algo_example_NN_multilayer_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset - CIFAR10\n",
    "\n",
    "Let's first prepare the data from CIFAR10 dataset.\n",
    "\n",
    "Source: http://www.cs.utoronto.ca/~kriz/cifar.html\n",
    "\n",
    "The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. \n",
    "\n",
    "(we will be using batch_1 only out of 6 batches -- so 10,000 images in total)\n",
    "\n",
    "The classes are [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]\n",
    "\n",
    "| no. of Classes    | 10            |\n",
    "|-------------------|---------------|\n",
    "| Samples per class | ~1,000        |\n",
    "| Samples total     | 10,000        |\n",
    "| Dimensionality    | 32x32x3 = 3,072|\n",
    "| Features          | int 0-255 each pixel     |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from random import randint\n",
    "from pandas import Series,DataFrame\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import math\n",
    "import time\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unpickle(file):\n",
    "    import cPickle\n",
    "    fo = open(file, 'rb')\n",
    "    dict = cPickle.load(fo)\n",
    "    fo.close()\n",
    "    return dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# source: http://www.cs.utoronto.ca/~kriz/cifar.html\n",
    "# get the training data, batch 1 out of 6. do unzip the tar.gz first\n",
    "from time import strftime\n",
    "import os\n",
    "\n",
    "if os.name == 'nt': # Windows\n",
    "    path = 'cifar\\\\data_batch_1'\n",
    "else:\n",
    "    path = 'cifar/data_batch_1'\n",
    "    \n",
    "cifar = unpickle('cifar/data_batch_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def array_to_image(arr):\n",
    "    im = arr.reshape((32,32,3),order='F')/255.0\n",
    "    im = np.transpose(im,axes=(1,0,2))\n",
    "    return im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array_size = 3072\n",
      "[5 5 5 ..., 0 2 2]\n",
      "file_name = jumbo_jet_s_001329.png\n"
     ]
    },
    {
     "data": {
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EgBq6ogSAGrqiBEBbvO6KokwuuqIrSgCooStKAKihK0oAqKErSgCooStKAKih\nK0oANFx4YjwwxqwBTiR9nP8ya+1D7Rw/02EF8F3gyUz0S2vtn7ZZhyXArcAaa+3XjTEH00SxzQnU\nYy2wDHgte8vV1trvt0GPq4CTSc/HrwAPMTnzUa/HubRxPsajEKtE21Z0Y8wpwJHW2uXAJcDX2jW2\ngx9ba1dkf+028inAdcC9I8RtL7Yp6AHw+RFz0w4jPxVYkp0XZwJ/zeTMh0sPaO98TFgh1nbeur8P\n+DcAa+0mYJYxZnobx99fGATOprYqzwpgffb/bcD7J0mPyeB+4EPZ/33AFCZnPlx6jJ4bOo5Ya9dZ\na6/KXo4sxDrmuWjnrfs8YOOI169msjfaqEOVo40x64HZwJXW2rvbNbC1tgSUjDEjxVPaXWxT0ANg\ntTHm8kyP1dbabROsRxnYnb28BLgDOGMS5sOlR5k2zwdMTCHWyXTGTVbFh9+SVsk5D7gQ+JYxpnOS\ndHExmZUwbgY+Z609DXgUuKJdAxtjziM1sNV1TW2djzo9JmU+rLUnkfoHmi7EKtFOQ99MuoJXWUDq\nXGgr1tqXslukxFr7NPAyaYHLyWRXVrILGii2OVFYa++11j6avVwPLG3HuMaYM4AvAmdZa3cwSfNR\nr0e758MYsyxzzJKNu7cQa/aWlueinYZ+F3A+gDHmBGCztXZnG8cnG/ujxpjPZv/PI/VwvtRuPerY\nL4ptGmNuMcYclr1cATzRhjFnAFcDK6212zNx2+fDpcckzMeEFWJta/aaMeZ/k36YCvAn1trH2jb4\nPh2mAd8GZgKdpL/R72jj+MuAa4BFwDDpReajpGGVhoptTqAe1wGfA/qBXZkeWydYj0+R3hL/ZoT4\nQuCbtHc+XHr8I+ktfFvmYzwKsUpomqqiBIA+GacoAaCGrigBoIauKAGghq4oAaCGrigBoIauKAGg\nhq4oAaCGrigB8P8BlabCV1GWlawAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22af47fad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# image data is uint8 array format \n",
    "# 3 RGB channels and 32x32 pixels means 32x32x3 = 1024x3 = 3072 numbers in one array\n",
    "# 10,000 arrays in dataset\n",
    "lucky_num = randint(0,10000)\n",
    "sample_arr = cifar[\"data\"][lucky_num].copy()\n",
    "print 'array_size = {}'.format(len(sample_arr))\n",
    "print sample_arr\n",
    "print 'file_name = {}'.format(cifar[\"filenames\"][lucky_num])\n",
    "#show image\n",
    "plt.rcParams['figure.figsize'] = (4, 4)\n",
    "plt.imshow(array_to_image(sample_arr))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Convolutional Neural Networks (CNN)\n",
    "\n",
    "Source1: http://cs231n.github.io/convolutional-networks/\n",
    "\n",
    "Source2: http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html\n",
    "\n",
    "\n",
    "\n",
    "Note: Most of the knowledge below was gleaned from [CS231n stanford course (Source1)](http://cs231n.github.io/convolutional-networks/), its figures and explanation are excellent learning materials.\n",
    "\n",
    "Convolutional Neural Networks (CNN) is essential normal neural nets with the \"Convolution\" part to transform raw input into meaningful features. Most common image, video, or other \"dense\" input problem.\n",
    "\n",
    "### What is convolution anyways?\n",
    "\n",
    "The math definition is: \"the integral of 2 products after one is reversed and shifted.\"\n",
    "\n",
    "$$ (f * g)(t) = \\int_{-\\infty}^{\\infty} f(x) \\cdot g(t - x) \\; dx  $$\n",
    "\n",
    "This image from wiki is much easier to understand.\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Comparison_convolution_correlation.svg/512px-Comparison_convolution_correlation.svg.png\"  title=\"source is wiki\" style=\"width:450px;\" />\n",
    "\n",
    "In matrix form, below is how to convolve an input matrix with another matrix (called a kernel). \n",
    "\n",
    "The convolution is essentially an element-wise multiplication and then sum of the result.\n",
    "\n",
    "**Note: the kernel needed to be flipped (in every dimension) before doing the multiplication.**\n",
    "\n",
    "Here we have a 5x5 (green) input matrix and a 3x3 (orange) kernel, doing a 1-each stride (moving the kernel 1 cell a a time). \n",
    "\n",
    "<img src=\"stack.gif\" title=\"unknown source\" style=\"width:400px;\" />\n",
    "\n",
    "\n",
    "Even more useful is to look at how it was applied in real application. \n",
    "\n",
    "The most (visually) obvious is as a [filter in photo editor -- Edge detection, Blur, etc](http://docs.gimp.org/en/plug-in-convmatrix.html). The kernel is mostly a mask (cell value either 1 or 0) to enhance or hide away features.\n",
    "\n",
    "<img src=\"http://i1.wp.com/timdettmers.com/wp-content/uploads/2015/03/convolution.png\" title=\"source is timdettmers.com\" style=\"width:370px;\" />\n",
    "\n",
    "So we know what convolution is now (roughly), let's move on to CNN architecture.\n",
    "\n",
    "## CNN architecture\n",
    "\n",
    "We're going to use the structure from [here](http://cs231n.github.io/convolutional-networks/). The source website actually contains great explanation on many concepts, so I will only do a short summary for our purpose.\n",
    "\n",
    "Note: For the sake of easier debugging of numpy array print format, I will use this notation [Depth x Height x Width]\n",
    "\n",
    "+ **INPUT** - [3x32x32] - will hold the raw pixel values of the image with 3 color channels.\n",
    "+ **CONV** - [12x32x32] - this layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as  if we decided to use 12 filters. (with each filter as [3x3x3])\n",
    "+ **RELU** - [12x32x32] - layer will apply an elementwise activation function, such as the max(0,x) thresholding at zero. This leaves the size of the volume unchanged.\n",
    "+ **POOL** - [12x16x16] - layer will perform a downsampling operation along the spatial dimensions (width, height).\n",
    "+ **FC** - [10x1x1] - (i.e. fully-connected) layer will compute the class scores, where each of the 10 numbers correspond to a class score, such as among the 10 categories of CIFAR-10. \n",
    "\n",
    "Let's get into details of each layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### INPUT layer\n",
    "\n",
    "Not much to be said here. we will use an image matrix of [RGB,Height,Width] = [3,32,32].\n",
    "\n",
    "### CONV layer\n",
    "\n",
    "First, we will use a filter (aka. kernel) matrix of RGBx3x3. \n",
    "\n",
