tensorflow_learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow Chessbot - Predicting chess pieces from images by training a single-layer classifier\n",
    "---\n",
    "**[Link to Github source code](https://github.com/Elucidation/tensorflow_chessbot)**\n",
    "\n",
    "Other IPython Notebooks for Tensorflow Chessbot:\n",
    "\n",
    "1. [Computer Vision to turn a Chessboard image into chess tiles](tensorflow_compvision.html) - [Blog post #1](http://www.samansari.info/2016/02/learning-tensorflow-1-using-computer.html)\n",
    "1. [Programmatically generating training datasets](tensorflow_generate_training_data.html)\n",
    "1. [Predicting chess pieces from images by training a single-layer classifier](index.html) (*This notebook*) - [Blog post #2](http://www.samansari.info/2016/02/learning-tensorflow-2-training.html)\n",
    "1. [Chessboard Convolutional Neural Network classifier](tensorflow_learn_cnn.html)\n",
    "---\n",
    "\n",
    "In this notebook we'll train a tensorflow neural network to tell what piece is on a chess square.\n",
    "In the previous notebook we wrote scripts that parsed input images which contained a chessboard into 32x32 grayscale chess squares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Init and helper functions\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import PIL\n",
    "import urllib, cStringIO\n",
    "import glob\n",
    "from IPython.core.display import Markdown\n",
    "from IPython.display import Image, display\n",
    "\n",
    "import helper_functions as hf\n",
    "import tensorflow_chessbot\n",
    "\n",
    "np.set_printoptions(precision=2, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the tiles in for the training and test dataset, and then split them in a 90/10 ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading 8294 Training tiles\n",
      ". . . . . . . . . Done\n",
      "Loading 922 Training tiles\n",
      ". Done\n"
     ]
    }
   ],
   "source": [
    "# All tiles with pieces in random organizations\n",
    "all_paths = np.array(glob.glob(\"tiles/train_tiles_C/*/*.png\")) # TODO : (set labels correctly)\n",
    "\n",
    "# Shuffle order of paths so when we split the train/test sets the order of files doesn't affect it\n",
    "np.random.shuffle(all_paths)\n",
    "\n",
    "ratio = 0.9 # training / testing ratio\n",
    "divider = int(len(all_paths) * ratio)\n",
    "train_paths = all_paths[:divider]\n",
    "test_paths = all_paths[divider:]\n",
    "\n",
    "# Training dataset\n",
    "# Generated by programmatic screenshots of lichess.org/editor/<FEN-string>\n",
    "print \"Loading %d Training tiles\" % train_paths.size\n",
    "train_images, train_labels = hf.loadFENtiles(train_paths) # Load from generated set\n",
    "\n",
    "# Test dataset, taken from screenshots of the starting position\n",
    "print \"Loading %d Training tiles\" % test_paths.size\n",
    "test_images, test_labels = hf.loadFENtiles(test_paths) # Load from generated set\n",
    "\n",
    "train_dataset = hf.DataSet(train_images, train_labels, dtype=tf.float32)\n",
    "test_dataset = hf.DataSet(test_images, test_labels, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool, lets look at a few images in the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5768: Piece(n)\n"
     ]
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2722: Piece(b)\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5919: Piece(k)\n"
     ]
    },
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    {
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     "text": [
      "4806: Piece(k)\n"
     ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6362: Piece(p)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize a couple tiles\n",
    "for i in np.random.choice(train_dataset.num_examples, 5, replace=False):\n",
    "#for i in range(train_dataset.num_examples):\n",
    "    #if hf.label2Name(train_dataset.labels[i]) == 'P':\n",
    "        #print \"%d: Piece(%s) : Label vector: %s\" % (i, hf.label2Name(train_dataset.labels[i]), train_dataset.labels[i])\n",
    "        print \"%d: Piece(%s)\" % (i, hf.label2Name(train_dataset.labels[i]))\n",
    "        hf.display_array(np.reshape(train_dataset.images[i,:],[32,32]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. Now that we've loaded the data, let's build up a simple softmax regression classifier based off of [this beginner tutorial](https://www.tensorflow.org/versions/v0.6.0/tutorials/mnist/beginners/index.html) on tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training for 6000 steps...\n",
      "\t500/6000\n",
      "\t1000/6000\n",
      "\t1500/6000\n",
      "\t2000/6000\n",
      "\t2500/6000\n",
      "\t3000/6000\n",
      "\t3500/6000\n",
      "\t4000/6000\n",
      "\t4500/6000\n",
      "\t5000/6000\n",
      "\t5500/6000\n",
      "\t6000/6000\n",
      "Finished training.\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(tf.float32, [None, 32*32])\n",
    "W = tf.Variable(tf.zeros([32*32, 13]))\n",
    "b = tf.Variable(tf.zeros([13]))\n",
    "\n",
    "y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
    "y_ = tf.placeholder(tf.float32, [None, 13])\n",
    "\n",
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y))\n",
    "\n",
    "train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)\n",
    "# train_step = tf.train.AdamOptimizer(0.01).minimize(cross_entropy)\n",
    "\n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "\n",
    "N = 6000\n",
    "print \"Training for %d steps...\" % N\n",
    "for i in range(N):\n",
    "    batch_xs, batch_ys = train_dataset.next_batch(100)\n",
    "    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
    "    if ((i+1) % 500) == 0:\n",
    "        print \"\\t%d/%d\" % (i+1, N)\n",
    "print \"Finished training.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.996142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "print \"Accuracy: %g\\n\" % sess.run(accuracy, feed_dict={x: test_dataset.images, y_: test_dataset.labels})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like it memorized everything from the datasets we collected, let's look at the weights to get an idea of what it sees for each piece.\n",
    "\n",
    "# Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualization of Weights as negative(Red) to positive(Blue)\n",
      "Piece:  \n"
     ]
    },
    {
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      "text/plain": [
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     "metadata": {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Piece: K\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Piece: q\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 100
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Piece: r\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\nI2zPGVMXBTqyvJPstnr07fqf/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 100
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Piece: b\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\n2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 100
