pyspark/dl/example/tutorial/simple_text_classification/text_classfication.ipynb

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   "source": [
    "# Text Classification with BigDL : A Tutorial"
   ]
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   "source": [
    "In this tutorial, we will walk you through an example of training a neural net to classify text documents using Python API of BigDL. This tutorial is based on the CNN model of [BigDL python text classfication example]( https://github.com/intel-analytics/BigDL/tree/branch-0.1/pyspark/dl/models/textclassifier). And the model/application was first described in [keras blog](https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html)\n",
    "\n",
    "The example is a typical text classfication problem using the [news20 dataset ](http://qwone.com/~jason/20Newsgroups/). The dataset is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. We will use BigDL to train a model using part of the dataset, and classify the rest, identifying the correct newsgroup label for each newsgroup document. We will show you how to use BigDL together with other python libraries and use visualizations to check training process. \n",
    " \n",
    "## Table of Contents"
   ]
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    "* [Setup the Working Environment](#Setup-the-Working-Environment)\n",
    "   * [Install prerequists](#Install-prerequists)\n",
    "   * [Start Jupyter](#Start-Jupyter)\n",
    "   * [Start TensorBoard](#Start-TensorBoard)\n",
    "* [Initialize BigDL](#Initialize-BigDL)\n",
    "* [Prepare datasets](#Prepare-Datasets)\n",
    "   * [Load data](#Load-data)\n",
    "   * [Prepare the train/val dataset](#Prepare-the-train/val-dataset)\n",
    "* [Train a neural network](#Train-a-neural-network)\n",
    "   * [Define the model](#Define-the-model)\n",
    "   * [Train the model](#Train-the-model)\n",
    "* [Evaluate the prediction result](#Evaluate-the-prediction-result)\n",
    "* [Visualize the Training Process](#Visualize-the-Training-Process)\n",
    "   * [using Tensorboard](#using-Tensorboard)\n",
    "   * [using inlined plotting of notebook](#using-inlined-plotting-of-notebook)"
   ]
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   "source": [
    "# Setup the Working Environment"
   ]
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    "##  Install prerequists\n",
    "Before running this example, you need to have some prerequists installed. \n",
    "* Install `Spark`. This example is tested with **Spark 1.6**. Refer to [Spark Official Site](http://spark.apache.org/) for detailed instructions. \n",
    "* Download and build `BigDL`. Follow the instructions in [BigDL Build Guide](https://github.com/intel-analytics/BigDL/wiki/Build-Page).\n",
    "* Install the Python development environment and related python libraries.\n",
    "    * Install `python` and `pip`. This example works with **Python 2.7.x and pip2**.\n",
    "    * Install python libraries using pip\n",
    "       * Install `jupyter`. Jupyter is a notebook environment for you to execute this ipynb notebook. If you are not familiar with jupyter or met with any problems during installation, refer to the [Jupyter Docs](https://jupyter.readthedocs.io/en/latest/index.html). \n",
    "```bash\n",
    "pip install jupyter\n",
    "```\n",
    "       * Install `tensorboard`. Tensorboard is used to visualize traces and statistics of BigDL training and validation process. Refer to [Tensorboard Installation Guide for BigDL](https://github.com/intel-analytics/BigDL/blob/master/spark/dl/src/main/scala/com/intel/analytics/bigdl/visualization/README.md) for detailed instructions.\n",
    "```bash\n",
    "pip install tensorboard\n",
    "```\n",
    "       * Install dependency python packages used in this example\n",
    "```bash\n",
    "pip install numpy scipy pandas scikit-learn matplotlib seaborn wordcloud\n",
    "```"
   ]
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   "source": [
    "## Start Jupyter\n",
    "\n",
    "* BigDL python API is based on pyspark. To enable communication with Spark cluster through python API, jupyter server needs to be launched using *pyspark* instead of using the regular way. Refer to [BigDL Python API Guide](https://github.com/intel-analytics/BigDL/blob/master/pyspark/dl/README.md#launch-jupyter-on-standalone-cluster) for detailed instructions on how to launch Jupyter with pyspark and BigDL. \n",
    "\n",
    "* After sucessfully launching Jupyter, you will be able to navigate to the notebook dashboard using a browser. You can find the exact url in the console output when you successfully launched Jupyter. By default, the dashboard url is \n",
    "```\n",
    "http://your_node:8888/\n",
    "```\n",
    "or if token is enabled, the dashboard url may look like below. For more details about security of Jupyter, please refer to [Jupyter Security doc](http://jupyter-notebook.readthedocs.io/en/latest/security.html)\n",
    "```\n",
    "http://your_node:8888/tree?token=abcded...\n",
    "```\n",
    "* In the jupyter notebook dashboard, open this notebook(.ipynb) and try execute the code cells in the following sections. "
   ]
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   "source": [
    "## Start TensorBoard\n",
    "Tensorboard is a web-based visualization tool which we use to visualize/inspect the process of training and validation. If you don't want to visualize the training process, you can skip this section.  \n",
    "\n",
    "During the training, BigDL will write traces and statistics in protobuf format in a designated local directory (on spark client node). The structure of the log directory looks like below\n",
    "```\n",
    "/logdir/run1/train\n",
    "/logdir/run1/validation\n",
    "/logdir/run2/train\n",
    "/logdir/run2/validation\n",
    "```\n",
    "We can launch Tensorboard anytime to point to the root log directory (as long as the log directory already exists) and visualize the learning curves alongside the training. To make illustration easier, we create the root directory beforehand (actually if you didn't, BigDL will create it for you when writing the logs). Later when setting up the training, we will tell BigDL to write logs into this directory. For detailed usage guide about visualization using Tensorboard, refer to [BigDL Programming Guide, Visualization via Tensorboard section](https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#visualization-via-tensorboard)\n",
    "\n",
    "Now follow the below steps to start tensorboard.\n",
    " * create the root log directory\n",
    "```bash\n",
    "mkdir /tmp/bigdl_summaries\n",
    "```\n",
    " * launch TensorBoard using below command. \n",
    "```bash\n",
    "tensorboard --logdir=/tmp/bigdl_summaries\n",
    "```\n",
    " * Navigate to the tensorboard dashboard using a browser. You can find the url in the console output after tensorboard is successfully launched. By default the url is\n",
    "```\n",
    "http://your_node:6006\n",
    "```"
   ]
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   "source": [
    "# Initialize BigDL"
   ]
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   "source": [
    "Now we start building our application.\n",
    "\n",
