlbLbf3wbAHUx7c7RTepWXnr62kqZFSviGr9HVRaesVvT+fQPagW7DhcuZEvUfSxOYll78hoA+nqLCRD2XQwGd+NNBcwdYC6y1OVm7l9zX5irLV2r01y6+brQFnBd5mYwd7IFYdtrWtbqYa7ssoSuFnydn8eOLUvsmrt20mtWS8rtTD7s3petc2yuszwBcdmauvmkJns1FzIUNp1PULHnwlx2ULjeFi/WCYj2zKThFaNNlkr35+EQZS4/X+t5apG7ctP7nbdPZa7oPHTHbM5sOnX/Wruep6ixNtxChKCwn2pJ49O6527psaQds2Mmk526oug4juM4juOUMq6K4qw4+rRUIQfjxBUolJYVK1cBsPGhRwEY6Ct61fMWavLsvgFVWmbGXnlnHA10tRcj1h/95/8CIB06eaA7ji5WnnOW1uX0MytlZxxVhWXv93QSyuyoGrbfcX+lzMOrVgCw/4COfI/262d48EFNjbJ3b5HqZzDoiODk9RsA2LZNlc6GxuLr7ovL/B0L3lefCuRL7VnCYCgmqNjoM1dSAFasUPsxO7cJJWWB0HYtu4aNdo10ZG37TJExhSZNN2LnsXpa8HS+TFWKqY210qt4epzJj923WqlGaqXHMVUjn7Bl9jF//vwR57HnwJILm+qY2qSpMTZJwNJDpeq22etYyCfZ2OtYlix0Jid5Evaye5m3T2XKorWx1u7l/0Nhd3kqMrNZ8/xAYc/2W1BmT/kkwDyNWRm5gjqZ7NR7KY7jOI7jOE4p46oo2ojA1JS0R2+j0F27ND7QUufs2FksJN/QpNXds0eTX69cpUlb22Jv/6Hb7q6UPbpVyzT0RfXxlDUALH3m2QAckCJ2Zda5ZwDQuVXVwbbH9Nj9W3ZWymx+QJejWrBaE4LvjvE2hw6pGrNlUxEf2dam9dm3T0fNIehoftasIslmxyH9vP19nnB7KpAv5ZQuz5dvs9FnmlLBtuUJWI10tGxqy/bt24ed19I5pHFXphbu3q02a8pROhq19/nSULlKBCPVJaMshU5ZOghncpHHk5Ylqc6XyEuV4mrKnNmiqYYw0q7OO++8/5+99w6z66rO/981faRRb1YvVrElN8mW3DCusTFgCBB6CA4hlHT4JkAwSYBAwi8hBUJoiYGA44ADBGNMd8HdkmxZlmX13vtImhmNpu3fH2u/d6/ZOjMeAZ65Y6/P8+i5d+7Zp1zNOnvOelfZAICFCxcCAFauXFkau2yZ5oUzusQGxI1mAQKqPLmaXaQe5ep23njbUk65X07P5CqxteW8ZU5Rfi3hNs6N/LmoDdTs2bMBADNnaru7Z57RqOLBgwdLY/P5nW3Qimytp+uy587VR/7siqLjOI7jOI5T9vSrolgZH0uPH1Ov0VZmjhwRcwAkKi81+nRdV5WexE8c0oq4w0f0dcYcVfe2Pq7L9O164PHS2Io29UaPNah6uejylwIAVsSF6h9dnfIPFy9eAgBYcLk2z960+zt6fa3JQ9jzqDbIHj9ZPY0hw2KeQrt6ImPPSFVRZwzXyupDQ/W7bNynymSL8RC64tdqOpEqY53yJW9kbZfwo6fK1zGxst5WhHJbrmZQNbFKypYtWwAAe/bs6bYPVRyrxDO/kLk0VCFZgQokRZJj6fmy2bE9Xr6IPbHX3VvjY6c8Kcon7SnHtEhp4We0FeYm2spRqi5UGRctWgQAuPrqqwGk+wJICvj27dsBpPuL1fvAqTm/ecV1URUs7ZfXXdSAuZyUGqdn8mUie5tvihYGyLfxeYM/79+/vzSG893cuXMBAFdcoV1RqB7++Mc/Lo1lZT6XoWReY1FOba5m0vaK7r3crstJ+XZF0XEcx3EcxymkXxXFqip9uuaydSdOtJhteinV1eqx0i9ob0/KzdGmqIRM1gq5lkb1NFd99yc6YH9SZVo6NV9m4qLL9HgTdJ97v/4VHXog5R+iSZ/yL/y9dwIAGs7T/o7HH3ysNKT+gD7db1yquTXD56vnsWuPesZdSB7Cod2as9AUPeK6Cep9V5u8yNYTmpsj0nM/Jad8yKvWrOdK5ZAeK9WRXJUDkrdI9YXLnFlFkXmHVButOgh078/Ic1HhtIvXk7x/IilSD/PcxN56JvbWC8wpD/g7KsrzyitG876K9j230cZpk8xLBJJacvbZuvQplz9jTvqZZ6a+tFQbmd9Fu7VV1KyEpm3TNovyD/Nq7CJFkWPKSalxeqanSna7Lf+dFi2tmiuK3MfOlVS7zz1Xu6HQdqmgU/kGUq5tnl9ro0ykpyX8rA3mObe83qLjDRSuKDqO4ziO4ziF+IOi4ziO4ziOU0i/hp7ZlJpLOU2dOi1dSAwZbNq0CQDQ1qKya7uJErQEDXMsPlOLWHYu14KUvau0mKX6ZJKma2JxyYwLLwQAHIhFMR0x/N1QnYoTGio1IfuY6HPz+NjWoe3Zren6dmvi66EN2gh8xHRtoDxirErWB5tS6HD3Hm2VU9Gp19MQzz1uXAqrMKS+cOEiOOVPby1EGDJgYj8Tl21oLC9aYSJ1vkwfcOoygUyoZnGLXQpt2jS9h1jownCKbW7M6+P15O0hmIxtrzNf5q8ohNdb81inPMhDX0UtjfKwrYV2z/0ZGmZIzqZBMLTHsN2aNWsAAPfffz8AYP78+aWxCxboYgTbtulypytWrADQveE2z8Vz0O55TTb9I7fxIopC6075U9T0Pw/TFtlE3saL8xwLqezxWETFsT//+c8BpLSiC+NzBJDmYc7hs2bp84gt1uIc3lMBji2o4t8A3n9MJ7JpHQON3zGO4ziO4zhOIf2qKG7Zot4jn7JbWpKKUnoaj6revl2a5FxbN7Q0pjKuQnVwiyp2a+59CABQ0axFMSeqUsLonIsu0TcjtSlmW5c+nddGr6K1M6khQ4aqJ9wWn5srRuq1TL/k8tKYjd+7S6/huCawHlqlbXYWLV4MAGhHevqvnKnLEA4R/e/du1+/y9YtG0pjpsYlAY8c8obbgwF6gPTyrEeYtzrgzzZZmkn7LFTJvUfb0JWeLhUaKpRU8Oy5OZaKYq4I2m35ElZFSlK+ID2/i1UPvYhl8JAXLPXWPiRfrs+OZ0EKE/dpF9bOWMRC5ZDJ/lRgrJJHFWbJEm1NxmIBth4BUjELGxuz/UjREpl9WU6yL6qjU34URXFyipaqzFV0zt1UAu1YNtp++umnAQB33qlLANPub7755tLY887TZYDvvfdeAMlmaa8W3gP5tdh7jHadLx/oiqLjOI7jOI5T9vSroniyVZUQtlbYvy+pKLU1qp50tqvXcDyqjfUVqRlwfWyds2vZagDAsXXb4pbYNmHGrNLY4fN1Wb7Y+Qb11Xr8+lp9at8XjNfcoJ5mbYV6JW0n9BpGRc8BACo3rgcAVK3RvMiwWfMc1t+vTb6nXJ7Gro85Nc0t6k0MH6aKzsmOlDf29Ept4N14pBlO+UPvrqj9DNsYcAxzYGxDV6ofuUrInEXbrJUKIBti5yqfVWbofdIb5b72eLw+NkKm9120qH2uXlKFKWop0hcVxxlYcgXRqmn58mJFzYBp07TF3L5sw+3zz9flUam4UE1hjiGVRSAtkcZ2JJs3bwYA/OxnPyuNYZ4XcxWplvPcVlHM8w5pr7bFSN6w2ylv8vY41paLmq4DxWox50jOtVSkaYNAat10zz33AAB27tzZ7fic0wHgJS/RhTlos3y1S7byfsmXTy1qN8Yx+ZKqvH/KAb9jHMdxHMdxnEL6VVGcN3ceAGDvPn06L8oxGT9en8RDRcyjkvQs235EPYFtj6kaV9ei3m5bg+Z5TXnJktLYE3H5PLTpcYfW1MZXVXSOdyUVRRr0XENj8+umqGoeHpnUzLFXXAwA2LNZl1cb3arezaE1GwEANVOTN1EV24U3NqknPSNWpp4xMTVOXhcrAqdOngyn/GHFZZEqQeWDSkeR6kivlnZOr5b5LVaxo2pDtSbPMytqPEtPlRXS1hvNlRjmRdKTtRXSPGeuuthz8r3nepU/eT5qUUPiXJWxyg3tIVe3qc7MmTOnNJbqIFXzhx9+GABw2223AQB27UqLHDz11FMAgNe+9rUAgIULFwJIVdBAqprm8ZjHznPbfEbact682Obf5kvCOeVNb7+vXGUsysHNl/Wj3TBqwuX6AGDqVK0ruCh2PFm9WqOWnDsnTkxL9DK/9sorrwSQ8sut6sgxjOJQmS9SFHnt0vEx6wAAIABJREFU+bZy6irhiqLjOI7jOI5TSL8qik+t0oqikcO1N5EYR6HxsD7t19bHnoZt+jRd15Aqk46vWg4AqN6pFXJtQZ9zh553KQBgyMzZpbFtsaq5gjlWQT3O+tq4RGBn8jyGVOr1dAXd1lGpXkRlW8rzmhhVwaMXaT+lvY88AgAYF5WmI4+vSl/mbB37slfeCACYNVa98X1Hj5SG/Pz+BwEANdUpx8cpX/Kehjavj0odFZp8qSgg5UpxDFVHeqw218v2NQRO7f/Wm4fN49ieXqy45jXz+PS0bR4X98vzLu056em6olj+9LQ8mP0s75Vo1W3mk1PlZl4rK+hZ6Qyk/olUVp58UiM/VP6sTdKutmzRCM0552hOue1Xt2PHjm77MweMx7HKPW3SVrICxfdKT73tnPKiKDfxucZY++ZcyHmPubJU+djLE0h5sLQ/RonyXolA+htwySXaWWXjRo0qLlu2rDSGS7OyDyOvhX8rrJ3mUaHelk0dKFxRdBzHcRzHcQrxB0XHcRzHcRynkH4NPVdW6umqY5ubHbEEHQCGjVSJNlSohDx8mDa6bDcJ0Ief0gTTmijNtkS5eOpClYDbOs3XYfiERTFBQzA1NSr5ho4UNmuoVSm5Mz43h8oY6jOx8dYOPd7kxbrk3pGN2ianvVELAY6tTc20583TsvuxsXF3e5SZ12zYVBqze49K2tOnTYVT/jCZnyEEhiaAFEZjOK6olQzDFWy4zTAIQye2tQLDfITHY7iit1AMt/F8QApv89wMRzIsYkN4eRNuhkpsmNnbiwwe8iX87O8xT2mgndkCENolbY+FT0z+t+E7Fkk988wz3a5h+vTpANL9AaQl09jQmKE/NuAGgK1btwIAHnpIF1ZggRZDz7bZN8PdTJnobalNt9/BAW31dIqPrO3Stjj/8XhMkeCcDiSboE1xLOc/pkgAwJQpUwCkBttXXHEFgO7FLEyb4N8LzrX270YOz1n092Og8TvGcRzHcRzHKaRfFcX2k6q8NJ/Qp/T6YWl5vn2HtShkWIcmnk4eodtW3/3T0piaw7pfY516pmMu18aXMly93nDSPInHXFF6I9XV+lWHxyf7qqqUTDosXkfoYpPhuAi5pKTSxqga1Q7VQoAZMel1y0+1QWzNyeTdbnt0KQCgcqwud7X6uCZjd5xI3sS4MXrN9fXp/8ApX6iG0PO0nmvemoPKCgsBgKR0cKkyepZcEo0J1kDPS1YVtfzoaZttrcDiFSoyVBCLlBV63/n3tV5uT21VnPKD9kDbLCpAov1SdbS2yPdUWmgPLGKxxSx5MRcLA3g/UEUHUmsSjpkWiwWtTV18sbYko5pDxaaoYIutT/LrtOp7X5aCc8qH3n5feesc/myLAmkDVMG57ayzzgLQXVHM503O15ynbWsntkrj/E5VnQo4kIpZWEg4adIkAClaVNRCLf9O5VTU4oqi4ziO4ziOU0i/Kop84p4c8/ImTEpNLNtb9Qm+/aiqHvtWaZ5LW4z1A6mlTd10XW5n1AJt8Noc2+TUVJlmslAvmeqgRO+iJubaVFWlp/W6evV4mYdTcVK9ig6Tx9gVn6mbo7AyZq62c6hdr3mHLc+mvBzZpqrR/pWaU1k/R3MW1+9NOYrt0XM5YfJsnPIlV9aKcp+opIwoqdbp9mJTViof9G7plVJ1seRNkouUFCp/Rc24SZ6rw2uh12w9V14flU5us+2AXJEZPOTLglmFLVcwOP/ZfNl8qUmqMFymj/laQFKuqZ6sW7cOQFoOzeZn0ZaZm0jl0toWmx/z78bdd9/d7VqsYs/rok3nKr997zmKg4Pe8msJf5ecBzkH2/F8HT9+PIBks1Y55/zJuZv5h1T+7D3Bc+T54La1E22WLaJ4HCqK9jqZ457bZTm1H/M7xnEcx3EcxymkXxXF3bt3AwAWLVZPcWKsfAOATbGiufmg5rHsfVIVuirjYR4drU/7Z116GQAgVLKxsW7vkJRHRb+ZDnTrCVXu5sX8hDe84Q2lsZOmqYfR2q7eaEU8ZTAr6IQuPVBXp25srVVvdsqlmie5fPOzpbHVsbH2wRUrAQBz5+o5G0cmJWjTIc1bnDZ1Opzyh94elQrbpJqqS64O2iXGmOPCHBhWfVJ9sbk1PAdf80bbNl8wb37d27JP+fJ+/A62yppeMo9XlCfjDYsHD7ShXJ2x8DMqgrZinmoHmTdPl2FlbiIVGAvzBHeYaBCQ1EMg5Xlt3rwZQLp3rNIyOS5vylxFNjZeuVLnVWvrVMCp3PDeK7JVV8QHB7TLvuQqUlG09s15jnMZ7YlqYdHyjnlUiNEYO0dyfs9zxdkJAEjV+3zmYePuovx1Rnjya/EcRcdxHMdxHKfs8QdFx3Ecx3Ecp5B+DT2PmaDJpC2xlUz7iRS+G1alkuxPf3a/frBfQ9DtIYUORi3UApIhUzVUfOJkTE6u1FBcZ0ghuerY2qa0d2y4vWH9en3dtK40tqpG5eZ5c7U4prNVQxqVXSbxu5Ov+llT0OM3TNbQ8eRz55fGHr3/Pj1lDDceXqlrXNdPSqGXvfu1fP7c8slXdfoAQ3k2JJeHCBjSZdgBSM2uGcJgGIQhDhsiy0MaDKGwGMCGKhguZOitqJ0NYXiGIUCG/2zjYl4fj1N0PIb8bANlpzzJ2znZhPm8uIM2ZO2PNsICqnPP1TmS4buiwhC2sWG4ja1vaPMAsGGDLlDA0DO3sTk3kMJ0s2fPBpBC0GwxxaIWe328rry9ifPChGk/1aUi1WS7nKc4RzI0zPnPztu8F5i6QPtkmoOdy2ljvF9opzwPkNYuZ2un++67r9vxbXoH51w29+bfgnJK8XFF0XEcx3EcxymkXxXFs6MieCy26Th8KHmEm1atAgA0bl4LAKjujC1qhqcCkOkLtfy8tTIuwxdVwqquqExWpCbaiIpiVbU+Czc167nu/tH3AQCHDu0rDd29Wz3gmVO0jU1VXGqwS8xzdFdc7qoyfiaqLLV06OczL7yyNPTJ1epFdB5Wj3r3kw/qmBm/WRozbYZ6N9v27YFT/lBJ7K1YhFDpsMof96cnmS/TZ73bnpK3WfBiPVd60ra4Biheqq2nxG8qlnY/KkVFjZp5flcUyx/aDH9/1k6ovtAWmWBvWyFRSZ41axaA1FyYaoq1Uap3VAnZjon7nn/++aWxvJ4VK1YASAqObUPCe4XXdcEFFwBIbXd+8YtflMayqTIVeqpG1rZp/325h53yo0hh4zxFVc/+bhn1YTucM8/UtnosULHQjnl/sBiKtmwLE9mwm03jeW57fbw/qIKzsIvLW9p2aBzL6y1qZTXQuKLoOI7jOI7jFNKvimIVm1+3qZd6eNvO0rZ1jy4DANS26BN9W62qFWdcdllpTFdsqnokOry1cVm+aoles1lyT6SGJwUAVA5TT3PaWdrW4fjq5HlMPUu95I6olLRHJ7mzI3nLXfGROsScSeZOdrKsfmLKv5l11bUAgNXf/TYAYPgxveAdj64ojZl/QbyOulMbLTvlB9WzXLmzUH2h92hzqKjeUcVhXiDVG5sDyLFcBipvZ2MVRao4VFTohRblKObKIj3YombaXG6Nnq/NReP5y6khrFMM7SBXK4BkB1TsqD7S7oCk6rHBNvO88qUB7X7M56JSyRZQNi+LqgzzsmhfVCGB1KqENjhzpkZ8qNLYJdM2bdLFDKgWUVFkbjCQ7sdyUmqcnunt90R7yXMUrf1wDO2PyiJt1x6fcy7vBargfLVtm3JFkvdY0ZzLfMZFixYBSPm19h7jGJ6bc285tXFyRdFxHMdxHMcppF8Vxe3rNN5f0RKVjB0pT/DkLs3nq29RD3XMtOgFzEhLRB04rE/hw6AeZl2U+SpF9wkmp1CiukghpDIqi799pap9xxem5XZGjlWlsuOYqjJsqt1R4CGAamNbzHepja/VaeyM2Vrl1zhFve/mbdv0nM9uLo0ZNVG/X/vklIPplC/0KG3OE6FawwpR5iYWKXVUOJhLQ0+WHjGQPGFWjVJ9LPIwOTZvlF1U3Zo38u6tGTG9497ygnwptPKHdsbfY5EyzLw+KtZWNZ8/X7s5sME2VY+iPFoqNaz45PGp7tnqeo6lYkOb5D0EJPunLfK6eBy7BBu/J3PJWAXNpf2AdO9a1ckpX/L5ztpubs8cy8gKkOZs2izth3ZlO1fkNsZ7glX+di4nzKvN5157nDxixHxELmsJpLmftsqfixTKgcJnesdxHMdxHKeQflUUTzbrU/bQk/qUvT1WAAFAVbt6hNW16gW0xyXunvnmN0tjjkU5r6JdVZja+MBNv6NLkgcipYd7/awq5jNWVsVFxKvTV9/bqtcVsvwuVlUDpvozbqtp1+O0RiHoeF0aOxT6vv6o5t9Uih6/tiM9l+9Y/hQAYPK4a+CUP/z9M1fRenu5+la0QD29xLznIj+3VdD0QukJ5/ll1rPui6pHBYb5jHmfRqsa0jPndfKc1rvPe/M55UtvfTWpyNFeqeZZZYT2yXxA9oGjOsccQyApdVR1aHdUV4rG0ia5zSr2+f55Xq9VBvPPeO/ZvEh+Zq/DKX966wnLOYg2Yu2HtvDII48AANbHHsp5FMaeI1cxaXP2GphnyDmSaratuOZ7zt08DhVve528dl5X3lexHHBF0XEcx3EcxynEHxQdx3Ecx3GcQvo1dlQXl8rbtVYl4Mb9e0vbhoteShMjc60qG3c2p/BCgMq/FSdjCDoup3cyNsHuQgqZVHZ1j0t3xRAKR7R2GDk7ftgRwxchLgXI8wFAR2cM/8VG4KFNz9lWxWKWdLjmuFtHbNzdWde9sAYAWuN337fqWTjlT1Ebmxy2F2FSsk3eZwiM4QWGJPKWNfY9t+WtSGwSdl5YwlfbDJufMdyRF7XYRO2ixGx7LUAKq9jQulOe8HfF36dtrcTQM+2JbTnssnc//elPAQD33ntvr8e39FQQVVSM1VvBQr6Nx8lbTdnvxXuQtllUvGMLHpzyp7eUG/6eWaBiQ7ocw6bZu+KSukUFevl8l9tsUYpPnpZj7TU/Xj532+PxHmJaRzkuZOCKouM4juM4jlNIvyqKEydNBAB07Ndl67pmmQXgqeqFmHzNn5E81qouVT5qY/uaCm7icmOS1J6aWIgi8Vm4rl69ydoa9TibmlKi6PB6baDZfEyf6Jua1bPu7EzKTXVUGbu6ooceFUn+Bw7rTF5KXbu+r6rWNj4tNbFIpiuds6FF9x9W130pN6c8oepGj9UqKXlCNSlS3OipWtUG6L6kU97ANfdm7c88d09FKHZ87uXmCdf2OPRq8zY+Fm+4PXjIFRgLbdHaQQ7tgsUttFer6uU2khcI2LFsWszj8Lh2jG0ZZY9T1Oon/4zfic3vgaTYOIODXM0rUptpI2yHZJthcw7LbZbY+StfqpTzPaNCVqnM7xPaPdVs+z5fYKFokQOek9dejktMuqLoOI7jOI7jFNKviuKxE1oafqJBn/Dl7DNL24aO0qasbHY9ZZqqjTvjclAAcOKEPo3XRqVuxAjNsTlzji7t1HQiqTT1USXcsV0bW1ZV6T6bNm4BALS2pPyxsZN1CZ3xIzSPbM3atQCA6ZNTs++jjaoy0rOoqlaPYMhwPc/MGfNKYyeM1hy1p5ctBwBs3qiL2A8/Y3xpzKSJ2pS7peXURp5O+cFG2XmLDgs9QXqGRe0N8vyYIlWPHio94jxHxyo3eR4Yt9nz5KoglU+OKWqPkx/P1cMXBjbHlvY0fbrOtXPnzgXQXdHg753KCO2BrzYvi++p3PCV94HNDWTrkryljlXlea9x/6JWTfl18tr5PYvu03JaGs3pmZ7yBoH0+2W7GbaUsYpink9Oe6eNWNvIVTyeizZolWnaKo/Pe2Ps2LGlMVyWj9v27dPFRdhax873vObx4/X5gPns5ZQD7oqi4ziO4ziOU0i/Kor7D2ilr9ToaY+dTKremCH69Nx2Qj3KNVtV+duyPSmKFaIeKpfnC9AqptXbtgIAOjqsh6DeSHOsmh45QhXLk7HZNxVCADgWK5lbT+rYXbv0nBv2Hi6N6eSyfmwMXqXHr65RdXTt9rT4/NCYn9B5Qs+xd79eZzh6sDTm0En1KFqafDmpwQA9S3qeRUoKXxsbGwGkKlIgeZv0MPla5N3m+ZAcy8+t95t7tXy1+TKE2/rSpLvoXKQo58wpT/L8LqvYsRp0d4za0GasEs73PSmKtlE888QmT54MICkiuToNnKo2UnGxS/jltpfn4VqK1HGguyrF/YtURqf8YJV60RzE32tvyzLm9kLljjZi7wXmtOYRFc7lu01kk4oix9CWrV1yvud9x3srr84H0r3FbVT2XVF0HMdxHMdxyh5/UHQcx3Ecx3EK6dfY0YSY4Ll3d2y0bcJ3E2KTYoHKuXf/7H4AwPQ556Qx4zWk0d7WvSF2e4eGL6QrHa8yttmpYNPr2G9n714NccycngppqoeqDNwaQxJnzjlXf241ZfCBIQ09buhguxw9bmdIibObNqwGANRXqbRdN0SP/8jyx0tjGo9rq4bxYyfCKX96W984D4UVtXMgPYXP8tY6wKnNkovOlydscx/b7Ds/d95Uuyg8x+PyeEWtKfoSwnYGlvx3ZMOuebFIXuxktzFUxibdU6Zood/UqVNLY2lXe/fq/P70008DSOFB2+6GoblJkyYBSMn/3BcAtmzR9COG/2iTRYU0ObkdA8nOixouO+XHDTfcMNCX4ER8pnccx3Ecx3EK6VdF8chhLfgYNXoUAKCtPSWpcgmeKVPUQx0yhOXuw0tjJC7zVxU904qK2By4NnqqRhnZsU0LSIYN11LzEbH1zTPPqtp36aUXl8ZOGKvnbG2P3iebfXckj5WKJJ1Y6er+jF1dl/4rg+j3embFgwCAowf1WupMcuq4ceO7Hc8pb0aMGAEgJU1TWQFOLfzgzzYZOS826UtBSD6WyootNqBiwkTovKWO/axoqal8bP4Zv4ttVptvc8qXvNm6VdN62lbU0J2K4uzZswEkBZBFKEBqIUKlMi8wsUolCwLYLoStQWbOnFkaw0KZdeu0vdjBgwe7XW+Rsp43+bbf11s9Oc4vhyuKjuM4juM4TiHizUcdx3Ecx3GcIlxRdBzHcRzHcQrxB0XHcRzHcRynEH9QdBzHcRzHcQrxB0XHcRzHcRynEH9QdBzHcRzHcQrxB0XHcRzHcRynEH9QdBzHcRzHcQrxB0XHcRzHcRynEH9QdBzHcRzHcQrxB0XHcRzHcRynEH9QdBzHcRzHcQrxB0XHcRzHcRynkEH/oCgiq0XkqoG+DscZjIjI9SLyvYG+DouI1IrIWhEZP9DX8kJARG4WkYcG+joGAhGZLyLLn4fjuo0+z4hIEJHZA30dA4GIPCwiC5+H4/6ziLzndPcb9A+KIYQFIYT7n89ziMjXROQTz+c5nF8eEdkqItcN9HWUAyLyZRFZJyJdInJzH3b5OwCfMvv/rYisEpEOEflowfHfIiLbRKRZRL4nIqPNttEi8n9x2zYReYvZdn506g6KyPvM59Ui8riITOVnIYSTAL4C4IOn+/1fCMSHkFvj/+FxEVkhIjcO9HWVA73ZWA/8LYBP92V/t9FfHyJym4jsEZFjIrJeRN450Nc0UERHrVNEmsy/q3oZfxOA4yGEFeaz94nIXhE5KiJfEZHa+HmViHxTRBpF5EciMszsc4u148g/ArhFRGpO5zsM+gdFx3G6sRLAHwB48rkGishiACNCCI+ZjzcC+ACAuwvGLwDwJQBvAzABQAuAz5sh/w6gLW57K4AvxH0A4O8B/DmA8wF8RETOiJ+/H8B3Qgg7stPdDuDtnBBfZFQB2AHgSgAjAPwVgDtEZMYAXlO50JuNdUNEJgK4GoBVzN1G+4e/BzAjhDAcwKsAfEJELiwaKCJV/XplA8OjIYQG8+/+Xsa+B8A3+IOI3ADgQwCuBTADwCwAH4ubXwsgABgL4BiAd8d9ZgK4CcC/2QOHEPYAWAv9nfSdEMKg/gdgK4DrAHwUwB0Avg7gOIDVAC7Kxv0lgGcBHAHwVQB1cdvNAB7KjhsAzAbwLgDt0MmlCcBdA/2d/V+339M3AHQBOBF/Px+In78q2kAjgPsBnN0XWyg4fiWAfwJwEMAWAH8UbaPK2p8Z/1EAt5mfLwHwSLyOlQCuMttuBrA52usWAG+Nn88G8AsAR+N5v/VL/L88BODm5xjz1wD+s4dttwH4aPbZ3wG43fx8ZrwvhgEYGt/PzX43n4rv1wCoje8fA7AEwDQASwFU93ANGwBcOdA2Vg7/ADwN4HXx/VUAdgL4fwD2A9gD4HfN2DEAvg/9w7EUqqo91MuxfwfANgCHoA+lJZsG8DUAnzBjrwKw0/w8CcB3AByINvwnZtsSAMvjdewD8M/x87poX4fifbEMwIQ+/B/0amM9fK+f93V/t9HnzXbnRRt9Q2a/HwSwF8A34ud/EcftBvAOxL/BPRxzJoAHoHPnz6EOwG1FNho/szZdAX3w2hRt8A4Ao5/LNtHDfN2H739zb/dfNrYG+rdsivnsdgB/Z36+FsDe+P6DAN4d378HwOfj+7sAvKSHc9wC4Kun8zt8oSmKrwLwTQAjoRPl57LtbwVwA/QP3FwAH3muA4YQvgzgvwH8Q1BP4KZf6xU7vxIhhLcB2A7gpvj7+QcRmQvgfwD8GYBxAH4I4K5Mbu+rLfw+gBsBXABgEYDf7Ou1ichkqDL3CQCjoWrFd0RknIgMBfBZADeGEIYBuAzAU3HXvwXwUwCjAEyB8QpF5Aci8qG+XsNzcC6AdacxfgH0YRcAEELYhPiHN/7rDCGsN+NXxn0A4BkA14vIFKhXvAn6/T8QQmjv4XxroOrOixoRmQD9/11tPj4DqjZOBvB7AP5dREbFbf8OoBXAROgf3Hf0cuz5UFX4rXE8j9mX66qA/kFaGfe5FsCfRQUEAD4D4DNBVaUzoX+QAeDt8TxToQ+174H+cYSIfEhEftDDKZ/LxnJy+3Yb7UdE5PMi0gJVsPZA52FyBnROnA7gXSLyMuj8+BsA5kDFn964HfoAPwbqnL/tNC7tT6Dz+JVQR+cI9J4BerDN3uZrEZkWQ7/TejnnwpjSsF5E/qoXFXUOgK4Qwk7zWbd5N76fICJjoDZ7TfzbdjWA1SLyGgAHQwg95SWfts2+0B4UHwoh/DCE0An1FPP/jM+FEHaEEA4D+CSAN/f7FTr9wRsB3B1C+Fmc4D8NoB56c5O+2sIboH/sdoYQjsDk8/WB3wbww2iTXSGEn0EVlpfH7V0AzhGR+hDCnhACHwTaoRPopBBCq73hQwivDCGczjX0xkiod9xXGqAqp+UoVFHsbRugfwTeC3Xg3gfg8njuzSJyp4j8QkRen+1/PF7jixYRqYY6qv8VQlhrNrUD+HgIoT2E8EOomj5PRCoBvA7AX4cQmkMIzwD4r15O8VvQKMlDIYQ2qMoc+nh5iwGMCyF8PITQFkLYDOA/ALzJXONsERkbQmgKKcWhHfpHeHYIoTOE8EQI4RgAhBA+FUJ4ZQ/ney4by8nt2220Hwkh/AH0//YKAN8FcNJs7gLwNyGEkyGEE9B59qshhGdCCM3Qh79C4gPZYqiNt8X58funcWnvBnBLnNNPxnP9Vnx469E20cN8HULYHkIYGULY3sP5HgBwDoDx0HvzzVD1tIiiOTm3W74fBn343gL9u3IUKpT9DYAPisgnReSB+MBuRZLTttkX2oPiXvO+BUBd9uRuc0y2Qb0J54XHJOjvFwAQQuiC/u6tUtJXW5iUjc3zlHpjOoDXR2+zUUQaAbwEwMQ4Gb4R6rHuEZG7ReSsuN8HAAiApTG5vkdF6FfkCHr+I1tEE4Dh2WfDoRNPb9sQQtgWQnh5CGERgDsBfBz6h/nTAL4FjQb8s5jimHhtjadxfS8oomL3Dahq+0fZ5kMhhA7zcwv0D8o4pBxHsg09082+Qwgt0LBbX5gOYFJm3x+G5v8BqnTOBbBWRJaJCB8AvwHgJwC+KSK7ReQf4gPxc9GrjRWQ27fbaD8TH7YegkZG3ms2HQghtJqf83n2uWz2cLRVcrrz8v8Zm10DoBNqt4W2+Rzzda+EEDaHELZEsWAV1K5+q4fhRXNybrd8fzwoHwohnBdCeBc0pP5FABfFf1dCw9n2b8hp2+wL7UHxuZhq3k+D5kIAQDOAIdxgkphJXz1sZ2DIfz+7oZMBAEBEBPq732XG9GQLOXugk1zRfkBmO9CQCtkBzb8Zaf4NpSIYQvhJCOE3oCG/tVA1BiGEvSGE3w8hTIJ6v5+X56dNxNPQP+R9ZTWMSi8iswDUAlgf/1WJyBwz/nx0D5cS5kbug4YHl4cQjkLzluz3PBvdQy4vGqLN3gr94/W6XkKfOQcAdOBU++6JbvYtIvVQRYU8l31vyex7WAjh5QAQQtgQQngzVEn5/wB8W0SGRhX0YyGE+VCV/5XQfMLn4nRsDDjVvt1GB44qaPoByefsPTg9mx0tItYu7b753/NKqANFdkBDyNZu60IIu3qzzZ7m61+CABUCitiglyxW1Og278b3+0II3Rw6ETknXvOXoTb7RAghQPMszzNDT9tmX2wPin8oIlOiR/hhqJcIxDwVEblAROpwquy9D1pp5JQn+e/nDgCvEJFro1Lx/6Bhj0fMmJ5sIecOAH8qIpNFZCRObYfxFIA3ibbQuAjdPcXbANwkIjeISKWI1InIVfG8E0TkVTH35STUa+wEABF5fcyTAtTDDNz2XIhITbRhAVAdz9nTff5DqMdp96+O+1dA/6jWxYkW0BDoTSJyRbzujwP4bgjhePS4vwvg4yIyVEQuB/BqmOq9ePz50GTzL8SPtkBzbCZA83O2x3GToTlMtiL7xcQXoBP6TTE01ydi2s13AXxURIbE/++397LLt6G/08tieOpj6P5H7CkALxdtK3MGNO+XLAVwTEQ+KCL10cbPEa2mh4j8toiMi4o+FYxOEblaRM6NdnUMGu57TvswVydwAAAgAElEQVTuq40ZfgZgUbTnPu/vNvqrISLjReRNItIQbeIGaLj13l52uwPAzaJ9L4dAw6eFhBC2QUOtH43z3aXQCl+yHhpNfEWc/z8CdWjJFwF8UkSmx+sdJyKvju8LbbO3+boP/x83RttBVCH/CqpYF323dmhxjp2Xvw7g9+L/zaj4fb6WnUOgeZZ/Gu+3LQBeEu/pK6FFOORKAD/qy7XbCxvU/9C96tlWm87AqdWprHRthObtDDHjb4FWmO6A5paVKq6gk8NTcb/vDfR39n+n2MCroZN3I4A/j5+9Jv6uj0IriBdkNtOjLWTHrgLwL9Bw3BZo7lI7AInbZwF4HDpx3A1NeLZ2eHE8/2Go2nM31FueiFTZzMrs+XGff4Cqn03QhPp3meP9CMCHe/m/uD/arv13VS/jlwG42Pz8tYL9bzbb3xL/r5uhk91os200tBVJcxzzloLz3Zed7/z4ezgI4P3m879ArJJ9sf2DquEBWpDSZP6xKv4q9F7VOQ7AD9D3queb4++LVc+7AFwRt9VBnahjUIXufTi16vl/oGk/R6APTbyO26BV2U1QVeQ34+dvhhaZNEOdvM8izdMfBvCjXq71OW0sG/+/AN7oNtqv9jsOOrc1RrtZBeD3zfZT7Dd+/qFoR32pej4TwIPQtIF7oCrarZlN74n29+c4ter5/dEGj0Pn2L/rzTbR+3w9Ldr4tB6u9dPxWM3QB7aPo4cq+jj+Ffk9EK93X/z//Cpidb7Z/g4A/25+roLmKx6FhtKHxc8nQlXxmtP5nfKP3QseEdkK4J0hhJ8P9LU4A8uvYguijY+/GEKY/pyDBwEicj2APwgh9Lma+/lGtC/dSgAvDSHsH+jreTEhIg3QP4RzQghbBvp6flWiOvhfAJaEX+MfO7fR8kJEvgVgbQihRyVyMCG6ktIfB9N0+9d03H8CsCmE8PnnHGz38wdF58XG6dhCzNm6GtquZgK0Z9xjIYQ/63VHxxkkiK4EcQ805PxPUBV80a/zwcpxfp3E9IbD0CjP9VCV+NJf94OVo7zYchQd53QRaN7WEQAroBVyfz2gV+Q4v15eDQ337Yam2bzJHxKdMucMaPi3CRoefq8/JD5/vGgURcdxHMdxHOf0cEXRcRzHcRzHKaRfF+P+4u2vDwDw8EPPAgA62ypL2xYvWQQAGFqrXU4uW6KLV9RUp6r2u354GwDgZa9fCADYf+AZAMDO1csBAA/84vHS2I27taPEoTbtXbnqFwcBACOGa0Py6uFDS2OHderzcueBIwCAWRfrz2dfNr40Zs+hw3rcVU0AgMO7dMzMeXUAgAsuTj0yq7q01Ri7WtRUNwAANq8/VhrTcVLbPC1bri28dm9u6amvklMGVFZWBgCorta+wA0NDaVtFRVqC21tbQCAceO0ZdfYsWNLY/h+1iy17wsuuAAAMHmytsuqrU123tmpXRdqarSZfldXFwCgtVX70x45cqQ0dt++fQCAadO6tx1bs2ZN6f2992pXih07tCdtc3MzAODYMbXH48dTv+LKSr0nq6qqul1XfX19aQw/4+u6devcdsuUD33oQwEATp7URTEefPDB0ratW7cCSHZGO6aNA8kOGHlqb9d2jrRRvtox3IfH4XEt3FZXV9ftZ9qfRTt/nLqNn9tt+bntPjNnzgQADB2qc/+nPvUpt9sy5qyzzuoW7rS/b/6eCe2I9gkkm+c8RVvt6OhADm2X5+BcO2LECADAsGHDThnL4/A83MdeB+fNPHI7e3Zqxblw4cJuY3bu1NX7pk9P9ZKcs595Rp95fvCDH/Sr7bqi6DiO4ziO4xTSr4pi4zFdYe/wQV1552Rresp++BFdzraiQ5+mX3fTnwAARo9OSxJu3aGr+nztP+4BAMydq57C3v3anWD6BeeUxjbMVQ/g2E5V7l55lio69UPUQ+is6SqNrWnX98+u1Z6UT+5WZXLvg2kVoVkT1DMdO0zPeaRK1cLhI9RzPdqY1MK6avVuFi3UFX4ee0TVnV27DpfGHNynXsiQIX1ZucoZaOiVDhky5JRt9B4nTNCVy6goWjWDCiTVDHrAVFvolRbB41D5sfB66I2uXavLAVvlaOPGjd2us6lJVXEqifY6qcTQE+bxeb1AUjhPnOhzH2hngKFSYlVu/t5pk7RFq7hQJTl6VJeXpWpepKJwW66e8GfajT0X7a1IfSzaD0h2zPPZMT2pjwDw+te/vtv3dQYHVAKtXfE9txVFPmhTHEubpTpnoU1wLOe7XGG08D7htqIx/IwKaD7v2+uiPXOeZwQISNEf3of9jSuKjuM4juM4TiH9qiiuWaVPw0Ma9Cl93oKkFt77U83Ve+87fxcAMCYqifsObC+NqarSJ/g77ngCAPCat1wNADj/vFcDAEYeSWpP85rYA/WYepr716qq0hGXTB0+3qy7HaP918ybBwC45IL5AIBNTZtKQ57ecB8AoGGsqig3nqvq0YSJuj5328nkhe/aoYrNqqc1F3P7Nr2W48eTB1xdo8/oFy6ZD6f8GTlS7ZFen1U5mB/IfEOqcFbVGD9e812pLNp8G6C7Ysdt9DR5rtyLBoBDh3S5T+auLFu2DEDKcwFOzUmkIpPnIdprHz58eLfrsl44/w9cURw80BatokibHj16NICkZFilLs+L5SvVaNoUkOyB9kXFJbffos+osNj7IM+dJLR/e508V348i829dAYPnKes0s25kfbCV6vq0U5o+7niZ+dnvs9VQn5e1B2mSOkkeR4jX/ld7LXsjxFR2vvBg1pP0dLSUhrD90WqZX/giqLjOI7jOI5TiD8oOo7jOI7jOIX0a+h5y+Y9AIBLLtdQ3egxo0rbpkzVljL7jz4GAPjX/3wrAOBEa0rebK/aCgB43WtfCwA4v2YxAODYv2tYeMP9j5TGdrSqVNtVFdswdMQwXtx+wEb+onLcibsBAMPPmAIAmHfNpaUhZ1/9XgDAwZoNAICWNm0CH7pUCh45alxpbE2NJsbu3KzXPrRew3njJ6Swyt49GsKprG6CU/7Y8CyQClcAYMoUtZdRo9SeGXawLRUmTZoE4NRQBkMSNizGsBzDDRs2qM2xUMWGfBmC2bNH7y22y2lsbCyNseFBey6Ga2xyP0PjeSjHhvl4XWPGjIEzOGAo1tokE//POkuL7pgeYaGt0Z4YFmMI2toZi6TyYilrOyQvhuH9YIsRaJd5ERfvDxuKzAtpmL5hQ9D22M7gwba8IQzh5gV61iZoA7QJzuFFhYMcSxthWLmoaKsv8Hg8Z2/FLIcPH+527ZxfbeEKv+dApU+4oug4juM4juMU0q+K4rmLVDWsqFXvr70rKSMz5+qlVNRoAcnqXZqkP2vWeaUxN864GQDQ+aC2sVn+8X/Vn4+rimLKU9AaH4Fbo5LI9H8qijUm/5RaUey7jZN7tRBgw+3/WxpTd+9UAMCC96jSeXC6JoWvO/gz/S5t6en/8F71bo4ejiepUI/4cOOh0pi556r3Xj/81DYOTvnB5qd5UjJwqufKMWyTA6QiESo0HEPP0hbH7N2rbaS2b9dCrm3btE0TPUybPE2FkqrmxIkTASSF0R6H6g/34TXZ5uF5OwiqQvb7XnjhhQBSo1hn8GCLRaggzotFfCzKsqoFbY3FTFQSi9p18DO+ciz3tcoQ74NcdbS2SIXeXjOQ7h17PO6fq0/2XilqbeWUL7mKZ+cgzmFU/jjWFgn21BibKl/R2HyfvJG73S8v5svbONmx3J/Hs2PzCBK/i7Xlogb3/Ykrio7jOI7jOE4h/aoo7t2jitqJuHzdiOHJc22KK9g1RSdCuvTJeUT7xaUxe763DgCw7tavAgBGRZ2QT7vd2mjWamPtEdNVaRk2XhVAOpidLclbadyp6kvbvh3xeDGnoc6Uz+/Vbcs/+RkAwPw/0pzFc87TFj0PP3FraWx9rS4V1TBMlcTde3YDAMaOT7lgU2aoN9/cdGrzT6f8OOccbea+a9euU7Yxr4VKDJdnsnmMeYsGepHMKdyyZUtpLHMS2TYhz/2y+S08Ll/Z1obKIpBaonB/5pnRI7Y5ZPRY6d1S1bniiitKY5YsWXLK93PKGyobVi2k3VLF4+/a5vJxP9oIW+lQJaRNAUmxPnDgQLfjsIWTbffB68hbSdk8yalTNYpD+82bINt8tLwlT5HquG7dulOu2SlfcoXOzlN8z7mwKIcvX7ov/zlfBtBuY35svi+QlEmeiz8XLQ2Yt+8hbE1lr4NKPP9G2JZk+VKa/Y0rio7jOI7jOE4h/aooNjRoFuGwofo03dKcqo9qRL3aTVtVRZk7/SoAwKRnklex7Cv/CQAYHn+mr1hXp/uedf2N6VwvV6WvYqrm3VTEyjmJj8adrcnTbD+kHmbzMq1k3nbX/wAAGremJfyqKmIVU7uqMKs/+yUAwOL3v0PPN+IlpbG7duiSfSNqujdpHmoV1OMxj/Goe7eDAVYtU42wHiJVFqp49BZtDgw9X6oqzDt89lltyr5pU2runue+9MWLpPdNr3T37t2lbVQZmWdJhYbq6ObNm0tj6c1SzbnqqqsAAJdccklpDBuLFy0p6JQ31iap4lEZoWpRlI9FqJoUNZ5nTiJtO8+NtXbN/XkuKotWuaG6SHWHdkcF1DZMzq+zaPk/qveuKA4OOGfyd2vz87gtb8ZtVT3O0XmFNLHdHjgn0g75ms/b9j3za/lqcyqpMubnzLtn2Ovj3E0lsWgZyiIVtD9wRdFxHMdxHMcpxB8UHcdxHMdxnEL6V8cMKgWfaNawwLbtKTw2bLheyjnnadHAgi5tdv3slz9RGjMpRuDYPjhM1aKR2R95PwCgftGFpbFdxzXkUHkiJjm36c9dsVClsiOFzRpGahuTqtdq6Hru9YsAALv+4+ulMbu/r824aytjAninysNPfvVbAICLPvbh0th9BzXEsWOHhmDaO6L8LOm5vK1NPxs7ZjSc8odhVra8YTgXSEUADE/nYREghSe4BvPy5csBpIIVGyLLWzKwKICvNiTB0AbDFQzv2TANQ38MufE62VLHhmt4jiuvvBJAaoFji2MYsikKjTiDhzy0R4pSJvI2TixQWbVqVWks0yiY0kCbJ72trUsbtc3hWXTF4pjzzz8fADBnzhwAKeUDSGHGPB3C3gcDFbZzfjkY4u0txYUpDHlRH3BqcQnnNqYwnHHGGaVttD+ek8WF+Rri9jgjRmjBLP8m2GIbto3iPZC3JisKkedFMTZMXVTA1Z+4oug4juM4juMU0q8u1oH9+nRdU61P7Q1D0tP/5LP1qXzetMsAACc++R0AQEdzWiLqcHyVGTMAAFd+4m/15/nnAgAazco8tW2aWHpSosJSrZ5wZ/SIA5KHUFGpn1V16fU0NJwJALjoDz5QGrOqSp/6N3/32zo2Ot0dB1URCrffXRq7+LeuBQB8a7228Rk5RJ/Hp02bXBojMYG8paX78mpOeUIVjV6kbW/AbXlivk2af/LJJwEAjz76KICU+E+FxhbHzJypSjmXVmMjZJ7HKj5UFKnwsDCFLXaA1MCbY6lqMvmaSg0AzJ07FwBw9tlnA0gFBVZB9aXQXhjkzYD5WtSImGoJC0KoJNKugWRnVB+p3HCpR9tMm2N4XCouVNiBtLTZihVaZEiVhvucd15ajIEFObyfePzeCl6c8qZoqUZClTEvirKqMW2XEZ+82JDzIHCqCs7j8jzWdhi9odLNZTH5twFI82Xekqeo9U3e3Juv9pwDpSQSVxQdx3Ecx3GcQvpVUTxyRJ/Aq4KedvZZycOcOkGVi+qN+ux6YPnjALovy3esXhWVl7xflb668zSXcNXSJwCknBYAmHuWLks1fIQegU/ynTEVrM7E//dFL3bt2sf0nLW6j/VY573rD/UcOzWv8vjSR/T40WHd+fD96bvcoK1yhg9Vj/rY4bUAgA3rkgdzokMvpGbowCzJ45weebsNm1NIpYQqBr2/tWvXlsYwJ5FKImHT6sWLF5c+o8JHZZJeMs9pPU1+xmtgW5tzzz23NOaJJ/T+oArEPDDeL7bZNxVFesn0vq2K6Llegxeba5Xn0hYpbrRl2gybVq9cuRJAdwWQ9kobnDVrFoCk5NCmgKTU8Ph5ax0A2LhxIwBgxw5d7ID3U94UGTg1f5evtjm9M7gpmnfyHFqb10d7Yw4h58inn34aQIrCACmiw7xtRnGoDNpzU+nmvJkvYACkfEOem68cY1vp8J7k/Zcvo2qxqnx/4neR4ziO4ziOU0i/SgN1QzWG3xQbbT/1bFJXpkzTPILWxzXnpQOsUk7Mu+EVAIDxV+pyYp/7gja9/vIX/i2OSKOnTddl1G75sFYjz5mjSgmi00yPGAD+7hOfBAAcOJiqsAHgmmtuKL3/o79QFXPx794MAHhgjXq3VU3qXTQiNdbsWKo5Ndddptf5/Z9rM+W2tnR9LY3qFbc1n7qQuFN+UC3Jl+ADkrJBb3bPHl0ScunSpaUxzGeht8jcPza0nj9/fmksvddcDck9zqLPmIdocx4vvliXwaQ3+vDDDwNI+TjMOwNS5SoVSX4n66kX5dA4g5dcfSvKjWL+IVU92gzvCyDl1lJRZFUybceOpS3yXFQErd1yDPfjuZmHy9xFIOWh8Vw83kAteeb86uR5rLaqmHMk7aVoTmJuLO2HcxvzYWmvQOrywIUFmF9Le7LzPSujqXgvW7YMAPDII4+Uxli1Ekj2WbRYAdX6rVu3Akg2W1T17Iqi4ziO4ziOU1b0q6J44ZJ3AgAOn9C8lgMHnyltmzVEvdCjT2hfQvoFQ6pTXsu033kTAOCuhx4CAHz5C58BAFx3nfY/PP+CC0pjv/KVrwAAPvvZzwIA/uVf/xVAUlw+85nPlMZWxOX5PvD+vwQArI/LqX3vzjtKY8bN0JyFP373uwAAZ7xEPZDDP/o/AEDKlgEOP65K0pnX/x4AYMaZMwAAGzekXLCaSt2jrrIaTvlDD5bVlTbfKl/wnZ6rzbdiXhUr46jyzZ6tyrddco/VnTwH8xitIkPo3bL6lK9WSVmyZAkA4Mwzz+x2fI5lPg0ArFmjy08yF4372O/r/RMHL73lKBbl8zGnisvx8ZVQIbHvOcey8p72YsfOiJ0reG4qlDaHl/tx6Umq+bRNKjpAUoeo1FONcdX7hYPNE6QiR6WPtmIVN77P+8gyd/Y1r3lNaexFF10EINlPrq5z3gfSPMz8bfZjtJ0hHorPKKym5n3EpQKZAwkkJTHvlWj/rnCO5nfpb1xRdBzHcRzHcQrxB0XHcRzHcRynkH4NPU+acxMAoKFNk+gPPHi4tK32iD6zHtkZm7bGzyvOm1caUz1LS9dbntbw2Mte9koAwF984IMAgDMmpCV5KNt+8hO6BOCemIx9IobqKPcCwIdvuQUA8OrXqhR95LCGP55Zt6Y0ZuVSbW9y/O1v13Ndoe1MDv74TgBATUjJrkcPaLl8OKwhmHHDVKLePy7J1yPrNbl17/ZUSOCULwx7MUGa4WALC1aYdG9DugybMOzBNjSPPaYtme68887SWIaGmZB/3XXXAQCuv/56AN1D0Gzg/a1vacoGw3425MZQ3ate9SoAqZE3Q+MsvrHnZvj8pS99KYAUMgGKm9A6g4OiQqi8xYiF4TrOlwx98T5gCBlI9sq2IRzLUJptpUM7pS3xnrFjGMpjex2+sh0JbRVIIXEu78dUCRu+88KWwYktJCG0KW6j7dEu7RjaCVvUXHaZLuph5zQWojBUzLGLFmkLPhsqZmHXM89o6hyLbGzDbbbWyxts5w3mgXSP8Z7g3wrbHsf+LRkIXFF0HMdxHMdxCulXRbGzUp+cJ42ZBADYVDe7tK19vz4xV3do0j8XrKk+Z545gD7Xvvz6lwEAXvkKbZfT1qqJoh1dRcvcqBdZEb1nPtFbLaRhuHqfh45rmbrEx+exo0eVxhyIydYdJ/Q6h89UD6N+nCpLnfuTKhNiq5y2PaqcTqISWpG8ZcQili6kczjlC5OZqbpYdYIqyOrVqwEkr9SOYXsENtNm09a77roLQPck7Btv1OIstl344Q9/CCB5wFQjgaRIUkF529vedsq5qTayLQ4VxQULFnS7FiB5tZtiQRe/i20l4Uri4MXaBd/TpqnO2DFs3UE7YFEWk/6t0sL9aENUz7mvXVaSLW5YhMAiFqvysACA+7MZMl95vwFpOTaqPSwwsG1InMGFbYcDdFcW+T5fus+q4lTt8qVKqUxzAQIgKYosUKEdUcmzxYYPPvgggFRMRRujvQOnFq3Q9u1cS6iQUwEtUhT5Pi+c7C9cUXQcx3Ecx3EK6VdF8favvg8AUHNC1ZPRY0anCxmhOQH0ZRmRHzM85YKdrNSWB21dmmdY3anKxs6YY/WN//5Gaexd39O2NZddfjmAlEvDNgwhJPWxK67rVx3/O4LoVXR0peX1OuP4ELVIGaoKTkX0QNqNolgVv0U4qt74sDHqXYxqSS1GjrVpQ86Zc1JepVO+2PYIQFLcAGD9+vUAkpJiF3wnVBSZ28iGrFy6jy1sgNQyh6rIl76kjeWZU0g1B0iKD73kefNUgbeqHxVINpmnKsS2I1xeEEgeND1fqjnWu2WDcV8ebXCT5ygS2geQfu/M86ICSHumjQJJ1WbDbeZesbG7VYi4jdfA49jG81QJmb/I66SiyNxbIN0HtFted1FLKWdwkC/ZlyuMRWOt6kgb43xFdY82Ycey4fbChQsBAE899RSAZFdsGwYkO7zpplhzEaNB1h55bNoq7ZJKpVUG+bclXy7T3pd5O6D+xmd6x3Ecx3Ecp5B+VRQP79XFuFv3xiaWC84vbeugMpftY5WcrkrdL1RWdNu2ZYvmu/znFz9/yjl/+61apcyn/pR/k57MBfSs9bUrRDXGFMmxqLk9vqmp1f+6mqHqnTSZc/I7nGhXz6CtXQ/U2Z6+3YghQ+N3OOWSnTKEntyOHTsAJI8TSE2vJ03S3Nt8kXcg5b7QDvkzPU5r58zNovpNTzpfKhBInjRVoDzvzJ6T34Gea35NQMrr4TnpCVtPlt+Ln7myOLjJl/Cz0FZoD7Q32iJfgVT1yeOwUpqqtK1IpSpOpZL2b49Hu+Q2qjB8tYoT78E8L80Z/BTZZZ6bSGivQIqOUEGk/VENt7bGCBGjQcw75752juRnjFLyZ5vHmI9lBT/vBTufcs7Oo1ZFCmr+ffsLn+Edx3Ecx3GcQvr18bQmaA7L4aPq9R08cKK0rXNWXCQ+/swn2M6mNAbxyZtP41ReFl+keV7/+3+pF90XPvc5AMDX/utrAICrrr4agFVjktoT0L2/FhWT7p/re3rfzGss9a0r+L5SoWOPNerY1mMpR3FUlfZ9Qkd9wZ5OucHl7pYu1eUZqSwCSR2hB0v7tBWX9EhpP/QM+TPzaYDUU5HVdewRxuX0+LM9h/Wk7XGBpMDwuqg+ct+iSjqOoYfdbtRwbvPq58ENlYyQzatF6nFe7U97s3ZHFY+qCXtxUhlhbzkgzcPMx6JNW1vsqb8j97Wf87psfqX93Bl8UCWmTdj5NFfWchsG0hzI/fJojlXsqAayNyLHskOEVR/zc3NutLZHdZ3HobJYlEuZH7dIQeX43vI0n09cUXQcx3Ecx3EK8QdFx3Ecx3Ecp5D+LWY5olJyVbVKwkfbj5e2hTr9bEgM4rbFUO/RPalJ9dio7IaY9FlTGcN38XF3jGm38+a3vAUAcO89vwsglbdTdrZUSPeGswxxV5jQWilxPw7pPKEScEujhk5SeUFqFl5Xqwms40Zrs+KxtWeWxowaoddRUzsCTvlz7733AkjNUW2o2IaCLUXhhTwUlofygBReYOiEIQ0m6rMVDpDCMkzD4FgbvmDYg+fi8YuKbnr6DkWNmvlqw9zO4CEvbioKoTHMy7AYQ8W0RTbKtu+ZpsFiLCb9M3UCSPdPKZWnILSWp3IQjikKkefhuyK7dQYHvbWEoQ3ky4n2ljbBUDaXLGXbJgA455xzAKQlIFncUmTnnHNZfJLbsCW3R5vCk48p2kbyBuP9jSuKjuM4juM4TiH9+ng6coR6p8e7VP04HNUZAGiOnmvdSC34qIpKXcu61IZkqKin+/W7ddmzVQ9psv+ff/ivAQC1E1P7haZWUwQDoCo+9VdX8ysn77LjpHonQ4aqAni8U1s2dDQfK42pCdFDHaLehGzQa+/cpw00rabSUaWezMlxej1PL1PvpKUxqVC11bEIoUvHvuWtvwenfGGyM4s77CLtVGDy9gZWweB+VG/ypcXYPgEA3hLV8EsuuQQAcOuttwJIS/pRoQGSmknvuKhlDc/F6+G181qKFpynN87j2+/WW0K2U95Y9binwhRrt1QUmZTPhsFsH7J9+/bSWNogl0aj3RU1OOZ1UI3J25IAScXJFWu2bLLqY16Y5Sr3CxvaKuc5KtS2BRPnKdoJFW5+bgunxo3TBT+4OAFtjIqijSDxnIzi8DjWHvPm2TxO/jcCSEVgHDtQBSu94Yqi4ziO4ziOU0i/KoqjR6o6IUFj8cf3JNVvv+gT9wULFgAA9jz8KACgbeP60pgjKzT3ZeoMbVL8zx/7OQCgoku/xqXXX10a+7UvaPPt8WN1aag5scw99tTG2PGTSmNvi0v/jZusuV/rNmtbh6fMouFXvfw3AQB1Deo97HlU1Z2T7eoN2Cy1mmGqDrUM0efw73/lOwCA1qa0tFvJsecF4XY45Uu+mLttek01I89HsbleXLKPXigVDyqVNs+RC8rPnKm5rVzejM2JrTLDtg3cRq/ZKpZUZng9PDc9WL4CSU3i/mz9Y5dC43ehwnnjjTfCGRxYpZmqXq4oWtWRds682M2bdXEDKosbN24sjeX+zAWjMsJ8L9o+kBrNU0mnMmlzwbg/lwakbbL9jlVeeM+MHq156kXKjTO4sfNe3sqJ+X0215vzMSMmtFUuo2oVRdYwcBk+zomM3lBxBJI9c/6jImjtkeNpz1Qk81Zl9vry72bzEfm9fAk/x3Ecx6TxkxQAACAASURBVHEcp6zoV0WxUvRpePiwWCUUkue6+YAqh5csvAgAcCIqinXmCXrTf34dAHDtf/wrAOA9H/pLAMCtn/4XAMBP7vtxaezwUeqpfuSv/lZ/HtG92vmP/+TPSu8/+je3AADe+863dxszZfKM0vs3vuNmAEDFbl2ofv8D9+l3itttu+PJZ50LANjboipPbVBPY+SY1HCb31x8+bNBAVUWKiw2pzCvWqMaZ5cRY47KgQMHuo29/XZVkq2ieM0113Tbh0uhUWGkagIAs2bNAgA8+qjeL3fccQcAYNq0aaUxTzzxBIDk5bIZLJUZq/Twe3IMz8VKVgD48Y/1PrvnnnsAuKI4mLBqId9TaaZNWtWR6jPtiQ2JqSrbHEWqhKwg5b75K5Dsiw2NqarwuEC6x6gWUjXftm0bgO65lFSJ+Jrn5TqDj3wRAaum8febLy1JGwFSviKPk+cdzp49+5RzMsJz/vm6vPBFF+nziO2WwnPniiLzG4E0b1KhpLJIddzC+TfPTbR/P3hPetWz4ziO4ziOU1b4g6LjOI7jOI5TSL/qmA1DNcE4BE3oHDFscmnb/u0qvx78jRkAgPqJU3XsnrSm7pGHHwAAbPmfbwMA/uj3tZn2JXGt54M79pTGTpmjoZIzxmk4pDWuGc31m1961bWlsZ/7ooYrlj30MACgrlYT9y+/IRXHzJ+i1/rkpz8NADi6UxNa62M0pbkzhXTOuuRSAMDj2zRcN2WchllseJEh56oKb+MwGGB7ECYjM3QGpNAdw2bcZkMHbB3C0N0VV1wBIIWTGcYFgNWrVwNIoUCG4BYvXtztWoC0fi6LCx54IN4jW7aUxrAY5vrrrweQQoBM7i5q9MqwzeOPPw4AWLFiRWkbwylsaeIMHmzomQUf+TrJtgiL4xl6ZtNshtLsmucMqy1cuBBACkXTXm2BiV07F0ihPRt6ZgEAm9yziIAhRJv+MX36dADJbnkuX4988MIwa94KBzi1mIVjikLPPA7nYM6vNj3njW98I4BkUzwX53sb8mW6z0033dTtuLa1E+dzFn8RFgWy6BDo3nrHnruoxVnROtD9gSuKjuM4juM4TiH93B5HlbuujqHx5ONL2zpb9bN9HeoJzr1J1Y8NX761NKYe6j08+7kvAQCGTdAWN+fFtjid0+eXxra1qkrSfEJbNXCdvxD0GC0nUwL/2WdpS54LztbE1ZN10QutSorQ+m99FwCw5bvf03PHR+z26HyPnXtWaeyhsepBb7lng46tY/uUpB5WxOupEn9WHwwwOZkKhU08ZjIyvc7589UOi1p9UMWjV3rdddcBSKofkFRHHm/JkiUAgLPPPhtAd2WGBSqvfOUrAQAXXnhht2uyY6hoMwl7504tzCpqt0PP9Qc/+AGA7t4ytxUlZjvljS0ooX0VLSNJ+LtmQckFF1wAICl/a9euLY199lltK8aCL77S7mzLprxwhmNs1IVLrfEcLOrid+B9BqR7gzaZt6pyBh+9tYLJt/Fn2+qLqh0LnEaNGgUgFUPdfffdpbHcj/Mx2+JQUbTKdL5kK+3y/vvvL322dOlSAKlV1KRJk7rtw4IaIEWeclu1Pw90E26/ixzHcRzHcZxC+ncJv/pFAIDaGlUthgxJOSZDajWvq6NSn+Drf+MGAMCo+5aWxrRs0AbY9Ue0xcjSj2hbm/OaPgAAGHv9y0pjO6OKV90al/GBSn9dsVuC9RDaOmJ7iLq4lF+bPuFv+eb/lsZs/Jw28G7ojE//8fPW2CBnyW+/rTR2ZYcqSe0tOra2Vj2Q0GWey0X3C+7xDgqo4lGps/l5zMGiwsFX6zUyl4s5NA8/rPmw116rubKXXnppaSyX7iNUZuhhFnnaVFKYY2PzzOixssUN8w35uVWZqD7SG2c+mG24TfXTNqF1Bgd23utJUbRjKktLn6r9M6+L9mrtjGo581qp3DAPlzmLQLInnouKDls2AUlRZI4ir3fOnDkAUs4ukGwyv1ecwcvpNJcuUsMZ0aHdUNGmImhbO1Fd5PKTzP3mHGfnP86b3J/7MHfbQiWR56Z9M68RKM5JBLqriEVLs/Ynfjc5juM4juM4hfSrovjud/0pALOszfFU7TN1tuZsrd+oeX1VI/VJ/oKPfbQ05rH3vAcA0HVMFcXaRj3Oyg99BABwxsOPlMZO/E1tAtwwVxtn1g1R1ae9M1ZQIXnNHcfVm21aqk2LV3/nLgDA3gfuLY2pjdXSLFI+Gh3pya94HQDgrDe/qTR267e+AACo7lQvoKjlK714bwg7OKCCyMbAVh25+mrNkaXKwgpO+7vNF4enZ8nm1VZFpCJJpbK33JXc66bCw8beALBy5UoASVGkN0u1iNWB9pz0lrmE3+TJqUMB83fYlNYZPBRVUlLVy18ttGUq1mxWbO2PqgsrPakI0haZwwgk5Y/n4n1lVRRuo1rOvF5WVVOpBFJurVXH7XU7g4+86tnaRq4S0q7tGCp/tD/aBtVnu4Qf50TmFvKV5ylaTo+vnHNtNwrOlzwHG3mzG0XRd+FnvS3X5w23HcdxHMdxnLKiXx9PT57kE3Ps1xXSE/PR6H227dbXfc3qTe4dkZ7S5338wwCAtR//ewDAicNaiTk65h+23nVXaezKH/0UAFA7T5fVGcMn/CH6hN/anKpCD23X6s/GdbqMYBVi/zpz7dQ+m6KSOON1WmV63p/8MQBg6ozk3bbHPMZQpd+hsqBXInN+bCW0U77Qc+XyZKxEBpKyRi+SfRStx8rqN1bGUWFkLuC99yb1mooMl4SiqkePtVt+bfRC2YuLuZAbNmwojWF1M8dyf1YB2n5iVDonTpzY7ZVLWQHAuefqEpW2UtsZHFhFMVcnelMU8/2pLLLa2H7GvC6qJ1TRbb9O3g88V35cexz2SORyf7RX28s0z7ekymMVRVcXBxe5wla0ja92rs3JcwqpXrMnKJBsjPMy51Me3+biknxe5s9A6hLBaA67BNDu7XfKlVNSNGagcEXRcRzHcRzHKcQfFB3HcRzHcZxC+lXPXPW0Nk7tChqCaGtP4d/NrSoHV1dpknPbDg2/Hdt2sjSmY+YMAMBV//RvAIDHPvOPAIADTy0HAFjxeSjD26u1dH17fCU24Mv/BAa5KTIfMGNq67Xh7JK36FI/m+fr6KZaTYKtqExhDanUsHJFVfdlpIIpa2HoucY0T3bKl1e84hUAUjjYhmvzhq5MvrfJzS97mbZuYoLyY489BiC1n7HLOLHhNsPHDLHxuDZhn2EVJmPz1Yb5eM68eIXFAXYsGyvPmzcPQAqr83sDqVin2m130FHU+iYvrLNj8m0MweUNs4EUemP7kalTdRlWht1sS6m8gTBDh7QtINkij5uH+uy58+UHi5alPJ12K87AQxspKijJt+VL+QFpbrSN3oGUnmMXRODcyrmcCyzYVIj8uITh5OXLl5c+Y+iZ83q+j71Ovs+/S9F4b4/jOI7jOI7jlBX9qiju37cXAFBRod5pV2d6qm6PaluoUE+ws+lovMCkwu14Whta7puoys3sD/whAGDC41rKvuuun5TGHtiszV7rKmLRSFd3D7PatMdpjefgc7xEj3jaSy8ujRl3gy7tc8++rQCA79/2dQDA1y48N+6UjlfTFZf4idJkqD7VU6enYdv0OOULW+BQ8bDKB73OfKky6wFToeM2erBsTsyCEyB5j/RU8zYPlp6acFvPk8ofVRsqM1RdbDPZyy+/HACwaJE2x8+X/7PHy1uROOWPnYP4e8xVw6ICENoXVUEuW8YG8kBqUUPFjy1wqNJYBZo2ThuiDdqihPx+4jVQTbI2z+OxIT6V9aK2Js7gIFfPioo7ipTEnshVSLsgAotYqH7ThmmX9vh5QQpfc9XQfodcEbR2mX9PfqeiApaBsmFXFB3HcRzHcZxC+lVRbD6m+VhcRs96rsJlpKJHCNGfa6qTatHSqd5r62Ft4bEpqnFTztBclkXvf19p7PQdql62NOo5G3eowijH1NMcMiwpQs1Qj7VhqjZRHnm2qihbZW9pzB0bvg0AeHj5UwCAyRPV03jq6V8AAIZXji6N3bp1EwCg7WT3xb5tjiI/q64Y2LJ3p2+wTQxVEyqCQPI66QFSJbGKG5USqiPMO2TjYqvqUekryhnLj8tzsuUDj2/zcrg/98ubHds2IwsWLACQcjCL1MOelpxyyh+rejz1lM5la9asAZB+n7013KaiQWXR5gLSFmkzeUsRq5DweDwn7cueO7+O3sZSheE5i5YjpGrkDA44h1EJtPMNP8vzD4vmJNpGrkjbfbkf74+8gXdR7mO+rJ4d09OSexxTdO7ecmiLztGf+EzvOI7jOI7jFNKvclZdVDJIl82FiZ5gTRQuKuMjrEga09mlH9bHsW3xdfseVRi37Up5XuNHamVn52hVapoqNC9rKDRfpqIqPdFXDlNF5dAJ9Th3PqwLhB86mCqlG4aq0rlwhiqRo0fr8R6672cAgKfuWVsaO6RK1aMF52nFaGus6O40OZlUgE62pqpup3xhRTPVuFpjy/QOe1s+jF4ol9FjbhdzF7lsH1CcxwL07gnT00yN3NO+uTLDn6kKrV69urSNeWFUGXvzZF1RHHzY36Ot+vx1kFcyO86vQm+5ej01oLaf56oet/WmOj7Xvn0dk8+X+TJ99hpy9TJXQMsBn+kdx3Ecx3GcQvxB0XEcx3Ecxymkn0PPerrOKMt2dqXQXFUMmdVWa+irqioWe9SYEBrD0ZWaQN3armXqI5s01Nt5Ism9bc26bdO2jQCAFc8sAwAMqdXzDG1IxSwjGmJTzWYNBw5r0IKF8xb8RmlMZynczaRuDRmPGKkhyPqKiaWxNbELhEh3mbmmNoUMS8nhBY1hnfKDodiiRHp+xvBC0bqgefiXzYRZHMNUBCAVttRmqRrEhrT3xDXSWVTAcAXbhABpHVOGu3m9DDPbEAdD13nrlKJWOB56dhzn+aKnptr2s9Lf1qyoxZK30Cka21t7sZ7gcfI2OUXH6+3c3NZT+NtejzfcdhzHcRzHccqKflUUO6PqQXXCLl+XWnjo65ChqnbU1iVVRWKj7hAftKVT1ZiuSi1CCaljCcZP1v3bRdvhnGyaAQAYPU7b2DQMTS1BKkWvY8e27QCAyVN0abPFS64ujWk/GRXAVrZf0OOmJaNMImunXo+A5fhRAbUNOQO/kzctHgzYhtNAd2+vtERj1vKjKBmZHiULCdiihEs+AcA111wDAJg8eTKAdL/weHa5vyeeeAJAUgvZhse2LWFDcC4XuGuXFn9xWT6rgObXzO9W5Mm6oug4zvNFrqJZxS5XG/tSbJereXauy9sr5Y22i4pPio6Tj+lLa5782vMl/Xr7Tv2Fz/SO4ziO4zhOIWLznRzHcRzHcRyHuKLoOI7jOI7jFOIPio7jOI7jOE4h/qDoOI7jOI7jFOIPio7jOI7jOE4h/qDoOI7jOI7jFOIPio7jOI7jOE4h/qDoOI7jOI7jFOIPio7jOI7jOE4h/qDoOI7jOI7jFOIPio7jOI7jOE4h/qDoOI7jOI7jFDLoHxRFZLWIXDXQ1+E4L0RE5GERWTgA5/2uiLysv887GBGRm0XkoYG+jnJERP5eRP5sAM77zyLynv4+72BGRIKIzB7o6yg3RGSciKwTkbrn4dh9mmcH/YNiCGFBCOH+5/McIvI1EfnE83kO55dHRLaKyHUDfR0DjYjMFZE7ReSAiBwWkZ+IyDyz/WYR6RSRJvPvql6OdxOA4yGEFfFnEZFPiMguETkqIveLyIJsn+tE5EkRaRaRHSLyhvj5iHg9jSLy3yJSafb5DxF5TXb6TwH45K/+v1J+iEitiNwqIttE5LiIrBCRGwf6ugaavvy/iMi1IrJWRFpE5D4Rmd7L8cYB+B0AX4o/14jIt+N8EXLbF5Gr4zGPisjWbFuViHwz2u+PRGSY2XaLiLwvO/0/ArhFRGp+mf+LwYaI3CYie0TkmIisF5F3DvQ1DRQi8nYReSL+X+wUkX8QkSqzvSn71yki/9bLIT8E4KshhNa4/6dFZEO8R9aKyO+YYz8v8+ygf1B0HKfESADfBzAPwAQASwHcmY15NITQYP7d38vx3gPgG+bn1wN4B4ArAIwG8KjdLiLzAdwO4BYAIwBcAOCJuPndAFbE65oB4DVxn0sBTAwh/J89cQhhKYDhInJRX774IKMKwA4AV+L/Z++84+0qyv39THohIQSSQEiDhB6KCiKIAteCoGDBgoJXLGDDhih2AbFc7F7levXn1StIuxZQAQULCKj03ksCafT0npz5/fHOd6/Zc9be5wThlOR9Pp9kn7PWrLLPftfsme9bxv5OnwcuCCFM68V76gu0/buEELYCfp22jwVuAM5vc75jgUtijCuzbVcDxwCP1rRfDvwP8ImafW8AIrAVsASzZ0II2wGHA01f9DHGBcA9wBFt7m9j4qvAtBjjaOw9nx5CeEFdw3zQtJEyAvgoZiv7Ai8DTtLOvP/F+sOVwP/VnSiEMBR4B3B2tnk5ZnObp33fDSHsn/Y9N/1sjLFf/wNmAy8HTgEuAH4OLAXuBPYu2n0auAtYCPwUGJb2HQtcXZw3AjOA44G1wBpgGfC73n7P/q/pczoL6EgP2zLgk2n7EckGFgFXALt0xxZqzj8Q+CbwJDALOCHZxqDc/rL2pwBnZ7+/CPh7uo9bgYOyfccCDyV7nQUcnbbPAK4EFqfrnv8M/zZj071umV3v6m4eOyT9TSdl204GLsh+3w1Ylf1+DvClFuf7L+CQ9PPXgE+mv+0/gektjvkx8MXetrEesuPbgCPTzwcBc4GPA48DC4B3Zm23xCYES7DJwJfafa6YqvYw8BQ2yGrYLPAz4PSs7UHA3Oz3icCvgCeSjX442/dCbLC2BHgM+FbaPgz7Ynsq2f31wIRn4e9yPPD3bN/IZKM7tzj2L8AxLfbNzZ/FYt/LgdnFtpOB96af3wecmX7+HXBAi/N8FlOCet2+etiWd0o2++bCnk/GBuhnpe2fSO3mYxPQCMxocc7tgL9hfeWfgB+Q+tnSZtO23MYHYKrcg8kmLwDGdmWrtOifn8Hf40RajBuwgd5DQGix/6XAA12c/7fAx9PPz0k/u7EpikcA51EpK98v9h8NHAJMB3YEPtfVCWOMPwJ+AZwRbRZw+LN6x86/RIzx7cAjwOHp8zkjhLAjcC42qxsHXAL8rnADddcWjgMOxdSx5wOv6+69hRC2BS4GTscGbScBv0oxJyOB7wGHxhhHAfsDt6RDvwRcBmwBTCJTK0IIvw8hfKqbt/BS4NEY41PZtueFEJ5M7qHPt5nd7wB0xBjnZtvOA2YkF/dgrJP7Q7b/Rekeb09uqLNDCGPTvjuAl4cQhmOK5J3Ah4FLY4wPtriHu4E9u/le+y0hhAmYDd6Zbd4aUwy2Bd4N/CCEsEXa9wNgFbAN9gX7rjbn3hU4E7P3bbJzdue+BmADoVvTMS8DPhpCOCQ1+S7w3Wgq0nTsCxjMLjYHJmOD2vdhAzpCCJ8KIfy+m9cv/y67pXsBIMa4HPvy363z0QDsDtzbnWt1gzuAf0t9yMHAncmN92SMsVV86CZhvyKEcGYIYQWmpC7A+l2xNdYHTgWODxYXdxLwCqyv6Sp06BxsUrQlNhl/+wbc2oexfvtAbOKzEHuGoIWttuufQwhTkmt3Sjev/1Kan+2cdwA/j2nEVkNbG0796T7Z+Z+TfnZjGyheHWO8JMa4HlOayjf//RjjnBjj05hf/q09fodOT/AW4OIY4+UxxrXAN4Dh2MMuumsLb8a+DOfGGBdis7Tucgzm+rokxtgRY7wcU2AOS/s7gJkhhOExxgUxRj3sa7EOdWKMcVX+RRRjfE2Msct7CCFMwjrDE7PNfwNmAuOBI7H3XOdmA5tsLS22LQCuwjqulZgrOo/NmoR14Edinf9wqkHuT7AO+dp0jltT2++EEP4rhPC30DkOeGm6j42WNOD+BfC/McZ7sl1rgdNijGtjjJdgavlOKeboSOALMcblMcY7gP9tc4k3YmrG1THGNcAXMOWmO+wDjIsxnhZjXBNjfAhTH47K7nFGCGGrGOOyGOM/s+1bYurQ+hjjjTHGJQAxxq/FGF/T1YVb/F02w1T2nMXAKOqps+FnyiWYqnRDuuZ5wBeBk0MIX072e2YxGd3o7TcnxvgB7LN4CRYisDrb3YGpVqujhQK8GVNb70gD/lNanTcNyPbBbH5N6g9/uwG39l7gs6kPX52u9cY0SW5pq7Ton2OMj8QYx8QYH+nqwiGEdwJ7Y99Bde/rQNo/v13Z8A+xvvSP6ffnpJ/d2AaKedzJCmBYoZjMyX5+GJtdOBsfE7HPF4AYYwf22edKSndtYWLRdk6LdnVMBd6UZp+LQgiLgAOwWJHl2ID2fcCCEMLFIYSd03GfBAJwXbCs/paKUR0piP8yzD12rrbHGB+KMc5Kg9bbgdOwgUQdC+n8BfxFrMOejLlsTgX+EkIYkfavxDr/+2KMy4CvkAbFacB7fIxxjxjjp4BvA5/BlK6BWIe5b2jOwBuFuYM2SpJidxYW1nJCsfupGOO67PcV2EBpHFUsn3iY1jTZb4xxBeZm6w5TgYmF/X4Gi38CUzp3BO4JIVwfQtAA8Czsi+u8EML8FMw/uJvXbPd3WQaMLpqPpvUXaZ0NPyOi8alkv8djrswfYoOAvTH7HUKzurtR228dabB1NTZpfH+264mYkjESZb/alQ0/nWxXbGg//JvMhu8G1mN2XGurXfTP3SKE8DpMWDg0xvhkTZN/x8StWW1O09KGQwhfxyb+b5Yi+Vz1sxvbQLErJmc/T8FiI8CCQ/VlRwhh6+K47s7And6h/HzmY50DYNm62Gc/L2vTyhZKFmCdXt1xUNgO5mIRc7B4nDHZv5FSBGOMf4wxvgJzCd6DqTXEGB+NMR4XY5yIzYbPDN0sG5Hck5cBv40xdpXNFrEBaR332+lCPrjeE4uXnBtjXBdj/BnmHt817b+NbjwrqZMKMcY/YK6VG1JHdwOwR9Z0FzJX48ZEssmfYF9WRybluzs8Aayjs/22osl+k0tqy2x/V/Y7q7DfUTFGDf7vjzG+FVOo/wP4ZQhhZFJBT40x7oqp+K/BvhS7pIu/y51kXqLkHpxOa7febdhA9lklhDATe18/wuz3xmS/17OJ2G83GIR9NqLsFxawYTY8NpuQUhxbfn8PxCZUYg42WMvteFiMcV47W23VP3eH1Mf9GAuJur1Fs3+nvZoILWw4hHAqFhL1ykwBrbuHZ6Wf3dQGih8MIUxKcVOfocqYuxXYLYSwV7BaRacUxz0GbN9zt+lsIOXncwHw6mClNAZjSQGrsaQS0coWSi4APhJC2DaEMAYLyM65BTgqhDA4ZY7lCt3ZwOEhhENCCANDCMNCCAel604IIRyRvuxWY2rJeoAQwpuS6xhsRhm1rx0hhNHY7PiaNJss9x+a4r5Is+PP0zkrGoD0Bf0nbAYqrscU0gkhhAEhhLcDg4EH0v6fAu8MIWyfOvWTgaZ4tPR8fY3KZT0LOCi57F6MBXaLA4FLu3rf/ZT/wjrow2NzVm5booXV/Bo4JYQwIsUgvqPNIb/EbHD/9Dc+lebJwS3AYSGEsWmCnNccvA5YEkI4OYQwPNnwzBDCPgAhhGNCCOOSYi9FYn2wMjO7py/sJZh7r0v7TbT7u/wGcwUemezoC8Bthcs+5xKa7VcleFSPbkh6JkPaNyDtG2y/hmGFK1kD2R8AH0nvexZwQGp3IJuO/TYIIYwPIRwVQtgs2cghWFjLX9ocdgFwbAhh19RXfLFVwxjjw9jg5pRgJY72w7J+xX2Y9/DVqb//HDA02/9D4MshlVIKFiP+2vRzra2265+78ff4Nyxs4shoWcV1bfbHPFy12c4Z1wFj8gl7COHTwNuAV8Tm+PP8/M9uPxv7QJbUv/KP5qznPNt0Gp2zU5XpuggbyY/I2n8WyzCdg8WWNTKwsHirW9JxF/b2e/Z/nWzgtVhCyyLgpLTt9emzXoxlEO9W2ExLWyjOPQiT759KD9vHsM4kpP3bY/Egy7DEle8Vdrhvuv7TmBp0MTZ73oYqs1mZ2bumY87A1M9lWLD+8dn5LgU+0+Je35Hsdnk6Vv+mpP3fwAbVy7GO4jRgcJu/66uxIGj9Pgz7klyAdao3Aa8qjjk1vc8nMLfOFsX+04BPZL9vjimgi7GA9YFp+z7Azb1tW8+RvU5Nn9Oq4nNS1vtBtM/iHIcNwLub9Xxsej6U9TwPeEn2mZ6fznVbsu8y6/lcLKxnIZY9qfs4G8vKXoapeq9L29+KxbEuT/b2Pap++DO5TW3I3yW1eTmm7qxMz8y0Nu97Kyzbdnjxd4zFv2nZ373cd0VxzncBPyj6h/OS/f4RGJW2b5OuPaS37a0H7Hkc1pctSnZ0O3Bctr+TPaftn0p21Z2s5+lYzN1S4M+YmvuTwsYXJHs8ic5Zzycmm1yK9alfaWertO+fp5D1qzX3+ldM9c9t+NKizX+Tsr+78ff9OnBy9nukGrzq32eKY57VflZfdhs9wQqovifG+Kfevhend/lXbCFYAeAfxhindtl4IyDYih8fiqnodg9e91fYF8ElXTZ2uk0IYTPsi2+H2D42aqMghPAV4PEY43d6+LrfBB6MMZ7Zk9fdVAghnA/cE2NsqURuLASLOb8KeF7cAO9DN8/drX7WB4rOJseG2EKK6ToYm41NwGrK/TPG2OPLgjnOMyHYCjt/xlzO38RU7ufHTaXzd/o9Kdzhacyr80rgQmC/np7AbqpsajGKjrOhBMyduhCreH83FhvlOP2F12LuvflYGM1RPkh0+hlbY+7fZZh7+P0+SOw5NhlF0XEcx3Ecx9kwXFF0HMdxHMdxavGBouM4juM4jlNLq3VenxOmTZ3SyO2m6QeILer0hpb1gHN0bNU2dOewPsTshx/pZ3e8aXHGGWdEgPXrrZTWsGHDGvsmTrRFXe680+r+ciritgAAIABJREFUXnedlc566qmqxNWgQfWP2oABNlfTeQEGD7ZFLE4/3VZauuaaawC47LLLANh552qBgEMPPRSAdetsEY8lS5Y0/Q7Q0dEBoDIJDBw4sOnaefiJ2mpfKjHX1Kbc9p73vMdtt48ybdo0q42x+eZAs13oc5TNrF5tK67JBqCyS9mMPnPZx9ChVbk62bj2rVxpCZqrVq3qdN5QdNBr165tul5+zfLZWbNmTae2ajNkyJDa+82vr2stW7bM7bYPM3HixAjV51VnP6U91iF7WbZsGVDZiF7z88luRN35ZXd6Le0+P073rnvQe8ifG6F+X+fJ36+YPt1qmF9//fU9ars9OlAs4yE7YtNIMb20jpksB42x/Ck7tKuBYkd2Lv2sLQPp/AE9d3hf1R9QR1I3cCo7su22267pNT9OqJOpG6ypA9NgdNttt206Xz5IXbzYlr5Vx6Pz5NdTR6b7K+8l/718f+X7bnXPTt9EdqbBmr6woLLbcgKRf9YaWKqtvsxGjhwJNA/iZBe6hl51jvy85Rewzls3GCi/mOvOp591fPmlmx9f2r/TN9HnpM8wH8SVfU/ZFjrbTdn/lYNC6GzfQpMeqJ6lckBXZ1dqI5tV2/zauqbek9rkz4LO025A/FzirmfHcRzHcRynlh5VFHd8lZWe22KkqR9jNsul3/SaJgqr1tgMdvGKaqnPJ5bazwMHWONhg22cO3r4kE5t73/MZOY18rQ0FBL7dVAmP45Ky9WvXmszj6Uxzazzm49Jaak2tHurTU2kksbQeZ+vIt0/KJW1fGYnt97+++8PwFZbbQXA8OHDG22kyJQuNp0vV2Y02xw3blzTPqmG+bXHjx8PVG5uzVzzNuU9a+Zapz6Ws+I6N4grM/0HfcZSRPLPsZWCkX+usjnZpNTsdiqP7FX7chdx2bZUsOvcbe0Uz7KNqLNR3Zcr4f2LOuVP/VwZYpCHVpRqeNkP5rah80ydauso7LbbbgAsWmQrU951112NtmXoQ53NtvLMlGE/7drWtal7lnoCVxQdx3Ecx3GcWnyg6DiO4ziO49TSo67nRQO3AWB9uuzgoZVrbtRQ2zZ8qEmzg9el4OlVlZS8fKgFka5caxLtGsmxm9mxI0dmLpN1y23fqmapdlW69szVcxrbdn7sQgCWjHk+AH8YbS7EIbEK/I7JbxwKt0WtE6NM6+6clN2gPJ/TNykzh3O22GILAGbMmAFULuMVK1Y02siFUbop6lzPZQC0kll23HHHpu1QJbMsXbq06bz5fZbuivIectdO6dare991rhGnb6LPSO63uqxnbZOd5IH8SqxSwH2eKZqfHyoblpu7tJO6a8uuyuzqumtsSBKWrpXbrc6dJ/Q4/Yc6t6vsWtT1Se0qOAjZ9Q477ADAi170IgDmzZsHwIIFCxptu+rLu7oW1Pe5pX3XHavKBD2N9/SO4ziO4zhOLT2qKC5Po2G9rshGx+NHW9D0qOFJUUwJK+vXVbOIjvU2E1y8PKWapwH3QhMP2XxENdsdHFIJhWgzjgHpfGtTwsp2U8c02m6zwNoOm2Az6QEr0jU7shlwei3H+A1FsKb0Q+OoRuJKXTkgpz9RlwCi2WgZxJ8rKGUAdDnLzWfL2qa6Xw888ABQKZSjR49utN16662bjq+r+1XOfMvf6+p/tQv47+3Aaqf7yIb0mdfZWZmEktuDbLhMpCpVyPwaUnlKG69LfGmXzFImsZQlRnIbrUuugXr10JOw+gft+qJyX3faijrVsexz77nnHqBSx+UlAli4cCEAjz/+OFBfI7d83tr1p2WSYV15nN5Ww11RdBzHcRzHcWrpUUVxfSG+reuoRtkr1qYRuGaRqU7Oukz8WGohiqxJ2zQvDCl+cEkWz1gVcrWR+JIVtm/P7UxJ/Ni0RY22o+fNBGD1dCtrct9yi528+o5K8RwxPM2Oy5lB25UrFKOTfs/ebwyuJfZH2sXltYuhKsvMtCvVoW1PPPEEAH/4wx8AuPXWW4Gq/A7AK1/5SqCKk2xXOqSujEO5vdWKB7k62m7lAKdvIUWjTonQ56gyTlIt8rivstCv7KBcdQU620NZ0Lvu2mWJkVwRbGVfdauulOp2nY3WeQOcvku7fqZUq0u1uY6y/FNuP5ttthlQxQDedtttQGXDuaKolbj0DJTx4flxalOuZFRXfF523S4e3hVFx3Ecx3Ecp0/Ro4qiKk6nOtlNo+plq9elV/t91DC7tboa1cMHJX99UujWptd8/D14YMr2SxPN8eMtrus7r9kegEkXX9Rou3DYCAC2mvcYAB/fx7JXb55TZWWvX2U3NjCUWXk1ymBoLu7dyJjOC2jqaM967heUn1P+e6nY1SkX5Uy3jPGqa3v33XcDcOONNwJVTIxe8/McfPDBQBW/WBc/2Gpt0nYKS93stjtxjE7foFRlckVcsbVlzGldJnNZyF3KS53CUdqe1Jo8Y1rxtuXyl/mxrWIc27VVdnYdulZdZrXT9yj7pTwWsLSxOg9NK5W5jMkFGDPGPI2yH3lzVFUitystsCCVUXGNdUq8bL9VZnN+P2UGd1/CFUXHcRzHcRynFh8oOo7jOI7jOLX0qOtZyRzrg41PV62vZNh1a1OB1OSUXZYyVsYMq9wEAwalAOiU4aJEF+WIhCbvXgqOHmqS8dffsBMAey1P7rvNqxIjgwZYgOmqJUsA2P/JuQC860W7Ndp85/KH0v0MStdM7sXawjllUoPef2jVxOnjtAuslhuuDPDPi6OWAc9lCYTc3bd8udV7uuGGGwB48skngXoXxaxZs4BqjdJddtkFqNbkza9Rlu+pc4OUAd916//WFQl3+ial+61dQeu6MIiyULf2lWuX58eX69nWlYsaMcLCfcryPXWlmrqz/njdWr/l+dxe+xfqP+vKcZXJdnV9WmlT5atcyFC5nssEFdnTU0891WirvnX8+PFA5Z5++umnO91fK/L9pc3WlZPq7ZJkrig6juM4juM4tfTsFCspiakSDuvWZAHyRdkRlc55ekU12h6i8g2NMjMpQDS9dmSq3rLVdpHPvNaWPTs02mj/qatMpVm73aRG2xFveikAq350jh37oC3b8559q5T4P0+3n++fbcU2hw9pLtFQl3SjcXg1+8na1BTqdvouZcJKHsT/yCOPAJUSOGrUKACWJIUaqplqGYhfp9g99pglVd11111NbTQDzmejmvmqQKxmudtss02jTRlI3a5MhH5W8LZmtXWFmr3MSP+hXYkk2XK53F9+XCtFPVfsSruQDUk1zO1MJXnUpt39lfvaqSrtyqP0tirjbBj6LNslKLUrj6PPuexrpSyPHTu203nUZyspUH1u7hHSEn4qU6ZFD9THQ/PyrV1RJtmUy1rm9FbCiyuKjuM4juM4Ti09G6PYplhxGbQ3IP2+Llc70mQhBo3A05Hp0MWrqhH40QdOAeDY8CgAS697EIC1SQkceNCLG23XTTP1Jb4gFd6+/AoAxj34UKPNpw84EID3zrcZh2IpGy+5utK45eY2TXNd3btXGOkXaHH4clYKVSmFMo4rV1DKGJpWMTYA9957L1Api5rdaqH6PPbx2muvBeChh8xWNUvOZ55l6YiyuHGdKlQWhi3jaJz+QbmcXl2JkTJeNrdbxRKWZZeknueUMWFl3GDeR8qGZWdSjfLzlucrFZa6mLUyDjG327rYL6fvUn5OdUWqyyXy6rwjrco15Uuhlkus7r333gA873nPA6rFDgCuv/56oLJVLXYgpRE6e5BEnQepjGcsy0DleIyi4ziO4ziO06foUUVRsYRavq6tmNYYcWdZTI0DVIjVRtxL09J9++wyodH2hFGm8iz63RUArE1q46APv9vOsM346lpLLYZm8IH7ArDmhpsBWPZ4FWP2ssUWh/aGfW35nl/8zRSmzUcktSYf/Kf3p03VxCB7x1KWPMyrX6AZp2Z5dQpbOautW8KvREWI86XQpA7qWpMnTwZgypQpTfcCVdbz7NmzAXj44YeB5qxnZfQ9kzizuuw9j03sP5Qxp3XZlqU95IWx65RIaK9slOq5zpfHmpWFtuuU9bLIt6hTWsprtlsGzelftFvmtN32Um2UHcrrkveRqiwhlfH5z38+UC1ksOWWWzbaPvqoeSkVmy5VfMKEavyhWEfFNpYZ13VL/paLMOR2Xxc/3JO4oug4juM4juPU0sOFpTRb1Gx0Q2d7UjvstzXr7PhxW9sM4ZNbVzECA8+7EIDly20EPvSdbwNgwO5WZy5kGUoDB6T72sJmEwMOsVnEyjPPqq582VUAfOjwwwD4+1TLhnp0gc0YhgzM/5Qd2d3CgIYCU5PR1/K9On0JZRPX1Xsr1bcy/rD8OW+jY2+66abGPtXlUnzYDjvsAFTZ1Lmit88++wBVDS/NjNUWquw8xdLUqUvlfbV6b/k2Vxb7D1Ks62yy1VJ5UCkYUkbaLWVZqnlSIZWBn2fiK+ZXtl5Xw7G0r1Ihqquj2Go5zfx9etZz/6D8DHO7LL0j7fpc7VOmvTwsuWdGdq46tNOmTQNg5MiRAEyfPr3RVmqjYshlw3kWtTKhFYsrtbAu/rB8XuoUxe5k/D+XuKLoOI7jOI7j1OIDRcdxHMdxHKeWnnU9DyiXvdvgEwDQkTJH1g+14NSP7mznnX7uuY2Wi++bA8Bmx5nLeejrDwFg4CqTmwcPG95o++fL/gDAoEEmC+/38lcCsPZv1zbarLjxdgAm3XkHACfsewAAJ19kiTBDszo3sVEIPL1fDcezzJVYuNGdvo3cFnKH1SWnlC68ujblMmfz588H4MEHH+zURi7niRMnNh2Tu+SU6LLjjlZY/uabLRFrwYIFjTY6Xu5zuVPq7q90ibcLvnb6Pq3ct1Dv0ivbyGVWus7qbFE/y82tZIFtt90WgEmTqkUO5GZTKSgF/9cVV5b7rl1R7vJ+y3vK35fbb/+gDIWo66/qypWJsq+Va1hJLfmCCOrf5XK+++67AbjiiisA2HXXXRttd9vNlvZV4qD63Lzgtq6la2gJwHYhFu3sus613pO4oug4juM4juPU0mdWSa9G2OVsL1cy7HXZWvvhrc+34PxD/nw2AEsfmt9oO/wAK3Uz92X7AXDbeaY2rl9tiuKQIUMbbcekwP8txtr51q21NoPeeFh1F09awOrCu6x0yeu3t1Ilf3qBqTWXXTuv0Xaz4WlWrADroF9rSo102uL0ZeoKu3ZnRlii2ecDDzwAVMucQecA6ryMAzTPKqXAKAhbpRuULACVaqMgbs2aS5U0p1SbNuS9OX2HsuRGrh7q51JFzhXFskBwaQ/5+cqi10qekpKt5CyoEluUCKDixbkiWJbmKVWVdvdZh9ty/0K2oM+/7rMtbaFuqVH1p7JH2VFekkz9p5RD2aM8M3mfu/322wPwwhe+EKjK5CihEKpkFiUSakk/JYXldt6dEk7P5Dvm2cQVRcdxHMdxHKeWHlUUVSamQ4W3853lbCH9OiDT3FasttnCHjuNA+Cd9/wZgOVXWCxhmDmz0XbRe94MwGXXWFmbF+21JwDnnnceACuXV4t2n3raaUA2+l9qs4mB223baBMPsiV9On6clMk/XgnAR458PQA3PlSVI1m+xM49SGV3YufYzNh4914gpz9Qqi75zK5VQeA61VFtFZuogtm5MrPTTjsBlepSnj8vuqoZ9Lhx9kzsvPPOQPNyUlJt7r//fqBSFjVTryvD0Kr8RP5efCm0vk9ZaiP/zKRGl0V8c+VG7csYsLo4XJ1Px0jBUammvK3ixNRGtq6SI/l9SfkuyZeyLJcjrIu/7I7q6PQdys8p73PLGFnZSt5PqfyN+kYt3Sd1L/fU7LmnjQ/22GMPoFIUFWOYx3xvt912AOy+++5AtUDC5Zdf3mgjD5FiFeVB0rXrlHMhW87tuyxl1dO4oug4juM4juPU0rNL+CkrtE2sXmz8nmYK66u2I8dbkesTVtwCwLDzLwBg2TaW+Tn8fUc32g7Z0Ub9L44207j44ktsR1oHcHqKMwAYmIplr13bvGRUXJGN6A+wwsarL7wUgKW3WFbUjFF23ne+9M2Ntl+/1GYPY9Jkdn1NJGL5fp3+wTNdEkzHaUapLGfFrGy++eaNtsp2loqj2admzVquL983Y8aMpmNVeBvgtttuazpOWahSevKZdavC4L4UWv+kXL4sVwZLlbFdYWPZnpYr0/lylUeqiZaa1Gs7FV5qj56DhQsXNtqUGa1SLGXzdcpgWQze4xH7L2VMad43lcXXRW4T6tdkYzqfvC3qK6FSB6VeX3PNNQCcfbblP+Qx37fcYuOPN7zhDQA873nPA6osaKiypnU+LQGoa+fxjOVylnoP+bPabknVnsAVRcdxHMdxHKeWnlUUFdeSfs/HxpWSSNNPq7Ol8d43xWIOZv7ofACeToLf4MNfbufd/wWNtmOizR7uXGgxBiNGmHqiGIQ8hmuzzWxfGYc2NKvptX6UxTesPuZI2/DAdwFYOs/qIx0Zq9nEFTtblt9Nd9u+kcP0HrJai1UqNE7fp4z1qqvPphlgqdBANVtUTKJedazqIUI1+5wzx2qBaqaquJk8pkzZdUKL2ivOEap4SMV/3XnnnUAVq5hfu64eWf6+y/fu9G3qMplFqR7LbutqLbZaIi/PHJUqrnhDxXfJ/vI+V+pjqWorQ7XuPQjFbuUxXOX9dif72enblApbXR9UZvPntqv+TbGJslWp4cp0hqoShKpGaElVKX/qk6GKfVQfPjPlRrzgBdX4Q323jlf+g86juHGoFPKyhmhtlZRe6ntdUXQcx3Ecx3Fq8YGi4ziO4ziOU0uvFNwOaU27kLliOxr77HXpGvvhkN2rsjOv/dMPAVgxy1LVh77yZbbjGAsqHbhuTaPt36+zgNPbbreg0nETLKB1yFBzcQwfUbk4Hn/cXHIK8l+/3u5GAalQBcRudfCLAVh82dW246p/ADD6Z79stD3x+A8A8O75VmC2Y5W5Cgdmw/KG59lLbvcr6oLjWwVd524CuRfkrpDrYcKECUBzYLVcDkp4ue+++wB44oknANhnn30abVsV5Za9QlXOQctIyQUtl7ZKOEDlpindPXVLTjl9n7KMSG6/rZY/y12z2le6rmWjeRFtufq0T/aq8iF1ReV1D3VhEHJZK9FFiTTlMn057ZaedPoXrYq8Q+vi67kty90rl67sSDampfigsq077rij6R6mTp0KVO5qqPps2bD6TxXghipx8OqrbZygsCG5nvOQDbm7y6Uq25VX62lcUXQcx3Ecx3Fq6dlkFtJMNXSe5WnEumKN7dtpOwuM/tDcv1XHX2Mq4fotbQQ/4oRjARiURvQL5z/aaPvHy64A4P3vPw6A5csssPqvf7Xzvetd/95oO3uWzXgfWmUB1jffdCsAl156SaPNpMmmNn7okycBsMX77Pjl19mC4GtmVwU59/3zxQC8db+3AvDTP1lg6+jhWTBuUlN9pN4/KIPk6wLiy6K/uQojVUUqiWa+kyZNApoVQAVLa8baLiFBbcoC2XmRYhXhlsKjwttaPjAP1FaZnbryJ6K3F6h3uk+p/uYqRZl0VRbeLtvX/a5kFKiUFdmvklmkUssOobJ3JcBIEc/Pr20q76TSUlKIpALlx7Ur51SnOjl9F31O6oPq+iLZrGxZyXz5z1Lv1F8piSVPZpFtqN9UYopsTLYMsOOOOza1URmo3Ob23deWEJYHSaqhbDrvc5XkVd5n/uz2dpky7+kdx3Ecx3GcWnqlPE4jEDFTFteutdnCFhNshvmp4TYSH/Ozixptlmxps9BBn7YYwI5dbWQfl5sSeNZZ5zXa7rP38wGYvr3FGNx6i8UedHTYzGHUqGrmMTjFLZ77i3MAmDXbYrfywfvcOXMBOPvHPwXgg5/9tB171OsAWPcf32+0XfrnvwNw7PP3AuDqGXYPj8yuimwOHZIUII+h6Re0Kg8CnZU1qXF5HIoURcWqSEnUIvR5jKFm0lJihFSY/HqK19J96fdcfdTMWstUqRi3XlUuJ7+G1KEyxi2nTuF0+hal0pwr4fpZNlO3xJ3sSkpLqe7kyohKNT3++ONAFZMoW1esLFT2VSr0uW1LmVRMWF70GJoVRdliq6X88mu53fYPys+yLk5an69UZsUl5tuk2KlvU4k82SVUsbYTJ04E4N577wVg7lz73s/ja9VXy4bVv+a2tvfetuSvYhUvvvjipntRTG5+X4pRLAtw5z97jKLjOI7jOI7Tp+gVRVEZv+s7qhnCmqGmwnxke1Nctv/BfwOwdPacRpthH3gfAENee7gds9iyOO9LbdZQjcAPe+1hAKxYbaPzMMRmF2MnWDHsu+9/oNH2W99JamBMs9Ck6KxKMTEAI9KM+pZbLF7mFz/+CQBve+tR1vYfNzTadlxvcYtbXfIHAE54uymgJ82v/twRxbz5WL0/oFleuUQYtC7GrSxjqGJUpJxsn5aQlCKTzx4Vi9VOQRGKndExionJlSNl940fb7av7H5lXksBgipuUUqnZtp1yozT95EdlK/QOus5/6yljpfKpGIT89haxct2p6B1q6X2csVESo3sVTau5yrPRNXz2U75d7vtX5R9bp1daZv6qTxmVoq00CIEik3Ml00V8gKpYLbIK0PI/uQlkjqYx4XLZhWrqH711lst/yHP3N9iiy2Aqj9Wke46e/UYRcdxHMdxHKdP4QNFx3Ecx3Ecp5YerhMQsv9hRUc1Tj1kikmqL/6nBX0u3szk2CEHv6TRZlAqrB0fM1fZoNWWxDJ5mMnOH3jTGxtt182zcjVr1tt5Jw0wiXr8TAvoX/h45RZ8zxGvtfMNM1fG6mj3tW59JQ93JFdxR3KNrFtrsvbylJAz5Pijq7azZtu177DXV970JwBes/erG20uvMbciqOHNa/v6PRNcjcyNLtB5HqWK0wBy3mSiIKhFZivtUUVcJ27SZRkIlezAqN1DzoWKneFSojcddddQLMbRO6Uvfay5CqtTbpw4UKg2cWt0jlyy8hdnbsn9d7rXOFO36JMBMgTOcpixXVFqrVNn7ncy3K35UlYZcKMEgN0TH7eMmC/zq2ofUpQkP3rta6UTpnUkruy6xJcnL6LPjv1PXWfpbbJfZv3U7IThffsvvvuQJV0VZcYohAhheOo9I1cyVD1kXI9a5+Kc0PVN6rcmFzQjzzyCNC87rnuT/elpJY8gaa3cUXRcRzHcRzHqaVHFcUBwWaNq9NsYMaE6vJH3f0bADqutUSQ9Sl1feDKSmlZ/3ZLZmFwmsWmEjchlbyJWcHYVQpqHpeUyRWmqgxLpXDGbTOh0XaSSjXMt6XNhqVA0zAkK7+QxtQDUkB1HGmKzcqL/mz3vXJ19T4V+D1pGwDW/p8V7n73p6sCnzdsa7OHxU/2nVmD0xoto1cXdC/1QjNKKRYqjZC3V0B1WR5k5cqVjbZSBTXDlDIj1TFXX6RaarlJJbdouSqoAqg169YsWcsG5rNbzcK1fKCUn1yJ0j27otj3kcJSV7C3VN3Utm4JP6kcUs3rEgFkezpe9qbz50WLpYSX95XbmZ412aAUFj0HebC/bFFKepncUrZ3+j5SiWUHskHoXEJMCndeNF62pMRBJfXJLnPbkG1JJVR/rGNVWiy/n5tvtrGKFMa8NI+SanRf8uao7M6VV17ZaKu+Ws+UvhtUSg0q2223fOVziSuKjuM4juM4Ti09qiiqGs7awTb7e/3QWY19k39lxa5XDrHR9LDHTN3ryAq6DlycZqwD7LbDcvs9qCzJ+iz2ZGDalkbg61KBa0bbjDgufLLRdO3T9vOgValY8XKbXcTsfOsX27bBabIdUxzjgKRmDlyXLS+USt6s3s5iF5anWe6kyy5sNDn0ZaaO/rSqTOL0YaS6lYVeoZrdSiUpF3mHShVUfGFZkiRXKDUzVTyjZtaaCefKjNQVxclowftcFSpVFr0H3YuW9oNKmVQsTR15IXGnb1Nnr0I2V76q1AhU9iXbkU1LYZfynF9DiovsVupMXkBe28o4sbpl2qSiSKXR74rvze+zjHmsK2PlMYr9A31e5WcL1edaxsrmfZnsRQW21TeWSwPmx2mJVdmq+u287I6WolS5Jtlw7hVSnyulW8XmFauYe5v0DCmWV4qiYsih8/dPT+OKouM4juM4jlNLz2Y9p2HpwDRTeHhApRbe+74TAVg80EbgQ1AGXraMzQgb1a8ZYDPJkJTJgYPT21hXxSfE9NbWr7Jt6wbYLHLY9OkAbP3obdV9XWrL8q0bY6rMute8w86bFfAempTDIUPTUlaLbbS/NrUZmGWtDlpns+4BKZ4nrDQ1cunQSuVZsNDaD/Kher9AxX/LLFCoYks0o9TsNI/hU1xgubyfZoi5wqhF58tixFIa8wxpzT7LZcnyWJZy5qtZs37PM1f1vvRedKziJfP3lSumTt9EqrQ+x1z1kI2UMYo5Uu2kdsjO8liwkrI4txS8/Nq6Vql45oqJnpVyaTMp63nBbT0TijWrU1J1X7k65PRdZJ/6LHP1Waq3+l7ZSN4nadEAFdiW+lgXryt7VEUInV/9a+5FUVvFL8ou88oYyprW86L70nn0fZK/TxXaVhZ0Xsxe8Yr5M9ST+DDFcRzHcRzHqaVnl/BLFRSHDbSZ5u8XVLEwl232bwCsT7ULo5a2y8JJ4tKk8HUUuxQbky8aHpuaNmo3rl5kasgr5lS14zrusuV1hk6zGceVj1v2qrKpAQYmRZLG0lMT0nXSTDgbcg/QkkNr7eqDh6bai1lMxNMPp7idIT5W7w+USzpJYYFqpiolUepNrigqxuTGG28EOsdz5bNbzTClkmhWqpl1Hpul2aza6jU/n2azrZZsy9XHUplUTcdcdVQbxWQ6fRfFRskG8uUa9fnp85fN5GqhtsmW1bY8Bjqr7XqV3eUxYfq5tLccqSflc1AqlvnPZfxh/rue2Tw71em7lHU+c6TISVWWmpfbhPpJxQPK3qXOrciW6JWtKQNZyp+em7oVBYl1AAAgAElEQVS2Uq+1L89SLo/X73q2cmWw3Kb4xlz51rb8PnoSH6U4juM4juM4tfhA0XEcx3Ecx6mlZ13PDbetjU+HZF6CjuUmu8pF3HjNssEbP8ZyQ2vKQghD0rVvGbpdY9vCFSZRD09VRwauMbk4d8k1AmCLi9eXWgjFMZ1vd/CA0Gmb03eR20o2nLtiH0ulnOSmVZvchaeirGUgdV25g1YB/nUFkYXaymZzF0xZdFnukLprqyxEee38WfDCxf0HlVJScP6ktJBBvk1B9HKd5Z+17KhVOESeYCA3oNrqGZG7LLebMpmrLqxC6Bpl8lSd27u0zTzhRa68vEyP03cp+7K8bJNcz7JH2Xm+7N1ll10GwF/+8pe2588pC7+3Ky9VbsufhXKfzqO+Ow//0fvS8yI3c13yTr6QQk/iiqLjOI7jOI5TS48qigOTmidVLgyoZn8asZbqW9O4vMxMaUdz7knjtWNtmqVsOb3R9B2f+woANz5is5O700RjaF4MtosirSG7qY5Yf6PNs4yQ36bTx9Hi7nUJIWU5nLL0B1QzyVZKYq4SlvtKJbEuiF9KTN0MWD+XZVB0TP5eWqmWudKjayqJx+m7SO2WWpF/ZlIXy2UkcyW8TBKR0qLX3E5KlVEqyPjx44Hmch9Sc3QtFanPEwK0T7bXKrkrv6buV/vyMiS6viez9C/02UppyymXdaxDtis7Kvtp6NzflcXZ87byLuk8df1oqYyX/X6uFpbb9J7yBMreThx0RdFxHMdxHMeppUcVxUFDrThmHNAcuwd0lhDrJMX4r8RGpViYdL5BA6r4hEMOPBiAhdfMBuDuWRbLMqgKb2mt/NXsGEDz+2sX39B5wSqnL6KlnOo+S810pVSU8Sj5tnJmWRezUqohUmZU2mTevKq0k45TYWXNdnPVsSzFUyo+dYWWy/eZx/No9u6xin2fUhGsK5tUlliqUxRLBbFUyKGyK9mt1Espl3lJKdlOriBCc/keIdsrr5nbZLkUoNSeXFFUIWOVDHL6F3mZHH3eU6daKTstUlAXX1t6SepstywDpVfF1+axgVLey5I6eQytYnB1fKvyTfk+3Xu7ova9tfykK4qO4ziO4zhOLT2qKC699VwAOpLW1pGPjvWzVIrnaOCsywwIleLyk8UWT3DfglRsc2larmrAM7uJ1oP+2LlNQ5Q54Bldy+kZpExolpfP7PIMaKifsQptUxupLLnaohmwFD8tWfbAA1YYPo/HUds999wTqOKwcoWlLFxbLnOWx9OUWc56zWNkNIPOVUunb6L4QGX/5rajTHxlPW9IMd+6WFhtU3axVBXFWuUxZrJtFaKXGpNnKeuZW7jQlkvVc6bztlOEpGbmGc4637777tvt9+n0Huq36rLeH3roIaBajrTMsM9/bqUo5rYhtXnbbW0ZX9lqnXeoVBsVB5wv4VdmVJf9fk6Z+S/qvELtls58LnFF0XEcx3Ecx6nFB4qO4ziO4zhOLT3qel58088BiDWBnb1ZJuYX/0hui0E2bh6cXM6reuDaldh8Zg9czXmmyM2gdUNzSheJ3Ba5m6EM2pdrbJdddgFgjz32aOwr1zEVSqjZfPPNO93D3nvvDVRJLXmCiu5Lbke5a+QqqXNTy8VRJhJA60QEp+8hN5kC73P3mH6WXeizrivrVCaL1IVXlG5fnU8uw7wYstzHsvE617jsqyx5I9vO3XDaJneiEsv0zEAVnnHwwQfj9H1Km8s/7zJZRPaYu6e1T25p9atKrpo8eXKjrWxLZZpuu+02oLLBPDyntK2tt9666ViAWbNmAVVoRZkM1i4ppSxFBZ2fgZ7GFUXHcRzHcRynlh5VFAePsJlmd0bTPcmQkV23ea7wEiP9Ay3Pd9VVVwHVjBY6ByMrgWDatGmNNvfccw9QqSqajc6cOROogqmhUmTKJBkplfnsVuVENAPef//9gSoou+54qUsiD5ouE1+k8OTvV8frb+L0XebOnQtUyUj5Z18qiO36orKwe6miQ2W3Y8eObTr/ggULgOZEAyWzlEWLc3RuKZ9Si+qWD9R5ZONSFvfbb79Gm7e85S1AVVLF6du0KqLebl9uE1KZ1Y9q0QQpgEpCgSrhqtXSkrlSqWS+Rx55BKj6+7zskuzv3nvvBaq+UvdbV6i+LPJdp+z3VgKhK4qO4ziO4zhOLaG3Cjg6juM4juM4fRtXFB3HcRzHcZxafKDoOI7jOI7j1OIDRcdxHMdxHKcWHyg6juM4juM4tfhA0XEcx3Ecx6nFB4qO4ziO4zhOLT5QdBzHcRzHcWrxgaLjOI7jOI5Tiw8UHcdxHMdxnFp8oOg4juM4juPU4gNFx3Ecx3EcpxYfKDqO4ziO4zi19PuBYgjhzhDCQb19H46zMRBCODeE8Lrevo+cEMIRIYTzevs++gshhGNDCFf39n30RZ4r+w4hfDiE8LVn+7ybEiGEGEKY0dv30dcIIYwLIdwbQhj2HJz7uhDCbl216/cDxRjjbjHGK57La4QQfhZCOP25vIbTfUIIs0MIL+/t++gLhBB+lDqRjhDCscW+d4QQbgwhLAkhzA0hnBFCGNTmXHsAewIXpd+3CSH8NoQwP3Xi04r2Q0MI/5PO/2gI4cRi/8tCCPeEEFaEEP4aQpia7ftECOHJEMIdIYSZ2fYXhxAuzM8TY/wtMDPd30ZH+jv+JITwcAhhaQjh5hDCob19X32JEMIOIYRVIYSzi+1vS3+35SGEC0MIY9uco8m+uzo+hPCdEMLCEMI/QgjbZtuPDiF8tzj9j4BjQgjj/9X32h8JIZwdQliQ+oL7Qgjv6e176i1CCDNDCH9M/Vus2X9CCOGGEMLqEMLPunHKTwE/jTGuSse37HdDCJNDCP8MITwdQvhmcd0/hBD2Ls79DeC0rm6g3w8UHWcT51bgA8BNNftGAB8FtgL2BV4GnNTmXO8FfhFjVOfWAfwBOLJF+1OAHYCpwMHAJ0MIrwIIIWwF/Br4PDAWuAE4P+3bBng3sD3wQ+Brafsg4JvpnkvOBY5vc+/9mUHAHOBAYHPsb3ZBOTDfxPkBcH2+ISkh/w28HZgArADObHOOJvtud3wI4YXAC4CtgauBT6ftm2PP0BfyE6cv8UuBf/8X3mN/5qvAtBjjaOAI4PQQwgvqGrabrG4krAUuwPq4OuYDpwP/09WJQghDgXcA+QTpFFr0u5id/i+wHfA6DQxDCG8BHoox3lBc4rfAwalPbk2MsV//A2YDL09/vAuAnwNLgTuBvYt2nwbuAhYCPwWGpX3HAlcX543ADOzLaS2wBlgG/K633/Om/A84CxvArEyfxyfT9iPSZ74IuALYpTuffc35B2KDlSeBWcAJyRYG5faWtT8FODv7/UXA39N93AoclO07Fngo2ecs4Oi0fQZwJbA4Xff8Z/B3uRo4tos2J7az33RvB9RsH5T+BtOK7fOAV2a/fwk4L/18PPD3bN/I9JntjA1az03bdwbuSj+fBHymxb29GJjV2/bXg3Z+G3Bk+vkgYC7wceBxYAHwzqztlliHvwS4Ln0OV7c5978DDwNPYYPShk0DPwNOz9oeBMzNfp8I/Ap4Itnwh7N9L8QmBEuAx4Bvpe3DsC+6p9JzcT0wYQP+FkdhfXv5rH0FOCf7fTrWT4/qjn23Ox54C/DVtP1VwCXp5+8Db2tx/qOBv/a27fT2P2CnZKNvLuz3ZOBR4Ky0/ROp3XzgXaTv3Bbn3A74G9Z3/gmbOJxdZ6NpW27TAzBV7sFkgxcAY7uyTVr01xvwd5gBxDb7Twd+1sU5Xgo8UGxr1+9eCuyUfj4PeDMwGrgZGNPiGpcD72h3HxubongE9scZg3Wc3y/2Hw0cgnUIOwKf6+qEMcYfAb8AzogxbhZjPPxZvWNng4gxvh14BDg8fR5nhBB2xBSnjwLjgEuA34UQhmSHdvezPw44FNgLeD7Q7Xim5J66GOsAxmIDn1+lGJORwPeAQ2OMo4D9gVvSoV8CLgO2ACYB/5md8/chhE919x664KXYYLru3kdinfG93TlRCGELbNBwa7b5VkDxLrvl+2KMy7GOejfgAWD3EMIYbJJ3ZwhhMjYg+EaLS94NTAshjO7O/fVnQggTMBvNP6utMbVxW0yp+EH6DMC+NFcB22BfuO9qc+5dMdXs6NRe5+zOfQ0Afod9rttiCvVHQwiHpCbfBb4bTVWajn0hgykimwOTsUHt+7BJAyGET4UQft/mmqMx19jHa3aXNvYgNtDbseY8dfbd7vg7gZeEEIan93lnUmd2ijGe0+J278Zc25skIYQzQwgrgHuwAeAl2e6tsT5xKnB8UsBOAl6BqWNdhRKdg02CtsQmDG/fgFv7MNaPH4j1WQuxZwZa2Ga7/jqEMCWEsCiEMGUD7uGZsjuZzXaj370DeEXqW/fGxJEvAd+JMS5qcY0u7XZjGyheHWO8JMa4HlOeyjf//RjjnBjj08CXgbf2+B06zwVvAS6OMV4eY1yLDTaGYw+36O5n/2bsy25ujHEhyS3aTY7BlIdLYowdMcbLMYXlsLS/A4u1Gx5jXBBj1EBgLdaBTowxrooxNhIRYoyviTH+y0HyIYR3Yh1Hq4HYmPS6tJun3Cy9Ls62LcbUGO1fTDOLMbXnKewz+AvwauwL47uY4vD6EMKVIYSLQgiTsmN1X2PYiAkhDMYmpv8bY7wn27UWOC3GuDbGeAmmpu8UQhiIhQZ8Ica4PMZ4B+Z6asUbMVX56hjjGsyF2imOqgX7AONijKfFGNfEGB8CfowN8HWPM0IIW8UYl8UY/5lt3xJTi9bHGG+MMS4BiDF+Lcb4mjbX/BLwkxjjnJp9LW2spm2dfbez0Tsw5fSfwBTgPzAb/XCwxJW/hRB+kb6QxVJs0LFJEmP8APa3fwkWdrI6290BfDHGuDrGuBLrZ38aY7wjTSJPaXXeNCDbB7PxNal//O0G3Np7gc+mPn11utYbkwu8pW3Sor+OMT4SYxwTY3xkA+7hmTKGzjYLrfvdr2J//yuxwfBgYA9MODkn2e0JxTWW0kW/urENFB/Nfl4BDCviIfLO5mFsZO70fyZinycAMcYO7LPOlZLufvYTi7Z1X1CtmAq8Kc02F4UQFgEHANukzvAt2Ix1QQjh4hDCzum4TwIBuC5YFn9LReiZECzL82vY7PjJFs0026z7kq1jWXrNFb7RVJ3asmJf0/4Y47kxxufHGA8FZmJfKjdjA9nDgf+jeVCr+2o1K+73JMXuLEzVKjvzp2KM67LfV2BfGuOoYhzFw7Smyb5jjCswt1t3mApMLOz7M1h8H5jSuSNwTwjh+hCCBoBnAX8EzguWGHVGGhC3JYSwF6Y0fbtFk7Y2VlBn313Z6LdjjHvGGN+CPbtXYd+Zx2Mq492YS1OMovPAc5MiDbauxjwj7892PRFTMkai7Ge7stmnk62KDe2Xf5PZ7N3Aesxua22zi/66J1lIZ5uFFv1ujPHpGONbYox7YhOb/wQ+hNnpHdjz9L7kWRCj6KJf3dgGil0xOft5ChYbAbAcC/wHIISwdXFcd2fcTs9Qfh7zsc4AgBBCwD7reVmbVp99yQKsk6s7DgpbwVwqYg4WfzMm+zdSimCM8Y8xxldgLr97MDWGGOOjMcbjYowTsdnvmeFZKhORXDw/xlz1t7dql7mGO7ntWrRfiP2tctV+Typ36Z35vuTKmU7h+k6uva9grsUdgDlpRn89NhMWuwCzs9n+RkWy2Z9gX15HJmW8OzwBrKOzfbeiyb7T33/LbH9X9j2rsO9RMcbDAGKM98cY3wqMxxS4X4YQRiYV9NQY466Yyv8aupf0cRAwDXgkhPAopjwfGUJQ4lZpY9sDQ4H7yhO1sO9uHZ9CAd6LucBnArelz6fORnOX4KbMIOx5F2WfvYANs9mxIYTcLvNjy+/vgdgESszBJsm53Q6LMc5rZ5ut+use5jYym+1Gv5tzPPDPpI7vDtyQvAi3Y3YsurTbTW2g+MEQwqRgJRA+Q8rCJPn4Qwh7BatVdEpx3GNYhqbTNyg/jwuAVwcrxzIYG3SsxpJKRKvPvuQC4CMhhG2TW+nkYv8twFEhhMEpZumN2b6zgcNDCIeEEAaGEIaFEA5K150QrB7gyHRvy7BZLSGEN2Vu1oVYp7q+O3+IEMKQZLMBGJyuOSDt+zfMjXlkjPG6bpzuEiyOJz//MOzLE2BoaK7l9XPgcyGELdJs+zgsGQLgN5jb5sh0zBewL9jcnQoWK/qzGON8LPZ0p/TFfDAWSC4OxAK1N1b+C+uwD0+uuW6Rwmx+DZwSQhiRlIJ3tDnkl5iN7h8shvdUzHbELcBhIYSxacKcZ6BfBywJIZwcQhiebHxmCGEfgBDCMSGEcUnRl0KxPoRwcAhh9/QFvgRz93XHvn+EDTb2Sv9+iMUAKybyF+m9vCQ9V6cBv44xtgqfKO27u8d/C3ObrsCSGvYJIWyGDWQ3JRutJYQwPoRwVAhhs2QTh2ChPX9pc9gFwLEhhF3TAPCLrRrGGB/GQnhOSf3dfpjXQdyHeQ9fnfr/z1H1WWB28+WQynMFixl/bfq51jbb9dfd+HuE1OcNSb8PC5a9rP2D0v6BgL4nWmWCXweMCVl5Jtr3u7rGeOCDVGOZWVh282ZYCNJDqd1QLLv/8rZvKvaBLKl/5R/NWc95Rtw0OmerKvN1ERbHMyJr/1ks43QOFmvWyMDClI5b0nEX9vZ73tT/Aa/FBhWLgJPSttenz3YxFp+xW2EjLT/74tyDMFfXU+nh+hjWeYS0f3vgWqzjuBgLeM7tbt90/acxtedibLa8DVVmszKzd03HnIGpn8sw1eP47HyX0iITOO2/Itlq/u+gtO+vmNq0LPt3aZtzzcRmpiHbVp47ZvuGYiUelOV6YnG+l2Mz8ZXpPqcV+3fCVJlB2bZPpOfwLmD3bPvtwJ69bXvPkT1PTX/bVcVnpaz4g2if1TkO+D3dz3o+Fnt+lPU8D3hJ2jcMm0QtwdSMj9E56/lcLMxnIRbDp/s4G8vKXpbs6HVp+1uxgPzlyU6+R9Uvf6adTRb3fQrZs5a2vS29l+VYfcSxG2jfbY/HJiwXF9u+k733SdnfbS4bkM29sfxL9ncl1q8tSc/qcdn+Tvabtn8q2VF3sp6nY67/pcCfsUnETwqbXpDs7yQ6Zz2fmGxwKdbHfqWdbdK+v56SbHxKi3udRud+c3Zhx+X+U9r8fb8OnJz93rbfTW1+Drwp+30y9r21EPhmtv1N2OSo7WesL7+NnhDCbOA9McY/9fa9OD3Lv/LZByt8/MMY49QuG28EhBDOAS6IMV7YZeMeIoRwOPD2GOObe/teNjaSwrAI2CHGOKu37+e55rmy7xDCh4DJMcZPPpvndeoJIZwP3BNjbKlEbiyEEMZhg+TnxQ3wNnTz3NcC747mnm7dzgeKzsbOhnz2KWbrYKxczQRS5mOMsa4ItOP0O9LA+8+Yy/mbmAr+/LipfBk4/Y4U3vA05uV5JXAhsF+M8eZevbFNhE0tRtFxuiJgcVsLsSzcuylWYXCcfs5rMXfffCys5igfJDp9nK0x9+8yzD38fh8k9hybjKLoOI7jOI7jbBiuKDqO4ziO4zi19PTi3C5ftiZ03cTpLc4555wIsH69VUi4/vrrG/vWrbM6yEOG2IqBs2ZZTsCwYVUlmYEDBwKwbJnVS1271srkLV1qlThWr64WMdA2veoY/T50aFX5YeTIkQBsvrktCDFgwICm85c/l/eVH5O/F71P0dHR0el8avPkk0+67fZR3vnOd