    "In terms of neurons, the no. of weight (connection) for each filter is 3x3x3 + 1 bias = 28. \n",
    "\n",
    "Since we will have 12 filter slices (also called \"depth\"), we will have 28x12 = 336 connections total for **CONV** layer.\n",
    "\n",
    "<img src=\"http://cs231n.github.io/assets/cnn/depthcol.jpeg\" title=\"source from stanford course\" style=\"width:250px;\" />\n",
    "\n",
    "The formula belows calculate the shape of the output matrix. \n",
    "\n",
    "For input **W**idth (or height) = 32, **F**ilter width = 3, zero-**P**adding = 1, and **S**tride = 1 :\n",
    "\n",
    "$$ \n",
    "\\begin{align}\n",
    "{Output}& = \\dfrac{(W + 2 \\cdot P - F)}{S} + 1 \\\\\n",
    "{Output}& = \\dfrac{(32 + 2 \\cdot 1 - 3)}{1} + 1 = 32\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "So our output matrix from this layer is [32x32x12].\n",
    "\n",
    "### RELU layer\n",
    "\n",
    "we will apply a Rectified Linear Unit (ReLU) function here. The output will be the same dimension of [32x32x12].\n",
    "\n",
    "$$ f(x) = max(0,x) $$\n",
    "\n",
    "Or put it another way:\n",
    "\n",
    "$$   \n",
    "f(x) =\n",
    "\\begin{cases}\n",
    "x  & \\text{if } x \\gt 0 \\\\\n",
    "0 & \\text{Otherwise}\n",
    "\\end{cases} \n",
    "$$\n",
    "\n",
    "\n",
    "### POOL layer\n",
    "\n",
    "Downsampling from [32x32x12] to [16x16x12].\n",
    "\n",
    "We are going to use a max function of 2x2 matrix inside the 32x32 layer, therefore the output will be halved -- to [16x16x12].\n",
    "\n",
    "<img src=\"http://cs231n.github.io/assets/cnn/maxpool.jpeg\" title=\"source from stanford course\" style=\"width:400px;\" />\n",
    "\n",
    "\n",
    "### FC layer\n",
    "\n",
    "This is a normal NN output layer, connecting all [16x16x12] input to [1x1x10] predicted output.\n",
    "\n",
    "We are going to use [Softmax function](https://en.wikipedia.org/wiki/Softmax_function) to constrain all the probability output.\n",
    "\n",
    "$$ z_i = w_{soft} \\cdot f_{POOL,i} $$\n",
    "\n",
    "$$ \\sigma_{soft}(z_i) = \\cfrac{e^{z_{i}}}{ \\sum_{j=0}^m e^{z_{j}} } $$\n",
    "\n",
    "However, there is a chance that $ e^{z}$ becomes very large, which could make the computer croak by trying to divide two large numbers. \n",
    "\n",
    "Therefore, we can add a constant $C$ to both numerator and divisor, so the output magnitude is reasonable.\n",
    "\n",
    "$$ \\sigma_{soft}(z_i) = \\cfrac{e^{z_i + C}}{ \\sum_{j=0}^m e^{z_j + C} } $$\n",
    "\n",
    "$$ C = -\\max z $$\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss function\n",
    "\n",
    "Base on [Softmax Classifier](http://cs231n.github.io/linear-classify/#softmax), the (cross-entropy) loss function is like so:\n",
    "\n",
    "$$ L_i = - y_i \\cdot \\log( \\; \\sigma_{soft, i} \\;) $$\n",
    "\n",
    "$$ L_i = - y_i \\cdot \\log( \\; \\frac {e^{z_{i}}}{\\sum e^z} \\;) $$\n",
    "\n",
    "$$ L_i = - ( \\; z_{y,i} - \\log \\sum e^z \\;) $$\n",
    "\n",
    "$$ L = \\frac{1}{N} \\sum L_i $$\n",
    "\n",
    "This roughly mean that for each instance, only use the predicted output ( $ sigma_{soft} $ ) of the correct class for that particular instance. the rest we set to zero.\n",
    "\n",
    "For total loss over the whole dataset, we can just sum up each loss $L_i$.\n",
    "\n",
    "## Forward Pass\n",
    "\n",
    "This describes the flow of data.\n",
    "\n",
    "INPUT --> CONV --> RELU --> POOL --> OUT (Softmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back Propagation\n",
    "\n",
    "The equations and derivations are from ([this slide](http://courses.cs.tau.ac.il/Caffe_workshop/Bootcamp/pdf_lectures/Lecture%203%20CNN%20-%20backpropagation.pdf)). Backprop in [stages](http://cs231n.github.io/optimization-2/#staged) using chain rule is very useful.\n",
    "\n",
    "For the **OUT layer**, the Softmax backprop is per below. \n",
    "\n",
    "$$ \n",
    "\\begin{align}\n",
    "\\frac{\\partial L}{\\partial \\sigma_{soft}} &= - \\frac{y}{\\sigma_{soft} } \\\\[5pt]\n",
    "\\frac{\\partial L}{\\partial z_i} &= - \\frac{y_i}{\\sigma_{soft,i} } \\cdot \\frac{\\partial \\sigma_{soft, i}}{\\partial z_i} + \\sum_{j \\neq i} - \\frac{y_j}{\\sigma_{soft,j}} \\cdot \\frac{\\partial \\sigma_{soft, j}}{\\partial f_{POOL,i}} \\\\[5pt]\n",
    "\\frac{\\partial \\sigma_{soft, i}}{\\partial z_i} &= \\sigma_{soft,i} \\cdot (1 - \\sigma_{soft, i}) \\quad \\text{,where } \\sigma_{soft} \\text{ is for } i \\\\[5pt] \n",
    "\\frac{\\partial \\sigma_{soft, j}}{\\partial z_i} &= - \\sigma_{soft,j} \\cdot \\sigma_{soft, i} \\qquad \\text{,where } \\sigma_{soft} \\text{ is NOT for } i \\\\[5pt] \n",
    "\\frac{\\partial L}{\\partial z_i} &= \\sigma_{soft, i} - y_i \\\\[5pt]\n",
    "\\frac{\\partial L}{\\partial w_{soft}} &= (\\sigma_{soft, i} - y_i) \\cdot f_{POOL,i} \\quad \\text{, for weight update}\\\\[5pt]\n",
    "\\frac{\\partial L}{\\partial f_{POOL,i}} &= (\\sigma_{soft, i} - y_i) \\cdot w_{soft}  \\qquad \\text{, for pass-through to the lower layer}\n",
    "\\end{align}\n",
    "$$\n",
    "<br/>\n",
    "\n",
    "For the **POOL layer**, we can pass the gradients directly back to the \"max\" input, otherwise it is zero.\n",
    "\n",
    "$$   \n",
    "\\frac{\\partial L}{\\partial f_{RELU,i}} =\n",
    "\\begin{cases}\n",
    "\\frac{\\partial L}{\\partial f_{POOL,i}}  & \\text{if } f_{RELU,i} \\text{ is } \\max \\\\\n",
    "0 & \\text{Otherwise}\n",
    "\\end{cases} \n",
    "$$\n",
    "<br/>\n",
    "\n",
    "For the **RELU layer**, again we can pass through the gradients directly if $ \\; f_{CONV} \\gt 0 $.\n",
    "\n",
    "$$   \n",
    "\\frac{\\partial L}{\\partial f_{CONV,i}} =\n",
    "\\begin{cases}\n",
    "\\frac{\\partial L}{\\partial f_{RELU,i}}  & \\text{if } f_{CONV,i} \\gt 0 \\\\\n",
    "0 & \\text{Otherwise}\n",
    "\\end{cases}\n",
    "$$\n",
    "<br/>\n",
    "\n",
    "For the **CONV layer**, there are a few steps. \n",
    "\n",
    "First, let's look a an example of convolving the [2x2] kernel $w_{r,c}$  with [3x3] input $x_{r,c}$. The result is a [2x2] $f_{r,c}$ matrix.\n",
    "\n",
    "Note: **$r$** = row index starting at 0 and **$c$** = column index starting at 0. like so:\n",
    "\n",
    "| $w_{0,0}$ | $w_{0,1}$ |\n",
    "|-----------|-----------|\n",
    "| $w_{1,0}$ | $w_{1,1}$ | \n",
    "\n",
    "So for forward-pass with stride = 1, we have all the $f_{r,c}$ like so :\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "f_{0,0} &= w_{0,0} \\cdot x_{0,0} + w_{0,1} \\cdot x_{0,1} + w_{1,0} \\cdot x_{1,0} + w_{1,1} \\cdot x_{1,1} + \\text{bias}\\\\\n",
    "f_{0,1} &= w_{0,0} \\cdot x_{0,1} + w_{0,1} \\cdot x_{0,2} + w_{1,0} \\cdot x_{1,1} + w_{1,1} \\cdot x_{1,2} + \\text{bias}\\\\\n",
    "f_{1,0} &= w_{0,0} \\cdot x_{1,0} + w_{0,1} \\cdot x_{1,1} + w_{1,0} \\cdot x_{2,0} + w_{1,1} \\cdot x_{2,1} + \\text{bias}\\\\\n",
    "f_{1,1} &= w_{0,0} \\cdot x_{1,1} + w_{0,1} \\cdot x_{1,2} + w_{1,0} \\cdot x_{2,1} + w_{1,1} \\cdot x_{2,2} + \\text{bias}\n",
    "\\end{align}\n",
    "$$   \n",
    "\n",
    "Then we can calculate the gradients -- partial derivative with respect to kernel weight $w$. \n",
    "\n",
    "$f$ here means $f_{CONV}$, the result from CONV layer:\n",
    "\n",
    "$$ \n",
    "\\begin{align}\n",
    "\\frac{\\partial f_{0,0}}{\\partial w_{0,0}} &= x_{0,0} \\\\[5pt]\n",
    "\\frac{\\partial f_{i,j}}{\\partial w_{r,c}} &= x_{(i+j),(r+c)} \\\\[5pt]\n",
    "\\frac{\\partial L}{\\partial w_{r,c}} &= \\sum_{i=0,j=0}^{n,m} \\frac{\\partial L}{\\partial f_{i,j}} \\cdot \\frac{\\partial f_{i,j}}{\\partial w_{r,c}}  \\\\[5pt]\n",
    "\\frac{\\partial L}{\\partial w_{r,c}} &= \\sum_{i=0,j=0}^{n,m} \\frac{\\partial L}{\\partial f_{i,j}} \\cdot x_{(i+j),(r+c)}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "For \"bias\", the partial derivative is 1, so we can just update it with the sum of gradients:\n",