    "First of all, we need to import some dependent python libraries. These are either python standard libraries or 3rd party libraries, convinient for visualization and data analytics. "
   ]
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   "cell_type": "code",
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   "source": [
    "import itertools\n",
    "import re\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "from wordcloud import WordCloud\n",
    "import random as rd\n",
    "import datetime as dt"
   ]
  },
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    "Then, import libraries from BigDL python API. \n",
    "  * `dataset` contains some convinient tools for downloading and simple processing of common datasets such as MNIST and news20.\n",
    "  * `nn.layer` contains definitions of layers and activations. Refer to [layer.py](https://github.com/intel-analytics/BigDL/blob/master/pyspark/dl/nn/layer.py) for a list of defined layers/activations. \n",
    "  * `nn.criterion` contains definitions of critierions (i.e. loss functions). Refer to [BigDL Criterions](https://github.com/intel-analytics/BigDL/wiki/Criterions) for a list of supported critierions. \n",
    "  * `optim.optimizer` contains the `Optimizer` and other utilities for training a neural net.\n",
    "  * `util` libraries contains some common utilities. `util.common.Sample`, in particular, is a class that contains two fields, `features`, and `label`, both of which are ndarrays. Training and prediction both accept an `RDD[Sample]` as input.  "
   ]
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   "source": [
    "from dataset import news20\n",
    "from nn.layer import *\n",
    "from nn.criterion import *\n",
    "from optim.optimizer import *\n",
    "from util.common import *\n",
    "from util.common import Sample"
   ]
  },
  {
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   "source": [
    "Last, call `init_engine()` to initialize BigDL Engine. Now the initialization work is all done. For details about engine initialization, refer to [BigDL Programming Guide, Engine section](https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "init_engine()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Prepare Datasets"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Now we start preparing the input datasets for training and validation. "
   ]
  },
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   "source": [
    "## Load data"
   ]
  },
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   "source": [
    "First, set some global variables for the following processing. "
   ]
  },
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   "cell_type": "code",
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   "source": [
    "batch_size = 128\n",
    "embedding_dim = 50\n",
    "sequence_len = 50\n",
    "max_words = 1000 \n",
    "training_split = 0.8"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Then we use the `news20` tool provided in `dataset` to load new20 dataset and word embeddings (here we use [GloVe embedding](https://nlp.stanford.edu/projects/glove/)). `get_news20` will download the dataset and parse it into a list of tuples, each tuple in the format of (news text string, newsgroup id). `get_glove_w2v` will download the GloVe word embeddings and parse it into a dict `w2v`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 19997 texts.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(19997, 400000)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts = news20.get_news20('/tmp/news20data')\n",
    "w2v = news20.get_glove_w2v(dim=embedding_dim)\n",
    "len(texts),len(w2v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now let's show one of newsgroup text samples using wordcloud and the corresponding class id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the newsgroup of the text is 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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MtC4wv4PT6Wb5yn2A6NWe3SuJj0pX+FwW1LIoLugpDlbcHhNurwmZJAanW49U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9LDhO0nVXJzyhxuTvGesP7koZwXjX0AgNGh3sXPXccBn234Gl+kMrhb2dONSLWfe6/YbDTQZjIy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TeR\nS247ulSWfQ0vsYm6I5R1XlpnXP04fX43DaQ3HeYNA+d0K+tvyE6P55mHzvtV+zyhuLKbalr45est\npOWLas9Rs46ePKTJLhKM7zBsQSlVMS5uIvIQgvl/GfaO0BQvY5PBZiFO2TcOLv0Nno4czo7WB3BZ\nfwhTuyukSNXzUMQ+32Mtr2AGUCVv6/jJjt1wB27bmp67EJTItXcg017fy7GBx1WNo/V+XLaV4SsL\nKuRa0V9Dpr2JSG6ZR9uX2A+97stpeh2H8Wnx0NQDpMrpyGOfxmX+GIfxbwHPeqLkNDtEtqdrv/qM\ny4ePot1u45yiIbTb7dz2/ddcP2Y8bTYrc/PD253vOl9cG7s3HSYmXsvba/+AQtnzHWj9sj08cvU/\nEQSBJz64KegN+6WHP+Wrt1cDkXNlZw1I5szF4Q8GX761ivKDdSHbPnfYA5jbrURpVXyy87Gw7Z3E\n0eGEujFHadW0NRlRafvOBqFXiLebmUmnAWB0GkMK5p2t69jdKhr0z8m4AYXk17eFrG36jp2GdVyf\n/2hE5ZGisl3M1rOsogSFRAKCwBVFfc/Gcyzgf3Y82mTnHrcBW+PZ4s+uYOlBpSBIwROKCc3V65uf\nx7EDAIfxr7gd/tzCEkQB1cWpx2PDYXwcj8eIXNedeD9kP86D2JqvwOOqDfJUAEEGHj+6QY8Vh/Ep\nANzOEhSxTwd5r2/7cjtFJ6Le9OUwPoOzPdhByOsx7gFE+6vLthJP8xVI1ZETgABsqRFtqKfk5HJ6\nQafw1SmV6NVRrCor5YoR4b8vB3aUs3tT5wHgjEsmhRXKJqOV5x/8r69+JGrvSJGYGsf8S7snAuqK\ndUt2+QRzMGij1ZjbrVhMNuw2Z9jf6Ujg9rj4peEfGB3VzE0X10eLrYQDbd8wIfFWllbfD8Cs1Edp\ntVcElHvLDrZ9C0CebjYH2r5lctKdIfs7kXFCxTFbLTaaagyU7CinZEfgpnn/jr9TbxVVQ2evue2I\n+1hW9323shprKTXWUrYb1nBh1m2c6yeUDY5GdrX+gtFp8NVvdTRhcDSyu3U9DbZOpwhv3a71a63l\n1FrL2dO2EZfHhQcPZeb9lJn3s8OwhlZHZ+7hyfp5QQ8OXctrrGU43KLwMDoNGJ0GWuyh+a4zdTFk\n6mKIVaqIVqrQyDvbMrVZ2LFGJNVw2J3HNSwjGP5x8xvsXL2Pnav3HXVbTuPf8bjKA4SyVL0Qpf49\nVMnbUaceQp2yH3XKPtQp+1Am/oAi9u9Io85HkOgBAVnU5b3q0264HbvhdtyOvQiSROTRf0SVtAp1\n6kHUqQdQJS5Hpr0Z8ZzcueE5218Mf7v2wmPG1nxtp6AUVCCokGlvQZn4g9hXygFUKTtQxP8LiTxQ\nyLgsn+I0hU8GEqovbz/h+nJZPu1VX277ui5CWYZMezOqxOWoUw/4/qkSfxYPMYIWt2Ov78ARKdRy\nGWq5jDZb4I1cAJ4+bR5nDSrilu++CtvOJ68v7xypTMr8y6aEfee1P39BU20r+pQYrrpvQa/G/Wsh\nI080O3k8ngBHsL6ERJAyOelOZELn3tRkO0CSSmS1i5FnEiPPxOio7lZudIhj+j/2zjtMqur84587\nvWzvvcLSe0cQEEWKCKJi72JNYoxRE0vUaPIzaowtsfcuigoiRRCRIiBVyrLALrCN7X16ub8/zszs\nzpbZ2WVBSPg+D89w7z33vGen3Pe87ftaXXVYXXXsqH6bMbHd0xPfffITD1/xEjvX7Wfnuv3cf9G/\nMDdaKdhbzFO3v8X2tbk8es3LWEw2/vX79yg+VE7xoXJuHv9ojyXonXIWc0dZ2YeaCjGqgqOvBPhn\n3t9QK9rGmcqsx5iT7N996kCjcDWOihLlNkpJhYxMnb2S5WUfMTpqKp8X/Ycr0n7vWcsv7KrbyNmx\nF/Jlyatcl/4nzK5G31jAN16nNPDR0WcZEz0NgzIECQmX7KTCKtjNQlURfHD0ae7s5d/xqDNUWIsp\ntRQwInIKaytEzePoqHM7HF9jFfE4q8tJhjGSCUnNyXRP3vIGDTVNPL/yz7z4xw8BuPu5a7tNidqT\nkGWZ7d/vYeoVne/6g4HL5t+SUKmbhibihbYDPTFmhSoHVDko9XMh3IXbsQ9JldElmbJb0BhKygS0\n0V8iKf1LuCRVOurQP6LwtMCz1yzAa0U7Gv6KNrZzd7uj4R/ILk9jecmINlp0X1Oo/cNBkhSKUnsO\nSu0k7DW34LJ93zxH43Mo9ZcgKQInD7Ynq7WcHpNV/1iLIwWayFdQ6tr2uJVUGahC7kShOxd79Xxk\nd0ObMYEwPFEkWn2yZzd/Xr0StyzzyKRzqLaYeWXrFkI1WrIiAzclqDxWx4Zlv/iOJ8wcQnR85+VB\nKz8T+QDVZfVcPKjzjmrg33bynXUPEZ/S/YYJwSBnaBrb1x8AREepPkNOTEkm+Ce5uWUnaoV47nvr\n/l2ys815l+xEQqLBLnj6tcpwbK46DKru5dycPWcEQyeKWufta3OpLK1l0/JdXHDjZPqPyqLwwDH2\nbjqEWqNm38/CCzTxgmFsWvEL42e2z2vfFZxSFvMZnMEZnMEZnMH/Ok4pi7mmTFgWMckiy1d2y0hK\nYbVFacL5smQVQyL6IMsye+oPBpxrRORoJsed1+b8VyWftTnndQmrJS1HTXl8U/o20xIup8p2DKvL\nxJ76zZhdDZRammNHQyLG0ytkEAcbd1HnqCK/abdvLOAbnxUygBBVGGfFNPMFS0i+3V+xJR+ry4xb\ndqOQgt8nDQwfw0dHn2VYxNlU24VLMU7Xca1liMd1XWu1YFT5exIuvvM8NnwjusB4uXztNkeHvL7f\nfSgSUBb+aykl+eXoDFqGTx3Ig+/dCQgr9+1HFvLtW2t898y5fRrXPHiR7/jw3iLefOhT9v8siEas\nZhuR8RGMnTGUO58VPZrvmvwYRXnHMDWYuW+m8Ch4PSjLGt7p7C1qF7K72u9YUnalPlWJQj2o82Ed\nQB16fxtr2W92rWikoDRcjMssvqdu5