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Piece: n\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\nWlne26urpWT/ACPdzHF4LEYJU6H2L+W9uj1eqP/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 100
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Piece: p\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\nK0oU6mEy+VLFU7Sa0u+t9NLdN9PO4ZtUoYjHOth5XTa07aa/if/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 100
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print \"Visualization of Weights as negative(Red) to positive(Blue)\"\n",
    "for i in range(13):\n",
    "    print \"Piece: %s\" % hf.labelIndex2Name(i)\n",
    "    piece_weight = np.reshape(sess.run(W)[:,i], [32,32])\n",
    "    hf.display_weight(piece_weight,rng=[-0.2,0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool, you can see the shapes show up within the weights. Let's have a look at the failure cases to get a sense of what went wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 mistakes:\n",
      "---\n",
      "\t#3366 | Actual: 'P', Guessed: ' '\n",
      "Actual: [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]\n",
      " Guess: [ 0.44  0.04  0.11  0.04  0.14  0.02  0.21  0.    0.    0.    0.    0.    0.  ]\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a\nHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAAgACABAREA/8QAHwAAAQUBAQEB\nAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh\nByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ\nWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG\nx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APUPmKLnB470EfMOF6UA\nYOcL+VGDzjbQAduM1c0+COe7CzHChc4zjJpNRt44LrZCQFIBIz0qntPPI/OlAO0ZNXtLRnugnGzB\nLcD/AD3pmox+XeuuQRgdQKpjHP3RSj7gGT9c1u2ECWuXyzs6jqV4/WoNUi8xhOMjACkZH+NZXPzA\nk1//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "\t#2634 | Actual: 'R', Guessed: ' '\n",
      "Actual: [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " Guess: [ 0.28  0.2   0.15  0.15  0.13  0.07  0.02  0.    0.    0.    0.    0.    0.  ]\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "\t#1520 | Actual: 'B', Guessed: ' '\n",
      "Actual: [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " Guess: [ 0.31  0.05  0.16  0.08  0.29  0.03  0.07  0.    0.    0.    0.    0.    0.  ]\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a\nHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAAgACABAREA/8QAHwAAAQUBAQEB\nAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh\nByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ\nWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG\nx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APTyvH3B6dKntrZ7m4SM\nLx/EcdBS3ds1rctGF+X+EnuKr4bB+WlG4LyP0rV0dgssm7qV6YOai1aXzbsbOirjOKz8Nt6ZP0po\n/wBWDnjFbWixcySn0Cj+Z/pUetRkTxuP4lI/L/8AXWVzjFOjHmGMcgMQDz610VqEtYREuTzkklef\n1qvqpEtsGAIKN6jv+NYrcZ6/nX//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "\t#3202 | Actual: 'R', Guessed: 'r'\n",
      "Actual: [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " Guess: [ 0.    0.    0.    0.3   0.04  0.    0.07  0.04  0.01  0.5   0.01  0.03\n",
      "  0.  ]\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a\nHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAAgACABAREA/8QAHwAAAQUBAQEB\nAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh\nByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ\nWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG\nx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APfXdYo2kdgqKCzMegA7\n1hQ+N/C9zY3V9Dr+nyWtpsFxMs6lYt5wu49skECibxx4Wt7G2vZvEGnJa3RcQStOoWQoQG2nvgkZ\n+tbsUiTRJLE4eN1DKynIIPQio7uD7VZT2+7b5sbJuxnGRjNfNX/CNPpGuSeHdU0G5udStoo3T+zb\nGWdLu3jIVJmAuox97gjbwe+aH8OvquvR6Dpfh+5t9Vu45XI1Kxlt47W3kyskyg3UgGCQANvX3AB+\nk7C1+xadbWm/f5ESR7sY3bQBnHbpVivINX8Z2GnfGifUba1vdVhtNF/s+4GnRrIYpmm8wA7mUdFP\nQnnj1pNM8bafqHxps9RurW90mG60Z9Pt/wC0Y1jMswmV8DazDoe5HPHcV7BWT4on1K18LapPo6M+\npR2ztbKqbyZAPlAXvz2rwjSrPxPpliII7LxBlnaV9/g+3lO9yWb5mnyRknH8hRqtl4m1XT3tZrLx\nBtLK48vwfbRHcrBl+ZZ8gZAr3TwncapdeFNMn1pGj1OSBWuVePYQ/fK9q//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "\t#535 | Actual: 'R', Guessed: ' '\n",
      "Actual: [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " Guess: [ 0.27  0.18  0.16  0.15  0.13  0.07  0.03  0.    0.    0.    0.    0.    0.  ]\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a\nHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAAgACABAREA/8QAHwAAAQUBAQEB\nAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh\nByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ\nWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG\nx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APUh5jEKH5JwOasSWV1H\nNHCfvP0G7rR9iuvtPkHhyu7G7tVY7gzAy8/Wk+YAYYcGuqjnikhWXemMdcjimTXUCQuwljLBTjDD\nNcwDkt8wpcHHaug0ldtgpP8AESag1iFVjjkVVU7sEgdeP/rVjYOD8w/OnRx73RSfvNjP410VuVt4\nEiAJCjGcrz+tRaifOs2AU5U7hyO341gFQCRn881//9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mistakes = tf.where(~correct_prediction)\n",
    "mistake_indices = sess.run(mistakes, feed_dict={x: test_dataset.images,\n",
    "                                                y_: test_dataset.labels}).flatten()\n",
    "\n",
    "guess_prob, guessed = sess.run([y, tf.argmax(y,1)], feed_dict={x: test_dataset.images})\n",
    "\n",
    "print \"%d mistakes:\" % mistake_indices.size\n",
    "\n",
    "for idx in np.random.choice(mistake_indices, 5, replace=False):\n",
    "    a,b = test_dataset.labels[idx], guessed[idx]\n",
    "    print \"---\"\n",
    "    print \"\\t#%d | Actual: '%s', Guessed: '%s'\" % (idx, hf.label2Name(a),hf.labelIndex2Name(b))\n",
    "    print \"Actual:\",a\n",
    "    print \" Guess:\",guess_prob[idx,:]\n",
    "    hf.display_array(np.reshape(test_dataset.images[idx,:],[32,32]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like it's been learning that pieces have black borders, and since this pieceSet didn't, and it was a small part of the training set, it just fails and thinks we're looking at blank squares, *more training data!* \n",
    "From the label probabilities, it did a reasonable job of thinking the pieces were white, and their second best guesses tended to be close to the right answer, the blank spaces just won out.\n",
    "\n",
    "Also, lets look at several random selections, including successes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#4662 | Actual: 'N', Guessed: 'N'\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#4761 | Actual: 'N', Guessed: 'N'\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#4079 | Actual: 'n', Guessed: 'n'\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\nwsLV2TNtYpbEqc7mEkjk9Onzj8jX/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#7440 | Actual: 'p', Guessed: 'p'\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\ng4r2fSYb9NDtItTkSS+SILM6HIZh3r//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#8233 | Actual: 'P', Guessed: 'P'\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx in np.random.choice(test_dataset.num_examples,5,replace=False):\n",
    "    a,b = test_dataset.labels[idx], guessed[idx]\n",
    "    print \"#%d | Actual: '%s', Guessed: '%s'\" % (idx, hf.label2Name(a),hf.labelIndex2Name(b))\n",
    "    hf.display_array(np.reshape(test_dataset.images[idx,:],[32,32]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manual validation via screenshots on reddit\n",
