2_TrainSyntheticModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a model using Synthetic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "from datetime import datetime\n",
    "from six.moves import cPickle as pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading Synthethic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160000, 32, 32, 1) (160000, 6)\n",
      "(10000, 32, 32, 1) (10000, 6)\n",
      "(3000, 32, 32, 1) (3000, 6)\n"
     ]
    }
   ],
   "source": [
    "file_name = 'Synthetic_data/SyntheticData32.pickle'\n",
    "with open(file_name, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    train_dataset = save['train_dataset']\n",
    "    train_labels = save['train_labels']\n",
    "    valid_dataset = save['valid_dataset']\n",
    "    valid_labels = save['valid_labels']\n",
    "    test_dataset = save['test_dataset']\n",
    "    test_labels = save['test_labels']\n",
    "    del save\n",
    "\n",
    "valid_dataset = valid_dataset[:3000]\n",
    "valid_labels = valid_labels[:3000]\n",
    "test_dataset = test_dataset[:10000]\n",
    "test_labels = test_labels[:10000]\n",
    "print(train_dataset.shape, train_labels.shape)\n",
    "print(test_dataset.shape, test_labels.shape)\n",
    "print(valid_dataset.shape, valid_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accuracy(predictions, labels):\n",
    "    return (100.0 * np.sum(np.argmax(predictions, 2).T == labels) / predictions.shape[1] / predictions.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "image_size = 32\n",
    "num_labels = 11  # 0-9, + blank \n",
    "num_channels = 1 # grayscale\n",
    "num_digits = 5\n",
    "\n",
    "batch_size = 64\n",
    "patch_size = 5\n",
    "depth1 = 16\n",
    "depth2 = 32\n",
    "depth3 = 64\n",
    "num_hidden1 = 64\n",
    "num_hidden2 = 64\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # Input data.\n",
    "    tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n",
    "    tf_train_labels = tf.placeholder(tf.int32, shape=(batch_size, num_digits+1))\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "    \n",
    "    # Variables.\n",
    "    layer1_weights = tf.get_variable(\"W1\", shape=[patch_size, patch_size, num_channels, depth1],\\\n",
    "                                    initializer=tf.contrib.layers.xavier_initializer_conv2d())\n",
    "    layer1_biases = tf.Variable(tf.constant(1.0, shape=[depth1]), name='B1')\n",
    "    layer2_weights = tf.get_variable(\"W2\", shape=[patch_size, patch_size, depth1, depth2],\\\n",
    "                                    initializer=tf.contrib.layers.xavier_initializer_conv2d())\n",
    "    layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth2]), name='B2')\n",
    "    layer3_weights = tf.get_variable(\"W3\", shape=[patch_size, patch_size, depth2, num_hidden1],\\\n",
    "                                    initializer=tf.contrib.layers.xavier_initializer_conv2d())\n",
    "    layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden1]), name='B3')\n",
    "\n",
    "    # Sequence length\n",
    "    ss_w = tf.get_variable(\"WSS\", shape=[num_hidden2, num_digits + 1],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    ss_b = tf.Variable(tf.constant(1.0, shape=[num_digits+1]), name='BSS')\n",
    "    \n",
    "    # Digits\n",
    "    s1_w = tf.get_variable(\"WS1\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s1_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS1')\n",
    "    s2_w = tf.get_variable(\"WS2\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s2_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS2')\n",
    "    s3_w = tf.get_variable(\"WS3\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s3_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS3')   \n",
    "    s4_w = tf.get_variable(\"WS4\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s4_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS4') \n",
    "    s5_w = tf.get_variable(\"WS5\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s5_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS5') \n",
    "    \n",
    "    # Model.\n",
    "    def model(data, keep_prob):\n",
    "        conv = tf.nn.conv2d(data, layer1_weights, [1,1,1,1], 'VALID', name='C1')\n",
    "        hidden = tf.nn.relu(conv + layer1_biases)\n",
    "        lrn = tf.nn.local_response_normalization(hidden)\n",
    "        sub = tf.nn.max_pool(lrn, [1,2,2,1], [1,2,2,1], 'SAME', name='S1')\n",
    "        \n",
    "        conv = tf.nn.conv2d(sub, layer2_weights, [1,1,1,1], padding='VALID', name='C2')\n",
    "        hidden = tf.nn.relu(conv + layer2_biases)\n",
    "        lrn = tf.nn.local_response_normalization(hidden)\n",
    "        sub = tf.nn.max_pool(lrn, [1,2,2,1], [1,2,2,1], 'SAME', name='S2')\n",
    "        \n",
    "        conv = tf.nn.conv2d(sub, layer3_weights, [1,1,1,1], padding='VALID', name='C3')\n",
    "        hidden = tf.nn.relu(conv + layer3_biases)\n",
    "        hidden = tf.nn.dropout(hidden, keep_prob)\n",
    "        shape = hidden.get_shape().as_list()\n",
    "        reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n",
    "\n",
    "        logits0 = tf.matmul(reshape, ss_w) + ss_b\n",
    "        # This concat only allow us to have a one hot vector like the others (to pack the results in the following lines)\n",
    "        logits0 = tf.concat(1, [logits0, tf.zeros([shape[0],5])])\n",
    "        logits1 = tf.matmul(reshape, s1_w) + s1_b\n",
    "        logits2 = tf.matmul(reshape, s2_w) + s2_b\n",