wHc9m0+a7o9yO5anJZPW8i5u10LtpU01OGP4KrwfkYyyGZc\nloWojLecQFkee6gTWW67ILBwO5t5y5X6ue1ayy2hUPVBFfJbHA1da3Th9Q09fd50bC4nGqUKCTCo\nw3ls8lTsLhcGdeBM76/f/hFXC1dmsNnYpwPGnTeQT15aBQgL/9JbxXc1WO7v7iJUnUy1TXwHmpwV\nAISo47G5GvzOh6jjMTkqiNOL32ff8LlsrnyBKYmPd0uul6PcC1mWMYTqMXvaWJobLOhDtCRmxrJv\ni3iGXXzHubz1+Jc9YjGfUoo5Pj2WFe+tRaMVb4qiRXH9zVkX81rB53xetBIZmQd3Px9wrq8nvNTu\n+Qkxbfv+JuqEW7fIcpBJsXMZHCHiQhqlniERExgeOclv/LbaCr84CMgdjgVQtYoZb6v9AZuHNvTc\n+PkcNuXS1SpFpaQiTpfChqpv6RvaecOOMpOgwEwLDedgnb9yWv7+ekwNZlZ9+hP11WJcRz+4zct2\n8uJd7wDwm39dy+jpQzE3WCgvrGqe7921rPnsJ55Z+SAgiBH+csmzJPeK55zLhEv68StfZOLcUdz7\nRnPmcVHeMcwNZt/x8z+Ihg3nG6/lqW9Fidzgif06/VsDQVImIzuP+I5d1m9RGRcEVJjHDUlkvyt1\n04MarjJc6VPMAC7bqoCK2WVd0iJbWYlSH1xph6RMQ6ESiU5u5wGPrPUBFfPxyvLK6UyWy7aqzTmV\n4aqgZCn1F+NoeJI2SXVBQqv0fyyqFIpOm8BYTDZf72Svm9fbgakzBNvbef2yXb7Espb3hEYEH+Lr\nLnIGp9FrYAqH9hRTeayON/5PxNvv/GvXkuy6igTDEPLqv2ZD+VMYVSKRV6MIaXNeowjBRIXvvjBN\nCtHavuQ3rCA7rGdKz867bBwv3PsR29bsw2yy0n90NrJbZv9W4cpO6RXPwV1He0TWKaWYFUoFM25o\nqzgBRkUNYlTUIGRk5m/8AwvH/yuoOb3WcLGlEI1CS7K+bXPufmGjAFhY9CILi/5Ntf0Y6YYchoQP\nYGHxSxSZD+Jw25ib0vEDa0j4Wb6xQq4Yr2qnnjJaE8/35YJIoN5Rjd0tWrvV2Cv4uWYVlbYSVpR9\nxIQYkQhic1vbnDeqwhgddS7/PvQAf+zTToy0FWINojPLucZQBsX4K6EbHrqITSt24Xa5+eNLNwSc\n59N/LmHmTeIzmnaN6C0bERvmK3EDWPrG98y+ZSoZ/Zut0dm3nMvXL3/nU8yWJisKpYLQCNF2UKFU\nED7+xHN8q/TzcDQ2l4PIrjJsVbNRhdwuEryknl+DQu3ZTEi6IMcPBikEZNE+0+3YHXC8295cJ61Q\nZSMpwoNem6T0kFh4FKbsDOyJOm5ZLRRzIFlt/mbJgEITnCUiKSJRqHr5WdsnGss/2eRr0TjnhuB7\nLkPwvZ1Lj1b5FHNX+kH3FO54bB73XPoislvmm/dFUqLT7uLmB2ZjDGu/9NLbJjJYOlKAc5P+z/d/\nCQWTEx/FLTtQtHiWtnc+UpvF6NhmuubBUcFt5FrjvMvH+R3f+FCzp+/Pr92E3ebwGY8Dx/Vm4Ljm\nLP4PdnUtV6gjnFKKORhISERpgnsY1DtqeevwKwD0CsnB6rZSb6/j5qw7/cZ5XciXpd2Fw21HKal8\n565M+wMOtw2VQuMroh8R2bx5mJHY3NLPOxbwG39j5kN+8jKN/bkuU3yYSknlGxelieP8hCs5P+FK\nv/FGwto9r5AU9A0dhk7Z+Y7Z7BSlK6sLD1Fvs3Lb4DG+Yv/4tGjmLDin0zkACveXMue2aQHHlOZX\nkNzLv0VgSq8Eig8c8x3/8bVbeO7ON1n+7loApswfy8wbp5DS2/++nobKeCsu2zqgWcnI7iocDY/j\naHwapW46Sv2FKLVeN+Tx/0S6ThYioVBl43bsEutzdlx/C+B27G3+v/OAXw11V+FNVPu1ZcnOAr9j\nhSqbrqTESKpMOEmK2e1y8/U74jsVFRfGxFlDTorck41+wzNY8MCFvPbE175zyz/dxI9LdzJyUl8y\n+iZiDNVRV9VEeXENe7ceprxYcAZ0VnfdGRQdEMZ0dP5EwquUO8Pzi9czfUQf+iR3PQHttFPMAK+O\nfDSocYdN+UxPEB0++oWJONjCog8DNrJor1RJ3YV65mDHtmdJdwW76jawp34TMxODK93xxpgb7TZc\nssySgv3MyRaW3IZvttN3RBZ/u+lVUnoJa/oPL1zX7jyyWw6uh3Ir3prWPDYjpg7knT3PsGWFUD4r\n31/HraMeYMHfr2DuHYEV/3FB0qCN+gAAR9O/cZpeA9nqWaQVl+UrXJavkBQiy1Wpn4PKcBWSqvu1\npZIUPHG/756Wlqi7MfBgd13g611BJwQeJ0tW66xq7+cRNLpgyR8vNqzY7VNAs64ej0rV9Y5cpwsu\numkS4dEhvPa4UM71NU2Ym6z8uHQnPy7d2cndPYcmi40tB4sI1WsZnp2MUqGgqKqOvGJRMjqydyoR\nRh2Hy2totNhIigpjX2E54/tlUN1oxu15HuUWVTAoPZ7Y8BBkGXYdKaW8tpGhWcnERzT3fz5YKkJ1\nJdX1TOifiUqp4FhtI3uPljE0S/QMiAkT3r+CsmqKqup9MrqD01Ixd4Z1ldswqlxY3VZ+qhY72QZn\nPTaXjUZnQ4dK+XTCkIizGBLReY2kF00O4dIP1Wipt1mZndXc9iwkwsjXr3/P7/55Nas+FXEyq9mG\nrp0m6Kl9k3wxlbMvHtOurJScBIoP+RNPFB8qI7m3vwtdqVIybpZoYjFu1nBWfrCOV+79sI1iVigV\nuN09SFDnyQ9Qh96NyngdTtN7uCyfILuaCRa87GBO09s4Te+g1E1FHfpnJFU3iOu7kNTXfE+z21uW\nzQEGtlZiSl+p14nASZMlt2rdGmQYwDdcOnmsdove+AEAtUbFzCt7pqzvVMY5c0cw9lxRQ7xq0VZ+\nXpNL4YEy6mpE6CUiKoSImFD6j8xg1JT+PS7fYnfwlz/14AAAIABJREFU+zeWcPnEIZRUNzCiVwp5\nJZW8s3orF44W8v7y4QqevG4mb6/ayuicVP722fecN7Q3jRYbTreb9fuOADBv3EAeeH85L90qYvYF\nZTXEhhn5w5tL+PCeK3wy73t7KZdOGEy4QYdCkiitaeCFJeuZN24Qj370HQBPXjeDygYTLyzZwPwJ\nQ9iQe4SZI4NvL9kSp7+GagdvHV7ELdnTkZAYGjESALfsRq1Q+yzn/zUEspg/e2E5ETGhZPRL9mUd\nqtTtfzXm3z2LJ298GYDMgamMPn8IDpuD3RvymDJfxGYuvPVc3nnsc0ZPb3bpffP6aq59uDlR5PPn\nv2XE1EHEpooMfKvJRu7mQyT3iqc1krPj2bRUZI3nDM/E3GglJqlnGnVIiijUob9HHfpbXLb1wmK2\nrgQ/ZSjjsq7CZVuLJvzvQSc8Nd9u7fK65BaKSVIYA46VJAOyLKxqpXYimqi3uywvWJw0WV5FLJs8\nr51Y8q0hd4/oweJw8M7m7azOy+dIdR1mh4NQrYbE8FBGp6fywLS2yZ3/WnRXt2R1BpfswpsvrpQU\n3PHYPG59dC7KTjZ6LtkdlOvY5nRy3ftfsL+8ghtuGMGydzvOoWkNQ4j4fP5WsZu6LCtkhQDNFub5\n/Xpx6yXHR5bit76xI7hrstj07CwoZUL/DM4d2hzb/WF3PpdNHMLQTGG9FpTVsD2/BKfLxcwRfdm4\n/ygTB2SyOa+QcKOOKYOEB2xc33S2F5SQV1LJwPQEVEoFewvLabLYfMaAQiERFWrgyknNRDlr9xTQ\naLGxetdBak3it7q/uJKDx6q4cEx/xvdLZ31u23ymYHHaKGan7EIClEE0pDe7LIyLbr9Uod7R1hU3\nI/m3x7W2ZSWBeZNbo7yohtunCr5ZLzl8sHN0Z63LSl6k3JOVnWgM5WBdtU8pA/z59QW+5I35d4nM\nYZW6/fd5/OwR3PlPUc702bNLee7ON9EZtPQelulTzFOvOIuSQ+X8cVozp+7sW6Zy3lXNjeL3/nSQ\nz59bRmOt2GUbwwwMPCuHB97zj/8D/Pb563nhd0IBfPvWGmKSo3hr11Ndfh8CQ4lSOwmldpKnlEdk\nnTpNbzVnEssO7HX3o1WmodAE3wdZdtd3fTkt46+dJaQpIsHV6JHVMftbj+AkyZIUwv0vu0weWV1z\nocty18ZbPFzZl739CXnlVX7X6ixW6ixWIvUnxwq3ue1YXVZcspvcBvHdSzemYnKaKLdWMii8H0aV\nEbPLjE6hQyEpfPkilbZqDjYWMCi8H5GaiIByjlTXsaNYMGYt2bPfp/i6gt6x0RytrafWbMHh6tl+\n0R2tT69R02jx36gZdRqaLM00uk1WG0ZPyZNCIaH0vD9ev5vJ2jzWYnOg16hZvHkfZpud22aMZVt+\nMXKLzvOaVs9DvUbN9OF9mD3a3yNQXF2P2Sa+S9bjYFA8QzByBmdwBmdwBmdwCuG0sZjv2PZXFCh4\nZeQjzFn/m85v6AA/VHzXhpLzZOP5ez/yWconC4HKpapKa6kqbZshW1HXxA+785k/cQjLtuVhttrJ\nTowmdUofABIVVu6fOgKny8WugmPsLCjF5XZTVFXPsGvGM+ZG4bXYVXCMPimx5BZVkF9WzeCMRC59\n+nI+WbuLa6eK+twwvZYfdueTmBnnJ0upUFCgdnHJmzcBMGNEnxP2HvkgGVAaRH9ppWE+zqaXcTQ+\n7bnoxtH0Etqo94KeTnYFzqpuC7ev2QPQaVxboe6Dy8P97XYe8Gtb2dM4WbIkpcj2ll3HPK8FgYa3\ngezsWj3pu5tFqMRrLQ9LSeS6McOJCzVitjsorK0nObzrSXzdQZmlnNUV67gx80ofoU6tvRYJBdX2\nWrbV7sIlu9EptSglBeHqcGrtwkPQ5DRhc9vZVruLs2LGoFd2HJvPiI5gWEoi+8sruWz4IOob/oZa\n1ReXJ79CqxmFy12G01mAVjsRm20TBv1s7I5dKCSRXPfafD0G/Xwh22ZnxFP/7rH3ofX6vBiSmcQX\nG3fz2Mff4ZZl7r94CnPGDODxT1ezcf8RQFjEQzOTWbj+l3bnXrdP/CaPVNRS2WCid1IMDRYbr6/Y\nTHldExabo937vJgxog8PfbCCPUfLsDqEZfzg/KmcMzibP727jLySSoqruuEp8+C0Ucz39r3R93+N\nQs0jA+7ocOyje//NF8UfMzhiGEtKFwH4Er7a48q+9dF5XV7Ppy99R11VI2k5XSOmWP7hRnasy0Pv\n6aBlaeqegk7LSeDCG9vGu9qD0+1mTZFgp5EkiQO1VfxxRLOr//uFgq2svrqR8kJBPvLkl38gLiIE\nh1O4p4qr6lhw/hg+XLOdyyeJetI+yTH0Sozm+cXr6ZUYzd7CciKMOkZkJ5MaG8HziwVDmPfa8Oxk\nGsw2DFo1KTHhvvu9aE9WmEGHTq0iTO//8HfYnNRW1BMSYcDSaCUsOpQGDzlKZHw45YVVxCRFUVtR\nT1xq+z21g4OEKuQO3PZtPq5nLytVsHA7RL2u7K5DUgR2L/rmbxHj7oxtTKEZh8sqElCQHbhsq1Dq\nZnVpjcHiZMnysom57RuFKHcDbsfu4JjXZEuXa5hX5R3y/V+vVvHK5XOJ0Hct4aynsL/xIFqFhlJL\nGcVm4cqdHHcWe+v3E6uNIlwdToW1kianiRCVkdyGAyTpxXNIpVASphZjbG57QMWsVan45IbLfcdm\nSzFudwNajeB1sNm3oFCEA0qQnSgV0djsQjlXVQu+g+ioN3z3h2h7lgWs9fq8kCR4/OrzsTmcaFQq\nX5XIU9fPxOZRklpPjszfr50BiPEAfZJj+XrzXl+S2NkDs9B4suhHZCcz6JY5qJSKNn2jX77dX0do\n1SqevmEWVrvT5+ZWSBIalZL/3H4RDpcLtbL72fmnjWLuHdLMohOqMjIwvOO+qEalnotTrmBfw27m\nJYsPNsMorI72KDnnLmif1KQj5O8p5tVHhcKfc9PkoO+rLqvjjcdFw4lzLhJf/qXvr++SbC9ikyKZ\ndc0Ev3NOVxkW20+E6GfgctfjdtfjksWubUyiqKWN1RsZEFXjd9+Nf2n+0r3+F9F+zml3UlTbwP6S\nSnKLKjBomsu7vM3jqxrNHC6vISc5lkazlaGZSRypqEHvqfPL8dTvea+53G4iQ/RsO1TC9BF9fPd7\n0Z6sEL2WgrJq31xebPxmG2qtmpJD5RjD9dRXNVLmYR/rNzKb3sMzfGOOTzELSOo+4GvC0NUMcbHh\ncFm+RGUMTOAC4DR/5Hes1AUuH1PqLxC9hz0JUs7GF1BqPQ1Netia7VBWT8vRifU7Ta/5zrnMH6II\n75zAwWVd2uWEu8PVzR6jfglxv5pSBpiWMMXHbTA/dY7vfGzcWb7zhPdDRkZCQkZmXaWophAJr4MI\nUQVOGGwPBv1FiO+qUCgazVBEO02F53gQoMLhPIROJ3gPJOnEUnIGgradBNX2zrU3Ru1RxppWpW2t\njzuDTtNB2e1xKGU4jRRzSzw37E8Br58bL5KQ+oe13V1Pig3MtRsMvnh1NQAh4QamXjwq6PteuO8T\nTI0Wxp0/mNguMOEEC5UyAVBQb/oYhWRApUwESdAJbi8v8Y2bmenvDl77pSDacDldHNwl3JRKlZLM\n+CjfTrNfquhBfNWU4b77fjv7LBSSRGZ8FG63jEIhMSC9Oava63b2XvPO41Xs3vu9aE/W6ys2M3FA\nJmv3CFdmtsfCTslJZNO3O1EoJBrrzMQkR/mYxsyNFsqPVPnGjL+gec1O88eo9BeKAynIh5dsEZna\nHii6WdPsbHoepXYCkqr9TaXLKr5XLkszgYNC3ReFOjBhhaSIRWW4AqfpHUC4mO11ItyjiXgepM4J\naGR3NW7bepT6OQHHdSSrK3KATmUpPFabpMrykY04zZ+j0M0QCXodze8qxdHwdIfXO4LJ1pwMFG08\n8RSXgeBTvp2c9x5LSJwdO669W7qB1gqlZRqSUBeSpCPEeG0PyTv5mD78JITDjhOnpWIOU4cEvH5N\nhnjwyi0sG++XOFLTfeupukzEcdYtFvGo6VeOC5rEffXnW9iyei8anZqb/zKXVZ5Wbz0Ju/MQdmce\nKmUiLncdeu04zLb1oAWbS7h4mhx2vjy0l4t6DWi+zyriKUq1kntevB4gqJaPLZWqIsD4lteULTiH\nW7uL2sO154