assignment1/features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for the bonus section.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Done extracting features for 48000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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      "iteration 1300 / 1500: loss nan\n",
      "iteration 1400 / 1500: loss nan\n",
      "CPU times: user 21.2 s, sys: 990 ms, total: 22.2 s\n",
      "Wall time: 17.7 s\n",
      "lr 3.302112e-08 reg 8.257687e+04 train accuracy: 0.404939 val accuracy: 0.427000\n",
      "lr 8.711412e-08 reg 3.096167e+04 train accuracy: 0.412673 val accuracy: 0.422000\n",
      "lr 2.373991e-08 reg 2.239706e+05 train accuracy: 0.405204 val accuracy: 0.419000\n",
      "lr 5.799264e-08 reg 1.059675e+05 train accuracy: 0.388959 val accuracy: 0.408000\n",
      "lr 1.691348e-08 reg 4.739152e+05 train accuracy: 0.391224 val accuracy: 0.406000\n",
      "lr 2.375395e-09 reg 1.388003e+06 train accuracy: 0.393245 val accuracy: 0.403000\n",
      "lr 4.829990e-09 reg 1.122216e+06 train accuracy: 0.401510 val accuracy: 0.401000\n",
      "lr 1.002233e-09 reg 1.293353e+07 train accuracy: 0.384429 val accuracy: 0.392000\n",
      "lr 6.131083e-09 reg 1.328688e+06 train accuracy: 0.389245 val accuracy: 0.387000\n",
      "lr 6.646268e-09 reg 4.629901e+05 train accuracy: 0.392673 val accuracy: 0.380000\n",
      "lr 9.170292e-08 reg 8.215108e+04 train accuracy: 0.385265 val accuracy: 0.376000\n",
      "lr 9.856227e-10 reg 4.028767e+06 train accuracy: 0.386857 val accuracy: 0.375000\n",
      "lr 1.140997e-08 reg 1.000117e+06 train accuracy: 0.381755 val accuracy: 0.374000\n",
      "lr 2.687024e-09 reg 3.300896e+06 train accuracy: 0.380184 val accuracy: 0.373000\n",
      "lr 2.106068e-09 reg 4.117332e+06 train accuracy: 0.382327 val accuracy: 0.368000\n",
      "lr 1.850623e-09 reg 9.365323e+06 train accuracy: 0.364245 val accuracy: 0.362000\n",
      "lr 9.189933e-08 reg 1.883317e+05 train accuracy: 0.369082 val accuracy: 0.360000\n",
      "lr 8.459511e-09 reg 4.176652e+06 train accuracy: 0.349306 val accuracy: 0.358000\n",
      "lr 2.351045e-08 reg 6.372761e+05 train accuracy: 0.364531 val accuracy: 0.355000\n",
      "lr 4.921256e-08 reg 2.472489e+05 train accuracy: 0.369673 val accuracy: 0.355000\n",
      "lr 5.195636e-08 reg 5.634373e+05 train accuracy: 0.339388 val accuracy: 0.333000\n",
      "lr 1.374674e-08 reg 3.774136e+06 train accuracy: 0.318776 val accuracy: 0.330000\n",
      "lr 2.661231e-08 reg 1.767427e+06 train accuracy: 0.298408 val accuracy: 0.308000\n",
      "lr 6.571657e-09 reg 7.965587e+06 train accuracy: 0.301469 val accuracy: 0.296000\n",
      "lr 4.147408e-09 reg 1.038124e+07 train accuracy: 0.284082 val accuracy: 0.295000\n",
      "lr 4.622470e-08 reg 1.590837e+06 train accuracy: 0.281714 val accuracy: 0.288000\n",
      "lr 1.163559e-07 reg 6.789463e+05 train accuracy: 0.290551 val accuracy: 0.270000\n",
      "lr 9.865697e-08 reg 1.132825e+04 train accuracy: 0.249388 val accuracy: 0.267000\n",
      "lr 2.838868e-08 reg 4.462864e+06 train accuracy: 0.247347 val accuracy: 0.253000\n",
      "lr 5.357338e-08 reg 2.603166e+04 train accuracy: 0.243816 val accuracy: 0.243000\n",
      "lr 4.699405e-08 reg 3.081613e+06 train accuracy: 0.244041 val accuracy: 0.237000\n",
      "lr 1.410677e-08 reg 1.076725e+07 train accuracy: 0.244041 val accuracy: 0.233000\n",
      "lr 9.477743e-08 reg 2.163276e+06 train accuracy: 0.241082 val accuracy: 0.229000\n",
      "lr 2.081170e-08 reg 9.271321e+06 train accuracy: 0.202959 val accuracy: 0.222000\n",
      "lr 1.021271e-08 reg 2.037671e+05 train accuracy: 0.234898 val accuracy: 0.202000\n",
      "lr 5.213844e-08 reg 1.099206e+07 train accuracy: 0.157061 val accuracy: 0.165000\n",
      "lr 1.348602e-07 reg 5.293188e+06 train accuracy: 0.180510 val accuracy: 0.152000\n",
      "lr 1.605769e-08 reg 1.206140e+04 train accuracy: 0.116959 val accuracy: 0.130000\n",
      "lr 9.233279e-10 reg 4.419222e+05 train accuracy: 0.124204 val accuracy: 0.126000\n",
      "lr 1.067795e-09 reg 5.444495e+04 train accuracy: 0.114531 val accuracy: 0.125000\n",
      "lr 1.377715e-09 reg 1.979807e+05 train accuracy: 0.117714 val accuracy: 0.120000\n",
      "lr 9.872359e-10 reg 1.569267e+06 train accuracy: 0.119837 val accuracy: 0.118000\n",
      "lr 2.128520e-09 reg 3.028309e+04 train accuracy: 0.110469 val accuracy: 0.116000\n",
      "lr 1.478129e-08 reg 7.587397e+04 train accuracy: 0.104735 val accuracy: 0.115000\n",
      "lr 4.770381e-08 reg 6.908900e+03 train accuracy: 0.115469 val accuracy: 0.115000\n",
      "lr 5.575513e-09 reg 1.753272e+05 train accuracy: 0.124653 val accuracy: 0.108000\n",
      "lr 3.038872e-09 reg 2.942441e+04 train accuracy: 0.101571 val accuracy: 0.105000\n",
      "lr 3.583743e-09 reg 4.763747e+05 train accuracy: 0.118429 val accuracy: 0.104000\n",
      "lr 2.399255e-09 reg 5.243778e+05 train accuracy: 0.093184 val accuracy: 0.101000\n",
      "lr 6.018047e-09 reg 2.711564e+04 train accuracy: 0.112102 val accuracy: 0.101000\n",
      "lr 3.449256e-09 reg 7.898890e+04 train accuracy: 0.090673 val accuracy: 0.099000\n",
      "lr 2.116730e-09 reg 1.774667e+05 train accuracy: 0.100612 val accuracy: 0.098000\n",
      "lr 7.514587e-09 reg 1.006468e+05 train accuracy: 0.091694 val accuracy: 0.097000\n",
      "lr 1.294353e-08 reg 2.472126e+04 train accuracy: 0.098224 val accuracy: 0.097000\n",
      "lr 6.721072e-09 reg 8.545352e+03 train accuracy: 0.095776 val accuracy: 0.094000\n",
      "lr 2.285245e-08 reg 8.603339e+03 train accuracy: 0.101837 val accuracy: 0.093000\n",
      "lr 2.894816e-08 reg 2.170831e+04 train accuracy: 0.089510 val accuracy: 0.092000\n",
      "lr 7.748629e-10 reg 3.919120e+04 train accuracy: 0.090837 val accuracy: 0.091000\n",
      "lr 3.486153e-09 reg 1.001208e+04 train accuracy: 0.084898 val accuracy: 0.090000\n",
      "lr 9.891418e-10 reg 9.346445e+03 train accuracy: 0.090020 val accuracy: 0.088000\n",
      "lr 2.250948e-09 reg 6.679268e+04 train accuracy: 0.080143 val accuracy: 0.088000\n",
      "lr 1.477122e-09 reg 1.688465e+04 train accuracy: 0.080163 val accuracy: 0.087000\n",
      "lr 9.743330e-08 reg 1.419431e+07 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 3.572987e-09 reg 1.807532e+05 train accuracy: 0.103837 val accuracy: 0.078000\n",
      "best validation accuracy achieved during cross-validation: -1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/linear_svm.py:96: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss += reg * np.sum(W * W)\n",
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/linear_svm.py:96: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += reg * np.sum(W * W)\n",
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/linear_svm.py:122: RuntimeWarning: overflow encountered in multiply\n",