    "        logits3 = tf.matmul(reshape, s3_w) + s3_b\n",
    "        logits4 = tf.matmul(reshape, s4_w) + s4_b\n",
    "        logits5 = tf.matmul(reshape, s5_w) + s5_b\n",
    "        return [logits0, logits1, logits2, logits3, logits4, logits5]\n",
    "    \n",
    "    # Training computation.\n",
    "    [logits0, logits1, logits2, logits3, logits4, logits5] = model(tf_train_dataset, 0.975)\n",
    "    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits0, tf_train_labels[:,0])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits1, tf_train_labels[:,1])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits2, tf_train_labels[:,2])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits3, tf_train_labels[:,3])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits4, tf_train_labels[:,4])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits5, tf_train_labels[:,5]))\n",
    "        \n",
    "    # Optimizer.\n",
    "    global_step = tf.Variable(0)\n",
    "    learning_rate = tf.train.exponential_decay(0.04, global_step, 10000, 0.95)\n",
    "    optimizer = tf.train.AdagradOptimizer(learning_rate).minimize(loss, global_step=global_step)\n",
    "    #optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss, global_step=global_step)\n",
    "    #optimizer = tf.train.MomentumOptimizer(learning_rate,0.9).minimize(loss, global_step=global_step)\n",
    "    \n",
    "    \n",
    "    # Predictions for the training, validation, and test data.\n",
    "    train_prediction = tf.pack([tf.nn.softmax(model(tf_train_dataset, 1.0)[0]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[1]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[2]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[3]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[4]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[5])])\n",
    "    valid_prediction = tf.pack([tf.nn.softmax(model(tf_valid_dataset, 1.0)[0]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[1]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[2]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[3]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[4]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[5])])\n",
    "    test_prediction = tf.pack([tf.nn.softmax(model(tf_test_dataset, 1.0)[0]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[1]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[2]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[3]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[4]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[5])])\n",
    "    \n",
    "    saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For visualization\n",
    "acc_synthetic_batch = []\n",
    "acc_synthetic_valid = []\n",
    "\n",
    "acc_SVHN_batch = []\n",
    "acc_SVHN_valid = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 17.129694\n",
      "Minibatch accuracy: 20.3%\n",
      "Validation accuracy: 38.8%\n",
      "Minibatch loss at step 500: 5.888487\n",
      "Minibatch accuracy: 63.8%\n",
      "Validation accuracy: 63.4%\n",
      "Minibatch loss at step 1000: 6.148942\n",
      "Minibatch accuracy: 62.8%\n",
      "Validation accuracy: 65.9%\n",
      "Minibatch loss at step 1500: 4.570237\n",
      "Minibatch accuracy: 76.0%\n",
      "Validation accuracy: 70.4%\n",
      "Minibatch loss at step 2000: 5.593374\n",
      "Minibatch accuracy: 65.9%\n",
      "Validation accuracy: 73.8%\n",
      "Minibatch loss at step 2500: 4.552387\n",
      "Minibatch accuracy: 74.0%\n",
      "Validation accuracy: 76.7%\n",
      "Minibatch loss at step 3000: 3.234842\n",
      "Minibatch accuracy: 80.7%\n",
      "Validation accuracy: 78.1%\n",
      "Minibatch loss at step 3500: 3.285993\n",
      "Minibatch accuracy: 79.2%\n",
      "Validation accuracy: 79.1%\n",
      "Minibatch loss at step 4000: 3.306557\n",
      "Minibatch accuracy: 81.0%\n",
      "Validation accuracy: 80.2%\n",
      "Minibatch loss at step 4500: 3.408890\n",
      "Minibatch accuracy: 83.3%\n",
      "Validation accuracy: 81.0%\n",
      "Minibatch loss at step 5000: 2.910397\n",
      "Minibatch accuracy: 85.4%\n",
      "Validation accuracy: 81.9%\n",
      "Minibatch loss at step 5500: 2.859982\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 81.5%\n",
      "Minibatch loss at step 6000: 3.805582\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 6500: 3.669700\n",
      "Minibatch accuracy: 79.9%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 7000: 2.473035\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 7500: 2.720575\n",
      "Minibatch accuracy: 85.7%\n",
      "Validation accuracy: 84.2%\n",
      "Minibatch loss at step 8000: 3.215557\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 8500: 2.356167\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 84.9%\n",
      "Minibatch loss at step 9000: 3.215524\n",
      "Minibatch accuracy: 83.9%\n",
      "Validation accuracy: 84.6%\n",
      "Minibatch loss at step 9500: 3.142681\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 84.9%\n",
      "Minibatch loss at step 10000: 2.537821\n",
      "Minibatch accuracy: 87.2%\n",
      "Validation accuracy: 85.0%\n",
      "Minibatch loss at step 10500: 1.992284\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.8%\n",
      "Minibatch loss at step 11000: 2.471822\n",
      "Minibatch accuracy: 84.1%\n",
      "Validation accuracy: 85.1%\n",
      "Minibatch loss at step 11500: 2.369192\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 85.5%\n",