yguKqe66b6c0VnD0oja0AqaxdtYdK80UgKCdlT3iApJNwuNwqlgqwB/iULjvoHcDQI\nYnul9iwU2rNRqAchqbJb0HG6kF2iLafbthmH6XVkZ37z32CY3+m6/eCxKmR3PbbqS1AZb0Opn4mk\nTAUkZFchTvNCnE2vem5oLvVRhz0SlAh16H247ZtwO0TLVJdVcE1bKyajMl6FQjMWSREPkhLZ3YTs\nKkJ25uHy9Ht227ehUGV1qpg7kuWVAwSU5bZvE2OClRX2MPYar5fBhb32FlTGW1AZLvW8fwKyqxSX\ndTXOppeQ3ZVIishOWcy8sDgcfj6QYL6X/8tQdanxyxl0B6elYg4We+p3olUIl1ROqCgP+qrkMy5I\nmtctkpGv3/oRAKfThUKpYPb1wfHV1lY28uojghv76ntmkpQRi+M4Uuk7gkbVi+iw+zxHwgUVrhKu\n/JaKuDXOO85ex1aHk0arjQaLlQaLjcKqOsb1TiPCIMpLas0WwvU66swWoowGqhpNJEUGl0ijVat8\nVnJrSAqJyZeM8Tv2wtsApd0NhqdG2GVd5VNgAkqQVCDb6chdrdSeg8pwZbvXOoI6VHwmTvOnyM6D\nOBqfwtH4FMIakeio2YIq5HcoNGODEyLp0US+ib3mRr/4quyuxNH4XJfW2x1ZJ0QOoNRORmW8GafJ\nE8uU7TibXsLZ9BL+1l3ze6jQjERlvAV7bft1uXcv+paqJhMVTSYqG02Y7Ha/6ytyD9Ln8bZc/HkP\n3x1wrQcrq1m+7wBbjopNXUFVDXUWK2qlgiijgX7xsVwwsC/T++d0YBPjk7vo5itJi4rgieU/APD9\ngXxUCgWTe2fx52lnE6bTsb+8kke//Z59ZRUkhotN5Z0Tx3DhoPYbvYx86j8ANNra5rWc3683Lxxn\n3XFPYORT/+nx9dWYLby/ZQdrDx2mqLYei8NJtFE8m4YkJ3LRkP5M6R0ccZAMrDmQz9K9eewuLaei\n0YTdKZ7lOrWa+LAQesdGMyo9hWl9exEfGtiI7AinjWLuaib2m6Me4YjpMGqFiFfa3FZkWeaIqWvZ\nnV5YzXaWfbDBdzzu/EHEBdmc/D8PfkZjnZleg1KZd4uIzTgdPVvz1xaKVq8nDot+3oNBqyYuLASF\nJFFW38ia3AJf4X1pbQMDUuLpnxTH9iOlaFWqoBVzT0NSpiO7OsrYdYHcweciaVAZb0Ideg9df0/F\nnNqo97HX/cGX0NTSMvaXpUUd+oeAXZ7avU2ZhDZmEY6GJ3FaPhEn5cDZpc1QBb8JaE9WF+QAXZKl\nDnsQSRGGo+nFVnLaflZK3TTU4U/5GoC0h2/39jyH9qPffs/H23a1e83pdlNS10BJXQOr8vKZ8ksu\nL82fHbBj1b6ySp5evZ6fDhf6nV+0ay9lDY08c9EMbvjgC2rM4jd2xBMjv++r5SRHhDEiNbnNnL3j\nxAa3qLaeOou1x+uOewK946J7dH3fHyjg3q+W0WTz33yVNTR5Xg+yIvcgU/tk88zcGX75La1hstu5\n87MlbT6TltcLqmooqKphRe5Bvti5h68WXN3u2M5w2ijmvw1qZtc52FTIqjLxcJuReDYJumiUCiUV\n1mqWHVvP/NTp6JV6JEmi0SkoBGvttUiSxMUpV3TLWl75yU801Tdnys4NMulr3Tc7WL90J0qVgrv/\neSVKlfgxul3dYyY6FaFSKqg3WxmZmcKm/CKSIsOICTWiU4n3OSchBpPNzqDUBNRKJWty85k6oPvc\n08cDXdz3uG0/AeCyb0B27MXtKgR3jWDbkt0g6ZGU4iGmUOWg0IxDqZ+FpIjrlkwvOYakjEcb/QEu\n60pclq9wO3YLt6ukR1ImodBOBkBluLwbjS88kAyow/+KKuQ28TdaFou/01kgXLuyA0kRgqRMRlL1\nRqkRsUmFbiqSoothnhayvHKAgLIUnn7KXZWlCvktSt1snJZPcNt+RHaVInsSvCRFHAr1YJSGi309\nrSFMxLzboTPdcHfbDc9Z/2pOMpvSO4snLji3S+sbkZrEx9t2kRkdyTk5wvoamBhPbGgIjVYbuWUV\nvP/zTmrNFtYcLOCjrbu4dvSwDud7ef1mtCoVL14quP6dLhdPrPiBapOZjYcL+e3Cb4gPC+Efc6az\n5Wgxr2/0NGQBPt2+u13F/PH1l/kd93R5U0+g5RqPd30/HS7kN58txiXLRBsNXDVyCL3jYgjVaiiu\nE3rhmz372XSkiNV5+dzz5be8fFnH4ZV/fPejTymPz0xj1oA+JISH+p5zNWYLByqq2HykiG2FJVwx\novvNTM4QjJzBGZzBGZzBGZxCOG0s5pblUf8+9DF/7rcAgDSDf5vAQeE5PLHvVcbHPMyU2POwe9ow\nHk/Sl+yW+cpDVA+QNSCFgWN7dXpfQ62J/zwoyo8uuf1csgY0J00cR+ORHoPd7eLFvT+ysiSPElMd\nDrebCI2IvYyKTeWl8cFxQs8fMxi3LKOQJOZFhrVJnvFeA+ibFEtOYkxQ85qcdqYte5nM0Gg+mBzY\nJXTflsUsOuJPJjAkOpkvprYuT1Kg0IrmH97XEw4/nmcJpe58lLqead7eESSl4AxWhdyGittOuKyT\nIkeVgTr0TxD6J2TZiRTQ8yWhT9jrd0aWnYBETEjgbHyNStnpmNaYOSCHlMhwhqW037b0nJwszu/X\nm3lvfITN6WTJ7v0BLeaSuga+WnA1/RKaywStTid/XiyqA7YVlfDNrdfSOy6as3tlsGRPLiBctLtL\ny9udszV6uu64p3E867M5ndz39Qpcskzv2Gg+vfFyjJq28106bCBPfvcjb2/axvcHClidl8/UPu17\n85bnCj6CYSlJvHX1xe3mCUzr24vfnD2WGrMFYwC3eGc4bRRzS1TZajssKXDKLiptojbWqArB6CFW\nd8tuvAk9iiD4tlvipxW/cOxoM3/unJuCI/Z45eHPqatqJDkrjivvnt4lmZ1h2w+5QfFmB+Lg/seu\nVbx78GcGRSVyfe/R6FRqam3C9Zdg6FoM2Kt428tobX2uq1mvRlXnP9DfDpjI7DSR4FZjs3DP5q+6\nJOPE4tfbhWW88gyfXHgZY5O6T6jfErsqyvjNd0v4/oobUSt6rr2hW7bjcFWiVIRid1WiVwlXvtVZ\njEYZh0s2oZB02F2VKCUjpQ1vkxoheNXtrkr06gxszhI0ylicbhNKSY9LNuF0N+D0cJXrVCnUWTcT\nrZ+KQtIi40bRQ3W4SoWiQ6XsRa/YaAYnxfNzYQkF1TUBx8aEGP2UMsDYjObwRkJYiC9mDJAVLfJd\nyhqaKG/oOL7+v4Ilu/dT0Sjeh8cvOK9dpezFXZPH89HWXdicThbt2tuhYvZCrVR0mLznRZTh+HjV\nT0vFPC56KH/dKzIMpydOJEkfi4REqaWSZWXrGBstfPu19hpeLXgBAIvLTIQ6Ervbxv19H+2SvEWv\nrQEgPFoo+clzRwQaDsDmVXtY8+VWJEni909fEXRz7ZOJ5cX7idEZ+eyc63v0IdtTMKo0bJgdXOee\nVGMkqcbm2vBTSzH/d0GpkDrcGHcX1eaVKCQtEbrxNNn30mgTiVROdz1u2YJKEUGccQ5N9r2EagZi\n0PRB8mRlN9n30mDdRqh2EBa3iWONn6BVxaNSRKBTpSF5ujFplPFIKKgyr0ClCCNSf5I8Ji2Q4Mme\nbp2M1BqpEW37SXsziQHSIv0Z5IwtrEtvQ47/Zaw5KJJ8w3Q6hiQHZmfUq1WkRYVzsKKaXwJ4G8Zm\npLIi9yBbjhbz+PI13Hn22ONWwB3htFTMv+19FYtLhbJcW/kzFVax+4zTRnF+wllcmCQSQPJNB5iV\nKPpsHjUf5oLEi1hU/ImPMScYHNh5lL1bRA3rzKvFDzmQkjU1iizJF+//1HdPMG7vrqIrlJztwSW7\nKbc0MiQ6+ZRUymdwamJIXAI/XHFzj89rUGdTbV6FQZ2NxZGPXi0SqCRJjVYRhUYZg91VjsWRT6Ru\nou8YEONVmVidRdicZSgVerTKZDTKGHTqdOosIlHUrMzH7DhIrPECaiw/Ei11LcGrM7hkmS1Hilif\nL7L+91dUUtFoosFqxWx3YnOKf8GgPeYxrar5cR2i9Wdaa+mFcp8KcTKgyvKT7zlba9uFxVlCuHYg\nYZp+NNj2YXIWEa4dgNLjtYg3HD/5kxf7yioAaLBa6fdE8GV8NaaOe5/ff+7Z7Cg+RkVjEx/8vJPP\ntu9map9sZg8UPZcn9c4MmGnfFZyWilmtUHFxynkAvtf2EKmO5phVMF5Fa2L4vmIlRZajuGRX0JnZ\nX7wqaBhVKiWzrmu/lWRLvOah6qwuqyM6IYIbH+ycRKE7aI+SMxhcvOotjlkaqbIKN8+u6hJ6ffZE\nm3GH5j/k+7/T7WZV6QG+KdxDbl0Fx8zCNahSKOkVFsPFGYO5stdIv61Or8+e4K6BYuOgUSj59771\njIhJ5ZWzLuWV3A28dWAz2WExPD/uIgA/a7d1vLj9WPHxY/bK1ylorGbzhXcTovZ/0JmcwqIZ/fWz\nZIVGs2Tagh6X3xP4Im8vr+76mcN1tRjUaiampPPSebP9xuytquCR9as52lBH36hYnp4ynd6Rwg3q\nfYQ/tXkdH+8TVur1g4bz+5HNte01VrHZnLnwXWqtVmwuJ0du+2O7awECrueFbSIj/p09O5BlmRlZ\nvXl4/BSMmn4YNX0ABekRd1PR9CUgiIEiDZNQezjG0yNELXGkvplDwHtOxoXkqw1vRkKoN1dCzF1r\nWU+kruu/nUDYUXyMB5esJL+qrYtao1QSotUQFmKk1mxpUzfdHtTKwA/4zq6fCjCo06i2CKpQnSqB\nUE1vnG4zEdpB2FwVIEkYVElUejZOPamYa0yWzge1A6e742qZ5IgwFt9yNS+u/YnPd+7F5nSybN8B\nlu0TrTkjDXrmDenPdWOGd7t+2YvTUjEHi6yQXqQZMgBQKVT8VL2OmYlzglbKlaW1bPh2JwATLhhK\ndHxb91JrrPxkk+//1WV1XNz33qBktYwXv7PpMeJTg6uR7iouy2pOOHlg61JSjZHc3i8wwYjD7eKh\nrUvRKlWMj88k2SBiuSanjeXF+3lk+3IanXZu6+s/z8piwQwVpw9hYGQi68ryuWfz15SY65ie0o8v\njuzi/3YKgo//nNXcWMQbLz7RseJLMofy+I4VLCvO5dLMoe2u3eZyMjc9iMYJ3cCXG/eQFBXGxtwj\npMZEMChTxCgPlVYxa3T7JBEtsfpoPg+u+47HJ5zLOelZNNrtFDe27Wjz1u5tvHjuBaSFRXDvmuU8\nvG4Vn1woylI+zd0NwNcHc/l0zuW4ZZkbly0iIzySub3FGqJ0wl236Zrb2FRaxOWLP+1wLUCH6/n6\nYC5fHtgHwMez5xOm1XLXqqX8c8sGHho/mZZFInEhF3X697eG1IZO0gt/JRap71mlnFdexXXvf47N\n6UStVHK5pxPS+f1z6BMXTZiu2fr93effsMKTRBQI0n8B+5hBlYw+VHyOEgpk3J6Nk78SDtf2/O/L\n6zXoHRfNExd0bLx1FZEGPX+ZcQ6/mTSOL3ft48tf9nKwQtDM1potvPnTNj78eRd3n3MW148Z3sls\nHeO0VcxrK0Xd3jelaymxCJdWkj6OGQkTOSd+DBLeOJj4gI6Y8kk3ZJKsDz4J5us3fsDlFDuorjSr\nOJXgcrv9aDDnt1LMUTqD37n2oFep+eq8m0gyhLdJ3Lq93wSmLH2JT/K3t1HM+Q0iYe6zc66n1NLA\n+cte5ruS/WyY/XtidEZ+OHaIHdUltEbLePGJVMxz0gfyj12r+Oro7jaKeUmhsP6UksTs9IEnRL5G\npeRASSU5ybEcKa+lq4+nl3ds4cr+Q7i0r1hftN5ARnjb7lXXDxzO8HiRpX31gCH8btU3vmsf7hMb\nz2sGDKVPlMiWv3bAUN7Zvd2nmLuyFqDD9by7Zwc3DRb5GV5Z1wwYyt83rfUo5tMTz/2w0eeifuGS\nWZyT03HykOO/iL8gGEgtNkVSB9W5HZ0/HkQa9JQ3NmF1OBmaHDgprzuIMui5adwIbho3wuc2/3jb\nL3y5ax9Wp5P/W7kWg1rN/OHd23Sclor548JvWVUuXGIzEyeSpI9DlqHUWsFHhUs5Zq3k6vTZ1Dtq\neevwKwD0CsnB6rZSb6/j5qw7O5VhabKx/CMho8/QdPoOzwhqbcG2kFz/rXAbeuPXLe8LjQiORN9q\nd9JgsRKiE25YpULC7nRR1WAiPTaSkpp69haWM3lgNiqP60vVTRdYirH9doVRWgO9wmPZU1Pa5lqy\nUXgY9Co1KYZwz7kIYnSiFCVOH8KB+spuracnEKHRMzU5h+VFuZSa60nyrLHWZmZDuejXOi4+kzjd\n8bmlOsKs0f38Ssm86JMS28Ed/jhUW831gwJvqgCf2xrAqNZgapEcdKRekJ9kRTSHEjIjoiioD5w1\n3J215NfV8NA64SHxvkJrx7OAl+f+1YPL+bpkCwCXpI7npuxm6+es7+4H4B9Dr+OVQ8spNdeQHZrI\ngwMuIcPY3Ezl7QLRHOTzog3Isszk+EH8Lmc2OmXPJGRuLRSby/SoiIBKGeBYQ0OPyDyDwOiXEEt5\nYxNFtfVUNZm6XP7WFfRPEMRDj886lytGDObqdxdistt546et/12K+bVvhTt48uBsth8qwS3LXDxh\nkK+l1zelP/CEhwks0+jPcDMqciAP7H6Oq9Nnc9hUwPQEEd/qFyZ28QuLPsQpOzt1Zy//eKMvkWvO\nzZODXnuwLSRLjwhr0quYu9p6EmDp1lysdicRRuEqiw4z0isxmtyiCnYUlDIgLR6LzcHy7XnMHdsx\nV3YwqLA08nH+drZUFVJiEu7JRocVi9OB3d0AbtMyAAAgAElEQVQ+dV7LuK1WKd7vcE2zW0+jVOGS\nf10L4tLMoSwrymXx0T3c1k8k931bnOtb10UnyI3txfE0THAj075a84dO1fnPvGW6kNyN5KFg1uKW\nZZ6fKno3T8/quG0rwDclwiO2smwn/x55K25Z5t6d75BqiGFaov8G4F/7F/PY4CtI0kfz970LeSb3\nK14aeavv/hXHRP/sF0fcQohKz6N7Pub1/BX8NqdnuKG9WdCh2sCtLwtr68grrwo45gx6BlNysvjh\noNhcf7h1F3dNPr5+AMGif0IcZ2WlsXL/IUrrG7s9z6mfQXAGZ3AGZ3AGZ/A/hFNSMcdFhBAXEcK6\nPYex2BxYbI42TFkKSerU2kgzZLC+6gfWV/3AlpqN/Fj5PZW2cnbX7eCX+h0d3ud2ufn6zR8AiIoL\nY+Lszt2FvwaUCsFR7XS7cbrdbDtUTJPVzuHyGjQqJSXV9aiUClRKBVa7E2s3O1ptryrmvGUv80be\nJjJCorij31nc0e8snh49h7fOvoLe4e27XhWSAoWkaHPuVMKE+CwS9GF8eXS379w3hXsxqDQYVBqm\nJff9FVcXGL0iotlVcey45siKiCIrIorDdc2u68P1tWSGdy350LuWQOvJjojiQG0VB2qr0CpVfv9a\n48viTXxZvIl5qePICkmgV2gi81LHsbBoY5uxl6SNZ2B4OlGaEC5KGcv+hmLftS8KN3JZ+kQuS59I\nVkgCcbpw5qWM4/vy3W3m6S6SPLXJBdU11Fus7Y6xOBz8efHKU6aU6b8dFw3uT0KYCEG9tuFnvtt/\nqNN7dpYcw2zvuAZ805GiTqmCTHY7+8pEeM77vegOTklXdoheuITqTFZcLjdGnQaT1Y5OI5Z7fsIE\nHt/7MiCaWCTr45CRKbVU8O2xdcxMFKUUEhIDw0VCikt2oVaoGRY5CqvbilruOL60YdkuyovEg2rW\ndRNRqU7NOt+5YwewKa+Q3kkikSYyRI9CkrhzlnDbuN1ywD7JweKvO1Zgctr5eMq1jIpt21yhcx6c\nUxcKSeKijEG8nLuBPbXHiNWFsLWykLkZgwERH+8O9ImHe3KZ7eK2oaO5a/VS+kbFMiUtE5vbxZbS\nYuZ0IWnr2oEi6e2ZLeuZki7io+/v3ck9o7pGvuFdC9Dhem4eMoIHfxSx5bGJqQyOS+BIfR11NguT\nUjP95isxi0zXVEMzfWuaIYZCU9uchMwW8WSDSovF1VyOdNRcwTO5ovTK+wr0KEHKjP45vLJ+C2a7\ng1s/+YrfThKNQTKjIzHbHWwvKuWNn7ZytKaOnLgYDlT8+u5styz7lW3ZnS4sDgd6dfe+7ydrPiCo\nObUqFc9dPItr3vsch8vFbxYuYXxmGmf3yiA2JASXpyyqymTmUGU1m48WUVLXwNq7FnTYYeq69z8n\nKTyUidkZDEpKIDkiDK1KSb1FUO4eqqxm0a69FNeJUF9348twiirmc4cFjj9dkzGbRJ34wS4rW88x\nq/ixJuvjuDr9AibHjQYgTB3us9AaHM1lG+fFzww4/yJP7bJao/KRipyqGNun4y5EPaGUAQ7UVxCn\nC2lXKVtdTo40dS1R6FTDpZlDeSV3A0sL9xFvCEWGgCVSBQUVaDQqUlq0/czLO0ZsbBhRUUYqKxqI\njQujrs6M0ailsVFYUWazDY1GRVycoDs9dLCc8AgDsbFiZ52bKxLo9u0rYezYXiQnRxII0zJ78diE\nc3hl5xbuX7sCg1rNoJj4LinmeTki9+BIfS2XfS1aRV4zYCgX92nORH9q8zoAPtu/mwa7eAj1f+N5\nQrVaHho3mdm9+vrWAnS4nguy+1JuMgFw3w8rqLaaSQ0N5+5RwcX/5A7sFW2AJC5Zlnlk0BUATI47\nMdn1t541mnX5R9h7rIIdxce48cNFbcYoJYkHpk0iMzqKBR9/2c4sJw4Ol4uL3viQJqtQdI02Gyab\n3e/dXHOwgKFPvoRSknzJpCFaDQMS4nwdrrywOp1c8uZHADRZ7Z3OBxCi03Y4n7e9o3eNgeYDfGvs\naD4vhqUk8f61l/D7L5ZS1tDExsOFbOygZSOIDAllJ8/M0vpGPt2+m0+3B/a4zB8+iBvGds4Q2RFO\nScXcGSQkzksQP2bva3vY1/ALNXax8x4Y5t+Cq6ypkbe2b+f348Zj9uzENEollSYTf//yLuosFhJC\nQ0+YLXjH3y71ew0WgbivTxQSDGFUWBppctjaEHE888v32Fzdc5GfKkgLiWRkbBo/lB0iXh9KvD6U\ncXEZ7Y7duvUwhUeryMqOIyUlim+WiJBITGwoa9bkcuWV4/jq620sWDCFdevyGDUqi6VLRUlS3z6J\nbNt2mJsXTGbjhoNICond3xRxw42T2LD+AOHhol74wIEyxgbJFje/7yDm9+14E9GaCGRsUqrfOe/3\n+55RE7hnVPv1vfeNmej3GmgtLV/bg7dcyvvaEdKMIjxSaG62LotMVaQZg2uA0jxPHIebRDnltISh\nnYzuHgwaNR9edxlvb9rGsn0HOFrjafMJRBkNjEhL4voxwxmYGE+D1dqiiPPkwC3LvlrbzuCSZZ87\nvt5ixdCOdep2d20+71wdzucZ09U1djRfSwxLSWLlnTfw5S/7+D6vgNzySmrNFt/3PsqoJzsmmtHp\nKcwckENsgOztRTdfydK9eewoPkZRbT0NVhsOlwudJyk5OTyMoSmJzBsygOGpSUH9LR3htFTMwSLN\nkMni0i8AKLOU+qzn6zJuwexw0icmliN1dXyyW7BM3T9hInsrKthUXMzAuDgSQ7sfI/g1sHbRFtRa\nNUdzSygrrCYhLZqywmpu/dt8DKHd53SdnzmUZ3av4Zq1H3Bh2kDMTrGR+bEsn4LGaoZFp7CjuriT\nWYJDnd1CXl0FTU4bTQ5hndVYzb664hC1lhCVhoGRiT43c8t7gDb3tXdPa1yaOZQ//byE4qY6ruk9\nssP8hSFD0jh6tIp9+0oZOjSd0mPiIXzB7GHY7S6Ki2t8blJvz223x202ZmwvGhotVFQ0cvBgOXHx\nYSQmReBwOCkurmHmLJGZX11zpgnBvFThDn7t0ErGx4g4/6LiTSzI7hpZxOVpE3h6v7BQh0Vm0i8s\nlWJLNfUOE2Oj+/jG5T1893GtV69WccfEMdwxcUzAcWE6HfsDyOpsHR1df+GS9jPMR1//LM/cNYdh\n1ihKKoXXsHdqLA/fNI3MpOYyujcXb+Kz73bglmXOGZkDwN1XTkanUXHO7f/mvceuIiUugpe/2IDN\n4fSto7ymkasefp9V/74j4LoDwUs1eryfQaD5Lx8+mMuHDz6ueQYkxjMgMb7zgT2A00oxz1n/m6DH\nfj3hJWrsVZwbNwOAUVHj/BKPInU6YgwGfi4p9j2sy00mDtVUkxUZxZAEf+Jzi8vEs3l/AMDkbMQp\nO3hqyMLj/ZO6De96vGsBuLX3s2xatpO41Ggy+qfQVG8mo38KemNb3t2uYEHf8SgVChYW7OTpX773\nWc1j4zJ4evSFrCsv6DHFvPbYIe7Z/LXfuSJTLXdv8nf/fT71BoZGJ3d4T3v3tbynNWak9uOx7csx\nOe3MTe/4B1xQUIEElHkU8rBh6QB89OFGampMLLhlCuVl9bz11lpKSmoZM7aXT8kvWvQzhYXVnHfe\nQM6akMO2rYcxGDRERYUwbFg6b721FoDiohpGjMhsV/7/CqYnCtakYnM1d24VXATzUsYxI6lr7sGp\nCUOosona4f/b9zm1dhNJ+khuyuo5NqhTHf/8YA1P3D6L5DhRp//4Gyt46r3veflPwlu3YtN+lm3M\n5T/3X0qoQctDr3wLwCuLNvD7yyeREBNGk0W4wavrTZgszbHfRrONlLhmjoMtNT9woHEPVpcFnVLP\n2GgR3qixV6KW1AyLPDllS6c7JLk7RYu/EpYdW0eBqRiLy0qqQSjO/mHZhKnbEkCkG5Ios5ayokyw\nHLnk5lrbGzNv9/0/uEpQf+Q37eXV/Ed/VcXcei0ATw1ZiOyWkRRSm9czCAyby8mYxf8i1RjRKS+2\nzeZEq/Xf07pcbpQtyFucTjcqlTh+4/UfALjxpklIkoTXGHe7ZSSpmX7R5bGwlacBD/IZnB4Yff2z\n3HX5JK6a3ryhWb+zgIdeWcoPrwga4Jue+JiZ4/tz8Tki3Ldycx4AL376I0ueXcA9z33FleePIDku\ngk++247T6eLK88V8FbVNfL56J0/cPstP7sv5T3B7djPf/paatbhlF0ZVKPG6ZOK0wtV7zNoc8622\nVdIvbChKSUmVrZwSyxF6hfTHqAqlxHKEZH0GAAWm/WQZ+1JqKcSgCkGW3RRbDvvN2xH21K9EI+nJ\nCeu878GvidPKYp6RKN5Ml+xib70g5theu48Kaw2haiMDwnoxILwXURqxM0zQJXF52rUAHGzMQ6fU\nkx3in1j236ayvEq49esZBMbq0gM0OWxcnDmk07GtlTK0VaZepQwwdpyIF7dOxmt9fCoq5Pwjlby7\nUBD+7MkrYdEbt/2q6+hoDTv2FDFsYM/0nP5vQ2aSf+mbXqfGbG3B/nasln+8t5p/vLfab5x3A5kY\nE06TxcaGXQUM75OC2y3z0+4jAMRFhZIc1z4rYGvsrNvEOXEXsrDoDW7KvBedUs+7R55nfLTwXhhU\nRk/73qOsrljM6KjJfFz4Ctdk/JYfK5czNf5CAD4pfJU/9f0nG6q/I93Qix11G5kSO9s3L4BO2XHo\nzu62cLBxg/jb9H0JUQmXfoOjgjLrAZL1AzCqIqmwCh0Tp8um2LyHFMNAKm2HidVmUGrJpcFRSbJ+\nAKHq5ryHKpuoxqi3l5EZMgqFpPLNC/jm7gyn3pPgDM7gDM7gDM7gfxinlcXshVJSMjhCJCh4X4+a\nS3m74EueyXubvmFZ/GPwHzA5m3j3yGsA9A0bgMVlZmP1j1yb3vP9ZL1YVf45ABuqliEjMyh8DBcm\nXY9aIeKy/8j9DZPi5jA2ujnGtbFqOesqv+H+fi/5zbOhahlAu/OcQc/B6Xbz6v6NhKq1bZpZ9AQG\nDkzp8TlPFrIzYvnrvaIcZd7Nr/zq6+hoDa9/uJ7//N8VJ3lVzShraMIty+wrq2BwkkgQivO0/ius\nrSO3rJIxGalE6HXkllXSLyHWx7E9Mi2ZvPIq+sTHUFrfyO7SMkakJvnxO3trn4vr6jm7V9f6/mo1\ngR/zslvm8dtmMmVE+5UAiTFhmCx2tucV8/BN5yPLMn99YwUAZw/L9osxB8LwyLPICR3IgcZfqHNU\nk6BMIUQVztmx0/3G7a7fyoSY88k05lBuLaagaT8qhZojJmF1DokYw96GbcRqRThzWMR4v3kBEpQd\n/+YONW1kWKSwvr8tfYp5qX/F5KxjXcXbDI6YzopjzzIr+U9srxUNdEZHX8aKY//ixuzX2Vm7hCnx\nt1FtO4pRFc3ikr9yVcYLvrmXlPydIRGz0ClDkSQF9Y5y37yAb26tIjB392mpmGVkDjWJ2MT22n0U\nmctQSSrGxQzh5qyLSfHEnw805jIlbhrQzJX9WdEHONwO1IqeIbBviR2169lR+yMAt2Y/gl5p5KOj\nz7Oi7BMuSLoOgJywoRQ07fNTzEdM+8kJHdJmnluzHwFod56eRpOzjpVlbzEqStR4J+tzUEgKSi2H\nCFNHE+Jxv2yqXszAcBFSaH3Oe9z6nkZnDbIsY1SFowyy5ebJwJO7VqFXqvmxLJ+9tWX8dcQMjCrN\nr72soPDah+vYm3cMWZYJD9Pz+L3iQTPv5lf8XL0tj2//80fEx4RRVFrLDZeNZ8Lo5oYLga51hL+9\nsIwHfyeSKx95ZgmP/bH9elIv7vnr5/ztPtGfXKdTY7E6ePDJr3j2UZGEtH13Ia9/uB4kuO0aQRI0\npH/nm5onX1pBXn459z0h6oefekg0hFm9fj+LV/6CxeO2HTEojVuvEd/dDxZtZt+BYzQ22ThcVOWT\nd8G5g/glt4TXPliHy+3mqovGBPVebDx8lB8PHeHSYQO596vlALx6xVyOVNfyxsatXDSkP39evIJ/\nXjST93/ewYLxo3jwG9Em89vbr+PjbbtYMH4Uz65Zz6XDBvHAku/457wZPv7t33+xlMtHDCZCr+vx\nlpAZSVEUlFRz/tj2We4SY8KorG1CrVT6SJ68S6iuNzEgK6Hd+1pDLTU/c7016S3PeaFT6rG6zABY\nXGa0Sh2xmngOm0Tse0rsbJYc+5DJsRdQba9od95AyAmdSLrRm1y4h0prAccsB7C5mzjYuAGLq54K\naz5Kz7yl5n3khE3kUONPRGqSUUpqFJKKMusBbC4TMiI3REKBQRnB8Ki5Pln5jZt884q/R8ydagic\nIX7qPCWDwNrKn8lvKqLO3kisVjz0+4VlMTa6Wam5cHPUXEq6IYkUQzpLPOVSGoUWi8tMg6P+hChl\ngI3Vy5gQK8oWEnSCjGNczPl8W/q+T6H2CR3KouLX/O47bMplbvJNbebxztHePD0NGTdKSeVnkW+t\nWUGYOpo99euYEHsJBmXn5WOt7wHYVbsGm9tChnEg2SEnppa0O1hatI9qq4kkQziPDp/OldndJwQ4\n2Vi+Zh/PPnIJGanRQdM85h0q55mHL8Fmd/C7hz71UziBrnWEXhmxlJTVIUkS6SnRnY6fec5AvluX\nC8Ds8wazet1+Zp3bXPP8/Jvf869HL0WW4d7Hxe/2rWev7XTeP/3mfLbsPOxTyF5MGpfD1Al9cbvF\n+3PJLa/5FPOSlb/w6Ssiye/SW17jghbreO6N1fzrkUvRalTc9Mf3g3ovAKb2yeasrHSfJby/vJIN\n+Ue5atQQhqUkkV9Vw9bCEjRKJduLSpneT+S7fJ+XT3pUBN8fLKDBamNl7kFqzRZyyyoZnS42JtFG\nA9eOPjHUwFdMG86T761meJ8U+mfGU1QhKg7qm6yMG5RBYnQYS9fvZcrI5vycMQNENcKqnw90qNC7\ni9FRk1hY9Ab7G3dhc1nJNPZBlmWOmEUf61hdIkXmAlIMmVTbK7o8v8PdTJtqd1tQK3RoFHr6hk1m\nQPi5vmvlViGvxLKXUdGXsLbiTUZFXczeuu+wu82Mj7maYvNuX9MXSQKl5L+xb2/eYHBaKeZn894N\neuzXE14iVhvHtASRLbirbhs6pZ4r0284UcujwlrCl8WvA/hewZ/+r1fIQEzORqpsglNYISlpdNaT\nHTKozTwt52g9T08jVBWFURVBgq65TKfWXsbIqPNxynaqbSUYDJ3/AFvfA6BSaNApjexv2BxQMdut\nDh645R2GjM5i8qzBpGYG1/4wEIoOV7L8860suHdGm2vrLvjdcc/fGnargwdveIPBY7OZPHsoqVlx\nHY5d/P5G1i37hac/6npC1VMPzeODRZupqmniirmjGDOs/fIql6tZaYeG6DAaNBgNGuob/TmdA13r\nCOed3Y/vfsxFQmLK+JxOx08el8NDTy0GhGLetL2AR1tY2bV1ZqIihIuvqgdquRev3MXPO48SFirK\nBRubmv+uaZP6c98TizDoNX6bA4Di0loe+ecSAOJjgucyaLKJMiIv37JBrSZEq6HRy7hltWHUqkmL\nimB7cSk3eZihnl69nhvHjaCwpo5ZA/owd3D/NnNrTiAt8Hlj+lBZ18Tjb62krsFMUqxInl0wV9SS\nJ8aEsTW3iL/c3OxyPnuY2Ky8+Nk6YiLaVsW0zMgGoWy9uCDpSt//b83+c5t79Uoj12bchdPtQOUx\norJD+pEd0sxo9+iAl/3mdbncfvN6ISofmishAIrNu6mxFwFgctYQo80kQpPEstJnKLPk4ZCtnJfw\nOxJ04jtdat5HlCaFcssB4nXC3f9T1Uc0Oqv8lHx76Bs+yTcv4Jtb2Y6noCVOK8X89YSXOh/UAm7Z\nRbJeZGqm6IX1aevkjTweyMhckSbaUQ6KaJ9oQKPQkWHsQ4FpHwBKSUWaobdfFqF3no7mOFnIDBnM\nusqFNDnrODf+OipshRSZ9+P2tEQcGTWdOkeF79zIqOlt7gEotx4hRBVJgXNXh7Iaas1sXrsfQ4iW\nnEHJJKaKTNIjBwRrU1mJoP0cOTEHlUpJxbE6DuwpYcCwNCJjQpFlmdxd4sdWeayeAcPTiIkP95Nx\n5GA5kdEhhEeJh3/JkSoO7islKT2aqOgQJIVEdFwYRQUVvnv1Ri0lhys5uLeE5PQYeg8SFkxFaR0H\ndhcxwNOnOzI2lIZaE5vX5GII0ZEzOJXEtGiqyup9mfEt59YbtVx4zXi2rNnXrc8mPTmKh+6aSX2D\nhet+/w5fvSVKAF0uGZfLTYNHCdU1mJvf40YLjU1W7A4X4aH+te2BrnWEqAgjDqeL6hoTmWmdM3Ip\nlQoG9xN15N/9mEuf7AS//uDRkUaqa03IskxU5P+3d+fRUVV5Ase/r5ZXe/ZNshATSNgSNgmKjWhk\n0bCJInYD4/SZAdEZt8buhjPa2q0jPcd26Z5zRqennWl6DoMNtuPYjfsoKsZmVfY1IUASQlhSSaUq\ntVfNH6+opKAC4Sg9NT2/zz8h9aruexWq3u/d373vd69s/dxgMHJRbfhX19bz1q8fIBBbvOWjTQfj\n23bua+Gp5TPJybo4qBQWZPD4w7eTm+0gFB74sqSfNTTRdM7JGbdWdrQiL4eCNAdPvfMRmxqP4QkE\nGFdcSCQKO1vauDZH+4zvbWtnZEEeVYPyWfnW++xuPYUvFOLHdbei6r9eQN66evlFj40fVnzR4wtn\njKf02hyOnu6g4hrt/7KmvJj1m3dTkO5g9uzRRHXw0juf8726b/Hhfm1RiN/8/WJ2N7fx+x0HmF41\nlJryYrY2NrOvpZ0RRXlMLNfOu29/dQBFUdjR1MpDMyZxoPV0fF815cln0xsuyGx2ubRleN0eH9lZ\ndrzeAKpqpMcbYMeuY1w3phSH3Rx7jh+bVeWLrY2MGVUc/zyNSp/OqPTphGO1H84HSINiYnbh4wQj\nfgw6FQWFIqt2wXb+5/1D18Z/v7P4aXSK4aLO0vySZxPfQ592tfekDqiDldKB2RcK4Qr4sRmN6BUd\nze4uuvw+hmXl4o4VOHeoasK26/J7C0gccR/iXECbNDEpWxtDWte8hr8Y/NdXpfeZZyqk3acFh7FK\n8vKGoKWzm9xaSs+oUxPGl/u2c6k2roap+YlpwyH2sZTZqtEp+thxlXB38Q8TnnPhYxe+BrQADjAs\nrf8LjVAojKIoqKoBi7U3nb7qsdeYec9EHLFylTpFob3Vya9//gG3zZ/Aiz96k5XPLcCoGjjRoKW1\nsnIdPPPIWn7xWy1YKYrCzs2NbP7kIMt+WIc71iN8edUGFiy5iTNtnRw/1IaqGpgyawwfvrGd2rna\nGFRuQTqvPPMWd993C6fbOhlaVUR7q5PVz7/LbQtqeOnvtHvZV7y4kFAojE5RUE0GLFYtpfXVF0dQ\nY+NyfdsurRjYuFx/Hn5yHarRQCQS5a66cfHHZ02t4oGVaykv1bINfVPMaQ4z/7T6Uw4fbWfp4sT7\nOPvb9p/vfsWXe7TPdJfLx49+9ntuGFdG3a3anI2ykhzUAaz3fN7sadrY2rIV/8HLqxInaz2ypJYn\nnnsLovDwXyWuT37+OJIdA8D0KcNZ+oM15Oc6WLVSG+OrvbGSR59cT0mspnlZn4uHaDTKYz/5HVmZ\nNnSKQmW5NmHrvsWTWb5sKk89vwFV1TOqchBLFg7se3hH9QhuqShLCKZpZhMv3VmHPxSKV7iaUFLI\nhJLe89SmR3vvm//5XTPxBkOYDPqE6nP/ujAxTf9NCEej6JTeM+F1ZUU0tp9j1wktm1dTXkxLRxcL\nrq/GHwpx/Iwz3vMMRxKHT24YWsLEIVqA3dfSzpzxw8lx9F5c7W89zTUZDoqz0wmGwgn76i8wX+j8\n2PrnWxoZV13Chg92k5+Xht1qotPlpX5LQ3xxCotZJRSK4O9ntahkPdZ2XycHXS2Myyzn35s+ZnKe\nVkd+d+cx7iiayH+1bCEKzC+ehEWvfb/XHv+MhYNvuuyxX+mk3ZQOzOsO78ZqUDHodORabETRPkhr\nDuzEoWpv1GowJmzryxvuwaJLvJ9Nh0IoEroq48yTc2fFx4/L7CMotg7hrL+NnrCbSkdvCrfCMYZt\nHdpCGarOzLyiJUnbKbNrKa3+2vlT6Btgk9nT9RGDzBU0uLdh0Tuozph22dckk5XroLK6iIYDJ6me\n0JuWTc+2M3fxDQnP3fzJQTzdPuo/3IfL6aHxYBtV40vRG7Xe1+F9rXjcvnhJzKZDpzhx9DRPv6xd\neNjs2mcnI9vOjs+PMHfRDXxZfzjpcVkdZu15mw4x915tQZMtH+3H3e2j/oO9uDq03lHj/laqJ5ZT\nMbqYhn3av6+mV356cdoOYOmib7F0UfJAotfrWPngjH63lUwrYFAkj5qhpZz1enD6fZSMyyMyRPuu\nPPRgLfnWxB7m2Q4PNWNLk7a5oWUXJr2ByXkVeEJ+VJ2Bs1EtRf3S8/OpP3eEqfYRdAZ6SDNaOJXZ\nxTM/nkOO6eJe7PU3m6mcZOc7y6qxGzI56d3LTudxAEak1/LQBYEc4Pv391/d68y5bn77ylIUBYKh\nMIse/DdAC8xVwwqveIa3yWDAqNeD4qUrVp/fYRxEd/AUVkM2YXoIRx34w25UvQ1/2IVRZwXAGzqH\n1ZBLMNKDWZ9OIHoKlQKg/3PUZw3H6AkEmFhaTCgSSZgQZjYY8AQCZFotOHu8dPv8uHx+RsVmizt7\nvKSZzWw83EjN4KL47O8jp86iKAqtHa54WxOHlPCrj7dyttvD8rrJnHS6+Mf3v+DEWW0c+qbh2ne1\n7/4nD7uWX23cxjUZDhbfqI2L144cwh+PHMdmUslx2Dhw8vRF+7qc+q1aLz0/18Hu/S2YTAZCwTDd\nbh8FeWlkZdriJXJd3T4cdhPdbv+A2/+kfQ93lUziN0c/5lp7frxinDvkY9u5BtwhHxa9Gg/KV1NK\nB2aDTk+n30eGyczmU83cU1FF/cnjBMNhuvxar0fV6RO29VXpGME/N/4CgDOB03hDPfHlH6/Uu21r\n48H0/IzBJ/Ysxqy3MmvQXzIm40ZGZ5ebFA8AAAVaSURBVEzCFdRSrq83v4In5CJLzWNawYKEtgZZ\nSgnEUhvecA9FlsTbFM6383qzNo6SrJ3zx3P+WJIdz59COBKgyfMlFr0Dk85KT6gTq2Fgt08MhJrk\nVg+LVWXK7dVMnds7GeaDN3fg9WhZlMV/U8ve7U3x+Znp2TYys+1s+fQgE6cMi59EfvDT+Rw70s6z\ny19j9j018cpbXbFgC9oJ5/s/+zbHDp9i1cNreGHd32K2mbh55mhunXf5yWIGgz7e7oVtpxpPMIDZ\nYKDJ5eS1Q7uYXjIUnaLQ1tMNwIcnGphXPgKbUTsxPfLkespKcpgzPfkM056QH5PewDHPOd44vp1H\nh0/jQJe2glZdYTU6FFY31nOyx0lVZjG+UCBpOwCRaISekBOLPo0Tnj1kmYris/6DER+q7spqwc+4\neSTLf/I6qtGAzx9kyXe+3vdl5sjK2LH0cNavXeSd9u0nEHYRivow6dOJRMMYdRbC0QDZpgoy1FIA\nzvoP43Z/gkmfjl4xEQi7GJ4x/5L7O9bh5N6asazZthOrasQf0tL1Jzq6yLZZsZtVOjxeWrtc1I2o\nQKcovPrFdgBau1yMHlSAL5S4+MzIonyG5GdjMvZ+526sGMz1Q4rRx27Pun1MJdPCkYQhiMLMtIR2\nhhZks3L2zUSjvUML15UVMq50ECha1ivZvi7n9j4ZkmhUm2z1358doHbysN5O2WhtUloklg04//Ny\nfOEgiqJgUPTU5ldzqLs1vi3NaOWW/CrafE7K7PmsO76JwbY8rs+pvESLX48UGBFCCCFSSMrXyr7w\nyicSjfKHoweZXabNEL5w24VXR/5Yz/So+wiqTqXMPvSqzm7+/yhK9Bv5mzY3neHtdVu5f2Vv3d3H\n71vNs//y3YTnBXxBnn/8DdIzbfh9QR56cg4Hd7fw2i83AlA27Br2bj/GC2vu4+SJc7z3u+0seew2\nnlv5OvcsmYI5Nv67/tVPsTrMuJw9LHrgFl5csZ6howpp2H+S+5/Q7gs2W1XW/3IjNrsZl9PD9/5h\nAQFfkBdWrCc9y4Y/VtD/wafvxKgaaD56mnfWbmZZ7PXtrU5eXLEeIKFts1XlvXVb2PiHndTOGcsd\n350cn5T2v+XNxn3MKx/J6v1f0upxce+wsdS3HccUGzPNtdipzMwh1zKw49zQsotZRaNZ27SZU94u\n7iwZz4YWbQLgzKLRvN2yixJbNg6jma6glzZvJ3cPnpA0la3RKtt/U5+3q6ErcIJGl1Z8I10dTCDi\nRtXZsBiycQVbCUW8WPSZlKfNoCug1WJodL1PulqCxZCNN9RBIOJmeMY8LlUw+M1d+7GqRpxeL95A\nkOJMLUt1xu3B2ePVhv/sNtLMJirzc/hjU3O8KEma2USn10ebq5tvj6tKKGQiLm+gvfCvI+UDsxD9\n8fuCqKohPus5GJt9azDoB1QjPBQKEwqGMVu0QB2NaDOaDUb9JZ8X3783iBqrm32p/UVjE2WStZ2K\n+i7s8k2chC4XSK807fh/RYPrPYIRL2WOWkx6bZZ/lAjKJRKVyV7Tn3A0ij7J323D3kPUjazot9Py\n5/r3/nMigVkIIYRIITLGLIQQQqQQCcxCCCFECpHALIQQQqQQCcxCCCFECpHALIQQQqQQCcxCCCFE\nCpHALIQQQqQQCcxCCCFECpHALIQQQqQQCcxCCCFECpHALIQQQqQQCcxCCCFECpHALIQQQqQQCcxC\nCCFECpHALIQQQqQQCcxCCCFECpHALIQQQqQQCcxCCCFECpHALIQQQqQQCcxCCCFECpHALIQQQqQQ\nCcxCCCFECpHALIQQQqQQCcxCCCFECpHALIQQQqSQ/wG3YwDDK5LLYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff76c0da710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_idx=rd.randrange(0, len(texts))\n",
    "wordcloud = WordCloud(max_font_size=40,background_color=\"white\").generate(texts[rand_idx][0])\n",
    "print \"the newsgroup of the text is %d\"%(texts[rand_idx][1])\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Then we do some text analysis and broadcast the word countings and word embeddings to Spark cluster using `sc`. - When using pyspark to launch jupyter, a SparkContext object is already created for you as `sc`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data_rdd = sc.parallelize(texts, 2)\n",
    "\n",
    "# break the text corpus into tokens (words)\n",
    "def text_to_words(review_text):\n",
    "    letters_only = re.sub(\"[^a-zA-Z]\", \" \", review_text)\n",
    "    words = letters_only.lower().split()\n",
    "    return words\n",
    "# calcualte the frequency of words in each text corpus, sort by frequency, and assign an id to each word\n",
    "def analyze_texts(data_rdd):\n",
    "    return data_rdd.flatMap(lambda (text, label): text_to_words(text)) \\\n",
    "        .map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b) \\\n",
    "        .sortBy(lambda (w, c): - c).zipWithIndex() \\\n",
    "        .map(lambda ((w, c), i): (w, (i + 1, c))).collect()\n",
    "        \n",
    "word_to_ic = analyze_texts(data_rdd)\n",
    "# take the top \"max_words\" most frequent words while remove the top 10 ( may not be meaningful,e.g. \"of\",\"the\")\n",
    "#broad case the word count info \n",
    "word_to_ic = dict(word_to_ic[10: max_words])\n",
    "bword_to_ic = sc.broadcast(word_to_ic)\n",
    "\n",
    "#prepare and broadcast word embeddings \n",
    "filtered_w2v = {w: v for w, v in w2v.items() if w in word_to_ic}\n",