      "  dW += 2 * reg * W\n",
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/linear_classifier.py:69: RuntimeWarning: invalid value encountered in subtract\n",
      "  self.W -= learning_rate * grad\n",
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/linear_svm.py:115: RuntimeWarning: invalid value encountered in greater\n",
      "  coeff_mat[non_neg_margins > 0] = 1\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "##\n",
    "\n",
    "learning_rate_pows = np.linspace(-9, -7, 8)\n",
    "regularization_strength_pows = np.linspace(4, 7, 8)\n",
    "\n",
    "jitter_power = 0.4\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "\n",
    "def jitter(value, power, step):\n",
    "    return value + power * step / 2 * np.random.randn()\n",
    "\n",
    "def search():\n",
    "    learning_rate_pow_step = learning_rate_pows[1] - learning_rate_pows[0]\n",
    "    regularization_strength_pow_step = regularization_strength_pows[1] - regularization_strength_pows[0]\n",
    "    \n",
    "    best = None\n",
    "    best_val_acc = 0.0\n",
    "\n",
    "    total_iteration_num = len(learning_rate_pows) * len(regularization_strength_pows)\n",
    "    current_iteration = 0\n",
    "    \n",
    "    for learning_rate_pow in learning_rate_pows:\n",
    "        for regularization_strength_pow in regularization_strength_pows:\n",
    "            learning_rate = 10**jitter(\n",
    "                learning_rate_pow, jitter_power, learning_rate_pow_step)\n",
    "            regularization_strength = 10**jitter(\n",
    "                regularization_strength_pow, jitter_power, regularization_strength_pow_step)\n",
    "\n",
    "            print()\n",
    "            print(f'rate: {learning_rate}')\n",
    "            print(f'reg: {regularization_strength}')\n",
    "            print()\n",
    "            \n",
    "            svm = LinearSVM()\n",
    "            svm.train(X_train_feats, y_train,\n",
    "                      learning_rate=learning_rate,\n",
    "                      reg=regularization_strength,\n",
    "                      num_iters=1500,\n",
    "                      verbose=True,\n",
    "                     )\n",
    "            trained_accuracy = np.mean(y_train == svm.predict(X_train_feats))\n",
    "            val_accuracy = np.mean(y_val == svm.predict(X_val_feats))\n",
    "            results[(\n",
    "                learning_rate,\n",
    "                regularization_strength,\n",
    "            )] = (\n",
    "                trained_accuracy,\n",
    "                val_accuracy,\n",
    "            )\n",
    "            print('*' * filled + '-' * (80 - filled))\n",
    "            if best_val_acc < val_accuracy:\n",
    "                best_val_acc = val_accuracy\n",
    "                best = svm\n",
    "    return best_val, best\n",
    "\n",
    "%time best_val, best_svm = search()            \n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results, key=lambda x: -results[x][1]):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sn/KaiXi9Xst1hZqampKsxpDJxMsSnIneGytXtteBP6vqUgARmQl8GXgaeJxu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M1mIy8vj3PPPTcm4udyc3NxOp1B24mIqUhtABIzKHi5iJyuqkvDribOaN68OYMGDWLhwoW1Dj2l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\nL1cA/BZQE+XUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52ca7ec320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.428\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pDGQ9ZPE2DnKw6JuF3L3iSkHoSuXDtbHvFrH/jP39eVzwujKyb1J4NiXTA6Lj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o1cHzEf09GgwRMOJLa4XlkpyWTqdj1miymBNHBICP3n93rfFFoQ2PS3i6rhD6LO4tU4hGR892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b2VrWxlK1vZyla2sqe0zxWROjru2WTqdrOF119/AwCwf3iEV175IgDg8PAQXYLKvbBi+Tg8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\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L0gKTEY2Vvb07GEzrQqiVQwJhDerQ4nYc4wIXMT47OUF3hTQRq+kUYaBwI6R2dZe6uP0W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tAAY+J0QXSTZHSnNnNOpDK20MO2Eg+86GOTPn4xMkxLcd9o8RN827b3c6dr2HYYh0btbrbDJB\nujDv0efK7qnrxvuA4XvVlJ17hw/w/R8Y1eNkOkVRLt3XV41vueWgapv0SSltkCSlBGfMQniu635i\nb95Vo9Esy2wgxjm3DuZvvnMLzdjsD1cvXIAm3tu6hJAzaO9snI2zcTbOxtk4G2fjpxyfaUVKao7H\nfZMR/Z+//btoUiT8C1//eTx8aDKzN995Cx98SCqaOFrJmhmeHBnyddSK0eqarD3N55jnKdi4bhmz\ngCAvpkWeoaLM4dbtR3jn3XcBAN0ggCKY4tajB1Clyaj3/Qi7143yZfvc3tJ0b03yJwDsHJzHzS9+\nBQDw9pvvYPGWyT5dd2n4NZ1OMaY2BlIWCBsmg+pu9DA+NRWLKAwghLnHaVIhK7WFNRajBbKFeRat\nyMU2mXNK7SGl1ibCDaDo9X58++GSONvew4KZ7Pj83lVsXzAqpYqynU8bjDFUBZmziYblLKeLBZy6\nbYkjUFLlZjKbo0xMJlSOMxQzUiLqCj6R+fMih0oSuFRtch0Xvm8qjIo7KKncVC7mcCVluEyBMYKy\neI4ir7P/ZYYipWMrmqsqtU8bWjML1yipbSXmeDhFKzIVi+k0x5wqLBUvUdZfXwnk9DddKPi1qaMG\nHg5OIfbIoLMAQJVxl3O0903l6cpLr+LBI2Pw19u/Bi80v//oo9soSMTw8osvYItK7Wl6Cl6TKsv1\nyeanw759NoPBAAllnO+9/w48qgYOTgeISJHmOIlVwDaiABHBXlVVYUFZe1XmqCq5os5hUFQlY7JC\nTDCfUpWdy3EcL31stITWS6VbPVb7cq6W7z9teL4HSU1UHh89xa23jJKt1d5Ad8dAeFHkwaF79PwI\nwqbb1kYOQAWftgCpgNB3MZ+RYWsQW9UfOKDINFEqDWXNfRkqqtYWRQrlE6nV9fHo3iEAIJlPEVJV\nuMjWrSw6mFHro9PBAO5lguzTFAVVqb0wsF5O23GAg57xVfuZCxewoOVz5/AxHjwwEMeHj49wMkvA\nae8IPeDoxMyN3l4Td4hS8Pt/fgvjifnevZcP8O9eN/SCtMjwhcumunrjYAM++btxj4PTemX6ecnm\ntcGWhEPV3mYrwOaeeYcbvQ24BO9wxmxV8NL5PbDaD4trSJqLjpzix6//KR58YCgY271d6Nrba3Ab\nrdTQO84Fcwwzc+/K86wKrCokptQnRQYxrr5mqnQHV17CZGoWNWMZgEtr3R3XFbrUKxRBhAmptZ/2\nB0hT8/ezWR/3CZp8ePcBOLW7icMQmtfQGLPQXkCE8vr8dF0XxKXGYnKCEdESKiWQEkydJQuMRuZc\n2t7agiT4T+YFUhL/5KzCPCGD5eeouilwZER/ePOdpZdhVWmU5bIyvtp3c2kaKqywRyn1TPXRcz0E\ntBet9sCs///1d9VjtcVMWZZWAXjcH+Gttw3c2I5ibHWpoqvXW4ufaSClHIXNbRM0fPXVV+DQJve3\nv/4qJuOrAICfvPMO7jwwJoCj8VMk5EC+yCvMEvMwX7z+MnotcwgP+lOkUqKizWc86MPxzUJVEMhK\ns0COhlOcnJgFcmFnCzu0kR7sncMO9dLb3ti1+PjG1fNW2VQV6y/8OG6j3TWBTWtzjFOClAbjCYYj\n893NZhNPyRlaVRIZBVjtjQ3ktHDCIEQp602ZAUxYPUqltO0hxlAiJRw8FCFSZZ6Xq3J0iQeyUXlW\nSZWwLnhg4LxmfAFP75pA72/+/Jr2BzxA3cN5OmWYUr84JwAYqdvSkmNxWls3OFCV+f3wJEVJSkLX\nqewCGI6nRq1C95cXJRyPlGChj5wWrGYMkoJaWeZQJNGGFChJNluU1VJdphWKerE/RxkaK1JjraV1\nSBTCsQaIx4MpBic1hMHtM1GCWRPYIkvRImuLjXaAqBkC2ryfstRIK5pX2oWmd33jlZ+F3zFzsxFH\nOLlrDqjpZA5Rz8fZ2LrZny6m8FUdtC7WvsM//MM/sJBcmi7/3b17d63rvwsFh5Q2WjMLwfmuZ80/\nlV4xklQKDmNw6VDyHQ5GD0bLAuf2TNAebmwjo62nBLc98IRWULXVN2OfuCk+T2NmxjkK2iinkyl+\n9CMTSL362hfhURAfNBrYJp6R54Vw6/5xfGlYqqocBUGKqqoASJS5eS6tZmQ5fF4QWtjh+OgJUnqO\nslIABTZhEMMlZW46TVFMFvTsYPkpW432ejcogAXNu6OxxINHZj46WqFuxiAxBKOALghi+M26CbZj\nTY23GwG2rl8GAIyLBf7lD97Bj+8bruUim6LVMPsIRxu/800TfEznEs2OgULeH8wQcnMAf+XSOTSl\nebeP7/cRN8y9NloCnQatxWrJifn0waBp8fq+g01SvW5tbSAiF/qG71jT3yAIbKcKRyx71bkuw2Jm\n9t+iKPB08ARvvm8Sa9f3cW7L2M54cPALXzaBUfmzX8YhQa9lqVAQHHbrvQ8xoh6Src1tbJ0z+/1k\nNsFwaAoFJnH92bXusNFsI6dzSngMrkNKczdARcHf22++hXQyst9du4pUVYWcAp55VsBhdZcOChho\nDZVpiUlszpbmLEFJ+6KCxoL6aVZFhpTuazbzLNc0Xcxt/9s0XyBP6f09R3JaKo2jE5P83713iKru\njqFXDWjZM4FfvdblSvD0DAeuqhCGwTONha1l0QqcB+AT+aBSSVRFrVIUePjQxAcfdN6FeMEkqoIt\n0Frj/s6gvbNxNs7G