    "We'll eventually build a training/test/validation dataset of different proportions in one go, but for now, lets build a wrapper that given an image, returns a predicted FEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hh7DvT2MRvIx+cCkomh0rY92Fco5/diAXwL9/997bf37gz99+u/bve3+Hd6oOARnfOSLZ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\nrkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's see the 5th tile, corresponding to F1\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\nvw3p9/pml/Zr+cSssh8oCQvsjwAq7iq5xg9q/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validate_img_path = 'chessboards/reddit/aL64q8w.png'\n",
    "img_arr = tensorflow_chessbot.loadImage(validate_img_path)\n",
    "tiles = tensorflow_chessbot.getTiles(img_arr)\n",
    "\n",
    "# See the screenshot\n",
    "display(Image(validate_img_path))\n",
    "\n",
    "# see one of the tiles\n",
    "print \"Let's see the 5th tile, corresponding to F1\"\n",
    "hf.display_array(tiles[:,:,5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 tiles\n",
      "#0 | Actual: '?', Guessed: 'R'\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\nvFKhR1OpjDKRgj/j39Kg0y2ntLVopmyA58tfM8zYmAAu7aM/lX//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#1 | Actual: '?', Guessed: 'N'\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a\nHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAAgACABAREA/8QAHwAAAQUBAQEB\nAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh\nByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ\nWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG\nx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APWVUbRwOnpXOXPjbR7W\n7vbNluGvLWTylt1i+e4bAJ8vnDYB55GBycDBra0/ULHVbRLqxninhdQwaNgcZGcHHQ+1Wtq+g/Kq\nGraj/ZeltcJEZpyVjghB5llYhUQfUkfQZParXg3w9q2g/wBof2rfW92J5VmjMaYIcqPNdiRnluAO\niqij1qEaLovizTRrOkxf2des8q22oW6KrtsdlDHHEkbbc4bIIPY9I9G1CXULDdcxLDeQyPBcxKch\nJUOGx7HqPYisrxRDqHnaZe2Ud1Ito0j4tFRpEkK4Rgr8EY3oeCRvyBxVTU/EWreJNJtzd2Fxp8F2\nqxw6QpK3F9MVyyOxAKRA5zwCVBJwOD6B4b0j+wfDWnaUXV2tYFjdlGAzY+YgdhnPFc5GotvG3iC3\nXhZVtrzb7uhjJ/HyRWkv3R9Kxng1i08US6tZ2+n3Km1W3hFzO6GH5iz4wjfe+TP+4Kv/ANteK/8A\noG6N/wCBkv8A8aqrZwanNr99q2pRWcLT2sFukdtIzj920rFiWVevmgfhX//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#2 | Actual: '?', Guessed: 'B'\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\nCo2INxKt3ILHOPYk/XtX/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#3 | Actual: '?', Guessed: ' '\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a\nHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAAgACABAREA/8QAHwAAAQUBAQEB\nAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh\nByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ\nWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG\nx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APWVUbRwOnpS7V9B+VG1\nfQflRtX0H5UL90fSloopF+6PpS0UUi/dH0paKK//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#4 | Actual: '?', Guessed: ' '\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a\nHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAAgACABAREA/8QAHwAAAQUBAQEB\nAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh\nByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ\nWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG\nx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/ANMk7jyevrSZPqfzoyfU\n/nRk+p/Og/eP1pKKKU/eP1pKKKU/eP1pKKK//9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validation_set = np.swapaxes(np.reshape(tiles, [32*32, 64]),0,1)\n",
    "\n",
    "guess_prob, guessed = sess.run([y, tf.argmax(y,1)], feed_dict={x: validation_set})\n",
    "\n",
    "print \"First 5 tiles\"\n",
    "for idx in range(5):\n",
    "    guess = guessed[idx]\n",
    "    print \"#%d | Actual: '?', Guessed: '%s'\" % (idx, hf.labelIndex2Name(guess))\n",
    "    hf.display_array(np.reshape(validation_set[idx,:],[32,32]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh my, that looks correct, let's generate a FEN string from the `guessed` results, and view that side by side with the screenshot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: rnbq1rk1/ppp11pb1/111p11n1/11111111/111PPP11/1BP11Q11/PP111111/RNB11RK1\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/rnbq1rk1/ppp11pb1/111p11n1/11111111/111PPP11/1BP11Q11/PP111111/RNB11RK1)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/rnbq1rk1/ppp11pb1/111p11n1/11111111/111PPP11/1BP11Q11/PP111111/RNB11RK1\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hh7DvT2MRvIx+cCkomh0rY92Fco5/diAXwL9/997bf37gz99+u/bve3+Hd6oOARnfOSLZ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\nrkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# guessed is tiles A1-H8 rank-order, so to make a FEN we just need to flip the files from 1-8 to 8-1\n",
    "pieceNames = map(lambda k: '1' if k == 0 else hf.labelIndex2Name(k), guessed) # exchange ' ' for '1' for FEN\n",
    "fen = '/'.join([''.join(pieceNames[i*8:(i+1)*8]) for i in reversed(range(8))])\n",
    "\n",
    "print \"FEN:\",fen\n",
    "\n",
    "# See our prediction as a chessboard\n",
    "display(Markdown(\"Prediction: [Lichess analysis](https://lichess.org/analysis/%s)\" % fen))\n",
    "display(Image(url='http://www.fen-to-image.com/image/%s' % fen))\n",
    "\n",
    "# See the original screenshot we took from reddit\n",
    "print \"Actual\"\n",
    "display(Image(validate_img_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A perfect match! Awesome, at this point even though we have enough to make predictions from several lichess boards (not all of them yet) and return a result. We can build our reddit chatbot now. \n",
    "\n",
    "# Predict from image url\n",
    "\n",
    "Let's wrap up predictions into a single function call from a URL, and test it on a few reddit posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getPrediction(img):\n",
    "    \"\"\"Run trained neural network on tiles generated from image\"\"\"\n",
    "    \n",
    "    # Convert to grayscale numpy array\n",
    "    img_arr = np.asarray(img.convert(\"L\"), dtype=np.float32)\n",
    "    \n",
    "    # Use computer vision to get the tiles\n",
    "    tiles = tensorflow_chessbot.getTiles(img_arr)\n",
    "    if tiles is []:\n",
    "        print \"Couldn't parse chessboard\"\n",
    "        return \"\"\n",
    "    \n",
    "    # Reshape into Nx1024 rows of input data, format used by neural network\n",
    "    validation_set = np.swapaxes(np.reshape(tiles, [32*32, 64]),0,1)\n",
    "\n",
    "    # Run neural network on data\n",
    "    guess_prob, guessed = sess.run([y, tf.argmax(y,1)], feed_dict={x: validation_set})\n",
    "    \n",
    "    # Convert guess into FEN string\n",
    "    # guessed is tiles A1-H8 rank-order, so to make a FEN we just need to flip the files from 1-8 to 8-1\n",
    "    pieceNames = map(lambda k: '1' if k == 0 else hf.labelIndex2Name(k), guessed) # exchange ' ' for '1' for FEN\n",
    "    fen = '/'.join([''.join(pieceNames[i*8:(i+1)*8]) for i in reversed(range(8))])\n",
    "    return fen\n",
    "\n",
    "def makePrediction(image_url):\n",