0aoPtclS6oylKtWWf3jiROt9rxsSXYMnW1n+fLlQGWDo0ZVtYDHjLHFHZ580mq6X3XVVQCsXGnx91OmVKXyZDuzZ88G4MADrWrNdttt12ija+jZ0TGyRb0nQBmcjbaDBg1q+h0qO9fxH/nIR9xu+zCvetWrIsDw4cMBePrppxv7pk613MIbb7wRgHnzrGSulSM1RowY0fS69dZWEnTwYKv1Pm3atEbbCROsZrz6Wj0bapvb2pw5Vutbdr7TTjsBcMwxxzTajBtnZRx171tuaaVKH3zwQQB22GGHRls9U7pP2f3tt1dlby+77DIALr3Uqi9df/31PWq7rig6juM4juM4tfS0othn0MwBqpF8rqw4To5meVI11qxZ09i3eLGt2rXZZps1tclnwDpes0cpNfo9n7FqFjt27FigUgB1Tc12oVKBhM6bn69Ex0ttye1eM/JSfamjVB2dvodsR3ZWp7CVSl3+mUsN1HE6n1RHKXf5cVtttRVQqSpPPdV5hcD58+c33YOUl1zN1DVzlQiq5yO32zLWXsfUxeB7XH7/QHajvkx2BZWaV/ZPpa3kx+sYKd/y0EBlE2oru5at5XYuFV1jCN3Dvffe22jz6KO2mvCiRVZ3Xs+RVE3dC8Duu+8OVP29+ucZM6rFuaSYXnzxxZ3eX0/gIyPHcRzHcRynlk1GUVyb1JiLLrI6qw888EBj3+jRtr720ce8HajivRxHaPap2WQem7Vw4UKgOf4LmhW3UunTvjrlr5wlS/GWeljGjeXny2MdhZSZMrawTkHXjFwzaP2evxfNvnP1x+mb6PPTa65Al/GxUr1zxVqKiJREKSL6XYoLVHYr29l5552BSl1ZsGBBo61ssTxfnU3K3rRP9pwrg6WSVKcstVPHnb6H7Eg2m6t6sqXHH3+86Zg6+5Gap1eRq+tCfZr6dyndddeWXT/xxBOdzi81XV4gHa82++yzT6Ot4i1FqVRCNUaR6tjTuKLoOI7jOI7j1OIDRcdxHMdxHKeWTcb1vG69ufiWLTN3ywP3P9jYN2asBbc+9aTJ2O56dkoUqjB58uRO+8okE7nV8oSXOncxVC6J3AWt4+QGkVtO7ozc3SdXi1xtch/mbou6kjn5Me3QMXVuvnYJM07fQDZZJqVAFU5Q2kWdLcoGS7dvfqy26VpKoFFIRn5eudvKcIq6RC09B2XoRB4OUedGLNH9eTJL/0B2s+OOOwLN/ZXCJsqySLkdlMlZelWSjEKGoLJv2ZwSEdXX5v2pXMNlko3cw/nx2267LQAvfvGLAdh+++2BZnez3kOeYJvfC1QudoWC9DSuKDqO4ziO4zi1bAKKoo3Wr0nFX39xzgUA7L//fo0WDz9sBTT/4+vfBOBLp9rSvuMnTOyxu3T6NiqUqpliro5IBdGrZp95YomUw1LV0HnqAu2lBEolVFJLXjBb19T5pLKsWLGi0UZKTFnqoS5RRfehY9Qmvz9XZPoPso+y7Ad0tpm6BCvZQVn8uk41lwKkV6kfUhZzO9NxaiuFSKo5VIXApeKXSnZ+7fL5ErkKVb5fp28j25A3J7dd9WUqSVYmZEH1OZd2U/bTUPWpaiObLRcnyK8t+1Th7twTqftRMowUxD333LPpHFDZpZJiZNd54opU0LykT0/iiqLjOI7jOI5Ty0arKMZos4lLLr4EgC9/7QwA3vymIwE45OUvb7Rdvtxiar79ve8D8NnPmaL4lS9/qdFm3PhtnuM7dvoyUhI1o8tnhFJXSlUw/11KTFlgu1TuoIrJ0qtUFs1KdQ7orHSq+HedQqkZdrkkWk5ZaLu8z/xevUB930efkdSYPAZQqrNioaRk5HFQ5fFqo+dAKgjAY4891nSNMr41t3HZoOxM95IXqX/kkUeAapk/FSZWLFhuk7of2bTUKCk6+XvJ1Xan7yJ7kT3l/V5pj7KjvERNGbdaem9ytVA/y6akCJax3/nPOo8URambUH0/vP/97weal68sKZ8pLSeYK+YqyZMr7j2J9/SO4ziO4zhOLRuVotixvpphXvS73wNwxhnfAuDoo48C4CUHWGziU9nMdUga/X/0wx8E4Fvf+S4An/v8qY02X/2KqYtjt+ydEb3Tu0iFKIvAQjV7VNZa3RJj5XJ5pZKYx99odrvNNqZi77ef2exee+0FNCuB//znPwG45pprms5bpxbWZTC3olSB6opre6xi36dUhtshFS5XMmSLsicpiHPnzm06Jj+uzMRvV/xadr/FFlsAMH78+E5t5syxGHIpSlIWc5tUBqueU/2eq0Z6PnsrzsvZMGRzdcucyjbURn1RXbWHulhsaO7D1aasAFCnhsvOFTur2PFc8VQsotTvsupArlCWmdX/+Mc/ALjuuusabeQ50vKDPY0rio7jOI7jOE4tPlB0HMdxHMdxatkoXM/rk8v5oosuamz71rf/E4D3HvceACZPNjfeA/fdB8CUqdOq45Pk++D9dwLwgfcdD8B//fD/Ndp87gtfBODLp58GwBZbbPXsvgmnX1AWRYXKnVCW6KhL9pB7Qa4Mtc3dICq78PKUcKVirXVrPWtNUpVmuPLKK4EqEQCq5ITSraL7q1sfWuRJO+V78ILb/Ye6tZllg7Jp2W9ut6XrTEH1ct/mNqDjdI1265lrjWe572TzuetQSSx6RrS2rs43c+bMRlvd17333gtULvH8fHIZut32D/R5qX/NE5NkW/p864qpl9vKcIf8fOV5lJiifjW3mdJV/NBDDwHN/ajOrX54+vTpna4p9BzqGXjyyScBuC+NVfLj6sr19ASuKDqO4ziO4zi19GtFce1aG8FfeOFvAfj+D85s7Dvu3e8CYO8XvACAhQttlD5psqWpL1pajf6HDLbx8uRtrfzIoEE2g/nYxz7UaPPNb1pSzOeTsnj6aaYsjtliy2ft/Th9F6lxeq0rIaIZsIKb81moguxLNUPByZpNAhxyyCEAvOIVrwA6l27Ig7JVlPWlL30pUCmMUhYB7r77bqAq2loGgufn03vRNevK7LQqbuz0PUoVOS8NI5VQr7LbvHCwbFpJLFIfZRe5+qiyHrJlXVvKSK64yG532WWXpvPcc889jTbz588HqiQB2ZsUl/vvv7/RVnav8+j5rCsm72Wd+hdS0/IEENlh2Qflv5cFt/Uqz0yeoCLblO1PmjSpqU1etknPhNRCqe55clW55J7uS7acJ4zpO+HGG28E4PLLL2+6T/de5gIAACAASURBVKjK69R5tHoCf2Icx3Ecx3GcWvqlorh6tRXgvPA3vwHgv3/0PwAcd9y7Gm32mGklFFSAeOgwm5U8sdBG9Hdd+b1G2xFb2qx2z/3eCEDssFl3x/pK/TnxxI8B8PVvfBuAL55yCgCnnmqvY8a4srgxoxlnWbQ631YWD84p1QypN5q5vuxlL2u0Peigg4BqdqtCxoqFyWejKiciFeeAAw4AKoUFqnibO++0GFzFeuWlTUqkBpVlferek9N3UWkZqTG5qleqE7LjXFGUIqK2Uj8UpyX7Bdhjjz2ASj2RvemYXO2R7ZVLDG677baNNlK1dR4p4mqbF/uWqqOSUlJ76tRyVxT7F+rv8n6vVMrrKGMUpUyq763ziKigtWJnpQDmfbp+1thCqqNKPEH1fMibI3vXsyVbhkqJlwp5yy23AHDwwQc32rz61a8G4NZbb+3yfT8X+BPjOI7jOI7j1NKvFMWVK20k/+tfmZL4s/89C4D3vvc4AHbeeYdG2yVLbQQ/Yrj5+ec9ZgVYH/rnDwB46bQHG20fX2SxLjdcY+PmvV9iy/x1dFQFNAekWcNJJ34EgDOSsnhqilU85YtfaLTdfHNXFzc2yoKpOeWSZZq55kW0pWIojmW77bYD4FWvehUAL3rRixptpYooLuaOO+4AKpUkn91qFqsZrLJJX5Bic6GKddFMWrEwQjNj6KyOlvGM+Xtx+j6ymbpM3zIDv85uSxVPSEncddddG9ukPirzWLGEZawYVFnTalMq9lDZ8g47WL+uZ1DPW66OSvmRkq73kqve7aoROH2PslB7bhulx6PMWq7bJjWvrkC2tslDo9hx2UoeHymkcEotzBVFqYNSvfVsKPs5t90yG1ux6UcffXSna+WZ0D2JPzGO4ziO4zhOLX1eUVyxvIqj+VVSEs86+xwA3peUxB12nAHA4sVVJqqUxEeftJnrrH/Ysnz7Tp4NwIAhVQzXlK1tdtIx/5cA3Cxl8YAjG23Wr7PzDEgzmE+c+FEAzvjmNwH40ulfabT94hc+D8CoUb2z3I7z7FNmCNcpi5oBS33JVRjFCUqJKTObFacCVQxhma1cLlcFVezY9ddfD1TLm22//faNNjvvvDNQqY6aWSve5a677mq0ldJTZljXqTBej67vUy5Xl6s0sgfZsuwiVzv0Geu4UuWTugdw++23A9XyeaXt1GWZqv6hFJ2HH364073vuOOOQPWMzJs3D2h+DqQO6TmTulOn3Lii2L/YkFjo3Mb0s+xcMa91S/opC3/LLc0bKHVPz0aegVzWs9UzoFjyfJ+eFynnde9FqqOUeNXPzWMy9V2gmOOexp8Yx3Ecx3EcpxYfKDqO4ziO4zi19FnX87JlFiD6y1/+urHtnHPPB+ADH3gvANtvbwkBcjkPG1YtjzP/CTv+4WvN5bzflNkADBhirom16yrZOaYfp060feFRc3Hf9I/qz/O8Fx0BVC5oSdMnJRf0177+7UbbL3/5qwB8/vOfAWDkyKrchNM/KV0Guds1D/6Hyl2RL5emcguHHXYYUJXDUTmQvFTJgw8+2PRaloLIC7sKuadvuummTvcrN7QSXOT+lpslD9RW4ozch7p2XSFbd+H1fUp3a51rTm5f2UG7ZSpVhknH5sH1CupvlTSS25DcarNmzWq6hzxRS/cu993UqbYggmwzd3vL9axnTkktcvlB5Yb2sk79g3KZ0zrb1WtdMktZikz9ppKi8n5PfaHalqFGeZ+rEA3Zn56XvNyY7FDHKZlFYRN54ou+S+R6Vv+ch40osVH33tN4T+84juM4juPU0ucUxSVLbOT9m99cBMB551/Q2HfCCe8HYLtp04BKSRw+woJL5z9Wlfl4+ForqL1/UhIHDrY2a9al2QnV7CSmH1evsz/H5G1sFqDkFoBbrrXXPfc93M6z1mYPgwbZzOGTJ32s0fY/vm4JLt/+jt3DSSeeCMCw4VVArNO/kLKmmWKulpSzWbWRighwxBGmSO+///5AVQKnbgF4/VyqJGVwdn5fUnw0y73uuusabTQzVUFkzVhVmkeB3FAFfN92221ApVTWqUx5MLjT/5C9qhSI1I+8XJLQPhUkVgB+HsDfyh7qkki0Tfaaq4NCSouuMWPGjKZ7yYP9yzJO+TMi9Py4Et4/aNfnlsW09ZqrxbKTslSS7EqlcAC22mqrpjazZ88GKjtXv5jfh5JapPLlXiY9Q6U3SOp4rlDKi5PfD1QLLEBl63lf3ZP4E+M4juM4juPU0mcUxSVLbAR+zjnnAnDRRb8D4KMfOaHRZtKkyamtqXnDh1s8wbzHTFmcde1/Ntq+eKqlkQ9ISmIVk6hlnPKr2y8Kb1iz1maeU7apSujEBRYreeu1tk/K4vq1ptYMHlz9KT/1yZMA+Po3vgXAGd/4etp+cqPNkKFVPKXT95FSUc5yoVLzNOtUGQ+phwD77LMPUMV4SalTHOI999zTaCsFsCw4q5lsPkvVtcuCsHmsl66h+BuVy9EMNr9PzYR1DzpPby1G7zy75DYl21FJmTLuq66NbFD2kS8VqW250gdVDG+u9ug8ZTHuOtWxVNb1mis4aqtnQ+fJY9ZK9d3p25Qqcf551ymI+Xao+mPZS1nYWnGJUKmDUhLlkdE18/Jl6kdLpTxX1KUy6lkoF1yYO3duo61USymfKluWL6UpJVHPYU/jiqLjOI7jOI5TS68qiosWVQtjn3++xQP+368uBODEj34YgClTpjTaLFliM1aNyh993LLfZl17JgD7TZ3XaDtwiI3610lJ1Ew1KpMqm92meMVIbHpds64aR0/ZOsU5zP8VAHfdaH+63V5g2atr11WFXUekmcFHP/ohAL6RlMXv/ef3Gm1O+KDtG5ZUUad/UDd7lEKhGeEuu+wCwAtf+MJGGymJisWSyqdYwFwB1IxVSG0p411yytl3fg4dd//9tlSl4mRGjx7dqa2WZLv55pubzl+nzDj9j/xzFLJb2VkeB6X4xTLzVLGK2g9VbK1UGB2j5yOvDpDHfEFl/7lqVGa0luTbpdzoVefJbbWuYL3TdykLY+efW5lZrza5jUklLOO3pdTldi41XEtellUfcqVb8eVSJDUeyTPspSjqPnVf8+fPB5o9NDpeyrnayOsE1fOh7OmexhVFx3Ecx3Ecp5ZeVRTvuvvexs/nX2CK4sknfwKASY36clUtIcUBLltpM8nb//ZDAF6xw2wABgwd3Wi7fr2Wnmq+puYkTTWZkrrYmMEqI7qjmsGsXmtj6hmTbZZy6/0/B+DOO2xG/bzn7Vu1XW2zCSmLH0+1Fr/61TMabfbd12LWXvKSf8Ppf+T2o9miZrB1M1bVcJPioRmnlJW65fC0TbPZumzNcp+OyWfWUg6V7SwVqFxGDeDKK68EmjPu4P+zd95xdldl/n8/U5JJ7wkJIQkQQGpAYRUBARuCgr2i4rp2d13ctYC6LutaEHdd9WdZ3XUtFBXFtQBKUREQEXEpEpqUQAIJCemZJFPP74/nPPd75sy5dyYK0/K8X695zdzv93zLnft8zz3n85TTN0vP/nZlcWxgtmI2lKp9++67L1ApI6Y+W5u1a9fW2uaZyxa/aPU70yoAprCbDf7oR+pJsrqK0D9mMo8TLimNeTZ1SYXybP3RQa4WpvZlNptXhEht1/bln7cpgakabnkPecxsKT7SFHirhWjPRBqbmy/j2qh2p8WX52p4umygvS9T8ocaVxQdx3Ecx3GcIj5QdBzHcRzHcYoMq+s5LenRFKXk++67D6iKaveJO45/T2zTtosOei4Af3rkWwActFeVUNIr9tb6LgNkr0mSWWq1t2MTsSKeUo2jx8XTPb5Rg1A3NGmiwkFLDtDr9fQv1TAhuuiW33UXAD29lQQ+rrVvORNnZJOX70hdCeYqsNIyljRy44031tpYCRpzJ9jSfXa+tOC2BV/XK/mRkrs0zEWRutysHI4V3LbzWOD2VVddVWtryTV2HiuhY+5rqAKr86ULnZFHqeSNUc8FmwbRW1C/LZ9n7mSznbSsk7nOzH6XxD78jDPOACo7hMp9Z2VC/vCHPwDVEmdQPVeWaGDPValUk9miXdtsNH2P9qyU/hfOyCP/nBqFGlifZnYFlS1YmI/1vea+TZP4rESNubfzBJpSf2/9ch4iBFX/mYdj2HnSsY8db23z0KD03ocrbMIVRcdxHMdxHKfIsEoCByUzzDP/7t0AfOKT5wLQ3q6zxpe+5MW1NltjwKkEHVUfuOxZACwXHW3/8aEv19oetlhnt129GtBa0xNtBiKJVNkUZ9298Xfc3JrUZd20We/n1g1HALDs+LcDMH2KNuruqhTFKdNUfbnppt8D8I3/+SZQLUEIcMQRT8MZPdhsr6TYmZJi6oup4mlRbmt/xBFqPxZQbQH+6QzT1BorhZBfs5T4ks9U999//9o+K3ljM2g7/zXXXAPAb37zm1pbSybIA6zTQG37XzQK0HZGBvWKtqf7zJ7MPlLFzpQ+U8uttNI992giotk8VPaQF6A3NSRVewxTTcze0mLYptjYNex3SSW1a5myaKVGUhu19l4eZ3Rgn12+RGoJ+9zTftTUN/ttSp31y9ZPQ2Xnlthn/Z1dMy1fZol+a9asAaqkljSRxmzenq288Hb6HOb9qT0TqcfGPE5p8thQ4oqi4ziO4ziOU2RYFcUJE6v07xe84CTdNkFH5ef8y78CfX38r33Nq4EqZqVrp/4+5DBV5+5u+bta29sf+CIAyxZFRSREZbE2NE5m1vG3zT3HteiWTZur0jy3b9RrPPVEVRInxsmDzRjSpax+c8NvAfjmt7SEzpln6jKEL3/ZK2ptmpr7L1rvjFzy+MCSqmezT4t3KSluppJYyRCLVdxvv/1qbUxVsaLc9gzYrLYUq2izZCtnsmzZsto+m2WbWnjFFVcAcO211wJVXCL0X8y+FJPpiszoIS+UXfrsLIYrX6YPKjswRcNiCvNYrhS7likjplCmMVeGPUdWcimNG7Pz1FOw0/diaqVts+8IVxRHL/kiAn1K2mVKuX3+qRfHVDzbZuXK7HzW10HfGGyolDvrc1Pbtf7ZCm9b7G2qAFqcb17KqfSdYOfL7TJ9L42OHwpcUXQcx3Ecx3GKjJi0xZYWHT0ff7zGHZ77qY8D8E8fPafWZvt2jTv5mze/GahmjR07NHbxwIMPr7W9p+nvAbjtPl0277BFOiPoIsbJ9BmZ60i+tTkqiVtiPOKmp9ZaHP7sdwAwOYZAdMaZgsUnXHvtdbW2F1xwAQDvf/97ATj11NMAaGpyFXG0khdgLal6hs360uxRi+myGarZ7jHHHANUGaJQxS2aurJ8+XKgmp2mKruphYcccggAhx+uz0CqcK9cuRKAq6++GoBf/vKXANx5551A32WhctXH3m8a+5PHojkjF7NTUytS1cI+W1NPZs+eDVTKIlRqoCl/VpjYzpuqHvk1bUmzm2++GaiWJoPKnsz25s2bB8CJJ57Y79r27Dz00EN136fFh5ma78XgRz955nEpvtZUPOsr0zamKFpcrdmY2bl5c6Cy1btihRJTG01tT5Vu89pY7LedJ/Uy2b1bf5/HeqfKoKnpeX+aKpRLly7td42hxBVFx3Ecx3Ecp4gPFB3HcRzHcZwiI8b1bLS0qAz7jGccDcBnPnNubd+HP/xPAHz5K1oG593v0pI6WzarO6RzZ1XW4cCDtbhwzQV9T3RBL1G3XZdUrrTWJpW2N8Xz3LbhSAAOP+EdtTZTose6q9NczhoYe+21ujbuRRdeVGt71lkfAOCUU14IgIi76EY7eXHp1LVlbpB8bdE00P+xxx4DKleEufDMxXHsscfW2h544IFAFXx98MEHA1Ux4tSlbW5qC6g2F0la+sEKav/qV78C4I9//GOfe2jkpislsxju3hv55MkmpUQOKztjbi0L0oeqyHUebmDuaXO/QVWc3dx4ViLE7DYtpm3PioVImK2bexCqxBkLvchJXeT2Hsw9be+ltNZzo7ARZ+RSSqizkANz36ZlvKzPtgLb+drM6bNhYQ5m33kZmzTEwv62faV1pq0Uj/X35sIurf1s17RwDLuX1Hbz4t5DjT8xjuM4juM4TpERpygazc16a0ccfkRt26fP1QSXj/6Tls75whdUJfzbv9PyM+1JodjOHarUHPAUVWPukTMBuOUuPeaofdprbTds1dH6rev1Wkec+C4AJif1YW2mMn16TF65TpNXLrrwQgA+/OEP1do+/6RTdu3NOqOGUiJHXsbBSNtYkkiekJKXEEkxJdFmxFbCYcOGDbU2Fphts1krpv2LX/yi1sb+vuOOO/ocvyuKYNq2XjkHZ3RifZsltZiiDZVqZ8lX+TGpMnL88ccDld2aomikz4epMma3ps6kz4Ep4VdeeWWfNqbgmGoDVeknU8ndNscOpSLV9vmakmik/dS0adOAqo9ME1LqXcP6trxIvCne0FfJhsoe0/7e2piNWgF46+9T+7Tj7VnKl4SFaqlMS9YaalxRdBzHcRzHcYqMWEXRaGqqbvHQQ1Xx+/Sn+5bO+eIXvwTAu99ZxRTu2KGj853bdYZ5wAEaw3VfkyqLN9z66VrbjnG63NlhJ2jM45Q48UjjHabFZfl++1stpn3RRecD8OGPqJL4/Oe7ijiWycvjpEqKqSK2z2aWqYKSq42m0FhMVb54PFTqyGGHabytzWrTUjo247XlpH79a42ZTRXF2267DajiuBopiXmcZV7YNj0+j9t0Rh65TaaYDZuaYuVr0rIhFgObF3+332nJGlv+zGIVLT6rVLrEYrZMxbR7SeO8TH2xtmZvFkNp14MqnjF/Pkv260tPjg5yBTm1YbMT6/9Khd/NE2OKotlPKVbV/raYWetzzXbTPtNiCU11zO8TKqXTVE1TFO2ZSO/XFMS8+Hy6bKDts/MMNa4oOo7jOI7jOEVkiGM5nqCL6Yzw3nu0OOanzlV1cHISE/OOd+hSe3kszfi2mBV6/wO1tjaLmDNL4w87OnS2nMbY/OEPWjT2ogu/A8B7/0GVyeef9KIn5i1VKwk6I5B58+bVtd08MzhfNq20LX/u0lgby/y0IqvPfvazATjiCFXU03gZUyZNNbz00kuBvpmiFtdiKpCphvViK9P3ZDPZdPadL1q/Y8cOt90RSltbW4D+nznUV2wWLlxYa3PCCScAlQ3lCnhqF6bymApj9mG2nT4PZldme9YmXcrSFM4HHtC+2p4Li7G1QtxQFe7O1cJG6mFvb6/b7Qhm6tSpAfoX14aqD7RYQIttTRVpW3xg//3VY2g2Vur3rB81u7bz2LVTdc/uw5RFe13q783OreqFqeCmpKfnzjP10/uze7a+d82aNUNqu64oOo7jOI7jOEVGqaIYTxZ0lP3gA7pg/Wc+8++1fTbyfv/73wfA44/riL5jp26ft0cVh2Mj+dWrNWN0ftx3+x2VKvP9i78HwJlnqpL43Oc94TGJPrsdwcyaNStApbqks0dTVWwGaPEsqdpis87BqI/WxjLmbMmoZz1Ll7dMs1Jteb7rYhb+LbfcAvTNmMvjYvIMvzT+JlcSS9mF1t72uaI4cpk0aVIfu01VCtuWZ/KnqozFwx59tNa1NSXEsoxLsbVGruYNpn5hmlFqz4zFeZkas2LFCqCKuU2vlT9Ppdp7yT632xHMlClT+iiKadayxR3aPlMUFy9eXGtjy5rOmjULqD5/s4nUNqxPy5cNzJXvdF/+u6QA5ktfWix5yXYb1ay1Ptzuc+3ata4oOo7jOI7jOMOPDxQdx3Ecx3GcIqPa9ZyfdtXKKkHlE5/4FAA7owv6Da9/DQDd3SoPz5o1tzo6/g+2bNEg6Xvv1VIQP/jBD2ptzjrrLOBJcTkb7gYZwcyfPz9A2S1gCVOWMJAne0B/N5+5GcylkLrl7DyWTGVB/JZkkC6xZu44W7LPgrLtHqByD5prxNwheSmc9P4alb4x94cdv2XLFrfdEYq570rlYvLEKrPRdLkyc0ObDS5btqxPW1uuD6rkkzyBsORSy+3MXM5pAqGFO1gigBUdtmcmPV/J1ZyTl0Xp6elxux3BmO3a55Um8eVL5JnNmUsaKtvNXcMlV3G9RJLSknkl13X+ut6+UmmyUshHTl6Wat26de56dhzHcRzHcYafMaIoGtUofs0aLcD6iY9/EoBNmzV49APv/wAA3d1V2/HjdeZ62+1aYuQ/v/Kf2vYD76+1eeGLXvpk3bThs9sRzOzZs/uUGSmpcDbztVljWiDW9uVqY35sihWTNTXHFJZ0mbN8KcCSemOJLfUKbZeWkxpMv2Dvb9u2bW67IxQrMZInMkH/z9g+Tyv7Af0LVtsykiV125INTN0xOy0lvNi18hI9aRFtUyhNtcxVmVIx7VJyWL4v+e12O4KZNm1anz7XCmhD1X9akkjJJnZlbJMrgIM5tmRj+b68TekY22b9fV7GB6pnyfp5T2ZxHMdxHMdxRgRjTFHsz+rVWiD2M+edB8CaNRrvcvbZH6q1scKtn//8ZwF473vfC8DLXv66IbtPXFEc0cydO7eP7aZqYbrUY7qvVMjaYl4Gs/ydxazsvffeQDXztOLCUKk1+Yw4VQ/zYsR5fFhJacyVxTRuLVdBPUZx5GJxXruiEKeKYr4vj/NLyyZZGRtTHfMYsdRG7Zl55JFHgEr1tuX6oH/cmNlkXsIppZGCk8cJd3Z2ut2OYEwNN6XaFsaA/n1XIyXZyO2nkbrXCDs+L3lWWhKwtK8edj5TD9MSOnlc7uOPP+6KouM4juM4jjP8jHlF0VizegUAX/nKVwFYvvzO2r7eHp2Zvvb01wLwyle+fkjvLeKz2xHMjBkz+hQuTsmfodISUabI5SpJiTz7dJ999gGqGWdaTDuPhzSlJs16tiXP7L7svPlsPCWfWadt8kLdHR0dbrsjFFMUjZKykSskpdjaeqQqodm2HW+v86UjAWbMmNGnjcWapep8noFq5220JGCeOZqqjrmKv379erfbEczkyZMDVAq3ZTpDpS5alnwjO82z8EtZz3mBbevbSjHkZn+WhW1t0tjxepnR9iykdmn3kS/Tlx6be6mG2nZdUXQcx3Ecx3GK+EDRcRzHcRzHKTJwRP0YYY/5ugbk8096NgBz585K9qqKe+yxJwzxXTmjjXxd55S8EHUa6J8X5a5XqiY9tx1vbc11lp7XMJdGqbRC7irOXR2pS9AoJSA4ow8rZG1u2tRuLUnAtpkdlNx4ybrefV6X1ijPXXv225JdoH/BZHO3pSEOeUiEuSDtvkvPUO6mLlFK1nFGLtZ3pskd7e3tQH93ctqX2bZ6a92X1gHPC9OX+ty8/zU3si12kJ8b+ve96XNo95OHbJQSa4arP3ZF0XEcx3Ecxymy2yiKphpu3KilQu659+7anvXrNQj1Oc/V5fmSGrKOA/RXAtOZne3Li6yWinLXU/FKbXNKKma9ZZ9Kgdp5QfDSfeZKTEkBbaREOiMLU89KSVSmjOQlPFKbyvetX78eqOwhtYu8/EyuyqRKnimd+XKSaRKWUa+sSek5sfOZTaeqTJ4s4IwOzGZLCnIj9S1vk6vNpeUs7be1MdtNE6Fyxb1UUD5X5/MlARsp8btaSH4ocEXRcRzHcRzHKTLU5XEcx3Ecx3GcUYIrio7jOI7jOE4RHyg6juM4juM4RXyg6DiO4ziO4xTxgaLjOI7jOI5TxAeKjuM4juM4ThEfKDqO4ziO4zhFfKDoOI7jOI7jFPGBouM4juM4jlPEB4qO4ziO4zhOER8oOo7jOI7jOEV8oOg4juM4juMUGfUDRRFZLiInDPd9OM5YQESeLyI/GobrvkdEzh3q644FRORNInL9cN/HSERE3i4inxuG67o97yIiEkRk6XDfx0hDROaIyD0i0vYknPuHIvKCgdqN+oFiCOHgEMI1T+Y1ROSbIvLxJ/MazhOHiKwQkecO930MNyIyW0R+IyLrRWSTiPxWRI4Z4LBPArUvuPi/3CEi2+LPlcm+18QObLOIrBWRb4nI1GT/50RkY7zunsn200Xk89l1vwa8XkTm/mXveuQjIuNF5Osi8pCIbBWRW0Tk5OG+r5GAiFwgIqtFZIuI3Csib0n2nZ7Y4TYR2R4HF0+rc65xwEeAz8TXDZ8HETlDRP4Qr71KRM4TkZZkv9tzHRp9brsbg+gX/1ZEbhaRDhH55iBOeRbwjRDCznj88uw56BaRn8Z900TkimjfF4pIc3Ld/xKRl2bnPhf4xEA3MOoHio7j1GUb8GZgDjAD+DTw0/TLL0VEjgKmhRBuzHadGkKYHH+en2z/DXBMCGEasA/QAnw8nuuvgKcBewDXA2fH7dOA9wEfTS8QO8GfAW/889/uqKEFWAkcD0wD/gm4WESWDOM9jRQ+BSwJIUwFTgM+bgPBEMKFiR1OBt4FPAD8X51zvRi4O4TwSHw90PMwETgTmA08HXgOaqtuzwNT93PLqdf/jCHq9ouRR+Pr/xnoRCIyHjgDuMC2RXHMnoEpwMPA9+PutwO3APOAJcBL43mOBuaHEP43PX8I4SZgqogc2eg+Rv1A0dQjETlHRC4WkW/HWfry9M3HdmeLyJ1xVvgNk3Kl4LqJM9WlIvI24HTgA3H0/tOhfYfOriAi5wOL0C+AbSLygbj9tGgTm0TkGhE5MDmmrm0Uzt8sIv8uIo+LyINxdhis85NMzYx2eUHy+hkickO8j9skCZuIdvhAtN8HReT0uH2piPw6zlAfF5HvDeZ/EULYGUK4J4TQCwjQg35BzqxzyMnArwdz7nj+lSGEx5NNPYC5jvYGrg8hdAC/QDtM0NnrZ0IImwunvAZ44WCvP1oJIbSHEM4JIawIIfSGEC4FHkQHIojICVHR+seoSKwWkb+240Vkloj8JKo3NwH7NrqeiLxRVL1cLyL/lNqoZN4Su3byeoGIXCIi66JNvifZ91dRGdkiIo+JyGfj9jZRhcmUu9+LyLxB/m+WR5sBCPGn3vs7A/h2CCHU2d/HU7ztxQAAIABJREFUngd6HkIIXwkhXBdC6IyDywsBUxzdnhvQ6HNL7PmDIrIG+Ebc/v5o24+KyJsbnV9E9haRa2PfeLWIfMn61dxm47bUxptE5CwRuT/a5MUiMjPuq2ur9frjQfwvGvWLhBB+GEL4EbB+EKd7OrAphLCqzv5nAXOBS+LrvYFfxc/iOmAfUVXxP4C/r3OOaxjATkf9QDHjNOC7wHTgJ8AXs/2nAyehBrw/6pZoSAjha2iHcV4cxZ/6hN6x84QSQngDOsMyFew8Edkf+A6qFswBLkcHkuOSQwdrG29Fv4AOB54KvGSw9ybqrroMnU3ORJWIS0RjUCYBXwBODiFMAZ4J3BoP/VfgSvRLbSHw/5JzXioiZw1w3duBnegz8d8hhLV1mh4K3FPYfmEcKFwpIsuycx8rIpuBrcDLAYsHWw4cJyITUGXGJm4HhBAuqnP9u4BldfaNWeIX0/7o/8zYA1Ub9wT+BviSiMyI+76Efp7zUYWs7pesiBwEfBm17/nJOQdzX03AT4Hb4jHPAc4UkZNik88Dn48q0r7AxXH7GfE6ewGzgHcAO+I5zxKRSwe47pdFZDtwN7AafV7zNovRL8lvNzhV0Z534Xl4FtVn4vY8AAN8bnugfd5i4G2icXHvA54H7AcMFCp0EXATak/nAG/YhVt7D9pPHw8sADaizxDUsdVG/bGILIqDykX1LtigX9xV6vXJxhnAD0II7fH1HcBzo50eh9rte4CfhRDur3OOAe10rA0Urw8hXB5C6AHOp/+b/2Ic7W9AZ4KvHfI7dIaDVwOXhRCuCiF0Af8GTEAffmOwtvEq9MtxVQhhI0k83yB4PXB5tNHeEMJVwM3AKXF/L3CIiEwIIawOIdiXVBfawS6IqkhN/Q4hvCiE0PAeQgiHAVOB16Fus3pMRzu2lNNRF8Zi4FfAFSIyPTn39dHFshCNBVsRt9+BznJvRBXeT6MDi/eIBvpfKxpDMz251la0095tEJFWdCL6rRDC3cmuLuBjIYSuEMLlqNv0gKgOvBz4aFQm7wC+1eASrwB+Gj+nTtRFWk+ByzkKmBNC+FhU2R4A/gt4TXKPS0VkdghhWxKy0IV+6S4NIfSEEP4QQtgCEEI4N4TwokYXDSG8C3WpHQf8EOgoNHsjcF0I4cEGpyrZ86Ceh6jgHon2FW7Pg2CAz60X+OcQQkcIYQfaj34jhHBHHOScU++8cUB2FGrznbH/+8ku3NrbgQ/HPrsjXusVol6gurZKnf44hPBwCGF6COHhBv+LYr/4Z1C0YQARmYg+399MNn8dtbnfoYribeig+nMi8pVop3m+xdZ4nbqMtYHimuTv7UCb9I2HWJn8/RA6u3DGPgvQzxuA6HpaSV9lZbC2sSBru7JOuxKLgVfG2egmEdkEHIvGjrSjA9p3AKtF5DIReUo87gOoq+wmUfd5QzdNiTjA/A5wVq4KJmxEO/r0uN+EEHaEELaHED4FbEK/CPLzPwL8HFX0bdt/hBCWhRBeHd/bdWif8zZUlbkLDdQ2pgAlF96YJCp25wOdwN9mu9eHELqT19uByagibjGOxkPUp4+9hhC2MziXF8TJSWavH0Ljn0CVzv2Bu6PLzgaA5wNXAN+NbsXz4oB40MQv7evRL9p3Fpq8kcYDZCjYc3L+us+DiLwEnQCenLoQ3Z4HpsHnti7EZIxI3o8OZMMbou0au9rv/m9iw3eh7uB51LHVAfrjQVPqF3eRujYMvAzYQP/wireFEA4LIZyFupw/hE74m1FV9enSN9N5Ctqv12WsDRQHYq/k70VoUClAOxrIDICI7JEdN9gZuDMyyD+vR9HOAgAREdQWHkna1LONnNVoJ1g6DjJbQl0uxkrg/DgbtZ9JpgiGEK4IITwPdRHejao3hBDWhBDeGkJYgM6Ovyx/fhmJVqr4qpzb0S/+RgR00FqihUI8WXStvh34GHAIcHtUdn8PHJY0PRCdAY95og1+Hf2yenn8fwyGdUA3/e21Hn3sNbqkZiX7B7LXBzN7nRJCOAUghPCnEMJr0RipTwM/EJFJUQX9lxDCQahq/yL+/KSOfjYlmqm8APjBAMcOxp77PA/xC/S/0NCVP5YOcHseFPnnlvfJq9k1G54ZFTQjPTb//m5GJ1TGSnTQn9pxWwjhkUa2Wq8//jMo9ouDpJENN4zRjbYsIYSfoy7sm2Pbm9lFO93dBorvFpGFMZD1Q4AlBdwGHCwih4smMZyTHfcY9b9cnZFH/nldDLxQRJ4TlY1/RN0iNyRt6tlGzsXA34vIntHN9MFs/63Aa0SkNcYwvSLZdwFwqoicJJoU0yYaiL1QROaJJtxMive2DZ31IiKvFBH7st+Idro9A/0TRBNnjhWRcSIyQUQ+iA5MflfnkMvRGacdv0hEjonHt4nI+9GM0N/E/afHNhJjxj6BBvrnfBZ1O21HkzaOEpHJwAlo1qpxPJopujvwFbSDPjW64gZF0LCaHwLniMjEGIN4RoNDfoDa3DNFY3L/hb4D/VuBU0RkZpwgn5nsuwnYIpqEMCHa7CGi2fGIyOtFZE5U6E2R6BGRE0Xk0PiFvQV17w3GXueKlhaZHK91EhoC8sus6RnAJSGEoksuIbfnhs+DiDwbDQN4edBs0Hq4PSfswueWcjHwJhE5KA4A/7lewxDCQ+jg5pz42R0NpLkC96LewxfG/v0jwPhk/38Cn4h9lNUlfHH8u2irjfrjQfw/GvaLItISxxnNgH0P1MsEvwmYLkk5pniOhcCJ1FHV4/nPBd4bNz0InBD7gGPYVTsNIYzqH9T3/1x0cHdBsn0J+oXakrQ7G7gT7dS+BUxM2n8YeBydfbw+Hrs07tsP7VA3AT8a7vfsPwPaxIvRhJZNwPvitpfGz34zKtUfnNlQXdvIzt2Cyvnr48P3XrRzkbh/H/SLZxuauPKFzC6fHq+/AVWHLkNn0/Pj9s3xHq4BDorHnIeqn9uA+4G3Jef7GfChOvd6PDoJ2krlonjWAP+73wNPj38fjM5o2+P7/QVwZNL2E8CquH8VWjtuVna+E9H40HTb59AB743AwritLZ5j3nDbzxDY5+LYv+yMn6n9nB73nwCsyo5ZATw3/j0HuBT9YrsJTXa6vsH13hSfh/VoKZ5HgOOS//v34rluj/a8Kjl2AZoItib5zOw+LgDWxntfDrwkbn8tGoDfjk7avkDVD38IDawv3eecaKOb4v38EXhr1qYt7n/OIP7PrfF9LxjM84DG4HZnn8nPsnO6Pe/i51ay57j9rGhXj6IJWbXv3ELbfVFX/1a0H/oa8PXMxldHe3xf9rw0Af8QbXIr2od+spGt0rg/XhRtY1Gde23YL6JjlZD9nNPg//sZ4IPZtrPRGN16x3wMeH/yehqaELkZTQxqjtuPAm4Z6DO2L7cxj4isAN4SQrh6uO/FGVn8JbYhWij5P0MIiwdsPAoQkecD7wohDDqb+wm67t8Be4UQPjCU193diMrXJmC/0DgRZEwgWt7soBDCmQM2fmKv6/b8JCJaIuzuEEJdJXKsICJz0EHyEWEXvA+DPPcl6IC7X2WBPu18oOjs7uyKbcQYrxPR2dk8YibkUH8ROc5gEZFTURVGgH9HVe2nht2l83dGPTHcYQPqxXk+8CPg6BDCLcN6Y7sJu1uMouP8pQga57URrYB/F9mqDI4zwngx6t57FA2jeY0PEp1Rxh6o+3cb6h5+pw8Sh47dRlF0HMdxHMdxdg1XFB3HcRzHcZwiPlB0HMdxHMdxitSr3fOk8IvP/nsAmLBOly6cGDpr+yYvPASAHXvtB8Dj6KIEPZ3bam2kRxN+mrp7Aeju0N89Heo+H7+9KqvV3BUXNejVfV07tKh7b4f+FqpFD5qClkdqadbyYuOadAng1nQp4Hi+lnjPLc36Wnr1HnqT0mRNcfg9Po7DO3p13+a2anGCbbO0HuhdU48A4D1vPq1eEWNnZOAxGvVx2x2hnHTSSQFg0qRJALS2Vn3QggUL+vyeOXMmABMmTKi1aW5uBrASG4jEPnKc9o3jx1fl6uwabW1tANxxxx0AXHvttQDceuuttbarVq0CYNs27d+7urr6XCfF7ufggw8G4KUvfSkAxxxzTK2NXXPrVv0O6O6O/XVL9RXXFDtmu8ZznvMct9sRzCGHHKL1yOJnaLaXbjM7TPcZZuvWJn9ttp2ez35bW7MZsy+AyZMnAzBtmq7OOHv27D6vUzo7dbywcKGWwd1///37nCO9pmHXtN8pvXG80dbWNqS2O6QDxabNunLUjPgWm+ObBti+QRfC2L5JV0ya0KsdR0vy/Vxr36GdQG9cSTJ06fYJ46vMcREd/IXe2DnEAWNPPF3oTf/Peq3mpu74Wzs/aa46zO5YajNEg+ztsQ8xvpbqvXT2aOPOELe1xvMlxjZxxox49C6tbOU4jrPL2Bdf+oXaE/up/Hdv0i/bl2m9L6+0rWFt7Fr2euLEamGNuXPn9vltX8TpF7INQu24xYu1AtWee+7Z73z5INAGiin2hVz6AnZGHk95iq6YZzaRTnLyCUvpM823mS2nA8S8bW7Ptj09xrblz82mTdUqeDt36mqFNiC01x0dOmiZEb//0/PZe8qfm1KbocafGMdxHMdxHKfIkCqK07q3ANDWraPqnp5qedPu9nW6L46cW3qjathTKYohjmslNMffOroOcbTfk4z6g436Y5su+92rb7lXqpnruBY9rq1VZwbdsW1Pso59U1QFaY6ydYvu64wz2J7WaqQv4+P9iV1bX6/trWa5DzymLpI7N63AcRznycQUkVRps79NRbHfaRtTMnJ3nfVtqavYlBVTTXL3r6mHAHvtpUv1mrvOfqcqYX5NazNrli5VnSpMuRpqr1MaufSckYd9/vaZpraWu4Ztn4UwpPtyFa70+efPgr02VTs9xtqYW3nt2rVApRqmTJkypc/92jOQhmxMnTq1zzGNVHo7LrX9ocCfGMdxHMdxHKeIDxQdx3Ecx3GcIkPqeu6NWb/bx03XDc3VOLWltW/QtEi8tabqFnua9fiOKCV3NEWXSWzbJEnWkcTgVNQNIi3mxmizm6k13RH3bRgfM61jEkp3InV3RTl4+05ts7NTf69fr1l7O7dVro4dPXrN9vZ2vVRMfNnQXbXZGMfos2csxHEcZygouZ5zt1vq4iu5weqxfbtWlNiwYQNQueSsH0wTAnI3smU2p21yF5/d1/Tp0/tcDyr3Yu6CTt2Ou/JenOGnXpIH9E9WKiUv5WES9vlbaEQpWcTOk2fhp8+EucTN1WzHlMId7Lh16zS07t577wUqm4bKni1px45Js6HtmnacVSgYKlxRdBzHcRzHcYoMqaL44OR5AHQGHR13dVWXly4dne9ER/3tsexMR1c1ku+IJW464/B2R9BR/85unbG29myptZ3Ypo3mzNLgz/kzdNQ+qTWqjzGhBqBzux7fuVlH61vijGNjZzVL2dSl59sc73lbR5zBMim+p6TsRLO+h/FT5wPQ0qMzm5aWSsWcMV7/burw8nzO4LjzzjsB2HfffWvbBipf4jhQ2Uka7G/Kim1rpLjZvnoqDVQKi6knpiyaCpKqKJs3b+5zD6bKpAkBphjmSQLLli3rcy9Q1bLLE3NK78GVxdGBKXRma+nnnSevlMrG2L5SiRso20FeL9TssqSy59dM7dv27dihJftMXbc6nxs3bqy1nTNHayqboljqwy15xZT3Y489tl+bJxP/VnEcx3Ecx3GKDKmieMU6HZVvCvGyvVNq+6b2almE7lj8uiPGGDaFKg28JcYrSlQSW5p0BD93ho7EnzK9Kri9YLpea/ZUHdlPFFUNWzpUdWxOwgmax8XSEbG497aJqniuk6rg9oqdum1VhxbQfLRd76t9ZyzimS5OEYt9t4rua43FvXu6k5T2YAVw+8dWOA5UM9c//elPAHz1q18F4Mgjj6y1seLD++2nKxrZKhulorLO7ovZQxpHlSt9Zm+pomFxUnZ8vkqGKYJQqYSmKJpqYiqKxSqmbe18pULZpsaYsmj3aTFdS5curbW1VWGMUmkUVxRHF42KS9crTp1+tgN9zqXz5+ctxR/mqweVYiitjR1nqrjZcPrc2HNSKsWTX8OeF1cUHcdxHMdxnBHBkCqKYarOANtE1cOeUMXsdcbBv0SVcFyMYwyJ6tiBzkwntj4CwBGztc2Rk1X52ytsrq7VraP0pi1xlhxVSOmM8S4dSWHOuGZ0s8USio7sJ4+r1nqeFxXOJeM1W+/emGH9UHNUGLdVCk53fH/NMXu6tUmvKVQznI7mOJOmf6aUs/uxZUsVX3vzzTcDcMMNNwDVGrm33XYbAD//+c9rba2gqxVytULGRx11VK3NKaecAsD8+RozO1zLQDnDR77eLfQvTl1SYExJzItfm9r3+OOP19o++qguw7py5co+r9ev16VbUxXFFEBb4szUlDTOy8jvz86TZj3bPnt/pQxUo7SetDPyyGMB0880V6JtXymO0fbl8Ycp+bJ8jcjbNIoPz7Oo83WdoVIbc7U+tdM8NniocUXRcRzHcRzHKeIDRcdxHMdxHKfIkLqee7pjmntNPk3WcY6JH6FLZd2WHpVjmxPX7JyJmwB42mId3x48QV0PM7c8BkD3trW1tl0SZefx6pbuaVYXXRcaCN3dXEm4zS3qymjpVdd2d6+ev3VblRzT1qvXWtiqAdlTJmibaZPU1TG+uQqmXrE5rmUdXdidth5l8n6lSSVpCR5YvTth7gRznz300EMAfP/736+1ueKKKwC4//77gSoZIHehQOWKuPvuu4GqxMKVV15Za3PJJZcAcNxxxwFw8sknA3DooYcCZXeIM7awNZTNPqB/AkCp4LZh27Zt0wUGHn74YQAeeOCBWhtLYjFXswXpm2stdc1Zwe0DDjigz/2tWbOm3zXzEjpGWuonX1faSF11+brAzsimlCRi2OfdKJklL+GUn6dUwNts1cIazJ7S5yb9Oz1/6vbObc1+W9vUdm1fHnaRns/+Hq5ELFcUHcdxHMdxnCJDKiU02bJ6cXjaRDqzs1F5TCDp1eD+PdrW1VocH1e7O6BXlcOmx3RfZyw/s72pKmcjE3XG2tWmSmLHuKnaNiafdFO1bQ06m2jt0sDszq2xdMPG6tpEcXHCNp0JzN2uKmbzVL3vlsnVmLu7R2fHK9rjjLhFr90cqhnxuKgkBk8s2K2wmestt9wCwHnnnQfAjTfeWGuTlwPJFZB0VpnPpE3xsd8Aq1atAmDFihVApT6+6U1vAuCZz3xmrW0+W3bGBpbkMS5J0DOFOk8WKZVWMntavXo1UNmQqd5QJWSZ3eYKYJpIY0WGDznkEKAqeWPnTY8vLc8GfZNZcgWotPRaPWXJGZnkSR2pHeRKYslmB1Lf0vOZwmdquPWZ1h9aGTKobMx+D6Zwdz31PiW3y7RNvqTgUOOKouM4juM4jlNkiIOTYsxBVBIljdmLsYgWqzhlkt7aYXuNr7XZr0nLLUxep79t2bwN41Ql3DZzUa1tmKAqXk+rHt8ei3z3oDNqkfSt67bxE/X4idN09D5u5pxai62PaNHjLY9pTNmsHo2XnLpdYxoWNVWj/86ZWgbICouv7dKZdBqOOCFo3E0vPrvdHbDYF1MSP/OZzwBw1VVXAWXVJF+6qhRjlW8rxTFaG1uQ3pQfU4lSZeakk04C+qo/zuinpLyYSpGrFamyYftsCbIHH3wQqBSXdCkyK5mTqyWmvNjyYwB77LEHUBWKN8UmLZydF/m213aftkQgVPGRts/s1wvPj17yPi21y7xvbBRXm/eRJVXP4mDNns2e7Dqpp8VKOuVF3Uvqdb1yTel7yd9DSQ0fbhXcFUXHcRzHcRynyNAW3K79Ff32kmyJg/veXp2VHrCXjtr3balmrLJRZ7XbREfpm1pnArBjps5Gt02bW7Vt09lrV29cErBDz9sqquyMb64yjJqa9Fqr12qbnds0VnHBvKrY94z9NEN0R6uO/tesVnVmTqdmpE5tr+IZ95yoSuSiqXoPa9fFgttNVXxQU4hjdI+X2S2w2K7Pfe5zAFx99dV99qcZobmCWJqxGvlMs5T9Z2qlndfUIVM303uxTOjFixcP8p05o4HSkna5YmO/U3Xb4g3Nfh95RBc72LRpU7+29WLCTBGcMWNGbdvChRpwbktOLlmyBKgKyKfH2T2bKm9qj9kxVAW7TRmyrGrLpk7/LhVGdkYupeznvN/Ls6BT6imKqcpnarjZtb22Y9JsfLOtmTN1/GFxvyW7SrObU9Ls/HqZ0aVYdC+47TiO4ziO44wohlRRbG2NWUJxfCohmd1GvXGPGbrtkIkaP7Vg3cPVCTq0huGqSbMBaJ+7r55nnCqJLc3V27nuOl3+TFr1fE972uH6ulNH+M1NVezKFZdeDsCVV/9GL9Ops4oFey+ptXnNa04H4NA9NUuvI8bHdG3UmUYb1YL347bqrHvBVJ0dT2/VGUcHSUZpr96rT27HLumM9Re/+AVQZTfnM+A0JjBXBfMYnZIqVK8t9I+BsbamDv3ud7+rtb3rrruAKmZsuJeOcp4YGtmO2YypcWnGvP1tKp4t2Wfb62Ukp1jcoamIUC05aXZvCsusWbNqbZYu1Vjvxx7TChP33Xdfn/tN4yPzWnQWA2nXSa/l8bejg9yDUorZG8ySe/UUxXRJSVMS7Xf+vKRLrFq/aXZkSnmpj8zjfhu1afQs2T0PV81bH6Y4juM4juM4RXyg6DiO4ziO4xQZYh0zurGi21dC5f6VWC5m6WyVWOd3qMt56rb1tTab47J7O6eq63lbmwaVTh6nLt7HHry31vai//o6AK8+/XUATGnVNt2xDM+111xba3vppVqi5KUvPk2vvYdKyRf84H9rbS7+7k8AeMo73wbA7DnqFunaoi6Y0FMlx4zvUPl6ZpMGxM6eqEktD29Jlu1pie6PQUjnzujCXAnmMgO47bbbANi8eXOftuYWSQOhzT1hrghzr5jbIW1r17Kg6ZL7Ir9GnhyTFk225f7MdWfJLV5mZHRjn33JdWU2YwH8qZvN7DUP8je3XaMCwHYtS1CxotopVuJm6lQtZ1YqWWLk4RWW3AKV/ecuOitlApULfDDuSmfkMJhkllISn5HbhpGWBbPQCgtdMLsx+za7h/42W1oe0xiMyzhfOrNRmI8X3HYcx3Ecx3FGFEO8hF8sWWPKRk+lUsycqLPDPds0OaR5XSzomhTGXjs1Bia3aVB0S6eqcp0TdYb4819XZT72XKTB+Mcdd4y22a7JJk1Rzbzl1t/X2h502MEAnPzilwMwMRbPXv1oVfLmp1doMsJj7RpA3TxPS+B0r9WZx7hYUkfvS9/LhA6dscwar+rMqqZqNtBjBXCHZ41v50nEZrDXXXddbdsNN2hylakgptCVkkUaJaak+0ttSzPXvAi3zb5te5q88Nvf/haAE088EaiWWHNGN6Xl63Ll2hSW1L5MSTQbyZXEkjqXF8q2tmnyiS0naSqMtU3LiaxcuRKAhx9+uM89mOqTKo72t13bEgzSNr6E3+jC7Kbkzcj7sFJfmSev2D6zH1MRoVLOc9u1IvGpkrd1q5bEs+X+TFlMPT3pUpn58fnrektolp6tgZYlfLJwRdFxHMdxHMcpMqSKYnMsgdNDrbp2bd/0iXor06Lq1hzL2OxIRukdEzXGpDuO1ttiAesHH9BlpW699dZa279+4xuBqiimjf5Dr80yqtH65Ek6a+iM5XdEYumSlmQpK+KsOx7XFQt6i5Ue6a5mwhKLcjfF9zmxLS4nFaqYmua4r6nJZ7djldQeTUHJ421Ky/LVi6FqtFSU7cuLFKd/5+fNZ9xQzaztt6svY4uS4mIqnsVhpaqe2YGpjXncbIrZnqmE9ttU9DQW1pRKO4+VGknv74EHHgCqZQPtXkr3kJc8MdUojdktLY3mjFwaxR3mn6HZTdq2tA0qRdBsECobNZtNl5uEqqA7QHu7jhOs4LsV4E6PyfvhvJ9P7byexydVKIdLSTRcUXQcx3Ecx3GKDHGMYpw92iLvyaRg2gTdNi6O7Md16Qh6e9KmMypz2+NdTxVV7n53za/1/MksY+1azTi99lrNbrZYK/P/p0VXQ6/OOpvi8oEh3lhvSzWK74rLDUosnt3dq2Ps8VisYTVz7em1mAOdmUtcn7C1OVmmzdQcF2zGHDYTTGOy6qmEpWKr+bJPjZbwG4xKUm/2XZpx22y5psC7+jImKKkUuTJnfWIaX2VKiyl1eVZ9qrRYsezZs7UqhdmXKYHp82Cqjqkwc+ZozLdl20P/GEdTNe2eSrG69uyUqgDY3/WWVXNGFnnfVopVzGMTG3lmzG5MScwrUEClKOZ2nsZ+m82aAr9qleZTpMtP2nNhz1KuGjaKuyx5evJ44qHGFUXHcRzHcRynyJAqiuPG64x1Z1TRmpOU3wlxEtvcrTNXsdku1ey2N9YebJmgdYtWP6wZ0rfcrEuQTZ4yqdb2xht1m432TztNayS+7GUv63dfPT16LVMWY7lHmsdV/55e6ZvlJ91xZmBvoTeJG7PRv/3G3m81i2iNF+nBJcWxSlqnK6+VZZSyRweaNQ5mCbLSEn55nEspPjK/n+GOjXGeGPJYqZQ8czS1RYv1q1encN68ebW2Bx54IFAt1Wf2b31wqsrYefP7SdVMq3uYx6o1Urnt3nNlESpVNM9IdUYmeWZzidyu0/7KjrN95i2x+rZpbURTEs1GzHatnmJaI9H+Nhs2dXzNmjW1NqY6msqY99mlKheN4hjzGrhDjSuKjuM4juM4ThEfKDqO4ziO4zhFhtT1HJr0cr2xNEygkombY1mcEEvT9AaVjZuTsWxzLNDdGsviXHOtJrEwTre/+93vrrWdP38BAF/96leBquDx8573vH73VRW6HBevrZJyS+Iqbo6u5+YulabHd0UpuCvK4i1VUHd3TLIJrbptZ1C5uCtxTzfHwt9NTe7aG6ukgf65i81sLv8NlevB3BUYDz3WAAAgAElEQVR5wkspiD8/tuROruf2LrXNC4I7o5tSoor9bUH+pZAHsy9zt9l55s+fD8ARRxxRa3vAAQcA1ZJmViDb2qblSMwNaMv6WSJMWmIkd/vl7rtGYRHmdiyVx3FGB40+37zvKvVl1uda+IGVs7GkqrRvM7vLQxZKdmRt80L1abKWtbFrmC2b6zh1e5eWKMxfN1qidShwRdFxHMdxHMcpMqSKYk9UAnuiWhiSYao06Ui5M3TFfVFV6a5G1eO6YnLITm37wEMrAFh40D4A7L10n6ptvNaSJUsAuOeeu4G0QHF17W1bdUYwbnwc9TepopimzzfFhJdJ8Z5bd8TlpHbGshEks92oLoZWnQlv2R5nA00Tqzbx/pCqCLczNrBZqM0iofHMN39tM0mbjeaKYhrcbQkCuTJZKqxc73Xa1s5naqgX3B4b2OeaqnL5tlKJEbMDK3ljCxjsv//+ACxbtqzWds89ddlUs1tTTey3qYjpNmtryqKpkVAtjWbPUaNl0fKC8yVFvFFCjzPyyBNUSokqRqnfs215gW3rRy1ZCvqr1aaG2zFmiyl5UsuWLVv67bNi3KldQ9lTky+MkNr3cCmJhj8xjuM4juM4TpEhVRS7YiFqG0w3NVWzgq5YBieIjvJ7bDSdLLXXvF1noeNiSZnJkzX1/LGHdNH4tWvW1tr29uhs4vc3/R6ARQsXAzBzms6I91mytNb2xz/5MQDnf/sbAEyJM9jLf3ZVrc1fPf0oAPaapYvN996nCmVbuyqKvcl/csckPX5Tk85Stu3Q9zC+uZoR29J9vV4eZ8xhM8PDDjustm2ffVTtXr58OVAui5NTL/6wtDxfvbbpvlwdLKmbNhO2sg55iQlndGKfX6pk5KqGqRZpbK2peaa+WEHsBQsW9NkPleKXqzNWcqRUqsauVSpEnBctzpdFS+02V8ItRqykoLpKPjrI+6dSHHejpVDNnk3ps1jCfKlJqOzO7NLOa8fY9vT4PIY2bfP4448DlRJp/am1Tfv9vAxQSVFstOjCUOC9v+M4juM4jlNkaJfwixm+zcTZI9UMc9OOOGucuCDe2Qr9Hf3/AGxdB0Br0FiX459zPABf/+JXADjnnz9aa1rL1ovxgn/9+jfq9pil/LxnV9nP23bo+a654Rq9r169l2OOPq7W5mUvfyEAHVtUtWxaq0VkZ6Pnax9XzcK3TlXV8k9bY2xij+5rS+MniMtQedbzmMNmp0ceeWRtm8V03X///UAV11JSCW2bKTD5YvFprFYez2iz6DSmxfaVZt3QN9P0qKNUOX/KU54yyHfrjAZKinA9u0ht0RSQvGixZZCmbS2uy1QUO9/q1auByvahUnms7aJFi/qdz+w/jz+036lCadvMlu13qo4OplC9M/IoFZvOFbaSd8Sy+c3+rM+1eME+y/hmfazZT74EH1R2l8fZprZm+6y4t8Xgzp07t891IK260vd5TK853Cq4K4qO4ziO4zhOER8oOo7jOI7jOEWG1PXcMl7dGL3d0aWWJHds3KaBoB1z1W27bbxKv5O6t9batLU/CsDjKzUh4ODDDgfgrWefCcDy39xYazshuiKe/oxnAjBvrq5Jum2nuk6aWqug6Ze+6lUAHP0CdUe3RW/wHlOqQO2mnesB2PzgHwGY3alu8I64fnX37GrN08c7VYJ+fLOOw5sm6nl6SFx+sRxQE17QeKxhroN99923tu244zSM4frrrwdg3bp1fY5JXQt5oe080L+RG7EU9FwvicW2W7FjgKc+9akALF26tM95ndFNKewgd+nla85C9fmbu83WszXX88MPP1xra+41K6VjAfwW2H/PPffU2ppLzlzPljSQJizk17b7yhNX0mvNmDGjz72kYRWekDU6Kbldc5u132k4wvr1+p1tYQ556E7at5mbNy+QbZRK8+ShQela5nYNu7a5v61cTppIk/fZpT68FBYylPiT4ziO4ziO4xQZ2iX84tJ2zS1xxJwobO1dGnj6cLeOtJfMXqJtt95WazO9M6aqr34QgB0TdBZ5yMK9AVj2mtOri8UJwI446t/apQqe2NJ5SbXv0KkzhLmTdXY7NRYEb9n4WK3Nzkd0Njx96wYApsRC2dtj2YjN0+bW2t6zUq/ZMn0hAF0SldOm7dU1m+MsudcVm7GGzfrSIqsvfKEmQ/3yl78E4Kc//SlQDsLO1cF6Szyl5AWGS2Ui8rILprZYog3A8cdrgticOXMGvKYzejBFpFF5jZJqYaqdHW8JAhs2aD9oymDa1tQYU0/smFWrVtXamiJpqk+pGLYdn5csMeUmTeoyVdyWCzT7TRVFs/9GS8M5I4+Szeb9pn22adFrs1Hbly+rVzpvrjqXngmzvzzpJD2fbbMEGrNz8yRZUgv0VcbrUW+hhqHCFUXHcRzHcRynyJAqirXi2aFQALhF47KWr9EYwmkLtEBxy+TVtSaTux4BYH6nKiQbH4zK4madse6ctbjWtmecKpPSEgvNRvVSemM8QVNSAiLe18RtOhvp2aCz3c5VK6prb9fl/MbH47qa40x1T41D++2qKpZy+zgt9dDapvEyXbEkT5MkMQemrrpis1tgSsdb3vIWoCoz8qtf/QroWwqhXjHZ0ky43vJ86flyNcniufbaay8AXvnKV9baWoziYGa5zujBVOVSiRGjpDpavKzFVJkNmn2ZagOwceNGoIpJNOXFrm3lc6BSCW2fxXClCqDFOuZlSCwOMVXsbfnAxYv1O8BiFNMyUXb94V4OzRkcg1G/zWZNuTMbhP5L9tkxFg87GKWy0dKA+fNSKnFmz4nZnimLpUL1jRZNGG413BVFx3Ecx3Ecp8iQKorjxEbDNkqv9kmT3kp7h84o79fJADOWHFFrsyGOpqdsUfVuWo/ODNo2rABg084qBrB7kqomTI5LRMVRe+iOs4GdSUHiOMPojDGJPe06K5mUFPueFOMMt7bGpc0Wafzhrx7WmcLq7irreeoeSwDY2atxiONa9DyJoEgPcUYhHi+zO2AK3dFHHw1UKomphTfeWGXs24w3nwmXqLe0Uynm0dQaU10+8pGPAHDyySfX2uaL1ztjA1NcGi3/aPaWZo6akmHqoKnRdmwaJ2hqydat2j9bbKL9Ts9r2LVNYbesaqhUGHtGrK2phfYbqpjE/P7sWQLYvHlzn/fkjGwaZfrmqp7Zbqpw22dvdmc2Vsruz+MM82uX1MKc0rOVVxawfr8zGVvUUxLT7YOJMX4ycUXRcRzHcRzHKTK0iqItV9fbf0YXiEuRNWkdxdUbdPZ3Z3O1zM7ShVrbrXv1AwBMikv6tcUM5LlxxgDQsUPfWse2GCcTYxV7u+OIfGeyKHdHrIfUE7PrmvR1a2uyzE6rZjeHBZph/eNHdOb7aIfOahfvvazWth27Z509tDbpeZt6q9l3V9Dz9YZqxuuMXWxGaSrIs5/9bAAWLNAlK7/1rW/V2l522WVApYDYTHgwMTVGqvTYNQ488EAAXhXrhp566qlA35peztjEYgIbqWmmypgCCJUCYkpzvrRZGlNobUzVsRgxU3ZSdc8UlVwRSmPMTJnMl+cz0vs0NdOuYe/T7j+9pmfyjw5Kyl9Ortily/JZv5bXSMxjvtO/6yl2pSX3BlOX0+7LlO6ZM3V8k8Yo5u/BntVSPGJJlR8KXFF0HMdxHMdxivhA0XEcx3EcxykypK7n5jguLaVvSHQ90xyDNpvUjbFiQ+VODlM0YWTfubFw5sSHANgZk1nauqsA0QkxSWRydHN3d6m03NEVA7h70ruIbor4qnOcFnrtnb5XrcXD3XrNWx6O7pC5+wFw2LynxPNXbpEJTZZgEN9bb/w3J0W+e2OJoG4qF6Ez9jH3gpX4ePrTnw5UpUAA9t5bwxv+9Kc/AXDLLbcA8GAsB5UWlc3LN5gbIy2ibW5uS1o56KCDgKqMScrChQv7vLbzpy5L+9vchflyV87IwxJDSoXYjbxIMFSuW3PzmsvZXHypq88StszNZiETk+KiBOkSZ3nClpG62+yaeVkTc0mny2DmbsCS286un96HM3Kxz8n6trQPyhOczC6tTFJ6nCVI2etSqShzPef9aSmZJV/coBTKYPdnv+05sb4yLT9WLyGxVOJsuBKxXFF0HMdxHMdxigzp1ErEZnQNGoUsWFMqpeXR9jijbNfR9cIJWux62mwtjTC1qwqE7ooJLm2Y+hELc/aI3UytbUtLLHjZrb/XbteR/QOJ4PJot46p5y3V8iZ7zNECyh1x+b/xrelSfJZgoG+0qzvOunurGUNvbNMyxDXPnaEjLTBs6p2pNfnC8mlg/uGHHw5UwdimzFiSQHuStFVvNloqW3LllVcCcPXVV/e5ZqqwHHrooX3OY/dpAdbpPdu2s88+u/+bd0YUZgONMNUjVUhMfTGbs9emlKQJKqb0mS3mimCKqTF5CahUCbR9uepo502VT9tmbe38peQGLyY/Oqin8qWYHdpnmvZ7hiVZ5cXi034vt9G8Xy0lruT3UypJlpfdycv6lNqUrlmvnx8qXFF0HMdxHMdxigypnNXS0te3X6I1xBIGUXHrlGqG0D1O4wC3MBWAOztVsWnp0tczuqrZ49RWHaVLt86Em2IEYhNxhry9UntWrVG1Z0O7xtJMnq2xieNmVvEOC2IR7Y6g975mg6oqLbHkT1OoFJd6pLMBUxR7qf+/cEYnVuLj4osvrm176CGNp73vvvv6tM3VOajUESuPY2qkqUKl2JV8xnnPPffU2tx111199uVlIlLVJX82G81g7dquKI58LE4r/Xyt3EyuyqRKhql2ZntmD6VSOva3XSMvLlxSUew8eQkT6K/Y2P3lywpCpSTZ+ex1qh7a+3VFcXTRaLyQ90/pZ2t2bIqi9bVmV6miWK/UTSnWNY9bLMUP5jafK+iN3kMe35gyXPG1rig6juM4juM4RYY4RrF/BlF/rPhkfCVJUcy4zF+QGEvTqrMH6dFMoh3d1ZJOWzt1drttmy42f/cfrgLgtpt+CcDOnkrBmbVQCxG/4OXvAmB2LKrdFap/T3vMlraU7ZicXbtPKxhur+JefQ9N0ncz0GQvghd/HWvccccdAFxyySW1babqmdpoM8NSwew8jiWfsaYzzbxYq50nVSjzjOh8gfl01lzv2fQixaMbyxBOFZdcfcszSaFSYex3HjeY2mKudgxGGbHfudoDlWJoWdP22xSiUoyZXcveU6pGeYzi6MJsopGiaJT6PbNjU/fMQ1MqFp/bS97Xlgpu5/1yqhZafLrZWq7Ep89YvYz90hJ+w4Urio7jOI7jOE4RHyg6juM4juM4RYbF9dxIRu2KyR1NmJu6atvaq+5kiSVvmkMsnBmLVze1Tqu17W3bA4CF89WN/PhKXR+6hesAmNg2sdZ2j70OAGDJflo8u31nlJB7q3F0a/xPNccSN63xmuaR7kyKaZO5E3t66ycEhDC8krLzxGMuDisXAtW6t7l7r1GySJ50MpgSDQNtH6iNu57HJuYKK60VmyYzQV8XWu5izhNfzA2c/p2fz2y8lDxgCTD2rKTXnjpVkxRnzZoFVOuk23VKpVDy86fXtPvygtujg7yvLIXc5KVl0rACC1Ww/tiOKfW59cJxzG2dJk6Zjdq+UlKgXTvvs/PyaKU2uZu6dF9DjSuKjuM4juM4TpEhnVrVS0FPCRaUb4kgUo28rWSOmGJXyxGJs4ukWPeOnji6jzVZO+PyfkccrOrhnvsdUGv7J61jTPtWnXV3xySW7kTtC/Fi3Tbax2YyTX3uQbf1/d3bQDVqcqVmt8DUwb+kYOpgnp/B0KiY7ECFZ53RSalwel5gu/QZmzJi6p4tQWbb0+UbTc3JlXBT8EpJCbYcpSXbWEkdqJYCNEVx2jT1GFkSQkkZzBWXkqI4mOQIZ+RQKsZu5Il6KWZLZgOmCuaFvNO2eQkxIy0DlfebJXvKl9rLbXUw/elIslNXFB3HcRzHcZwi4kqB4ziO4ziOU8IVRcdxHMdxHKeIDxQdx3Ecx3GcIj5QdBzHcRzHcYr4QNFxHMdxHMcp4gNFx3Ecx3Ecp4gPFB3HcRzHcZwiPlB0HMdxHMdxivhA0XEcx3EcxyniA0XHcRzHcRyniA8UHcdxHMdxnCI+UHQcx3Ecx3GK+EDRcRzHcRzHKTLqB4oislxEThju+3CcsYCIfEpEzhzu+0gRkcNE5Ibhvo/Rioi8SUSuH+77GA5E5CARuflJOO94EblbROY+0ed2FBEJIrJ0uO9jOBCR74jIS56E875HRM7d1eNG/UAxhHBwCOGaJ/MaIvJNEfn4k3kNZ/CIyAoRee5w38dwIyKzReQ3IrJeRDaJyG9F5Jhkv4jIx0XkERHZLCLXiMjBDc43B3gj8NX4epyI/CD+v0M+IYvn/3S8/noROU9EJNl/uIj8QUS2x9+HJ/teJyKrReTB9Lwisq+I3CAizbYthHA7sElETv2L/mGjhDgI+bqIPCQiW0XkFhE5ebjvayQgIjNF5H9FpD3+f143wCH/CvzbYI4XkWVReHhcRN6bbG8Vkd+JyF62LYTQAfwP8MEn7t2NHUTkgvh8bxGRe0XkLcN9T8OFiBwiIldEuwqDaH8YsAz4cbLtddFe20XkRyIyM9n3ORHZGPv/PZPtp4vI57PTfw14/a5OcEb9QNFxdmO2AW8G5gAzgE8DPxWRlrj/lXH/ccBM4LfA+Q3O9ybg8hDCjmTb9cDrgTWF9m8DXoJ2aocBLwLeDjrIRDu6C+K9fQv4cRx8tgDnAk8F/g74YnLOLwD/EELoya51oZ17N6AFWAkcD0wD/gm4WESWDOM9jRS+BHQC84DTga/Um/yIyHzgROBHgzz+U8D7UHv+iIjsEbf/A3BJCGFldomLgDNEZPxf/K7GHp8CloQQpgKnAR8XkaeVGib91VilC7gY+JtBtn87cGEIIQBE+/wq8AbUbrcDX477/gp4GrAH2lefHbdPQ235o+mJQwg7gZ+hgsDgCSGM6h9gBfBc4Jz4YXwb2AosB47M2p0N3AlsBL4BtMV9bwKuz84bgKXol2EX2rlsA3463O95d/5BBzq9wI74eXwgbj8tfuabgGuAAwfz2RfO3wz8O/A48CDwt9EWWlJ7S9qfA1yQvH4GcEO8j9uAE5J9bwIeiPb5IHB63L4U+DWwOV73e3/G/6UJODXe69y47YPAxUmbg4GdDc7xS+D1dfatSt9L3HYD8Lbk9d8AN8a/nw88Akiy/2HgBWhn99u4rQ3YHv9+BfC1OtffM37m44fbBofJ7m8HXh7/PiF+Hv8IrAVWA3+dtJ0F/ATYAtyEqmrXNzj3G4GHgPXooLRm48A3gY8nbU8AViWvFwCXAOuiTb8n2fdXwM3xPh4DPpt85hfE620Cfg/MG8T/YBLaD++fbDsfOLfB+7p6sMcDd5l9ATfG+18U/4etda7xJ+D44baPkfwDHBBt9FWZ/X4QnYCeH7e/P7Z7FJ3gBmBpnXPuDVyL9qVXoxOAC0o2GrelNt0EnAXcH23wYmDmQLZJnf57F/4PS4EwiHYPAMcmrz8JXJS83jfa8RTg1cCn4vYXoBN90Mn36+qc/3TgV7ty72NNUTwN+C4wHe0ov5jtPx04Cf1H7w98ZKAThhC+hqoZ54UQJocQdgv310glhPAGdMBxavw8zhOR/YHvAGei6trlqLI2Ljl0sJ/9W4GTgcNRxWvQcSJR9r8M+Diq4L0PuERE5ojIJFQtOzmEMAV4JnBrPPRfgStR5W0h8P+Sc14qImcNcN3bgZ2ozf93CGFt3PVdYKmI7C8ircAZwM8bnOpQ4J7Bvl904Hlb8vq2uM323R5izxS5PW5fB8wSkYXA84DlIjIZ/UzOLl0ohPAIOmE7YBfub0wgIvNQm12ebN4DVRv3RAfoXxKRGXHfl1B7mI9+4b65wbkPQtWJ02N7O+dg7qsJ+Cn6ue8JPAc4U0ROik0+D3w+qKq0L/qFDGqH04C90EHtO9BJACJylohcWueS+wM9IYR7k22pzeXk9jzQ8XcAz492uQQdSHwBnYx21bnGXagC6WSIyJdFZDtwNzoAvDzZvQfaRy4G3iYiL0D7y+cB+6HiTyMuQgfws9DJ+ht24dbeg/brx6MTnY3oMwN1bLNR/y0ii0RDfxbtwj0UidfZm75226efDSHcT5zwoH3CcSIyAX3+lovIkcABIYSL6lxml212rA0Urw8hXB7UbXU+/f8ZXwwhrAwhbAA+Abx2yO/QeTJ4NXBZCOGq2KH/GzABfZiNwX72r0K/3FaFEDaiLtLB8np0Rnd5CKE3hHAVqqicEvf3AoeIyIQQwuoQgn3xd6Ed5oIQws4QQi3xIITwohBCw3sIIRwGTAVeh7ofjNXAdWinswN1Rb+33wkqpqOz5cEyGVVBjc3A5BinmO+z/VNCCL3AO4EfoF8ObwU+hg6QDxWRX8WYnkOy47fGe9xtiAP8C4FvhRDuTnZ1AR8LIXSFEC5H1fUDYmzny4GPhhDaQwh3oG7/erwC9ZJcH0LoRF1VA8ZRRY4C5oQQPhZC6AwhPAD8F/Ca5B6XisjsEMK2EMKNyfZZqFrUE0L4QwhhC0AI4dwQwovqXK+uTdVpn9vzQMe/D7XLn6DPyTHx+AdE5Mci8msReWV2/G5nk4MlhPAu9H97HPBDoCPZ3Qv8cwihI2ioy6uAb4QQ7gghtKODvyJxQHYUauOdsb/8yS7c2tuBD8c+viNe6xXRBV7XNqnTf4cQHg4hTA8hPLwL91APs6VB2W18vi9BFfBFaPjR54H3iCauXCsiF4pIaqNb0cHwoBlrA8U0jmo70JbFP6QxJg+hswln9LMA/TwBiAORlfRVRgb72S/I2uZxSY1YDLwyzi43icgm4Fhgfuz8Xo3OUFeLyGUi8pR43AcAAW6KwfR1FaB6xAHmd4CzRMQmSP+Mdqh7oS6VfwF+KSIT65xmI/W/dEtsQweoxlRgW1QR8322f2u831+EEJ4RQjge7YCPRN2c56Munn8F/js7fgrqDtotiIrd+ah68LfZ7vUhhO7k9Xb0C2UOVYyj8RD16WPvIYTtqNttMCwGFmT2/iE0tABU6dwfuFtEfi8iNgA8H7gC+K6IPCqaBNU6iOs1tKkCuT0PZJMPhRBOCSE8FY2v/Rg6ePw34Huox+qzaSIBu5lN7ipxsHU96il5Z7JrXdB4OSPvdwey2Q3RVo1d7af/N7HZu4Ae1G6LtjlA//1EYra0K3b7HyGEZSGEV8d7vA4d270NVRnvQl3txhT6DzwbMtYGigOxV/L3IjQWAqAdqH15JkHMxmBn2M7QkH8ej6IPP6DZuOhn/UjSpt5nn7Ma7dRKx0FmK6gLxViJxttMT34mmSIYQrgihPA81MV3N6q+EEJYE0J4awhhATrb/bL8+WUhWoF94t/L0HjHVSGE7hDCN1H39kF1jr0d/WIfLMvpq9ovo3KPLgcOi5+FcRh93af2WX0RdQfNBppDCA+hsUGHJe0WAOPYNdf4qCX+X76Ofnm9vIHrM2cd0E1/e69HH3uPLqxZyf6B7P3BzN6nhBBOAQgh/CmE8FpgLqp0/EBEJkUV9F9CCAehqv+LGFxw/b1Ai4jsl2xLbS4nt+ddOf6jaBjHY6gL++YQwmY0ti59Ng+kb/iFU6YFDT8w8j58NbtmszOzCW96bP593oxOoIyVqAs5tdu2EMIjjWyzXv/9RBIHpPfT12779LMisg8wHrVnku3z0O+PjwGHoKE/XWR9KX+Gze5uA8V3i8jCOCP8EDpLhBinIlrOo43+svdjVF++zvCTfx4XAy8UkedEZeIfUTdHWnuv3mefczHw9yKyZ5Tr8/IXtwKvES2ZcSTqujMuAE4VkZNEpFlE2kTkhHjdeSJyWoxB6UBniT0AIvLKGBcFqoIE29cIEXmGiBwrmkk8QUQ+iA4sfheb/B5VOOeJSJOIvAEdSN5X55SXo3E76TXGx2cCYFx8Tzb4+zbwD/F/tQD9v38z7rsmvof3xHOYIvbL7JpvAW4JIdyKKlkTYtzciWhQt3EC8MvoKtod+AraoZ8a+mahNySG3fwQOEdEJsb/5RkNDvkBarPPFI3p/RdU3TZuBU4RLSuzBxoHbNwEbBGRD0b7axYtBXIUgIi8XkTmRIXflJIeETlRRA6NX+BbUHffgPYev0R/CHxMRCaJloJ6MfUz+a8Cnmr2O9jj4//sBPQzAE1ceHb8It4PjZG2mOSZqNvPiYjIXBF5jYhMjjZxEhrqkz/7KRcDbxKtezkR9YYUiRPJm1EbHyciR6OJfMa9qDfxhfH74CPowMr4T+ATIrI43u8cEXlx/Ltom43670H8PyTa4Lj4uk0aZ8rn/fCF6DN6XLz+x4AfhhByJf2zqDt/O2qzR4nGfp9A3770eDTzefCEEZAV9Zf80DfrOc0+XUL/bFXLfN2Exu1MTNp/GM04XYnGmtUyrtDO4dZ43I+G+z3v7j9o5/5w/DzeF7e9NH62m9EM4oMzG6n72WfnbgH+Ax20PIjGKnURs3fRAerv0I7iMjTAObW7p8frb0DVncvQ2fF8qsxmy8w+KB5zHqp+bkNnk2km8c+AD9W51+PRSc7WeL1fA89K9rehQdqr0U7v/4AXNPi/zkYVkwnZ/y5kP0viPon3viH+nGf/p7j/COAPaHzk/wFHFK53BzA12XY6GkKyAjgx2X4ZcNpw294Q2ffi+H/eGW3CfixL/gQaZ3XOAS5l8FnPb0KfJ8t6fgQ4LrGh78Vz3R6fhzzr+TvxM9uIDprsPi5As7K3oarIS+L216LKcDs66fsCVT/9IeBnDe51Jlrupj3eczGzM2n/feDVu3I88Cvg6cnrZWjf8Thausm2v5+Yye0/ff5/c9C+aFO0mz8Cb03297PfuP2saEeDyXreF3WxbgV+gdYH/Hpm06uj/b2P/lnP/xBtcCva536ykW3SuP9eFG18UZ17XUL/PnRFg//fIfF5SfvS10V7bUfDImZmx5yIxh7HdW8AACAASURBVOmn2z6XPJML47Y2tI8fsMpA+mNffmMeEVkBvCWEcPVw34sztPwln71ooeP/DCEsHrDxGEBEPgmsDSF8brjvxRCRQ9GyOUcP972MdaICsQnYL4Tw4HDfz19KVAe/BfxVeAK/7KIidBs6MVs7UHvnyUVEvgfcHUKoq0SOJkTkIrS02Y8GbLxr5/07YK8Qwgd26TgfKDpjnV357GOM1olouZp5xIyy/8/ee8dbWtX3/u+1T5legIGBocwwDAy9N2NQEDSggkYs5KrRJJpcE68pNzGm3FzTvcnP3HtzE6NJTGI0qIhGUcASEAEFwZEyVOlthjqdKafs9ftjrc9e373Oc84cDJzZB77v1wt2edZT9jzfZ521Pt+yYow9tayd4zxfhLTizRUkhfijJFX8+OdzYOU4zyc5vGEdyevzGpJK/LIY40279MJepLzUYhQdZ2cEUpzWeuAmUsbYH0y4h+NMb95AcvetIYXZXOCDRKfH2Zvk/t1Ccg+/zweJLxwvGUXRcRzHcRzHeW64oug4juM4juM04gNFx3Ecx3Ecp5H+nTd5/hh99pZd6OdOpw75CqKtFKayYbGV2+TxcygLH3S3f/7pm3PMC3wG5z/DG9/4xgio7ADDw6X+cX9/eoxmzkzlBmfMSCWyli4tidLf+MY3ANiyZUvX/tr3uOOO67T97d9OpRuPOCItQTswkBat+MIXvgDAl770pU7bRx5JCxKMjo52XYP2AZg7dy4ACxakVZt22y0tCbxo0SIAdt+9LDRx0kknAfDoo48C8MQTTwCwbVsp5dfX19e1/wUXXOC226N86EMfigA7dqTyk9dcc01n24MPPgjA4GBaEr3VSv2etR3ZZ233sje92jbaR8fRcS3aVturbMuisp31NlvLXdvqc9t9DjzwQADmzJkDwEc+8hG32x6mr68vQrmX6seg2NTQ0BAAe+6Z6mmrT7Lvly9PJXePPfZYAPbdNy3YpX4aih3rWWi32wBs354Wj1m/fn2nrfrEAw7orgl+5513dt5feWUqGan++dlnnwVg06a0GuDmzaUEYm27uq5Zs2Z12ug7vd59991TartTOlB8oVCHoZtrv4v5dSSmmzFzZl7y0PQ5Q1vTajatlgaT6bV7VKtP3re8FFm4MNmNHnT94YXSWekhVsc2MlImGuoE1NnpD67s1HYK1o4tatv0B1Ln1DXYP+D14HTr1q1dv0WdIcDdd6eFT558MlX8WLNmTdcx7Dk0mLzgggtwehvZjP1DKtvRwEl/fK3dyjY2bkx9pGxd9m9tR9vquHd9tnZdD0qbBpVN+0GxY53PthlvUAnwlre8pev3Or2N+pnZs8euOCobWLw4rRapgaK97+prdb81KZGN2T68RsfRwNGi65F93nVXWoLdTsLuvfferutU/6kBor1O2b7+Buj4ul4o9m0n7FOJu54dx3Ecx3GcRl4UiqKws9HODLOV3YIzktttzkC9dC88O5xUk/aOpKKErBq2R0yj7LMOfa4ovhTR7LN2eUCxNc0+NRO0aouYN29e1/5WvRGaqdbKTAhjbU+zbrlT5F7be++9x7Sp1RvNanVNUGa1S5YsAWDlypVjrlPqqDN9qMMFoKjkCj2Q/Vrbrl1vepUyIlUaitohFUU2o+fD2nP9nZ4rq6LULnEhtdxep85VH89iXepO7yP71H22yrLcvur3pMJZpW6vvfYCirJY95/W1rRN/Xxtn9ZD88wzzwBw2223AXDjjTcCxcMCY13NdaiRdXvr2ufPn991XTqG/TdwRdFxHMdxHMfpKXyg6DiO4ziO4zTyovAh1YHMYAKzh5Lk29dKrwcsSW6Wn3vPL5QDjCY5d3R0OO+bA6v7i4w9qnM871fvTAeefvppoLg2bNZabX9yV1j3glxjcqfVwfZ77LFH5711qVlk09b1K7fK0UcfDcBP/MRPALBixYoxbbSfjtPkEpRrQ9dZ72N/pzN90P1rCjM49NBDgeKqs8geNmzYAJTnQC5ofQ8lYL8O3G+y5zoZRjZok7qUhFAnFMhubThEnUgjG7V2a4/t9D62/4SSuAKw3377AaWCg+67tW+Fz6ivVRvZjQ1FkE0p0e+ee+4BSqKKdfnKdteuXQuULGj7LNiQDHsuuZVtQpX65zpkyT43ui77d2IqcUXRcRzHcRzHaeRFoShq1miVkbqml8rh/O7vfQCA897whnKAdgpUDSGrJ/nVBrD29ekcz/PFO9MCBSOvW7cO6J4x1jPVphmrZomyKc0oNes9/vjjd3oNsnOrsGiGqTqMqr1oS0o0BfZDcwkROyN3XnzYAH4piEpYUoKAtVvZtgLrpSTK/lU2x36nV7XVvrb2qBSaWnW0tfKkFtlrhvJ82eNpf/udvX5oLrPi9C6qQ6v7bT0ptfdGbVQmB8r9lq2pTe1RAXj88ccBePjhhwF46KGHgGLf1o7Ub0rV3GeffYCiMNrjSGXUProma+f6LfoboWfC/t4TTjgB6K63O5W4oug4juM4juM0MsWK4kRFq6tteXWUkVhG/YP9VZkEtC0rI62itDz2aBqV773P4QBcf00qJLxx61EA/OuF93XaHn5Ymrm86vTj8r6rAVi0uMysZw6kcw+PqgxDUob6VAS5ba4t5Niv6mfaNrHVXFTZ6U3OPPNMAC6++GKguwC1ZoRSMzRjtTE22qbXo45KdnjWWWcBcNhhh3XaNsUiQlF6bHyLSptoxim10eMIHYvswaqFitmTuiEFz8byaT8pdrI3qYT2OZB68tRTT3UdR+VEFGdlr6Mua2LjJPffP5UyU0kfKYu6JhujWJfkaVIdVUzeXrPTuxx55JEAPPbYY2O2ybZkR4rJtnGMsikpdVIQFVP4wAMPdNoqJlELDdTxttYro+PqVWVtpCxCsVntr9heqZs2/lBeJj0feg5PO+20TpuTTz55zO+bSlxRdBzHcRzHcRrZRTGKTcqi3ue4wFzgumViA4aHFHeozNE0q9i+I43k33zehzptr7vmfgBm9acYsFZI6k5bh+8rPz2Ga9OZ4yfSa0gzjv2Xlhiu7//gi+lcQ2lWOnMwzVj7BvJaqCY0pg5jbId80r6ypfVCLx7tPK+8/vWvB8p6nk0ZaULKilU89F6KjBRFvWpWaqljC5uKEit25o477gBKrKMrik4TEy3/KAW7KRZMyI6biiArJlHxXXWcls0c1f46l5RFq8JLXZSCruLKUkBtjG19nU3L/0lJckVxeqD4bd0v2++pH5WKp+LcTZVP1D/LLtVX3ndf8SrWhazrxQ6akG0pJlfLnULpzxVnKYVR6uj999/faSvFUwr66aefDsCpp57aaSPbb1pScCpwRdFxHMdxHMdpxAeKjuM4juM4TiNT7HqewB2Wk1dC5biNsbjvWgPJ1fxU8iDw+7/3OQC+dNHtAMxslUDouSElB/S1s2tYUnInOab89JaKs+bvBmNybTz9UPEnL93jnQCc+vIkcf/tP74PgL32TcdrB3vdrXzu/KmlMjvuDpyuyGWgNZRt0L0CoOVO02tTkWoFI+tVLreJ1k9WckDt4rb7yQUzXikcx6mRrdRu2yb3XV1SRAkqq1ev7rSVS0/Pii1KD92uYr2XK1vuaVt2SgkAsv9jjjkGgIMPPhgo7kcobsnaNWdLnPka5dML3UuVvLHhOUr4kHu6KQFQ9qc1mH/wgx8Apb+2YQmyR+2vZBm9WttVgpQSuuS2trYme5bbXNepkjo2LEnneOUrXwmUEjg2OUbhF00lzaYC/6viOI7jOI7jNLJLplidMjHBlojJ76NGzFndM6UaNj2TigEfekhS92aQigvP7VsGwMhQGdFrDBw7M+E0C+jPxbTbbasAquB2+m5UxbpHSmD1zFYa3X//6hSwfdTBvwzA/Y9fBMCceU+Zn6LyOPrnbXX/brw8znRDBVmFLVEjVVCzxHoZMUu9hF/TMnqasUpRuf32pJgr+FoKC5RZ84MPPggURcYeT4HQmoV7oosDxQ7qJBZrH7IjqeRKCJGS+MMf/rDTVs+I1B3ZnYrC2yLDaqPjSoGR2gOluP1NN90ElOLH2kfLVkJJyNHzpONPlPDi9DbqYxcsWACUhBW7rU6GsolKss3rrrsOKMlWshGbHHPggQcCZTlLFZ/XeaztqH+Wqq7EFJXYgfIsqK1UTSWuSB0HOOSQQ4BSIk3eKqug7urlJ11RdBzHcRzHcRqZUkWxWUlMxBw7GPpyvGAewW9aV0bS++7zdgDmDZyY2kgVzEWwB8zPkTAZc8xjK6uFKpRtwwVDS9vS574gFbIolApnGBxIs5s4mmcei98MwH2P/HOn7fzdc2xOPmdfPlnXTH3Mv4DTy2gWKuXDxrdIzajjuLR4vEUzTMW16LMthKzZ6NVXXw3ALbfcAhRF0aouiovRbFmzU3tuFQvXjFWzZL3uqpILztRjleY6rqtJcZNKLqVaRatlk9YWpeqozMfy5cuBEmtll4eUzen4dWkdgHvvvReARx55BIC77roLGLsMJoyNJdOrx+xOX+oSR7bPlTqt+ys7ko1AiUmUbQnFh5900kmd76Tw1THjOqd9NvSdrkH2rlJnAKtWrQKK8q7nR14iW+xbfbaeDymnVkXc1fG1/hQ5juM4juM4jUzpMFU1ppt0tXaOE5SKt31rUliOOeLnO20WDqYZQMwK4uDsNNPcvDktxj2n/0RzsjQaD4oFy0pi59xG1YxZ6Yxajs9cFVX7dp7NttopfnHeQMrAO3T5mzpNb/7RxwBYsnhB/k1ZsQw2htKZTmhmqIXflfEGZearWZ+KBtvMNsXDqI1iaTTbtQW8r702FYC/8MILgaKoaB+rFkpd0QxV8Th2BqxCsEcckWJ6pfSooKsy8er9nJcGtfpmbUA2LJVbio1iFaXAQInzksKirGQ9D7at1BidS+qJjRtTG+2ncysmTLGLUOJvdS4dbzKFk53eRPe9XoIPipqs+61++YYbbui0USy3bEyxfypoffjhh3faql+uFWjta5+J+jt5haztnnLKKUCx4e9+97tAiSnX8wOlWoAUSf0mW3xez+iu6p9dUXQcx3Ecx3EamdoYRbpjE0O/+TyaFMS+vLTehqezGrftwE4TjdyHQlJ3Hn3qq/lASVVZttc5nbZDz64EYHA01WAKUWPinGVqryOM5Ovrxq6yp7edrDrVfWynGcNgOKLT9omHU5bfPnsrviHHrJkF6ge9pte0QrXhpOY1xYxIxajjZqDEZGkmqVgvxXgpHgtKfEtdP1Hqoc3kHC92xc6+NZvVkmqKx9FvUQwjlFn3rqrX5bywTBSj2BTPp1ha2Y5ehTKb7Xv108oClS3ZtsuWLes6t54LG0+m/bQMmpQlPTv2mZGaKfuVGuMK+fRFtqH4bRvjatU7KKqcjXFVf6msaal8K1asALqX3FNGvc5R17m1qD9WVrVe7bN18sknA3DQQQd1HV9trVdIy8LKrrWP/b27uj92RdFxHMdxHMdpxAeKjuM4juM4TiO7xv+Z3bbReJ5jvpT1TyV33UlHplI4M8KxnTaSX9//394PQDukBIDhkVTM8sEnLu60fdsb/xiAa69MQa59cVHeoqUCDZ1kkywdZ1exdZW3yEkx+k7JLW0tcVUk6rNemRJwbn8gJbXstafc1O4Gma7Uy5FZN0NdaFtYV57cICq8Wh9PxVuhlFDQcfVq3cn1dcjF1hT0LPeh3DI6ntoo+cDur0SEukxE0+/c1W4RZ/I0BeXXy/RZlEClgu4qD6Ui2nIhQwm9UGKV2srebCkduacVkqFEFdtGBYdVXkevej7kzoPiEtfyfnLbWRelJ7ZMLxRqIFuTO9ii8BzZj3Xpqu9S8p7K0Fx//fUAfOUrX+m0lS3Jhs866ywAXvOa1wDdLmglDH7+858Hii3bZ0tu5PPOOw8ohbzVByv5xp5b7vNXvOIVACxatKjTRs+JJ7M4juM4juM4PcXUlsep1LxhU9C6vy+NmOfPSaP+GSEFdPZTln3aPpJmGH//1ymJ5dJLPwvAqtWfAGBopASyfu7ffweAf/r49wH4/Q+m0X//aJqVhrZdoD4Fy460k0IZYxrhz5xflgPatjml47eCrkeqaFZnzJKAM/rSzGfBvBQ022o9CMDggBfcnq7UCqBdAF6lbSZS/tRehVeltuh7q3Zo1qjv1KbpuOPRpA5ptq3EHM249957704bqYNaxL4ucQJjFUm7NJvT21g70/u6ULxtI3uVzcgWlTSipc7sflJYpORoX7vEmUrcyN6UxGJVFCnh2l9lnPSqpS2hKPUq4yOb9mLy05d6IQNrl1KeZQOyEdtGJZOUvKclIb/61TR+sP3WOeekRNgbb7wRgMsuuwwo9ig1EooiKdX6ne9855hzS21UIqEURZUo07VAeV60oIJ+ixK0YNcnZbmi6DiO4ziO4zQytTGKOa5PZWf6Qjl9H2n28PZ3fSg1DUnBGxk1MQd5ab1WX1I5Hr03HWj3OecCsG7r1zttd4yk0gnvfu/xALzqrJSu/vLj3p+Pv7TTVhMBzQhGQi7v8PgXO21ecXIqqP3AXUk9UsxjQMsMld8y0Erp+O//5T8F4B8/9a58AlNwu2EZQ6d3UayWZn9NMXv1klO2dI2UDW2zBbahe3ar/RSjo3Prsy27I+oisFZJ6dh13k/HUSyZlgq01yFlRiVN7PVJ6dG57FJYzvShjlEUVi2XrSiOSgqg1BqrRkthUcyr7FZqvLV5bdM16Di2CLJUQsWf6TqlKCoODIryKaVG191U3sSZHthlTaEobgA/+tGPgNIf2wUQhGxUsY2KA1d/pRI2UErmyA4/8YnkpVRMoRR0KLammNmVK1MpPqv6SYHUUpd6plTqScsLQvH0yHalKMqGoRQY31VLUrqi6DiO4ziO4zTSM1Wf2yNpNP7Nr6cYgZkxjfoHWibzOOSl0YbS6HwgK4v94WgA9pj75k7bpzdfntqGVMxy/4PSTPjhZ76QPi86t9M2xjTKD/05Q649dvx89Y3/CMCpx/wSAGvuT7NjDfD7QlFwhoeT4nLxRSnO4e/+4WfT9XrS87RFKtqwKZpeU6t6VvnrFGqv4sGa4vzq5c2k5iguRzNaGKtmKpZQmYL2OnQc/QbNWG+77bZOWymIiiHTAvU2/kbxZPotrihOb+ol/CyyHamBUrulcOgVSmFjHUeZ0lJIrE1KoZFNSj2yx9NzoG3KYK6Xw4RSBFmxv1aNcaYn6iO1hOnNN9/c2ab7rVjquvoDlL5LdqTPUqStYqn+XYXfZe/1UoFQ7K6OL7fPj86p36DnqL4mKDarc0pZtF4r/a7678hU4Yqi4ziO4ziO08jUKopaRi8vuWeLGY7mD7GdlLm+PIYd6CuXuCMrdaOKccwj+X5S7aPR4ZKBt8fcswF4cnOKW4ykeJYRUmzD2o2XdtruPveVAMzsPywdZ1S1Esv1DQ+nmcYJp6RMvsfvTzE2o50MUDP6b+Vadu00C9mwIcXo7LnIxMt42vO0os70bapHp9le06xPs1DNGvWqtjarWrE12kf15PRqa81Jkamzqq0yU2cwq4ZXreZAiQerM66b8PqJ05vxYmublIw687Su8QlFxZOCqLpwsvWjjz6601YKjexV9ets3cPx6jtqX/v9ePbqtROnL1ru7oYbbgCKsghFkVZ8n2zWxmZLtVM/pf5UnxUnC6Wm4jXXXAMUe9Ryevpsz1HHitv+UHas65Jdat96CULbRvGW1nulbV5H0XEcx3Ecx+kpfKDoOI7jOI7jNDKlrmetYKfRqXUTDPZnl3Nfdu2pjMhICUoOnbI62bWXXdhxOO07IxR5eCAnqOw7Py2h88DjSVqeMSO5ntvtUvx13bZvADB/1ssAmN23PF+wufh8zt33mJP335CvNy/t1y4ydKuV/llDTNsWL84BtyPrO23cITK9qIOlmwoX16476yaQG6Euzq1XW4BV+8k9IVd007JkSiyRG1nuFXt9Nui/CetqlCtQ7mg9o02JObu6CKzzn6MOtJeN2n5Zbl7ZUF0wXvZn38tlqMQALfMnNx4Ut1/pP8eWjardiqIO24Bii7rOpiLN7oaeXlx55ZVAWbLRuoqtK9gy0TKn9Wfbp8mmZI96BmTnKoUDpf9VAoza2lAIlWXSuXT8pqSb8X5Dk+3qdarDflxRdBzHcRzHcRqZUkWxRXeg8UC/KQqcl/NrxzxbbKXRfssubdZ5VYXsrD7m4a6dOLTyEn2D7RT0umxxUha/fNmfA3DSy8ySZEOpkOembd8GYPGCEwDY8uwTnTb9s9L1HX7kgfn6UtmdOJIX627ZmUyeJZNm6Js2pZn23DlmFuGz22nFRGrEeEvt2VmfZq/1cdTWlvPQDFpqjkpBqLyIlEUoCS51WRw7s1Zii2a1mhErgcZeU1Mx7/p4uzqw2vnxsfdsvMQUaw+yq7oQuxKqHn744U5b2dPq1auBErgvdaVJAdQzogB+m6glhbJWT6S+W/WxThLwRKvpz5o1a4BiG1KYofRBdVFua7vaT3ZdL+eovhPgv/yX/wLAqaeeCsAnP/lJoCzpJ1Ucipope29KXtS5dD26dl2L/S1Cz4SOb3+blPLnsozr84krio7jOI7jOE4jU1xwW6NhxaWYCwlSFFOsVSmhY2bAWXVsxe7xbbuVRufRlKhpZf2xLy+tN7MvxRice84fAPDLv35mp+0f/clPA7BjKC3X88SmbwHw0EOrO232npWUyYMPToriaMwxNszvulyA1kC6vpmzcqzZ7DQrD7HMgF1PnF7UsYkTKYxNSlvdvi6zY2eK9VJ9UhT1qqKtMDZuUZ9tDI9mpjqu9pdy2fRb6jIjTUsWuqI4/bCqR10Yvi4BBUVRVIyWCrFLWbz33ns7bWt7leKnpdcU0wWl6LFUHSmTNuZR+8umZXcqv2MVxUWL0pKqKgFVK03O9MPGbUN30WspdnVMqu23tGSf7E4qs5RK20fKfg48MP1911J+iv1u8tBom2JxrWIpO9b16NyK8W1aNEH7q/SPXX5Sv0UK5znnnMNU4oqi4ziO4ziO08gUK4oKJswZaSZmcbSdVMFf+MU3APD5T6Timq04OGb/dkc51PFyzBQlLiXmmMF2SLPOkM85q5VmnB//62902l579dUAXPHtj6Yv2k8BsM/iEgsWQpotH3hgmlkPDqZzxR15RhPLLHzHaJoJ/eVffzD9tpH02/pt2IyLMdOKOjuzSWGr46KsMlPHlkykxtUxj/qsGaxib6DMbqUSSvmx8Td1gWLNpDUbb4pLbMrunsy1O71NU6F42Zmynq3qKJs+4IC0mIGWe5TCYWMUpRIeeeSRXfvWr1CW+zv00EOBErOl40KxYak9sv+HHkqeH2ubixcv7nqtY8Sc6YfsU/2X7dPUd8lmdb9trLcylp966qmuthdeeCHQrSi+6lWv6tpHy09KYZRSDbB8eaqKct111wFw0UUXAeUZAVi1ahUAe+65J1DsXWq4Vdf1O9VG51L1AICvfz0tHHLFFVcArig6juM4juM4PYIPFB3HcRzHcZxGprY8Tl4DObbHBvD3ZSn5ox/9PQA++/G3pjZhUTlAdkO3+roLxQ4OpED+kXZxZQ+TyywMPgrA6GhyQZ96yokA7Lfs0E7b/Zcmt903vn4ZAK9+9QljLz67nnfbbUE+d3JptGIKmo4mUaXVl1yD73rXq9NvaCW5ub3dlIeYYqe/85+jLk8wkbtWTJQkUruirbuvXtt2ou/rAt4qSmsDq+XKkHtPyQZqY69F7pi60LZ1G9br/zrTB2ujSvioQx1sQoDay62motkK1rfr7yrp5LjjjgOKK1qldWyCiV2LHErohHU9y5aVdKWkGCU5WFfk0qVpgQWVh9K5PExi+iK7UaKeXLNQbFT2om3W9azyNQqXOO2004DiTpYbF+D2228HintatnXSSSd1XQuUNcuV0HV1Dl174IEHOm2UDPOa17wGKP2nkr/sOs5Ctvv9738fgJtuuqmz7e677wa6w46mEu/pHcdxHMdxnEamWNfKpUVymZw+M9kbGk7BnS1ysGdMS+3Noozk+5QB0k7H6W/l5XGGk0LSNkVnlPDywEMpnbxvdjpuqy/NUtvtMqIP+Xr6B7qTB0LbFkxOY+qBGel6RoeT0tLfn/YdjaWA5sx5SjpIQdcDgyrIaUs2eJD1dELqmZRFq9jVSSya1drZrYKX63IOTeVxxkPnsUqKAv0VNN2UOCBFRkqMPuv6rOqi66yXQLNtdGwvQTL9sHZRL/fYpJLLDmRnxx57LFCUnLvuuqvT9o477gBK8oFepVLbZ6ZOnFEbm2Cwdu3arnMowUC/4fDDD++0Peyww4CiatbPmTP9kCdEfY8th1T3p7KFpvJKUvGUhHLWWWcBRfWDojrqeCeffDJQ7Mr2deprX//61wNwwgkndF2TbSN7VlmbRx9NHs6mcjuy1a997WtAd/F5bbOLLUwl/hQ5juM4juM4jUyporhxS4oZeObJpGgcdNCyzra+LLANjaY2659NqeH7LHxTp00rLgFgdCQpIf1ZoRvIM8wdo2WU3t9KMS9L938dAP/+9f8NwE++ciUA7ZGi6LVaacaycWNKjb/l5jQzfvqpjZ02P1yVYhg2PpXiEQZaaWQ/MpKudySU5f4efeSqdI5wn84w5t/CqzZML+xsFpqX59MsUbGAdpmmelk/zVw1U53MEnlqY+NbNItVAdd6oXooymG9FJau2xayVdt6YXobb6nZrS+TNv2wNjWeojiReqxYxZe97GVAt51JuVGMlcrYKCZMMYtQignrXLJflQ+BoihKAdf1HnzwwUCJH4OiDtWloJzpi2xOfZqNz1PMoJREvaq8DZT4WZVV+u53vwvAmWemxTZkw1CW7hO1HTV5fKTuycPT1OeqxI3iDfW97TulPqpwtzw/tuC27Fttpxp/mhzHcRzHcZxGplRRnDc7zQLmLk0+eRsTowF7K1el3rItqXEnn1ZiJbPiFwAAIABJREFUVm66NsUftEMa7UcV4M5KYl+rqB4asYfR3QD4mbPT0n2j7TQrGRgoP72dYx5brTzDzlnZI+1yvECKr+lkiubJQ+xPs5WPf/K3Om1vuy0V8z7ymKReDrdz0e8RU+y2z8fo0wllCk+0PF+dEW1noXpfF5HVqz1uXWi7jmPUtUBRF5Xhp9l307lrdbBJdanVxnrf8f4NnOmBvefqy+rlJCeycaknK1asALrtTAqIlvmTIqiCx4phhLF2L7uzyr22SblRjJmyqqVUQonzqlVuL7g9fak9IFaRPuOMM4CiCipr3t5vqdRSGaVWq3i1VRGlSEqprPtG+7lWF9Vfy84BbrnlFqAoiuqfZZ/KcLbnVPF6LeG37777dtosW7YMgGOOOYZdgY9WHMdxHMdxnEamVFGUCtfqpDvbDLy8nFQWGWcOps+fu+hPO22W75eW9wutNPofjsrw1Hi3KJQxh3G1tFxgO81gB8jxWDvMTFPJ1Cg7Of2z9JuJQ2ilY+/YljOs+9IM4bQz0+h/2fISn7DysBVdv7cvF02MLTtT33mWq9M7SMWrs4Ht+1p9s7PQWtmo6xMq/gqK0lPXXGzKTtV7XV9TlrKoYwv1amMe67qM9b4WG5PjTA/sfbQ2BxMrivX+UhaVFWq/UxyV6spJ0bF2Viv09XHtcVQjUcv9KU7S1tWr4y1rVb5+7/Q+6ge1JKQykaEoa1LdZAuquQjFxq666iqgKIyKBbzyyis7baWCH3LIIUBR9aT22WdCfaTqfCoW8p577um0UXaz2mr/3XZLHk673J+UTi2/qtcTTzyx0+aoo44CujO1pxJXFB3HcRzHcZxGfKDoOI7jOI7jNDKlrueB/irV3Kxjl1f36yyF98wzyV2x1+LFnTY/euArALz6jPcD8Oj9cmUkOZeh4s5t9aXjtEcrd0rs7/4MxJjdgAPp+oaH0nEH+0vZkOHRXIh4IBWa/cB/PweA156b3OBHH7+y07Y9ks4d2yo3kb8PxdXuI/TpRdPyZjVNpWREnZBSFxy2BV3lnpD7V0HddYmeic5tXYw6d+1yVltbdqIukdJUHsKX8Ju+NJW+qW2nu2/s3ib7r+0XirtORYb3339/oBTntnZW27JchkpKgFLkW8et3YH23PXz2bRE2mSK2ju9w+tel0rbyR1s3bWL87hAfaUSnuxSe2effTZQ7vv1118PlPIzch1DKbgt97Fc2TquTZJSiRslqOjV2lxdDk3JK0rIsm1l5ytXpjGE3Or63VCei121yIH39I7jOI7jOE4jU5vMomSRPMoetQHzOTFl2/Y061y0aI/cpoy85y1MM93Lr/hfADxyX5o9vuqVPw/AjFDSyUdjmhG0Qg5ujVIx0/FGTembGHIywqiWVUtt2/2l4PazI2kZqQ3P3AzAJV/9OwCOPuEgAIZ3lNlypzRP0LVklcYqMB5XPa2oE1YmCvifSFmsVQ19tkWvFbytWagKFys5wC5TNV4ZG3u8erlA7aOZcZP6Uhf9tupNU0KPMz2wdit1orbXiRK1pApqOT0VM4ZSokaqjmxQ6o9VQ+pkLpXWsckI9dJ/ugapkfZZ0vFUHkoqj03YaVqi0OldVAJHappVm5X0VNuIvd9S6LRN6qAKwivhBMYmBapvtCVvxHhFuG0fKVuXPUsNV19ri2m//OUvB+D4448Hxi7/Z4+3qxY5cEXRcRzHcRzHaWRKFcU6LsXObu+9OxXYPnBF8uGHvjQL2LG9FBd+dn2KW9Ri4YMr0uj8sSdT7OJtq9d02r7mrJ8FoEVq2xfTbHf2YFIaR4yKEkmz5K2jafYwd0G6rvseXNVps2ZNKha75vH03XlvSjGKQ9u1nKAphaI4tlwOJ7TSb8HOQLxo8bRiovimyRSyFrW6p+Pa2eMRRxwBlHIgipe59dZbAbjiiis6bVevXg0UpUfHb4pnHE85airjU8czWuqyPc70QUoJwM03J+/InXfeCYy1TUtdmkn2ZtXoelnKOp7Xqj217RUvjFmUoLqOidrKFnXOpuUIVR7FmR6oTIyUaimCUBQ52VRTfyU1T4qi+lEVi7eqnux4vPJi9rg6pwqB6/h6tftrv7rAvC3tpP5eMZhN6qFsf1fFhbui6DiO4ziO4zQypYpi/2AaVXdmhANFRVl5VFL+CHnsmjOGZ83fvdNm1uxcjDXHEM5ZlNv2pRH43N3L7PYjH0mK4tmvTUW69z/46LRhSNnPZrb7bF5IfEeKa7n4y58F4L4HrjXXlwvL5nhD2jvyudPxImYGUmdY55CfYIpsd6KAoo/VpwPPpbh0UxzjeMukaQZr1RbFJmqpsiVLlgBlZq34MChZesrg07mtcjSe8jdRgeWJ1KV6qUJn+mDvmY11fT6YKCvfcZ4rymiulzuFot5NtGSj+kAto6d4WsUuatk+KP1dXYReNm3VQrXVsyQF0O5b95v6LCX+9ttv72yTN0kqY318iyuKjuM4juM4Tk/hA0XHcRzHcRynkSl1PWvd5b//208AsNv83cqFtJJ828qVt4dyWRzrNhsd6Q40HW2nNkN5UeUd24pUK4X2qqsvBeDpiy8G4A8//JcAlCIMcP5JycV37H4pePaOrWsB2DRaSt5cf11yQw/OTPL3BW89P33O60JbmTh01p5WSZ5cuqRrfee+rm1ObyPX82RcshOttyzGc3XYc9UFjBV8bYO6FbBdl75RoLW9nvpzU+mbOlGgydVRu80dx3Geb+SKbUpe0nfqP5tCg+r+V4WtFcJj+0j1rda9bbF93dq1aXygBBj1+yrNBGV9crm7db3qy+24Rq7rulxVUyKhu54dx3Ecx3GcnmJKFcVIUjt+6dd+BYBrLy9lPmbNSqP8/lwWp4yczXJS+a1Uk04pkJFciHW4jPrnzk2JL9u3plF+GHkYgBmDaUTfGiqzifPOfxsAs4dT+YSDlp0OwEYjmHSWuxpNwa1aja/dKYXT/UsB2p1LzxtDHNOmc3ycXkYz1/oVygxQM8y6WLVtM1ExbqHjKJC6Pp6d9dZlF5rOXVOrok3L/dUB2vZ4vnSf4zgvNLZkGDQvI1qXWWpK3FMiipK3VBbqySef7LR51ateBcC++6ZFO/T3Xsezy/2tWpVK5EktlFfHlopSQXAtF/jYY48BZVk+q4DW1zyRN8cVRcdxHMdxHKenCB5n5DiO4ziO4zThiqLjOI7jOI7TiA8UHcdxHMdxnEZ8oOg4juM4juM04gNFx3Ecx3EcpxEfKDqO4ziO4ziN+EDRcRzHcRzHacQHio7jOI7jOE4jPlB0HMdxHMdxGvGBouM4juM4jtOIDxQdx3Ecx3GcRnyg6DiO4ziO4zTiA0XHcRzHcRynkWk/UAwh3B5COH1XX4fjvBgIIXw2hPDGXX0dlhDCeSGEz+3q65guhBDeHUK4dldfRy8SQvjzEMKvvQDHdRv9TxJCiCGEFbv6OnqNEMKeIYS7QwgzX4Bj3xBCOGJn7ab9QDHGeESM8aoX8hwhhH8JIfzJC3kOZ/KEEB4MIZy1q6+jFwgh/H3uRNohhHdX244MIXwjhPB0CCFO4lhHA8cAX8mf9wkhXBJCWJM78WVV+xkhhH8KIWwKITweQviNavuZIYS7QghbQwjfDiEsNdt+K1/XbSGEI833Lw8hfNkeJ8Z4CXBkvr4XHfnf8ZMhhIdCCJtDCDeFEM7Z1dfVC4QQPhNCWJtt7EchhPdU28e1sYZj7Qn8LPCJyezvNjp5dnafXkqEEN4VQliV/y0eDSH8RQih32x/fwjhByGEHSGEf5nEIT8E/HOMcXvef9x+N4Swfwjh+hDCuhDCR6vr+noI4cTq2P8f8Ec7u4BpP1B0nJc4twC/DPywYdswcBHwC5M81i8B/xZj1KCyDXwdOH+c9h8GDgaWAmcAHwwhnA0QQlgEfAn4H8DuwA+Az+dt++RrWg58HPhI/r4f+CjQpPh8FvjFSf6O6UY/8AjwSmAB6d/sonpg/hLlz4FlMcb5wHnAn4QQToCJbWwc3g1cFmPctrP93UafM+Pepxo7aHqRMptkH4uAU4Azgd8029cAfwL8084OFEKYAbwL+Iz5+sOM0+8CvwN8CjgQeKMGhiGEtwH3xxh/UJ3iEuCMbO/jE2Oc1v8BDwJn5X+8i4B/BTYDtwMnVu1+B7gDWA/8MzAzb3s3cG113AisID34w8AQsAX46q7+zS/l/4BPkwYw2/L9+GD+/rx8zzcAVwGHTebeNxy/j/SH4GngAeD92Rb6rb2Z9h8GPmM+nwp8L1/HLcDpZtu7gfuzfT4AvD1/vwL4DrAxn/fzP8a/y7XAu8fZtiI96js9xv3ATzZ835//DZZV3z8GvMZ8/mPgc/n9LwLfM9vm5Ht2KKnz/Gz+/lDgjvz+N4HfHefaXg48sKvtbwrt/Fbg/Pz+dOBR4L8DTwJrgZ8zbfcgdfibgBvyfbh2gmP/LPAQ8AxpkNSxaeBfgD8xbU8HHjWflwBfBJ7KNvwBs+1k0mBrE/AE8Ff5+5mkP3TP5OfiRmDxj/FvsjL/9rfuzMbG2f9K4B3ms9voC2O79X2S/f428Djw6fz9b+V2a4Cfz33MinGOeSBwNanv/A/gb8n9bm2j+Ttr0y2SKndftsGLgN13ZpuM01//GP8ev0HDuIE0WPyXnez7CuDe6ruJ+t3LgZX5/eeAtwLzgZuAheOc41vAuya6jhebonge6R9nIanj/Jtq+9uBnwIOAg4Bfn9nB4wx/j3wb8BfxBjnxhjPfV6v2HlOxBjfCTwMnJvvx1+EEA4hzeZ/DdgTuAz4aghh0Ow62Xv/XuAc4FjgeGDS8XohhH2BS0kdwO6kPypfzDEmc4C/Bs6JMc4DfgK4Oe/6x8A3gd2A/YD/Z475tRDChyZ7DT8u+foOBO6eZPvdSIOGW8zXtwCKdznCbosxPkvqqI8A7gWOCiEsJE3ybg8h7A9cQHKFNHEnsCyEMH+yv2m6EkJYTLLR283Xe5PUxn1JStff5nsA6Y/mdmAf0h/cn5/g2IcDHyM9D/uYY07mulrAV0n3dV+SUvJrIYSfyk3+L/B/Y1KVDiL9QYakiCwA9icNav8raUBGCOFDIYSv7eS8HwshbAXuIg0sLsubJrKxJo6i277dRp9HJrhPkOx3d5IK9otZAftN4NUkdWxnoUQXkiZBe5Am5+98Dpf2AVI//kpSn7We9MzAOLY5UX8dQjgghLAhhHDAJM//Crqf5edCl81Oot+9DXh1ttsTSeLIHwP/J8a4YZxz3EkKORqXF9tA8doY42UxxlGS8lT/+L+JMT4SY1wH/CnwM1N+hc4LwduAS2OM34oxDpM68lmkh1tM9t6/lfTH7tEY43qyy2mSvIPk2rosxtiOMX6LpLC8Nm9vk+KYZsUY18YY1XkMkzrQJTHG7THGTiJCjPH1Mcbncg0/Lgvz6+ZJtp+bXzea7zYC88z2jXSzEZgXY3yGdA+uBF5H+oPxf0mKw0+HEL4TQvhKCGE/s6+uayEvYkIIA6SJ6adijHeZTcPAH8UYh2OMl5HU9JUhhD5SaMAfxBifjTHeRnI9jcebSerGtTHGIeAPSErOZDgJ2DPG+EcxxqEY4/3AP5AGT7rGFSGERTHGLTHG6833e5DUotEY46oY4yaAGONHYoyvn+ikMcZfJtnVaSRX8Y68aVwbG+dQC+m2b7fR55EJ7hOkvu9/xhh3xOT6fysp7u62PED/8HjHzQOyk0g2PpT7x0uew6X9EvB7uU/fkc/15uwCH9c2Gae/jjE+HGNcGGN8eGcnDiH8HGnANt7kYmc02SyM3+/+Oenf/zukwfAAcDRJOLkwhHB1COH91Tk2sxObfbENFB8377cCM6t4iEfM+4dII3Nn+rOEdD8BiDG2SffaKiWTvfdLqraPjNOuiaXAW/Jsc0MIYQPwk8A+uTN8G2nGujaEcGkI4dC83weBANwQUhb/uIrQC4hmm+P9ka3Zkl+tejKf0qltqbZ1bY8xfjbGeHyM8RzgSNIflZtIHeq5wBfo7lx1XePNiqc9WbH7NCnMpe7Mn4kxjpjPW0l/NPakxDiKhxifLvuOMW4lud0mw1JgSWXfvwssztt/gaSE3hVCuDGEoAHgp4FvAJ8LKTHqL/KAeNLkP+LXkhT39+WvJ7SxBtbTbd9uo88z49wngKdiTsbI1P3szmx2XbZV8Vz75X83NnsnMEqy20bb3El/PSlCqh7xEZIq+fRz2dfQZLMwTr8bY1wXY3xbjPEY0sTm/wH/jeR6v42k3P7X7FkQ89iJzb7YBoo7Y3/z/gBSbATAs6QAVABCCHtX+012xu1MDfX9WEPqDAAIIQTSvX7MtBnv3tesJXVyTftBZSskl4p4hBR/s9D8N0eKYIzxGzHGV5NcfneR1BhijI/HGN8bY1xCmv1+LExxmQjjdjtkku3Xk/6trGp/DMXFcrvdll05B1G5YEIIs4A/I8XfHQw8kmf0N5JmwuIw4EEz239RkW32k6Q/XudnZXwyPAWMMNa+x6PLvvO//x5m+87s+4HKvufFGF8LEGO8J8b4M8BewP8CLg4hzMkq6B/GGA8nqfyvJ8VJ/jj0k+wIJmljhlvptm+30RcOe59gbJ+9ludms7uHEKxd2n3rv999pAmUeIQ0WLN2OzPG+NhEtjlefz0Zsmv9H0ghUqsnu18DXTY7iX7X8ovA9dnLcBTwg+xFWE2a+IjD6HZlj+GlNlD8lRDCfiGE3UkzYWXI3QIcEUI4NqRaRR+u9nuClP3m9Ab1/bgIeF1IpS4GSB36DlJSiRjv3tdcBPxqCGHfHOfx29X2m4ELQggDOaPszWbbZ4BzQwg/FULoCyHMDCGcns+7OKRaa3PytW0hzWoJIbzFuLDWkzrV0cn8Q4QQBrPNBmAgn7OVt4W8bTB/nhlSFt14XEaK47HHnwlonxmhu5bXvwK/H0LYLc+230tKhgD4d5Lb5vy8zx8At1buVEixov8SY1xDij1dmWP0ziAFkotXkgK1X6z8HanDPje75iZFDrP5EvDhEMLsrBS8a4JdLibZ6E+EFMP7hyTbETcDrw0h7J4nzDa79wZgUwjht0MIs7KNHxlCOAkghPCOEMKeWdGXQjEaQjgjhHBU/gO+ieTu26l9hxD2CiFcEEKYm8/1U6SQkStzk8namKjt2230eWAS96mJi4B3hxAOzwPA/zlewxjjQ6QQng/n/u5lJEVX/IjkPXxd7v9/n9JnQcpa/9OQSx+FFDP+hvy+0TYn6q8n8e/xKlL4yPkxxhsatvdne+sD9HdivEzwG4CFIcW/i4n6XZ1jL+BXKGOZB0jZzXNJrvD7c7sZwAmkhJbxiT2QJfWf+Y/urGebfbqMsdmqynzdQIrjmW3a/x4p4/QRUqxZJwOLNIu8Oe/35V39m1/q/wFvIHXYG4DfzN/9dL63G0nxGUdUNjLuva+O3Q/8b5I77gHg10mdR8jblwPfJ3Ucl5ICnq3dnZLPv46k9lxKmi3vQ8lsVmb24XmfvyCpn1tIqt4vmuNdzjhZlnn7VdlW7X+nV8+A/e/BCY51JGlmGsx39f7RbJtBKvGgLNffqI53Fmkmvi1f57Jq+0qSKtNvvvut/BzeARxlvl8NHLOrbe8Fsuel+d92e7YB/aes+NOZOKtzT+BrTD7r+d2k50dZz48Bp+VtM0mTqE0kNePXGZv1/FlSmM964HpzHZ8hZWVvyXb0xvz9z5AC8p/NdvLXlH75d4HLx7nOPUnPzIZ8PauB9z4XG6vaLiJl385yG31e7XfC+9Rkv/n7D2U7mkzW80HANSQX6xXA3wOfrGx6bba/32Rs1vNvZBvcTOpj/2wi22Ti/vqAbOMHjHOt3yap/PZZvtxs/zBj+9UPT/Dv+5fAb5vPE/a7uc2/Am8xn/cn/d1aD3zUfP8W4Es7u8f64/eiJ4TwIPCeGON/7OprcaaW/8y9D6nw8cdjjEt32vhFQAjhQuCiGOOXd9p4igghnAu8M8b41l19LS82ssKwATg4xvjArr6eF5oQwp8BT8YY/8/zfFy30SkkhPB54K4Y47hK5IuFkArFXwMcF5+Dt2GSx/4+8AsxuafHb+cDRefFznO59zke6QxSuZrFpJpx18cYn/dlvxxnV5AHNVeQXM4fJangx8eXyh8DZ9qRwxvWkbw8rwG+DLwsxnjTLr2wlwgvtRhFx9kZgRS3tZ6U4XgnKXbJcV4svIHk7ltDCqu5wAeJTo+zN8n9u4XkHn6fDxKnjpeMoug4juM4juM8N1xRdBzHcRzHcRqZ0sW5/+JjF6YUpFYan86ZUU4/d24qON7fn75Tm1ReLDE6OppfU93Z4WG9ppJjTeqo9h8YGOj63GeaDvT15W2tfA3pc19/adTK+/UPplXh5s6ZA8DQ0NCYc8+ebcs9AbGV25Z6uVu3ppjU7dtTDdLzf/pVAadnOfvssyPArFmzAFi3bl1n29KlKc9l1apVADz2WCrfaG1XNqHXvfdO5elkl8uWLeu0Xbw41S/esiXVVpWNqO3ISLGjRx5JdWeffjrVc125ciUA73jHOzpt9twzlRTTte+xRyqbd9999wFw8MEHd9o+++yzXdc5Y0aqMrF6dSkF9s1vfhOAyy9PlUBuvPFGt90eZWRkJKXy535VfaVF/aratNvtzja978t9pNrKttVP223jtbXHrdvKtptQ32qfJ3u9ADt27Oi6Hj0jdp/6mgcGBtxue5gPfvCDXbY7b16pOy073rw51VbX/Z45s1Tv2n333bte1a