2TgbZ+NsnI2fcny2ZHOtsLNlspj/4Z/8JlRpsrFuI0SHyutMfA57+yYjP+6f\n4vCRIQHe+ugeyhoaOz3CK5cMw37T6+HJcYmHx+a7slJDUi8g7kW4/9io+45OR5bQPi8qXG+bzGaT\nNXFASpTuVge8Jplxbnv6rHpxfNpohCECipDbzSYC8tF57/AJXr1psr5ep4VkarLY+STHMfm0jOcP\nkFK0/9LLl20bAC/souGWUNJkC1kxx17HqLzarRCDubnOqOGhZES0PBohJKZv0OxgSJ9JcxeyJE+Q\n/in+1i+aKsEXr+2sdX9FJmz1RSsfoWeuw3E0UJp7zRZLDxDX9TAbm2c6GWVQ3GR1caiRUQUlzwpw\nL4KkPj95mUIUtbJHwAuoIuS4yAnK0FLVvqvQFbPGi0WVW4IxY8r6gBT5+kRsMGXJ164n0G5RbyYu\nbDuf0XSBk755H6p0bXYmfI6cKmWdOMIBVWBDT+Fgp4lsSv4vs7k1rlQVQ6eGippb2GyZ7HhvN4BI\nTJbUf9yEJJXM9PgR5JapIDIeA5RdttZ1jwPwk5/8eKW3lGONKMuqQF77j2UZ5uTlJISAR5mgw7m9\n3/lsigV9hoPB8120agWoLKBL876ZNtUqANjodjAgsYSqGDhhaFwzgKC9VdWe1ktjQcbWz4LPdRuY\nUBVhPp/jhFrfzJMUbu2dozU8j9pEMG2rmcBKW5oyR0hwHDwHgAPXqXsoautDNUtmtu2VrEq4BPkJ\nh2E+H9HnOer8td8/Baq6ssyRkoij1+qsdX9acWiqRgwmcxyTutTj3MLbWvtwCQ4rHA+NrMYoU0zJ\n9LZUgOObz3R6TexutuE8MM+q192GSz5JDx4+wd6uqU6dO79h30/sO3hlwzyPFyKOaUZVwESCF6ba\n0QlDVLSfJflzKGi1hCbkIo5CbNBe3Wz6iMO6rViBMpvQfSXW6FdJiQVVEqezmZ1bjbCJ/skpZnPz\nvJwgwDFRTpji2OyYNXvj6mX8J182NI3NjfM4fGiq90+fjHC8MIjCzn4X4GbtPn36lq30CMYB/Bdr\n3aMfxQAZG7PSmA0DwPb2LjRRNd6QEm7dRi1wrVCHcQ6fKqr7nR5qenRR5FBa2zOsKksktGYHo4mt\n0DvCtVW7tEjtnE9mE2QE98syByfpussFaDqhUOurhNM0x5OnRtwwmc4sXcO0ePnLvRellHbvVlpb\nQ9JVEnoYRdZnEvjL0B5faXcjVtZ7XTWuygqq3k+0xBxmbz+8f9u2KNvqrrfffKaBFKS2DrI73TaC\nkEwiZWVlmpwL9ChICJwAg2FtypcjoHr17kaIydEhACBkBdpOhUe0Ac7LCmOSXx8f30EliT+iYfFQ\n7ghkifnM7u4GXnzJwIqhD3SaZhGBL83MViWZnzZ834fv1+6qEZpUpp9N5+AkqdcsBGPmM36ocPEi\nuTw/fILx1ByceSnhk7nfTusAab5AuTDlcy0Ta6rpOAxBRRvlYgJOeLMvfIwmZB0RRJgszGa4cz7G\n179m/t4vfuMVXL1oSpgcEutMh+moss0zHeHBc0l6r3Mw1wTDSTEHiA8hpcScTBZVpVGRkeFovoAk\nZIvxAEWuoFD/m8L2Acs5g1P30aoEVk3ms9R8QVkWSDNzKFSyssic0steU/maSijgWUzdLELanCoJ\nl7h1ybREVdal56VyjjHY5qzNjQY2yLQQKDEfncKnORV5LhRdaNwMMTk2G/Pp3SOEbXLX3rqMTtO8\ntzB0QYJXLAoGQUHo9Qv7WJCEd7+7JiQEUDl8uR7qBtd+4CJfmPt6eHSMBQUGmklw4vkwrS2XSBaF\n7fUVuAFcx7F8PKkAn5SjqtI47Zv5295LrRt/oSrwGjdQSxiVcW7L8RractbYc0iFfu61G3jvY2Nz\ncvj4CNpZyrzrQEqWhTUK5IxBk4KWQaPMzd7z4MGRbdQKKJSltNfmua4NBKFTtFtkUtloWmVxUVTW\nYd/xPJtocUdgOjP7nicVfAre58QF/bTBBEdCi2iULcBdn34vrHO77zfgUZLqBsJKrTxHYXuTzH99\njpKC/0Yc4WBnB43IBA2LorJQmVYSi9RMwq5qWUd3xrQNWHQc2ecRNAUOrpnP+IHA8Ng825Pp+msx\nih07J6LYR0hNoYPQtxy2bJbgeGz2zSzPkdFaX2QZiqI2Gi1tz9S40cRkOrPBZgsOksQEUou0wOFd\n02VgdNLH+LqBA2/eBDZ2zPvc3W/j41NjJuyHObLUwH+LRQ5Jz12srfcC/CBCTP1EIydAg3oSbm30\n8OCOsTlwGLdrQJbLd8I4B6dEoLezv7SN4YDrec+o3WoIjHGOU2puD6XBaipCkdu9s8hSLAj6V1pD\n8Bo+01jGHuuvxcl0hhP6m0Uprd0Eg4ZH72F13y3L0sJljPNnjDptn0DHgessm7avurSzlURMSml/\nvxpIMcagKZGpWIWMAvbJokJOfK7YWw+0O4P2zsbZOBtn42ycjbNxNn7K8dlWpDgA1J2qU2SUibor\nlvWMMVSU3fQfP8Tk1GTbNy7voUfW+QdbPSR9kwXMx2OUioExcyvjZIbDpyf051xskrlgkqW2KqGq\nHElNPOyEttNzUUmEDaM48V3PlmlXiWqfNhzHgaDM13cZNsmfhasKDapOaThojk12Pug/RkgeM1eu\nvoDjE/P78WSKTc/8/Y2NBtqqAVVQ5tFxkCbUtkFw8KCuAFRIiH06nCsox2TU18/t4RvfMMTHn/nZ\nm7h+zUBHritWeNV/ubz6SWM+lXDIcM8TgKg7Eqz0S4ybAn5oKlWzUQLfr0vwAkfH5v6SZI5GVFf/\njAFg3fNQMGV7LOZZak01wVb8pQBIqpCk2cKqOqRcGk1KpZCRMq0s1i9DCzBbZZktZphNTYnb9UII\nmmfpvLC9vlyHgZFxm6M5Aqo+NH2BFhlXhnEbWbFAQN/LHQFG0A93HYAqdR1HQufm3fbvVzgaGGi7\n026if2SeXS48MFLauJ7AhUumm/mA5tQ6I/ADWxFTTNveXVprDIfmex4d95d9IKscgTDvpBlG6NBa\nLNMYimDYwI8xSxOkJPzwBYcfePS9AnPyMsuSBEHXwKXGSJFUOHCWPcX0UsGjVojY4jlSv87OAZqn\n5CMkFSRVCWbTqW0/EzaaiDxzvY3AxTipBR3Swrj3HzxF/9S8HyEYIUT1hWgwuuatroM4MvvHxsY2\nJFWghdBotWuvmwBOQP3Ymg1cEWZ+PO6fYJqYym19bZ82BOPWtLDdbCCkdeY6Ljxq6TGbzAGQX5sT\noubye3EDMSnFSlUhm5tncLpI0O8nyFLyzVIVFFX7G40ALlWHR/2nGLEaRqpwm9750eVz+PzVC3RN\nEVIyWJvONdJkqYhcd3R7kYXquaMwpX07LxJUpfmi0ckIi4l5ZkWlkFO1Jge1fAGMCpEgxdlihqoq\n4dJ8rlKFoqrXgmOhwQ/vPcSUBAPv3v4IvS3qJ1jN0KzbMfEURWHWa5UnqGrjVrb+TTIusLtroPqt\nziY86pkZuB4e0r1wLhCSqKFEbvn6aZrZe7x99z5KqoiFnIGxpcpVKQ3QueSFPnzaUzdabbSpcto/\nHWBOxPMsz0x1H4aUrUiYwKoCUtb7+fr3qDTAae+upLImvgzL6SCEeKa6xKyP1NKEU1bVf2DUu9wn\nPM+D7y/butWw5qr5MGPMipygYf2ilJYgYAeZdECvHWLNc/EzDaQyLeHW5UDOwclpXOplQ+IsyzCb\nmMXy+P7HcOlA/Tu/9lUkpyZ4enQ0Rp+aAadpAcljBBSMzB4eYU6H536vC6du6Bp7WNS9g9IZPNds\neCenYzQaZEgWu1bCr/Unyyc/bWjNjNILQOBK/Mwrxk7hj//kz3H42ByKL1y7js6G4RqUZY7hyGDs\ncWcHUWyCi4cPn6JDZoweX6CzsWe4OACqJsCEKf/6UYi7hyZwPJl4EE3zHLY7F3D/0Mg5uTrBL3/j\nH5pncukAdTCr8CznhK2xwwnB4Xm183xpe2yBcUhmnm+rFcBxzd9Y8Ny4yQKYVamdxHGjhUrVDnca\nXDiYk/OxLAvUQDxjS4hVOI69xmSRWPn+ao+mYrVfk5KYE8yrnoPnJsCQEwdhNp6gItsBx/HhB+aa\n82xhlVHwYBsHu0yjVUO7nmNVYDs7u6iYxOSJmQOVkvAIioHglv/QacbwY/NupeOjHddmeaGFwh/d\nu4eSIJ3BfIrtc5cAADpb32kYbBU6A+qt4Oj4Cd5++x0AQKGYtShhnFn592avgxapBsusQI23ZmmJ\nssqRU4CbAeDcHNbM8zAnRdB0PELQNvfCuYOyhvYEB2jz1lpZ+EKqyvIlPLG+xcM8zW0fNIBB1Uoh\nqVD3u223XWy0CdZ0PRR0YIwnlVXGpukEI+p76TouKimt7QfnzEj8ADTCLrhDCjfh4/TUvI9WaxOa\n1LRZXqKoeStphust8/k0STAhxW4l19tvOGf1UkYqK8w4cayaATZI0cbkFCUlpmkJzGn9jKVETF0j\nYj9ERfyhRVngdD5DRfuu47jW/iWZz5GQCpkzgTAkuxI3wNMRcbWm97C/a/iWvd19nExI+ZVmcG1A\ntN7hBACNhguKDcAALGgOHR3PMUtIxZoBgjosKO6Dkbv8zoUL9vnkSYLxiVEiyiKHozQ0maomWW6D\nbM1hD/BZWiB9YCBOxiWcuxR87HZxcJ6U2d0WKtCBjdLaXOjnODMuX7qCjZ4JpBp+hKpuoOwKk2QB\nKKWC0PXzY+BEDUEhbTB96fJVVGTaK7MFSpXbBLNYVHC92rZGoSK+pW7EmCdmbiZpiln9e6WWUCBj\nNqhy9LLrRA2LrTM8z8UeBYtB4GMyqW1sOCpdr/kll4mvQPtlVT3DV673gqqqEHievU7P856xSbH/\nviyfgfbq4XouKpoDShfLxu2VxJwKEpUKsM74bFvEqCV/RXBhMeqyUpbXNBgMIHjdyVvCpY7MEUuR\nZ4Rjj0e2rcRkUWCU5SiUeZhVKVHmdfQs4QW0IbgaG3WrktDBzesXAQD7B5cQxyb4mAyOsdUw2X2e\nZUv5s1r/EJZVhenIBHn58Bhfvmn4V/l0jnffMz4gFy6cx4UXDFk+qyo8emxIeFs7MV688XkAwHd/\n8OfY3DGbd7PxMbiSiGJz/Yw70DThktzFYEybYxFj56px1n3vnW/ij/74twEAP/5+DJ+y4P/2n/wm\ntnYNB8cEJcsu4uuMTs+HH9RYvYYi7yKmY3BR2e+aT4mILV1MpiSlTjJsbZkAVjgR8rodiF7gdDAw\nZGOYhrhVVU/40hIrW602GJUklFI2kJJS2nYcRVHYQKqqKiwotXieqiJnzOL/SkrLTcizHC5l5wXT\n1iPK84Go5t8wFx0KMsIots1U4Xjo9jbgOLUz01BCqgAAIABJREFUu1w+cw34FGw6TFlOC4834Yfk\nbl8qXL9hNu8HB3v49p/8MQBg1J9gtGEOqzYF4esMR3CwunIH2CDj5OjEeslwL8Lpqcm2m4GPdttc\niysEstS8X60ry3tkrILjckwXRKgvS1P1BOA6HDM6BCejIXYuUGd34SKvyyRCWL6Gg2VCUlWlvVb9\nHJ5uxn9q+d9q5TvqP+m6HHluqlZZOrPebVUlIengr6rSVjYrp4CUyvIEOee2S4FULRtUKY2lP0/e\nhySybqfdWHKIXA9OPQtkBQerge2nDw1lq4pJVUJRd4es4dsqThF6KOkdlA5DanktBVr082iUgnoW\no9IS0yIDE3VlgFlPt6LIl67rKscWcXnOnzuPvW0TSHUjB7tdM8cXZYqqdqePItsYuPYTWmd0uw2k\n1LS8qjSq2vEkF2B1xQ8KnCplisM2p446O5CeCVSLxdzaOMzvvg9kY2in3t8FlhVGZUnpjHGk9fsJ\n2tjvmbNhkYzhTsw9XL+xBYcCykWYWCsR+RwWD+f296GozwFnvL58aKVqs3t4fmhFIGVRwquFDB5H\nQWvi3NVrll9ZZiXKMlkR/UQoycanSIe4/75xpR9NR1gkJrnLi2LJvWIMTs2FdRzrHsOqHCqjdfkc\nfEXf97G9ba5td3cP09pv66+wM1kNpATnAMUKnPMlWbyqkOfF8nNC2KCq1Wrh9PTU/pu6GLJYLOyZ\n4LmetS7hmi096jTHnAQTs3TNpGatT52Ns3E2zsbZOBtn42ycjb80PtOKFK8q1Ckidz3U9fXhbIHj\nocmqK1XhHEnG33I8DMYmiy0LhR4ZwH3ti+cxIRn8nSd9vP3xIW4/Nf9eOA58ggo2OzEapG4rswlu\nnDM9kc51GggKk3XvRxfw4U++BwBoNJoQ1y8BMDLUuhL1PLBQJSUGpLp5fO8BWl0Dz/3cFz9vIcvp\n4Ah+0/x++8J1DE9NmXMyGeDgvFHUte/cxLd/bMzvlApxIQWaLVLYOQLjpOZvPMCd26b8/DPf+M/g\nUpb1nT/9Nyhz873H+Qz/1z83zTdf/vxN/Pp//uvme7iD5yIswGRJda89z/OhKbvOFpmlL6VphTKn\nCo3fwuameW9pnIKzulzsoka2lE6Q+SV8bbKOhebIq6Vi0mLgvo+QzOriOMZsZu5vNpstm3OuKCzL\nsrTZi/grMp9PHJzZsgBX3KrQOq0YrZgaZFYJOJX0Q8HRrrlQTKBJ7ufdzW2AFGGD8Rg68uFvmLnd\njJtIyeU9TVIwkte7rpFjAwDzBHhurrsVN+HF5veXr57H+ORFc4GZRIP4aHG8vv1BtxngPsEWjVYH\nmmAerJT0Z+nCmko2z+8hDExlYr7IoGtVZVmirOEMruD4LnxFTZexfB9pnoMTtFCkKSS9LyacJdtI\nVeCqdr9WFlpDlSEk7kPgr79lffM738ec1LlBECAjHk+eZ5DEf5zPK1QEQzuiwoLmrdJ8ue6VspUt\nzhn0imRbCAFd24HoZeWTcVgX7iDyrbWHWFE8pmmKIXGRsjxFXtXVnvWrpzV3bDif44QqDknhoEHm\nuGXkIiX4Ag4gYN5tw3HA/Jrvp1ARRJ/NU6SKIaDGyo7jWAhvq9EAp5udj4fYoLl8bncTJf2N+WyE\nMc0NX1ZIiTPEGAMnuDF9jsbTSitU9RzKimUjaM9DQe9HQ4HTc3eqCuXEUCUevvUmoi2DPPidJkjw\nh8oRKAHIFRdta55e/zeMStHtmDNj85VfxS9/3TS9fvtbv4X7H34TANDjPVw7b5rbF2Vu96Hn6gnJ\nmLUjUFC2Ogym4dOa29k7jykpXafjU2RU3WWC2+cznIwhiAemFwtksyGcujrX6iIgqoiWIdrbBn4t\nJ0O7LnzPtxxQwzcyD0xpjbKuBHNpoVm+PkJrYDfq/7i/v4/bt+/QrTPwlfewqjJcwnSuhfyrqnrG\nlLcsS4tMhFFkOVKrXKkgCOy/abfbtmGzVAoxzW1HM7C6ygwH08Tc72D6/0P7gyov7cVKV1t8MkkS\n9KkMFzgag4GBxu49fIKFJjmj4+Py5VfMF3EHKcn8dy4ssLWzD+fHplQ5S+6gSTyWNEshaKPpRgKa\nmrFun+vhIpUZg8UUm0TW3jjfw6wyn2FSWgt89RxwgpQKs6k5fB4+OkZEgeDmZIYeydMdJnDrLXO9\nF156FXuXXgAA3H3vDYAO55uvfBUfPTSwyr/95o/x0pUniGlzm2cFHlKw5qoUX/icacC603FRZObf\n+K4DgdpuQaHfN8/0/uGhPSA4f/7XPxxUOLhgFqSqXOv4nJVD5PS9vtcG2TohTZYT0fPEksQqM8QN\nc33bmxF2WvtIyUrp7pMEi6H5D8ZgYdz5bA4maglzAy3iQgwGg2UrmNVmlyuOuIG/PrdGc6CkG+CK\nISLO02YngkdzQSsXvOZFuQ48j0rEqoSkkncOhin5Q4nFFG4zRuCYoEcVHqQgf6oGA3w6aCMfnErM\nUipwj8ir/tJnKBYCr33JQMBQHtKav6Nma9/jw9vv4zY1Af/Sz34VBZEuAyHQoObas8XQchOm0ynS\njKw8sgSOs/RlmVBz06IooMQSrnJcB4oILpWG5Xt4wrEbj1A5YmqNwrm2nkehGyCMakI6Q0bz7Hna\nUtx6/0O7oRoiq/m9Vtr6ik2nU5QUuEpVYEbzKAp95OSBZTrjEGlXcmhoSDq8jFfPsuVE3ZjZ91vY\n2THzM/B924KHCYmEkqDDYR8PyNNCCIGMnhVfE4bWmoERGXY0T3BCbZ72YmE5LYxpZGQrwhRHbYel\nXQ/10nTaEZpktTI8PIKEj4gSBuEJ1Hr3TJbgBAc6fmhbOqVlAZcShpxxjAjebAkHOiRrljJDRa1h\nimD9EzhJC8zJ70pJDUHBXyA4iorg1lxBE8zHFYdX814mQ6SJSV7T0LOQKltk1LaHDmr9LDtU2/ZQ\nFRoEuXtRhG99/1vmGT1+H4I8h7yqwLnY0BUgPDsvajrCOsPwfmhP1toGiNAaDnECe5u7JMwAhBti\n2DdrXla5TVYmJ08RNMgpP08hZ2MLE6dVijQnaFAqXLtsKCeiuoAjOkvKsrJEe8a53UezLENaUOJT\nOdZzrLXdXPsegaWvU6/Xs5BakeWr1uafyEdmnEEWy8R6lTgOthr4KrveHcexlB3GmD1DOp0OOmSv\n9OjRY+shp1auz9EBEmp8f/h4hm+sc2/rPYKzcTbOxtk4G2fjbJyNs/Efjs+2abGR5wEAqkpZ+WkU\neMhr8qpQiCgKd1wH1cJEyE+O+7h2xZRpmetAcJNxNSTHS1euWFJelZeoZR6DyRhu00Scl69fh1sQ\nof3oKTaoatXcj3Bpm/osNQIUSa0mWLL1y+cwcyyzAl5sMvr79++DUwWDv/w5CLeWzPqIyTl12j82\nMCeAZnMLU1Lw9Xav4yvf+FUAwL/6rX+Kb77xthViSiwj4P/061/Ci1cvAQDSySn8TROF/+Pf/O/x\nz/7X/xEAcO/wXXQJYrx67dozbrDPOxzhWyPKNKmgibXrCB95Tsab5QRzMgNNZgqcxANeYGA/ANCV\nwM6myeR2uw2o0kNKmWrB2pilpBRK5sgokyqqwrrpCjJjAwxaXBtvcjBoUUvlGXrbdUPojbXvUUtp\nIS3GYGE7Hwq8luEzDla75TouBMFrDlMQlG0Npom99ijy4IUtBIG5jnZnF00ih7teCIfgwJp0Dhh3\nbKcyGfU4nVrn6tE0A6M50+nuIaPus/PRZO17PHl4F9ttc80hV5iMTEW4zBa2AazjOrYaO5vN8OSp\nIaUKzm22p5TEaF4TzzUYJFRVK+y4fUeh64LXxFnHRRTUVh4d9HbMPGAoUBFBOw4baFH3gYcPH+Iv\nPjQGiAOqrK4zpFRI09q2QNiMs5KwDdDTrMTXvmrcq+fJFHf+4g0AwM72lu1VHgQu2h1zv5xzIgTT\nCmTMVjDarYatuOfpAjduGMhnc2MTP/jxDwEA/eEIi9ptnQGDwsyzWDjwaRNbTycEggsJJlyUGJ2a\nvaPVFMgJ5otaTWhGLthKgNcEeo/ZCjdnwIj2vcPjPtIK4KR2NH3uaA4kc3CqvnWbTUS0z2kIImwD\nne42NL1zL2wgJJfuvFhguiB7h+eAvaJGG5oq52VZ2AopuEZA7PVSMFREE9GM2958DBqiVu0tCutA\n70sOpgGmP5nWUAsamHCxOD00P7/7O6hbrmazAQ62TVUjjNuYU7/R2v0beD67FaWWwgZdAZmunbdL\nFFQZj5ttW/lsdjYhSaU47D8BFQ/xyuWLGNZzSzDTWLy29nE9zOgdb3W72Ke91+EOOl2jpsvK3PaE\nXIXPtNbICf6bnp5gNjN/w+XrnyOGPG5+7nQ6aJHwa5Dl9p0KIeyz54zXgksouUQZPG9pS8Q4AwPD\nLqlEX33tC2gTSnF6empJ5UVRWAsbzjkuXDD2HOPxxKo1hXBs5wahPYzG5vO375+udX+fbdNitYTJ\npNQWYmrGEa5evgQAGA2eYIv8Or74hVfx7ddNo1HP8wDPHDIKeqm68Tg4c/DSFdN+xXNd2439mz98\n26poZosCL543EybSCW4fGSuFHz+4hy5JzL/x87+AmFRSKob1g9L5+pyFNJuj0TaTxI0aeHr3EADg\nB3dwSv48mgW4eNEEhb1tFwmpWI77Q2zToRJ6wOc/Z+CbH3zrAI8eTaHq0jSM6gowjtGHt80hM6ue\n4srnzTb89//Lf1R3DsFv/4t/ilc/Z+DDz73yKlY9cLDy0zr2B71tB1FMvlWVQlVQw1avg9AzB19V\nZJABQRzChXCJv+BVCAKzCB3uokMu0EJpCOEgo42x0fTRoQVRlhUSgkuSbIpC1gfj0jNEqQIa5Izv\nuLXAA41GiBsvm+ccReu0njTD4xwe8XnKtEBA88BVChy12gwAQVWpUijoAI1bbThULq40szySVqsB\n5XLMU+Ngnh4dGwwRQBDG8JuGP6KgqY2IadzZonuMmm0c7Buumd7o4JiCpsf9GRTNzzBar7UIALx0\n5SJKShbyPEe/b5SV+WKBmP7mZruNMbnSl1mK/ik1xXWEnbNCcJT1YgRDIDjicOlt1CUfN1kUdlOH\ncNCktjvtjo9zFPxnicJwSopBBzh5bILIb3/zW7j1wcfmM7XByxpDOMJuugBsc2StFSZj891xGKBD\nPjqclRgNjII29F0IgrRCz0Ezrl2hTRubeq0wrED/urLclYLl0JIOUy1sMjYaDVGR9MzzfNsUOmy3\nwGu4cN17ZMpaE6SLHIMTA+21u6ZVD2Cg0PqQDnzHBhN5kYNzk1i6no9H9827vX3nKXJ40KRODQLX\n3qvreHCcGl7ywMjl3w1i2zJHyQrFnDD6rcj6KQnPQduhNV2sH/Ab5SXBdo5jD1pZwSq5mCdRVXUT\n3uW74Yw94+dUi8wM9K5XDr9n970lvMSgS2qX9PQeFK2XKGrg6tWXzfMJYzw+PrT3XqvkFgQRrzMm\nkwmK2sSoqJBrMz+qUiInSG2jt4GI1Lyz+QxRi9ZM2UZJMOLJcR9lXVFggNdsoY5eFvMZXKIGnOt1\nsLNlFIwaQED8yKJaKpSBJUeYc2FVzButBsbETy7S9dtuCc6tkrHRjNEj3uxoOLTqXKaX/DANBsZq\nYqIDp3ZWFxqi9t8THBolopZ5Ll/5ha9YZfv3/vy70GQ1I5VEQc2f+4O+Pdc5X6KKnDvwHbMnyVyh\nSGnfw3qebmfQ3tk4G2fjbJyNs3E2zsZPOT7zipQld0ltVTlaKxzsm2rRwU4XARnL+VHTsuq3N9q2\nIShjjs3mmcPAGeAT8fHKwR4av/w1AIAb+viDb5pS/aQAnoyob9LFHrg2Uexbb72HN48N6dYLe3iN\nqkCx4yIi+GJVAfBpYzKdIaGIffv8Fdy9fQgAuHv3Afa3TbkwiGJMYyJNei4cgniyUkLUvitKWThu\nb/cAjx7desZgpqDo/t2P70DuGX8hLSPc48ZM8U//7e9jd9eQ83/jN/8n7G6ZZxpFIWpCu3GfMz+y\nlf/964ZwU9vvrrPRwXxGhMSyQINUOX4Yo9U8Z372IwstDEdP0dkwmUgURPBZ7Ua+gO97KMlQ0g2A\nbs9kIKOptr0Lk6zAhCBDJbkt6fY2W2ht0nNzl5+PGgF6e0SInT4H+bMoICnDFQ5HMzbv3xMaVW0O\n6gr4DZPBtOIGmrXHUhAgpQxRK4UOwXytbhfC86Epy5tOZjgdmgoCA7M+OFVV2qywqkpbAWhvtLFL\n7/nC/kV0uyajBGcYU2PTuL0uKGQqTzOqVPjNpQLKcQXi2h+r10FI2d/T4xPbv42XFXL62fM8SJsE\nM3Q2O+iSM3Oz0cD580b1lCSJhV9bnTZALvgnR/ex1TGfH/dHGJ4Y6G6jp3DrI7Mu3//4LgakcBTP\nkfv5vm8hCc65fXd5kcMvCYYuYvyE4LwnTx9gQTBNMp/ApWd/OhphMCBogDEopWwVSitt/ak20wiC\nm/nZbnbwg9e/AwAImz3b/80v5mCUHUulrAptkeVoEtzRIyHMpw3GlvNRFsBoaK7Rc89hq2fWRpal\nkEQ8L3MFTVWJtJBIqZLoFQqDATmGpyUyKGiq3lVVYX2NuND2viulkVjY1EGrSb5GWqMiDEwjh0W7\nlF42s1brHztMMyuK4WCoLb1dP7RO31FcYUQO9vkit4VPrWAbJXMN60WmmPnvuj+1+iu3PQ1NYqdS\ne7bas7vRxYsvXaOPpJjODPyT5hlyqu5kz9EkfbFYoBbN6qJEwerG7FhpLL7sEpAsgDAye1JHdTGf\nmjl76733rUrP8z0cM2bnR1WWttLGpMSVa4Zs7oWBNb32o9CKF5RSFnLTWltlXeVxSBLA5P7698i1\ntt0qGo0IW9tm/7rz8cfPwIiq9u/TS9haOMy6oq+abkqpIJlGf2iq6cPJKbYPzH6ze+4AH3z4ET3H\nCh6t5clkgqPHZFzb68Gh/a0qFdwaCRA5rl8wVWpRLakWf934bFvEgFlreDCJRUqbi+9CVcS1YUZe\nDADtRoSvfOkLAICAldC1Glkv1VgSCppr25DSUy42O2bB/srXvwzFzIR7572PrYFbp+Xj6/S9e7uX\n8Id/Zja83/mjb+MRHbg//8oNvPSSkZh/Unfqv2r0ByOcnJhNWjo+tgjCO31wH8OxmfBdofH4gYHj\nmKrgd0zQIViAh4/NpLgYnIdHEYsXRJ/wJM04Gk8QUdlzL9pE/6FpuHnrB2/g4EXiM+y2sEutagRK\nTEe1S3NzGUgxhoA4D3/dkJJjNjGH2lZvC35Ub7gjMFroTPpo0DUryZASDNRoNdGkptAMAoJUPK4K\nkCwmkBTgRS0XG5V5b3cPK+TEI9nb37LXMRrOLTfhwqVdtHcooFPTFdVehVzTgYn1VXt5mtgg3w8i\nC5PIKkFAQdXm1gZc2sjdILbKmf5wAEUbUCOKMZuZv99qRajKBpgi08Q0AQep9pS2XdeZ9qxqS+UZ\npqTqGjy9jYcfE0/o6jVcuPICXd8eYuIBBp31A/7j/gAZmU+6hba2JJWUKOtGnkrZli3CcWwwIKDt\ngSarpXEtFxzgLtqb5hBvNhqIu8TFCJt2I93qNvHokbFe6G20EYYGkkySUwxn9L2+wuETMuDNNbzA\nzE3/OXrEmL1/2fqmNv8bjU4xOu7TdTXw+KlZM8PhGC7RB05Oji3PZT5LbHsbITi962dh8frZ1Z0C\nuBAYDk1w4mSV7VJQpQmGBKMWZWmVp3mR45iUWEG43lxlDBYWkRJIiB+jIMA9ar5cLhtqCyFsOyql\nuTVE1mxprpjLHJJ5FlbJFjlqjpTWEh5Bg6HgmNG7cBhHIyIIV1UoyMXdjQQC4v5VeQFd+0Q8x+Dc\nAQjqYhDwqAm2VMpybja2NrGxafaGZDrHgt7VfJagqFWjVWUVpOvb2WjbeswRITbIaPeXvvJLeOX6\n5wAAp/0HNsBK8rlV66XPAe0xMAsxMc7h1K1UVImSlKP5itGwlKU9k1qtFopa/h8E9p06woHrust2\nKAD6A6Po3tzeQUzdPCQ0XG/ZisnaQLAVg0oYTihg2oKxeg5VK5n9p92jVrZZN4SDnW2zL7ieiyJd\nqvD0ElW1F+P4Gp5vnnEYLs0187yCyz0kZP78vW9/F5Fv5uH21ja6TfNzNhzAJ+oFq6TlUAsNQNV2\nPAXKwqzLg80IX7xp3m81XC9YPIP2zsbZOBtn42ycjbNxNn7K8ZlWpDQ08pKIljDEcACotIamUqh2\nHVte5ExaaEOL0BLHGbCsbKFWX1AWDw1NWWEndvFrXzGkQFcWePOWIawORxkkNVq9vL+Ff/j3jDru\nT777Dt5913xmK/Zx8YIpE/LnqEiNxmNkZOXPPQdb50xbgSKZQVBZfZzkKAn+y9O7UI7JFLYv/wwY\ntds4PX0M0TDlYz9ognEXjHQMq+1OpAIOyXdrEmcIfTKpZBqPJ8b0rLcZocFM0+LJsIdkWmeoM9vq\nAgw4f+3ap94fUz2rHlRlAyI2763dAhhljovJAjm1jlHSs82FG80GPL/2BhKQVPlIkwKVFnCjWtVY\noM6CW62W9fPZ3Wtje8dknu+8dRszKmlPxiFaOyZL6m7EtjVHupBwqXSrnsM8TkChQe02Wu02HLcm\nzPrYOzAKzzCOkZOiJk0Sm4lWUlkDT1kVGA/NXHCERp7NUcoazqxs+xRoQNV+aUzY9hzKLVBMzLse\nHidQmhSlEFBUEdrZq8AjUgzG6xPquetjTsoUUWrMyNsoz0vkVImZLRb296W07YShNLMKpdUGQ0Up\nMZwmuECNeMP2JjQROJnvICACPxfAbG6e1/XrN3HUNxnleCZROiaLPJ4DpaAqVNyBS3CnK57HQJbZ\nagxjwlat8yxDRe+3s7OBkpSVV669hMHJIQBgMh5jQgraNEttpUnKkrzKKMMlqA8wsJ9L1AWtFTZ6\npgoVxG3oeg2cOw+PYKiqKKyfThAEmJP6sUjXhaGV9UaSEphQ497RJMHGrnmHjbYDn/bZMAjRtaaM\nHFOqYA0nc6TWM6ugnoc0B58hIGsop/aFksipIpFLhoRI0RoL7JwjA94it9BJ6AUWSiueA/ZqNNoQ\npCDMZYVFRgreZGHnI3cdeCs9FQvyGGSLDIzOCcEFNKsNPNe1IWa2IhsEAn/ja4Yy8mu/+KuIqWrI\nSoY5PcdpukBJquKsWN97UCll4UsmhD2VmVaYkjAhSWYrzdgr27808H07X2RRwasFODB+cBukVvZ9\nH979+wCAgwuXrFBAyRKcfmZMW6K+1tpWwIClt5YsSysucZ7Dh1DwZY9F7nrYIWiv0WjgKbUQcxzH\nsr8dx7FedWD/b3tfsiTZkV133P35m2LMrBmNoZooDATU3Wg2yTZKpImkZCZyzaW+TH+hDWXakDSa\nyZpsI0VJ1mwRQBeAQg05VGVmzG92dy38vvsiqQYRlQsYF34WVVFZGRFvcPd3/d57zmmYoNE0w51r\n2xZCaCgSeX786WNU2/8KAHjnnYdMxBAqhYEfNyLKoVP/rNDJBJ3z7zVmhTT38/Lj9+7irbm/JuvN\nv0ZBTtNxOtZ30vvX0jhmJAih2LsKzg2Dx4FZP5EYxMK6roNzjmvDNtKA8INMSoG7R/6C/Mkf/Tt+\nzyQRyKn3CgJ464G/qf/5z/4Un33uB5tUDQylopU4nOb59bNn7FC+3a65D+ZkscUk84P8aDTjwHG3\nqWHgAymhn+LeQx94rRYLqNQzvOI0xYe/+RN88fj/AADapsTQ1STQ0iJ/XqwRkXP888UpX9Pf/72f\n4t4dX1I7OTlHpHrWg2K6trPuoEAqVkeDeGSkIGmhWbzasNq1KSVMtKRjn6Ck0pzSAmnqyzg6SblP\nxrUWSnYoNq/oOxSbIcc6wzF5et26k2J27O/h/QdzLBekCL8s8Gbrg4hRNIIg+YzOSeT0YI6yw9PQ\nztbIEv9543EOZ3157fadY+TUF9W0DpstlQ3ajoN/LSRkbwTtAE3p8aZucXZ2hoQYbaPxmP2ypJTQ\n6FlhCo5ZShKbhb+fRVszYy2/eIn5jOjX4yVAi8v06HCBvJOXF3BUtrCyQkXBZ2st1qQGvt5uYem8\n8vEINZk3N1UF009kIbicoESMq/UG28pfl/nt+2ho42SkgNB9v9UzaJ3Rd7T4h//tmblFWWNFwnvr\nssYxMf4mR7fw5WO/wdGvkUN3zvfv+MOUvHGbTCZ4l7wuP/roE/z85760/+If/h4ffuBL8Zv1lnuf\nvDAiPaCcgRAYPMIcWLBSyIgf+lJKjGisfPnlY3yPXBXe/eAHqOjh/td//T+Y0p7GEd5528/9qjqs\nLGStG/pehEBJm7OT56+QUQl6Mk7QV0MnE4G22/F7tyQtc3W1Rdv1hq0tusby2GyalntT8zxH05ex\nqgY6pfVxW1KdBBjNDFYFmcjWGklG46oroehxU7yGqKpUESYTP9YnSvJxbrdbrEhsc1us0FLgEkcp\nBD2Au72+nK5tuFy/x+OjP8U/K9T2pSbAkhSB1gLvP/LM8Pt37jD1v2sFCmpw2pYF1tQaUtWHlzGF\nHDYmkZaQFKwKZ7nkZ0zHwbuOItho8BPt13l1R0DTRtXqGLHWcDTmrY5w703fQjKdzVkNHUJ5/QsA\nkaI+NPTf2YtgOvT6QpFMYSmQUq8RSMVKeq9d+HViPvVr1WSc44zKykI4dqBQSjD73hrBTGZh1HBc\nraQAjfxPyxJPvvDrxJPHnyOnUuz7P/gxjmntOjl5gdsk/fDRxx9jQ+X3rz77K/wWeZm+//AIoM3d\nuDpsBx5KewEBAQEBAQEBN8R3m5HqzPUOfbuXOtxzZufXQjIDA27QnRKRu/Y+n173/46imPVirDVw\ntKOYjxP8yR964b2m3LL9BFTENidSGPzgfS/WVaFB2/rvKQ9OtQNffPk1+q749WqFzdbv7jdVixcv\nvUbO3ekIdyY+vajqBimVK7YvnyKmLYhMZ1DyBACQoMXd4yM8pR1Js5dc2deUk04wq0YJifceed++\nH33yCYraH9OLk7OBGagGr7226fBvDzj7qk/iAAAX+ElEQVS/0TjDmLIUcdbignwCXz6pufQ4SWdo\naVfYmgo26kX0JCravUVxzullpTOYdoN+61w3FiTHAwHN7Jwsl4D0WZnZUcZsys26QrGi3c6dHHcm\nnvWUixoRZQiS8eF+icYYtsiQUkDTueSjMRSVrYplxRlVaywU+6MPDdpCCiS0Q0zTBEJoOFKl1FEC\nrVP+P9mXs1UCQ+KC20pCJv5C3HrwBrrK76Q6W2BJXlCj1iGlhOnm6nCxytYKTMi7bzybIx357M+v\nvvoKJc0HJ8DNqjqOUdK9sxBwPaNGSM5qWmthjMPFpR/nRVlxtkoIoCQG3XK1xj1qSP/lp7/C1099\n5nW1XeKE2GNORpxBqootXC8k+Rr9ylVVcuNunufMiptO59ht/bm8utjh4tJf4//+5/8NL3/qma46\nUjg99c3f7733CEINhA+vZdQ3lQs4moRx5FhbJ9Uxnj338/cv/uIv8Yf/4T8CAO6/9R6U9NnOLMlR\noNdy0qyFlKUHsi+HjgZEWqGl8svL8xXmRC7ZFSmXnXW8wnTqM+RKKTSU/aurDo3pRXMV1q/WKLYD\nM2pohAYSahhu25qJFFoJVMTg2jVbXK79eCibEucXQyNxTlle14wOOz8AF+sFcmpuzrIRRpRlGE0m\nSDb+3p6/PEdR+TLsZfEKvWVha1v0/xDCDWkD67xOUU9EwF6biHA8f601SIk089s/+m18+P6HAHy2\nUdHYnE7nuHvHZxJ3dYFdQcKVvZbWAdgvo0kI1hzzGlq9FdP1lo6+faCzFrq3QYo1RqSJluUTWOe4\n7KyUxIgyVzKKOJMZ7YkpCeCa792+1ZbrWfJWoWvt8N6DT9IgJRstaw1yKo3OJjkiYm/nccQWUQ4W\nUW9tJCQzLtF1fBv98Vpoqmy05Zo9MfM8xcO3HwIA/uDf/wgZMfCrokA+9q+nsznqta8GvTl7gvsT\nnwnWDVAsyLu1+FfI2jPG8A3cNx+81mcgJTpK30IITvXVdccTOo4lGw9KSQaiPaVWWO4fiXUCQ5c9\ngsDRlAKI2YgpwcCgmgoDZk91MJyetd3hl+nLL59gRkFSXZesYmyt4wfO6WqLq52/abMsxZQetrey\nDuuVfxhOkWK78gvxWFuMjibIf8eHOk9ePMfTE8962lVbDMykQajzg/ce4cef/BYAoNi1OD/zn5sk\n8V7fiGR2Tj85vg0qbTDOfJ/QZneB7ZoMLzHmktY4PQIoMFhv1kgpKOkKi01BTCZoXgA22wrrbYO6\n9se1vFpAwafzVSRYZVlBoyLfLSkl7j/wgcWvPnuGkh6MsZxAWQoQ0gmzsHbt4UrDKo5h6Zo2bYnZ\nMdGsxxMuG1d1g6LohfPWiCkYTuIIug8I6xIpKTsrJZCkGSwFm0kScw+AjmM2N470CF1JfS+uhaQS\n2OzWFLOx7z0RwiGlIOjo3n1EZNK9vHx88DnqJOcg6Wg+Q0EBTNN1/JBQEGhoUa/aDk3TzxnJEbyD\nhKAUv7MdhBBoif7eNjUS6ouycMxmPD6+DUWO1c9PnmBb+2DCyJrLqPPpbewomjamwpT8sZrq8Pso\npeSyx9HRHJMJBTBpir//278BAPz8736B7a7/HodLYjb95oeP0FGg8fDhA9ymng5rDZU0aL1Sjvtb\nLl9d4MlXvt8jTVP87Gd/CwA4Oz3Hz//GSyx89vgMF+f+d4rtyiuPwgfjmvpeRvlhizcc6y16bzRa\nzhcXGywv/XWMtiXaPe+2zYrK7GroUYSTMORPF8cKo3HGzFwphy9pisIzNgFEOmbxw9t3b6EsvPjh\n4vIcIvE/r6oOk3Ev2Jsjp2BLvcZj5+XqAmNDwWlXIaE2gSzNEWf0/XcEuwxcXr5CWfn7KRMHS2O5\nLho0Zc/+o1IRqzEa7r+1FuxpN57N8VNac//TH/8pjmd+DLStRduSSHBdoXdrn07u4PYtH0AV9eHC\nsU3dwILmnFQw6N0TBBQHUu5aL15f6lLScW9pFGve4EgpIBwQkwNCHMfc/uI3dXTu1nIJW0qJXuti\nP5CCEMxelEbsec8e3i6h5GD+HkUKQvln5EcfvIvTrz4DAJRlAUHrtFIKab/uS8MtEkKIPdZeg9ZK\naNrc2q7isfro0bv4vd/1rPu8+wrVqb8vWsdQzs+vttaQxo/zN44sEnLsaNcSdkevcVhbTyjtBQQE\nBAQEBATcEN95Riraiyw5nSkkpzOllLzDg7NoqdHRGIOM/Lm6th2a0PvGM9JckmJ4LaRCQ93+UaQQ\nRVTG6rpeBgdJLBFRuroqDZwbmizLHTlei8ObI89Oz2G7Yzomi2LXCwIOTWtOSG44rbY7XNEueyss\nbk8o8pcbEFkM9+98H//46WNsyEE+yRKMiHW0IxFLwDd/v0UswTfffIiryyUdh+CG0zjW1+5B3zDo\nvsF36p9jsbgAOp8dWC5rkLYg8izm3VOaxezFllSanejrXYUxifLV5Q515W/CrqhRFh1envtzqaoa\nR0d0Tsqx2CpMxA3Ps+kc0Ts+w/Ds2Qtmm5S7BjU1T965fcwMjdXF4bYUUZqh6UsCdYc48cecpiO0\nhsQ5kxJ2c0XvsOiJTdYCio5XQMAQS6/rGozjDKCdndaamTej0Qig91gXc5k6S1t0uT/+yeQIt2/7\nexvHMXLKemZjjYQyWytxfvA5Sh37hn94McgLarqsGsNigtYBhk7M7mc/xLD/cs5dn7sCqEjXpq5r\njCmNLmCQE9liPMvwgvTSOlhMj/pzyZFmfkwczY/w4sRntmbzMRLadb88P1yfZz6f4/jIZ/EmkzFS\nOt800YPIbnYb6cT/zqvzr7G88uPk3r030XVUnq4tQOXWtmlJ74wyi6lmmxfnHBIisWRZjjXpxiVa\nYzr12Qwd5Zz1ttYwo04KcEmJ2UrfCseWKbAdl1raqkO58VmZeTJnFmZd16ipwVzt+aQlSYKU1lY9\nTuCs5myVgoOg+RtLOYy7o7v43tu+dWA+VlicfgoA2L28RKX9fdusctTklziZAJgcKvs7YLVZMtM1\nrSto0pGKopgZakopRKmfS/lsyoyEznSwCZWddQTT8wOqDmjdwMSE5RL8fHYX33/oMxk//uSneO/7\n7wEA7k6P4FpqwWgKPqZNU2BD5IyyrCAoO5z9Gu2/b0LXdRCqX4cdXyBrzV6Z0Q4sOusgexadsxhx\n60OGgkrLnTGQe9mbNE25ZBzrmH8ulYJgYewh6/X/gVtvHLfOvBZ/VgxaWc52LH75bz54F6n1LTfn\nL5+gLPuMqUZG5b889aQfwOvZ9c+T1WqJzc4ApMdX7CyXBn/yo/s4Hvv7IssLTPqCldIQJcUMhYEl\nLcM0llAgC6NiBEXi0i46LET6TgMpKRUvxnCA7Xp6ouT+p6auOAWpVcQXP9IaFS0IOhIDq6SqUFU1\n90JESqFuBh+wPnVuLPYYLhFT8p2IUJBgZFl1YBmFzvGk6A4WcPNCbOe92J+KUNDAqKuCP1thSIo6\nIWDpof3q1QKX5Gf29PwU9275xffk1RV+8fgzLBdX/D29Z5YUg2LvfDzFfOqDuGrXsACpVpLLLXEc\nDywrqdAnJcWByclmq/Bq48sfFhXGVIYQdkhDJ1mKCTGuxmLCZdSyLLnUYqzA5cIP9O12g6qqUVDZ\nTuuEDWNH4xwjKmMtFqeISJjNGYeMau5v3LvPLLb799+AUn1KWDMDKnqNWR8lCTp6UApY1NQz1BqH\nhBbsyVGG1lKfm4th2p7RVqAhKrhONHqDL+u8PWZvGK310PuVpCms7vtiNDcCJZFDTQyeySjHjIRm\nVeypzQCgtERC4ovq7iBY+m0wkGhofKxXO2xpDjSd495AISXTnoXYc2IUuMZzYsNVJ6CEwG7rx/zz\nF2eYzXxEnOkYb1EpdnFxjpKuUTLK4YT//TR1GE9junYGIGPy6XiELPfz+OLl4U1S9+89wKzvGUkT\n7u+JdYSYPDX19DYMPzwkLkmaYFdU3C+yXm9wh9aqqvTjNB/1C7tCQg/r3a5l0ccXz07w/IXv/Zrk\nY4xJHHB6fB8F9RadPf+c564E2IsM3/Qw+xfg1afpNYANBVK37t0aGimd4oXHt1n4113XYbXqJQkU\ntE6R0gYk1Rp3j6lUV6ygIvr5ZIKczLyXq3N8eeLXvF3tIJWfLxNoNMSWrRsLQUKZrju8PIuuQ0Ey\nJ9V2x5vASMfMKG07wyrcVVVf6zFSNDfyLOf1wtYt0Bher1Sa4Y0Hnq35w49/Bw/f9ozOO7fexIje\ng67BZtUzHlt0JPWyaXZYbclYfPESixWZYZeH90hZZ7EfOnMwY633mIUPhHZUgrbO8lgRUlzreZXk\nCuKshdjrheq6jiWDRCwH82eAr6k3Db7ef9z/3btZiK7j665eo0dK6phXjKbccQvKNJX43Z/4a6/z\nMZqONkpO9RVT6GiQFYGUsFRWL6odim0FSz6FdVWiomt09/YSk0mvgi8h6T6KOIOjjZ6rtvzsbbsU\nrepZz/dYANgUhz37Q2kvICAgICAgIOCG+E4zUq0xkJTlUVqxToRzLTeiRjKCMX2KO+ImQmMt21iI\nKIITvYOz9rJpFD3Xbc3Nc13TsmCd/x7/t8CgL9N1QNP0vlkWLfmWwYLLRZ05PJ1RVxVWa1+CE1Ky\nX5c17Krh07X9R1pwQzz/IoBmt8OKouvPnz7B9USqgKNsghMWmkQPZZxgufE7oda0GHc+qi7rAilF\n9LGO97J3kplyUh7WVKdMipL0jEYThYxKT0077HCdlGjofkppuWzgiQG91UCMUe2v+8XFOa6uFrB0\nneM8gyJGX5IkvJMqdi3u0s5+ks9wfOTZeQoayxWxvfbEWbe7NSxdp56AcAiEUszkqeoKZeWPf7Xe\n4Ej78227HfKcdImyKQSl0HabFdZ0LMZ0w72RfhfYl5GFVFxa01EMEVMpwAlA9SwWh4ZmqISBJu2Y\nNFeI9WDd0mcno+zAJmVQoyb9ftU0zKZs2o7LlDAW4tck8K//xHGWw4Ec2WminZ2d4623fDnye48e\n4m16/ezrL1BS+XC7q/DywpMqIlkio2tyVi9xft6n+WseN5vt4WKOR7M5Emq2TeOYG28jKaBEL4Yq\n0J+w1jGk8tdk8fKKx9Q/vTrDlrJsWgK77QY5ZTqOZzk31P/d//onTMaUgUsS/MYDKvHLlI/fi3bS\nMWUZa1ApIViHragOZwn3EELwZ8EJrIh1VO9aZBFp9HVdn6BHawy3TTR1hbZfM7sOcTJoLuU6xvyW\nn2efn19gtfH36oOjB3DGH+fp2XOUVAWwJsJ62WcYR9zQbS3QUkmlLg8vsydZusfutjDOZxPaegdH\nWnO2A1pqlredBbpeuNKBDhG2aKFonmgdQcUSmoggx3fv4h7pfI2mx4iJ4CHt4BtY1wUqaqNo2i3K\nyp/DxdUZzi888WdTXnE2ujOHZ06lkNdaVQYdRcsN33meM/vSi6Rafu/1Z1zfOH59lhpj+NiyJGEt\nqP0MVPQNZSyHoZrjq0r9e18ncyqYldrUDSyN1Txx0LE/32zsMO4zTy4CZE+LHsa2k84r+gLQ4ynG\nR6NhjbIW5ZpIFlIiIT9bKMF6Wk4OxDQhNFRNgpw2AeDXHplMYJ1/bZrD1pvvNpDqOvbs8YERnZwx\nXP6xTcP9C8baa/XbHkJKDoQQWVhjIWRf8hnSoV3X8c2TQnA6M45j5D21uqr4s4yxzAaMlWb24N5X\nfyv+4O13UPYqrJ2Fpc8zTYOKjqW2LSxdB2/mSNdEgFO8WkjOFwr6I6VVMJERFCkRKwdMaPFP8xzH\n5GGUj2YYTXqq8Bhpz1jKRkjpfJNYI6G+FR0fRrluW8tun3EcQ/T18j3z1l1VcG9aliRsVO2cwGrl\ng8wsH0NFw8JW1zUi6n+QIkHFZrMCVdWXdHPcOvK0+QcP3uC+BvcmsFz6he3yYsH19LZrWP6gqw8v\nJ1RFgTT2AZuNBJqu92hbYbur+LMnZByq4FhOYzrOWDrh4vICFZUF4ypBnntFcQDo5AhVRz0AKh68\nI7sONDSRJA5pQguYNexN6FqHljyionEKSwPFycP7Mqyz/ICqyhJrCsCtHYInBwe1169jvqHEfU1p\n31hIoiMLIfj/okhhQabhT75+hhfnvoR2sdzhikq8WnbIScthtSxQVhzRoWfJ2deYi8fzI96s6Sga\n6NpU9gAAp8Rg9KoVCmJ/fvrpZ+ioD2a1WQK/9Oc0SjXaco2J8sezHad4REreabnARd8XlY+R05xy\nKuZ1D84OwqD5iL3yEiVhbG8we2DQL4aHpaeC06YBllX3n3z5Am9/n7wP5ynqpvebc+xtaUQLRfR2\nFUlIZaFIdd86A0MPzNl8hgX5mnVti/XKz7nF5RXqXtC1scwANMZiRYLEKGvcIvarHB0+F6eTIx6n\n1nXDc8IZ9PWmrh3KVsZYXrettVyadrDc2xdpIMsSjIh1OM9vYZbRPYxyOArEymKLTUcBf7HAtvDn\ncv7qKU7PvgQAXC1OUJMyvoDkMpsUh2++GwzBvJQKXe8aYB36J0Kcj5EQi9xgB53l/D2mZ35KBaF7\nKYQMzjl0NOadE2z43TiBWPQJCsOm48ZYVrH3vcr9e4fzcQDaPl5/jTjKOculwDRNIGncywiApM1p\nW7A3oo5T6LgPhOwg0RBJTqhAaKhMwNA9csaz2wGfBOnnOBT4tXWC+x2F1AA5cEibIBa960aCHckF\n9fPo2xBKewEBAQEBAQEBN8R3mpGKYs2lOie8Lxngy2x96cXre/SR8KDx1HWDjpQQgnf6pmpQlSUk\nWRSkabLnkm3RUeNhmqbchFjXNWcA6rrlz7V2yD51puPo/HUi7//y+3/EthhlU8CRpolra7Sm92Ey\nsNJHwq6r0duHORjWBNIi4/QnpIMShhkrSZLBnHofvbYsESfUAPrBDzF99JF/S5pCUcYEcQSQqJ1K\nMm/MBUBEGooyVYf2t8rUQdFOcF3uYMgmRWvF2Yvtdsv3II9HiEjM0HQdnj/3zZhRfIV8mtH5xBjl\n40H0DRFnGAHLzbKT0Ry3Zr7MoGWMzcLviOuyQkK7/+VyjTjpNZkk2ra34tkcdoIAttsdlrQzeuON\nY6S9nRAED5BEp1wqkkJxA3+WJWzPoeMIFxeXAICqalCWDUZ9qdFFKJshyyKpBiEB1nFK0xx2TOQH\nI1GSrlIuJVTaly9sTyBjtuohkFJxc/92ux2aV8XgiWWMGTJle6/dnnXTPnrWT8/6W2/WPIeePXuO\nX/xPXwJ5+uwEr5b+XHaNg3P+ejWNRNWTM5qGtWusHUROXycjNZ3MEfdenaYb7CekgOtJKBjWFaUU\nNiSk+MtP/y+adkjrn554lqEUDs4a1gqTEvjZPyZ0zAbbshctHUgA777zNhzZTcF2bMORJ8lQ2pNy\nYPMdeH5SCC7RWOeYQLOvjbdcb4Gv/Cf+8OOHuP89P99XZYm6puzfaouOxs5xPkYaxXwPL652eEVC\nwkfTDJMxseasweLCX5PTF2dYUFYxjhOMaK2pljU6YvA12RaW1oFscrjlVhKPhzKVsFxyd9ZyO4jp\nOs4etm3LvmxekLknS0jOeKdpjvFoitnUZ++n+X2kiT+2um1wsfLnZUyLoqCs2+Icp6dPAQBPn32B\n9ZYIRRrIsqFEL6nq8I3st193juh4jUuMQz+hm67hTEcEB6RUEnZDhtaL/lLW2LZwlGUUpoYQApHr\n/89A0/oq2xIxUcLjNEZN5au26gYbnSji7JoQAirqS9AdD9DXWW/a1gwPUjMI2jatgpA++9+Vjv0m\nnROYzoiMk5VQmlpFrBm8CH0atl/+4OA46+iMgWAxVrV3Pyy3JrjWQFK1wTQS9HhG17Rsc1U3h5XZ\nxa9bEAMCAgICAgICAr4dobQXEBAQEBAQEHBDhEAqICAgICAgIOCGCIFUQEBAQEBAQMANEQKpgICA\ngICAgIAbIgRSAQEBAQEBAQE3RAikAgICAgICAgJuiBBIBQQEBAQEBATcECGQCggICAgICAi4IUIg\nFRAQEBAQEBBwQ4RAKiAgICAgICDghgiBVEBAQEBAQEDADRECqYCAgICAgICAGyIEUgEBAQEBAQEB\nN0QIpAICAgICAgICbogQSAUEBAQEBAQE3BAhkAoICAgICAgIuCFCIBUQEBAQEBAQcEOEQCogICAg\nICAg4IYIgVRAQEBAQEBAwA0RAqmAgICAgICAgBsiBFIBAQEBAQEBATdECKQCAgICAgICAm6IEEgF\nBAQEBAQEBNwQ/w+9Lyf+AkRLLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52c7c02828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "- some of cases seems fail because they have color histogram which quite close to color histogram of images from this class\n",
    "- few misclassified image quite similar - like truck and car\n",
    "- HOG takes into account direction of local gradient but for such small images it doesn't play well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "rate: 0.012227097115929585\n",
      "reg: 2.5293429426136258e-09\n",
      "hidden_size: 100\n",
      "\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 2.302442\n",
      "iteration 200 / 1000: loss 2.302705\n",
      "iteration 300 / 1000: loss 2.302583\n",
      "iteration 400 / 1000: loss 2.302436\n",
      "iteration 500 / 1000: loss 2.302608\n",
      "iteration 600 / 1000: loss 2.302238\n",
      "iteration 700 / 1000: loss 2.302642\n",
      "iteration 800 / 1000: loss 2.302652\n",
      "iteration 900 / 1000: loss 2.302621\n",
      "train accuracy: 0.13908163265306123\n",
      "val accuracy: 0.125\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "rate: 0.011845552477261625\n",
      "reg: 3.4301610500565337e-08\n",
      "hidden_size: 100\n",
      "\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 2.302532\n",
      "iteration 200 / 1000: loss 2.302650\n",
      "iteration 300 / 1000: loss 2.303187\n",
      "iteration 400 / 1000: loss 2.303035\n",
      "iteration 500 / 1000: loss 2.302890\n",
      "iteration 600 / 1000: loss 2.302589\n",
      "iteration 700 / 1000: loss 2.302695\n",
      "iteration 800 / 1000: loss 2.302791\n",
      "iteration 900 / 1000: loss 2.302239\n",
      "train accuracy: 0.10026530612244898\n",
      "val accuracy: 0.087\n",
      "***-----------------------------------------------------------------------------\n",
      "\n",
      "rate: 0.011798427504697223\n",
      "reg: 0.00035656217222183276\n",
      "hidden_size: 100\n",
      "\n",
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 2.302601\n",
      "iteration 200 / 1000: loss 2.302603\n",
      "iteration 300 / 1000: loss 2.302742\n",
      "iteration 400 / 1000: loss 2.302390\n",
      "iteration 500 / 1000: loss 2.302569\n",
      "iteration 600 / 1000: loss 2.302645\n",