      "Minibatch loss at step 12000: 2.865984\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 85.1%\n",
      "Minibatch loss at step 12500: 2.333702\n",
      "Minibatch accuracy: 85.4%\n",
      "Validation accuracy: 86.2%\n",
      "Minibatch loss at step 13000: 2.200284\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 85.8%\n",
      "Minibatch loss at step 13500: 2.254526\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 86.5%\n",
      "Minibatch loss at step 14000: 2.261552\n",
      "Minibatch accuracy: 86.2%\n",
      "Validation accuracy: 86.7%\n",
      "Minibatch loss at step 14500: 2.252300\n",
      "Minibatch accuracy: 88.0%\n",
      "Validation accuracy: 85.9%\n",
      "Minibatch loss at step 15000: 2.490324\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 86.6%\n",
      "Minibatch loss at step 15500: 1.580817\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 86.4%\n",
      "Minibatch loss at step 16000: 2.451010\n",
      "Minibatch accuracy: 87.2%\n",
      "Validation accuracy: 87.1%\n",
      "Minibatch loss at step 16500: 1.849911\n",
      "Minibatch accuracy: 88.8%\n",
      "Validation accuracy: 87.0%\n",
      "Minibatch loss at step 17000: 1.909108\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 86.9%\n",
      "Minibatch loss at step 17500: 1.912497\n",
      "Minibatch accuracy: 89.3%\n",
      "Validation accuracy: 87.4%\n",
      "Minibatch loss at step 18000: 2.508141\n",
      "Minibatch accuracy: 85.7%\n",
      "Validation accuracy: 87.1%\n",
      "Minibatch loss at step 18500: 1.425579\n",
      "Minibatch accuracy: 92.7%\n",
      "Validation accuracy: 86.9%\n",
      "Minibatch loss at step 19000: 1.739893\n",
      "Minibatch accuracy: 88.8%\n",
      "Validation accuracy: 87.6%\n",
      "Minibatch loss at step 19500: 2.070806\n",
      "Minibatch accuracy: 88.5%\n",
      "Validation accuracy: 87.4%\n",
      "Test accuracy: 87.6%\n",
      "Model saved in file: Models/Synthetic_Model_Momentum.ckpt\n"
     ]
    }
   ],
   "source": [
    "filename = \"Models/Synthetic_Model.ckpt\"\n",
    "num_steps = 20000\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()  \n",
    "    \n",
    "    if os.path.exists(filename):\n",
    "        reader = tf.train.NewCheckpointReader(filename)\n",
    "        reader.get_variable_to_shape_map()\n",
    "        saver.restore(session, filename)\n",
    "        print('Model restored.')\n",
    "    print('Initialized')\n",
    "  \n",
    "    for step in range(num_steps):\n",
    "        offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "        batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n",
    "        batch_labels = train_labels[offset:(offset + batch_size),:]\n",
    "        feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "        \n",
    "        _, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "        \n",
    "        if (step % 500 == 0): \n",
    "            acc_batch = accuracy(predictions, batch_labels[:,0:])\n",
    "            acc_valid = accuracy(valid_prediction.eval(), valid_labels[:,0:])\n",
    "            acc_synthetic_batch.append(acc_batch)\n",
    "            acc_synthetic_valid.append(acc_valid)\n",
    "            print('Minibatch loss at step %d: %f' % (step, l))\n",
    "            print('Minibatch accuracy: %.1f%%' % acc_batch)\n",
    "            print('Validation accuracy: %.1f%%' % acc_valid)\n",
    "            \n",
    "    print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels[:,0:]))\n",
    "    save_path = saver.save(session, filename)\n",
    "    print(\"Model saved in file: %s\" % save_path)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance on Testing set\n",
    "\n",
    "Selecting 10 random images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JrDgvNA/N5/NdGwRUJUgxWBwOB6ampnDnzh0sLCwI0Uej0Yj0fKrWNcyiD0Ws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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc645274410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import os\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (20.0, 20.0)\n",
    "f, ax = plt.subplots(nrows=1, ncols=10)\n",
    "\n",
    "im_samples = []\n",
    "    \n",
    "for i,j in enumerate(np.sort(np.random.randint(0, test_labels.shape[0], size = 10))):\n",
    "    im_samples.extend([j])\n",
    "    ax[i].axis('off')\n",
    "    label = test_labels[j,1:1+test_labels[j,0]]\n",
    "    ax[i].set_title(label, loc='center')\n",
    "    ax[i].imshow(test_dataset[j,:,:,0],cmap=\"gray\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Training and evaluation this architecture using SVHN datasets\n",
    "\n",
    "This will require the files generated after executing \"3_Preprocess_SVHN.ipynb\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data from binaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This function reads one record from the binary files\n",
    "def read_binary(filename_queue):\n",
    "    label_bytes = 6     # Labels contain: Number of digits + 5 digits\n",
    "    img_height = 64\n",
    "    img_width = 64\n",
    "    img_channels = 3\n",
    "    image_bytes = img_height * img_width * img_channels\n",
    "    \n",
    "    # Every record consists of a label followed by the image, with a fixed number of bytes for each.\n",
    "    record_bytes = label_bytes + image_bytes\n",
    "    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)\n",
    "    key, value = reader.read(filename_queue)\n",
    "    record_bytes = tf.decode_raw(value, tf.int8)\n",
    "\n",