    "bfiltered_w2v = sc.broadcast(filtered_w2v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Prepare the train/val dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In the following code, we use the word embedding and word count statistics to prepare the input samples (`RDD[Sample]`). Then we split it into training and validation set. The split ratio is specified in `training_split`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "code_folding": [],
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing samples with embedding_dim = 50, sequence_len=50\n",
      "Generated Samples\n"
     ]
    }
   ],
   "source": [
    "def prepare_samples(data_rdd,\n",
    "                    sequence_len,\n",
    "                    embedding_dim,\n",
    "                    bword_to_ic,\n",
    "                    bfiltered_w2v):\n",
    "    print \"preparing samples with embedding_dim = %s, sequence_len=%s\"%(embedding_dim,sequence_len)\n",
    "    def pad(l, fill_value, width):\n",
    "        \"\"\"\n",
    "        pad the embedding to required length\n",
    "        pad([1, 2, 3, 4, 5], 0, 6)\n",
    "        \"\"\"\n",
    "        if len(l) >= width:\n",
    "            return l[0: width]\n",
    "        else:\n",
    "            l.extend([fill_value] * (width - len(l)))\n",
    "            return l\n",
    "    def to_vec(token, b_w2v, embedding_dim):\n",
    "        \"\"\"\n",
    "        word to vec\n",
    "        \"\"\"\n",
    "        if token in b_w2v:\n",
    "            return b_w2v[token]\n",
    "        else:\n",
    "            return pad([], 0, embedding_dim)  \n",
    "    def to_sample(vectors, label, embedding_dim,sequence_len):\n",
    "        \"\"\"\n",
    "        assemble the features (embeddings of words in each text sample) and label into samples\n",
    "        \"\"\"\n",
    "        flatten_features = list(itertools.chain(*vectors)) # flatten nested list\n",
    "        features = np.array(flatten_features, dtype='float').reshape(\n",
    "            [sequence_len, embedding_dim]).transpose(1, 0)\n",
    "        return Sample.from_ndarray(features, np.array(label))\n",
    "    \n",
    "    tokens_rdd = data_rdd.map(lambda (text, label):\n",
    "                              ([w for w in text_to_words(text) if\n",
    "                                w in bword_to_ic.value], label))\n",
    "    padded_tokens_rdd = tokens_rdd.map(\n",
    "        lambda (tokens, label): (pad(tokens, \"##\", sequence_len), label))\n",
    "\n",
    "    vector_rdd = padded_tokens_rdd.map(lambda (tokens, label):\n",
    "                                       ([to_vec(w, bfiltered_w2v.value,\n",
    "                                                embedding_dim) for w in\n",
    "                                         tokens], label))\n",
    "    sample_rdd = vector_rdd.map(\n",
    "        lambda (vectors, label): to_sample(vectors, label, embedding_dim,sequence_len))\n",
    "    print('Generated Samples')\n",
    "    return sample_rdd\n",
    "\n",
    "sample_rdd = prepare_samples(data_rdd, sequence_len, embedding_dim,bword_to_ic,bfiltered_w2v)\n",
    "#split train val sets\n",
    "train_rdd, val_rdd = sample_rdd.randomSplit([training_split, 1-training_split])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Train a neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Define the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we define a nerual net model, layer by layer. For details of module/layer definitions, refer to [layer.py](https://github.com/intel-analytics/BigDL/blob/master/pyspark/dl/nn/layer.py). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building model with embedding_dim = 50, sequence_len=50\n",
      "creating: createSequential\n",
      "creating: createReshape\n",
      "creating: createSpatialConvolution\n",
      "creating: createReLU\n",
      "creating: createSpatialMaxPooling\n",
      "creating: createSpatialConvolution\n",
      "creating: createReLU\n",
      "creating: createSpatialMaxPooling\n",
      "creating: createReshape\n",
      "creating: createLinear\n",
      "creating: createLinear\n",
      "creating: createLogSoftMax\n"
     ]
    }
   ],
   "source": [
    "def build_model(class_num=news20.CLASS_NUM,\n",
    "                embedding_dim=embedding_dim,\n",
    "                sequence_len=sequence_len):\n",
    "    print \"building model with embedding_dim = %s, sequence_len=%s\"%(embedding_dim,sequence_len)\n",
    "    model = Sequential()\n",
    "    model.add(Reshape([embedding_dim, 1, sequence_len]))\n",
    "    model.add(SpatialConvolution(embedding_dim, 128, 5, 1).set_name('conv1'))\n",
    "    model.add(ReLU())\n",
    "    model.add(SpatialMaxPooling(5, 1, 5, 1))\n",
    "    model.add(SpatialConvolution(128, 128, 5, 1).set_name('conv2'))\n",
    "    model.add(ReLU())\n",
    "    model.add(SpatialMaxPooling(5, 1, 5, 1))\n",
    "    model.add(Reshape([128]))\n",
    "    model.add(Linear(128, 100).set_name('fc1'))\n",
    "    model.add(Linear(100, class_num).set_name('fc2'))\n",
    "    model.add(LogSoftMax())\n",
    "    return model\n",
    "\n",
    "model = build_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To train a model, we first create and configure a trainer (`Optimizer`) for training.  Then, use ```Optimizer.optimize()``` to launch the acutal training process. \n",
    "\n",
    "For details of how to configure an optimizer, refer to [BigDL Programming Guide, Optimizer section](https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#optimizer). \n",
    "\n",
    "In order to use Tensorboard to visualize the optimization process, we need to configure BigDL to write logs into the log directory - we have created one in the above [Start TensorBoard section](#Start-TensorBoard) :```/tmp/bigdl_summaries``` . The logdir is actually set into `TrainSummary` and `ValidationSummary`. Run below code and you will find the output log indicating where the logs will be written to. The output looks like below\n",
    "```\n",
    "...\n",
    "optimize summary will be write to : /tmp/bigdl_summaries/adagrad-yyyymmdd-HHMMSS\n",
    "...\n",
    "```\n",
    "In my run, for example, the summary logs are written into `/tmp/bigdl_summaries/adagrad-20170406-150256`, that is, my appname is `adagrad-20170406-150256`. Run your training for some time, and you will be able to see the visualizations of your app in Tensorboard dashboard (the name of Runs in Tensorboard is your appname). In following sections we will show you what the visualizations look like.\n",
    "\n",
    "When the training is finished, we obtain a trained model `trained_model`. \n",
    "\n",
    "`TrainSummary` and `ValidationSummary` are the utilities we use to configure the log writting procedure. They can also be used to retrieve the logs for further processing after the training. We will see usage in the following sections. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start to train the model\n",
      "optimize summary will be write to : /tmp/bigdl_summaries/adagrad-20170406-150256\n",
      "creating: createClassNLLCriterion\n",
      "creating: createMaxEpoch\n",
      "creating: createOptimizer\n",
      "creating: createEveryEpoch\n",
      "creating: createTrainSummary\n",