    "\n",
    "$$ \\frac{\\partial L}{\\partial \\text{bias}} = \\sum_{i=0,j=0}^{n,m} \\frac{\\partial L}{\\partial f_{i,j}} \\cdot 1 $$\n",
    "\n",
    "<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization Method\n",
    "\n",
    "We are going to use a mini-batch Stochastic gradient descent (SGD).\n",
    "\n",
    "The gradient will have momentum and learning rate will decay based on epoch.\n",
    "\n",
    "\n",
    "## Computational Performance\n",
    "\n",
    "Since this code is intended to be clear for educational purpose, its performance are far from production level. \n",
    "\n",
    "I'm trying to use \"just\" Numpy array and only use other helper libraries as neccessary. \n",
    "\n",
    "---\n",
    "\n",
    "This ends the theory part. Let's start coding!\n",
    "\n",
    "...\n",
    "\n",
    "..\n",
    "\n",
    ".\n",
    "\n",
    "## Coding Starts Here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# X Format is [image_index, RGB, Height, Width]\n",
    "X = cifar[\"data\"].copy()\n",
    "X = X.reshape((-1,3,32,32),order='C')/255.0 - 0.5 #standardize\n",
    "\n",
    "#set size of input, features, hidden, target\n",
    "instance_size = X.shape[0]\n",
    "feature_size = X.shape[1]*X.shape[2]*X.shape[3]\n",
    "target_size = 10\n",
    "kernel_size = (12,3,3,3)\n",
    "weight_size = (10,12,16,16) #softmax layer\n",
    "\n",
    "#make a flat 10 output with all zeros\n",
    "Y = np.zeros((instance_size,10))\n",
    "for j in range(0,instance_size):\n",
    "    Y[j][cifar['labels'][j]] = 1\n",
    "\n",
    "#split train and test dataset\n",
    "train_split = 0.8 #6000.0/instance_size #0.8\n",
    "train_size = int(train_split*instance_size)\n",
    "test_size = instance_size - train_size\n",
    "index = np.random.permutation(instance_size)\n",
    "train_ix, test_ix = index[:train_size], index[train_size:train_size+test_size]\n",
    "Y_train , Y_test = Y[train_ix,:], Y[test_ix,:]\n",
    "X_train , X_test = X[train_ix,:,:,:], X[test_ix,:,:,:]\n",
    "\n",
    "#Xb = np.insert(X_train,0,1,axis=1) #add bias input, always activated\n",
    "#Xb_test = np.insert(X_test,0,1,axis=1) #add bias input, always activated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def convolve_3d_slow(data,kernel,bias_kernel,stride,pad): \n",
    "    #73.8 ms per call --> about 10 min per 8,000 images\n",
    "    if pad > 0:\n",
    "        tpad = ((0,0),(pad,pad),(pad,pad))\n",
    "        data = np.pad(data, pad_width=tpad, mode='constant', constant_values=0)\n",
    "    k_h = kernel.shape[2]\n",
    "    k_w = kernel.shape[3]\n",
    "    len_h = (data.shape[1] - k_h)/stride + 1\n",
    "    len_w = (data.shape[2] - k_w)/stride + 1\n",
    "    len_d = kernel.shape[0]\n",
    "    output = np.zeros((len_d,len_h,len_w))\n",
    "    kernel_rot = kernel[:,::-1,::-1,::-1]\n",
    "    for h in range(0,len_h*stride,stride):\n",
    "        for w in range(0,len_w*stride,stride):      \n",
    "            for d in range(0,len_d,1):\n",
    "                output[d,h/stride,w/stride] = np.sum(data[:,h:(h+k_h),w:(w+k_w)]*kernel_rot[d,:,:,:]) + bias_kernel[d]\n",
    "    return output\n",
    "\n",
    "import scipy.ndimage as ndi\n",
    "import scipy.signal as sig\n",
    "def convolve_3d_fast(data,kernel,bias_kernel,stride,pad):\n",
    "    #use scipy convolve then sum up\n",
    "    if pad > 0:\n",
    "        tpad = ((0,0),(pad,pad),(pad,pad))\n",
    "        data = np.pad(data, pad_width=tpad, mode='constant', constant_values=0)\n",
    "    k_h = kernel.shape[2]\n",
    "    k_w = kernel.shape[3]\n",
    "    len_h = (data.shape[1] - k_h)/stride + 1\n",
    "    len_w = (data.shape[2] - k_w)/stride + 1\n",
    "    len_d = kernel.shape[0]\n",
    "    output = np.zeros((len_d,len_h,len_w))\n",
    "    kernel_rot = kernel[:,::-1,:,:]\n",
    "    # np.rot90(kernel[d,c,:,:],2)\n",
    "    for d in range(0,len_d,1): # no. of kernel\n",
    "        for c in range(0,3): # RGB\n",
    "            output[d,:,:] += sig.convolve2d(data[c,:,:],kernel_rot[d,c,:,:],mode='valid')\n",
    "        output[d,:,:] += bias_kernel[d]\n",
    "    return output\n",
    "\n",
    "def convolve_3d_faster(data,kernel,bias_kernel,stride,pad):\n",
    "    #only works with stride = 1 and full padding the same size as input data though\n",
    "    len_h = (data.shape[1] - kernel.shape[2] +2*pad)/stride + 1\n",
    "    len_w = (data.shape[2] - kernel.shape[3] +2*pad)/stride + 1\n",
    "    len_d = kernel.shape[0]\n",
    "\n",
    "    output = np.zeros((len_d,len_h,len_w))\n",
    "    kernel_rot = kernel[:,::-1,:,:]\n",
    "    for d in range(0,len_d,1): # no. of kernel\n",
    "        for c in range(0,3): # RGB \n",
    "            output[d,:,:] += ndi.convolve(data[c,:,:],kernel_rot[d,c,:,:],mode='constant',cval=0.0)\n",
    "        output[d,:,:] += bias_kernel[d]\n",
    "    return output\n",
    "\n",
    "def test_loop(data,kernel,bias_kernel,stride,pad):\n",
    "    #only works with stride = 1 and full padding the same size as input data though\n",
    "    len_h = (data.shape[2] - kernel.shape[2] +2*pad)/stride + 1\n",
    "    len_w = (data.shape[3] - kernel.shape[3] +2*pad)/stride + 1\n",
    "    len_d = kernel.shape[0]\n",
    "    \n",
    "    data_size = data.shape[0]\n",
    "    output = np.zeros((data_size,len_d,len_h,len_w))\n",
    "    kernel_rot = kernel[:,::-1,:,:]\n",
    "    \n",
    "    for i in range(0,data_size): # no. of sample\n",
    "        for d in range(0,len_d,1): # no. of kernel\n",
    "            for c in range(0,3): # RGB \n",
    "                output[i,d,:,:] += ndi.convolve(data[i,c,:,:],kernel_rot[d,c,:,:],mode='constant',cval=0.0)\n",
    "            output[i,d,:,:] += bias_kernel[d]    \n",
    "    return output\n",
    "\n",
    "def test_fft(data,kernel,bias_kernel,stride,pad):\n",
    "    #only works with stride = 1 and full padding the same size as input data though\n",
    "    if pad > 0:\n",
    "        tpad = ((0,0),(pad,pad),(pad,pad))\n",
    "        data = np.pad(data, pad_width=tpad, mode='constant', constant_values=0)\n",
    "    k_h = kernel.shape[2]\n",
    "    k_w = kernel.shape[3]\n",
    "    len_h = (data.shape[1] - k_h)/stride + 1\n",
    "    len_w = (data.shape[2] - k_w)/stride + 1\n",
    "    len_d = kernel.shape[0]\n",
    "\n",
    "    output = np.zeros((len_d,len_h,len_w))\n",
    "            \n",
    "    for d in range(0,len_d,1): # no. of kernel  \n",
    "        output[d,:,:] = sig.fftconvolve(data[:,:,:],kernel[d,:,:,:],mode='valid')[0]\n",
    "        output[d,:,:] += bias_kernel[d]\n",
    "    return output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "single-sample test\n",
      "True True True\n",
      "10 loops, best of 3: 157 ms per loop\n",
      "100 loops, best of 3: 2.31 ms per loop\n",
      "1000 loops, best of 3: 1.9 ms per loop\n",
      "100 loops, best of 3: 7.77 ms per loop\n"
     ]
    }
   ],
   "source": [
    "#test convolution performance \n",
    "k1 = np.arange(0,12*3*(3**2)).reshape(12,3,3,3)\n",
    "d1 = np.arange(0,1000*3*(32**2)).reshape(1000,3,32,32)\n",
    "b1 = np.random.uniform(-1,1,size=12)\n",
    "print 'single-sample test'\n",
    "out1 = convolve_3d_slow(d1[0],k1,b1,1,1)\n",
    "out2 = convolve_3d_fast(d1[0],k1,b1,1,1)\n",
    "out3 = convolve_3d_faster(d1[0],k1,b1,1,1)\n",
    "out4 = test_fft(d1[0],k1,b1,1,1)\n",
    "print np.allclose(out1,out2),\n",
    "print np.allclose(out1,out3),\n",
    "print np.allclose(out1,out4)\n",
    "%timeit convolve_3d_slow(d1[0],k1,b1,1,1)\n",
    "%timeit convolve_3d_fast(d1[0],k1,b1,1,1)\n",
    "%timeit convolve_3d_faster(d1[0],k1,b1,1,1)\n",