    "    label = tf.cast(tf.slice(record_bytes, [0], [label_bytes]),tf.int32)\n",
    "    image = tf.cast(tf.reshape(tf.slice(record_bytes, [label_bytes], [image_bytes]),[img_height, img_width, img_channels]), tf.float32)\n",
    "    \n",
    "    # This is the size of the crops \n",
    "    height = 54\n",
    "    width = 54\n",
    "\n",
    "    # Randomly crop a [height, width] section of the image.\n",
    "    image = tf.random_crop(image, [height, width, 3])\n",
    "    \n",
    "    # Resize to network input\n",
    "    image = tf.image.resize_images(image, 32,32)\n",
    "    # To gray scale\n",
    "    image = tf.image.rgb_to_grayscale(image)\n",
    "    \n",
    "    # Apply random brightness/contrast transformations\n",
    "    if (np.random.random_sample()>0.5):\n",
    "        image = tf.image.random_brightness(image, max_delta=63)\n",
    "        image = tf.image.random_contrast(image,lower=0.2, upper=1.8)\n",
    "    else:\n",
    "        image = tf.image.random_contrast(image,lower=0.2, upper=1.8)\n",
    "        image = tf.image.random_brightness(image, max_delta=63)\n",
    "    \n",
    "    # Subtract mean\n",
    "    image = tf.sub(image, tf.reduce_mean(image))\n",
    "\n",
    "    return image, label   \n",
    "\n",
    "# This function create training batches running processes in parallel\n",
    "def input_pipeline(filenames, batch_size, num_epochs=None):\n",
    "    num_preprocess_threads = 2\n",
    "    \n",
    "    filename_queue = tf.train.string_input_producer(filenames, num_epochs=num_epochs, shuffle=True)\n",
    "    example_list = [read_binary(filename_queue) for _ in range(num_preprocess_threads)]\n",
    "\n",
    "    label, image = read_binary(filename_queue)\n",
    "    min_after_dequeue = 2000\n",
    "    capacity = min_after_dequeue + 3 * batch_size\n",
    "\n",
    "    image_batch, label_batch = tf.train.shuffle_batch_join(\n",
    "        example_list, batch_size=batch_size, capacity=capacity,\n",
    "        min_after_dequeue=min_after_dequeue)\n",
    "\n",
    "    return image_batch, label_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load validation/testing from pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_valid_test(num_valid, num_test, filename):\n",
    "    with open(filename, 'rb') as f:\n",
    "        save = pickle.load(f)\n",
    "        valid_dataset = save['valid_dataset']\n",
    "        valid_labels = save['valid_labels']\n",
    "        test_dataset = save['test_dataset']\n",
    "        test_labels = save['test_labels']\n",
    "        del save\n",
    "    \n",
    "    height = 54\n",
    "    width = 54\n",
    "    for i in range(num_valid):\n",
    "        temp = tf.cast(tf.constant(valid_dataset[i,:,:,:]),tf.float32)\n",
    "        # Randomly crop a [height, width] section of the image.\n",
    "        temp = tf.random_crop(temp, [height, width, 3])  \n",
    "        # Resize to network input\n",
    "        temp = tf.image.resize_images(temp, 32,32)\n",
    "        # To gray scale\n",
    "        temp = tf.image.rgb_to_grayscale(temp)    \n",
    "        # Subtract mean\n",
    "        temp = tf.sub(temp, tf.reduce_mean(temp))\n",
    "        if i==0:\n",
    "            result = tf.expand_dims(temp, 0)\n",
    "        else:\n",
    "            result = tf.concat(0, [result, tf.expand_dims(temp, 0)])\n",
    "\n",
    "    valid_dataset = result\n",
    "    valid_labels = tf.cast(valid_labels[:num_valid],tf.int32)\n",
    "    \n",
    "    for i in range(num_test):\n",
    "        temp = tf.cast(tf.constant(test_dataset[i,:,:,:]),tf.float32)\n",
    "        # Randomly crop a [height, width] section of the image.\n",
    "        temp = tf.random_crop(temp, [height, width, 3]) \n",
    "        # Resize to network input\n",
    "        temp = tf.image.resize_images(temp, 32,32)\n",
    "        # To gray scale\n",
    "        temp = tf.image.rgb_to_grayscale(temp)    \n",
    "        # Subtract mean\n",
    "        temp = tf.sub(temp, tf.reduce_mean(temp))\n",
    "        if i==0:\n",
    "            result = tf.expand_dims(temp, 0)\n",
    "        else:\n",
    "            result = tf.concat(0, [result, tf.expand_dims(temp, 0)])\n",
    "\n",
    "    test_dataset = result\n",
    "    test_labels = tf.cast(test_labels[:num_test],tf.float32)\n",
    "    \n",
    "    return valid_dataset, valid_labels, test_dataset, test_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define network architecture\n",
    "\n",
    "Same architecture but it now loads the training data from binary files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "image_size = 32\n",
    "num_labels = 11  # 0-9, + blank \n",
    "num_channels = 1 # grayscale\n",
    "num_digits = 5\n",
    "\n",
    "batch_size = 64\n",
    "patch_size = 5\n",
    "depth1 = 16\n",
    "depth2 = 32\n",
    "depth3 = 64\n",
    "num_hidden1 = 64\n",
    "num_hidden2 = 64\n",
    "\n",
    "# Binary files where the training data was stored\n",
    "file_names = [os.path.join('SVHN_data/','Train%d_64.bin' % i) for i in range(1,4)]\n",
    "# Pickle file where validation and testing were stored\n",
    "valid_test_file_name = 'SVHN_data/SVHN64.pickle'\n",
    "# Number of Valid/Testing samples to load into memory\n",
    "num_valid = 1000\n",
    "num_test = 2000\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # Input data.\n",
    "    tf_train_dataset, tf_train_labels = input_pipeline(file_names, batch_size, num_epochs=None)   \n",