/utddeACxcWBYjkU3oVccVg4NlNVid4+677+46rrXh/fZL1c5k59pf123tUtcuO9dn+7zouvTdfvvtN6W264qi4ziO4ziO08iUKoqawIVWHgz3lXFqzCPlVKsV2nE0f2+P0N2GkF5brbEzVtWQDa00So9odsuYc7f78kwzZFVQn80MoZXVxr6sNnZmAXn7jBllJqMZx9BwUhu3DaVZxPbRsvTl9nZSiYYnV8PT2cU880xa5Uz3fdGiRZ1tUvO67W+sAmL31z6a1doZbD2z1IxaM1arpGiWLfVR16DZLsDjj6eVLTdsSDWQt21LarZUTV0LwFFHHQWUWbjUzBUrykIxUkwvvfTSMb/P6S1qG7Sqh1Wm7WdrX7KnWvVQGykmdpv2qdVIey1SSLRNbZvOXSuKerXXX29rUlC1Td9NpGI6ux5563QvpRpD8XzoVVj7kd3ofqv/mz8/rX5n+9w52UMom9O51Q/Ks2LbyFMj74s8Nba9bFTXUD9z9ly63rr/h2K7uyqnxBVFx3Ecx3Ecp5EpVRRnzkyj7HbDqHh4JI+qg2aATfGG3fEnnZmmhrtxrDrXauV4wz59buWjl+O3Owpl2jaalcWWadM3kA7Q39ffdXVSGGfMsP+UUjHzLCAfb3h0aMzvHWmPnWE4vYdmqpp52pnr2rVrAXjyySe79rHxW0Jqnl6FlBWL4lqkGirGsOncUg2feuqpMcfXzLeeoavNSSed1GmreEtRK5VQZuSabTu9Sx0vaKkVDLWxqkUdG1XHctnj1qpHp6+dIHa8Vh+bVEd9V8dSWsWljm2v2zb9Bqe3Uf/XdL9rha4pRlH3Wf2e+jL1kVahrPepvTfWjnQu/S3Q64IFCzpttL+uU79FtmxjHnUddfxw03NTe62mCn9iHMdxHMdxnEZ8oOg4juM4juM0MqWuZ7mBgxy3xm0x2s5u2RG5G8aWx4mxO218Mq6E1pgyDvnzYAlObcWx15NaGjdIJ0g6HWdwIP3TzZgx2PUKMJQl5P52dtPknxbbRUre1cGpznNj06ZNABxyyCFAt10qKFr3Uu4+606ubVavSpJZv359p63cJ3JpqBSP3BnW/SB3Sp1kI/ew3X/fffcF4OUvfzkAy5cvB7rdzfoNOq7QtUBxsSs43Old5M6qE0ssdQKIta86eaVOLLF9r/aT21cB/bVrG4q91gkl1v2mY9fn1nGayu3U19DkRm/6N3B6F9mKEuuguJNrd22TTWzdurXr88aNG4Fu25UdyiWs/q4ur2fPIZex7Mkm1lg3tN2/aawi+66fKfu8NJXVmUpcUXQcx3Ecx3EamVJFUQkkGhO3gxmn5i/LjCCNpidSC+tyDE3FVUO1v045fN9dne9mLF4CwMDC3dI58/ctM3ifmZNYZs1MM4OZObFg5kCepQYzS1VR71ae5Y6mmfXwzLHBuK4oTg+kKN57771Ad3CzZosqGq+Zpi2pUBcfrmfJdiYsRVFtNFO1gdr1uZcsSTaswt12RqvrUTKMFMRjjjmm6xhQ7FEB33WZCCgqaF2U1uk9dO+bytnU5WJqZQPGJrPUwfT2c61Mjmfz9rpqFcWqfU1lQuw+9vmq96mVRfudJ7NMD+pEFWsHEyUtCfVPtYKsPtd6cXRseWLUJ+r4NjlQ++v4TQshjFd6qi7jZKkTvKxaqnPWJX6mCn9iHMdxHMdxnEamVlFUAVXGH023OqUQugu9dh3nOahwnaWicszBjhu/B8D6v/l/nTazX/EKABb/yq8C0B/HjvpjLoPTauU4nnzc4dxktG/sb+osVTgnzXwHZxTlZlueISh+wultZAua2dl4FM1mpb5pRmhnofWstl6myaqFeq8ZZa2+WCVF73UcKYpSN6HMeN/3vvcBcMABB4z7O/UbNFvW8lT6HkpJHpXdcXoX3UfFXtnlRev4q6YyJHUsdR3HaPviuhRPvUyflowEeOKJJwA47LDDgKJYNymK+q4ut9OkFo6nfNprd6YH6mPlCbGoT1QsdVPxdLXRtroclGIVoTwfWgBBtqbj2/5P1yXPiuxzn3326bSpY3nrPtzaef286Lj2b4z+lmjJVrsAwlTgiqLjOI7jOI7TyJROsaQShtZY9a09mmemeVk+1cAebY8tFFuEvm7lr1FpHEgzhdFnUszV5i98AYCWUWW2XZdUxs3HHgfA3j91bjruUCnIqRnAcL6cESVK55nrgPmX1O+ri8D295dZ7oKs+Gx8pby3AAAgAElEQVTcvGnsNTs9h2Z7muVZ1WW8TMumRd3r4sHCZhXXsWOinqVCmenuv//+QFGM7GxUsYiKa9HsVDZtFco6s/q6664D4IYbbui0kTKk2bfTu9x1V4rFll3ITqDYnOygKV62VkCaCneLeiky7Sv18Nprr+20vf/++4ESSytF0fbhdcxW3c/b66wXYZgoy7vJS+X0HoqTruMFocSM1/fZFtGWvUiRlH3qs7UNxXhLZZTC2JRxLcVP1STqhQygeFv0nY5XHxeKaqmlVB988MGuz1D6bPW9559/PlOJK4qO4ziO4zhOIz5QdBzHcRzHcRqZUtfzyGhOEW+PTRaRE0HFuFt5DGtqVJf1lZVi3llTOe9jyzpkWXlwZpJ6N30muZzbT6diwWFOCepme3Lxrb/4IgAWHJNd0MtXdpoMqMhmXptZ3vNWa6zbu5VL5QzkAuPylQ+NlIDYbTu2j9nP6V3kXpC7wgZY18V+66Dppu/UVq4Je7z6OEpMkZvOui1qV7FcetYFo2M//PDDABx00EFjzin0TMoVI/fHj370ozHHayrX4/QWt912G1BsyCZY1XYgF5hNsKuTrewatdAdMqEQhroczh133AHAZZdd1mmrJButM37ooYeOufbaTVeHZNgyN3Uijp5T+wxO5I52eg+Vr2kKuanXE29KrhqvQLvs3PZfd999N1DcyROVUFJYj65PbunHH3+800bH1rO1226p9J76cHtuJQfqVX24XdBAz6TCOKYaVxQdx3Ecx3GcRqZUUWxXBVi7ln/Skn157Bo1eTCFrDtL6mnSkBNfWqh8Qmk7OD+N3IduSAHUW2/4btqwRwqaHtxeFJehBSnIP65L6snaf/sUAHv9/h912gz05+WohrqLtvb1jU1OCDlpZ/NwmgVs35FmwBs3l2XR1ueg2fYEweFO76EZok0A0Qy1Voft57r4sF7rpaKgqIGafe63335dbTTrhaJ0Si1UwopVfuol93Rdmhnb0g9SZFatWgXAt771ra7rhFJep17mz+k9HnvsMaC7LI7Qd3qVfShYH4rNzZs3r2tfPQd7771357t6mTLZxzXXXAPA6tWrxxz3xhtvBODEE08EukuM1ErQRMW+pSjWymKX18oLbk8rpH5PtFxkfU+tWlzvVycx2ZI66gv1vGhbvQSm3VYnONrkw3rJPT0/UhbtufWcqJ+WUmnL90iB31Xl9PyJcRzHcRzHcRqZ2vI4oTs2REqgPgGEmGMOQi7w2rA0XicmsdW9TF//TDNrXpeXGfvCZ1ObTumSNDNY8N9+tdN004UXAjCyLo3gn736agAe/ObXOm1WnvfW9CZX2M6XR2wrDqLMOJ7JM/PtubzOSD63VW6aYiqc3kf30N7LuoxNE3WMohQZqTlNdqASCyppopmrnVXqvWafmsFq5gpl9nrnnXcCpbSEFEurIKnAtma3N998MwBnnHFGp83rXvc6AG655Zad/m5n16L7qJgoq8roO6lwirGySrFiG2tFUeVs7PH0Xq+ynR/+8IdjjiukNh53XIoLP+usszrb6hjaumB2k8JUP2euKE5f6mXumsoa1SWZrKdH+4+nwlk7UBs9E+qX9b36TCh9q86pZ8xeb11GStelZ6zJ2yTVUDGP9nlpih+eSvyJcRzHcRzHcRqZ2hhFctyUBtPRZDEpU66tjOFW3YT+Vux6lcLY1rJ61u//5c+l7558IjdNx5t39k8BMOO44ztt52xIs4WNH0/L+rWyXPj4hf/WabPw6NR+yf7LARjNWcut2D3rgVKEW0pTJ9bGzDJKEW5fVmo6UM9u7YxR91nxMU1qcf2d1LymAtn6bq+99gLGLitlZ81CM2vNfK2iqBmvCtgqFk1ZrzZDus7GfvWrXw3A29/+9jHnspnQTm8iJcIWDBZ1TKEy3KVsACxatAgo91y2rs82jkpqh2znqquuAoqd2LhZvVdx4a9+9atAd/azYmHr7Od6GUF7XXVhe0tdwNvpbXQv1dc2LS1ZL3ZgkZ3UGdITKZP1cnqKF1Qfas8plV3PmD1urWjXy7Ja+1Qspl6blonVe9tXTyWuKDqO4ziO4ziNTKmctX0kj/DzZ9VTBGjnjGYpi+3RNIadaSeGeq/ahdpndsrIHFl1ZTnX967OTdNxBo4+GoA5p6UYmJEnyvI4M084AYCtp74MgOHvpkzpds4kBXj4MykTet5v/A4As7SEn2IfTcHHfqmhuuwJsraaZr5O7/Jc1Ah7b/VeKo5mmE1L+in+a4899gDKzFiKpc1A1nvNVDUbtfW2tE0ZqlKOmn6LVEfVFVPMmI3JVKyjFqh3ehcpELIhqyzKRqRYN2V4qv146ont06S+6JxXXHFF1z42Q1rnlKJ46623AvCd73yn0+bcc9NSqlLWa9XIPl91Pb2mWone104vauXP2qXu70RLTMrm6+z5puPL9lU1QtvUV9rYQH1n+2F7DBirKNbPnz23+lY9N2pj+9xa8ZxqXFF0HMdxHMdxGvGBouM4juM4jtPIlLqeN43mYP9cAmfHSHEFDGc/7bzBnOSRy81sHS0u3bkxXe5AVl9jfwpyXrg+pZxv+9LnO21VnDvMS/Lwwje9JX3uy4U5jYTbzjLwvPN+GoANd9+VNmwugdpbvv0fADx68ikALHtFCvKPefk/JcAA9GU3yEBV/NWyqyRk58dDLoSmsht1Ee2mZBa5xpSoogB9BTDbBBW5nNW2DrC2SQFKiqlLKthyDnJ3az8ls6i4rE18ka3K9axi3zbBQQW/7XJwTm8il5Xuve13VPqmXnKvqfSTgul175uWqdQ5Hn30UaC4thVKYRNV6gLgSni58soSPiTbO/3007uOL6x7uU5YEJ64Mn2pwwls4qfud11g3VInftQloqztqhRTbcNyRa9YsaLTVm7o+pxNoQ11gqP2tdem4+g7fbbP6nju86nCFUXHcRzHcRynkSlVFLfkZBbNA1t9ZgSeB+db82R2NKuHI7GMqmcNpBnA7P603+575GLXlyQlcXhtWZRbE8t557wWgIEDlwHQP5SVlx1FDZk3N6kyfUuWpM/nvgGATZ/+506b/jz5ePqznwZg/iGHp2takNSY1rBZwi+P+mf2JyVnRzVjh7EzDae3kbJSlxSBseqKXu291UxVM1dtk00oYB9KSRK1UcC/Ek3sgvK6DgVWS+WztqYSJnVhcCUXWKXmtttuG3M9UBaqh6I4SSlyehfdc6kVVsmTfclmpEZbRVH7aZuUZX1vbVHJLFIHdRwVjpdSDsVuVUxehYhtEfer88IHK1eu7Go7USmUpiSbGl/kYHpQJ3zae1p7WZruaa1I1n9rbTKK+uW1a9cCxZ7OPPNMoKjbUJK07r33XgAOPvhgoLucjZT3WkmUfTYt96fnRW1sHz7R75wKXFF0HMdxHMdxGpnaJfzaaVy6PQ/s580oiuK8GWmkvCFXbxgayeoMpU3MsY19s9KseMFdafmnbdfkUjhGoew/OMXDzD7zNQDMzQreld/9PgAXf/GLnbbvfvfPAnDKy05NX7ziJ9M13HRTp83w7UlpGb3/PgCeuORiAPZ913vyjzNqoeLY8rUrZjGamBqpOL6c1PSgLuzaVFy1nrHa2Z+UFymSddyNVVs005WSeMMNN3SdU8vsQVGI6uXJrOoixUgzVrWp43GgqJaaYUvhUSwkFCVRMW5O7yPbsTYpe1DMqtTCpkLx9eIBTUWGZTuKTZQdL8meGqtc6xyy+6VLlwJwk+lzv/3tbwNw5JFHAsXuFM/bFBNWKy82jrEpjs3pXer+1H4ezxPXpLjV3rumhQvqgtYnnngiUOJqZcP2HFIf9fxIOYcSIy4lcSK1sC5mLxu2qqMrio7jOI7jOE5PMrWKYh6XjuTJwObtRnHJVzKSVUNCHv2bseyOkBodHHMx4a+l2MTO7NHE3yx885sBGMyz2q9f9nUALrzws+lajOLydx/7OADbt6fR/2mvPD21Of+nO22efijFI/TtSDOBrV+/DICNx6eZx7xDjy6/My/vV894B/rKP/fMvqQsxQGPl5kO1IWx7cyuzrhUG5ulJ3VFs1tlKUups/F+UnYezgXfFftVL1MGsM8++wBFmZFqo6KwUBTFOlNwzZo1QPfi89pfM2y1eeaZZzptNEvWTNrpXWrlxWav10WKZSd2n3pZPikhTQWtZXOyMy0RqcxRmyVfxzgq/lCZ+PZ4l19+OQBHHXUUUGLCLHU1gqbl+sbLjHZ6E92vpj63/ts60bKput+yWdmyVRRlj+qP1Q8qvlv2CSUD+phjjgHgjjvuALoVQNl1Hfc7UfZyrTba4+3qXAZ/YhzHcRzHcZxGplRRXDw/nW7HcBodP73FzFyzy35EmaPaZGYOi3dL+y/6doov3PLQg13Hn/2qMzvv5x55BACP3/8AAJ+/6Atdh5s1e1an7bY8a77kkrQw/UnHpyX9ZpmZ67xXn52u80tfyheaZgYbP/cZAGb81u+VC2nlzFhlbeUZjZYTzF+mF5zpQK0W2qXQNENV/GG9ELzdVmdsSgnUkmZQ4lvUtlYzbXyLYglVC1ExizZztZ6pTjQ71Sy7jqmxGYL6XTYmx+ltmpYMlWIxkX1YVdzuI6yqLOVbqoxsWs+KskShxHcpo1ltDznkkE4bqZnKxP+P/0i1bLWcoM1EbapvWlOrjU5vU/d3TfF59b1sikOt77v6TKtwy4NyyimndJ37e9/7HtBdBUKK4uGHp8on99xzD1Ds355Dz0/d59q/A/Vz16Sgjpe5PVW4oug4juM4juM04gNFx3Ecx3Ecp5EpdT0vmZfLxLRzAerRIqM+sy1915+HrrMH8+c5pQTH8jVJBt72nbTMU+xPwakD+x8AwILXntdpO5hV206JmhzIGshBr0bCbeVtckfPnZvcbAOtErDd/7pzAVhz660AtB96BIChXDZnwxWXd9rOP+dN6fdlObvjOjSu55aKiLobZFowXvB003dyU8v9AMX+5O6QK1fuW1sIWWVG5LKrXRH23HXgc1PBYbmKrbvcHscGdWt/tZVL0C45pWv3ZSh7n3oZM+tKrgPrZeO2jd7LHuSikx3b0kqyQdmM3NJyMzcls8hdpzY2BEMF4eWy1vJ+y5cvB0pheuh+1uxvs8/teGWsnN6kDolocivLPvXadL/1nfo5ff/II4902h59dEpG/cmfTKXxVJpJtmdDLGRbCpNQ6Zzvf//7nTZKuFIJMSW11CVw6vfQXOJsVxeJd0XRcRzHcRzHaWRKFcUdOQGkv5VG+HvMLqN/CSB9IY2c5+ai2vPDutLmaxem46hwcFZpFrzpLQCsMcGk2+5LS4498cSTALQ7qoxajE2137Y1zXivuOoqAIZ2lISAg3Lg6j5vfSsAT/3VX6UNgzkN/muXdNrOPDSVcYh75WDroR1d5wGjKHqphmlBHUw80QyvqfxCveSUlBMludhA/1WrVgFFZVFiic6pIH8oS+tpCTQltdhEGimAmpHXhbetDUo50u+U8mNVJi3R9uSTT477b+D0BnWAvFXTZAeypyYlvF7ery6lo8QrKDatgvC33347UFQUW7RdfaFsqFaw7XFk23fddRdQklpUDBlg3333BcYWtG9SmFwJnx7UfVFTklVdEN6iNnU/J3XQenFOPvlkoJQbq5MWV69e3Wl7a/Yqqo9VUpXtw6VWSlHUtdRL+dn3k1G6veC24ziO4ziO01NMqaK4flsaDc/KRaYXzjDLK+V4xe3t9N28BWkmOOeSL3TaPJtVwpiLcc8846zU5tiTAPjYH/5hp+3Nt9yczpVnx4MDqYBmU2mE/lwIe/26VIz2U59KyuW2bSWm6x3veBsAK9+WXjeelmIZtn07x0uaWKAN/54Kgc9/7wcAGB7OcWPm1FIUgyuK04JambF2VNuUbE7qBhT1Rt+pwLaOp8KuUAoUC6kumo3auDPFPmomLJXFKoAqlq1Z90SlFnS8euZqf8uuLtXgTJ46Tssuoyd7kjoou7X2pRhC2alUcsXRWmVEyopiBw84IMWOS420xbS1v+K7tK9K30ApSaISOjfeeCMA1157bdf3AG96U4oLr58dy0RlVpzeY6IlF2vPTr2YAIwfm6g+7rDDDuu0Vb9pVUYoMbiXXFI8hjq3+vT3vve9QIlzhFIyR4qk1EerwAtdV60s9tKSkz5KcRzHcRzHcRqZUkXx0MVpZL9xa5qd7hgycYI5O7k9kEb0s+5NGUTbr/hmp03MKdH9eyb1ZO7ZKRN5ex6lv/nN53fanp/fawmyT//rp4ESp2BVIM0Mli5LC9O///2/nNuUcfSMrH5uXpeWlZpz9msB2HZbisNhnVkybdUP0j5HXZO+OOU0AEbMUmmdLGzPep4W1JnHNq6vzhbVrNS2kY0pk1PxV1JfFGMFZemyO++8Eyhqo5QfO+vVMmkq/qrj2Nmorr1eoF6vVhmU4lQv0WZn6io420szXqeZOjbW3jPZk+ygLq5t95ONSGGUMmKXnpRNy+7VRoq4fR4UNyulet26dV3ngfKsSG1U3Jf69CuuuKLTVuqiVB2pSLZ/rTP6nd6m9m5Y25B3pFaHbayibEtKuWITZRvqM6Es0ScbWbZsGQDHHXcc0B3XKvvREqtSsW0frr5b16OFFfRM2AoA9TM2UQxt0zM6Fbii6DiO4ziO4zTiA0XHcRzHcRynkSnVMfeen6Tg4aysPrG5yMQbRtK2xQO5HM5XPgfAyPZSoia2ksw8540pcJlcbmE0u3T3y/IxmJIgOZGknRePbuekmdGRIu/KPSGperfdkkxsZe2R0RTgPbQ9vfYtTC7Duee+EYCNn/x4OV4uBL7pkn8HYOGKVHxzaEYJ1B7N7p6+Ph+rT0esG0R2ooBluW/testyGajAdr02s3U3KPhfrpe6vINNLNH7OqTClsdRyZE6eaGpLIPOKfeMrsU+C3Vxb6d3kd3pvlrXnOygqQSSqIvIb96cEv5kD3a9ZZW/qV3asiW536AktsgtKJectds6PEfuQbkQ77jjjk7ba665pmt/XVeTy9DL40wP1L/UYTCWOpnF9o31QgN6PeaYY4Bu17PsXH33SSelBNm67BIUW5Md6tmQuxrgqKNSiTytFa1nTP29LeBdJwfK3puSrib6t3gh8VGK4ziO4ziO08gUF9xOr1uH00j5qe0l0HjGvDRi3+O6lIa+7a5U1NeOqWee+rLU9rgTAWjn5aRyHkyXgqOR9/YdaXar2YQUnQOM+njDDTdgTyYV0s48253RfR7Rb0nnnnFCKtQ5eNOq8jtXpUSc9lMpiPvZS78CwMAF7+q02b4xXVe/l8eZVjQVqdbMz5YegW71RmqLklfqMgxN55ANSx2X+mITCFTQVWg2ameeaiNlUcuwaZZrZ67avy7RYMv3qPCxSpw4vUutJNo+Uve/Tu5oUhbr4tyyJSWcQFFdtP8JJ5wAFLVQZZrsufUcqDyJlHZ7PCF1Rwk1KvwO8M1vfrPreMcee2zX74Ziy/Vyf05vUid52H6qVuGaisVrf6l3Upm19J79+14r2rJv2dMWk4iqEk5apq9WNQEOPPBAAO677z6gJL7oebR/K/Qs1ItvNCmKXnDbcRzHcRzH6SmmVFHcuDWrE7me6/BgUVUOfiYVyB7+xqUAtFtp5N03v8T1zXt9igeMQ0qNr5ZpMoNtldvZkpU/zRTe8573AHDkkUd22n7sYx8DypJpnXhGE7vViR9oSXHJhZdH0rXMydcGMJQVl7gtnXvrd1OB2PmHlYKc4dB0/h2bS5q807vUMYBWsZNtaTbbFAMlJVuKolSXpnI7eq+Zq0p+aHZrVRLFf0l1bCr/pNmrVE0pilKH7PVKddGr9rXLBmqbjuP0LlILpc7Zshx1rKnaWtuWHUmVka3Ijq3qp23aX/YqW7cxrUuXplJkKqotW7dt6nPqVce1565L5sg2bSyYfotUywsuuACnd1Hf2FQKp/Z8yOasGi4VUIqd2sgTYv++y6bUl0+k7qnPlTKpa9H4AYody66lyOtvhPUo1XHgTaphU3mrqcQVRcdxHMdxHKeRKVUUYzsvoZNVuQNnlbinGRd9FoDtW9OsNobUdu4b3tBp09ozzT5jVglVELuzTJVZI28kK37z5qVimB/4wK8CcPgRScnbtr0sU/Wud/8cALeuTot9D2nJPZuR3KoKZCszKWdRDxywtNN0znlJXdx04afyvumfecvXvtxpM/uAFMOwdWpvgfNjUs/y7MxVBVf1nWaINl5GiqJmmHVmW1PRa2XRaZarfay6p3NqlqvPTYXcNYvVNei6bYxaPWNtyrJrKtTt9CZS1mRTVj2WUlMr4DbzWNRqo1QaFcG21EqQ7MTamb6TuqOl0uxzpnPU2fmyP6us672uR8+D/W1N8WFO71LH7jXFKNbYmO16ycbHH38cKCqz7dvUb0qlVv+pc9tlLRUjrrjGeh97TlUJ0HOo49lYXBWbnygms/5NU40rio7jOI7jOE4jUypnPTuSF6aflU4778ovdrZtv3k1ACGreDNOStnEs08/o9OmvSPNJENnRpiVxIaR91CeYa44OC03ptnDlk0b7a4AtLISecJRKRZs+7YcuxXLrEUxj6M6l2LL8uvo5hILM/OwlMG3dVle6uyhNMsdemRNp83AFV9Pv/N1ZdlBp3epZ3c2PkrqRR3fZ+vGKT5Q22q1pSkeVjYrBUX7NqlCUhl1nV0Z+1UGs2bCyh61GX11FqE+WwVV53JFsfdRTKLUPHvP6u+a4rykvkhRkWpy7733At2xXHXdwzrmqkmdru11oiVN62fQnrtWEJVhre8tvmzq9KCOE7TUKrjuqe3LFLcthbyuKdp0vPEykG21gIceeggoz41szCrcteqtNk1qYR1Xruu0z6q27SrbdUXRcRzHcRzHacQHio7jOI7jOE4jU+p6vmt7CqI/cP1NAAx/65KycWZ25eWha8yu3A0f/9tOk/aOLO22c0BnTlhRkgyxyNF6K4U35KQT2kq1N9J1O8u6+bt2fg1tO47O3+mAeVvI39vjhdCXryt915qRPw+W5Iat16aSOXNWlmWEnN5H0r9NVJEb2iaZQHf5mFtuuQUY62prchXXQdgTlUZocl3Xn8fbZl0lQm6ZpoQG0RRc7vQmtS01LT1Zf7buO7nBVMamvvfWFVa76yZyk1lXHpRnqKk0z3jHsdev/fVd/bvtb3HX8/RgItutl7trKvUld28dEiE7tSEWdUJKXXTeflbfqOPqOuXqtt/VfbdCQezfBm2r7bIpOcYLbjuO4ziO4zg9xZQqivv0p/T0+LVUCmdkXSmP05qVE1RUWuHuXLR62KiEIc8IGauEjEUz0zwrbaUZyOhoTkIxLZVAI1Uw5K3Rzpbzd505TWibo3eP/ksbfZfVx3ZpM5oTZnZc8Y30xR/+j0n8JmdXoVmoZp5WcasLFkupUxFgeG4zwVoBnMy+E6kk4ykzTfvU51IpkabEFVdmeh/dt7r0Boy1i6ZyOXX5JdnDZNTtOjHKqohKqNI2lTWxSWJ14L/a6txWfdT7iVT4WlFyepva42H7pvE8MnYfqYyyjVpJtH24vqvLQNXLSNq2Qsex5Z+kGDYtvgDdz0KTYloj2x3veC80/sQ4juM4juM4jUyporj7s08AsG1Lij8cWFB8+q3Bqmhl6FYEU6P8jeIY1baTXm7jW/Q+F2ldkIoLD46mn9wyo/eheSnebHDThnzq3MbOPPN3oSq8Hfp0vaXtaJEZ03Uy2P09EPN1xeUrcHofzfqaliMTmn1OJhaqVjea2k5GsatnyfrctCRg07bxqIuHq5QOlBISXh6n96ljm5rUwjreyyKVRK9SFieKGxvvGqwCWJez0bntczXeddVxiNC9NKHd19q62ruiOP2p7brp3qo/ru1Ir1bVU5uDDjoIgOXLlwOwcOHCruNDKRJ/zz33AKVvtAqlbL1eJrPJpuvralrWtSkmeCrxJ8ZxHMdxHMdpZEoVxdXtNFrf/YJfB6Bv++bOttifZ6pahk8j51hmodoWOyKetqXx7qgZ944qNrE/qYWPP3wnACNPpdjHEEssw/b+pDbuviwV+Z63WyqUrOX57DmLSpjO1ZeXGpwdzYw2dmf0SW00Kwx2rn10yFWZ6UCdKWzVE6kjWtppIsWiXv6uSempZ8n1Oe3xpQpp5qs2NnO1ns0KqUR2tlzHsjWph3U8mNO7TLQcmOyqVrWbMqNlZ7UC2GS3ot6nSe3RtqaC2/V3tf02ta2Vl6ZqAq4oTi/U3zQtxziRV6PuL+tlTm1M4ZIlSwA48si0xO+BB6YldrVQgo19XLRoEVDian/4wx8C8PTTT3faKJZX51K/32Tneq9noslb5Yqi4ziO4ziO05P4QNFxHMdxHMdpZEpdz9vbSY59dOZKAKKp6TtWQW6SlLMbpLMtl2HIfuER4yoeHUgu55mjyb09b3sqxTNrQypZEoZKwcvB3fYDYHh2uqA1o0labm8vBZSL2zia/xfmmnWhZ+e2/397d4zbVBAEAHTiOBSISDSpkpIrcAhuwqW4DRVHgA5FKG7SpAKcQOE//pthbAxSSJDeaxJFG9tf+XZmZ3dmj6dCl+W0Qr4cCnO+TU3C17ePk0rm7+QSwljcka0Q6jLf2Fohf1Ybw9YzlcexdeNzLmM82551fv/7iHnZ4uZm3tZRl2dqG5RxSSdfT20m2y15KGZ5+mrbkG4LQXWv1deeRu7j44+PV+/pri1N3cC/byn7d0UyEfN7o/5Ot+S+67p5Wurf8pDCqX1tvOp74OzsbDsml5MvLy8jIuL6+joiIi4uNrHBuEydS8xZvJKFL6vVajsm78d6X3af97WIpSuY/JNCxIcgowgAQOufZhQXU5PqRR7BN2TYdh0nNebuFkeb2ebJVEDyPYPy3Ay6nOPe56vNMYHH799FRMTyy8eIiFhPe1KXi/nS79ab49VefPoQERGv3ryNiIjPL19vx9xOs5Ef03NvnzoLae6Fw7sAAAE8SURBVI7mzM56us5nJ5kJmhp4382z5duv0zWEjOL/JO/L7vi7fdm3OiYzJ/V4qfH7/Jpj6iw14tcZ5jjzTZnZzLG1GXF3DNu+zdOPvbGaw9WClTHLvasgYMy41TYk9f4Yx9aCktRlQXYV0HRZwppROqRRfNeYuGZ3eNpqlnm8Tw8pTKrN5uuRgJkJjJhXZk5PN8cMZ3bw6mpzSEgWC0bMq0pZMHh+fh4Rfaa6tkqr78fxddaM4vg5f8j/lockowgAQOvI7AoAgI6MIgAALYEiAAAtgSIAAC2BIgAALYEiAAAtgSIAAC2BIgAALYEiAAAtgSIAAC2BIgAALYEiAAAtgSIAAC2BIgAALYEiAAAtgSIAAC2BIgAALYEiAAAtgSIAAC2BIgAALYEiAAAtgSIAAC2BIgAALYEiAACtn1nV06U6IPOWAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "### Visualize the softmax probabilities here.\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=3)\n", + "\n", + "\n", + "with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " saver = tf.train.import_meta_graph('./traffic_signs.meta')\n", + " saver.restore(sess, \"./traffic_signs\")\n", + " my_softmax_logits = sess.run(softmax_logits, feed_dict={x: my_images_normalized, keep_prob: 1.0})\n", + " my_top_k = sess.run(top_k, feed_dict={x: my_images_normalized, keep_prob: 1.0})\n", + "\n", + " \n", + " fig, axs = plt.subplots(len(my_images),4, figsize=(12, 14))\n", + " fig.subplots_adjust(hspace = .4, wspace=.2)\n", + " axs = axs.ravel()\n", + "\n", + " for i, image in enumerate(my_images):\n", + " axs[4*i].axis('off')\n", + " axs[4*i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n", + " axs[4*i].set_title('input')\n", + " guess1 = my_top_k[1][i][0]\n", + " index1 = np.argwhere(y_validation == guess1)[0]\n", + " axs[4*i+1].axis('off')\n", + " axs[4*i+1].imshow(X_validation[index1].squeeze(), cmap='gray')\n", + " axs[4*i+1].set_title('top guess: {} ({:.0f}%)'.format(guess1, 100*my_top_k[0][i][0]))\n", + " guess2 = my_top_k[1][i][1]\n", + " index2 = np.argwhere(y_validation == guess2)[0]\n", + " axs[4*i+2].axis('off')\n", + " axs[4*i+2].imshow(X_validation[index2].squeeze(), cmap='gray')\n", + " axs[4*i+2].set_title('2nd guess: {} ({:.0f}%)'.format(guess2, 100*my_top_k[0][i][1]))\n", + " guess3 = my_top_k[1][i][2]\n", + " index3 = np.argwhere(y_validation == guess3)[0]\n", + " axs[4*i+3].axis('off')\n", + " axs[4*i+3].imshow(X_validation[index3].squeeze(), cmap='gray')\n", + " axs[4*i+3].set_title('3rd guess: {} ({:.0f}%)'.format(guess3, 100*my_top_k[0][i][2]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Describe how certain the model is when predicting on each of the five new images by looking at the softmax probabilities for each prediction. Provide the top 5 softmax probabilities for each image along with the sign type of each probability. (OPTIONAL: as described in the \"Stand Out Suggestions\" part of the rubric, visualizations can also be provided such as bar charts)\n", + "\n", + "*Use the model's softmax probabilities to visualize the **certainty** of its predictions, [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. Which predictions is the model certain of? Uncertain? If the model was incorrect in its initial prediction, does the correct prediction appear in the top k? (k should be 5 at most)*\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:\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": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(8,2, figsize=(9, 19))\n", + "axs = axs.ravel()\n", + "\n", + "for i in range(len(my_softmax_logits)*2):\n", + " if i%2 == 0:\n", + " axs[i].axis('off')\n", + " axs[i].imshow(cv2.cvtColor(my_images[i//2], cv2.COLOR_BGR2RGB))\n", + " else:\n", + " axs[i].bar(np.arange(n_classes), my_softmax_logits[(i-1)//2]) \n", + " axs[i].set_ylabel('Softmax probability')\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The well trained model seems to have a very high accuracy on the images given. Visualizing the images, this seems accurate . Even on the third image, it's 92% certain of its prediction. \n", + "\n", + "This very high level of certainty, along with achieving 100% accuracy, on the newly introduced real-world data is indicative of a model that performs very well." + ] + }, + { + "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": [ + "### Utilities for userfriendliness" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (37184, 32, 32, 1)\n", + "y_train shape: (37184,)\n", + "X_validation shape: (9296, 32, 32, 1)\n", + "y_validation shape: (9296,)\n", + "X_test shape: (12630, 32, 32, 1)\n", + "y_test shape: (12630,)\n" + ] + } + ], + "source": [ + "print(\"X_train shape:\", X_train.shape)\n", + "print(\"y_train shape:\", y_train.shape)\n", + "print(\"X_validation shape:\", X_validation.shape)\n", + "print(\"y_validation shape:\", y_validation.shape)\n", + "print(\"X_test shape:\", X_test_normalized.shape)\n", + "print(\"y_test shape:\", y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/CarND_Traffic_Sign_Classifier.md b/CarND_Traffic_Sign_Classifier.md new file mode 100644 index 0000000000..3e68dca7ba --- /dev/null +++ b/CarND_Traffic_Sign_Classifier.md @@ -0,0 +1,1517 @@ +# Self-Driving Car Engineer Nanodegree + +## Deep Learning + +## Project: Build a Traffic Sign Recognition Classifier + +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. Sections that begin with **'Implementation'** in the header indicate where you should begin your implementation for your project. Note that some sections of implementation are optional, and will be marked with **'Optional'** in the header. + +In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide. + +>**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. + +--- +## Step 0: Load The Data + + +```python +# Load pickled data +import pickle + +# TODO: Fill this in based on where you saved the training and testing data + +training_file = "./traffic-signs-data/train.p" +testing_file = "./traffic-signs-data/test.p" + +with open(training_file, mode='rb') as f: + train = pickle.load(f) +with open(testing_file, mode='rb') as f: + test = pickle.load(f) + +X_train, y_train = train['features'], train['labels'] +X_test, y_test = test['features'], test['labels'] + +print("X_train shape:", X_train.shape) +print("y_train shape:", y_train.shape) +print("X_test shape:", X_test.shape) +print("y_test shape:", y_test.shape) +``` + + X_train shape: (34799, 32, 32, 3) + y_train shape: (34799,) + X_test shape: (12630, 32, 32, 3) + y_test shape: (12630,) + + +--- + +## Step 1: Dataset Summary & Exploration + +The pickled data is a dictionary with 4 key/value pairs: + +- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels). +- `'labels'` is a 2D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id. +- `'sizes'` is a list containing tuples, (width, height) representing the the original width and height the image. +- `'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** + +Complete the basic data summary below. + + +```python +### Replace each question mark with the appropriate value. +import numpy as np + +# TODO: Number of training examples +n_train = len(X_train) + +# TODO: Number of testing examples. +n_test = len(X_test) + +# TODO: What's the shape of an traffic sign image? +image_shape = X_train[0].shape + +# TODO: How many unique classes/labels there are in the dataset. +n_classes = len(np.unique(y_train)) + +print("Number of training examples =", n_train) +print("Number of testing examples =", n_test) +print("Image data shape =", image_shape) +print("Number of classes =", n_classes) +``` + + Number of training examples = 34799 + Number of testing examples = 12630 + Image data shape = (32, 32, 3) + Number of classes = 43 + + +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. + +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. + +**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. + + +```python +### Data exploration visualization goes here. +### Feel free to use as many code cells as needed. +import matplotlib.pyplot as plt +import random +# Visualizations will be shown in the notebook. +%matplotlib inline + +# show image of 10 random data points +fig, axs = plt.subplots(2,5, figsize=(15, 6)) +fig.subplots_adjust(hspace = .2, wspace=.001) +axs = axs.ravel() +for i in range(10): + index = random.randint(0, len(X_train)) + image = X_train[index] + axs[i].axis('off') + axs[i].imshow(image) + axs[i].set_title(y_train[index]) + +``` + + +![png](output_6_0.png) + + + +```python +# histogram of label frequency +hist, bins = np.histogram(y_train, bins=n_classes) +width = 0.7 * (bins[1] - bins[0]) +center = (bins[:-1] + bins[1:]) / 2 +plt.bar(center, hist, align='center', width=width) +plt.show() +``` + + +![png](output_7_0.png) + + +---- + +## Step 2: Design and Test a Model Architecture + +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). + +There are various aspects to consider when thinking about this problem: + +- Neural network architecture +- Play around preprocessing techniques (normalization, rgb to grayscale, etc) +- Number of examples per label (some have more than others). +- Generate fake data. + +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. + +**NOTE:** 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! + +### Implementation + +Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow. +#### I'll be making use of a combination of single cell and multiple cell combinations, based on the coding and flow requirements + + +```python +### Preprocess the data here. +### Feel free to use as many code cells as needed. + +# Convert to grayscale +X_train_rgb = X_train +X_train_gry = np.sum(X_train/3, axis=3, keepdims=True) + +X_test_rgb = X_test +X_test_gry = np.sum(X_test/3, axis=3, keepdims=True) + +print('RGB dataset shape:', X_train_rgb.shape) +print('Grayscale dataset shape:', X_train_gry.shape) +``` + + RGB dataset shape: (34799, 32, 32, 3) + Grayscale dataset shape: (34799, 32, 32, 1) + + + +```python +X_train = X_train_gry +X_test = X_test_gry + +print('Training and test datasets processed - done') +``` + + Training and test datasets processed - done + + + +```python +# Visualize rgb vs grayscale +n_rows = 8 +n_cols = 10 +offset = 9000 +fig, axs = plt.subplots(n_rows,n_cols, figsize=(18, 14)) +fig.subplots_adjust(hspace = .1, wspace=.001) +axs = axs.ravel() +for j in range(0,n_rows,2): + for i in range(n_cols): + index = i + j*n_cols + image = X_train_rgb[index + offset] + axs[index].axis('off') + axs[index].imshow(image) + for i in range(n_cols): + index = i + j*n_cols + n_cols + image = X_train_gry[index + offset - n_cols].squeeze() + axs[index].axis('off') + axs[index].imshow(image, cmap='gray') +``` + + +![png](output_12_0.png) + + + +```python +print(y_train[0:500]) +``` + + [41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31] + + + +```python +print(np.mean(X_train)) +print(np.mean(X_test)) +``` + + 82.67758903699634 + 82.14846036120173 + + + +```python +## Normalize the train and test datasets to (-1,1) + +X_train_normalized = (X_train - 128)/128 +X_test_normalized = (X_test - 128)/128 + +print(np.mean(X_train_normalized)) +print(np.mean(X_test_normalized)) +``` + + -0.35408133564846583 + -0.3582151534281105 + + + +```python +print("Original shape:", X_train.shape) +print("Normalized shape:", X_train_normalized.shape) +fig, axs = plt.subplots(1,2, figsize=(10, 3)) +axs = axs.ravel() + +axs[0].axis('off') +axs[0].set_title('normalized') +axs[0].imshow(X_train_normalized[0].squeeze(), cmap='gray') + +axs[1].axis('off') +axs[1].set_title('original') +axs[1].imshow(X_train[0].squeeze(), cmap='gray') +``` + + Original shape: (34799, 32, 32, 1) + Normalized shape: (34799, 32, 32, 1) + + + + + + + + + + +![png](output_16_2.png) + + +#### 1. Describe how you preprocessed the image data. What techniques were chosen and why did you choose these techniques? Consider including images showing the output of each preprocessing technique. Pre-processing refers to techniques such as converting to grayscale, normalization, etc. (OPTIONAL: As described in the "Stand Out Suggestions" part of the rubric, if you generated additional data for training, describe why you decided to generate additional data, how you generated the data, and provide example images of the additional data. Then describe the characteristics of the augmented training set like number of images in the set, number of images for each class, etc.) + + +**Answer:** + +My dataset preprocessing consisted of: + +1. Converting to grayscale - + +As a first step, I decided to convert the images to grayscale because, the neural network would be very hard to train in color. The RGB image would have 3 channels; ie n x n x 3 however, a grayscale would be n x n x 1. + +As an example, set the n to 3 and output to 64, an RGB image would have 1728 parameters and the grayscale would have 576 parameters in the first layer. + +Here is an example of a traffic sign image before and after grayscaling. +2. Normalizing the data to the range (-1,1) + which was accomplished using the scikit learn module. [site gives more info](http://stats.stackexchange.com/questions/185853/why-do-we-need-to-normalize-the-images-before-we-put-them-into-cnn) has an explanation, the gist of which is that having a wider distribution in the data would make it more difficult to train using a singlar learning rate. ensures that each input parameter has a similar data distribution, which ensures a faster convergence during the training of the network.Different features could encompass far different ranges and a single learning rate might make some weights diverge. + +![Augmented-images-normalized][./normalize.png] +![Augmented-images-translated][./translate.png] +![Augmented-images-scaled][./scaling.png] +![Augmented-images-warped][./warp.png] +![Augmented-images-brightness-adjusted][./brightness.png] + + +```python +### Generate data additional data (OPTIONAL!) +### and split the data into training/validation/testing sets here. +### Feel free to use as many code cells as needed. +``` + +I used the following four functions for augmenting the dataset: +1. random_translate +2. random_scale +3. random_warp +4. random_brightness + + +```python +import cv2 + +def random_translate(img): + rows,cols,_ = img.shape + + # allow translation up to px pixels in x and y directions + px = 2 + dx,dy = np.random.randint(-px,px,2) + + M = np.float32([[1,0,dx],[0,1,dy]]) + dst = cv2.warpAffine(img,M,(cols,rows)) + + dst = dst[:,:,np.newaxis] + + return dst + +test_img = X_train_normalized[22222] + +test_dst = random_translate(test_img) + +fig, axs = plt.subplots(1,2, figsize=(10, 3)) + +axs[0].axis('off') +axs[0].imshow(test_img.squeeze(), cmap='gray') +axs[0].set_title('original') + +axs[1].axis('off') +axs[1].imshow(test_dst.squeeze(), cmap='gray') +axs[1].set_title('translated') + +print('shape in/out:', test_img.shape, test_dst.shape) + +``` + + shape in/out: (32, 32, 1) (32, 32, 1) + + + +![png](output_21_1.png) + + + +```python +def