    "%timeit test_fft(d1[0],k1,b1,1,1)\n",
    "#print 'loop test'\n",
    "#out_loop1 = test_loop(d1,k1,b1,1,1)\n",
    "#print np.allclose(out1,out_loop1[0])\n",
    "#%timeit test_loop(d1,k1,b1,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def max_pool(x):\n",
    "    \"\"\"Return maximum in groups of 2x2 for a d,h,w image\"\"\"\n",
    "    #source: http://stackoverflow.com/a/31975845\n",
    "    d,h,w = x.shape\n",
    "    x = x.reshape(d,h/2,2,w/2,2).swapaxes(2,3).reshape(d,h/2,w/2,4)\n",
    "    return np.amax(x,axis=3)\n",
    "\n",
    "def softmax(z):\n",
    "    c = -np.max(z)\n",
    "    tmp = np.exp(z + c)\n",
    "    total = np.sum(tmp)\n",
    "    return tmp/total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def backprop_f_RELU(grad_f_POOL,f_POOL_1batch,f_RELU_1batch):\n",
    "    \"\"\"upsample f_POOL then backprop to f_RELU \"\"\"\n",
    "    tmp1 = np.repeat(grad_f_POOL,2,axis=1) #expand grad down\n",
    "    tmp1 = np.repeat(tmp1,2,axis=2) #expand grad right\n",
    "    tmp2 = np.repeat(f_POOL_1batch,2,axis=1) #expand f_POOL down\n",
    "    tmp2 = np.repeat(tmp2,2,axis=2) #expand f_POOL right\n",
    "    mask = f_RELU_1batch == tmp2\n",
    "    return mask*tmp1\n",
    "\n",
    "def backprop_kernel(grad_f_CONV,x_arr):\n",
    "    \"\"\" sum grad up to (12,3,3,3) per kernel dimension \"\"\"\n",
    "    c_img,h_img,w_img = x_arr.shape\n",
    "    # (12, 32, 32) and (3,32,32) -> (12,3,32,32)\n",
    "    tmp = np.einsum('ijk,ljk->iljk',grad_f_CONV,x_arr)\n",
    "    grad_kernel = np.zeros(kernel_size)\n",
    "    for k in range(0,kernel_size[0]): # no. of filter = 12\n",
    "        for c in range(0,kernel_size[1]): # RGB = 3\n",
    "            for h in range(0,kernel_size[2]): # height = 3\n",
    "                for w in range(0,kernel_size[3]): # width = 3\n",
    "                    #sum relevant portion (with padding = 1)\n",
    "                    h_from = max(0,0-1+h)\n",
    "                    h_to = min(h_img,h_img-kernel_size[2]+2+h)\n",
    "                    w_from = max(0,0-1+w)\n",
    "                    w_to = min(w_img,w_img-kernel_size[3]+2+w)\n",
    "                    grad_kernel[k,c,h,w] = np.sum(tmp[k,c,h_from:h_to,w_from:w_to]) \n",
    "    return grad_kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "def loss_func(z,y):\n",
    "    z = z - np.max(z)\n",
    "    nom = np.sum(y*z)\n",
    "    denom = math.log(np.sum(np.exp(z)))\n",
    "    return -nom + denom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def forward_pass(X,kernel,bias_kernel,w_SOFT,bias_w_SOFT,Y,input_index,cache_flag):\n",
    "    f_CONV,f_RELU,f_POOL,f_OUT = [],[],[],[]\n",
    "    calc_loss = 0.0\n",
    "    input_size = len(input_index)\n",
    "    for k in input_index:\n",
    "        f_conv = convolve_3d_faster(X[k,:,:,:],kernel,bias_kernel,1,1)\n",
    "        f_relu = np.maximum(f_conv,0)\n",
    "        f_pool = max_pool(f_relu)\n",
    "        z = np.einsum('ijkl,jkl->i',w_SOFT,f_pool) + bias_w_SOFT\n",
    "        calc_loss += loss_func(z,Y[k])/len(input_index)\n",
    "        \n",
    "        if cache_flag == 'Y':\n",
    "            f_CONV.append(f_conv)\n",
    "            f_RELU.append(f_relu)\n",
    "            f_POOL.append(f_pool)\n",
    "            f_OUT.append(softmax(z))\n",
    "\n",
    "    return f_CONV,f_RELU,f_POOL,f_OUT,calc_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#All Settings before running the program\n",
    "from operator import mul\n",
    "\n",
    "#build kernel = [Filter, RGB, Height, Width]\n",
    "kernel = np.random.standard_normal(kernel_size)*math.sqrt(2.0/reduce(mul,kernel_size))\n",
    "w_SOFT = np.random.standard_normal(weight_size)*math.sqrt(2.0/reduce(mul,weight_size))\n",
    "\n",
    "bias_kernel = np.random.uniform(-0.01,0.01,size=12)\n",
    "bias_w_SOFT = np.random.uniform(-0.01,0.01,size=10)\n",
    "\n",
    "loss,loss_test = [],[]\n",
    "\n",
    "#mini-batch setting, we need to cache some calculation for reuse later\n",
    "mini_batch_size = min(100,int(0.2*train_size))\n",
    "\n",
    "#learning rate for gradient descent\n",
    "w_SOFT_learn_rate = 0.02\n",
    "kernel_learn_rate = 0.04\n",
    "momentum_rate = 0.9\n",
    "\n",
    "epoch_size = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00%... 20.00%... 40.00%... 60.00%... 80.00%... run time = 1702.66s\n"
     ]
    },
    {
     "data": {
      "image/png": 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WA5Fhhn5LEOBvhzi3i1QYf6IEMUZioiw8+PlMjAYtj795jGMFo683l1U3s3FH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22af2c5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Algo starts here\n",
    "#shuffle_box = np.random.choice(train_size,size=train_size,replace=False)\n",
    "prev_w_SOFT_update,prev_kernel_update = 0.0 , 0.0\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "#forward pass\n",
    "#INPUT --> CONV --> RELU --> POOL --> OUT (Softmax)\n",
    "f_CONV,f_RELU,f_POOL,f_OUT,curr_loss = \\\n",
    "    forward_pass(X_train,kernel,bias_kernel,w_SOFT,bias_w_SOFT,Y_train,range(0,train_size),'N')\n",
    "_,_,_,f_OUT_test,curr_loss_test = \\\n",
    "    forward_pass(X_test,kernel,bias_kernel,w_SOFT,bias_w_SOFT,Y_test,range(0,test_size),'N')\n",
    "loss.append(curr_loss)\n",
    "loss_test.append(curr_loss_test)\n",
    "\n",
    "for e in range(0,epoch_size):\n",
    "    \n",
    "    #print 'epoch ' + str(e) + ','\n",
    "    \n",
    "    for m in range(0,train_size,mini_batch_size):\n",
    "        \n",
    "        cache_index = range(m,min(train_size,m+mini_batch_size))\n",
    "        #print str(m) + ',',\n",
    "        #test_index = range(0,train_size)\n",
    "        \n",
    "        f_CONV,f_RELU,f_POOL,f_OUT,curr_loss = \\\n",
    "                forward_pass(X_train,kernel,bias_kernel,\\\n",
    "                                     w_SOFT,bias_w_SOFT,Y_train,cache_index,'Y')\n",
    "     \n",
    "        #backpropagation\n",
    "        #kernel <-- CONV <-- RELU <-- POOL <-- OUT (Softmax)\n",
    "        grad_w_SOFT = np.zeros((10,12,16,16))\n",
    "        grad_kernel = np.zeros((12,3,3,3))\n",
    "        grad_bias_kernel = np.zeros(12)\n",
    "        grad_bias_w_SOFT = np.zeros(10)\n",
    "\n",
    "        #for batch in range(m,min(train_size,m+mini_batch_size)):\n",
    "        for batch in range(0,len(cache_index)):\n",
    "            grad_w_SOFT += np.einsum('l,ijk->lijk',(f_OUT[batch] - Y_train[m+batch]),f_POOL[batch])\n",
    "            grad_f_POOL = np.einsum('l,lijk->ijk',(f_OUT[batch] - Y_train[m+batch]),w_SOFT)     \n",
    "            grad_f_RELU = backprop_f_RELU(grad_f_POOL,f_POOL[batch],f_RELU[batch])\n",
    "            grad_f_CONV = (f_CONV[batch] >0)*grad_f_RELU\n",
    "            grad_kernel += backprop_kernel(grad_f_CONV,X_train[m+batch,:,:,:])\n",
    "            grad_bias_kernel += np.sum(grad_f_CONV,axis=(1,2))\n",
    "            grad_bias_w_SOFT += (f_OUT[batch] - Y_train[m+batch])\n",
    "\n",
    "        #update kernel and weight\n",
    "        learn_decay = (epoch_size - e*0.5)/(epoch_size)\n",
    "        w_SOFT_update = - w_SOFT_learn_rate*learn_decay*grad_w_SOFT/mini_batch_size\n",
    "        kernel_update = - kernel_learn_rate*learn_decay*grad_kernel/mini_batch_size\n",
    "        bias_w_SOFT_update = - kernel_learn_rate*learn_decay*grad_bias_w_SOFT/mini_batch_size\n",
    "        bias_kernel_update = - kernel_learn_rate*learn_decay*grad_bias_kernel/mini_batch_size\n",
    "\n",
    "        w_SOFT = w_SOFT + w_SOFT_update + momentum_rate*prev_w_SOFT_update\n",
    "        kernel = kernel + kernel_update + momentum_rate*prev_kernel_update\n",
    "        bias_w_SOFT = bias_w_SOFT + bias_w_SOFT_update\n",
    "        bias_kernel = bias_kernel + bias_kernel_update\n",