    "    tf_valid_dataset, tf_valid_labels, tf_test_dataset, tf_test_labels = load_valid_test(num_valid, num_test, valid_test_file_name)\n",
    "    \n",
    "    # Variables.\n",
    "    layer1_weights = tf.get_variable(\"W1\", shape=[patch_size, patch_size, num_channels, depth1],\\\n",
    "                                    initializer=tf.contrib.layers.xavier_initializer_conv2d())\n",
    "    layer1_biases = tf.Variable(tf.constant(1.0, shape=[depth1]), name='B1')\n",
    "    layer2_weights = tf.get_variable(\"W2\", shape=[patch_size, patch_size, depth1, depth2],\\\n",
    "                                    initializer=tf.contrib.layers.xavier_initializer_conv2d())\n",
    "    layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth2]), name='B2')\n",
    "    layer3_weights = tf.get_variable(\"W3\", shape=[patch_size, patch_size, depth2, num_hidden1],\\\n",
    "                                    initializer=tf.contrib.layers.xavier_initializer_conv2d())\n",
    "    layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden1]), name='B3')\n",
    "\n",
    "    # Sequence length\n",
    "    ss_w = tf.get_variable(\"WSS\", shape=[num_hidden2, num_digits + 1],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    ss_b = tf.Variable(tf.constant(1.0, shape=[num_digits+1]), name='BSS')\n",
    "    \n",
    "    # Digits\n",
    "    s1_w = tf.get_variable(\"WS1\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s1_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS1')\n",
    "    s2_w = tf.get_variable(\"WS2\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s2_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS2')\n",
    "    s3_w = tf.get_variable(\"WS3\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s3_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS3')   \n",
    "    s4_w = tf.get_variable(\"WS4\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s4_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS4') \n",
    "    s5_w = tf.get_variable(\"WS5\", shape=[num_hidden2, num_labels],\\\n",
    "                           initializer=tf.contrib.layers.xavier_initializer())\n",
    "    s5_b = tf.Variable(tf.constant(1.0, shape=[num_labels]), name='BS5') \n",
    "    \n",
    "    # Model.\n",
    "    def model(data, keep_prob):\n",
    "        conv = tf.nn.conv2d(data, layer1_weights, [1,1,1,1], 'VALID', name='C1')\n",
    "        hidden = tf.nn.relu(conv + layer1_biases)\n",
    "        lrn = tf.nn.local_response_normalization(hidden)\n",
    "        sub = tf.nn.max_pool(lrn, [1,2,2,1], [1,2,2,1], 'SAME', name='S1')\n",
    "        \n",
    "        conv = tf.nn.conv2d(sub, layer2_weights, [1,1,1,1], padding='VALID', name='C2')\n",
    "        hidden = tf.nn.relu(conv + layer2_biases)\n",
    "        lrn = tf.nn.local_response_normalization(hidden)\n",
    "        sub = tf.nn.max_pool(lrn, [1,2,2,1], [1,2,2,1], 'SAME', name='S2')\n",
    "        \n",
    "        conv = tf.nn.conv2d(sub, layer3_weights, [1,1,1,1], padding='VALID', name='C3')\n",
    "        hidden = tf.nn.relu(conv + layer3_biases)\n",
    "        hidden = tf.nn.dropout(hidden, keep_prob)\n",
    "        shape = hidden.get_shape().as_list()\n",
    "        reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n",
    "\n",
    "        logits0 = tf.matmul(reshape, ss_w) + ss_b\n",
    "        # This concat only allow us to have a one hot vector like the others (to pack the results in the following lines)\n",
    "        logits0 = tf.concat(1, [logits0, tf.zeros([shape[0],5])])\n",
    "        logits1 = tf.matmul(reshape, s1_w) + s1_b\n",
    "        logits2 = tf.matmul(reshape, s2_w) + s2_b\n",
    "        logits3 = tf.matmul(reshape, s3_w) + s3_b\n",
    "        logits4 = tf.matmul(reshape, s4_w) + s4_b\n",
    "        logits5 = tf.matmul(reshape, s5_w) + s5_b\n",
    "        return [logits0, logits1, logits2, logits3, logits4, logits5]\n",
    "    \n",
    "    # Training computation.\n",
    "    [logits0, logits1, logits2, logits3, logits4, logits5] = model(tf_train_dataset, 0.975)\n",
    "    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits0, tf_train_labels[:,0])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits1, tf_train_labels[:,1])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits2, tf_train_labels[:,2])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits3, tf_train_labels[:,3])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits4, tf_train_labels[:,4])) +\\\n",
    "        tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits5, tf_train_labels[:,5]))\n",
    "        \n",
    "    # Optimizer.\n",
    "    global_step = tf.Variable(0)\n",
    "    learning_rate = tf.train.exponential_decay(0.04, global_step, 10000, 0.95)\n",
    "    optimizer = tf.train.AdagradOptimizer(learning_rate).minimize(loss, global_step=global_step)\n",
    "    #optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss, global_step=global_step)\n",
    "    #optimizer = tf.train.MomentumOptimizer(learning_rate,0.9).minimize(loss, global_step=global_step)\n",
    "    \n",
    "    # Predictions for the training, validation, and test data.\n",
    "    train_prediction = tf.pack([tf.nn.softmax(model(tf_train_dataset, 1.0)[0]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[1]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[2]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[3]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[4]),\\\n",
    "                                tf.nn.softmax(model(tf_train_dataset, 1.0)[5])])\n",