    "    \"\"\"Given image url to a chessboard image, return a visualization of FEN and link to a lichess analysis\"\"\"\n",
    "    # Load image from url and display\n",
    "    img = PIL.Image.open(cStringIO.StringIO(urllib.urlopen(image_url).read()))\n",
    "    \n",
    "    print \"Image on which to make prediction: %s\" % image_url\n",
    "    hf.display_image(img.resize([200,200], PIL.Image.ADAPTIVE))\n",
    "    \n",
    "    # Make prediction\n",
    "    fen = getPrediction(img)\n",
    "    display(Markdown(\"Prediction: [Lichess analysis](https://lichess.org/analysis/%s)\" % fen))\n",
    "    display(Image(url='http://www.fen-to-image.com/image/%s' % fen))\n",
    "    print \"FEN: %s\" % fen    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Predictions\n",
    "\n",
    "All the boilerplate is done, the model is trained, it's time. I chose the first post I saw on reddit.com/chess with a chessboard (something our CV algorithm can do also): https://www.reddit.com/r/chess/comments/45inab/moderate_black_to_play_and_win/ with an image url of http://i.imgur.com/x6lLQQK.png\n",
    "\n",
    "And awaayyy we gooo..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://i.imgur.com/x6lLQQK.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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HNC679FKSyWT2oG7UyBEMHNAPz/NwXZcpp53K3Llvsuzdx1my8KW0khxcc0sp\nSTAUZdH8WdRsfJnH/zOTPr17MnHisfzjgXv48LUHWLXknYNuZmUCaluawVqLb1HSpSkWpymeatf9\nKvADDN9/4xH+dv/v+cYNX6W0tDtCwFlnns7s558jLLezYdVHBIKRz8RexPM88vPzeeihh/jRj36U\n3Yf88pe/ZOLEiUAz9dG6deuyzJTtQdcN6mur6dOziPcXvs8NN9zACSccn00vACgr38m8+e9lU6Ad\nx2HQoAH8c8YMXnvmL8QaqtEP5n4wzbm1s2wztVsXMOvZp7jows8TCFgU5OfzpS9exdNP/ptVC58m\nEWv0mWkOAgSQSqZobIqTst2s8aP5Xwqk6xLI68YFnz+d804eQ1hXtF4NUGGYJlWVZQwZ1JcTTjge\nx3Gz5A2plI2UHl+4+CJ2bFuLYZgHxm11EKGUwjRNampqeOCBB7Iz0U9+8hO++MUvcuedd3L55Zdj\nGAaVlZXMmDGDQAeFSH1q1jxWLH6LiceOZdSoUaxYsYKf//znWde453l8+OHH/OrO3+wih+d5nPf5\nc5h84jG8Nms6wXD0oM3MCr8GR9m2dUydOoVIJEwq1Rx1bdsOgwYNYsKE8ZRv3+jTxh7g8SIE2K5i\n1LhxXHPRFEb3L8Jx/Cq/WuYhFBAMwbJl6+gzdAjD+0RI2l4rXheB57rkF3SjuraB6uoqTLNZ6y3L\nRNN0Fn/8EYXdevvelM/AVsQwDKqrq9m2bRtKKVzXD7UJBoNYlkU8Hsfz/P5csWJF+4MgzVW1bdMq\nqra8z6233sKQwYMZOXJkllFG03zijJdefoXTT5uc/plq0YTi1lu/x6olb9BQsxPdMA/SKiKQnkNR\nSW/WrVsPQCDQTExoWSaO47Bx42aKu/XEc/fe07fPUArdNEg0VrG2LMGUySPTOS6i2cQyTIOKLeU0\nugZb165h2eb6dCnnPTtVKkkwFKKo91F8/cZb2L69zCffkhLHdfnTnx9g4ZIyhh5x9GHJgNEa/Jdt\nYVlW1o696667uO+++/jud7/LrFmzsCwLIQThcLj9QZA+6XvpqT9y910/p1evnlnCvsyexpOK3/72\nbh5++J9cfdWV6Z/5bWZ4y6aeMZXePYtZs/zdg2bqCiGwU0n6DhjK5p0eP/v5HcTiiSzjf2VlFd/8\n1i2IyEAKi7u3W8xmf0Eh0KXDqrUVBC3Fq3OXoAwLUM0mlpNKUjp0JDdedTKmkFhB3wRo9TUqkJ7H\n0cefxXMvzucP9/0ZIXx6nxUr1/Dt70xj5DFnE8kvPPT88fsBQghs26ZPnz5MnTo1S6gH8Otf/5r3\n338fgFQqhZSSiy66qM39h1I+6/0H8+dw2omjGTFiBFu2bMlSJ2maxv33/4WJkybz4CMvkl/Yg7q6\nuj3a8TwPyzSYfNJk1ix/L1vQ5mCsIVJJAoEQ4yZ9np//7E7eemtelg7q3/99ggf/+TjHnXyRH9t3\nEJRYAxIuXHT5eZw0sgRPmBjpU/2siaUbFsnq7Ux/ZA5LN9akg+z2zBJTShIIhqiu3MZT03/I/037\nGtOmfY9MeYQjRw7n2WceZ8vSp3jn9f8RiRZ27LE5TOC6LnfccQdnnnkmJSUlLFmyhIaGBpqamvj7\n3/9Ofn4+P/nJT7joootoamrKErLtDqUUVeVrOemkE/nBD24nGAyiaRqxeJyLL72C39z3GONOuYFz\nLr0F0xD06dMLYJdVKRM+ceyEYynfuiZLanGgVURJj3CkgMULX+XD1//Ko4/9kymnnZzdll73pS/y\nxz/8khce+39s3bSSUDjvgLumJRA0dd6b+yaPvvAhjUk3G1ia3TwIIUglksSa4vhF2fcMWVZp7qy6\nuirefv6PzPj7fZw0+aRdrjFNk/PPO4fTp5zCNddex4I3n2bSqV8g1lR3yPq6c4EMKUXPnj351a9+\nxbnnnsvPf/5zkskkwWCQnTt3MnbsWKZNm0ZlZWXb+fnKD/UPRQopL9vOw//6F5s3b2PN2o38+9+P\nsWqrx3XfvhsrEOLvd3+L88+aSkFBgR9u0kLhMq76Xr364jkJ7GT8gPe/UpJQJJ8VH79N3Za5vDj7\nKXr06LHLNfl5EW7+1jc4Y+ppXHbldej6V+jVdwi2nTzAexFFbU09judHSJuGrxq7+NSEpmHpfo5G\nKtW6LWiYFm+/+BA//8n3OGnySdi2nWVbzMBxHCKRCA/N+AennXEeO8vGU9yt52FdliBjGui6zpw5\nc7juuuuYOHHiLgTes2bN4q233uLYY48lFovRWqk6IQSOnWD0sWfw7yfuRtMM5r+7gPHjxjJ/0UYu\nuOI2XNfh6Ud/R+9iya/vujMbpNgaLCuAdG2SiUbC0ZL93g8toesGjfW1rPnoeWY/8wg9evTAcRxM\ns5n2SSlwHJsRI47gz/f9hq/d/FPOv/onByUKwzANNN1P38iss3v0asZUau1zKxBk6+bV9OtuctVV\nV/l2bnrj2RKmaeK6LgUFBdzwlatZ/tEbWIdorM2+IJOGnDm0MwyDnTt38sorr9CrVy8qKyvZuHEj\nGzZswLZtotEozz77bJYeKXNWssvJuhA4jk1x996cePbNzHl7E/Vxg7feWcz4Ey/CNC1eefZB+hYm\neO21V8iLRgHaPtwVAuklSSVjaLp2wFzuSkoCoQjLPnqTi86bwuDBg3FddxflSIuHZVm4rsvJJ5/M\nxPFDWLfqQwLpNIEDiczYb3nXvTiVUZimxfZNKzlj8gnQCsFcS2QGwamnnsrf/zWbVDJxWHmzMl6r\nSCRMLBbDdV0Mw6B79+7ceeedTJ8+nZdffhlN07KD4sYbb+Scc87BMAxM08yevJumSX19Q7ZtITQc\nO0W3ngM4c+AomhprSSRilPboy3tvz8JyNvPMS68SsMy0y7ejfm3/Xe0XCP8wub5qC2ec8Z3shNDm\n5ekksNNPO5UHHnmTUeNOOCTOzzqtIH7slUFDbQXdioakE3pkO94YAEX3bkV4diOOY/uz3MF/5n2G\nvxcz2bxlO48//l8WLVpEPB4nGo3Su3dvRo4cSXV1NV/5yld4//33GTNmDNu2bWPLli08/vjjrFmz\nhrKyMmzbprS0lKlTp3LuuZ9HiBZk+0LgOjaNdhJd04lG84nHGlm56AX++8j9BCwT23YwDKPVd5A5\nuO1oYO5PSCmJN1ZT2r0wPTvT5njJyFvavZCm+mo/suMQGCydVhCBTzIdiuSTSPklECzL6vB3iUQK\nNJ/tXXq5TYE9WMgccG3ZsoV77rmnzetmz57N2WefzaxZs1i3bl2b1y1dupSzzj6H3fvG36PoSCWx\njABbNqygpMBkUjp0xbIy5sqefZqtQx84eGEmQgisUJRUery0t9Bl5G1ojBMIRfxYtENgrHTexBIC\n204ydMR4Hvj7/cx6/sX0x60/hE/soFNbW0NRj3E+d5JjczhYWZqmEYvFmDz5JG655RYaGhro06cP\n69evp6ysjJ07d6JpGqeffjq33XYbGzdu5Je//CUbN24kHA7Tq1cv+vXrR+/evfn444+55JJLKCoq\nwk3F2rmrQHopysq2c/rUszpcFXziaUFVVQ2R/GLSKaA57YcOoRR9B47my9ffSPdu3bKrRHvybt9e\nztiTrsVx7UNiLu0w5VbTBJoQCE1Dujbde/Zn8nnfI5HI1Khrr9MFvYSguHtPXCeBrmuAACGy6af7\nYxltJgDI/OUSIh3IKWhqijNt2m1YloXCZ8JPppLU1/kZciXFxTQ0NjJw0CD+OeMh6mprsSyLwsLC\nZh5jKUkmEjTUNxAJ+a8j0+dZF4rS8dwUfQaM4Pwv3kk8Hu/EY/kX9DV0TjgnTCCUh5QuCp/bWCq1\nZ1rsJ4TET1ttOV7QNBwnwchxJ1PaZxielzGt2rqpP5b6jwtQVNIDz0mhazqaaM5Bz8hNjnw9Uvly\nt7fCtkva4Lpp8gPbzW6wFR7BaCnh/M4vBSnHJmVn3MYST7kYSmHqAk/ldgBrgFAGmrDRsHOek67h\nEbIEkZCBrklQNqlkOvhO+QOkpDgPlCKVbCQc0FHKBSUo7VaIVBI71URGhwWCgCkwdYNo2CdtaIr7\n0aTSU3sIYEW6E8zrfN+rNONM0vHAdglaOpGQkXvShqCB6ylicRclm0uYISC/uF+n90FKKeKJZq5f\nTRc0xV0UZPtHa4emZ6/klpJIyCBgtX0+1KqCCAG2IxnYJ8JlZwygsSntImzxEHsDgcjOeEoJdF3R\n2CRYuupjn1whx4uIQlBlj8FTHZM2KKTPcyS0jr1sQuEpeOPDHXy0qhpPtnZ985q4673b+jz9mfBJ\nCboVBrj23EFZEoTdmv7EK27mHcxfXIHjpk2dHPW7ED6TTL/SCCMHFuxCCCE9icyUMxN+rpEnm2l/\ntHSIkpReVqk0Tc+uGkr5RBC1DTYzZ2/MkmbkAkpJAoEAmzZXtNlumwrieoqSwgDHjCqhti6QNo9y\nIRR+PbgdTby6cCWWoeVYQfwAgSZ3DLIjO12CYUIgCo4NdpIO90gKWL1lC6aqQgmTXI0yTfNnyi+c\n1o+Jo7vRFHdzRm/TEnMX7aSqNoVp5pD0TghiSZfrPj+Eo0YUE0+6aGkvVCjkUx+hfELzZCpJOBQm\nFG79M6UgHo+RSqX8cyJPEQ0bvLqgnPeWVRENG+2ykOwNMqw8tdWN2Vi13dG2iYVvYsUTLvGUm1MF\ncaWGbStMzcLQ9o+CaMIBWq+XLvBNgEBQo7ZyOx8vnUPP/kfTd9gxOEnZzmm/T/sWsARBYaQjdXKn\nID4NEsTSfb4/FCRgaYRDBmYOZ2ItTTfier5JnrD9WCbTMPhg3nssW7YMTdeYeNxEhg0fxrx33mfJ\n0qXousaECRMYccQI5i9YyOLFizF0g2OOOYZhw4fj2LZviaYXu0jIIBzIsYKYBnGr7bHd6U16Wxq2\n10Lhd6j/0Jn/5RKZFtveoEslsQIa29e/T8WiH3Lpuccwd/5TLK+4jNEnfZlUXLZTZswfWDJ7n1yt\n980Bhrnu811uk76PzPGh+h6bdKUIh0P86U9/ZODAgdTW1rJ92zbuueceHnjgL/Tr14/a2lrWrlnD\nX//6V/45fTrdunWjqqqKFSuWc99991FVVZU1yzL3kKptUrq9hUq31V5zBye/8aBDgYBNH/6df/7+\nB0yZcgbV1TWcfOYXaaq/lGAokjP+1886DMPgzjvvJJFIcOWVV3LTTTdRU1PD7Nmz2bRpE1dccQU3\n3XQTlZWVPPnkk8yfPz+blXko4DOpIAKB9HzGkO7de+E6LqFQkHC0MO2OPDRezuEAXdfZuXMnw4cP\n5/HHH8dxHMLhMK7r0rt3b5599lmSySSWZeF5HrW1tZ0InTlw+EwqiFSSaNRA5B3HDd+4lXfmv8JP\nf/z/2FBRyLg++dTXyMMqbuxgIBPi4nkeF1xwAWeccUa2RqWUEsfx3f6ZSHCf41ln9uzZnH/++YdM\nDtEeCtJaCPbhBCUlgbDBx288hlH9H8658DxAMeG4iSz6+N/M/e+POPacO/HczD6mC58EmqaRTCb5\n/ve/z3XXXUc4HOaHP/whruvuMsYyp+tCCH784x9TWlrK17/+dRobG9OHqQdQaOFzRrd0AuxRJz2R\nSGFa1mE5NJSSBEIam1a+Q7ThEZ564RG6lRSjlOLyS87n8ovP4+vf+DpzX7+P487+DolGF3GQaGg+\n7cgkkI0ePZrXXnuNadOm8aUvfYlBgwZllQQyrlaDlStXcuSRRzJnzhwAkslkNtrgwMgLnuOQdDyC\nASubLNji7SuUMBk1sj9eop4la+MHRLADC4WmQ/maF7nru1+hW0kxqVSKQCCA47iYpsGPfvwT3vjC\nj0gmfGb1LnxyaJpGQ0MDJSUl3HrrrTz44IP8+c9/3iNEXynF1VdfzbRp0wBIJBJtpiPvDwgBnisp\n6dmTvsUBNmzZQdz2ozIMSJ8LuB7h0u6MGHUEw4o1dmx8AldpOQtHOCSQno3MQD664Z+R+CXFyJ7z\nKOmiW9GsG7rLzNo3ZMjsmpqaiEQihMPhVq8Lh8M0NjYSiUQOyibdERq9+/fm+KOGMWTJ+zz+7kas\ngJYhbQDdsmgq28zsNxaRVCaBcMfMf586CA3Pg55DTuf3901nZ0UlhqFnbU7XVfz8F79Gzz8aKyCy\n1aG6sG/IEOllxtPu4yoTL7Y77eeBglIQMgTvz1vAR+srCeVF/UgAlaX9Ade2Ke4/kFF9i4nbLgVF\n4cPO5SmERirmMmTseOoiV3LSqedR35AA/JdzxTVf4bkFISad+zUSTW6XiZVDZNJZPc9r9a+tVO8D\nAQHYUnD0sWMxEk2EivPRhG89ZFcQTddorGkgFA2z/OMlrNhYSyigH1a8ukpKQvkGqz54A7vsSS69\n5GJMq9nNeO6559G/cDsL5vyNUMQ47HLoDxaklIRCIYLBILquY5pmlktY1/0KAYFAoFN8xfsFAoTn\n0tCYImjYzHl5EZphgZItaX80lBNn3rz3MawA6IcXp66SkmBEY/UHz2NW/I1Z//4tR4wYSSbuXCnF\nl6+9iC+cdyq3fu/HvDPntxx3zm0kY+2FnXShIyiliEQizJs3j+effx7DMHYpJJT5/9dff53TTz+d\nCy64gOrq6gO6SVcKDEOjfMsmNqxzCQYDmJqO8nZ38wqNaDSCrglSyeQBE3D/Q2FYguqdZdSvvo83\nX3yYXr16Z4kWgOyhVmFhMTOmP8C5513M6g9fYfj4M0glPIToMrf2FlJKotEoM2bMYMOGDcyePbtF\nop2PjIVy/fXXc9ddd2FZFlOmTCEWix3QzbpSYFpWmjzbL2ktaOWg0D+4ObxmTCUlZkBnxfy/8P9u\nvYZevXrvwc8EvsfF9TwMXeee393J2Vf8ksGjzzjs+uNAIMM0v2bNGn72s59x5ZVX8tRTT7Wadiul\nxDR9cu0f/vCHvPDCC9nqwAda5t3v+Rk4BVNoukZTvUOBUc6559zihzUYrT+6kSZ6GzHiCMYNL6Bi\n2zp6DhiKk+p8qeUu+IO+pKSEZcuWcfXVV/Pb3/62Q7rVUCjEN77xjSxDZW1tLQfbSXT4K4hS6IZG\nXXUVUbOWoqJCXNcvydDWdtBxXQxdp18Pi3ll6+g7ZChOqo2Lu9AqDMPgueee4/nnn2fr1q288sor\n2LbdptmUmbmXLVvG9OnTmTp1KscccwyplN3q9QcKh7+CiDQBtzDQjMAutUzaQmZfohkWgq69x14h\nTdcjpeThhx/m6quvpnfv3jQ1NWXrgrT9U8Uf/vAHPvroI/79739z/PHHk0wd3Jnp8FcQQLqS/KIi\nNtcJfvp/txOJRJBKtrt4K6WY+/Yiep30nfTq0WVe7Q0yAYibNm2ipqYm+1l7yOxPysrKmleag+xI\nbVdBlCKbwSVylsVFNosrkzWX6z5QZOiIVPqefmpl3/G38u+354Nm+gnpbUEIlOdSOvQ7FJb0xssS\nKKjM12m5c6c0LTMIs1lz+2FwZGTPZb+3bE8q8KTENAyuuOJKVq5cSTKZ6vSGW2gCwzS55JJLSaXs\ndJsZggeRTe/NBVpmt7aFNhVE4bNJhAMGyYCR05z0gKlhmhrxlIvl7Y+cdIFUZjNpg9CRDnQbOIHe\nR0zodEuu4xM5NPeg//8pWyFxIauI+w5NE8STPt1POGggZTuE1PsA25Hpfs9tTno86WJognDIgDSl\n14UXnMsVl1+UdZl2Bpnot3giTjyeIBw0CQcN/7Oki6aR05x0XWrYTtuTZasKohQYmk+1smRt7R60\nP23cLZ1g30FXpLmjYkmXsUOLELnm6xUgpeDjreW4nr7L7KBciWqnM3aHgcDcxevi0/4M6W1SECrG\nkzodCa/So6Mj+qEMk4zjSj5aVdM67U8OMKBXlF4lYZ+9JYe0P66nqGuyWby6WXbp1X5iV63QRDYf\nxNAFrlSMH1GcY9ofhRUIsMWqZ3EbjbapIJapsWFbE4+9sBHHSbTJhyPSN9I0HaHpeNlKRq33vyYg\nZUsG9orwjUtHYLtyj0I9+wIhBCnHZecTHxNPOD7vVs7a9mfgqROOYHCfXiTtPWXPzIAqzTYYDAbw\nPInj2Oia3mZ8sCcVeWGDp9/Yyozn1pMXNvD2Q7Dkd64YSbeiALa7Z4GkTwpPKaJBg4eeX89zb24l\nHDTwcjSKdU3QGHeZOqkXXz5/CI1xFz1HgntSkhcJseCjBE89qwiIPYNH2jexDEE4aGBrRpuHZQqf\n6TyVjJNK1pNfVIrn+BVCW4MQoGsya2L5JGaf9BFba19gO55vYiHSpbRy1DYgkaRsRTzlkrLbkF35\nuRDhcJg169aTF82jtLS0OUuuFYkyFb2E8OltQjmkt2mJVNrEcnLMrCjSVkc4ZBCyjJwxj2iaT/Uq\n8OmQEjmkQ5JSohv+e2wLnd+kt/JS/Txig4a6ap566MfUVW7jjItv5chjzsROxVtVqgwnVctNeq4V\nRMtyBu8PF4jaZaPbquxp+ptf/eqXrFu3DsdxuPzyyzn//PNpampq/SxAsN/obXa5TVb2dui/PgG0\n9Eqdc9n3Jx1SJ8bfvu28lcK0gmxeu4jKsvU4Toql7885rCKA9xae51FQUMCTTz5JbW0tc+bM4R//\n+AczZ85k06ZNBAKBz3T/fNqwbwoiBKlkjGFHnkC3ngMBwTGTL8kmm3xWIYRg48aNfPGLXwRgwIAB\njBkzhnXr1nUpyKcM+6QgSkmCoTwqyjeSTDSiaYLKsvUgNHT9s5tPIaXkvPPO44EHHsB1XRYvXsz6\n9euZOnVq2yZWFw5JfOI3pZQiFClg+aJXePqhH9NUX42UkvmvPMyLT/wOAF0/eNWNDgaUUgSDQXbs\n2MH//vc/ysvLcRyHLVu2UFdXxyOPPEIwGPxM9cmnHa3sojuxAVIK0wqw4NWZzHr0F9ipBAWFRQwa\nPBQhNJa8N5unZ/wfdirmR29+RgZEJqfkxz/+MUceeSRvv/02oVCI8847jyeeeIL33nuPBx54gIKC\ngsMv3/8wwe7DX9v1S4H0XDzVdhCFUhIrEGbDyveYO+fBbNj4lClTuPcP96a/D7Jp7SJef+5+dMM6\nJIox7m9IKcnLy+P111+nuLiYb3/72+i6niUk6Nu3Lw899BDvvPMOO3bsyAZEduHQgUDhuB4tjyiy\nCiI0gZNMkNd3CDdfdQYR18VrLexFgdA0Vnz4SlqhPL5x4838/g9/5ORTTuLZWS+iaQIhNDasXEB9\n7c7PhKnlH5b6PFBHH300SqksQZqmaXieRygUYuDAgTQ0NGAYxmHfJ58maAIaYpKzLzqXs47sTTzp\nV2XOspooz8MIF3LixNEYhv9Fe68vHqv3G9Y0/vfEf3l4xj/ZsmUbP7jt+7i2g0KRTDRhp+KfiU1p\nhmrzqKOOYs6cOdi2nc2Sk1Ki6zpz585l+/btDBo0KFsgpgsHHxqClO0y/MiRDBvUHeU1W1D+yBUC\nx3Hp1rcXpRGdUFERfUoieG2dcgtBYXGvNEGxRlVVBb/5za/58pe/zMqVy7KKFY4UEo4UIKV3IJ7z\noEIIQSwWY+TIkZx//vlceumlVFRUZL+bPXs2v/jFL/jlL3+J4zhdq8chg3R8hG4xbGhP8Dz6D+xN\nwC/Ol6b9UQozYFG9YS3TH3+F+R+uoazer0q7x3sUAtdJMf6kiwiF83Fdhz59B3DS5FMoLCzk+BMm\n43kuKMWEUy8nkleC5zmH/WyplEpTmDpUVVWxfft26urqAF9BKioqiMVibNu2zS9J1oVDBAqJIGgo\nXp71Mk++/hGL127F1TQ/fKbFdSjdJJiK8frrC5FoFLKnmSWEwHVsuvccyJU33cesR3/F7bdP4/Ir\nriSZSpFKJjnzjCkMGn0WE065GDuVOOxJDzLU/VJKpk2bRr9+/XjjjTfIz8/PrhRf/vKXmTBhAv/3\nf//HmjVr+OpXv0ptbe0BpbfpQttQUhGKhChfvJStaAQCJspN7MnuroQgELBIpdw2GxNC4NhJuvUY\niBmIcPfdv+FPf7oXz3PRNYPq6mpOGDAyfePDn1dKSklhYSHf//73GTduHD/84Q+B5hoZmWtGjx7N\nU089xfnnn0/37t0577zzqK+v/0zs0T4NUEqhmyY6KpvD0uqbacs+9mlRJNLzCIXz+HDBC1xywem8\ns2Ahr7/xFu+8+x6vz53PM08/yZqPXvJNLUjzDB2eNreUkvz8fF5//XUaGxuzNTB2p7fRNA3XddE0\njT//+c88/vjjLWpgHJ5982mE2q1mYaed8X4dBxNN05FKopTATtSyfPlWrrjsEsrKyzENE13XGD58\nOJ6bJN5UTzTfr7+BUriuc5iFn6gs/9PcuXO56qqrsqtGa6uCYRh4nsfAgQMZOXIk7733HqeffjoN\nDQ105bwfmujU2i6EhmkFqasuZ9umZezYtpq66u0cO/liCgd9joraJMceewwlJUX0HziETdURTjn/\nFgzTpKJsPds3LaOibB0oiWkeXsF6mqYRj8cpKytj9OjR2WeTUrb6lyFrHj58OCtWrMi6grtwaKKD\nFUSlN+Up5vz3N2xe8wGpZAxNNwgEI0Tzi7FTKUq7FbCjvILysnLCkTxWL3qGik3v01hfRTLRiGMn\nMcwAhcU9OeOi71Dad8xhNSiklMTjcQoLCzvcT1iWBfj1MGKx2GHv3fs0ouU76dDEUkpiBSNIzyGV\njGEFwtipOIlYPYn0YWF9TRme0Y1hR1/Iy0/eDZD9DsC0gjh2krqaHYQiBWkz6/DYmGbcu927d+el\nl15ixIgReJ7XpqJ4noeu6yxYsIARI0bgum07Q7pwcOC6HqBh0qGCZBi4YcoF3+LIo8+guMcAkrEG\nGusrqasuI5loole/Ixg+5mRMK0hprwGsW/EuSikKinuSX9iDaEE34k21oBTdeg4m1tSEEGb7t/5U\nwM9clFJx1lln8fDDD2NZVgeBiCIdrxbghhtuIBaLZ8kJVItWP23Yn/ZApj8OhM2hFBQXRlFoNNY2\ndSblFuxUknCkmOFjT/MP/Up1NK05SlehsFNxbDtFj34j6TVgjN+AEKAknufSredgpJIkkzEkYr+n\n3Kosn1fuOlbgp38K0Zz+KXSdZDLOqaeewuc+d+ZetReLxZDSRdc0pFRZMgLvU5hyq+/HlNsMeYW+\nn1NuM4yQvboXELR0ltWXt68ghi4IWToaRjpeK44uBCgHWlgGQkDQ0gEdpRzwduVTzfxGR2AGDAxd\nYRqChO3i5pA8wJfFJ22wTL+0mpZjVhNd03BdRSLpYjvNsidScVRDrPONpYkdms9JFJrm3yMUNAha\nzaXhcgnbkSRsL6f9LtPR37ommmXPJWmD9CenRNIlaXu5JW0wvSwvlk9b5ZtYbjpKsVUF0TRIpjyO\nGJTPTZePoK6+KafEcaYhKK9O8sDjqzGM3PEc+e0rLFPn8s8NJJhmBsmV/mVkf+bNrTz9xlYss33Z\nfUeEv4QJ0f6U7RPHeZxydA++d81I4sncDYSWePyVTdQ12B32e0t3fEeREJoQJGyPc0/swzkn9vEH\ncY60T0pFOKjz/vJq7nlkJeFg7iYO30VvUVmxHctoPo9qruOuOrGCBHSSlp5TBbEsLbuCmDK3zIq+\naQWWqROydLwcK4hlNa8gUrUlu29amlYIoWkIBLaTQnoObTG2aZogkWZWDO1nZsWE7WHKdhREKQKh\naHaVS6XiSM9tW3bhy55ZQTKf5QJSKkJpZsXEfmBWdJW3C7Nipk988dtYQVpenLEpc8rNK/cfN2+G\nbzWzB8ks/zlpW+25B9kjVg2QSmIFQmxZ/xFLF75IOFrIMZMvJprfDc/xifV2/50mxB57kAPNzZuh\nZLKCIZa8/yLrl88jkl/KiWdcTSich+c5tOZCyMjecg+SK0il9tiD7E9uXk0TbNhagZGOkTs8fK2H\nEKSSmFaQ8q2r+N/0H7Liw1f54K3/MeuRX+K5TnM5hlZwsL1YUklC4TwWv/cyqu4jnnz0Pq69eBKv\nPfOnDnf0h4sXK0OjmrAlUnUpSM7hh+QEWL9yAZ7rYJgWQtMo27yCqh0bMczAIR1uo5Ri+6Yl3PWb\nuxg77mi+971pDOpbQPnW9ZhWkINej+AAwF9l/f/uUpCcQ+B5Dn0GHImuG3iei5KSwpLeFHbrg+va\nHRJZHyxk0oZ79BnOW3PfRkrJ+vXrqW1w6NajH46d4tN5SvPJ0aUgOUZm/6PpOpG8YpSUgKBbjwHY\nqQS6bh6ac7ACTTPwXIdYPMFdv/sDUipmPDST1eu24LkpDNM6pFe//YE9FETTtNwdxHzGoJTEDIT4\nYO7jPP73aTTUVQL+JnDdind55E83sWPrKkzrUDSz/Gjtl5/+E6P6SZ579il0XePbN3+TO3/6LV59\n4g52lm30zazDKI6uJfwo7N027bteoojHk6Qc99Cc5Q5h+D71AFU7NvLWi9OBzL5WZctDxBtreeO5\nv6BkW0UQDg6UkgTDeSz7cC7D+wb45/QHmXDMUQghKC3tzo03fp0/3P0LFrw602fMPNgC7wcIIXAc\nh3jCxvWanzDLaoKSIAKcecbxnD5hGFHDLxbThU5CKTTdoKZiK9JzEbslQinl15+vryknEatD141D\nZyZOn3fUVG7lCxddhOs42LYfDSGlxHEcpkw5nV49iqmrrfJlP4wghMBOOQwYNJiLz53EiN55eJ7n\nryjpS5CuTaj7QCYfNYD66hpitspZoZLPBARI6VFY0gsgvffY9QKlJJH8YkKRgvTB26HTv0op8ot7\n8vFHH2KY5i7EdqZpsnbNamrqmsjLL/ZlP4ygCUXS0xgz/mj650nKquLpymcqzWqCQugWbnw7f/vP\nO4w7/nhG9M8nkXK78hU6CSE0HDtFSY9BHHPSRei6sVvf+VzGJ0z9IkK0XkTnYEFoGolEIyPHnsAL\nbyzmwX/8E2iuOrt8+Qq+/d0fM2zsVD/+9CDLm2tICZGwwfy33+TdjYLLPn8snuNHPTQTx0k/72Pc\nkQPxEk00xlLoXWQCewelMANBxk06j7zCHun6hD7LpFKKwUccx9DRJyIPuQ26//4LikrpOfBovvPd\n29m6fUc2JunXd/2Oj1ZsZ8TYE9Orx2GmIgJcT9JvQF8G9cyjoqIuvbqnY7EUoBkGjZUVLFqcYpGd\nZHNlihPMLkKBzkJJSSAUYen7c3j92ftJJhrTXyh/tRCC5R++QuXOjZx/9U8p7NYHxz40KJGUgmAw\nwktP3U+3cD0vvzCLvr17pL9T3HPP7xjylwf49z9u49QLvkNhYXfsQ0T2XEApMIVi9fK1VG6xqKio\n9V3aXnJXL5YmoLqyhrq4jdmlHJ2GlB6BUJTVS+Yy5z93kUw07mmapskcKrav43/Tf0BTfeUhwVks\npUckms9rs2cwfniEOc8/y0knTkLX9ewK0qO0Gz//2U944L5f8vrT9xCLNaAb5qHjZMgBhBC4dpLt\nO2tB13ejHm0B3dQxtNyGoB/eUOiGSVNDFa89ez8Amqa3OvBV2tNVV13GvJdmYJgHd5D59UwirFv5\nIVGxkwf+cj8qTSyx+3W2bXP66afzvW9/hXkvzyQQCB905c41hBBY5q5EfnuukfsniPSwhZR+OYg1\nS96iqb7Sp0Vqh4tYSt99uHrJXGoqt6Zjsw5Oj2eI0lYtfp3v3Hyjz0KfJtpuCSEEpmkipeSGr32N\nwpBNxY6tGKZ1UOTen9j9VRweRuRBhJ+m6VG+dVWniw8BOHaSndvXHTxTRYGu6zQ11GPpNsefcHyW\nQrU1COG7qXVdZ8LRY9i2eRVW4FCMCMgtuhRkX5GukdJUX0mm+HTGdm/7T0MIjXhjTZt10w+M6Bq2\nnUQ5TRQXFeD7M33zurU/P49HUVQYorGhNs1LcFBEP2A4vI5EDwr8MJJQpDDNINlxqQeVviYYzvPP\nGg5S2IlKE/kJPUginiS/INDu9abpM9HU1jURTst+CEXM7Bd0Kci+Ih2m0WfQGLas/8iP1u3A7Mhs\n1kt7D8smUR1wCHyO5UgeVqQHp009m0gkmt0jtQWlJDsr6zn9ottIpRKHbOh+rtAJ2p/9kHKrPsUp\nt2p32h8Nx44zdsLnOGLMSZ2OPBBCwwqGUWnaHy0dSQot0lb3c8qt0DWkk2TCKZdRX1fd/Jxt/RZf\nuceGI4SjfmEkrYXs+yvlNpODrh2AlNvd0aaCKMAwBOGgQTJpYOSItEEqCFgapqkRT7lYnkYu2W1U\nOoc5YOmEAwauVDnbaGVkd1xFPOEiZTPhm0AgjLxO7ycUEE82xzTpmiCW8EkbIiEDqdgvsXApR6b7\nPb3XwF8x8gr9GLJ2B186CFlKj1jCzu65dCGIJV0MTRAOGSDSVE85gCcVkZBP2hBLuGiimSdrn6EU\nutRI2W2v+K0qiFJgmRobtjXx2AsbiSfiaLk6NRX+LBMJGlx51mA/9DvH40BKxUvvlOF6uVs9gKzs\no4cWMGlMN39ma5Ewvbeb7ZarjVI+i0xZZYIZz63H0PfPWdQp43tgWRrKz+NK3zw3sm8uj7F4bW1O\nZc/kiJcWB/ny+UP8d5orPi8pCYeCLF/t8vEHCtPa06HYpoIYmqC2wWbJ2tqchkQI4c9iA3tGuODU\n/tht1UH8xO0LbNvlxfllxBJOlm0jN237sk8a042h/fJJ2rmTXUpFNGywuXwrH62qIRo29gtx3OnH\n9aJ7UQA7x8RxkaDB4rW1LF5dQzho5JQ4rinucvqkXowfUUxT3M0pcVxeJERTQ7RNeds3sXRBOGBg\nCyOnCqJrMmtiOftBQRzHI5BmJsy1guia9E2slEsqxwqSYVYMBw1Cgf2jIHbaxHL2A7OioYlm2fcD\ns2I86ZJI5VZBdMPdhRdrd3R+k56jYZbhXtqf3LxC7Edu3swmPdeyi2aytUyff5q4eTPOlpzLnh6D\nmXvsT27e1tB1UNiFLrSDLgXpQhfaQZeCdKEL7WAPBRG5tPG60IVPFfak/dllky4EpFIpPKWTo3PB\nLnTh0wEhUMojFk9hWhZWWgGa1UCA6yoGDB7McUf2xzqkaAW60IX9C+W5mIEoE485kv7FEVwpEWR5\nsQSeYxMpHcCUSUfSv1cxeUGBd/gHa3ahC2gCEo7G+OMmMG5Yb3p3j/pUJ1leLAGe41HSv5j8gEA3\ngwQDpu9/7tqPdOGwhs9/JQIm/QYWIlMeefnRrP3kK4gCXddorI5R15CgZ8/uFBWEcD3ZtYJ04TCH\nT6ahHJfKigaEZjJwYA9/BclUmFIoDCtAzcY1vBOQFKoEyzfWMmDowP1ymtuFLhxK8BQENY8F8xYx\nZnhvNq9ehzBMUKlmL5bCL1C5dtkKpDAOLe7YLnRhP0MzDOymWt6atxMzGMDSdJS3m5tXKQiEgmhC\nI5mMHyxZu9CFAw+lELpBNGqglESlMxn2CFZUUiFF18rRhc8gWmQvZtB1HNiFLrSDLgXpQhfaQZeC\ndKEL7aBLQbrQhXbQbkbhLvQ2OTpRz7QpRDNFTC4PI5uz/kTOaYUysmdYBnMp+/6kt2mJ/Sl7y/GS\nK+xPOqRM5mN7pxltKojAD16MJ1xsx80taYMtsR1JOGTgOLnPSdc1QdL2iCdddD3HpA22xDQEkZCB\nruU2Jz0SMlCKLP3P/shJD1haut9zT9rgeopYws2maucCWpoOSUG2f3KZkx4OGgSstsd2qwqSoVop\nKQxwzKiSTtH+qDQtfIcrjfBdyQFLZ9GK6nQVpg6eJE1LI+jMrCpQUjFyUAFermk907KXVyWoa3R2\npf3ZR2SocyxTY+LobvuN9mfd1kY2l8c61++dhEpzeBXnWxw3uptPlJFj2h9DE7y3rCrHtD+KUCjB\nuq2Nba56bSqI7UgG9olw2RkDqKlvQm8nQUR6EsM00DWdlJ3ylamNh1AKAqbGpvIYf3l8FZahtduZ\nSkmEpqHpfpF7oN1Br5SvfLdcM4poyCeOyyWzYsDU+Nv/1rB2ayNBq33Z9waaLmiMuVw0pR+fP3cQ\njTEXTc+9jXXfYyuprE1hGbkbxFqaOO6684Zw1Mhi4kk3Z2aW9BR5EYNX3i1n5uyN5EUMZI7KL/vl\nHyzqqivanJDaNrHS+SGxhOubKq3VK0zPqJFIhKqaWmKxGH379iWZTDYTG+92UwW4robtSCIhA1PX\n2jSB/NrjYVwnRayxhrzCUkDheW0XF1VKYRm6X4AUn4UvZwqSlt00NSIhg4DZtux7C58u1VfCprhL\nPJW7QdYSAUtP93vuTE8NAZo/08fiLgnb9T/LATIsMgqIhg3COaQUUlJhWAYJq/WSD7AXm/TW7D4p\nJZFImJdeepFf/epX1NbWcuGFF/KTn/yk+frdfpaxIbObdK31TZJSCsMMsLNsPa88dS81FVvpO3gM\nZ1x0K6FwfptllDN0Py35YnO5guy+Sc+ZGaSxxyY9V7Z2S6j0hlpqOQy1S7/LXcZLzuwg9tik52pv\nlumL9goYfeKdtz+7m1RVVfG9732PdevWUV1dzfTp03n22WfJy8vbo5TX3kIArz97P2WbV5BKxli3\n/B0WvPYophXsijLuwgHBPimIYRhUVFRQXV2NpmlYll+Sa82aNX5hmH0YxEJo2KkEdTXlCKGhpYtK\nVu/c3G6Jsy50IZf4xAqiaRrJZJLx48dz5plnIqXEcRzy8/O57LLL8Lz260x0BOk55BV2Z8yxZ/nR\nldLPbhw36VwMMwiHeemvLhwa2KcVJBgM8vDDD1NWVpZVBl3X+de//kV5eTnBYNDfrO9125JAKMqa\npW+xbeMSQCCli6bprFn6NmWbl2MFI4d9fbwuHHzslg+i8NJMDe1ZR57nUVBQwIwZM7jtttt2+a62\ntpbp06ezevVqZs6cidaa96sdKCWxrBDbNizlqRk/Trt205s012X1krmUbV7BlTfdS7SgO57r5OyU\nvwufbXieh8xwRqeHbXb0KqUwLIvuJQXkhYPo7bhfdV2nqamJv/3tbwghMIxmPcuUDJ43bx7z588n\nEons1WZdKdB0gw/nP43nOn5mY1oShULXTRrrK1m+6BUsK9SV9diFnEABeQX5dCuIEAyY2c/TtD++\nyzaUF2XkkUdy/ZWfo0++3moNCZWOc3IcB8fxZ++WZpTvOvP/HY/H93p2FwKkkrhOCtHKRt+/v4Zj\nJ/FVpktBurBvEIBE0LN3T84863QuPmUEqZSNJjRfQRRgmCbV27azbFMFG9etZ+POJoKWvscELYTA\ndV2KiooYP368X6W1hRIIIfA8j2AwyLhx40ilUntlZiml0DWdfkPGoaTcQ0GkdFFK0m/w2D3u3YUu\nfBIowBSSjz5eTUVtLQs/WoPS/dTb7MgVysM1Ipw0fghb1m4gJUxEG5tgTdOIxWLcddddnHDCCXie\nX8wxM1i7devG9OnTGThwIIlEYq8GsabpJOINHDP5YsYcdw7pAC/Ajyg2zACnfv4bDB45iWSiMWdB\nlF347EIIgWPb9Bs6kgGBJGu21xOwdL/IZ+YiJcEMGmzbUc7mymS7cUZKKcLhMGvXrt2lYE3mO6UU\n27ZtI5VKEQgE9uo8REmJFYhQX1OO9NIbdNV8X4BErIFErB7LCnV5srqQEyilgWHz8aoypGagpcdc\ns4IIQUDaLHr3Y2o80Gk97N7zPPLz85k5cyZf+MIXmD9/fvoGzUf21dXV3H777Xzzm9/E8zx0Xe+U\nkigpsYIRtm1YzL8fuJXli15JK4DKSuM6Kd574zEef3AaDfWVGObeKWAXurA7lFIYAYu6jetZuGYb\nZsDKjqld7BOFwApYbR6OSCnJy8vj7bffZtq0aX6Nt1YGf8aTNWvWLH72s58RCoU6PA/xIytNGusq\neO7RXxJrqEbTWw8i03SDiu3reP7RX6bdvBr7pah4Fz47UArNMAmYxi7jeQ9daGs2znivbNvm17/+\nNVJKNE1r1YWrlMJ1XXRdZ+bMmXzwwQdEo9F2lcSP7Qrw3uuPpZXDQLbhHpaef2hYtnkFKz56lUAw\n7J+0d6EL+wK1Z+Bip3e4SilCoRArVqzggw8+AGj3fCOjUFJKXnjhBSzLascUUui6QaypjvUr3/X3\nNB3EW6k0p+qaJXPxPLeLhr4L+wV7pSCmabJ69WqUUp1y3WYUYtWqVS3cvXsqiVKgGwb11eXEm+rS\nv+tgRUhre111OYlYPVqLA8UudCFXaDcfZHcIIUgmk+i6jq7rHZ6QZ5Qom0DVJvzDP89z/LMNTcN3\n67Y94IXw42E8z0WmE6i61KMLuUanFSRzQNi/f388z+tU+Ejmmp49e2JZFolEoo2VR+B5LtH8bmi6\njuu4HbadUYZAMEIwlJfer7SSwtiFLuwDOlSQDMuKJgTJVIpRo47kS9ddRyKRQO8g50PTNFIpm4su\nvjirLIrm3N8Ms42mCaTnEs0vZuJpV1BXXd6ha9hfcWz6DBqLYVk4qWT2QDKjJp0w1PYKCvaQPVcQ\nNCdIZvp8f6n6p0n21vo8p7J30FiHKbeG7tPoGLqGUpKionzu/f09zaOwvRukv08mUySTCSzTQKbZ\nO4QQuJ5CE81pq570mDT1S3t1Oi49l2TmtD6dbqtL5cusNStMLpBhHlEqzbSh5y7lVuG3Cc19vj9S\nbqVS6X7PXZynSjOPtBwvuUq5Ffhtgn8PT+Y25Rav/fbaJW1wXEVj3CEWd9B0DYFAoairr9orQYQm\nmjMMFdimRsr2yAubGLuwmiiUTLX5MK2GrBiCoGllaYGUUpiGRjzh+jnMLUgbdu+GzPMoJRGi+fl2\nvaZ5HlBp2S1TIz9iYppazqbKlqR0DTGHRMrLzph7yt25zzKft0QoYJAXVhiGyKnsuiFwXEVDzCFl\ne+RKt2U6/18pyI+YhAJ7xgd+8rYlpmmSCBptdkWrCiKlz36xamM9f3l8NSk7kVPiOMeR9O4e5sZL\nR+C67ZOvKTKDx3cKdNTvQghs1+OR5zeQSLlZ4ga/sWY/t9D8wS00DcsK4jo2nrdbdPJujJIZ2S85\nYwD9e/bHtnNLHBcOGbz2fjm/f2QF4ZDRPLMpsoorMoK0+Czz3K1+thuuPnswRfkWbiuR2p9YdqUI\nBQyenbuF2W9vJZhD5hFNE8QTLpPGdue714winnBzShwXiYRYuFgy902JZe25qrZrYinlryJ+Z+bm\ngTMrk+cpTCMThNj+bzxPEmuqJy+a16G9JIRAKt98s12Jlg52FEKg6SaG6cf6O2mltxMJNq75iOLS\n/uQVdMdxbKxACPCTt1wn5dvB0g/IdFzQhcA0BFJ2LHtnISXp/mjuc19BFJpuoOtm+joP10miaYaf\nepyW07GTCE3DzH6mcN1U+o03C2noInuf3CmIL3tmvBi6yqGC+G2Cfw/DyJ3pKaXfF0Y7/GMdbtKF\naP7LBVq2l+GBaguZtN7t27dz2+2388jMmdi23eE9lAIhFIZuEA5HcZwUnufSULeDd1+dSSSvmONP\nvwYzYPHyk/ezfNErdO81mEu/9jsieUUsWfA8G9d8wPgTL6TvoDEoKQlECnCdJK6XyG4ccxkC1rK9\nbB+hMAMh1ix7m6XvzQGgV/+RnPr5b7Bl3YfMf/lhhBDkF/XgrEunUVm+gblzHkR6DqFIAaec8zUC\noWia5ELscp/9L3tu0HLs5Vr2zrS3V+cgBxJSyuzJ/e9//3sWvv8+t99+O7fccgs9evTIJmu1BqUU\nmm6SiFWx+N1n6NZrMEeMO5U5/7mLVYvfAKC4tD9jjzuXrRuWAFBZvoFErA47FeOlJ38PQPnWVXz9\nh49RX1fB2qVvM3D4MRR2H3TAgiP9A1mDmoptbFj1HgCpRCN1kz5P+daV2c/yC7tRW3kFFdvXsHbZ\n2wAEQlFOPPM6Aplo6K5Ig0+EQ1ZBfBdxikGDBnHLLbewdetWpk2bRigU6oAxJW2rC8Frz/6Jdcvn\noRsm19/2L7r3GsyqxW8QDEXJK+iOZhicfsG3eOG/v2X8iRfSvedg6mt30K3HQKp2bqJ7T18ZZj92\nB+VbV7F4QU8uvuFedL3/AYwg9mPUMti+eQX3/+KSXa5oqKvir3deuctnmm7tNR9AF/bEIaMgmaSr\nzEv1PA8pJSUlJWzevJmtW7dSXV3N6NGjqaury57mg7/a7B7+IqUk1lib/XdjfSUnnnkdUkl2blvD\noCMmkGiqZ9AREwlHCxl25IlI6VHUrS/DRk+me+8hnHXp93GcpG/LA/FYHa6dyp292QlomkZTYx2X\nX3ENk044kVQykeYA2HX/k1FY1/UwTJN//vMhEok4kbxiumjEPjkOCQWRUlJUVEQikSAWi2EYBtFo\nlIKCAv7yl7/wxhtvcPfddzNt2jR++tOfcuaZZ1JXV0cs5rOUR6NRdF2nsbExqzSapjP1wpv56J2n\nSSXjlPYZhpQug46YwMK5/6Vq52a69xzIzu3rqK8pZ8f2NfTqPxI7FWPVkjcZO+FsgqE8XNdh2OjJ\n5Bf1YtzEcygs6ZUOsQ9yIE7tpZQUFPeiqXEVU087GYVGfX39nhcK0IRGaWkpC997B4lJOFLg7z+6\nzKtPjD0UxOfNPXA9KpUkPy+PmTNn8uSTTxKJRADo1asXU6dO5eWXX+bmm29mwYIFfPOb32TmzJnU\n1tYye/ZsqqqqsjPn9ddfz5lnnklTU2OaVCJFt16DueCLP2P6b69j9cdvcMzkS3DtJHYyzrrl8+kz\ncDQNtTvxPJfaym1YwTCb131IbeVWFBKh61RvX8vyRS9z1U1/pKhbb+pqaw5Y/whNI5lo4qhJZ7H4\nfY0xRx3LkSOGc845Z2XPbqDZtVtX18jDD82guNcRnH/V7YQiUdx29mpd2BUiw+ecLgMNu/NiSUki\n5YLwZ+H93a2e9CgqLOI3v7mL9957j/vuuw/XdUkmk+zYsYMXXniBN998kwsvvJDa2lqi0SjPPvss\nffr04aKLLqJ3797k5eVRX1/PjTfeSCKR4OKLLmJnZXU6zziFretE8oqY99IMxkw4Cy3tLl29+E1O\nPPNL1FRsAaCmYgvS81i7bB7gr0CmGWTei9Nx7BS6aZGINxxQ8wrS5zqpBBMmX0i3HgNY8tZ0Lrns\nCkzTSh+ekibJCDN71tMMHHE8l33tDpLxpi7l2AsIwLFt/2hA07HSjO9G5kslPfRwPmceP4ao28Qr\nc9+n45DBTw6pFJFwmA8+WMizzz7La6+9xg9+8AOeeOIJdF3nhBNO4K677mLUqFHMmjWLWbNmMXLk\nSP7zn//geR533nkna9aswTRNfvKTn/Cf//yHK6+8kpNPnuxvapXyzQ7dpLT3UNavXMCieU/Rf8h4\nACrK1rFj62pqKrcCUFu9ndrKbaxf8S4AViDMptULWbNsHv0GjyMQjCBbkNgdSAih0VhfTa++w3hx\nRzUTJ4xv89ozLvoOrudh24ns2UkX2ocQYLuS4WNGc8ywnqxfvoxF66oJ6iKzgvgJSnqwgJGD+xNM\nVbMgqPByeBC2O5SUhMJhFrz3HlOmTOGdd97hkUceyRJAvPrqq5x00kksWLCAbdu2ccMNN3Dttdei\n6zoXX3zxLm19//vfZ9myZQwYMIClS5dy/AmT02EtCk0T5BWWIoRg4dzHCQSj6LqB57ksXjCbuupy\nAOxknCXvz6Guusz/dyrB2y9OByHIL+6JaQZJOKkDvYBkoWk6jpPKmsBCaLsQVgjNr6zlOja0ML+6\n0DE0wHahV+++jBjUE7d6AwvXVEBWQQR4nqK4Rx6VW7ciTJ1oQQGN3v5cQ3wlCYdCNDU10bNnT4Bs\nFK9lWaRSKf7f//t/3HDDDVx++eW8+OKLXHnllViWleXfklJiWRaRSATXdQkGg83hJCITRl+CUorG\nugrmv/KQH1RnGKz66BWEpmEYBo4d56P5T2IYfk7yx+8+S1NDNShFfkF3f0+yX3ujY2TMJZ9iaVfW\nGaHpSJUpLHSwJf00wZ+QdcugIE+wZMlazPwSdG0zoFrwYmmCeFMcEQoRNjUSCTt9pL9/OlukubVO\nOeVU5s6dy5AhQ/j+97+P67p4nkcikUBKybPPPouu60ycOJGNGzeydetWbNsmlUrhui5SSh577DHK\nysqorq5m/FHjfUbHtOzS88gr7I5umCigvmYHruviui7xeCOxpnpc1yWVTGT/2/M86qrL/dkYKCju\neWgwnKaF8DwP13WyeTme5+E6dpYnoIsKaS/hV3OiIWZTVFJAKp4gE+JqQDqd1rKo27yRWTWV6NKl\nui7J0MJ9q/HRHjQhSKVSDB48iK9//etMmTKFl19+GSEEK1asYNKkSRQWFtKvXz/Gjh2LUopzzjmH\nRx99lMrKSnbu3Mm7777LDTfcQK9evbj44ou59957sQIWjbEEIk0050mXvMJSAsEopg59+/XlqPHH\nMmjQYHr16ommCSzTxHZcUFBRWcHmzZv5cNH7bNu6naqqnRQU90ZJmd6gHzxNMawApmHwhYsuwbQC\nvkw0v7/lSz8imUwRCIRJNNWD1nZpsS5koJAKgrri3TfeYUV+iPraBgzTRHmJZi+WUmCYBolYDCF0\nTDN3BSrbgqbp1NXVceONN6LrOlOnTuV3v/sdp59+OgsXLmT58uXMnTuXO+64g4ULF3LZZZfRt29f\nCgsLGTx4ML/+9a9Zvnw5l19+Offddx+nnnoajY31ux4Yei55+SX0GzaJi885ljt+9Qt2VtRQW1NN\nRUV5ms+r2Tl1/PHHUVzcjX59e/LyK69z/Y3T6DNgFK6dYYg8CAqiFOFoPk88dCcjjxjMH/90P9Lz\n0qnJZFeOxYuXcNkV19J/2NH0GziKZLwxe00XOoAQ6JqitrYxTZjuY4/yBxkKUXf/bj+y0DSd6upq\nvvOd7xAOh7nwwgsB6N27N/379ycajdK9e3euvvpqKisr2bZtG6tWreI///kPO3fuBODVV19lwoQJ\n7NxZgWWZ0CId2HeTJpn8uWt49LGf89xzz6IbIYqKikD59ciDgQC24+BJia4HiMUSxJrqaGqsY8Kp\nX8MMhEjGG9HaqfS7v6CUxLSCzHv5XwwoSTFx0gn87a8P7BKAmElWCQbDfOmqC5j39kMY+vV07zUQ\nz7EPuGv60wy/mnNz8NohcZKuaRp1dXUMHz6ck08+mSeffJK8vDxc183GZGWUNxDw45J0XWf9+vVc\nf/31dO/endraWgyjNZNC4HkO+QXdCEZ7cNddP2LicRNRShIIBmloTKDrAteThIMBhPDDzBsb6rny\n2q+SV9QTz7XTe5oDD003iDXWU1O2jH8/OoNuJcW4roPWivnkug75+YXce+/vmfPmW/QZMIK4neo6\nC9kHHBIKkvFIFRYWsm3bNn7xi19QV1fH0qVLsW0bXdcpLCykuroagGg0ypgxY0gkEsTj8ezpe1tQ\nSqEbJlIqZsyYwb//+zSrVnxMKhlDSkVBYTGNjfVIz8M0LfoOGEr3biWUl5VxdDQPr+VsfYAh8BO6\n6urqOOusM9GE3uYmXAGmadLU2MDY4y/1syS7lGOfcEgoSCa0fenSpRQWFnLttdcyfPhwCgoKAEil\n/GBBXdezxXpqampYunQp119/PVu2bGHkyJHE4/FWWlcYhkl9TQXSqee66/6Po8aPp6SkmIBlkEik\ncD0PXdMIh4MoBbW1daxdu5bVq5azbdNKjjzqZBLxA8skr6TEMIK898ZjrF+5gF49ipk7dzHBgIGS\nrVtNniexTI1/P/4MN974TdavfIeTz76BHn2H+glVXWcje41DQkHAH/yzZ8/GsizWrFnD7373O8rK\nylBK0dTUhOd5WJZFKBTCMAyGDRvG2WefTTwe5/XXX2f8+PE0NcXYnc7Xr7dusrNsA55dT2VlBT/6\n4W2sW7sW23FpaoqRsm1M0yQvGiVgGfTo0YOzzzmPkKWxec1HjDn6tIOyNxeaTvmWlZRtXk5dZZiz\nzzyN5gTiPTUkE5NVXVVDfU0Z9TVlJE9tQIgub9YnxSGhIJl6I1dffTV/+9vf+OpXv5pdNdrCW2+9\nxRNPPMEpp5zCSSedRDwebzUV0y8nnaTvwCOoKp/Kd2//BXWV20Al22ld8OJr7zL4iPEcN/lskonY\nQdnoKuVH8uYXlQIab7+zqINf+DKahkFBcS+EALOrRMQ+YRcFydT6aDk7NZc1yN0UmmlTSpmtEpVI\nJDjxpJOYPHkytbV11NRUU1ZWTiIRJ5FIUFtbS0lJCYFAkPz8PHr16kVRcTEF+QU4jpMu1KNl28zK\nrdLsFYEwJ511HSedeS2JeAO2nSDeWItSZCvoAoQi+QQCEaxgGNMKYdtJPNcmc+LaUm5f9tz0ic/A\n0kJuIXDsBCeccS0Tp1yJ0HT0TrpsW5bB0w0zXa5O7CZ7bkkbWsreshTGvsKPGNq133O1H2zZF21h\nFwVJJZO4nkswECJDB2qaAZTyOmG/tkWus9tVAiQSywoQjYRxWrCa+J0M3bsV0bNHd8aOORIhNDRN\nZFlNMh3leS6u6+G6NoYuyItG0u0LbMfDNAOYnoaeZjVRgJPyzzICwSjBcD6Fxb3Tkgtk2rD3y75J\nlPSw09f7RAgqK3skHCIvEsY0c6sg0bBBMBDAMC1MM81qYgWznF+tdia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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/KQ111B11/P11bN1P1/11P111R1/b11P1111/111p1P11/11111111/pp111111/1kq1r111)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/KQ111B11/P11bN1P1/11P111R1/b11P1111/111p1P11/11111111/pp111111/1kq1r111\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: KQ111B11/P11bN1P1/11P111R1/b11P1111/111p1P11/11111111/pp111111/1kq1r111\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://i.imgur.com/x6lLQQK.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fantastic, a perfect match! It was able to handle the highlighting on the pawn movement from G2 to F3 also. \n",
    "\n",
    "Now just for fun, let's try an image that is from a chessboard we've never seen before! Here's another on reddit: https://www.reddit.com/r/chess/comments/45c8ty/is_this_position_starting_move_36_a_win_for_white/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://i.imgur.com/r2r43xA.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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cc9KpVJTXiOHVB3rmXIYpeP3FZ/l69P+IRHPJLyhi5bKFlJWuZe3qpUgpkdLP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\nBmAlw8B8Z0YXQuC4LvPn/gRs+0RVZp+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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/11111111/1111B111/bBK11Nr1/11111111/11b1B1B1/b11k1111/1b111b11/11111111)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/11111111/1111B111/bBK11Nr1/11111111/11b1B1B1/b11k1111/1b111b11/11111111\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: 11111111/1111B111/bBK11Nr1/11111111/11b1B1B1/b11k1111/1b111b11/11111111\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://i.imgur.com/r2r43xA.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hah, it thought the black pawns (on A3, B2, C4, and F2) were black bishops. Same for the white pawns. This would be a pretty bad situation for white. But amazingly it predicted all the other pieces and empty squares correctly! This is pretty great, let's look at a few more screenshots taken lichess. Here's https://www.reddit.com/r/chess/comments/44q2n6/tactic_from_a_game_i_just_played_white_to_move/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://i.imgur.com/gSFbM1d.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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hh2b6n+LB+Y1GQyeffLIOOeSQTHsJz2EuwWfcKRsXi0VNTk7qe9/7XqbZEZj1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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/111111k1/11111rp1/1111111p/1pp1Pr11/p11pKPR1/1P1111R1/P1111P11/11111111)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/111111k1/11111rp1/1111111p/1pp1Pr11/p11pKPR1/1P1111R1/P1111P11/11111111\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: 111111k1/11111rp1/1111111p/1pp1Pr11/p11pKPR1/1P1111R1/P1111P11/11111111\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://i.imgur.com/gSFbM1d.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect match, as expected, when the validation images are based off of what the model trains, it'll do great, but if we use images from chess boards we haven't trained on, we'll see lots of mistakes. Mistakes are fun, lets see some.\n",