    "\n",
    "        prev_w_SOFT_update, prev_kernel_update = w_SOFT_update, kernel_update\n",
    "    \n",
    "    if e % (epoch_size/5.0) == 0: #report progress 20% each\n",
    "        #a1 = np.sum(np.abs(w_SOFT_update))\n",
    "        #a2 = np.sum(np.abs(kernel_update))\n",
    "        #print 'w_SOFT_update = {:.4f}, kernel_update = {:.4f}'.format(a1,a2)\n",
    "        print '{:.2f}%...'.format(100.0*e/epoch_size),\n",
    "    \n",
    "    _,_,_,_,curr_loss = \\\n",
    "            forward_pass(X_train,kernel,bias_kernel,w_SOFT,bias_w_SOFT,\\\n",
    "                         Y_train,range(0,train_size),'N')\n",
    "    _,_,_,_,curr_loss_test = \\\n",
    "            forward_pass(X_test,kernel,bias_kernel,w_SOFT,bias_w_SOFT,\\\n",
    "                         Y_test,range(0,test_size),'N')\n",
    "    \n",
    "    loss.append(curr_loss)\n",
    "    loss_test.append(curr_loss_test)\n",
    "    \n",
    "print 'run time = {:.2f}s'.format((time.time()-start_time))    \n",
    "\n",
    "df_run = pd.DataFrame(data=np.array([loss,loss_test]).T,columns=['train','test'])\n",
    "df_run.plot()   \n",
    "axes = plt.gca()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set Accuracy = 41.20%\n"
     ]
    },
    {
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LVd/BUGL7wDRXl3QtGa+8\n9wHMZtsa85s2bIC7hg/D0L694OnhUWaNVquFl06H0OC6aNYwGj06dgAA5BcU4Ict2/HdTz8jzcZh\n5AuXLEOnNq3QuEF9m44jorKptW6I7Gf7/UhERKLMZjNW/LABn6/4tka+lrLGUFKCRd+sxOoNm2A2\nm50dh4iIiIjIadicT0REROQEAaEBGD55hE3HZCVnYv2CH5AQm2DzegXZ+di2ZCsObz6E0c+MQcO2\njYSO8/DywJjn7sLnT39aqYny5akbWRfDHh1u0zFF+UXY/PnPOLrtaKXWNJvMiN0Zi9idsYgZHIMR\nj4+Eh67sD9TLMvLJ27Dw0Q9gNvENZaodsnJzMeeLr2z62XfXuuHhMXfgnuFDoVGrbV5TpVKhV0x7\n9Ippj73HYvHWF4uRnZcvfPyib9eib6eOdttFvqCwCG99sVj434G71g2P3zMWowcPrNTzD/D1xf2j\nbsXIvn3w7tJl2H3oiNBxOw4eQq8/9+GWnt1tXpOIlPXlh/PRqUM7Z8cgolpo3vsLcc2GXSrc3Nww\n+cH78dCE+yrd1OXh4Y6xd4zCnbfdimWr12Dh51/CZMPvT28v+Bjrln8FrVZbqfWJqHrhdYqIqgOj\n0YiXX3tDeAdUlUqFRx6ciEcnTYRHOc1cFfH29sa9Y8dg1IihWPDRIny3br3QcbEn/8JX3yzHo5Me\nsHlNIqqZEpOu4JXX38KxEyedHYWIXFDStWS8/uHHiD0jOzuKIiwWC15f+AnyC8QGYQOAl6cnnp50\nP+64ZXCl1/Xx9sb9o2/DvSOH4+NlK7F6o/jw7xKjEfO//Bqfvjmr0usT0f/V6xEDdz/73DPgLCcX\nrXJ2BCIqR9K1ZMx6fyFibdx0pKaIOx+P195fiIRLic6OQkRERETkdLZ3CxARERFRld36+Ch4ensK\n18cfi8cnT3xcqcb8m2UlZ2HJjK+x98e9wsdENotE5xH23RVs5BOj4O7pLlyfGJeIj/7zYaUb8//p\n6Laj+HTKJ0i/Irb7EADUjaiL9gM72GV9ourgs+++R2ZOrnB9ZGgIlrz5OsbdOrxSjen/1KNDOyyd\nMxstG4sNEwGAvIICrNmyrcpr3/DluvVITs8QqvX38cEHM57DXbcMrvLzD/T3w1tPPYEJI8WHuHyw\nfCXybLjJiIiIiGquPw8cxC/bfhOuDw8LxarFn+LRBybYZbdVtVqNB8bdg88/eA+BAf7Cx11KTMSP\nP2+u8vpE5Pp4nSKi6mL1mnX46/QZoVpPDw8seHsOpjz2aKUa82/m7e2NV2Y8h1demA6VSiV0zKdf\nfo3EpKQqrUtENcPaHzbgzvEP2tSYHxkRjtatWiiYiohcxQ+/bsO4qdNtasyPCAtFq6ZNFExlX+u3\nbLepWa5Zw2isXDi/So35N9NqtZj20AOY8/wz8HAXvyfiyMlT2Hf0uF0yENVmPlFhCGpVfa5ZRFS9\n/LB5C+578hmbXmtEhIWiVbOmCqZyDKPJhMWr1+DBZ1+0qTFfatwQDSIjFExGRKQ8Ff/iXxX8RUS1\nG5vziYiIiBwsunU0mndtLlx//sg5LJu5FMUFxXZZ32w245fPNmHPd7uFjxk8cQi8/Lzssn6zLhIa\ndxD/ICwxLhFLX/oaueniTcIi0pPSsXzmNyjMFW9m7T22j10zELmq85cT8cueP4TrI0JC8NFLLyA6\nItyuOYIDA/HOs08jPCRY+Jh1W7ejqFhsN7OKJKen48ffdgrVeri7453pU9FOalbldW9QqVT4z913\nYvSgAUL1ufkFWLnpF7utT0RERNWTxWLBws8WC9eHBNfFlwvfh6TADdadY9rj/bdmw83NTfiYlWvW\nwWKx2D0LEbkOXqeIqLooKCjAl0u+EapVq9WY98YsDOzf164Z7r5zNKZNeVyo1mg04qPPvrTr+kRU\nvaSmpeOxqdPxxtvzUVRUJHzcrcNuwdrlS9AouoGC6YjI2dIyMvH0629h3qdf2PQ52rB+fbBiwbuI\njqoeDVXFej0Wf7tWuL5Jg/r4ePariAgNtXuWQT27Y6bga7kbVm/cZPccRLWJWuuGiH5dnR2DiGqg\ntIxMPPXqG5j78ecoKha/h3P4gL5Y+dH7iI6KVDCd8i4mXcFD01/E5yu+hdFoFDpGpVJh/OjbsOS9\neagTGKBwQiIiIiIi52BzPhEREZGDDZooPnE99XIqVr2xEiajye45ti7ZghO7YoVqdb46dL+9h13W\n7Xt3P+Ha9KR0LH3pa+gLq95oW5aMqxlY/eZKmM1mofrgyGBEtaivSBYiV7Ji0y8wCzYb+Pl446OX\nn0dInSBFsgT6+WH+9GnCO0vkFRbi96NHq7zuV+s2oETwA6UZDz+A1k0aV3nNsky9fxzaCk7QXrt1\nGzKycxTJQURERNXD7j/2Iu7sOaFanc4TX374HiLtPGDpZjHt2+KFqU8K11+4dBknT4ntTktE1ROv\nU0RUXSxb9S2ysrOFap/8zyN2b8y/4cEJ4zBMcBfXX7duhyx4jSWimuWXLdsx+r778ee+A8LH+Pr6\n4J03Z2HOrFfg7W2fAd1E5Jq27PkD9z71jE27svt6e+Ot6VPx+rQp8PbSKZjOvjZs/Q3pWVlCtSF1\ngvDx7FcR4OerWJ4hfXpiwujbhOsPHD+BtIxMxfIQ1XT1esbA3de7whpDTr6D0hBRTbFl1++45/Gp\n2HfkmPAxvt7eeOuFZ/D6s09Xq9dS/2SxWLB6wyaMf+pZnD57Xvi4kDpB+OStWXj6oYnQarUKJiQi\nIiIici425xMRERE5UGjDMES3aShUazKasGbutzAUGxTLs37BD0hPSheq7TayO9x1Ys2x5YloFoEG\nrcR23jCbzfjxgx8Ua8y/4cKJCziz97Rwfdt+7RRMQ+R8aVlZ2HHgkHD91PH3IbROHQUTAQ3C6+GO\ngf2F67fbcANiWVIyMrDlz71CtX07d8SQHt2rtF5FNGo1Xnp0ktBwgmK9Ad9u3qJYFiIiInJ96376\nWbj28YcfRHQD5YePjb3jNjQXHDYEANt37VEwDRE5G69TRFQdFBUXY/nqNUK1bVq1xKT7xyua56Xp\nzyAoKNBqncViwRdLvlE0CxG5nrnzF2DGq68jNzdP+JiOHdrh+xVLMXTwQAWTEZErePeLrzDzvQ+R\nm18gfEyHVi2w8sN3Mbh3TwWTKePHbduFa599ZBKCAvwVTFPq0XvHom6g9ddyQOnruV37DyqciKhm\n8omqh6CWTSqs0WfnIvvcRccEIqIa4d1Pv8Qr7y5Abr74YI8OrVti1cfvY0ifXgomc4xnZs/F+198\nDb1e/P7VAT27YdUnC9C5XRsFkxERERERuQY25xMRERE5UJcRXYVrD/y0H8kXkhVMA5ToS7Bh4Y9C\ntTpfHToP61Kl9doPjBGuPfTLIVz862KV1hP12/LtMJvMQrWNOyizOzaRq9i+7wBMJpNQbbe2bXBL\nrx4KJyo1fuRw6Dw9hWqPnpFhMov9TJdlw47dQse7a7WYNmFcpdcRFRUWhrGCO6T9sud3GEpKFE5E\nRERErigjKwt7BW/elZo0xri7xiic6P/+M+l+4dr9h48omISInInXKSKqLjZv2Ya8PLEm15effxYa\njUbRPAEB/nhy8iNCtTt27UE6d1slqlX+Oh0nXOum0WDKY4/gq0ULUS8sVMFUROQqzpyLF67VaDR4\nbPy9+PTNWQgLDlYulELi4hMQfylRqLZnpxj07y5+70ZVeLi74/4xtwnXH4w9oWAaoppJ7a5FRH/r\n9zMlH4iFxWJxQCIiqilOnxPfLV6j0eDx++/DZ3NnIyyk+r2WKsvps+LPX+fpiVeeehxvv/Q8/H19\nFUxFREREROQ62JxPRERE5CAqlQqte7UWqjUUG7Dr213KBrruwokEnNkntnN8h8HizfVlad27lVCd\n2WzG72t3V2ktW6ReSsWFExeEautG1oWXn5fCiYicZ8eBQ8K140cOVzDJ3wX6+WFg185CtUXFxTh3\n6XKl1jGaTNi0+3eh2tv690WwwK5l9nDv8KFCwwmy8/Kx8+BhByQiIiIiV7P3wCEYBYcs3Td2jOJN\nZDfr16sn6oWKNX6cT0hAYWGRwomIyBl4nSKi6mLNuvVCdf369EKrli0UTlPq9lG3CjXSGo1G/LBh\nowMSEVF10yAqEssWf4pHHrgfajVvFyOiv4sKr4ev3n4TD941utpeI/48fFS4dtxtIxVM8m+3DxkE\nd61WqPakfFbhNEQ1T72eMXD38a6wpjAlA7nxYgM8iIhsVT+8Hr6aPwcP3n1ntX0tVRUtmzXBioXz\ncdstg5wdhYiIiIjIoWrfq38iIiIiJ6nfsj68Ayr+MOiG478dQ2FOgcKJ/m/Hit+E6sIahiGsUb1K\nrREaHQrfID+h2vhj55Gdkl2pdSrrzH6xAQVqtRphDcMUTkPkHHkFhYhLEBtUUb9eGDq0aK5wor/r\nFdNBuPb85co15+87HouMbOvXH7VKhXtHDK3UGpXh7+uD4b17CtX+tNNxw02IiIjIdRwUvAFZp/PE\nkP79lA3zDyqVCl06ib2WM5nMSLh0SeFEROQMvE4RUXUgnz2HU2fEdqF+6P4JCqf5P62bG8bdfZdQ\n7Q8bflI4DRFVN3eMHIHvln+N1g4aKEJE1cvIQf2xYsE7aNm0ibOjVMmhEyeF6uqFBKNjG7FNBezF\n08MDbZo3E6rNzM5BVk6OwomIag6fqHoIatHYal3yvmMOSENEtdGowQOx4qP30KpZU2dHcTi1Wo0H\nxo7GV+/OQf2IcGfHISIiIiJyODbnExERETlI005iH7YCwNFt4lPd7eFa/DVcPi12U3H7ge0rtUZ0\nm4bCtccc/PwB4PJp8UbewDDH7JRN5Ggnzp6F2WIRqh0m2ChuT22aid8UdDU1vVJ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hAAAg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\nruTaarbouZuflbeNjV+7k5iSGLSxALijp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ag6paWlcUm/\nmxKfytiqRfM44+TjqykVkBXnz4HlWbh4SYydPDnV7Drt2kWPrhvmONHP7bTF5vHCO++mmp047ZvY\nsGPH3AaKH5/QevT+PeP4gw+MwkKX+0Ou2acqzz4FAMDqzqdgAIAaqmmTxnHBqX+N3bffNusoK628\nvDw+Hz8x1exeO+0QeXl5OU70S1t33zRVOT8i4ruZs5TzqREWzF8YN1zUP955dVjWUTIx87vZcdU/\nb47RH36RdZSc2Ofg3VPNvThwaMyZPS/Haf7rtKPOjSWLl1bbelCTfDN9eqq5ruuvl+Mkv9Shfbto\n3KhRLFq8OHH2m+/SvQ+gdnmvEk/zOGDvPXKY5Je6rr9edO/WNUZ/OTZx9sWhbyjnAwCrjIWLFsWl\n196YOLfrjtvHVj02q4ZEAFVnwMOPx1dTpibOnX3qydGkceNqSARkyflzYHlGT5gQ5RUVqWb33WmH\nHKf5pU0qsTdNn/19DpP8qE2LFnHhySfE5htukPO1gB/ZpyrHPgUAABH5WQcAAOCXtuq+STx6y/Wr\nRDE/4scye3FJSarZrhmVMDqu1SH17NyUT6OEXPr4g9FxwkFnrLbF/NdffCeOP/iMVbaYn1+QH3vu\nv0uq2eceT37yWlVSzGd1tnBR8oV7ERHNMrqJzxpNm6SaW5jiAkSg9vl83PhUc21atazU08KqyoH7\n7JVq7u1hI3KcBACg+vS79Y6YlXBBdJ06deLMv51cTYkAqsbkaV/HA/9+MnFuu622iL13S3euG6jd\nnD8Hluerr79JPVuZAmpVadakSTRt1CjV7My5c3KaZY9tto4HLr9E4RWqmX0qPfsUAAD8qDDrAAAA\n/FedwsI4udeR8edDDszk6fG5UrpsWey14/axYOGimL/wh1jww8JYsHBhFJeU/mJ2g04dqz1fRESz\nlF/GR0QsXVqUwySwfCUlpXH/rY/G4/c+k3WUTCxetCT+dcWAGPLsG1lHyamtdtg8WrRqljg36sPP\nY+pX6b8gBFZOcXFxqrmmTbJ5EljTxo3j2xRzRUXp3gdQu4yd8H/s3Xd0FPUeBfCbTd30QgiEFPpQ\nQm8CIlKkK4qK0gVURFGadBUVUKQICBasICgKUpTee5PemQABQktIr5u6eX+ADxGy89uwM7ub3I+H\n8zR7Z+bGc5y3+9v5zlwSyjWpV1flJg/3RNPG0H2hg9FoNJm7dvMWbsbEIrhMkEbNiIiIiNSxe/9B\n/LV+o2JuYJ8eCA8L0aAREZHlfDr7S