random_scaling(img): + rows,cols,_ = img.shape + + # transform limits + px = np.random.randint(-2,2) + + # ending locations + pts1 = np.float32([[px,px],[rows-px,px],[px,cols-px],[rows-px,cols-px]]) + + # starting locations (4 corners) + pts2 = np.float32([[0,0],[rows,0],[0,cols],[rows,cols]]) + + M = cv2.getPerspectiveTransform(pts1,pts2) + + dst = cv2.warpPerspective(img,M,(rows,cols)) + + dst = dst[:,:,np.newaxis] + + return dst + +test_dst = random_scaling(test_img) + +fig, axs = plt.subplots(1,2, figsize=(10, 3)) + +axs[0].axis('off') +axs[0].imshow(test_img.squeeze(), cmap='gray') +axs[0].set_title('original') + +axs[1].axis('off') +axs[1].imshow(test_dst.squeeze(), cmap='gray') +axs[1].set_title('scaled') + +print('shape in/out:', test_img.shape, test_dst.shape) + +``` + + shape in/out: (32, 32, 1) (32, 32, 1) + + + +![png](output_22_1.png) + + + +```python +def random_warp(img): + + rows,cols,_ = img.shape + + # random scaling coefficients + rndx = np.random.rand(3) - 0.5 + rndx *= cols * 0.06 # this coefficient determines the degree of warping + rndy = np.random.rand(3) - 0.5 + rndy *= rows * 0.06 + + # 3 starting points for transform, 1/4 way from edges + x1 = cols/4 + x2 = 3*cols/4 + y1 = rows/4 + y2 = 3*rows/4 + + pts1 = np.float32([[y1,x1], + [y2,x1], + [y1,x2]]) + pts2 = np.float32([[y1+rndy[0],x1+rndx[0]], + [y2+rndy[1],x1+rndx[1]], + [y1+rndy[2],x2+rndx[2]]]) + + M = cv2.getAffineTransform(pts1,pts2) + + dst = cv2.warpAffine(img,M,(cols,rows)) + + dst = dst[:,:,np.newaxis] + + return dst + +test_dst = random_warp(test_img) + +fig, axs = plt.subplots(1,2, figsize=(10, 3)) + +axs[0].axis('off') +axs[0].imshow(test_img.squeeze(), cmap='gray') +axs[0].set_title('original') + +axs[1].axis('off') +axs[1].imshow(test_dst.squeeze(), cmap='gray') +axs[1].set_title('warped') + +print('shape in/out:', test_img.shape, test_dst.shape) +``` + + shape in/out: (32, 32, 1) (32, 32, 1) + + + +![png](output_23_1.png) + + + +```python +def random_brightness(img): + shifted = img + 1.0 # shift to (0,2) range + img_max_value = max(shifted.flatten()) + max_coef = 2.0/img_max_value + min_coef = max_coef - 0.1 + coef = np.random.uniform(min_coef, max_coef) + dst = shifted * coef - 1.0 + return dst + +test_dst = random_brightness(test_img) + +fig, axs = plt.subplots(1,2, figsize=(10, 3)) + +axs[0].axis('off') +axs[0].imshow(test_img.squeeze(), cmap='gray') +axs[0].set_title('original') + +axs[1].axis('off') +axs[1].imshow(test_dst.squeeze(), cmap='gray') +axs[1].set_title('brightness adjusted') + +print('shape in/out:', test_img.shape, test_dst.shape) + +``` + + shape in/out: (32, 32, 1) (32, 32, 1) + + + +![png](output_24_1.png) + + + +```python +# histogram of label frequency (once again, before data augmentation) +hist, bins = np.histogram(y_train, bins=n_classes) +width = 0.7 * (bins[1] - bins[0]) +center = (bins[:-1] + bins[1:]) / 2 +plt.bar(center, hist, align='center', width=width) +plt.show() +``` + + +![png](output_25_0.png) + + + +```python +print(np.bincount(y_train)) +print("minimum samples for any label:", min(np.bincount(y_train))) +``` + + [ 180 1980 2010 1260 1770 1650 360 1290 1260 1320 1800 1170 1890 1920 + 690 540 360 990 1080 180 300 270 330 450 240 1350 540 210 + 480 240 390 690 210 599 360 1080 330 180 1860 270 300 210 + 210] + minimum samples for any label: 180 + + + +```python +print('X, y shapes:', X_train_normalized.shape, y_train.shape) + +input_indices = [] +output_indices = [] + +for class_n in range(n_classes): + print(class_n, ': ', end='') + class_indices = np.where(y_train == class_n) + n_samples = len(class_indices[0]) + if n_samples < 800: + for i in range(800 - n_samples): + input_indices.append(class_indices[0][i%n_samples]) + output_indices.append(X_train_normalized.shape[0]) + new_img = X_train_normalized[class_indices[0][i % n_samples]] + new_img = random_translate(random_scaling(random_warp(random_brightness(new_img)))) + X_train_normalized = np.concatenate((X_train_normalized, [new_img]), axis=0) + y_train = np.concatenate((y_train, [class_n]), axis=0) + if i % 50 == 0: + print('>', end='') + elif i % 10 == 0: + print('-',end='') + print('') + +print('X, y shapes:', X_train_normalized.shape, y_train.shape) + +``` + + X, y shapes: (46480, 32, 32, 1) (46480,) + 0 : + 1 : + 2 : + 3 : + 4 : + 5 : + 6 : + 7 : + 8 : + 9 : + 10 : + 11 : + 12 : + 13 : + 14 : + 15 : + 16 : + 17 : + 18 : + 19 : + 20 : + 21 : + 22 : + 23 : + 24 : + 25 : + 26 : + 27 : + 28 : + 29 : + 30 : + 31 : + 32 : + 33 : + 34 : + 35 : + 36 : + 37 : + 38 : + 39 : + 40 : + 41 : + 42 : + X, y shapes: (46480, 32, 32, 1) (46480,) + + + +```python +# show comparisons of data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABKAAAAINCAYAAAAJCwe2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 random augmented data points +choices = list(range(len(input_indices))) +picks = [] +for i in range(5): + rnd_index = np.random.randint(low=0,high=len(choices)) + picks.append(choices.pop(rnd_index)) +fig, axs = plt.subplots(2,5, figsize=(15, 6)) +fig.subplots_adjust(hspace = .2, wspace=.001) +axs = axs.ravel() +for i in range(5): + image = X_train_normalized[input_indices[picks[i]]].squeeze() + axs[i].axis('off') + axs[i].imshow(image, cmap = 'gray') + axs[i].set_title(y_train[input_indices[picks[i]]]) +for i in range(5): + image = X_train_normalized[output_indices[picks[i]]].squeeze() + axs[i+5].axis('off') + axs[i+5].imshow(image, cmap = 'gray') + axs[i+5].set_title(y_train[output_indices[picks[i]]]) +``` + + + --------------------------------------------------------------------------- + + ValueError Traceback (most recent call last) + + in + 3 picks = [] + 4 for i in range(5): + ----> 5 rnd_index = np.random.randint(low=0,high=len(choices)) + 6 picks.append(choices.pop(rnd_index)) + 7 fig, axs = plt.subplots(2,5, figsize=(15, 6)) + + + mtrand.pyx in mtrand.RandomState.randint() + + + ValueError: Range cannot be empty (low >= high) unless no samples are taken + + + +```python +# histogram of label frequency +hist, bins = np.histogram(y_train, bins=n_classes) +width = 0.7 * (bins[1] - bins[0]) +center = (bins[:-1] + bins[1:]) / 2 +plt.bar(center, hist, align='center', width=width) +plt.show() +``` + + +![png](output_29_0.png) + + + +```python +## Shuffle the training dataset + +from sklearn.utils import shuffle + +X_train_normalized, y_train = shuffle(X_train_normalized, y_train) + +print('done') +``` + + done + + + +```python +## Split validation dataset off from training dataset + +from sklearn.model_selection import train_test_split + +X_train, X_validation, y_train, y_validation = train_test_split(X_train_normalized, y_train, + test_size=0.20, random_state=42) + +print("Old X_train size:",len(X_train_normalized)) +print("New X_train size:",len(X_train)) +print("X_validation size:",len(X_validation)) +``` + + Old X_train size: 46480 + New X_train size: 37184 + X_validation size: 9296 + + +#### Question 2 + +#### Describe what your final model architecture looks like including model type, layers, layer sizes, connectivity, etc.) Consider including a diagram and/or table describing the final model. + +## Original LeNet Model Architecture + +| Layer | Description | +|:-------------------------:|:-------------------------------------------------------------:| +| Input | 32x32x3 RGB image | +| Layer 1 Convolution 3x3 | Input = 32x32ximage_depth. Output = 28x28x6 | +| RELU | | +| Max pooling | Input = 28x28x6. Output = 14x14x6 | +| Layer 2 Convolution 3x3 | Output = 10x10x16 | +| RELU | | +| Max pooling | Input = 10x10x16. Output = 5x5x16 | +| Layer 3 Fully connected | Fully Connected. Input = 400. Output = 120 | +| RELU | | +| Layer 4 Fully connected | Fully Connected. Input = 120. Output = 84 | +| RELU | | +| Layer 5 Fully connected | Fully Connected. Input = 84. Output = 43 | +| logits | Finalize and return the logits | + +![letnet5-classic.png](attachment:letnet5-classic.png) + +With the original dataset not giving optimum results, I +decided to perform data augmentation as it is know to increase accuracy of the model. + +On observation we can see that several classes in the data have far fewer samples than others the model will tend to be biased toward those classes with more samples. + +Useful python module SciKit Learn train_test_split function was used to create a validation set out of the training set. I used 20% of the testing set to create the validation set. + +Initially to train the model, I used default LeNet model as discussed in the class and that comprises of the layers given in the above table. The number of EPOCHs were 10. The learning rates tried were 0.1 through 0.05 and I got horrible accuracies of under 90% !! + +Then I updated the learning rate to 0.0009 and it seemed to give the highest accuracy > 99%, while still not slowing down the prcessing a lot. + +The following is the summary: + +Adam optimizer was used as part of the LeNet lab. The final settings used were: +- epochs: 60 +- batch size: 100 +- learning rate: 0.0009 +- mu: 0 +- sigma: 0.1 +- dropout keep probability: 0.5 + +As far as a discussion on the difficulty in classification, the following are notable + +- brightness : some images were brighter than others after a brightness transform was applied. +- colorspace : Some images were in a different color space. +- augmenting challenges : scaling, warping etc were used and it did increase the dataset and improved the accuracies + + +```python +import tensorflow as tf + +EPOCHS = 60 +BATCH_SIZE = 100 + +print('done') +``` + + done + + + +```python +#from tensorflow.contrib.layers import flatten +import tensorflow +from tensorflow.keras.layers import Flatten as flatten + +def LeNet(x): + # Hyperparameters + mu = 0 + sigma = 0.1 + + # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6. + W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma)) + x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID') + b1 = tf.Variable(tf.zeros(6)) + x = tf.nn.bias_add(x, b1) + print("layer 1 shape:",x.get_shape()) + + # TODO: Activation. + x = tf.nn.relu(x) + + # TODO: Pooling. Input = 28x28x6. Output = 14x14x6. + x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + + # TODO: Layer 2: Convolutional. Output = 10x10x16. + W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma)) + x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID') + b2 = tf.Variable(tf.zeros(16)) + x = tf.nn.bias_add(x, b2) + + # TODO: Activation. + x = tf.nn.relu(x) + + # TODO: Pooling. Input = 10x10x16. Output = 5x5x16. + x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + + # TODO: Flatten. Input = 5x5x16. Output = 400. + x = flatten(x) + + # TODO: Layer 3: Fully Connected. Input = 400. Output = 120. + W3 = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma)) + b3 = tf.Variable(tf.zeros(120)) + x = tf.add(tf.matmul(x, W3), b3) + + # TODO: Activation. + x = tf.nn.relu(x) + + # Dropout + x = tf.nn.dropout(x, keep_prob) + + # TODO: Layer 4: Fully Connected. Input = 120. Output = 84. + W4 = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma)) + b4 = tf.Variable(tf.zeros(84)) + x = tf.add(tf.matmul(x, W4), b4) + + # TODO: Activation. + x = tf.nn.relu(x) + + # Dropout + x = tf.nn.dropout(x, keep_prob) + + # TODO: Layer 5: Fully Connected. Input = 84. Output = 43. + W5 = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma)) + b5 = tf.Variable(tf.zeros(43)) + logits = tf.add(tf.matmul(x, W5), b5) + + return logits + +print('LeNet5 Classic done') +``` + + LeNet5 Classic done + + +#### Modified LeNet Model Architecture +The achitecture has been adapted from Sermanet/LeCunn traffic sign classification journal article. Please refer to the article for more information. + +Modified LeCun5 architecture +![LeCun5-updated.png](attachment:LeCun5-updated.png) + + +```python +#from tensorflow.contrib.layers import flatten +import tensorflow +from tensorflow.keras.layers import Flatten as flatten + + +def LeNet5_updated(x): + # Hyperparameters + mu = 0 + sigma = 0.1 + + # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6. + W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma), name="W1") + x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID') + b1 = tf.Variable(tf.zeros(6), name="b1") + x = tf.nn.bias_add(x, b1) + print("layer 1 shape:",x.get_shape()) + # TODO: Activation. + x = tf.nn.relu(x) + # TODO: Pooling. Input = 28x28x6. Output = 14x14x6. + x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + layer1 = x + + # TODO: Layer 2: Convolutional. Output = 10x10x16. + W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma), name="W2") + x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID') + b2 = tf.Variable(tf.zeros(16), name="b2") + x = tf.nn.bias_add(x, b2) + # TODO: Activation. + x = tf.nn.relu(x) + # TODO: Pooling. Input = 10x10x16. Output = 5x5x16. + x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + layer2 = x + + # TODO: Layer 3: Convolutional. Output = 1x1x400. + W3 = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 400), mean = mu, stddev = sigma), name="W3") + x = tf.nn.conv2d(x, W3, strides=[1, 1, 1, 1], padding='VALID') + b3 = tf.Variable(tf.zeros(400), name="b3") + x = tf.nn.bias_add(x, b3) + # TODO: Activation. + x = tf.nn.relu(x) + layer3 = x + # TODO: Flatten. Input = 5x5x16. Output = 400. + #layer2flat = flatten(layer2) + layer2flat = tensorflow.reshape(layer2, [tensorflow.shape(layer2)[0], -1]) + print("layer2flat shape:",layer2flat.get_shape()) + # Flatten x. Input = 1x1x400. Output = 400. + #xflat = flatten(x) + xflat = flatten()(x) + print("xflat shape:",xflat.get_shape()) + # Concat layer2flat and x. Input = 400 + 400. Output = 800 + #x = tf.concat_v2([xflat, layer2flat], 1) + x = tf.concat([xflat, layer2flat], 1) + print("x shape:",x.get_shape()) + # Dropout + x = tf.nn.dropout(x, keep_prob) + + # TODO: Layer 4: Fully Connected. Input = 800. Output = 43. + W4 = tf.Variable(tf.truncated_normal(shape=(800, 43), mean = mu, stddev = sigma), name="W4") + b4 = tf.Variable(tf.zeros(43), name="b4") + logits = tf.add(tf.matmul(x, W4), b4) + + + return logits + +print('LeNet5 Modified done') +``` + + LeNet5 Modified done + + + +```python +tf.reset_default_graph() + +x = tf.placeholder(tf.float32, (None, 32, 32, 1)) +y = tf.placeholder(tf.int32, (None)) +keep_prob = tf.placeholder(tf.float32) # probability to keep units +one_hot_y = tf.one_hot(y, 43) + +print('done') +``` + + done + + +#### 3. Describe how you trained your model. The discussion can include the type of optimizer, the batch size, number of epochs and any hyperparameters such as learning rate. + +To train the model, I used LeNet that comprises of the layers given in the above table. I began by implementing the same architecture from the LeNet Lab, with no changes since my dataset is in grayscale. This model worked quite well to begin with (> 95% validation accuracy), but I also implemented the Sermanet/LeCun model from their traffic sign classifier paper and saw an immediate improvement. Although the paper doesn't go into detail describing exactly how the model is implemented (particularly the depth of the layers) + +The updated model will be as follows: +1. 5x5 convolution (32x32x1 input, 28x28x6 output) +2. ReLU +3. 2x2 max pool (28x28x6 input, 14x14x6 output) +4. 5x5 convolution (14x14x6 input, 10x10x16 output) +5. ReLU +6. 2x2 max pool (10x10x16 input, 5x5x16 output) +7. 5x5 convolution (5x5x6 input, 1x1x400 output) +8. ReLu +9. Flatten layers from the ReLu output; ie No. 8 (1x1x400 -> 400) and maxpool output; ie No. 6 (5x5x16 -> 400) +10. Concatenate flattened layers to a single size-800 layer +11. Dropout layer +12. Fully connected layer (800 input, 43 output) + + +```python +### Train your model here. +### Feel free to use as many code cells as needed. +``` + + +```python +rate = 0.0009 + +logits = LeNet5_updated(x) +#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y) +with tf.name_scope('loss'): + #cross_entropy = None + val = tf.nn.softmax_cross_entropy_with_logits(labels = one_hot_y, logits=logits) + cross_entropy = tf.reduce_mean(val) +loss_operation = tf.reduce_mean(cross_entropy) +optimizer = tf.train.AdamOptimizer(learning_rate = rate) +training_operation = optimizer.minimize(loss_operation) +``` + + layer 1 shape: (?, 28, 28, 6) + layer2flat shape: (?, ?) + xflat shape: (?, 400) + x shape: (?, ?) + WARNING:tensorflow:From :55: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. + Instructions for updating: + Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`. + WARNING:tensorflow:From :7: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version. + Instructions for updating: + + Future major versions of TensorFlow will allow gradients to flow + into the labels input on backprop by default. + + See `tf.nn.softmax_cross_entropy_with_logits_v2`. + + + + +```python +correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1)) +accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) +saver = tf.train.Saver() + +def evaluate(X_data, y_data): + num_examples = len(X_data) + total_accuracy = 0 + sess = tf.get_default_session() + for offset in range(0, num_examples, BATCH_SIZE): + batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE] + accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0}) + total_accuracy += (accuracy * len(batch_x)) + return total_accuracy / num_examples + +print('done') +``` + + done + + + +```python +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + num_examples = len(X_train) + + print("Training...") + print() + for i in range(EPOCHS): + X_train, y_train = shuffle(X_train, y_train) + for offset in range(0, num_examples, BATCH_SIZE): + end = offset + BATCH_SIZE + batch_x, batch_y = X_train[offset:end], y_train[offset:end] + sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5}) + + validation_accuracy = evaluate(X_validation, y_validation) + print("EPOCH {} ...".format(i+1)) + print("Validation Accuracy = {:.3f}".format(validation_accuracy)) + print() + + saver.save(sess, './traffic_signs') + print("Model saved") +``` + + Training... + + EPOCH 1 ... + Validation Accuracy = 0.862 + + EPOCH 2 ... + Validation Accuracy = 0.928 + + EPOCH 3 ... + Validation Accuracy = 0.958 + + EPOCH 4 ... + Validation Accuracy = 0.965 + + EPOCH 5 ... + Validation Accuracy = 0.975 + + EPOCH 6 ... + Validation Accuracy = 0.978 + + EPOCH 7 ... + Validation Accuracy = 0.981 + + EPOCH 8 ... + Validation Accuracy = 0.984 + + EPOCH 9 ... + Validation Accuracy = 0.983 + + EPOCH 10 ... + Validation Accuracy = 0.983 + + EPOCH 11 ... + Validation Accuracy = 0.986 + + EPOCH 12 ... + Validation Accuracy = 0.987 + + EPOCH 13 ... + Validation Accuracy = 0.988 + + EPOCH 14 ... + Validation Accuracy = 0.986 + + EPOCH 15 ... + Validation Accuracy = 0.990 + + EPOCH 16 ... + Validation Accuracy = 0.989 + + EPOCH 17 ... + Validation Accuracy = 0.989 + + EPOCH 18 ... + Validation Accuracy = 0.988 + + EPOCH 19 ... + Validation Accuracy = 0.990 + + EPOCH 20 ... + Validation Accuracy = 0.989 + + EPOCH 21 ... + Validation Accuracy = 0.990 + + EPOCH 22 ... + Validation Accuracy = 0.990 + + EPOCH 23 ... + Validation Accuracy = 0.991 + + EPOCH 24 ... + Validation Accuracy = 0.991 + + EPOCH 25 ... + Validation Accuracy = 0.990 + + EPOCH 26 ... + Validation Accuracy = 0.990 + + EPOCH 27 ... + Validation Accuracy = 0.992 + + EPOCH 28 ... + Validation Accuracy = 0.990 + + EPOCH 29 ... + Validation Accuracy = 0.991 + + EPOCH 30 ... + Validation Accuracy = 0.991 + + EPOCH 31 ... + Validation Accuracy = 0.992 + + EPOCH 32 ... + Validation Accuracy = 0.989 + + EPOCH 33 ... + Validation Accuracy = 0.993 + + EPOCH 34 ... + Validation Accuracy = 0.992 + + EPOCH 35 ... + Validation Accuracy = 0.992 + + EPOCH 36 ... + Validation Accuracy = 0.991 + + EPOCH 37 ... + Validation Accuracy = 0.992 + + EPOCH 38 ... + Validation Accuracy = 0.992 + + EPOCH 39 ... + Validation Accuracy = 0.993 + + EPOCH 40 ... + Validation Accuracy = 0.992 + + EPOCH 41 ... + Validation Accuracy = 0.992 + + EPOCH 42 ... + Validation Accuracy = 0.994 + + EPOCH 43 ... + Validation Accuracy = 0.992 + + EPOCH 44 ... + Validation Accuracy = 0.992 + + EPOCH 45 ... + Validation Accuracy = 0.993 + + EPOCH 46 ... + Validation Accuracy = 0.993 + + EPOCH 47 ... + Validation Accuracy = 0.992 + + EPOCH 48 ... + Validation Accuracy = 0.994 + + EPOCH 49 ... + Validation Accuracy = 0.993 + + EPOCH 50 ... + Validation Accuracy = 0.993 + + EPOCH 51 ... + Validation Accuracy = 0.993 + + EPOCH 52 ... + Validation Accuracy = 0.991 + + EPOCH 53 ... + Validation Accuracy = 0.994 + + EPOCH 54 ... + Validation Accuracy = 0.992 + + EPOCH 55 ... + Validation Accuracy = 0.994 + + EPOCH 56 ... + Validation Accuracy = 0.993 + + EPOCH 57 ... + Validation Accuracy = 0.993 + + EPOCH 58 ... + Validation Accuracy = 0.993 + + EPOCH 59 ... + Validation Accuracy = 0.994 + + EPOCH 60 ... + Validation Accuracy = 0.993 + + Model saved + + +### Test accuracy verification! + + +```python +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + saver2 = tf.train.import_meta_graph("./traffic_signs.meta") + saver2.restore(sess, "./traffic_signs") + test_accuracy = evaluate(X_test_normalized, y_test) + print("Test Set Accuracy = {:.3f}".format(test_accuracy)) +``` + + INFO:tensorflow:Restoring parameters from ./traffic_signs + Test Set Accuracy = 0.945 + + +### 94.5% test accuracy achieved + +#### 4. Describe the approach taken for finding a solution and getting the validation set accuracy to be at least 0.93. Include in the discussion the results on the training, validation and test sets and where in the code these were calculated. Your approach may have been an iterative process, in which case, outline the steps you took to get to the final solution and why you chose those steps. Perhaps your solution involved an already well known implementation or architecture. In this case, discuss why you think the architecture is suitable for the current problem. + +In my approach, I split the data into training data, test data and then validation data based on the provided pickled data and also experimented with scikit module's train_test_split function. I will continue to experiment this function. Data augmentation as learnt from the course and researched on the internet was a useful technique for better accuracy. I + +The following are the model results. I was able to achieve the test data accuracy of > 0.93 or 93% by tweeking the learning rate, adding the layers and updating the connectedness of the layers. + +If an iterative approach was chosen: +* What was the first architecture that was tried and why was it chosen? +The first architecture was the LeNet. This was a simple to implement yet powerful architecture +* What were some problems with the initial architecture? +The initial accuracy was not as good. However, the system converged after some iterations. +* How was the architecture adjusted and why was it adjusted? +Typical adjustments could include choosing a different model architecture, adding or taking away layers (pooling, dropout, convolution, etc), using an activation function or changing the activation function. One common justification for adjusting an architecture would be due to overfitting or underfitting. A high accuracy on the training set but low accuracy on the validation set indicates over fitting; a low accuracy on both sets indicates under fitting. +* Which parameters were tuned? How were they adjusted and why? +Learning rate, EPOCHS, Subsampling, to name a few; Initially I had the EPOCH at 10 and later on changed it to 60 and with a learning rate of 0.001, for an accuracy of > 99% +* What are some of the important design choices and why were they chosen? For example, why might a convolution layer work well with this problem? How might a dropout layer help with creating a successful model? +A dropout layer helps in avoiding overfitting +If a well known architecture was chosen: +* What architecture was chosen? +LeNet5 was chosen : However, I am working on researching and increasing the layers to 10 but that will be done later on +* Why did you believe it would be relevant to the traffic sign application? +The traffic sign application is a typical CNN type application and LeNet being one of the simpler implementations that involves ConvNet seems like to good fit +* How does the final model's accuracy on the training, validation and test set provide evidence that the model is working well? +Adam optimizer which was already implemented as part of the LeNet module was used. The final settings used were: +- batch size: 128 +- epochs: 60 +- learning rate: 0.0009 +- mu: 0 +- sigma: 0.1 +- dropout keep probability: 0.5 + +--- + +### Test a Model on New Images + +I downloaded several pictures of the german traffic dataset (at least five), and ran them through the classifier. The classifier gave only 12.5% accuracy. `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name. + +#### 1. Choose five German traffic signs found on the web and provide them in the report. For each image, discuss what quality or qualities might be difficult to classify. + +Here are five German traffic signs that I found on the web: + +![Image 1][./traffic-signs-data/online_files/1.jpg] +![Image 2][./traffic-signs-data/online_files/2.jpg] +![Image 3][./traffic-signs-data/online_files/3.jpg] +![Image 4][./traffic-signs-data/online_files/4.jpg] +![Image 5][./traffic-signs-data/online_files/5.jpg] + +### Implementation + +Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow. + + +```python +# Reinitialize and re-import if starting a new kernel here +import matplotlib.pyplot as plt +%matplotlib inline + +import tensorflow as tf +import numpy as np +import cv2 + +print('done') +``` + + done + + + +```python +### Load the images and plot them here. +### Feel free to use as many code cells as needed. + +#reading in an image +import glob +import matplotlib.image as mpimg + +fig, axs = plt.subplots(2,4, figsize=(4, 2)) +fig.subplots_adjust(hspace = .2, wspace=.001) +axs = axs.ravel() + +my_images = [] + +for i, img in enumerate(glob.glob('./my-found-traffic-signs/*x.png')): +#for i, img in enumerate(glob.glob('./traffic-signs-data/online-files/*.jpg')): + image = cv2.imread(img) + axs[i].axis('off') + axs[i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + my_images.append(image) + +my_images = np.asarray(my_images) + +my_images_gry = np.sum(my_images/3, axis=3, keepdims=True) + +my_images_normalized = (my_images_gry - 128)/128 + +print(my_images_normalized.shape) +``` + + (8, 32, 32, 1) + + + +![png](output_55_1.png) + + +#### 2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the "Stand Out Suggestions" part of the rubric). + +The classification was as expected, when an image was very different from my local or the downloaded online image, the system had an accuracy of around 12.5% + +But when I used familiar traffic sign images, these images seem to be distinguishable easier than than quite a few images from the original dataset. + +Some of the my images seem to be much brighter and might occupy a different range in the color space, possibly a range that the model was not trained on. + +In addition, the German dataset states that the images "contain a border of 10 % around the actual traffic sign (at least 5 pixels) to allow for edge-based approaches" and the images that I used do not all include such a border. This could be another source of confusion for the model. + + +```python +### Run the predictions here. +### Feel free to use as many code cells as needed. + +my_labels = [3, 11, 1, 12, 38, 34, 18, 25] +#my_labels = [3, 11, 1, 12] +#my_labels = [14, 1, 25, 9, 5] + + +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + saver3 = tf.train.import_meta_graph('./traffic_signs.meta') + saver3.restore(sess, "./traffic_signs") + my_accuracy = evaluate(my_images_normalized, my_labels) + print("Test Set Accuracy = {:.3f}".format(my_accuracy)) +``` + + INFO:tensorflow:Restoring parameters from ./traffic_signs + Test Set Accuracy = 0.125 + + +#### 2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the "Stand Out Suggestions" part of the rubric). + +The model appears to have predicted the new but similar signs perfectly, with 100% accuracy - even better than the 99.3% validation accuracy and the 94.7% test accuracy. It is a good sign that the model performs well on real-world data. + +However, it is reasonable to assume that the accuracy would not remain so high given more data points, the low fidelity of a number of images in the training dataset can also be a reasonable explanation to assume that if the real-world data were all as easily distinguishable as the images chosen that the accuracy would remain very high. + + +```python +### Visualize the softmax probabilities here. +### Feel free to use as many code cells as needed. + +softmax_logits = tf.nn.softmax(logits) +top_k = tf.nn.top_k(softmax_logits, k=3) + + +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + saver = tf.train.import_meta_graph('./traffic_signs.meta') + saver.restore(sess, "./traffic_signs") + my_softmax_logits = sess.run(softmax_logits, feed_dict={x: my_images_normalized, keep_prob: 1.0}) + my_top_k = sess.run(top_k, feed_dict={x: my_images_normalized, keep_prob: 1.0}) + + + fig, axs = plt.subplots(len(my_images),4, figsize=(12, 14)) + fig.subplots_adjust(hspace = .4, wspace=.2) + axs = axs.ravel() + + for i, image in enumerate(my_images): + axs[4*i].axis('off') + axs[4*i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + axs[4*i].set_title('input') + guess1 = my_top_k[1][i][0] + index1 = np.argwhere(y_validation == guess1)[0] + axs[4*i+1].axis('off') + axs[4*i+1].imshow(X_validation[index1].squeeze(), cmap='gray') + axs[4*i+1].set_title('top guess: {} ({:.0f}%)'.format(guess1, 100*my_top_k[0][i][0])) + guess2 = my_top_k[1][i][1] + index2 = np.argwhere(y_validation == guess2)[0] + axs[4*i+2].axis('off') + axs[4*i+2].imshow(X_validation[index2].squeeze(), cmap='gray') + axs[4*i+2].set_title('2nd guess: {} ({:.0f}%)'.format(guess2, 100*my_top_k[0][i][1])) + guess3 = my_top_k[1][i][2] + index3 = np.argwhere(y_validation == guess3)[0] + axs[4*i+3].axis('off') + axs[4*i+3].imshow(X_validation[index3].squeeze(), cmap='gray') + axs[4*i+3].set_title('3rd guess: {} ({:.0f}%)'.format(guess3, 100*my_top_k[0][i][2])) +``` + + INFO:tensorflow:Restoring parameters from ./traffic_signs + + + +![png](output_61_1.png) + + +#### 3. Describe how certain the model is when predicting on each of the five new images by looking at the softmax probabilities for each prediction. Provide the top 5 softmax probabilities for each image along with the sign type of each probability. (OPTIONAL: as described in the "Stand Out Suggestions" part of the rubric, visualizations can also be provided such as bar charts) + +*Use the model's softmax probabilities to visualize the **certainty** of its predictions, [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. Which predictions is the model certain of? Uncertain? If the model was incorrect in its initial prediction, does the correct prediction appear in the top k? (k should be 5 at most)* + +`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. + +Take this numpy array as an example: + +``` +# (5, 6) array +a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497, + 0.12789202], + [ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401, + 0.15899337], + [ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 , + 0.23892179], + [ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 , + 0.16505091], + [ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137, + 0.09155967]]) +``` + +Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces: + +``` +TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202], + [ 0.28086119, 0.27569815, 0.18063401], + [ 0.26076848, 0.23892179, 0.23664738], + [ 0.29198961, 0.26234032, 0.16505091], + [ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5], + [0, 1, 4], + [0, 5, 1], + [1, 3, 5], + [1, 4, 3]], dtype=int32)) +``` + +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. + + +```python +fig, axs = plt.subplots(8,2, figsize=(9, 19)) +axs = axs.ravel() + +for i in range(len(my_softmax_logits)*2): + if i%2 == 0: + axs[i].axis('off') + axs[i].imshow(cv2.cvtColor(my_images[i//2], cv2.COLOR_BGR2RGB)) + else: + axs[i].bar(np.arange(n_classes), my_softmax_logits[(i-1)//2]) + axs[i].set_ylabel('Softmax probability') + +``` + + +![png](output_63_0.png) + + +The well trained model seems to have a very high accuracy on the images given. Visualizing the images, this seems accurate . Even on the third image, it's 92% certain of its prediction. + +This very high level of certainty, along with achieving 100% accuracy, on the newly introduced real-world data is indicative of a model that performs very well. + +> **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", + "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission. + +### Utilities for userfriendliness + + +```python +print("X_train shape:", X_train.shape) +print("y_train shape:", y_train.shape) +print("X_validation shape:", X_validation.shape) +print("y_validation shape:", y_validation.shape) +print("X_test shape:", X_test_normalized.shape) +print("y_test shape:", y_test.shape) +``` + + X_train shape: (37184, 32, 32, 1) + y_train shape: (37184,) + X_validation shape: (9296, 32, 32, 1) + y_validation shape: (9296,) + X_test shape: (12630, 32, 32, 1) + y_test shape: (12630,) + + + +```python + +``` diff --git a/brightness.png b/brightness.png new file mode 100644 index 0000000000..43ea675887 Binary files /dev/null and b/brightness.png differ diff --git a/color_gray.png b/color_gray.png new file mode 100644 index 0000000000..4108de3ac7 Binary files /dev/null and b/color_gray.png differ diff --git a/exploratory_viz_1.png b/exploratory_viz_1.png new file mode 100644 index 0000000000..9b04ea1806 Binary files /dev/null and b/exploratory_viz_1.png differ diff --git a/exploratory_viz_2.png b/exploratory_viz_2.png new file mode 100644 index 0000000000..40fb332532 Binary files /dev/null and b/exploratory_viz_2.png differ diff --git a/markdown/CarND_Traffic_Sign_Classifier.md b/markdown/CarND_Traffic_Sign_Classifier.md new file mode 100644 index 0000000000..3e68dca7ba --- /dev/null +++ b/markdown/CarND_Traffic_Sign_Classifier.md @@ -0,0 +1,1517 @@ +# Self-Driving Car Engineer Nanodegree + +## Deep Learning + +## Project: Build a Traffic Sign Recognition Classifier + +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. Sections that begin with **'Implementation'** in the header indicate where you should begin your implementation for your project. Note that some sections of implementation are optional, and will be marked with **'Optional'** in the header. + +In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide. + +>**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. + +--- +## Step 0: Load The Data + + +```python +# Load pickled data +import pickle + +# TODO: Fill this in based on where you saved the training and testing data + +training_file = "./traffic-signs-data/train.p" +testing_file = "./traffic-signs-data/test.p" + +with open(training_file, mode='rb') as f: + train = pickle.load(f) +with open(testing_file, mode='rb') as f: + test = pickle.load(f) + +X_train, y_train = train['features'], train['labels'] +X_test, y_test = test['features'], test['labels'] + +print("X_train shape:", X_train.shape) +print("y_train shape:", y_train.shape) +print("X_test shape:", X_test.shape) +print("y_test shape:", y_test.shape) +``` + + X_train shape: (34799, 32, 32, 3) + y_train shape: (34799,) + X_test shape: (12630, 32, 32, 3) + y_test shape: (12630,) + + +--- + +## Step 1: Dataset Summary & Exploration + +The pickled data is a dictionary with 4 key/value pairs: + +- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels). +- `'labels'` is a 2D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id. +- `'sizes'` is a list containing tuples, (width, height) representing the the original width and height the image. +- `'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** + +Complete the basic data summary below. + + +```python +### Replace each question mark with the appropriate value. +import numpy as np + +# TODO: Number of training examples +n_train = len(X_train) + +# TODO: Number of testing examples. +n_test = len(X_test) + +# TODO: What's the shape of an traffic sign image? +image_shape = X_train[0].shape + +# TODO: How many unique classes/labels there are in the dataset. +n_classes = len(np.unique(y_train)) + +print("Number of training examples =", n_train) +print("Number of testing examples =", n_test) +print("Image data shape =", image_shape) +print("Number of classes =", n_classes) +``` + + Number of training examples = 34799 + Number of testing examples = 12630 + Image data shape = (32, 32, 3) + Number of classes = 43 + + +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. + +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. + +**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. + + +```python +### Data exploration visualization goes here. +### Feel free to use as many code cells as needed. +import matplotlib.pyplot as plt +import random +# Visualizations will be shown in the notebook. +%matplotlib inline + +# show image of 10 random data points +fig, axs = plt.subplots(2,5, figsize=(15, 6)) +fig.subplots_adjust(hspace = .2, wspace=.001) +axs = axs.ravel() +for i in range(10): + index = random.randint(0, len(X_train)) + image = X_train[index] + axs[i].axis('off') + axs[i].imshow(image) + axs[i].set_title(y_train[index]) + +``` + + +![png](output_6_0.png) + + + +```python +# histogram of label frequency +hist, bins = np.histogram(y_train, bins=n_classes) +width = 0.7 * (bins[1] - bins[0]) +center = (bins[:-1] + bins[1:]) / 2 +plt.bar(center, hist, align='center', width=width) +plt.show() +``` + + +![png](output_7_0.png) + + +---- + +## Step 2: Design and Test a Model Architecture + +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). + +There are various aspects to consider when thinking about this problem: + +- Neural network architecture +- Play around preprocessing techniques (normalization, rgb to grayscale, etc) +- Number of examples per label (some have more than others). +- Generate fake data. + +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. + +**NOTE:** 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! + +### Implementation + +Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow. +#### I'll be making use of a combination of single cell and multiple cell combinations, based on the coding and flow requirements + + +```python +### Preprocess the data here. +### Feel free to use as many code cells as needed. + +# Convert to grayscale +X_train_rgb = X_train +X_train_gry = np.sum(X_train/3, axis=3, keepdims=True) + +X_test_rgb = X_test +X_test_gry = np.sum(X_test/3, axis=3, keepdims=True) + +print('RGB dataset shape:', X_train_rgb.shape) +print('Grayscale dataset shape:', X_train_gry.shape) +``` + + RGB dataset shape: (34799, 32, 32, 3) + Grayscale dataset shape: (34799, 32, 32, 1) + + + +```python +X_train = X_train_gry +X_test = X_test_gry + +print('Training and test datasets processed - done') +``` + + Training and test datasets processed - done + + + +```python +# Visualize rgb vs grayscale +n_rows = 8 +n_cols = 10 +offset = 9000 +fig, axs = plt.subplots(n_rows,n_cols, figsize=(18, 14)) +fig.subplots_adjust(hspace = .1, wspace=.001) +axs = axs.ravel() +for j in range(0,n_rows,2): + for i in range(n_cols): + index = i + j*n_cols + image = X_train_rgb[index + offset] + axs[index].axis('off') + axs[index].imshow(image) + for i in range(n_cols): + index = i + j*n_cols + n_cols + image = X_train_gry[index + offset - n_cols].squeeze() + axs[index].axis('off') + axs[index].imshow(image, cmap='gray') +``` + + +![png](output_12_0.png) + + + +```python +print(y_train[0:500]) +``` + + [41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 + 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 + 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31] + + + +```python +print(np.mean(X_train)) +print(np.mean(X_test)) +``` + + 82.67758903699634 + 82.14846036120173 + + + +```python +## Normalize the train and test datasets to (-1,1) + +X_train_normalized = (X_train - 128)/128 +X_test_normalized = (X_test - 128)/128 + +print(np.mean(X_train_normalized)) +print(np.mean(X_test_normalized)) +``` + + -0.35408133564846583 + -0.3582151534281105 + + + +```python +print("Original shape:", X_train.shape) +print("Normalized shape:", X_train_normalized.shape) +fig, axs = plt.subplots(1,2, figsize=(10, 3)) +axs = axs.ravel() + +axs[0].axis('off') +axs[0].set_title('normalized') +axs[0].imshow(X_train_normalized[0].squeeze(), cmap='gray') + +axs[1].axis('off') +axs[1].set_title('original') +axs[1].imshow(X_train[0].squeeze(), cmap='gray') +``` + + Original shape: (34799, 32, 32, 1) + Normalized shape: (34799, 32, 32, 1) + + + + + + + + + + +![png](output_16_2.png) + + +#### 1. Describe how you preprocessed the image data. What techniques were chosen and why did you choose these techniques? Consider including images showing the output of each preprocessing technique. Pre-processing refers to techniques such as converting to grayscale, normalization, etc. (OPTIONAL: As described in the "Stand Out Suggestions" part of the rubric, if you generated additional data for training, describe why you decided to generate additional data, how you generated the data, and provide example images of the additional data. Then describe the characteristics of the augmented training set like number of images in the set, number of images for each class, etc.) + + +**Answer:** + +My dataset preprocessing consisted of: + +1. Converting to grayscale - + +As a first step, I decided to convert the images to grayscale because, the neural network would be very hard to train in color. The RGB image would have 3 channels; ie n x n x 3 however, a grayscale would be n x n x 1. + +As an example, set the n to 3 and output to 64, an RGB image would have 1728 parameters and the grayscale would have 576 parameters in the first layer. + +Here is an example of a traffic sign image before and after grayscaling. +2. Normalizing the data to the range (-1,1) + which was accomplished using the scikit learn module. [site gives more info](http://stats.stackexchange.com/questions/185853/why-do-we-need-to-normalize-the-images-before-we-put-them-into-cnn) has an explanation, the gist of which is that having a wider distribution in the data would make it more difficult to train using a singlar learning rate. ensures that each input parameter has a similar data distribution, which ensures a faster convergence during the training of the network.Different features could encompass far different ranges and a single learning rate might make some weights diverge. + +![Augmented-images-normalized][./normalize.png] +![Augmented-images-translated][./translate.png] +![Augmented-images-scaled][./scaling.png] +![Augmented-images-warped][./warp.png] +![Augmented-images-brightness-adjusted][./brightness.png] + + +```python +### Generate data additional data (OPTIONAL!) +### and split the data into training/validation/testing sets here. +### Feel free to use as many code cells as needed. +``` + +I used the following four functions for augmenting the dataset: +1. random_translate +2. random_scale +3. random_warp +4. random_brightness + + +```python +import cv2 + +def random_translate(img): + rows,cols,_ = img.shape + + # allow translation up to px pixels in x and y directions + px = 2 + dx,dy = np.random.randint(-px,px,2) + + M = np.float32([[1,0,dx],[0,1,dy]]) + dst = cv2.warpAffine(img,M,(cols,rows)) + + dst = dst[:,:,np.newaxis] + + return dst + +test_img = X_train_normalized[22222] + +test_dst = random_translate(test_img) + +fig, axs = plt.subplots(1,2, figsize=(10, 3)) + +axs[0].axis('off') +axs[0].imshow(test_img.squeeze(), cmap='gray') +axs[0].set_title('original') + +axs[1].axis('off') +axs[1].imshow(test_dst.squeeze(), cmap='gray') +axs[1].set_title('translated') + +print('shape in/out:', test_img.shape, test_dst.shape) + +``` + + shape in/out: (32, 32, 1) (32, 32, 1) + + + +![png](output_21_1.png) + + + +```python +def random_scaling(img): + rows,cols,_ = img.shape + + # transform limits + px = np.random.randint(-2,2) + + # ending locations + pts1 = np.float32([[px,px],[rows-px,px],[px,cols-px],[rows-px,cols-px]]) + + # starting locations (4 corners) + pts2 = np.float32([[0,0],[rows,0],[0,cols],[rows,cols]]) + + M = cv2.getPerspectiveTransform(pts1,pts2) + + dst = cv2.warpPerspective(img,M,(rows,cols)) + + dst = dst[:,:,np.newaxis] + + return dst + +test_dst = random_scaling(test_img) + +fig, axs = plt.subplots(1,2, figsize=(10, 3)) + +axs[0].axis('off') +axs[0].imshow(test_img.squeeze(), cmap='gray') +axs[0].set_title('original') + +axs[1].axis('off') +axs[1].imshow(test_dst.squeeze(), cmap='gray') +axs[1].set_title('scaled') + +print('shape in/out:', test_img.shape, test_dst.shape) + +``` + + shape in/out: (32, 32, 1) (32, 32, 1) + + + +![png](output_22_1.png) + + + +```python +def random_warp(img): + + rows,cols,_ = img.shape + + # random scaling coefficients + rndx = np.random.rand(3) - 0.5 + rndx *= cols * 0.06 # this coefficient determines the degree of warping + rndy = np.random.rand(3) - 0.5 + rndy *= rows * 0.06 + + # 3 starting points for transform, 1/4 way from edges + x1 = cols/4 + x2 = 3*cols/4 + y1 = rows/4 + y2 = 3*rows/4 + + pts1 = np.float32([[y1,x1], + [y2,x1], + [y1,x2]]) + pts2 = np.float32([[y1+rndy[0],x1+rndx[0]], + [y2+rndy[1],x1+rndx[1]], + [y1+rndy[2],x2+rndx[2]]]) + + M = cv2.getAffineTransform(pts1,pts2) + + dst = cv2.warpAffine(img,M,(cols,rows)) + + dst = dst[:,:,np.newaxis] + + return dst + +test_dst = random_warp(test_img) + +fig, axs = plt.subplots(1,2, figsize=(10, 3)) + +axs[0].axis('off') +axs[0].imshow(test_img.squeeze(), cmap='gray') +axs[0].set_title('original') + +axs[1].axis('off') +axs[1].imshow(test_dst.squeeze(), cmap='gray') +axs[1].set_title('warped') + +print('shape in/out:', test_img.shape, test_dst.shape) +``` + + shape in/out: (32, 32, 1) (32, 32, 1) + + + +![png](output_23_1.png) + + + +```python +def random_brightness(img): + shifted = img + 1.0 # shift to (0,2) range + img_max_value = max(shifted.flatten()) + max_coef = 2.0/img_max_value + min_coef = max_coef - 0.1 + coef = np.random.uniform(min_coef, max_coef) + dst = shifted * coef - 1.0 + return dst + +test_dst = random_brightness(test_img) + +fig, axs = plt.subplots(1,2, figsize=(10, 3)) + +axs[0].axis('off') +axs[0].imshow(test_img.squeeze(), cmap='gray') +axs[0].set_title('original') + +axs[1].axis('off') +axs[1].imshow(test_dst.squeeze(), cmap='gray') +axs[1].set_title('brightness adjusted') + +print('shape in/out:', test_img.shape, test_dst.shape) + +``` + + shape in/out: (32, 32, 1) (32, 32, 1) + + + +![png](output_24_1.png) + + + +```python +# histogram of label frequency (once again, before data augmentation) +hist, bins = np.histogram(y_train, bins=n_classes) +width = 0.7 * (bins[1] - bins[0]) +center = (bins[:-1] + bins[1:]) / 2 +plt.bar(center, hist, align='center', width=width) +plt.show() +``` + + +![png](output_25_0.png) + + + +```python +print(np.bincount(y_train)) +print("minimum samples for any label:", min(np.bincount(y_train))) +``` + + [ 180 1980 2010 1260 1770 1650 360 1290 1260 1320 1800 1170 1890 1920 + 690 540 360 990 1080 180 300 270 330 450 240 1350 540 210 + 480 240 390 690 210 599 360 1080 330 180 1860 270 300 210 + 210] + minimum samples for any label: 180 + + + +```python +print('X, y shapes:', X_train_normalized.shape, y_train.shape) + +input_indices = [] +output_indices = [] + +for class_n in range(n_classes): + print(class_n, ': ', end='') + class_indices = np.where(y_train == class_n) + n_samples = len(class_indices[0]) + if n_samples < 800: + for i in range(800 - n_samples): + input_indices.append(class_indices[0][i%n_samples]) + output_indices.append(X_train_normalized.shape[0]) + new_img = X_train_normalized[class_indices[0][i % n_samples]] + new_img = random_translate(random_scaling(random_warp(random_brightness(new_img)))) + X_train_normalized = np.concatenate((X_train_normalized, [new_img]), axis=0) + y_train = np.concatenate((y_train, [class_n]), axis=0) + if i % 50 == 0: + print('>', end='') + elif i % 10 == 0: + print('-',end='') + print('') + +print('X, y shapes:', X_train_normalized.shape, y_train.shape) + +``` + + X, y shapes: (46480, 32, 32, 1) (46480,) + 0 : + 1 : + 2 : + 3 : + 4 : + 5 : + 6 : + 7 : + 8 : + 9 : + 10 : + 11 : + 12 : + 13 : + 14 : + 15 : + 16 : + 17 : + 18 : + 19 : + 20 : + 21 : + 22 : + 23 : + 24 : + 25 : + 26 : + 27 : + 28 : + 29 : + 30 : + 31 : + 32 : + 33 : + 34 : + 35 : + 36 : + 37 : + 38 : + 39 : + 40 : + 41 : + 42 : + X, y shapes: (46480, 32, 32, 1) (46480,) + + + +```python +# show comparisons of data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABKAAAAINCAYAAAAJCwe2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 random augmented data points +choices = list(range(len(input_indices))) +picks = [] +for i in range(5): + rnd_index = np.random.randint(low=0,high=len(choices)) + picks.append(choices.pop(rnd_index)) +fig, axs = plt.subplots(2,5, figsize=(15, 6)) +fig.subplots_adjust(hspace = .2, wspace=.001) +axs = axs.ravel() +for i in range(5): + image = X_train_normalized[input_indices[picks[i]]].squeeze() + axs[i].axis('off') + axs[i].imshow(image, cmap = 'gray') + axs[i].set_title(y_train[input_indices[picks[i]]]) +for i in range(5): + image = X_train_normalized[output_indices[picks[i]]].squeeze() + axs[i+5].axis('off') + axs[i+5].imshow(image, cmap = 'gray') + axs[i+5].set_title(y_train[output_indices[picks[i]]]) +``` + + + --------------------------------------------------------------------------- + + ValueError Traceback (most recent call last) + + in + 3 picks = [] + 4 for i in range(5): + ----> 5 rnd_index = np.random.randint(low=0,high=len(choices)) + 6 picks.append(choices.pop(rnd_index)) + 7 fig, axs = plt.subplots(2,5, figsize=(15, 6)) + + + mtrand.pyx in mtrand.RandomState.randint() + + + ValueError: Range cannot be empty (low >= high) unless no samples are taken + + + +```python +# histogram of label frequency +hist, bins = np.histogram(y_train, bins=n_classes) +width = 0.7 * (bins[1] - bins[0]) +center = (bins[:-1] + bins[1:]) / 2 +plt.bar(center, hist, align='center', width=width) +plt.show() +``` + + +![png](output_29_0.png) + + + +```python +## Shuffle the training dataset + +from sklearn.utils import shuffle + +X_train_normalized, y_train = shuffle(X_train_normalized, y_train) + +print('done') +``` + + done + + + +```python +## Split validation dataset off from training dataset + +from sklearn.model_selection import train_test_split + +X_train, X_validation, y_train, y_validation = train_test_split(X_train_normalized, y_train, + test_size=0.20, random_state=42) + +print("Old X_train size:",len(X_train_normalized)) +print("New X_train size:",len(X_train)) +print("X_validation size:",len(X_validation)) +``` + + Old X_train size: 46480 + New X_train size: 37184 + X_validation size: 9296 + + +#### Question 2 + +#### Describe what your final model architecture looks like including model type, layers, layer sizes, connectivity, etc.) Consider including a diagram and/or table describing the final model. + +## Original LeNet Model Architecture + +| Layer | Description | +|:-------------------------:|:-------------------------------------------------------------:| +| Input | 32x32x3 RGB image | +| Layer 1 Convolution 3x3 | Input = 32x32ximage_depth. Output = 28x28x6 | +| RELU | | +| Max pooling | Input = 28x28x6. Output = 14x14x6 | +| Layer 2 Convolution 3x3 | Output = 10x10x16 | +| RELU | | +| Max pooling | Input = 10x10x16. Output = 5x5x16 | +| Layer 3 Fully connected | Fully Connected. Input = 400. Output = 120 | +| RELU | | +| Layer 4 Fully connected | Fully Connected. Input = 120. Output = 84 | +| RELU | | +| Layer 5 Fully connected | Fully Connected. Input = 84. Output = 43 | +| logits | Finalize and return the logits | + +![letnet5-classic.png](attachment:letnet5-classic.png) + +With the original dataset not giving optimum results, I +decided to perform data augmentation as it is know to increase accuracy of the model. + +On observation we can see that several classes in the data have far fewer samples than others the model will tend to be biased toward those classes with more samples. + +Useful python module SciKit Learn train_test_split function was used to create a validation set out of the training set. I used 20% of the testing set to create the validation set. + +Initially to train the model, I used default LeNet model as discussed in the class and that comprises of the layers given in the above table. The number of EPOCHs were 10. The learning rates tried were 0.1 through 0.05 and I got horrible accuracies of under 90% !! + +Then I updated the learning rate to 0.0009 and it seemed to give the highest accuracy > 99%, while still not slowing down the prcessing a lot. + +The following is the summary: + +Adam optimizer was used as part of the LeNet lab. The final settings used were: +- epochs: 60 +- batch size: 100 +- learning rate: 0.0009 +- mu: 0 +- sigma: 0.1 +- dropout keep probability: 0.5 + +As far as a discussion on the difficulty in classification, the following are notable + +- brightness : some images were brighter than others after a brightness transform was applied. +- colorspace : Some images were in a different color space. +- augmenting challenges : scaling, warping etc were used and it did increase the dataset and improved the accuracies + + +```python +import tensorflow as tf + +EPOCHS = 60 +BATCH_SIZE = 100 + +print('done') +``` + + done + + + +```python +#from tensorflow.contrib.layers import flatten +import tensorflow +from tensorflow.keras.layers import Flatten as flatten + +def LeNet(x): + # Hyperparameters + mu = 0 + sigma = 0.1 + + # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6. + W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma)) + x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID') + b1 = tf.Variable(tf.zeros(6)) + x = tf.nn.bias_add(x, b1) + print("layer 1 shape:",x.get_shape()) + + # TODO: Activation. + x = tf.nn.relu(x) + + # TODO: Pooling. Input = 28x28x6. Output = 14x14x6. + x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + + # TODO: Layer 2: Convolutional. Output = 10x10x16. + W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma)) + x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID') + b2 = tf.Variable(tf.zeros(16)) + x = tf.nn.bias_add(x, b2) + + # TODO: Activation. + x = tf.nn.relu(x) + + # TODO: Pooling. Input = 10x10x16. Output = 5x5x16. + x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + + # TODO: Flatten. Input = 5x5x16. Output = 400. + x = flatten(x) + + # TODO: Layer 3: Fully Connected. Input = 400. Output = 120. + W3 = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma)) + b3 = tf.Variable(tf.zeros(120)) + x = tf.add(tf.matmul(x, W3), b3) + + # TODO: Activation. + x = tf.nn.relu(x) + + # Dropout + x = tf.nn.dropout(x, keep_prob) + + # TODO: Layer 4: Fully Connected. Input = 120. Output = 84. + W4 = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma)) + b4 = tf.Variable(tf.zeros(84)) + x = tf.add(tf.matmul(x, W4), b4) + + # TODO: Activation. + x = tf.nn.relu(x) + + # Dropout + x = tf.nn.dropout(x, keep_prob) + + # TODO: Layer 5: Fully Connected. Input = 84. Output = 43. + W5 = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma)) + b5 = tf.Variable(tf.zeros(43)) + logits = tf.add(tf.matmul(x, W5), b5) + + return logits + +print('LeNet5 Classic done') +``` + + LeNet5 Classic done + + +#### Modified LeNet Model Architecture +The achitecture has been adapted from Sermanet/LeCunn traffic sign classification journal article. Please refer to the article for more information. + +Modified LeCun5 architecture +![LeCun5-updated.png](attachment:LeCun5-updated.png) + + +```python +#from tensorflow.contrib.layers import flatten +import tensorflow +from tensorflow.keras.layers import Flatten as flatten + + +def LeNet5_updated(x): + # Hyperparameters + mu = 0 + sigma = 0.1 + + # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6. + W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma), name="W1") + x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID') + b1 = tf.Variable(tf.zeros(6), name="b1") + x = tf.nn.bias_add(x, b1) + print("layer 1 shape:",x.get_shape()) + # TODO: Activation. + x = tf.nn.relu(x) + # TODO: Pooling. Input = 28x28x6. Output = 14x14x6. + x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + layer1 = x + + # TODO: Layer 2: Convolutional. Output = 10x10x16. + W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma), name="W2") + x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID') + b2 = tf.Variable(tf.zeros(16), name="b2") + x = tf.nn.bias_add(x, b2) + # TODO: Activation. + x = tf.nn.relu(x) + # TODO: Pooling. Input = 10x10x16. Output = 5x5x16. + x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') + layer2 = x + + # TODO: Layer 3: Convolutional. Output = 1x1x400. + W3 = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 400), mean = mu, stddev = sigma), name="W3") + x = tf.nn.conv2d(x, W3, strides=[1, 1, 1, 1], padding='VALID') + b3 = tf.Variable(tf.zeros(400), name="b3") + x = tf.nn.bias_add(x, b3) + # TODO: Activation. + x = tf.nn.relu(x) + layer3 = x + # TODO: Flatten. Input = 5x5x16. Output = 400. + #layer2flat = flatten(layer2) + layer2flat = tensorflow.reshape(layer2, [tensorflow.shape(layer2)[0], -1]) + print("layer2flat shape:",layer2flat.get_shape()) + # Flatten x. Input = 1x1x400. Output = 400. + #xflat = flatten(x) + xflat = flatten()(x) + print("xflat shape:",xflat.get_shape()) + # Concat layer2flat and x. Input = 400 + 400. Output = 800 + #x = tf.concat_v2([xflat, layer2flat], 1) + x = tf.concat([xflat, layer2flat], 1) + print("x shape:",x.get_shape()) + # Dropout + x = tf.nn.dropout(x, keep_prob) + + # TODO: Layer 4: Fully Connected. Input = 800. Output = 43. + W4 = tf.Variable(tf.truncated_normal(shape=(800, 43), mean = mu, stddev = sigma), name="W4") + b4 = tf.Variable(tf.zeros(43), name="b4") + logits = tf.add(tf.matmul(x, W4), b4) + + + return logits + +print('LeNet5 Modified done') +``` + + LeNet5 Modified done + + + +```python +tf.reset_default_graph() + +x = tf.placeholder(tf.float32, (None, 32, 32, 1)) +y = tf.placeholder(tf.int32, (None)) +keep_prob = tf.placeholder(tf.float32) # probability to keep units +one_hot_y = tf.one_hot(y, 43) + +print('done') +``` + + done + + +#### 3. Describe how you trained your model. The discussion can include the type of optimizer, the batch size, number of epochs and any hyperparameters such as learning rate. + +To train the model, I used LeNet that comprises of the layers given in the above table. I began by implementing the same architecture from the LeNet Lab, with no changes since my dataset is in grayscale. This model worked quite well to begin with (> 95% validation accuracy), but I also implemented the Sermanet/LeCun model from their traffic sign classifier paper and saw an immediate improvement. Although the paper doesn't go into detail describing exactly how the model is implemented (particularly the depth of the layers) + +The updated model will be as follows: +1. 