      "iteration 700 / 1000: loss 2.302462\n",
      "iteration 800 / 1000: loss 2.302334\n",
      "iteration 900 / 1000: loss 2.302528\n",
      "train accuracy: 0.10042857142857142\n",
      "val accuracy: 0.079\n",
      "******--------------------------------------------------------------------------\n",
      "\n",
      "rate: 0.009741788677928909\n",
      "reg: 0.027515791061407766\n",
      "hidden_size: 100\n",
      "\n",
      "iteration 0 / 1000: loss 2.302590\n",
      "iteration 100 / 1000: loss 2.302518\n",
      "iteration 200 / 1000: loss 2.302719\n",
      "iteration 300 / 1000: loss 2.302715\n",
      "iteration 400 / 1000: loss 2.302157\n",
      "iteration 500 / 1000: loss 2.302328\n",
      "iteration 600 / 1000: loss 2.301957\n",
      "iteration 700 / 1000: loss 2.302708\n",
      "iteration 800 / 1000: loss 2.302434\n",
      "iteration 900 / 1000: loss 2.302410\n",
      "train accuracy: 0.10026530612244898\n",
      "val accuracy: 0.087\n",
      "*********-----------------------------------------------------------------------\n",
      "\n",
      "rate: 0.00462935424206487\n",
      "reg: 41.00073113166992\n",
      "hidden_size: 100\n",
      "\n",
      "iteration 0 / 1000: loss 2.309507\n",
      "iteration 100 / 1000: loss 2.302630\n",
      "iteration 200 / 1000: loss 2.302520\n",
      "iteration 300 / 1000: loss 2.302585\n",
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      "train accuracy: 0.10026530612244898\n",
      "val accuracy: 0.087\n",
      "************--------------------------------------------------------------------\n",
      "\n",
      "rate: 0.03751224680025372\n",
      "reg: 7.764750206250797e-12\n",
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      "\n",
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      "train accuracy: 0.331734693877551\n",
      "val accuracy: 0.333\n",
      "****************----------------------------------------------------------------\n",
      "\n",
      "rate: 0.03218240654344621\n",
      "reg: 4.430175522846506e-07\n",
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      "train accuracy: 0.2891836734693878\n",
      "val accuracy: 0.301\n",
      "*******************-------------------------------------------------------------\n",
      "\n",
      "rate: 0.06628664245967193\n",
      "reg: 8.991364336730562e-06\n",
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      "train accuracy: 0.4793877551020408\n",
      "val accuracy: 0.465\n",
      "**********************----------------------------------------------------------\n",
      "\n",
      "rate: 0.05749195756344965\n",
      "reg: 0.015720816868617375\n",
      "hidden_size: 100\n",
      "\n",
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      "iteration 900 / 1000: loss 1.850533\n",
      "train accuracy: 0.4155510204081633\n",
      "val accuracy: 0.408\n",
      "*************************-------------------------------------------------------\n",
      "\n",
      "rate: 0.03738994078384156\n",
      "reg: 106.53193502583996\n",
      "hidden_size: 100\n",
      "\n",
      "iteration 0 / 1000: loss 2.319968\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/neural_net.py:100: RuntimeWarning: overflow encountered in exp\n",
      "  exp_scores = np.exp(scores)\n",
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/neural_net.py:102: RuntimeWarning: invalid value encountered in true_divide\n",
      "  prob_of_classes = exp_scores / p_norm\n",
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/neural_net.py:106: RuntimeWarning: divide by zero encountered in log\n",
      "  loss = -np.log(prob_of_classes[range(N), y]).sum() / N\n",
      "/opt/notebooks/cs231/assignment1/cs231n/classifiers/neural_net.py:159: RuntimeWarning: invalid value encountered in less_equal\n",
      "  drelu[layer_1 <=- 0] = 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "train accuracy: 0.10026530612244898\n",
      "val accuracy: 0.087\n",
      "****************************----------------------------------------------------\n",
      "\n",
      "rate: 0.33799662278518616\n",
      "reg: 1.4235767847535157e-11\n",
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      "train accuracy: 0.5866122448979592\n",
      "val accuracy: 0.557\n",
      "********************************------------------------------------------------\n",
      "\n",
      "rate: 0.29978943503564986\n",
      "reg: 3.6317837466171403e-07\n",
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      "val accuracy: 0.553\n",
      "***********************************---------------------------------------------\n",
      "\n",
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      "**************************************------------------------------------------\n",
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      "************************************************--------------------------------\n",
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      "***************************************************-----------------------------\n",
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      "******************************************************--------------------------\n",
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      "*********************************************************-----------------------\n",
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      "************************************************************--------------------\n",
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      "val accuracy: 0.087\n",
      "****************************************************************----------------\n",
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      "val accuracy: 0.087\n",
      "*******************************************************************-------------\n",
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      "val accuracy: 0.087\n",
      "**********************************************************************----------\n",
      "\n",
      "rate: 5.018738878534267\n",
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      "train accuracy: 0.10026530612244898\n",
      "val accuracy: 0.087\n",
      "*************************************************************************-------\n",
      "\n",
      "rate: 12.369464851591902\n",
      "reg: 59.34638541439223\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 300 / 1000: loss nan\n",
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      "train accuracy: 0.10026530612244898\n",
      "val accuracy: 0.087\n",
      "****************************************************************************----\n",
      "CPU times: user 13min 58s, sys: 45.5 s, total: 14min 44s\n",