    "    valid_prediction = tf.pack([tf.nn.softmax(model(tf_valid_dataset, 1.0)[0]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[1]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[2]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[3]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[4]),\\\n",
    "                                tf.nn.softmax(model(tf_valid_dataset, 1.0)[5])])\n",
    "    test_prediction = tf.pack([tf.nn.softmax(model(tf_test_dataset, 1.0)[0]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[1]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[2]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[3]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[4]),\\\n",
    "                                tf.nn.softmax(model(tf_test_dataset, 1.0)[5])])\n",
    "    \n",
    "    saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 20.405554\n",
      "Minibatch accuracy: 43.8%\n",
      "Validation accuracy: 42.2%\n",
      "Minibatch loss at step 500: 7.099613\n",
      "Minibatch accuracy: 60.9%\n",
      "Validation accuracy: 59.6%\n",
      "Minibatch loss at step 1000: 6.144541\n",
      "Minibatch accuracy: 62.8%\n",
      "Validation accuracy: 61.6%\n",
      "Minibatch loss at step 1500: 6.607010\n",
      "Minibatch accuracy: 64.6%\n",
      "Validation accuracy: 60.3%\n",
      "Minibatch loss at step 2000: 6.106441\n",
      "Minibatch accuracy: 59.9%\n",
      "Validation accuracy: 65.4%\n",
      "Minibatch loss at step 2500: 6.356358\n",
      "Minibatch accuracy: 64.8%\n",
      "Validation accuracy: 64.2%\n",
      "Minibatch loss at step 3000: 6.186196\n",
      "Minibatch accuracy: 68.8%\n",
      "Validation accuracy: 66.1%\n",
      "Minibatch loss at step 3500: 5.962085\n",
      "Minibatch accuracy: 69.0%\n",
      "Validation accuracy: 62.9%\n",
      "Minibatch loss at step 4000: 5.373891\n",
      "Minibatch accuracy: 68.5%\n",
      "Validation accuracy: 63.0%\n",
      "Minibatch loss at step 4500: 6.036961\n",
      "Minibatch accuracy: 67.2%\n",
      "Validation accuracy: 65.9%\n",
      "Minibatch loss at step 5000: 6.064417\n",
      "Minibatch accuracy: 60.4%\n",
      "Validation accuracy: 65.2%\n",
      "Minibatch loss at step 5500: 5.986944\n",
      "Minibatch accuracy: 63.3%\n",
      "Validation accuracy: 67.4%\n",
      "Minibatch loss at step 6000: 6.268200\n",
      "Minibatch accuracy: 64.8%\n",
      "Validation accuracy: 67.2%\n",
      "Minibatch loss at step 6500: 5.646094\n",
      "Minibatch accuracy: 67.2%\n",
      "Validation accuracy: 67.6%\n",
      "Minibatch loss at step 7000: 6.621971\n",
      "Minibatch accuracy: 68.5%\n",
      "Validation accuracy: 68.2%\n",
      "Minibatch loss at step 7500: 5.719033\n",
      "Minibatch accuracy: 70.8%\n",
      "Validation accuracy: 66.9%\n",
      "Minibatch loss at step 8000: 5.169441\n",
      "Minibatch accuracy: 70.6%\n",
      "Validation accuracy: 66.8%\n",
      "Minibatch loss at step 8500: 5.072827\n",
      "Minibatch accuracy: 70.8%\n",
      "Validation accuracy: 65.9%\n",
      "Minibatch loss at step 9000: 5.122186\n",
      "Minibatch accuracy: 69.3%\n",
      "Validation accuracy: 67.7%\n",
      "Minibatch loss at step 9500: 6.103755\n",
      "Minibatch accuracy: 66.7%\n",
      "Validation accuracy: 67.9%\n",
      "Minibatch loss at step 10000: 5.356981\n",
      "Minibatch accuracy: 63.3%\n",
      "Validation accuracy: 66.9%\n",
      "Minibatch loss at step 10500: 6.386445\n",
      "Minibatch accuracy: 67.7%\n",
      "Validation accuracy: 68.4%\n",
      "Minibatch loss at step 11000: 5.919908\n",
      "Minibatch accuracy: 72.9%\n",
      "Validation accuracy: 68.2%\n",
      "Minibatch loss at step 11500: 5.394932\n",
      "Minibatch accuracy: 69.5%\n",
      "Validation accuracy: 68.1%\n",
      "Minibatch loss at step 12000: 6.074568\n",
      "Minibatch accuracy: 70.1%\n",
      "Validation accuracy: 66.7%\n",
      "Minibatch loss at step 12500: 6.205260\n",
      "Minibatch accuracy: 73.7%\n",
      "Validation accuracy: 67.7%\n",
      "Minibatch loss at step 13000: 5.330285\n",
      "Minibatch accuracy: 68.2%\n",
      "Validation accuracy: 66.2%\n",
      "Minibatch loss at step 13500: 5.371821\n",
      "Minibatch accuracy: 70.6%\n",
      "Validation accuracy: 68.3%\n",
      "Minibatch loss at step 14000: 5.942102\n",
      "Minibatch accuracy: 70.1%\n",
      "Validation accuracy: 67.8%\n",
      "Minibatch loss at step 14500: 5.636793\n",
      "Minibatch accuracy: 63.3%\n",
      "Validation accuracy: 68.2%\n",
      "Minibatch loss at step 15000: 5.455369\n",
      "Minibatch accuracy: 68.8%\n",
      "Validation accuracy: 67.4%\n",
      "Minibatch loss at step 15500: 6.063006\n",
      "Minibatch accuracy: 71.1%\n",
      "Validation accuracy: 69.2%\n",
      "Minibatch loss at step 16000: 5.479003\n",
      "Minibatch accuracy: 67.2%\n",
      "Validation accuracy: 69.0%\n",
      "Minibatch loss at step 16500: 5.887541\n",
      "Minibatch accuracy: 69.0%\n",
      "Validation accuracy: 69.5%\n",
      "Minibatch loss at step 17000: 5.800154\n",
      "Minibatch accuracy: 69.8%\n",
      "Validation accuracy: 69.6%\n",
      "Minibatch loss at step 17500: 4.882589\n",
      "Minibatch accuracy: 67.7%\n",
      "Validation accuracy: 68.6%\n",
      "Minibatch loss at step 18000: 5.955020\n",
      "Minibatch accuracy: 70.3%\n",
      "Validation accuracy: 67.6%\n",
      "Minibatch loss at step 18500: 6.041705\n",
      "Minibatch accuracy: 69.3%\n",
      "Validation accuracy: 69.9%\n",
      "Minibatch loss at step 19000: 5.558751\n",