5x5 convolution (32x32x1 input, 28x28x6 output) +2. ReLU +3. 2x2 max pool (28x28x6 input, 14x14x6 output) +4. 5x5 convolution (14x14x6 input, 10x10x16 output) +5. ReLU +6. 2x2 max pool (10x10x16 input, 5x5x16 output) +7. 5x5 convolution (5x5x6 input, 1x1x400 output) +8. ReLu +9. Flatten layers from the ReLu output; ie No. 8 (1x1x400 -> 400) and maxpool output; ie No. 6 (5x5x16 -> 400) +10. Concatenate flattened layers to a single size-800 layer +11. Dropout layer +12. Fully connected layer (800 input, 43 output) + + +```python +### Train your model here. +### Feel free to use as many code cells as needed. +``` + + +```python +rate = 0.0009 + +logits = LeNet5_updated(x) +#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y) +with tf.name_scope('loss'): + #cross_entropy = None + val = tf.nn.softmax_cross_entropy_with_logits(labels = one_hot_y, logits=logits) + cross_entropy = tf.reduce_mean(val) +loss_operation = tf.reduce_mean(cross_entropy) +optimizer = tf.train.AdamOptimizer(learning_rate = rate) +training_operation = optimizer.minimize(loss_operation) +``` + + layer 1 shape: (?, 28, 28, 6) + layer2flat shape: (?, ?) + xflat shape: (?, 400) + x shape: (?, ?) + WARNING:tensorflow:From :55: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. + Instructions for updating: + Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`. + WARNING:tensorflow:From :7: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version. + Instructions for updating: + + Future major versions of TensorFlow will allow gradients to flow + into the labels input on backprop by default. + + See `tf.nn.softmax_cross_entropy_with_logits_v2`. + + + + +```python +correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1)) +accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) +saver = tf.train.Saver() + +def evaluate(X_data, y_data): + num_examples = len(X_data) + total_accuracy = 0 + sess = tf.get_default_session() + for offset in range(0, num_examples, BATCH_SIZE): + batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE] + accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0}) + total_accuracy += (accuracy * len(batch_x)) + return total_accuracy / num_examples + +print('done') +``` + + done + + + +```python +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + num_examples = len(X_train) + + print("Training...") + print() + for i in range(EPOCHS): + X_train, y_train = shuffle(X_train, y_train) + for offset in range(0, num_examples, BATCH_SIZE): + end = offset + BATCH_SIZE + batch_x, batch_y = X_train[offset:end], y_train[offset:end] + sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5}) + + validation_accuracy = evaluate(X_validation, y_validation) + print("EPOCH {} ...".format(i+1)) + print("Validation Accuracy = {:.3f}".format(validation_accuracy)) + print() + + saver.save(sess, './traffic_signs') + print("Model saved") +``` + + Training... + + EPOCH 1 ... + Validation Accuracy = 0.862 + + EPOCH 2 ... + Validation Accuracy = 0.928 + + EPOCH 3 ... + Validation Accuracy = 0.958 + + EPOCH 4 ... + Validation Accuracy = 0.965 + + EPOCH 5 ... + Validation Accuracy = 0.975 + + EPOCH 6 ... + Validation Accuracy = 0.978 + + EPOCH 7 ... + Validation Accuracy = 0.981 + + EPOCH 8 ... + Validation Accuracy = 0.984 + + EPOCH 9 ... + Validation Accuracy = 0.983 + + EPOCH 10 ... + Validation Accuracy = 0.983 + + EPOCH 11 ... + Validation Accuracy = 0.986 + + EPOCH 12 ... + Validation Accuracy = 0.987 + + EPOCH 13 ... + Validation Accuracy = 0.988 + + EPOCH 14 ... + Validation Accuracy = 0.986 + + EPOCH 15 ... + Validation Accuracy = 0.990 + + EPOCH 16 ... + Validation Accuracy = 0.989 + + EPOCH 17 ... + Validation Accuracy = 0.989 + + EPOCH 18 ... + Validation Accuracy = 0.988 + + EPOCH 19 ... + Validation Accuracy = 0.990 + + EPOCH 20 ... + Validation Accuracy = 0.989 + + EPOCH 21 ... + Validation Accuracy = 0.990 + + EPOCH 22 ... + Validation Accuracy = 0.990 + + EPOCH 23 ... + Validation Accuracy = 0.991 + + EPOCH 24 ... + Validation Accuracy = 0.991 + + EPOCH 25 ... + Validation Accuracy = 0.990 + + EPOCH 26 ... + Validation Accuracy = 0.990 + + EPOCH 27 ... + Validation Accuracy = 0.992 + + EPOCH 28 ... + Validation Accuracy = 0.990 + + EPOCH 29 ... + Validation Accuracy = 0.991 + + EPOCH 30 ... + Validation Accuracy = 0.991 + + EPOCH 31 ... + Validation Accuracy = 0.992 + + EPOCH 32 ... + Validation Accuracy = 0.989 + + EPOCH 33 ... + Validation Accuracy = 0.993 + + EPOCH 34 ... + Validation Accuracy = 0.992 + + EPOCH 35 ... + Validation Accuracy = 0.992 + + EPOCH 36 ... + Validation Accuracy = 0.991 + + EPOCH 37 ... + Validation Accuracy = 0.992 + + EPOCH 38 ... + Validation Accuracy = 0.992 + + EPOCH 39 ... + Validation Accuracy = 0.993 + + EPOCH 40 ... + Validation Accuracy = 0.992 + + EPOCH 41 ... + Validation Accuracy = 0.992 + + EPOCH 42 ... + Validation Accuracy = 0.994 + + EPOCH 43 ... + Validation Accuracy = 0.992 + + EPOCH 44 ... + Validation Accuracy = 0.992 + + EPOCH 45 ... + Validation Accuracy = 0.993 + + EPOCH 46 ... + Validation Accuracy = 0.993 + + EPOCH 47 ... + Validation Accuracy = 0.992 + + EPOCH 48 ... + Validation Accuracy = 0.994 + + EPOCH 49 ... + Validation Accuracy = 0.993 + + EPOCH 50 ... + Validation Accuracy = 0.993 + + EPOCH 51 ... + Validation Accuracy = 0.993 + + EPOCH 52 ... + Validation Accuracy = 0.991 + + EPOCH 53 ... + Validation Accuracy = 0.994 + + EPOCH 54 ... + Validation Accuracy = 0.992 + + EPOCH 55 ... + Validation Accuracy = 0.994 + + EPOCH 56 ... + Validation Accuracy = 0.993 + + EPOCH 57 ... + Validation Accuracy = 0.993 + + EPOCH 58 ... + Validation Accuracy = 0.993 + + EPOCH 59 ... + Validation Accuracy = 0.994 + + EPOCH 60 ... + Validation Accuracy = 0.993 + + Model saved + + +### Test accuracy verification! + + +```python +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + saver2 = tf.train.import_meta_graph("./traffic_signs.meta") + saver2.restore(sess, "./traffic_signs") + test_accuracy = evaluate(X_test_normalized, y_test) + print("Test Set Accuracy = {:.3f}".format(test_accuracy)) +``` + + INFO:tensorflow:Restoring parameters from ./traffic_signs + Test Set Accuracy = 0.945 + + +### 94.5% test accuracy achieved + +#### 4. Describe the approach taken for finding a solution and getting the validation set accuracy to be at least 0.93. Include in the discussion the results on the training, validation and test sets and where in the code these were calculated. Your approach may have been an iterative process, in which case, outline the steps you took to get to the final solution and why you chose those steps. Perhaps your solution involved an already well known implementation or architecture. In this case, discuss why you think the architecture is suitable for the current problem. + +In my approach, I split the data into training data, test data and then validation data based on the provided pickled data and also experimented with scikit module's train_test_split function. I will continue to experiment this function. Data augmentation as learnt from the course and researched on the internet was a useful technique for better accuracy. I + +The following are the model results. I was able to achieve the test data accuracy of > 0.93 or 93% by tweeking the learning rate, adding the layers and updating the connectedness of the layers. + +If an iterative approach was chosen: +* What was the first architecture that was tried and why was it chosen? +The first architecture was the LeNet. This was a simple to implement yet powerful architecture +* What were some problems with the initial architecture? +The initial accuracy was not as good. However, the system converged after some iterations. +* How was the architecture adjusted and why was it adjusted? +Typical adjustments could include choosing a different model architecture, adding or taking away layers (pooling, dropout, convolution, etc), using an activation function or changing the activation function. One common justification for adjusting an architecture would be due to overfitting or underfitting. A high accuracy on the training set but low accuracy on the validation set indicates over fitting; a low accuracy on both sets indicates under fitting. +* Which parameters were tuned? How were they adjusted and why? +Learning rate, EPOCHS, Subsampling, to name a few; Initially I had the EPOCH at 10 and later on changed it to 60 and with a learning rate of 0.001, for an accuracy of > 99% +* What are some of the important design choices and why were they chosen? For example, why might a convolution layer work well with this problem? How might a dropout layer help with creating a successful model? +A dropout layer helps in avoiding overfitting +If a well known architecture was chosen: +* What architecture was chosen? +LeNet5 was chosen : However, I am working on researching and increasing the layers to 10 but that will be done later on +* Why did you believe it would be relevant to the traffic sign application? +The traffic sign application is a typical CNN type application and LeNet being one of the simpler implementations that involves ConvNet seems like to good fit +* How does the final model's accuracy on the training, validation and test set provide evidence that the model is working well? +Adam optimizer which was already implemented as part of the LeNet module was used. The final settings used were: +- batch size: 128 +- epochs: 60 +- learning rate: 0.0009 +- mu: 0 +- sigma: 0.1 +- dropout keep probability: 0.5 + +--- + +### Test a Model on New Images + +I downloaded several pictures of the german traffic dataset (at least five), and ran them through the classifier. The classifier gave only 12.5% accuracy. `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name. + +#### 1. Choose five German traffic signs found on the web and provide them in the report. For each image, discuss what quality or qualities might be difficult to classify. + +Here are five German traffic signs that I found on the web: + +![Image 1][./traffic-signs-data/online_files/1.jpg] +![Image 2][./traffic-signs-data/online_files/2.jpg] +![Image 3][./traffic-signs-data/online_files/3.jpg] +![Image 4][./traffic-signs-data/online_files/4.jpg] +![Image 5][./traffic-signs-data/online_files/5.jpg] + +### Implementation + +Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow. + + +```python +# Reinitialize and re-import if starting a new kernel here +import matplotlib.pyplot as plt +%matplotlib inline + +import tensorflow as tf +import numpy as np +import cv2 + +print('done') +``` + + done + + + +```python +### Load the images and plot them here. +### Feel free to use as many code cells as needed. + +#reading in an image +import glob +import matplotlib.image as mpimg + +fig, axs = plt.subplots(2,4, figsize=(4, 2)) +fig.subplots_adjust(hspace = .2, wspace=.001) +axs = axs.ravel() + +my_images = [] + +for i, img in enumerate(glob.glob('./my-found-traffic-signs/*x.png')): +#for i, img in enumerate(glob.glob('./traffic-signs-data/online-files/*.jpg')): + image = cv2.imread(img) + axs[i].axis('off') + axs[i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + my_images.append(image) + +my_images = np.asarray(my_images) + +my_images_gry = np.sum(my_images/3, axis=3, keepdims=True) + +my_images_normalized = (my_images_gry - 128)/128 + +print(my_images_normalized.shape) +``` + + (8, 32, 32, 1) + + + +![png](output_55_1.png) + + +#### 2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the "Stand Out Suggestions" part of the rubric). + +The classification was as expected, when an image was very different from my local or the downloaded online image, the system had an accuracy of around 12.5% + +But when I used familiar traffic sign images, these images seem to be distinguishable easier than than quite a few images from the original dataset. + +Some of the my images seem to be much brighter and might occupy a different range in the color space, possibly a range that the model was not trained on. + +In addition, the German dataset states that the images "contain a border of 10 % around the actual traffic sign (at least 5 pixels) to allow for edge-based approaches" and the images that I used do not all include such a border. This could be another source of confusion for the model. + + +```python +### Run the predictions here. +### Feel free to use as many code cells as needed. + +my_labels = [3, 11, 1, 12, 38, 34, 18, 25] +#my_labels = [3, 11, 1, 12] +#my_labels = [14, 1, 25, 9, 5] + + +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + saver3 = tf.train.import_meta_graph('./traffic_signs.meta') + saver3.restore(sess, "./traffic_signs") + my_accuracy = evaluate(my_images_normalized, my_labels) + print("Test Set Accuracy = {:.3f}".format(my_accuracy)) +``` + + INFO:tensorflow:Restoring parameters from ./traffic_signs + Test Set Accuracy = 0.125 + + +#### 2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the "Stand Out Suggestions" part of the rubric). + +The model appears to have predicted the new but similar signs perfectly, with 100% accuracy - even better than the 99.3% validation accuracy and the 94.7% test accuracy. It is a good sign that the model performs well on real-world data. + +However, it is reasonable to assume that the accuracy would not remain so high given more data points, the low fidelity of a number of images in the training dataset can also be a reasonable explanation to assume that if the real-world data were all as easily distinguishable as the images chosen that the accuracy would remain very high. + + +```python +### Visualize the softmax probabilities here. +### Feel free to use as many code cells as needed. + +softmax_logits = tf.nn.softmax(logits) +top_k = tf.nn.top_k(softmax_logits, k=3) + + +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + saver = tf.train.import_meta_graph('./traffic_signs.meta') + saver.restore(sess, "./traffic_signs") + my_softmax_logits = sess.run(softmax_logits, feed_dict={x: my_images_normalized, keep_prob: 1.0}) + my_top_k = sess.run(top_k, feed_dict={x: my_images_normalized, keep_prob: 1.0}) + + + fig, axs = plt.subplots(len(my_images),4, figsize=(12, 14)) + fig.subplots_adjust(hspace = .4, wspace=.2) + axs = axs.ravel() + + for i, image in enumerate(my_images): + axs[4*i].axis('off') + axs[4*i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + axs[4*i].set_title('input') + guess1 = my_top_k[1][i][0] + index1 = np.argwhere(y_validation == guess1)[0] + axs[4*i+1].axis('off') + axs[4*i+1].imshow(X_validation[index1].squeeze(), cmap='gray') + axs[4*i+1].set_title('top guess: {} ({:.0f}%)'.format(guess1, 100*my_top_k[0][i][0])) + guess2 = my_top_k[1][i][1] + index2 = np.argwhere(y_validation == guess2)[0] + axs[4*i+2].axis('off') + axs[4*i+2].imshow(X_validation[index2].squeeze(), cmap='gray') + axs[4*i+2].set_title('2nd guess: {} ({:.0f}%)'.format(guess2, 100*my_top_k[0][i][1])) + guess3 = my_top_k[1][i][2] + index3 = np.argwhere(y_validation == guess3)[0] + axs[4*i+3].axis('off') + axs[4*i+3].imshow(X_validation[index3].squeeze(), cmap='gray') + axs[4*i+3].set_title('3rd guess: {} ({:.0f}%)'.format(guess3, 100*my_top_k[0][i][2])) +``` + + INFO:tensorflow:Restoring parameters from ./traffic_signs + + + +![png](output_61_1.png) + + +#### 3. Describe how certain the model is when predicting on each of the five new images by looking at the softmax probabilities for each prediction. Provide the top 5 softmax probabilities for each image along with the sign type of each probability. (OPTIONAL: as described in the "Stand Out Suggestions" part of the rubric, visualizations can also be provided such as bar charts) + +*Use the model's softmax probabilities to visualize the **certainty** of its predictions, [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. Which predictions is the model certain of? Uncertain? If the model was incorrect in its initial prediction, does the correct prediction appear in the top k? (k should be 5 at most)* + +`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. + +Take this numpy array as an example: + +``` +# (5, 6) array +a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497, + 0.12789202], + [ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401, + 0.15899337], + [ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 , + 0.23892179], + [ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 , + 0.16505091], + [ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137, + 0.09155967]]) +``` + +Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces: + +``` +TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202], + [ 0.28086119, 0.27569815, 0.18063401], + [ 0.26076848, 0.23892179, 0.23664738], + [ 0.29198961, 0.26234032, 0.16505091], + [ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5], + [0, 1, 4], + [0, 5, 1], + [1, 3, 5], + [1, 4, 3]], dtype=int32)) +``` + +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. + + +```python +fig, axs = plt.subplots(8,2, figsize=(9, 19)) +axs = axs.ravel() + +for i in range(len(my_softmax_logits)*2): + if i%2 == 0: + axs[i].axis('off') + axs[i].imshow(cv2.cvtColor(my_images[i//2], cv2.COLOR_BGR2RGB)) + else: + axs[i].bar(np.arange(n_classes), my_softmax_logits[(i-1)//2]) + axs[i].set_ylabel('Softmax probability') + +``` + + +![png](output_63_0.png) + + +The well trained model seems to have a very high accuracy on the images given. Visualizing the images, this seems accurate . Even on the third image, it's 92% certain of its prediction. + +This very high level of certainty, along with achieving 100% accuracy, on the newly introduced real-world data is indicative of a model that performs very well. + +> **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", + "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission. + +### Utilities for userfriendliness + + +```python +print("X_train shape:", X_train.shape) +print("y_train shape:", y_train.shape) +print("X_validation shape:", X_validation.shape) +print("y_validation shape:", y_validation.shape) +print("X_test shape:", X_test_normalized.shape) +print("y_test shape:", y_test.shape) +``` + + X_train shape: (37184, 32, 32, 1) + y_train shape: (37184,) + X_validation shape: (9296, 32, 32, 1) + y_validation shape: (9296,) + X_test shape: (12630, 32, 32, 1) + y_test shape: (12630,) + + + +```python + +``` diff --git a/markdown/output_12_0.png b/markdown/output_12_0.png new file mode 100644 index 0000000000..3d7722694f Binary files /dev/null and b/markdown/output_12_0.png differ diff --git a/markdown/output_16_2.png b/markdown/output_16_2.png new file mode 100644 index 0000000000..7e5a0ae35e Binary files /dev/null and b/markdown/output_16_2.png differ diff --git a/markdown/output_21_1.png b/markdown/output_21_1.png new file mode 100644 index 0000000000..9fc3de4978 Binary files /dev/null and b/markdown/output_21_1.png differ diff --git a/markdown/output_22_1.png b/markdown/output_22_1.png new file mode 100644 index 0000000000..f000f4298f Binary files /dev/null and b/markdown/output_22_1.png differ diff --git a/markdown/output_23_1.png b/markdown/output_23_1.png new file mode 100644 index 0000000000..7a7ab80099 Binary files /dev/null and b/markdown/output_23_1.png differ diff --git a/markdown/output_24_1.png b/markdown/output_24_1.png new file mode 100644 index 0000000000..ad94e5c5bc Binary files /dev/null and b/markdown/output_24_1.png differ diff --git a/markdown/output_25_0.png b/markdown/output_25_0.png new file mode 100644 index 0000000000..639be133d9 Binary files /dev/null and b/markdown/output_25_0.png differ diff --git a/markdown/output_29_0.png b/markdown/output_29_0.png new file mode 100644 index 0000000000..4ff3ff4728 Binary files /dev/null and b/markdown/output_29_0.png differ diff --git a/markdown/output_55_1.png b/markdown/output_55_1.png new file mode 100644 index 0000000000..da3ee31833 Binary files /dev/null and b/markdown/output_55_1.png differ diff --git a/markdown/output_61_1.png b/markdown/output_61_1.png new file mode 100644 index 0000000000..4f8516601b Binary files /dev/null and b/markdown/output_61_1.png differ diff --git a/markdown/output_63_0.png b/markdown/output_63_0.png new file mode 100644 index 0000000000..8be00576e3 Binary files /dev/null and b/markdown/output_63_0.png differ diff --git a/markdown/output_6_0.png b/markdown/output_6_0.png new file mode 100644 index 0000000000..cb886b7fec Binary files /dev/null and b/markdown/output_6_0.png differ diff --git a/markdown/output_7_0.png b/markdown/output_7_0.png new file mode 100644 index 0000000000..639be133d9 Binary files /dev/null and b/markdown/output_7_0.png differ diff --git a/normalize.png b/normalize.png new file mode 100644 index 0000000000..a08bfbdb2c Binary files /dev/null and b/normalize.png differ diff --git a/online_images.png b/online_images.png new file mode 100644 index 0000000000..6631e8cbdf Binary files /dev/null and b/online_images.png differ diff --git a/scaling.png b/scaling.png new file mode 100644 index 0000000000..6e879a1461 Binary files /dev/null and b/scaling.png differ diff --git a/traffic-signs-data/online-files/1.jpg b/traffic-signs-data/online-files/1.jpg new file mode 100644 index 0000000000..614096e699 Binary files /dev/null and b/traffic-signs-data/online-files/1.jpg differ diff --git a/traffic-signs-data/online-files/2.jpg b/traffic-signs-data/online-files/2.jpg new file mode 100644 index 0000000000..901e2e2d4d Binary files /dev/null and b/traffic-signs-data/online-files/2.jpg differ diff --git a/traffic-signs-data/online-files/3.jpg b/traffic-signs-data/online-files/3.jpg new file mode 100644 index 0000000000..bd9327d9e1 Binary files /dev/null and b/traffic-signs-data/online-files/3.jpg differ diff --git a/traffic-signs-data/online-files/4.jpg b/traffic-signs-data/online-files/4.jpg new file mode 100644 index 0000000000..14a75e1ecd Binary files /dev/null and b/traffic-signs-data/online-files/4.jpg differ diff --git a/traffic-signs-data/online-files/5.jpg b/traffic-signs-data/online-files/5.jpg new file mode 100644 index 0000000000..c37c093354 Binary files /dev/null and b/traffic-signs-data/online-files/5.jpg differ diff --git a/traffic-signs-data/online_files/1.jpg b/traffic-signs-data/online_files/1.jpg new file mode 100644 index 0000000000..614096e699 Binary files /dev/null and b/traffic-signs-data/online_files/1.jpg differ diff --git a/traffic-signs-data/online_files/2.jpg b/traffic-signs-data/online_files/2.jpg new file mode 100644 index 0000000000..901e2e2d4d Binary files /dev/null and b/traffic-signs-data/online_files/2.jpg differ diff --git a/traffic-signs-data/online_files/3.jpg b/traffic-signs-data/online_files/3.jpg new file mode 100644 index 0000000000..bd9327d9e1 Binary files /dev/null and b/traffic-signs-data/online_files/3.jpg differ diff --git a/traffic-signs-data/online_files/4.jpg b/traffic-signs-data/online_files/4.jpg new file mode 100644 index 0000000000..14a75e1ecd Binary files /dev/null and b/traffic-signs-data/online_files/4.jpg differ diff --git a/traffic-signs-data/online_files/5.jpg b/traffic-signs-data/online_files/5.jpg new file mode 100644 index 0000000000..c37c093354 Binary files /dev/null and b/traffic-signs-data/online_files/5.jpg differ diff --git a/traffic_signs.data-00000-of-00001 b/traffic_signs.data-00000-of-00001 new file mode 100644 index 0000000000..e8a44b3c92 Binary files /dev/null and b/traffic_signs.data-00000-of-00001 differ diff --git a/traffic_signs.data-data-00000-of-00001 b/traffic_signs.data-data-00000-of-00001 new file mode 100644 index 0000000000..ae4eac4687 Binary files /dev/null and b/traffic_signs.data-data-00000-of-00001 differ diff --git a/traffic_signs.index b/traffic_signs.index new file mode 100644 index 0000000000..8303824014 Binary files /dev/null and b/traffic_signs.index differ diff --git a/traffic_signs.meta b/traffic_signs.meta new file mode 100644 index 0000000000..219e5780c2 Binary files /dev/null and b/traffic_signs.meta differ diff --git a/translate.png b/translate.png new file mode 100644 index 0000000000..7b47e5c96d Binary files /dev/null and b/translate.png differ diff --git a/warp.png b/warp.png new file mode 100644 index 0000000000..67c46aa838 Binary files /dev/null and b/warp.png differ