+Tm5pnMuDg7Y+zbgzVqRETWxvVzIjLF0VGHCuWCkZiSitSM\nDBQU8nRqBwcHVA0P07jdHYH+fkgVuEFHZpY61wy5u7lhWO8e6Ph4c1X2T0Sm8TyljOcpIiIiIqL7\ncTifiIiIyEaUDymHSSOHQqpYwdpVLK5CaAg+GT3igZ9nGgx3BvZTU5F8d2A/tGwZKzQEfLy9hbMF\nePjiO5Harl66jsmjPsfFc5etXcUqzhyXMWXULNy6HmvtKqpr1qqhUG7nhn0qNyGif3N31ws9/cfF\n2VmDNg85rouLUE7v5qpyEyLSWlxCAlLT04WyEdUllds8nL+vL6pVroSzkRcUsweOHkO3Th00aEVE\nRESkjtS0dHw87XPFXNVKFTGwT08NGhERWc6azVtx+MRJxVzfl55HWEg5DRoRkS3g+jkRmdKrU0f0\n6tQRAJCXn4+k1FQkpqQgMSUViSmpSEhJQVJqKozGAnjo9VbpqHcV++/fkJ1j8WPXrFQRHwx6DcGl\nAy2+byISw/OUaTxPERERERE9iMP5RERERDbgufZtMXzgK3ATXEAtLtz1erjr9ShrA4u2To6Owllv\nT+vczZ9KttW/b8SXU39Edpblv0Cxdfl5+fjl2+VY+NXvMOabftJqcdGweT3FjNFoxO4tBzVoQ0T/\n8PbyErq4MD0jU4M2DzIYDEI5c25KRET24VZsnHC2SoXyqvVQUjeihtBw/qHjpzicT0RERHZt2hdf\nIi4+wWTGydERH48fBWcnXrZBRPYjNS0Ns7/5QTFXrmwZDOj5kgaNiMhWcP2ciEQ5OToi0M8PgX5+\n1q5SNIU8TbsoHHU69O7SCa90fdqs65aISF08T93D8xQRERERUeH4LS8RERGRFfn6eGP6+NFo2aSR\ntauUeBmZYl/GA4CPF4fzSTvJial4761PsXfb39auYhW3btzGxKHTcPrYeWtX0Uy58LIoF1ZGMXfx\n/GXEx5q+yJuILMvXxxs3bsUo5lLS0jRo86CMTLGLGn28vVRuQkRai42LF86GBCu/z1BLhFRVKHf+\nwkWVmxARERGpZ9feA1izYbNirn/vl1GtahUNGhERWc7c7xcgMTlZMTfqrUEl7qbkRCUd18+JyN6l\nC143pHezzHucsoGlMOntwahdhZ8LiUgMz1NERERERLaFw/lEZDMkSXIA0BdAhUIiq2RZPq5hJSIi\n1X06egRcXVysXYMAxCUmCmf9fHxUbEJ0vw+HTUduTq61a1jN8H7vl7jfv0HT2kK5E4fOqNyEiP6r\ncvlwnDkfqZi7fDVagzb3y8/PR6zCUxn/UTE8TOU2RKS1lNRUoZyTk5NVP89UDA8Vyl2/FYOMzEx4\nuLur3IiIiIjIslLT0jBp+izFXKUK5fF6v97qFyIisqBT585j5bqNirmWzR7DE02baNCIiGwJ18+J\nyN6lpqcL5fy8vC1yvDljRsHVxdki+yKikoHnKSIiIiIi28LhfCKyCZIklQPwI4B2JmJXAHA4n4iK\nFQ7m246LghcB+Hp7oWzpQJXbEN1T0gbT/6sk/v7Va4s9Ufbk4bMqNyGi/6pdozr+FHj6YVT0NeTm\n5sLZWbsvqm/GxCIvL08oW7tGdZXbEJHWsrKzhXIe7no4ODio3KZwYeXKwcHBAQUFBSZzBQUFkC9F\noX6tCI2aEREREVnGZ7O/RFyC6cEvR0cdPh4/StPPjEREjyo/Px+fzJ4Ho9FoMufm6opRbw3SqBUR\n2RKunxORPUtKTUOi4E1wgy10zRAHXonIHDxPERERERHZHp21CxARSZL0MoBTMD2YT0REpKq/j58Q\nytWuJqnchIhKOimislDu7Anlp48QkWXVqVlDKJeXl4cz8gWV29wvMuqyUE6n06FW9WoqtyEirWXl\n5AjlrH2DOBcXZ3h7egplL16+qnIbIiIiIsvasWcf1m7aopjr93J31OQ6MxHZmSUr/4J8MUoxN6DX\nSwguE6RBIyKyNVw/JyJ7djFa7IEeAFAlLFTFJkRED8fzFBGR9TjwL/5l4i8iKtk4nE9EViNJkp8k\nSUsALAHgZ+0+RERUcsXGJ+DEufNC2dr8Mp6IVOSmd0VYhXKKueTEFCTGJWnQiIj+rUJYKCqVDxfK\nbt29R+U299t/+IhQrlHd2vD0cFe5DRHZqnyFJxxqwd/PVyh341aMyk2IiIiILCclNRWTp89WzFUM\nD8MbA/pq0IiIyHISEpMwf+FixVxouWD06/68Bo2IyBZx/ZyI7NmBU6eFco6OjqhZuZLKbYiIHsTz\nFBERERGR7eFwPhFZhSRJ7QCcBvCytbsQERH9tGw5jMYCxZyDgwPaNHtMg0ZEVFJVksrD0clRMRcl\n80myRNbSrXMHodxfG7cgLSND5TZ3ZGRmYvNOsYsZu3XuqHIbIrIGNxcXoVx6ujbnJVP0bq5CuZux\nt1VuQkRERGQ5n83+EvGJiSYzOp0OH457Fy6C792IiGzFvB8WIiPToJgbPmggnJ2dNWhERLaK6+dE\nZK92HT4qlKtXrSrc3dxUbkNE9CCep4iIiIiIbA+H84lIU5Ik6SVJmgtgA4Bga/chIiI6JUfiz01b\nhbKNakegXJkglRsRUUkWVjFEKHfl0jWVmxBRYTq3bQ29wJfZ6RkZ+G7Rrxo0AhYtW4F0gQsZA/x8\n0ZI3GiIqllxdxQbes3NykJOTq3Ib01ycxYbRbsbGqtyEiIiIyDK27dqLdZuV15h7d38etWvW0KAR\nEZHlnL9wCas3bVHMNapXB082b6pBIyKyZVw/JyJ7tPfYccQkJAhl2zRurHIbIqIH8TxFRERERGSb\nnKxdgIhKDkmSGgFYBECydhciIiIASEhOxnszZiPfaBTKD3jpBZUbEVFJVzZU7AYgt2/Fm7Xf0PLB\nqN2oJspXDkV4pVAEBQfCw1MPd3c9XFxdkJWVjSxDFpLiU3DrRixuXI3B2RMyTh87j6T45KL8KkTF\nlpenJwb06I4vf/pZMbtk5V94vHEjNK5fV7U+kZeisOD3ZULZN/r1hrMTlwOJiiNfb2/hbFpGOgJc\n/FRsY1pefp5Q7lbMbZWbEBERET265JQUTJk5WzEXHhqCt17tr0EjIiLLmvHVfBgVvkfT6XQYOfh1\njRoRkS3j+jkR2aOFq9cK5Xy9vPBU0yYqtyEiehDPU0REREREtomriUSkOkmSnAC8B2ACTJ93LgKo\nrEkpIiIq8RKTUzD0wym4dTtOKN+2eVPU51ONiEhlwSGiw/nK567Q8sF4+qX2aNqqIULCg01mPTzd\n4eHpjoBAf1SuXuG+1+TTF7F9/V5sWb0TCXFJQv2Iirs+L3bDms1bcfX6DZM5o9GIUR9/gm+mTUH1\nqlUs3uPW7dsY9v7HyM1VHnStWa0qnu3Y3uIdiMg2lC4VIJy9FXsbAX7WG843ZGUL5dIyMpCfnw9H\nR0eVGxGRvTJkZeHUmXM4F3kBkRejcCv2NuLi45GWno7s7Bzk5ObCxdkZbm5u8PRwR5mg0gguE4SK\n5cNRq0Z11KhWFW6urtb+NYjIzk2dPQ8JiabXS3Q6HT4aNwquri4atSIisoytu/fi6MnTirmuHduh\naqUKijkiKhm4fk5E9mTTvgM4F3VZKPviU23g6sLPdUSkLZ6niIiIiIhsF4fziUhVkiRJABYBaKQQ\nXQhgCIA01UsREVGJd/bCJUyYMQs3YmKF8oH+fhg1aKDKrYiIgLKhYsP5sbfiC30tol419Br0PBq3\nqA+dTvfInaSIypAiKmPA0J7YumYXlny3Ateu3Hzk/RLZM2dnZ7w3/B0MHjMBeXmmL+xLz8jAayPH\n4v0R76B9q5YW63Ds9BmMnTQV8YmJillXFxeMHzrEIucEIrJNZUoHCmfPRl5ERDVJxTamxcUnCOUK\nCgqQlp4BXx9vlRsRkT3Jys7Glh27sGnbTvx95Biyc3IU81nZ2UhOScH1m7fue83Z2RmN69dF2yef\nQJuWLeDl6aFmdSIqhrbu3I0NW7Yr5no8/yzq1qqpQSMiIsvJz8/Hlz8qP/nazdUVb/TtpUEjIrIX\nXD8nInuRmp6Oeb/9LpT10OvRrW1rlRsREd2P5ykiIiIiItvG4XwiUo0kSW8BmA5AbyKWAmCQLMu/\n391Gi2pERFRCxcYn4OcVq7Biw2bk5+cLbePm6oqpY9+Fn4+Pyu2IiIDSZcUG65ITUx74mbevF954\ntx/aP9dKlQuIXFyc0bFbG7Tt8gSWL1qLhV/9jqzMLIsfh8he1K8dgQlDh+CjmbMVs4asLIz/ZBo2\nbt+Jtwb0Q6Xy4UU+blx8Ar775TesXLcBRqNRMa/T6TB57LuoVrlSkY9JRLavdKkAeLi7IyMzUzF7\nLvKiBo0eLjklFanp6eL51FQO5xMRAOB2fDx+XbYSy1evRXp6hkX2mZubi70HD2HvwUOY/sVXeKZT\ne/R+sRvKBZe1yP6JqHhLSk7BlJlzFHOhIeUw5PUBGjQiIrKs1Ru34Er0NcVc965dEFgqQINGRGRP\nuH5ORPZg2k8/IylV7FlSL7V/Cp7u7io3IiK6H89TRERERES2jcP5RKSmz2B6MH8vgF6yLF/VqA8R\nEZVAeXl5OHlexsZde7Bm6w7kKtyd/9/cXF0xbdwo1JKqqtiQiOgeH18voVx66v3DKBH1quGjL8bA\nv5SvGrXu4+zijJcHPovH2zbGh8Om49L5K6ofk8hWPdPhKcTGx+ObhYuF8jv3H8Tug4fQuF5ddGzz\nJBrUqYWypUsrbhefmIjjp89i887d2LX/IHJyc4U7jnjjNbRu0Vw4T0T2ycHBAVKlCjh66oxi9owc\nqUGjhzt57rxZ+dQ08UF+Iiqe0jMy8MOiJfj1jxXIyRF/D2SuTIMBvy1fheV/rUWP55/Fq317wcvT\nQ7XjEZH9mzprLhKTkk1mHBwc8OGYkdC7uWnUiojIMrJzcvDtol8Vc54e7ujfo7sGjYjIHnH9nIhs\n2cpt27HzyFGhbHhwWfTq3FHlRkRE9+N5ioiIiIjI9nE4n4isIR/AZACTZFkWe2wxERGRCXn5+TAY\nspBpMCAhOQXXb8Ug+uZNyFGXcfjkaWQYDGbvs3SAP6aNG4UaVSqr0JiI6EHuHno4uzgr5oxG433D\n+W27PIFRk9+Ci6uLmvUeEBIejC+XTMWnY7/Azo37ND02kS15rXcPlCkdiE/nfInsnBzFvNFoxIEj\nR3Hg7hfpfr4+CC4ThMCAAOjdXOHs5Iyc3FxkZWcjITEJN2NjkZCYZHYvD3c9Jr47HG14YSFRiVGj\nahWh4fyo6Gu4HH0NFcJCNWh1v/2HxS4i+kdmET7LEVHxceDwEUyYPBW34+I1O2Zubi5+/m0Z1m/Z\nhskTxqBxg3qaHZuI7MeWHbuwcdsOxdxLzz2DBnVrq1+IiMjClv25BjG34xRzfV7sBh9vsRvOElHJ\nxPVzIrJFx86dxxe//CaUdXBwwOhX+sLFWfl7fCIiS+F5ioiIiIjIPnA4n4i0dhVAL1mW91q7CBER\n2Z8lf63FrB8WqH6cVk2bYNybg+DLC4qISEPevmLnnGxDNoxGIwCgZftmGPfZUOh0OjWrFcrVzRXv\nzxgBN70rNq7abpUORLbg6XZtUSk8HBM+nYboGzfN2jYpOQVJySkW7VO5QnlMnTAGFcLDLLpfIrJt\njzWoh8XLVwll/9q0BUNf7a9yo/tl5+Rg047dZm2Tm5unUhsisgc/LFpitWPHxSdg8MixeL1fbwzq\n38dqPYjI9iQlp+CTz79QzJUrWwZD33hVg0ZERJZlyMrCgt/+UMx5e3mi5/PPatCIiOwd18+JyJZE\n34rBe/O+Rl6+2DOlurZqidpVq6jciojoHp6niIiIiIjsB4fziUhLvwMYJMuyZb81KYYkSXIEUAFA\nFQABADwBFABIBJAAIBbAeVmWxVZfLNvNH8DjAJoAqHa3ZyAAXwCuABwBZAPIABADIBrAaQAHAeyQ\nZTlR684PI0lSdQC9ALTEnd/BH4ARd/7dfinL8udWrEdEVlI5PAxv9e2F5g3rW7sKEZVAosP5OXeH\n1CLqVcO4qe8ID+anp2bg2uUbSElJQ2pyGgDATe+GwKAAlAsrI3z8/3J0csSoyW8hJTkNB3YcLtI+\niIqDGlIVLPvhG6xatxHfLV6C+ETtP/qUKR2Ift1fQLcuHeHk6Kj58YnIuurXioCbqyuysrMVs+u2\n7sCQ/n3hqOG5YvWmrUhJSzNrm9y8XJXaEBEpMxqN+OannxEbF4cJI4dqes4kItv1yedzkJiUbDLj\n4OCAD8e+C71er1ErIiLLWb56HRKTTZ/nAODl556Bh7u7Bo2IqDjg+jkR2YKbcXEYNm0mUjMyhPJS\n+XAMefkllVsREd3D8xQRkW3SOThYuwIREdkoDucTkRbSAbwjy/JP1i5iyyRJqgTgZQBtADTDnUF3\nU1IlSdoLYDuAhbIs31axmyuAFwEMAPAE7gzgm6K/+6cUgAgAne7+PF+SpE0AvgawRpblAgv1uwIg\nvJCXq8iyfPFfWa+7x+8J4GGflCoCqG2JXkRkH3Q6BzSuUxvdOrRDyyaN4MBFFCKyEg9PsQsZ83Pz\n4O6hxwefj4SrW+FvGfNy83B433Hs3fo3Th+XceVCtMn9hlUMQaPmdfHUMy0hRVQ2q7ujoyPenzEC\nQ3qMxWWF4xAVZ06Ojnjh6U7o/FRrbN29F1t37cGBI8eQk6vecGmAvx8a16uDdi2fQLPGDXlRIVEJ\n5uLijCeaNhZ6On1CYhJWrNuIF5/upJi1hEyDAd/98pvZ2+XlaX5fSiKiB6xcsx4FBQWYOGaktasQ\nkZVt2r4Tm7fvUsy98ExnNKpfV4NGRESWlZ2Tg5+XLlfMebjr0bNbVw0aEVFxwvVzIrKmm3FxGPbZ\nTMQlJQnlfb28MOXtN+Hq4qxyMyKiO3ieIiIiIiKyPxzOJyK1HQbQU5blC9YuYqskSXocwHsA2uHh\nw+KF8QbQ8e6fSZIk/QJgmizLsoX79QEwBUCoBXbniHudj0iSNEiW5SMW2K8QSZJKAdgJoIZWxyQi\n2+Tl4YE6NaqhWYN6aNm4EQID/K1diYgIzoJfmOXl5WPQu/0QWKbUQ1/Pz8vH6qWbsOT7Fbh9K174\n+NFR1xEddR3LF61BnUY18erw3oioV014e3cPPcZ/NgxvdB+FfA7SUQmnd3NDl6faQKpUEfVqRWD+\nol+QlaX8JGtzBQWWQs9uz6J+rQhIlSvyaa5EhK7tnxIazgeArxYsQuvHmyLAz0/lVsCsb39EQqLY\nxUT/lpuXp0IbIioOfH180Kh+HdSNqIny4aEICwmBp4cHPNz1gIMDsgxZSExOxq2YWMgXL+H4qTM4\ncPgosrKyinS8VWs3oHRgKQwe0M/CvwkR2YvEpGR8+vlcxVzZMkEY/uYgDRoREVneijXrES/w2e2F\npzvD28tLg0ZEVBxx/ZyItHbp2nWMnDkLCckpQnlHR0d8/OYgBAUEqNyMiOgOnqeIiIiIiOwTh/OJ\nSE3TAHwqy7J6tzi2Y5IkBQCYBaCPBXbnijtPte8pSdJ7AGbJsmx8xH56AIsBdLNAv4dpAOCAJEnD\nZFn+UqVj/J8kSY4AloKD+UQlXlCpADzdphXCgoNRpWJ5BPj5WrsSEREAwNlZ7CO6r783unR/6qGv\nXbkYjcnvzsIl+cojdTlx6Aze6TUe3Xp3xqB3+wrfOKBy9QroPegFLPzy90c6PpE9u3bzFlauXY8N\n23ciNk78BhlFERsXj1nzvwdw54llrZo3Q/euXVBTqqrqcYnIdjWuVwdh5YIRfeOmYjYtPQMzv/ke\nn4wbpWqnjTt2YeW6jUXaVqcz5z6WRFTcOTk5oX3rlni6Yzs0rl8PDg6FnyOcvTzh5eWJ8NAQPNao\nAfr1AAxZWdi+ay8WL12Oc5Hm30/4u4W/oG5ETTRt3PBRfg0islOffP4FkpKTFXMfjB4Bd3e9Bo2I\niCwrNy8PPy9drphzcXZG7xee06ARERVHXD8nIq2dvngJo2fNQVpGplDewcEBY/r3Rb3q4jexJyJ6\nFDxPERERERHZLw7nE5FqZFn+2NodbJUkSTUArAZQ0cK7dgMwA0AnSZK6yrKcXpSdSJLkgzv9Wliy\n3EM4AZgnSZKHLMvTVD7W6wBaqXwMIrIDsfEJ+P73P/7/zx7uetSvWQNPNGmEJx9rDB8+6YOIrMRJ\ncDjfxdXloT/ft/0QPh4xA9lZORbpU1BQgOWL1uDi+cv45OsJcPcQu7D8pQHPYvXSTUiMM//puET2\nLDYuHl98/xM279iFfOMj3SutSDIyDVizeSvWbN6KmtWq4o2+vdGsUQPNexCRdTk4OGBAjxfx4Yw5\nQvlNO3ajdo3qeLlrF1X6HDx6HB/P/KLI2zs78SsMIgJ0Oh2e7dwBr/bthbJBpYu8H72bGzq1a4NO\n7dpg07admPXVfMTcjhPevqCgAO9/Mg3Lf/4ePt7eRe5BRPZn49bt2LJjl2LuuS4d0ZSfw4jITm0U\nHJTt2KYVAvz9NGhERMUJ18+JyBoOnjqN9+Z+hawc8e/Ph/XqgY6PN1exFRHRPTxPERERERHZN521\nCxARlTSSJDUAsB+WH8z/t9YANkiSVNQJ0yUQG8zPALANwHcAPgYwFsBoAJ8C+B7ACQD5AvuZKknS\n00WrqkySJA8AH6m1fyKybxmZBuw+dART5n2DLgMG4aM583Ap+pq1axFRCeQsOJz/MAd2HcH7b0+1\n2GD+v504dAbjB09Bbk6uUF7v7oa+b3a3eA8iW1VQUIDla9bjxVcHY8O2HVa5sPC/zpyPxNvjP8D7\nU2cgKSXF2nWISGMdWj+JsHLBwvnPv/keK9ZtsHiPjTt2YfgHk5BtxgVF/+XE4XyiEi88NAQ/zpuF\n90cNf6TB/P9q17olfv9pPp58vJlZ2yUkJuHrH3+2WA8isn2JSUmYOmueYi6odCBGDnlDg0ZEROpY\nvGylUK7n811VbkJExQnXz4nIWlZt24Exs+eaNfD6xovPo1vb1iq2IiK6h+cpIiIiIiL7xyvbiIg0\nJElSOIA1AEw9VscIYB2APwEcAXAFQBoAVwD+ACIAPAGgP4AgE/tpDmAZgA5mdhwEoKNC7AjuDONv\nkGXZ5MqQJElBAAYDeBeARyExBwDfSZJUVZblVHP6CuoNIFCF/RJRMZOdk4u123Zi/Y5d6PRkSwzp\n1xv+vj7WrkVEJYTOsWj3z7t+9SY+Hj4Dxnz1Lmg6cegMvpm+EG9PeFUo375rK3w/azHSUzNU60Rk\nCwyGLIye9An2HTpi1nauLi5oVK8O6tSoDqlyJZQNKo0APz+4ubnCydEROTm5yMrJRnxiEmJvxyEy\n6jLOyJH4++hxZBoMwsdZt3U79h0+gi+mfISaUlVzfz0islNOjo4Y/dYgDBk/UShvNBrxyZyvcOXa\nDbw9oC+cnZ0f6fgZmZmY+8NC/LFm/UNf9/f1gaenJ6Kv31Dc16N2ISL79uTjzTBpwmh4ehS2rPxo\nvL28MHPyREydPQ/LVq0W3m75n2vwcreuKB8WqkovIrItU2bOERra+mD0cNXOV0REajtw+CgiL0Up\n5hrXr4sqFSto0IiIigOunxORNeTl52Pur79jxdZtZm33+gvd0Kuz0iWTRESPjucpIiIiIqLig8P5\nREQakSTJEcAKAGVMxHYBGCTL8vmHvJaHO0+qvwZgs/KN2gAAIABJREFUvSRJHwIYA+ADAI6F7K+9\nJEmDZVn+WrCjG4BPFGLzAQyRZTlPZJ+yLMcC+FCSpGUAVgMo7Nv6IAAjAYhdOW6ewf/55wIAywEs\nBnAIQNzdn/kAqIjC/30SUQlhNBZgzbYd2H3oCMYMfg1tmze1diUiKgHy8/LN3sZoNOKz8fNgyMxS\nodH9Vixei5btm6F2wxqKWTe9Kzp2a4NlC/5SvReRtaSlp+OdCRNx8uzDPr49XHhoCPq80A1PtWwB\nTw/3QnN6vSP0ejf4+figSoXyeLxJIwBAdk4Odh/4G7+u+BMnzpwVOmZySioGjx6POZM/RL1aEcJd\nici+PdagHjq1aYV1W7cLb/Prij9x4MgxjBg0EI81qGf2MbNzcrB64xb8sGQp4hISC8292b8Plv65\nVmifLs78CoOoOAoKDESlCuVNZjq0eRID+/SEg4ODql10Oh3GDX8bGRmZWLd5q9A2efn5WLhkKSaO\nGalqNyKyvvVbtmHrzj2Kua6d2qN5k8YaNCIiUseiP1YK5Xp266pyEyIqLrh+TkTWkJaRgQ+++gaH\nz5wT3sbBwQHDe/fEc21aqdiMiOgOnqeIiIiIiIoXXtlGRKSddwDUN/H6TwBeN2PoPRvAx5IkHced\nof/CBsqnS5K0SpblWwK77QHA38TrmwEMlmW5QKTjv8myfEaSpK4ADgLQFxIbLEnSJNF/B4JqAqjz\nr39OBPC8LMs7HpJNuPuHiGxUj2c6o8cznR/4udFoRFZ2NgzZ2cjINCA2Ph6xcfGIiYvH+UtROHle\nRnJqmtnHS0lLw/hpn+N8t64Y0q+3JX4FIqJC5eaa/xZo18b9OH1U/Eu7R/Xl1B8x/48ZQtm2Tz/B\n4XwqtjIyM/H6yLGIjLoslPf28sRb/fuhW+cO0Ol0RT6uq4sL2j7xONo+8Tj2Hz6KmV9/i8vR1wT6\nGjBk/Af4aupk1KmpfIMNIioexgwZhDNyJK4KPKH+H1FXozFk/ERUq1wJz3Z4Cs0aNUBwmaBC88kp\nqThy8jQOHj2OLbv2IDU93eT+69SsjmfatcUvy/8U6qN3K2wJiYjs2Vuv9cdbr/W3do3/c3BwwMQx\nI3DhUhQuCL6/W795G94eNBD+vr4qtyMia0lITMJns+cp5gJLBWDkkP/eI5qIyH5EX7+BA4ePKubK\nlA78/wAsEZEpXD8nImuIvhWDsXPm4lpMrPA2jo6OmPDqADzVtImKzYiI7uB5ioiIiIio+OFwPhGR\nBiRJKg3gYxORLQBelWXZaO6+ZVn+S5KkMQAKm5LywJ0bA4wT2F0/hdeHFmUw/x+yLJ+SJGkWgPGF\nRAIBtMKdmwBYykv/+vs4AM1lWb5gwf0TkQ3Q6XRw1+vhrtcjwNcXYcFlH8hcvX4DO/8+jBUbNuFm\n7G2z9v/zij+RlpGJsYNfU/2JbURUcuUVYTj/l++Wq9CkcJFnLuHo/pOo37S2YrZqjUooXbYUbt+K\n16AZkbamzJorfGFhTakqPvtgHMqWLm3RDk0b1sfir+Zg2ryv8ecG5Y9QWVnZeG/qDCz5Zp7Jpw4R\nUfHh4e6OGRPHY+DwMYpD8/91/uIlTJ13CQAQ4OeL8NAQ+Pl4Q+/mhkxDFpKSkxGfmITrt2JQUCC2\nVFSuTBCmfzAOOp0OeXli73t8vb3M6k1EVFQuLi6YNGE0er72FoxG5WX67JwcbNy6Az2ef1aDdkRk\nDZNnzEZySqpi7v13h8Pby1ODRkRE6li+Zr3Q57quHds90tAsEZUcXD8nIq0dOHkKH339LdINBuFt\n9K6u+OjNQWhaR/l7byKiR8XzFBERERFR8cRvTYiItDEYQGFX5mQBGFCUwfx/mQ3gpInX35AkyeSV\nQZIkuQJ4zERkjyzLlngs63wApn7X1hY4xr91/9ffD+RgPlHJFR5SDn27dcWKb+Zi1vvjUKe6ZNb2\nKzduxne/LVOpHRERkJuTa1Y+8uwlXDwndnGTJa1fsVU4+1jLBio2IbKOFes2YOOOXULZBrVr4Zvp\nn1j8wsJ/uLm64oORw9DvpReE8jdjYjF17peqdCEi21QhLBRfTPkQHu5Fv6g4ISkZR0+extbd+7Bm\n8zZs27MPx06fxbWbt4QH8319vDF70gf/f8J0puDFR74+3kXuTURkLqlKZTzdoZ1wfvN2sfeERGR/\n1m7aiu279yrmurRviyeam/pqjYjItuXk5GL1xi2KOZ1Oh65mvE8iopKL6+dEpLUl6zdizKwvzBp4\n9ffxwdxxoznwSkSa4HmKiIiIiKj44nA+EZHKJElyBjDIRGS+LMvXHuUYsiznA5hlIuILQOnb8ooA\nogAkAnjYldXKVyEJkGU5GoCpIX9LT3A53v3fZbIsr7bwvonIDul0OjRvWB/fTZ2MD955E+5ubsLb\n/vD7H9h35JiK7YioJDNkZpmVP7jrqEpNTNuz9SByBG8kUL12VZXbEGkrLj4BM776VigbVi4YMz96\nD+56vcqtgHde7Y92Tz4hlF2/dQcO8P0MUYkSUa0qvpo6CaX8/a1y/AphoVgwZwYqhIX+/2ep6emK\n2zk5OcHTw0PNakRED3ilZ3c4ODgIZU+cPoPUtDSVGxGR1uITEjFtzjzFXCl/f4x6500NGhERqWfT\nzl1ITk1VzDVr1ABlSgdq0IiI7BnXz4lIS9k5uZj87Q/46vdlMAreRBYAygcHY/774yGVD1exHRER\nz1NERERERCUBh/OJiNTXGUBZE69/Y6HjLANgakqqvamNZVk+J8tyDVmWAwC4AggF0BB3+g8AsMhC\nPQHgsInXKljwOP/2uUr7JSI71qVNKyyYORVlAksJ5QsKCjBl3tfIyBS/ky0RkaiUJOWLIP/t1OGz\nKjUxLcuQjXMnI4Wy1WpVUbkNkbYWLl2O7OxsxZyjTodJY9+Fl6enBq3uGD9sCAIDAoSyC35bpnIb\nIrI1NaUqWDRvJmpXr6bpcVs2bYIfZ32GkLJl/v+zTIMBubl5itv6eHupWY2I6KHKh4WiYd06Qlmj\n0YgTp63zuYyI1DN5xiykpCrfeGP8yKHw8fbWoBERkXpWrdsolOvSro3KTYioOOD6ORFpJSk1Fe9M\nnY6N+/abtV3DmtXx1YSxKFNK7HxARFRUPE8REREREZUMHM4nIlJfJxOvnZNl+bwlDiLLcgaA4wDy\nAcThztPp9wBYBeB7AGfM2FeuLMvXZVk+IsvyOlmWf5JlWXh7AVEmXguy4HH+cUKW5QMq7JeIioHy\nIeUw/5OPEejvJ5SPS0zC4pV/qtyKiEqilGTznrgYffmGSk2UnT5yTigXWj4YOh2XHqh4SEhKwop1\nG4SyT7d/ChHVJJUb3c/LwwNv9OsllD10/ATORV5QuRER2ZrAgAB8N/NTDH21P1xdXVQ9VulSAfjs\nvbGY+eGEBy60Fhl2A4AgXnhERFbS9skWwtmTZ8Q+GxGRfVi7cQt27FG+aLpD21Zo/URzDRoREann\nZmwsjp1SvgRA7+aGFo811qAREdkzrp8TkVaiY2LwxqRPcTbK1OWHD3qudStMHzEMXh7uKjUjIrqD\n5ykiIiIiopKDV8gTEanP1BPrt1v4WC0BOMuyXFqW5RqyLLeQZfk5WZZfk2X5Cwsf61GYeiysXoXj\nid3yn4hKrLKlAzF9/Gg4OzkJ5X9bsw6ZBoPKrYiopElLSUd+fr5QNj8/H3ExCSo3KlxU5FWhnKOT\nI0oF+avchkgbv69aLfTUHwcHBwzo0V2DRg/q/FQb+Pn6CGWXrPxL5TZEZIscHR3R58XnsPLH+Xih\nS0c4O4t9BhJVNqg0hr3WH8u++xJtWjR7aCbmdpzQvkKCy1qyGhGRsEb16wpno69dV7EJEWkpLj4B\nn835UjHn5+ODMUOHaNCIiEhd67fsQEFBgWLuiaZNoHdz06AREdkzrp8TkRZORl7A4Emf4mac2Boz\ncGdNfESfXhjRtxecHB1VbEdExPMUEREREVFJw+F8IiIVSZJUGUCYicghSx5PlmWDLMvK36Bbn6nh\nfMteFX6HpW+CQETFUI0qldGzaxehbEamARt37VG5ERGVNAUFBUhJNPU26Z7srBwYjUaVGxXu5rVY\n4WzpMqVUbEKknQ3bdwrlGtapjXJly6jc5uGcnZzQ+nGxpzfuOnAQeYI3BCGi4qd0qQCMfXswVv/8\nA94e2A8Vw00tX5nm6eGBdk+2wGfvjcXKn+aj9wvPwcO98Kd63IgRex8RUpbD+URkHeXDQuHp6SGU\nvXErRuU2RKSVSdNnITUtTTE34d2hwkNdRES2bP1Wsa/Q27d6QuUmRFQccP2ciNS27e9DGD5tJlIz\nMoS38fJwx4yRw/Bcm1YqNiMiuoPnKSIiIiKikkeNAUgiIrqnvsLr5zVpYXu0niQ7ovHxiMhOvfJC\nNyxdux6GLOW7+m/dux/PtX9Kg1ZEVJLE3LwN/0A/xVx2Vo4GbQqXEJconPX0FhtqIbJlUVejhQev\nHmtQT+U2ptWNqIHla9Yp5tLSM3DmvIw6NWto0IqIbFUpfz/06/48+nV/HrFx8Th84hTORl7AtRs3\ncf1WDFLT02EwZCEvLw8e7u7w9HCHr48PKoaHonKF8qhWuRLq1qwOJyfxrxpuxd4WyoUGczifiKzD\nwcEBocHBOBd5QTF7Oz5eg0ZEpLbVGzZh174DirluT3dC2yc5pEpE9u/8hUuIuhqtmPP0cEezRg01\naERE9ozr50Sktj82b8UXv/6GggLxZxaFlSmDqcPeRmiZIBWbERHdwfMUEREREVHJxOF8IiJ1KX1L\nc1mTFiVbmizLcdYuQUT2wcNdj1ZNH8M6gTv7Hz1zFlnZ2XBzddWgGRGVFLeuxaJGHUkx5+TkqEGb\nwqWnit/p29WN50myfyfOnBPO1qtVU8UmympVUz6H/OOMfIEXFxLR/wUFlkLntq3Qua26T+e4fO2a\nUC6sXLCqPYiITAksFSA0nG/IytKgDRGp6XZ8PKbN+UoxV7F8OEa986YGjYiI1Ld5526h3GMN6sPF\nxVnlNkRk77h+TkRq+m3DJnz521KztmlUswY+evMNeHm4q9SKiOgenqeIiIiIiEouDucTEakr3MRr\n+QDsamhckqTSAJoCqA6g0t0/IQA8AXjc/WPdSbEHRVm7ABHZl4a1I4SG8/Py8nH+UhTq1qiuQSsi\nKiluXo8Vyrm4WveCyJycXOGstbsSWULkJbGPFY6OjqhRtYrKbUwLLRcMd70emQaDYvbSlasaNCIi\nut/5i8rnVEedDlKlihq0ISJ6OL3eTSiXZeBwPpG9+/izz5GWnm4yo9e7YdpH70HvJnZuICKydTv2\n7hfKPd6kkcpNiKg44Po5Eanll7Xr8c2y5WZt81zrVnin18twcrS1SxiJqDjieYqIiIiIqGTjcD4R\nkbrKmngtWZZlo2ZNiujuQP5AAC8CqGflOkVhVzdAICLrkypWEM5GRV/ncD4RWdS1yzeEcq5urnDT\nuyLLkK1yo4crKCgQzubl5qnYhEgbN2JihHK+Pt5wdrb+DSl8vL2ELi6Muc2PS0SkrUyDAddv3lLM\nVQwPg5ubqwaNiIgezlEndmGkg06nchMiUtPu/Qex58DfirkKYWH4ZekKDRrdcS7yglDui2++h7te\nL5Tt2rkD6lr5SbVEZBuir9/A5ehrijkHBwcO5xOREK6fE5EaFv61Bt+vWCWcd9TpMKTHS3jhqTYq\ntiIiuofnKSIiIiIi4nA+EZG6Aky8lqpZiyKQJKkUgA8ADALgYuU6jyLD2gWIyL6U8vMVzsbE8Qt5\nIrKsC2fFni4CAAGl/XHjqvJwmxpcXcXfHmZn5ajYhEgbCUlJQjlfb2+Vm4jx9vLCrdjbirmk5GQN\n2hAR3XNGjoTRqHyvyhqSdZ+iRkSUlS12IzQ3V95IhMieJSaJfSY6K0firBypchvzbdy2Qzhbt3YE\nh/OJCACwbc8+oVxNqQr8zfjOjIhKLq6fE5Gl/bTqL/y46i/hvIdej48GD0KT2hEqtiIiuofnKSIi\nIiIiAjicT0SkNk8Tr2Vp1sJMkiR1BLAAQGkrV7GETGsXICL74unuLpxNz+AphogsKzrqBgyZWdC7\nuylmg0PLWG04391D7KlsAJBlsNm3vUTCDFliw1meHh4qNxEjOiSWlcObZxCRtv4+ekIoFyFVVbkJ\nEZFpmZliaz56vfJnNyIiIiJbsmv/QaFckwb1VG5CRMUF18+JyJJWbt1u1sBrgK8PZowYhsphoSq2\nIiK6h+cpIqKSx8HBwdoViIjIRumsXYCIqJhzNvFanmYtzCBJ0qsAVqN4DOYDAL8tI7JxRqMRickp\niIq+Zu0qAIACM7I5ubmq9SCiksloNOLS+ctC2QpVwlRuU7iAQD/hbEKc2BNTiGxZXp7Yx7esLNu4\nGUWG4DBZfn6+yk2IiO739/GTQrnG9euo3ISIyLTYuHihXIC/+GcjIiIiImtLz8jE6XOyULZB7Voq\ntyGi4oLr50RkKXuPn8CcX5YI50OCgvD1hHEceCUizfA8RURERERE/+Zk7QJERMWcqdtk2dwttCRJ\nehbAfIjfvCUPwBkAVwFcA5AEwHD3j6n51sYAehS9KRHZk8ioy4iKvo6ElGQkJCUjMTkFicnJSEi+\n8/fJKanINxoBAJsW/Qhfby+r9s3KFruzPwC4upi6BwsRUdGcPnYeEfWrK+aq1aqsQZuHCyonfh+n\n27fEhlqIbJmri4tQLi0jQ+UmYkQvLhT9vYiILCE+IRHnLlxUzJUPDUG5MmU0aERE9HBGoxExt28L\nZcsGBanchoiIiMhyjpw4iTyBYVMnR0fUqVlDg0ZEVBxw/ZyILEG+cgUfff3t/68fUlKtQnlMGz4U\nfla+xoiISg6ep4iIiIiI6L84nE9EpC5Tj1R216yFAEmSQgEshPJgfg6ApQAWADggy7LZ355JkvQK\nOJxPVGL8uWUblq3dIJQ9e+EimjWop3Ij0+ISxZ/w7ObmpmITIiqpDu87gZcHPqeYq9OwpgZtHq5C\nlTChXEpyGgyZtvEkFKJHoRf8//y09HSVm4gRvbhQ9PciIrKELXv2wShwwVKzRg00aENEVLir167D\nYBD7HBNchsP5REREZD8OHj0ulKtWtTL0eq4bEZEYrp8T0aO6FReP0Z9/AYPgwzRqVKyIme8Og6e7\nTV1+SUTFGM9TRERERET0MKJPRiYioqIxdQWfXrMWYuYA8FbIbAZQQZblPrIsby3KYD4RlTwVQkKE\ns2cEnqKothu3YoSzPl68sy0RWd7Jw2eRZVD+Qs8/0A8160oaNHqQFFFZKHfp3GWVmxBpI8DPVyiX\nnpFp9af/pGdkIi1drEOpAH+V2xAR3bNp+y6hXIsmDVVuQkRk2qmz54Sz1atWUbEJERERkWUdPHJM\nKFevVoTKTYioOOH6ORE9ivTMTIz6fDYSU1OF8hx4JSKt8TxFRERERESFcbJ2ASKiYi7BxGs2s/Ii\nSVINAM8qxOYCGCbLsvIjzoiI/kWqVEE4u+PAQbz28osqtlFmzg0CgoNKq9iEiEqq3JxcHD90Go89\nofzU2A7d2uDMcVmDVvfodDrUblhDKBt5NkrlNkTaKCv4RNSCggKci7yIxvXqqNyocBeiolBQUCCU\nLccnvRKRRs5fvIST584r5kqXCkCD2rU0aERE1pCdnYPYuDjExN5GzO04xN6OQ8ztf/197G3MnDIR\nTRrUt2rPXfsOCmcjalRTsQkRERGR5SSlpOBy9DWhbI0qYjdnJSICuH5ORI9m6g8LcFXwIRrVK1bg\nwCsRaY7nKSIiIiIiKgyH84mI1BVn4jUfSZL0siwbNGtTuN4AHEy8vhPAcAsO5nPliagEqVaxAlxd\nnJGdk6uYvXD5KiIvX0HVCuVV71WYA8dOCGdD+IU8Ealk+7o9QsP5rTo2x7xPvkd2Vo4Gre6o2yQC\nnl4eQtnTx8SfOElky8qHhAhnz8qRVr24MPLSZeFsuBm/FxHZv/z8fNyIiUX09Rt4vEkjTY/9+6o1\nQrnObVpBp9Op3IaItBZ9/QZeeXMYkpKTFbObt+2y6nB+RmYm9v99WChbyt8fYSHlVG5ERGrq2qk9\nunZqb+0aD/j6x4WY/9MixdzapYtRrmwZDRoRUXFw5nykcFaqUknFJkRU3HD9nIiK6o/NW7DzyFGh\nbEhQEKYNf4cDr0SkKZ6niIiIiIjIFF7lRkSkrmiF18M1aaGsg4nX8gG8KstyvgWPV9aC+yIiG+fs\n7Iy6NcSesAwAf27eqmIb067fisG5i5eEsq4uzqgUFqpyIyIqqXZt2g9DhvI9nDw83dG6UwsNGt3T\n7pknhXK5Obk4uv+kumWINFKzWlXh7PEzZ1VsouyMLH6hdYQZvxcR2Y+cnFxcvHwFm3fuwfxFv2Lc\nlGl4+Y230aJrd3Qb8AaGfTAJpwSeYm8p12/ewvrtO4WyXdq1UbkNEVlDSHBZ5OYp37QRALbs3AVD\nVpbKjQq3duMWZBrE7qfbsnlTldsQERERWc6pc7JQTu/mhrBywSq3IaLihOvnRFQUF69dw1e//yGU\n9fHyxPQR78DXy0vlVkRE9/A8RURERERESjicT0SkrgsKr1t9OF+SJEcANU1EtsuyfNHCh61i4f0R\nkY1r0Vj56c//WLlxM65cv6Fim8L9+qfY0xwBoHrlynB2dlaxDRGVZFmGbOzafEAo2//tl+Hm7qZy\nozv8A/3QqmNzoeypI+d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h69//5hsN6dlTUeH2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\njh379mPv4SNoyYbE14wfx8Z8ajMMej0eufp63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HDfA2nPgKzW1NQU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\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enoyqBUmSfAHEOToHUWWSlJwiVFfrLg98iktKMH/hEnw79xcUFla8Kf9u9h06gn2HjqBz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\ncnUUInIiNzc3PDVnNtzd3bFk+SpXx2nQ1GsmYO7Ds6DT6VwdhYhaIdE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lzwC/ILCqSP/3TefMyb/A5cKpjUSFRbJFy8hHc+n46Dx0/YO4pqziYk4osf51h0jLeX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PTdNw+aQLYDDYd/oiAISGBOOGK6fi4vPH4cW35mLV2vVS161Y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\n5rxHfYQQcQAa68woAzC5KY35Z3kUdf/bN6Q9gGvseL/xADo18noGgJvsacw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sw4cMcY7d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f970Z9ISxBrBi2ruIhY7F0l3qjeuEfT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\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bbz4irvzL2VHSAE8mi4jIz8uLM379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\nginTPkNRUZHumTb/9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9NSfcXKFXRT/ZouyQLg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\n3HZ9n4BnABAeup/WRZ2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9IZfNXbw4xr3xRsbzvbp2jevO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eB6KaV3l6ckIiLyA28u+RrlVdprRoUFh+CxqdcjNNjdX71ERORLBvbro5zduXefiU2IiC50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\nEXnP6YICFCve5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8AV/eJbkDQPXpFrVXyPNyO+v3sKOMPM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hS/o1wY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\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zs3HABplLrqix+7KEJhsSyeh7Vlo7viD9YGYrA5sBmwLvo/m9olHAt8xsnLsfXOVYR7hu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\nsO5+pplNAU4mXyhjwMCeVbtOJz5LPbP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22ad7be090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "def show_confusion_matrix(cm_mat):\n",
    "    accuracy = np.trace(cm_mat)*100.0/test_size\n",
    "    print 'Test set Accuracy = {:.2f}%'.format(accuracy)\n",
    "    df_cm = pd.DataFrame(cm_mat,index=label_textno[0:cm_mat.shape[0]],columns=label_textno[0:cm_mat.shape[1]])\n",
    "    plt.figure(figsize = (8,6),dpi=600)\n",
    "    sns.heatmap(df_cm, cbar=True ,annot=True, fmt=',.0f')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.xlabel('Truth')\n",
    "    plt.ylabel('Predicted')\n",
    "\n",
    "#get lastest prediction\n",
    "_,_,_,f_OUT_test,curr_loss_test = \\\n",
    "            forward_pass(X_test,kernel,bias_kernel,w_SOFT,bias_w_SOFT,\\\n",
    "                         Y_test,range(0,test_size),'Y')\n",
    "#get the prediction to compare with target\n",
    "label_text = [\"plane\",\"car\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]\n",
    "label_textno = label_text\n",
    "for l in range(0,len(label_text)):\n",
    "    label_textno[l] = str(l) + ' ' + label_text[l]\n",
    "    \n",
    "y_pred = [np.argmax(r) for r in f_OUT_test]\n",
    "y_truth = np.array(cifar['labels'])[test_ix]\n",
    "cm_mat = confusion_matrix(y_truth,y_pred)\n",
    "show_confusion_matrix(cm_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CNN has no problem segregating big groupings like animals vs machines. \n",
    "It has problem telling aparts the sub-category like -- (plane,truck,ship,car) for machine and (deer,cat,frog,bird,horse) for animals.\n",
    "\n",
    "Since we only set up 1-layer process for convolution NN, this maybe the limit. Further training will generally improve the training set performance but worsen the test set performance.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000L,)\n",
      "Test set Accuracy = 9.55%\n"
     ]
    },
    {
     "data": {
      "image/png": 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W5LPy5+1sStrHlNg4S4cCQFluAb6iIx1v6IWTlxtdxg7Ao2MAKVsPKKkvWIQy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\nDxhHARflFvHqXa+avG7WM7NIPJHIjpU7lGUBmPrHSYSPCOPdZ5ZwYv+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QAAAf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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test out completely random selections.\n",
    "# as expected, the accuracy is always around 1/(no. of categories)\n",
    "print y_truth.shape\n",
    "rt = np.random.randint(0,10,size=(y_truth.shape[0]))\n",
    "cm_mat = confusion_matrix(y_truth,rt)\n",
    "show_confusion_matrix(cm_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}