    "\n",
    "## Trying with non-lichess images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://imgur.com/oXpMSQI.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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fFDYw0IKBDKmuzunbIx3McZ1Aa6ibW5LvDDjXw50EIlTX8fK6pJKDh1ecwGEx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\n7L6SSjG8d6PgHCC4996fG+bgm4SIEIYKecTZsP3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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/11kr111r/p1p11ppp/11pb1111/111N1q11/1111n1b1/1111BN11/PPP1QPPP/11KR111R)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/11kr111r/p1p11ppp/11pb1111/111N1q11/1111n1b1/1111BN11/PPP1QPPP/11KR111R\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: 11kr111r/p1p11ppp/11pb1111/111N1q11/1111n1b1/1111BN11/PPP1QPPP/11KR111R\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://imgur.com/oXpMSQI.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ouch, it missed most of them there, the training data didn't contain images from this site, which looks somewhat like chess.com, need more DATA!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://imgur.com/qk5xa6q.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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KDNF7RrjuLTGSQQnLUMqkN2p6MOmiLEoJ+qVAgefw25Ji0NbGjFAkTPtj6tgC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\n4D+O/wfdjBNNYw8iAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/1Qr11B1r/pp111pp1/11p111p1/11pn11pB/111qp111/r111bB1p/pp11b111/1k11q1r1)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/1Qr11B1r/pp111pp1/11p111p1/11pn11pB/111qp111/r111bB1p/pp11b111/1k11q1r1\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: 1Qr11B1r/pp111pp1/11p111p1/11pn11pB/111qp111/r111bB1p/pp11b111/1k11q1r1\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://imgur.com/qk5xa6q.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://imgur.com/u4zF5Hj.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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7Vq2lRmzh++5u6nRndTFYaDNUq7KGRZghlxkIugFA0vnSXLUJDj5VdlZITNUO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\n//Je16OqduKn+1AbHKRV2UrZ6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A56ABYRbvCdLTTrkj0nmAofstEi2KDckHqgctKvY/h8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\nNQGD0CoI4YYe3dp0BvNYV3Jfgj7Rs8G9uGso342htbaltNUGp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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/11111111/11111p11/11111R1P/11p11111/1p1p1111/11111111/111K1111/11111111)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/11111111/11111p11/11111R1P/11p11111/1p1p1111/11111111/111K1111/11111111\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: 11111111/11111p11/11111R1P/11p11111/1p1p1111/11111111/111K1111/11111111\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://imgur.com/u4zF5Hj.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://imgur.com/CW675pw.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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pVFVj4sTpgL1PylEfLpdO2dZKKndE8HhcyUOfRNfcFK0sdYIeleYFLh4IOGTS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\nMOk1G3XvXbB+23pqwjWoitq0HLv3zu0RPQq7E/QFnXTWxrn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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/111r1rk1/ppp1q1pp/1nn1pb11/11111b11/11bP1111/11N1BN11/pP1QB1bP/111R1RK1)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/111r1rk1/ppp1q1pp/1nn1pb11/11111b11/11bP1111/11N1BN11/pP1QB1bP/111R1RK1\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: 111r1rk1/ppp1q1pp/1nn1pb11/11111b11/11bP1111/11N1BN11/pP1QB1bP/111R1RK1\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://imgur.com/CW675pw.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: https://i.ytimg.com/vi/pG1Uhw3pO8o/hqdefault.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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MDIiO+Y1Xvm5y9SoAJIYo4lvNoiwMIQMHZXQ+63Zmr7nmS9Nzc5wyN4SIlHIJ7F3KTa1b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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/r1bqnr11/pp1ppkbp/1111N1p1/n111P111/11111111/11N1B111/PPP11PPP/R11QK11R)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/r1bqnr11/pp1ppkbp/1111N1p1/n111P111/11111111/11N1B111/PPP11PPP/R11QK11R\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: r1bqnr11/pp1ppkbp/1111N1p1/n111P111/11111111/11N1B111/PPP11PPP/R11QK11R\n"
     ]
    }
   ],
   "source": [
    "makePrediction('https://i.ytimg.com/vi/pG1Uhw3pO8o/hqdefault.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://www.caissa.com/chess-openings/img/siciliandefense1.gif\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/rnbqkbnr/pp1ppppp/11111111/11p11111/1111P111/11111111/PPPP1PPP/RNBQKBNR)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/rnbqkbnr/pp1ppppp/11111111/11p11111/1111P111/11111111/PPPP1PPP/RNBQKBNR\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: rnbqkbnr/pp1ppppp/11111111/11p11111/1111P111/11111111/PPPP1PPP/RNBQKBNR\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://www.caissa.com/chess-openings/img/siciliandefense1.gif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image on which to make prediction: http://www.jinchess.com/chessboard/?p=rnbqkbnrpPpppppp----------P----------------R----PP-PPPPPRNBQKBNR\n"
     ]
    },
    {
     "data": {
      "image/png": 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AoNvtURShR49W993XEQBACE25lJl8Lr1rq4QLTOXX9FpTFKn5IqdYWFDCssyw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\nfacVXJRZoNVrnp3zyOSnFgBQeC3EJEinQ5KkpF7MSIoMCfU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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Prediction: [Lichess analysis](https://lichess.org/analysis/rnbqkbnr/pPpppppp/11111111/11P11111/11111111/111R1111/PP1PPPPP/RNBQKBNR)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.fen-to-image.com/image/rnbqkbnr/pPpppppp/11111111/11P11111/11111111/111R1111/PP1PPPPP/RNBQKBNR\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEN: rnbqkbnr/pPpppppp/11111111/11P11111/11111111/111R1111/PP1PPPPP/RNBQKBNR\n"
     ]
    }
   ],
   "source": [
    "makePrediction('http://www.jinchess.com/chessboard/?p=rnbqkbnrpPpppppp----------P----------------R----PP-PPPPPRNBQKBNR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ouch, tons of failures, interesting replacements, sometimes it's a missing piece, sometimes it's a white rook instead of a black king, or a bishop instead of a pawn, how interesting."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}