      "Wall time: 3min 44s\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_size = X_train_feats.shape[1]\n",
    "hidden_size = 100\n",
    "num_classes = 10\n",
    "batch_size = 200\n",
    "\n",
    "# net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "\n",
    "num_iters = 1000\n",
    "\n",
    "# searching space \n",
    "learning_rate_pows = np.linspace(-2, 1, 5)\n",
    "regularization_strength_pows = np.linspace(-10.0, 2, 5)\n",
    "\n",
    "# random layout\n",
    "jitter_power = 0.4\n",
    "\n",
    "results = {}\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "\n",
    "def search():\n",
    "    \"\"\"\n",
    "    We have disabled hidden_size_space search dimension.\n",
    "    \"\"\"\n",
    "    best_net = None # store the best model into this \n",
    "    best_acc = 0.0\n",
    "    \n",
    "    learning_rate_pow_step = learning_rate_pows[1] - learning_rate_pows[0]\n",
    "    regularization_strength_pow_step = regularization_strength_pows[1] - regularization_strength_pows[0]\n",
    "    \n",
    "    results = {}\n",
    "\n",
    "    total_iteration_num = len(learning_rate_pows) * len(regularization_strength_pows)\n",
    "    current_iteration = 0\n",
    "    for learning_rate_pow in learning_rate_pows:\n",
    "        for regularization_strength_pow in regularization_strength_pows:\n",
    "\n",
    "            # randomize layout\n",
    "            learning_rate_pow_jitter = jitter_power * learning_rate_pow_step / 2 * np.random.randn()\n",
    "            regularization_strength_jitter = jitter_power * regularization_strength_pow_step / 2 * np.random.randn()            \n",
    "        \n",
    "            # define params\n",
    "            learning_rate = 10**(learning_rate_pow + learning_rate_pow_jitter)\n",
    "            regularization_strength = 10**(regularization_strength_pow + regularization_strength_jitter)\n",
    "\n",
    "            print()\n",
    "            print(f'rate: {learning_rate}')\n",
    "            print(f'reg: {regularization_strength}')\n",
    "            print(f'hidden_size: {hidden_size}')\n",
    "            print()\n",
    "\n",
    "            net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "            stats = net.train(X_train_feats, y_train, \n",
    "                              X_val_feats, y_val,\n",
    "                              num_iters=num_iters, \n",
    "                              batch_size=batch_size,\n",
    "                              learning_rate=learning_rate, \n",
    "                              learning_rate_decay=0.95,\n",
    "                              reg=regularization_strength, \n",
    "                              verbose=True,\n",
    "                             )\n",
    "\n",
    "            # Predict on the validation set\n",
    "            trained_accuracy = (net.predict(X_train_feats) == y_train).mean()\n",
    "            val_accuracy = (net.predict(X_val_feats) == y_val).mean()\n",
    "            \n",
    "            print(f'train accuracy: {trained_accuracy}')\n",
    "            print(f'val accuracy: {val_accuracy}')\n",
    "\n",
    "            filled = int(80*current_iteration/total_iteration_num)\n",
    "            print('*' * filled + '-' * (80 - filled))\n",
    "            current_iteration+=1\n",
    "\n",
    "            if best_acc < val_accuracy:\n",
    "                best_acc = val_accuracy\n",
    "                best_net = net\n",
    "\n",
    "            results[(\n",
    "                learning_rate,\n",
    "                regularization_strength,\n",
    "                hidden_size,\n",
    "            )] = (\n",
    "                trained_accuracy, \n",
    "                val_accuracy,\n",
    "            )\n",
    "                \n",
    "    return best_net, results\n",
    "\n",
    "                \n",
    "%time best_net, results = search()\n",
    "\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RUcEHPvCB/WrpP/ShD1FWVpYx/fa8mSnF5eTkMHPmTNas2Xf0d3Z2NhUVFV4r45xzGSw3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52afcd9278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 50\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, edgecolors='lightgrey')\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, edgecolors='lightgrey')\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((0.44734987845775687, 0.00022728715001198684, 100), (0.59451020408163269, 0.58899999999999997))\n",
      "The best params are:\n",
      "rate: 0.44734987845775687\n",
      "reg: 0.00022728715001198684\n",
      "hidden_size: 100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sorted_results = sorted(results.items(), key=lambda x: x[1][1])\n",
    "print(sorted_results[-1])\n",
    "\n",
    "(\n",
    "    best_learning_rate,\n",
    "    best_regularization_strength,\n",
    "    hidden_size,\n",
    ") = sorted_results[-1][0]\n",
    "\n",
    "print('The best params are:')\n",
    "print(f'rate: {best_learning_rate}')\n",
    "print(f'reg: {best_regularization_strength}')\n",
    "print(f'hidden_size: {hidden_size}')\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.56\n"
     ]
    }
   ],
   "source": [
    "# Run your neural net classifier on the test set. You should be able to\n",
    "# get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus: Design your own features!\n",
    "\n",
    "You have seen that simple image features can improve classification performance. So far we have tried HOG and color histograms, but other types of features may be able to achieve even better classification performance.\n",
    "\n",
    "For bonus points, design and implement a new type of feature and use it for image classification on CIFAR-10. Explain how your feature works and why you expect it to be useful for image classification. Implement it in this notebook, cross-validate any hyperparameters, and compare its performance to the HOG + Color histogram baseline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus: Do something extra!\n",
    "Use the material and code we have presented in this assignment to do something interesting. Was there another question we should have asked? Did any cool ideas pop into your head as you were working on the assignment? This is your chance to show off!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/mzAfTmC3It0?rel=0&amp;controls=1&amp;showinfo=0&autoplay=1\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# play rolling sound at the end of the notebook :)\n",
    "# very useful for long training\n",
    "\n",
    "from IPython.display import HTML\n",
    "video_id = 'mzAfTmC3It0'\n",
    "HTML(f'<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/{video_id}?rel=0&amp;controls=1&amp;showinfo=0&autoplay=1\" frameborder=\"0\" allowfullscreen></iframe>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}