      "creating: createSeveralIteration\n",
      "creating: createValidationSummary\n",
      "Optimization Done.\n"
     ]
    }
   ],
   "source": [
    "def create_optimizer(model,\n",
    "                     app_name,\n",
    "                     logdir='/tmp/bigdl_summaries',\n",
    "                     batch_size=batch_size,\n",
    "                     lr=0.01,\n",
    "                     lrd=0.0002,\n",
    "                     optim=\"Adagrad\",\n",
    "                     val=[\"Top1Accuracy\",\"Loss\"],\n",
    "                     max_epoch=2):\n",
    "    print \"optimize summary will be write to :\",logdir+'/'+app_name\n",
    "    state = {\"batchSize\": batch_size,\n",
    "         \"learningRate\": lr,\n",
    "         \"learningRateDecay\": lrd}\n",
    "    #configure optimizer\n",
    "    optimizer = Optimizer(\n",
    "        model=model,\n",
    "        training_rdd=train_rdd,\n",
    "        criterion=ClassNLLCriterion(),\n",
    "        end_trigger=MaxEpoch(max_epoch),\n",
    "        batch_size=batch_size,\n",
    "        optim_method=optim,\n",
    "        state=state)\n",
    "\n",
    "    optimizer.setvalidation(\n",
    "        batch_size=batch_size,\n",
    "        val_rdd=val_rdd,\n",
    "        trigger=EveryEpoch(),\n",
    "        val_method=val\n",
    "    )\n",
    "    train_summary = TrainSummary(log_dir=logdir, app_name=app_name)\n",
    "    train_summary.set_summary_trigger(\"Parameters\", SeveralIteration(50))\n",
    "    val_summary = ValidationSummary(log_dir=logdir, app_name=app_name)\n",
    "    optimizer.set_train_summary(train_summary)\n",
    "    optimizer.set_val_summary(val_summary)\n",
    "    return optimizer,train_summary,val_summary\n",
    "\n",
    "print 'Start to train the model'\n",
    "#configure optimizer\n",
    "optimizer,train_summary,val_summary = create_optimizer(model,'adagrad-'+ dt.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "trained_model = optimizer.optimize()\n",
    "print \"Optimization Done.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Evaluate the prediction result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now let's use `trained_model` to make predictions on `val_rdd`. `y_pred` are the predicted labels on entire validation set and `y_true` are the true labels. \n",
    "\n",
    "We compare the result of predicted labels and true labels using 2 metrics: *accuracy score* and *confusion matrix* ( both are provided in `sklearn.metrics`). If you're not familar with confusion matrix, refer to [Wikipedia Definition of Confusion Matrix](https://en.wikipedia.org/wiki/Confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "predictions = trained_model.predict(val_rdd).collect()\n",
    "\n",
    "def map_predict_label(l):\n",
    "    return np.array(l).argmax()\n",
    "def map_groundtruth_label(l):\n",
    "    return l[0] - 1\n",
    "\n",
    "y_pred = np.array([ map_predict_label(s) for s in predictions])\n",
    "\n",
    "y_true = np.array([map_groundtruth_label(s.label) for s in val_rdd.collect()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The prediction accuracy is 95.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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0BTrL67UaHewdKqMov+RBCo895rFmVnjrBe0X/OayzOYtV2wv7OGeDRs24PXX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7541b0350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = accuracy_score(y_true, y_pred)\n",
    "print \"The prediction accuracy is %.2f%%\"%(acc*100)\n",
    "\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "cm.shape\n",
    "df_cm = pd.DataFrame(cm)\n",
    "plt.figure(figsize = (10,8))\n",
    "sn.heatmap(df_cm, annot=True,fmt='d');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Visualize the Training Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## using Tensorboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In Tensorboard dashboard, you will be able to view the visualizations of each optimization process. Select corresponding appname at the bottom left column. Use tabs at the top to switch to different views. `Loss` and `Throughput` curves are under `SCALARS` tab; `weights`, `bias`, `gradientWeights` and `gradientBias` are under `DISTRIBUTIONS` and `HISTOGRAMS` tabs, illustrated as below. For more details of how to use Tensorboard for visualization, refer to [BigDL wiki](https://github.com/intel-analytics/BigDL/wiki/Programming-Guide#visualization-via-tensorboard)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<img src=\"https://raw.githubusercontent.com/intel-analytics/BigDL/branch-0.1/pyspark/dl/example/tutorial/simple_text_classification/tensorboard_scalar.png\" width=\"800px\"  class=\"pull-left\" style=\"display: inline-block; margin: 0px;\"/>\n",
    "<img src=\"https://raw.githubusercontent.com/intel-analytics/BigDL/branch-0.1/pyspark/dl/example/tutorial/simple_text_classification/tensorboard_histo1.png\" width=\"800px\" class=\"pull-left\" style=\"display: inline-block; margin: 0px;\"/>\n",
    "<img src=\"https://raw.githubusercontent.com/intel-analytics/BigDL/branch-0.1/pyspark/dl/example/tutorial/simple_text_classification/tensorboard_histo2.png\" width=\"800px\" class=\"pull-left\" style=\"display: inline-block; margin: 0px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## using inlined plotting of notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The training and validation metrics history can also be retrieved using `read_scalar` of `TrainSummary` or `ValidationSummary`. They are in the format of ndarrays and can be visualized using inlined plotting libs such as matplotlib, illustrated as below. Validation summary is recorded every epoch, so `Loss` and `Top1Accuracy` in `val_summary` has only 2 data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CbGQG7WnnaBtaumjv1KQoEREJjhEZtPExUdijLJRWt/LdJ7fw+P/uD3VJIiIyTI2IWccf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\nqwEut0KxRokAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7541b0650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_adagrad = np.array(train_summary.read_scalar(\"Loss\"))\n",
    "valloss_adagrad  = np.array(val_summary.read_scalar(\"Loss\"))\n",
    "# each event data has 3-dimension value, in the form of (iteration_count, value, timestamp)\n",
    "plt.plot(loss_adagrad[:,0],loss_adagrad[:,1],label='train')\n",
    "plt.plot(valloss_adagrad[:,0],valloss_adagrad[:,1],label='val',color='green')\n",
    "plt.scatter(valloss_adagrad[:,0],valloss_adagrad[:,1],color='green')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7541b09d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top1 = np.array(val_summary.read_scalar(\"Top1Accuracy\"))\n",
    "plt.ylim(0,1)\n",
    "plt.plot(top1[:,0],top1[:,1],label='top1')\n",
    "plt.scatter(top1[:,0],top1[:,1]);"
   ]
  }
 ],
 "metadata": {
  "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": "2.7.6"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "317px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": false,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_position": {
    "height": "809px",
    "left": "0px",
    "right": "1708px",
    "top": "107px",
    "width": "212px"
   },
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}