      "Minibatch accuracy: 71.1%\n",
      "Validation accuracy: 68.1%\n",
      "Minibatch loss at step 19500: 5.618520\n",
      "Minibatch accuracy: 67.4%\n",
      "Validation accuracy: 68.4%\n",
      "Test accuracy: 75.0%\n",
      "Model saved in file: Models/Synthetic_Model_SVHN_Momentum.ckpt\n"
     ]
    }
   ],
   "source": [
    "filename = \"Models/Synthetic_Model_SVHN.ckpt\"\n",
    "num_steps = 20000\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()  \n",
    "    \n",
    "    # These are required to start the processes reading training batches\n",
    "    coord = tf.train.Coordinator()\n",
    "    threads = tf.train.start_queue_runners(sess=session, coord=coord)\n",
    "    \n",
    "    if os.path.exists(filename):\n",
    "        reader = tf.train.NewCheckpointReader(filename)\n",
    "        reader.get_variable_to_shape_map()\n",
    "        saver.restore(session, filename)\n",
    "        print('Model restored.')\n",
    "    print('Initialized')\n",
    "  \n",
    "    for step in range(num_steps):   \n",
    "        _, l,  = session.run([optimizer, loss])\n",
    "\n",
    "        if (step % 500 == 0): \n",
    "            batch_predictions, batch_labels, valid_label = session.run([train_prediction, tf_train_labels, tf_valid_labels])   \n",
    "            acc_batch = accuracy(batch_predictions, batch_labels[:,0:])\n",
    "            acc_valid = accuracy(valid_prediction.eval(), valid_label[:,0:])\n",
    "            acc_SVHN_batch.append(acc_batch)\n",
    "            acc_SVHN_valid.append(acc_valid)\n",
    "            print('Minibatch loss at step %d: %f' % (step, l))\n",
    "            print('Minibatch accuracy: %.1f%%' % acc_batch)\n",
    "            print('Validation accuracy: %.1f%%' % acc_valid) \n",
    "\n",
    "    test_dataset, labels_test, prediction_test = session.run([tf_test_dataset, tf_test_labels, test_prediction])\n",
    "    # Accuracy on testing set\n",
    "    print('Test accuracy: %.1f%%' % accuracy(prediction_test, labels_test[:,0:]))    \n",
    "\n",
    "    save_path = saver.save(session, filename)\n",
    "    print(\"Model saved in file: %s\" % save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the performance \n",
    "\n",
    "Comparing performance on Synthetic and SVHN datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T0B56P2Le4IUlzb3bkT/7nhWjNoTij8Nq04WzmD94Grw+K25q+TrLujOCek/jhbnrNeTP\nf2AVrqxYMmFWWGmaVkbjL//LGlWyp1uxXWvGMCMwlbQE+8vPWrFV973LcjvZerucZCTcKciqkDXn\noboSohFLLJaWI8rKobTcis/rHPy6rCdJwxIbCd0aABM9hEmyc50wegyOPS0mhtljoLzGkmJ0W1Su\nHdmupzV7viUhLSKMHoLCElnd1p8ukSUhLQhsQqBpWGthrW2d72ld+7Tu97pee10O7Ji47Rpuu4bT\nLnDbrG2XXXSuu7fddg1XpyXH1kOE9VxGahGzvm+X2KOX8NNNaf1WAgTWNbp+P5HeFt3bous9kRZk\nacE4zPbJlibkWy8jd7wypLIi6c+ZJvI/v4HsaEX75OcHPX4ikJEw5n88CaEOtI/+FYWLlw14rBJp\nCkUnA5ZRSCUti8XRvcij+6yBa9laxLK1MGc+wmazKlZ//+tWVfT3/uGYVvseD6RpIg+8ZYkzfwDt\nocesTK/hnOPEQczvP4H24U8jlq4Z/AODnS+ZsNype7YhfvcjiNUbIRq2rDuv/y+UllvWnXmLJ60m\nlpTScj3ueQPtU3+HmFkMTLyrXEbCEAkicrtjWUwpiaZMIkmDSNIknDQI99q23oulLJFjdFp5DBN0\n2WWFsc6TtsrEYhjRiGXxcXtACjB0pKFbRWVtNitA32azAuU1jS7J0/MvZEiZFl5At8joXLvsGk6b\nwGWzhIizxzrtTuphMXGk1xp2DRya1ueYnl3keqNcz7ekJC2cbOm1JbQsAdZ3vzYGfVGFWgwdqetw\nrDN7uvZiuqxIz/+F9LFSIn/2PWRNpTXd0xSaK1SaphUT+sqvKP7JqwMep0SaQtFJrzIKoQ7k8QOW\nKDt9FIrKEMvWWTE7A9RyktEI8sfftm4If/QXEzKP3nCRpgmHd2M+/xNwudEefgwWLB95+YMLZ60A\n3/d8CG391pG3q/IU5g++gSieZRW0vCZAXqaSyN1vIF9+zipjcN+7LbeebWwCitPXkZbbImH0sOh0\nWnviiSSJV35NoqOD5D3vIunwpI8RdgemnsJpE52L1s92330Om8AwJbEeLq1Yyky7vdJLqusYk3hn\nnEssZYmvSOc6ljLx2DV8Tg2f04bfacPn1PB3bTus/V6H1sP91WXt6bZu2HpYYrpcXVrtBbQ9r1uZ\nvUVliOJZ1iTUA8SWXTuoaELg7hRddm1yBPZURom0kdGrrEjxLKsO4dK16cxa8/kfI4/sRfuLf0J4\n/ZPc2v6R505QuOXeAd9XIk1xyyNNExJxfMkY4T3bLTdmXbUlXpatRSxZjQgMPZbG3LMN+bPvIe5/\nN+Keh6dMkLSsPGXNiajZ0B56zJpmZiymdrlSi/nkFxF3vQPt3oeH99lEHPnsD61khCFU/ZamaT1F\nv/QstLci7nnICkTv5wlZSkkkZRJKGAQTBsG4QUdCJ5gwCCUMOuKd+xMGoYROR8IgmjSxawKX3bLq\nODvXLmHiuHoJlyZwzZqD22mzrD2dViCfx00kFifZKe6SnbEqScOKW0kYlpvOcu11bycNid0m8PRw\nY7kdGp6ubbuG2y5wO6xtzzXHWAKsW3zZlAC6IVEibXR0xbDK7S9C01XEHfeAZkPu2472l1+Z0MK8\nI0FldypuGWR7C/LYAYhH+xSglD2LUPYoVkkyCU4XWmY2cuEyy41ZsWRU8yjK5gbM738d7A60D39m\nUqvH9yoc+t4/sOKXxthdKFubMJ/4eyt+7N0fuO75ddOyTsXPniL2ix+SKC4nec+7STg8fQOjO2OM\nuoNzzXQAbzIYJNlwBT0aJZmdi56RQ0popAxJJGkQSho4NI1Mt42Ay0ZG55Lptvd+7bIRcNvIcNnx\n9SN0ZOMVzG/8A2LpWsR7Ptiv6FaDrGI0qP7TP4m4SSol8QeGbjGXdZeQb76IvFSF9pG/QOTMGMcW\njg1KpCluCWRrk1WksGwuZGb3KiyJ+zrTuzjdCE0b8xulNAyrevzr/2sVJF01NnPDDfn6IywcKqUk\nlDBojOg0RVI0RlI0RVJEUiZmVyxTP0HVRkrHqLuE6XRjTMvDpDvtv8ttmDBMTAluqeNMxXH7vLi8\nHtxdFquegdKdqe/OnnFItm6XWTodPtiK49BO7GeO4Jy/GOf6O/HNzCfDZcNhG50VU1adwfzWVxAP\nPoq25W0DHqcGWcVoUP2nL6Yp2fV6mGRSsuX+ANpNbCVWIk1x0yPbWjC/+reILW9Du+ehEZ1jvG6U\n8sJZzO99DTFvsRUQPw61tPpc8zqFQ00paYvpneJLpzHcLcQaIymaoylsmiDX52BG55Lrs+N32tLx\nSzZBdxB11z4NtFQS8dwPsdls2N/z+9idLmyaFSjutmk4zx9H+9G/olUssaYD8o1dnIgMtiPf+I3l\n8piR3z059JyFI0rkkAfewvzxdyxL6JJV1z1WDbJjg5QSXQc9JdF1icMhcHumRrjAeKL6T1/OnYzT\n0qiDgJlFDsrKp3Yy1mhQIk1xUyPbWy0L2h33oN337hGfZzxvlDIe7Zxw9xTaH/15ukDsYBim5boL\nJo10bFUoYWXsdcU+ddXoSRmSZCJJ8nINqbZWUvklJDNyepUqSOgmbXEDv1PrFF+OtBiz1nZy/Q68\njpEH5Es9hfyPp5BtzWh/8nmE128lVfz8P5AnD6H93icHFT2jQaaSUHnamjz67HGovQjFsxHzl1gT\nSM+Zf11XtpQS+dIvka//xmr/ECrjq0F2YNpbdRrqdXRdoqckhi7T2z0Fma5LDN1KFrXbBXa7IJmU\nbNjqJ5A5tgkiUw3Vf3rT1qKzb0eETfcGSCZM9r4ZYevbMrA7bk5rmhJpipsW2dFmCbTb70R723tH\nda5xFWnSys4LHdxH6Ne/ILLmTsIrNhBJkRZeXQHswYQV7B5K6ERSJl6HRobLiqsKOG1kuG34HLZ0\nlmC69EBtJY4DO3AWl+Bctwmnz5d2DzptVmkCl02Q7bHjHKUbcNDva5pW6vu5E4j734P85X8iFq1E\n/M6HJ2yeyHRbEvFO0XYMeeY41F+CsrmWpa1iCcyel55oXOo68iffQV44h/anXxhyLKEaZPvn0oUE\np4/FKZnlxOEU2B2W+LLWlhizXbOvZzxj7cUE508nuOPuAA7nzTlAg+o/PdF1yZsvhZi/xE1BifUw\ndXhPBI9PY/6S8fdCTAZKpCluSmSw3RJoa+9Ae3D0E+UO9UZpmJKOhEF7TKctptMe1wklDcKJ7tpU\n4c7aVFYAu0k0aeCwafidGn6bxNdch99M4C8uISM7QIbfYwkxp61bkLms0gmDZezJK5cxf/xtCIfQ\nfu/jiDnzR/1bjAVSSuQL/4Pc9TraYx9DLFw+2U0CrGmWqDyFPNNpabtaZwm1iiXI8ydBCKtI7TAm\nkleDbG9MU3LqSIzGKzpr7vARyBi5JezYgSiJuGT1Bu+k1ccbb1T/6ebYgSiGLllxW/fDXDRi8ubL\nIbbcH7gp3d9KpCluOmQoiPm1zyFW3I720PtHfz4pwemltrm9h/gyaIvptMWt7fbO7XDCIOCyke2x\nk+W2k+XuFlRdJRH8Tht+V+86VT3rQ0nTRL76K+S+HdBQB3YH5Bci8gqtdX4h5BVZsVWdNX/6tDmR\nQL7wDPLNlxAPPorY8rYxrxt2KyCjYTh30hJsTjfine8b9u+oBtlukgmTg7uiCA1W3e7F4RzdoGoa\nkl1vhMkrcDB34dQpRjqWqP5jcbUuxYnDMTbfF8BxjWvz1NEYqaRk2ZrhT3k21VEiTTEq5JG9mHve\nQHvkD6dEOrMMBzG/9gXE0tWIh39vWE/X4YRBfShJfSjJlVCS+lCK+qC1jRBku21keew91vZOMWYj\nq3M7wzW4dWtY30dK6GiDhjrk1Tq4WodsqIOrl6GtBXJm9BBunetwEPOZf0fMrkA88geIrKk5v+Wt\nghpkLYLtBvvfijCz2MGCJW7EGP2fxKImO14JsXydl9z8yZ/WZ6xR/ccqt7H9pRCr1vuYNqPvg2ky\nafLGCyHW33nzxSgqkaYYMbKtBfP/fAaxegNy/1vWJMl33Dt50/JEwphf/zxiwXKrZlU/7YimDK50\niq9rBVnKkBRmOJgZcFIQcKbXBQEHBdOzCIfDE/ZdohED0+S6NYBkKgVNV3oIt861rqO9+wNTxoV4\nq6MGWaivTXL8YIxFKzwUlY68xuBAtDTqHNwdYePdfry+qTFISymJRU3CQRMpYXqeHZtt+PfGW73/\nSCnZtyNCRpaNBUsHjjurOmtlfK69Y2rOHjBcUknJ8UNRHnz33AGP6d+PolBgueTMHzyFuPPtaO/4\nXeTmBzD/4ynkgbfQPvAniOl5E9ueaBjzib+zAr7f80FMCVdCCarbElxsS1DdFudie4JQwuglvpbk\nebm3PIuCgJMst21AgTmRwjOVkuzZHiGVlKzb5CMrp/9/ReFwQEEJFJRwc0bjKCaS9hadyzVJ8osc\nTJthH5sZJ6Tk3Mk4tReT1+3Lo2Varp3y+S72vxVl411+bPYJ/H9NSsIhg3DIJBIyCAdNwiGDSNjE\n6RT4AzaklBzea5Jf6KCotPP3vYlre40lNVVJEnFJxaLru7PLyl1cPJ+kuVFneu6NLV9am3QO740y\nI//630NZ0hQDYr76PHL/DrS/+r/pGB1pGMiXn0O+/EurOOrmByZk2qNwR4iL3/s2NTMXUD17FdXt\nCS61J8jy2CnLcjEr20VZtpuyLBd5fseIJj2eqKdZKSWHdkexOwR5BQ6O7o+yZqOPnOk39k1nLGmo\nT3H6WIxFyz3MuAHcW1PdEpJMmpw5FudqXYriMidX61MgobTcRVGZA+cI48b0lOTQ3giphGT1Bh8u\n9zhnDUvJ4T1WvNvytWOfSJBMmLS1GISDliALhwwiIRNdl/j8NvwZGv6Ahj9gw9e57lkWIhY1qb+U\n5HJNimTCpKDESVGpg4ysgR8OYWLvPV31x5av8+H1TX4QfjhosPP1MOu3+oeUYHK5JsnFcwk23u2f\nMokkiUSC1tZWdF3H6/Xi8XjweDz9ts80JedPxampSrJ0tZf8QodydyqGj6yrwfzq59A++y+I3Jl9\n379Si/mDp61pjz74p/0eM6TrdJamiKZMIp1ZkJGUSSRpUBdKWlay1hjBcJwSLUZZeTGzOsVYWbZr\nVPW8rmWibpTV5xPUXEiy8W4/Npug8UqKw3ujrFrvZXru1Bck44muS04fjdFQn2LOfDfnTsZZvNJD\nYcnYu8/Gkqkq0qSUXK62BG9+oYP5S9w4XRpSSlqbDWoqEzRcSTGz0ElpuZOsnOuLiZ5Ewgb7d0TI\nnm5nyUoP2gjcfCNB1yVvvRqibI6LsrljV+C0oT7F0f1RApk2AhkavoDNEmQZNtweMWxBEOowuFyT\npO5SCpsNCkudFJU48Pr73rMmov+kUgaH9rQTCsaZlmvnaq2dlbdlkFcwefcc05S89WqYktnOIRer\nlVKy45Uw5fNd6RIdE4GUkkgkQmtrK21tbb3WqVSK7Oxs7HY7sViMWCxGMpnE7XanRZvX68Vud9PW\nZMfl8lCxMIvMLB9er5eKiooBr6tEmqIPMpXC/PKfWxNmb7xn4ONMA/nqr5G//R/E2x9FbH0wbVWT\nUnKuJc7hKxGCcaveV7RTfHUJskjKIJYycdoEXoeVCel12PA5NLxOjZl+J2V+Qemvvkt+bhb2xz42\nrla7ibhRtrfq7H0zwsa7/Ph6xKI1N6Q4uDvKinVecmfemkKto03n0J4omVk2lqzy4HBqBNsN9r4Z\npnyBm1ljOCCPNVNRpIU6DI4djGLosGSVh+xp/VtqE3GT2uokNVVJ7HZB6RwnRaXO6xYObbqa4tCe\nKBWL3ZNSCT4SMnjrtfCYWKB7PhisuK3/oPXRIKWkrdkSbFcup/D5NYpKncwsceByWfez/vqPlBLD\nMDAMA13X09s9XyeTSRKJRJ8lHo/32ZdMprDZHPh8lksxHI4gsOPx+Jg2PUAgEMDv9+P3+3ttOxzj\ndz86czxGR5vB2jt8wxLBzQ0pju6PcecDgX4fDnRdp6mpCSklmqahaRpCiPR2f/uEENg6PUahUKiP\nGGtra8Nms5GTk0N2dnavtd/f16pnGAaxWIxoNEo0GqXuUohL1UECWSmc7iTRaDT9/he+8IUBv6sS\naYo+mP9kZP1WAAAgAElEQVTz78imq2gf/+yQ/nFkQz3mD55GIql6+JPsirjZdSmIw6axrshPjseO\nz9ktvnwOG16Hhs9prQfKlJSJOObTX0LkFiB+/5Pj7lYd74E2mTTZ8XKYBcvcFBT3fQJsbdbZ/1aE\nZWssE/itgpSSC2cTVJ5JsGi5h6Ky3r9NNGywZ3uEwlIH8xa5p4yLoydTSaTpKcm5U1aM2LxFbsrm\nOIcUGyWlpLlBp7oqSUujTkGxg9I5LjKzbb2OuXAuQdWZBKtu9zFtEuOCGupTHDsQ5Y57+tbOMgwj\nPQBfj/4eDMaSVCrVazCORKI0N4ZpbgoTCkXRbAnQEmiaJJVK9hJipmlis9l6LXa7vddrp9OJy+VK\nL263u9drl8uFqTs4flCnoNjHwmXdLjgpJR3tUQ7saSaZjJBXlCKRiBAKhQiHw+nFbrenBVuXgOu5\n+P3+tLgZDi1NOgd3Rdh8X2BEbvK9b4aZke9g9jzrISGZTFJdXU1lZSWXLl0iKysLm82GaZqYpomU\nMr092D6/358WYV1CLCcnB7d7+CVgkkmT4wdihDoMVt7uIyOr72+l3J2KISNPH8X89yfQ/u5pRCBj\n8OM7LWY7a4LsOteIM9LB+hzJhi2rKMvu3yc/pHYkEpjf+AfEtFzEB/90yAIt2G7Q2qxTPMs57Cyr\n8Z5xYP/OCF6vxuKVA9f56bK0LVnl6VfI3WzEoiZH9lnFK1fe5u3XFQSWtWfP9gjZ02wsWemZcgHZ\nU0GkSSm5Wpfi5OEYOTPsLFzmGXHhz3jM5NKFJDUXEng8GqVzXOQV2jl5OEaw3WTNxsmLZ5JSkkgk\nCIVCnDnRTHNzkGl5CcLhMKFQiGAwSCKRSIscu92O3W7H4XCkt+12O8mERiyikTPNRWa2K71f0yx3\ncNfQ2LV97etrt3Vd72U5icVimKbZy93Vc9vl9BCLOmlpsGHTHMxf7CF7uquXCBvtA0lrs86BnRFL\nrA9g8ZSmJeovXUiy4rbeIRdSSuLxePq3vXYdCoWIRCJ4PJ5+BVzX4nb3frhKpSTbXwqxeIVnxA+k\nwXaDt15rpnhuCxerK6mrq6OgoIDy8nJmzZqF1zv59dSaG61QlpmFDhYs9QyY7KJEmmJIyEgY8x8+\nhfb7f4JYvHLg46TkfEucnZdCaYvZhpIAG0oClKTakP/1r5BMoH3oU4iCkv7PYRgQbIf2VuhoQba3\nWtvtrciOVrhyGTF3IeLDn0ZoQ3tKk9IqeqmnJMmEZO5CNyWznEOOlRnPgbbqbJz6Syk2bPUP2p6O\nNsvFt2CZh+Kym1eoXbmc5NiBGLPmuihf4EIbRHilUpL9b0VwugQr1nlHVOpgvJhskRYJG5w4FCMa\nMVmyypMeaA3DSA+mXUs0GgW4ruunawFBJCRpbTYJByWZWYKCUhtSmr0sPj0tQF37ulxypmkCDGoV\nuva1zWZlTPYUBMFgMP07Z2RkEAgEiIbdBPwB5s6flhYFPp8v/f1TqRS6rqPrOqlUikg4xdmTYXQ9\nRekcO5rN6PW+lDItKIQQ6aXn6/62bTZbLxHm8XhwOp2DCi3Lgmnj8N42K25wqXvEiRw9qbuU5MSh\nGMvXeckbQghF49UUR/ZG0/+PQxWIpmkSiUR6/Z2uXQzDIDMzM22dCrb68HmzWLdxJvYBinUPRCQS\noaqqiqqqKurrrzItu5AVqyooKyvD5ZoaIRGmITnbmfG8bO3gv/+UF2nPPvssO3bsQNM0SkpK+MQn\nPkE8HufJJ5+kqamJ3NxcHn/88SErYyXSho+UEvndf4GMLLT3/XG/7w8kzEqzev9DS9NEvvkS8lf/\njdhwNzhd0NHaLcQ6WiEcBH8mZOVAVg4iM6d7OysHMnOgqGxYLs6mhhTHD8TY8kCAjjaDsyfihIMG\ncxe6KZ7lHFQEjNdA2+XGvOOeodd3CgUN9mwLM2+Rm9I5U+PGM1boKcnJIzGaG3RW3uYlexgxRYYh\nObQnip6SrNngmzITLg/Ud3RdJx6PE4/H0wHFXdtd+4UQOJ3O9OJyuXq9vnbp6VqKxRKcOtbCxao2\nsqfHcXlihMLdg2MsFsPn8xEIBNKipus+Ohz3jyW6DGw2LS2mNE3rI6oG2t8zvuraZaB4K8MwAPD7\n/em2d32PnuInmTTZ8UqYisXuQeuzDffBYKIIBAK0tHSkM3AXLPVQVOYYkSVNSknlmQTVlQnWbvT3\nclcPRixqcnBXBIfTehByusbGWppMJmlvb6e1tZVLNc3UX27B5gwSDAbx+Xxpd2LPWK+ersWOjo60\nMGttbaWsrIzy8nJyZxSx87U4m+8L4PGOTVubrqbQdYnbo+H2aLjcYlj9JBw0OLQnitsjWLbGOyRX\n7pQWaU1NTXzpS1/iySefxG6388QTT7BixQouX75MIBDgoYce4rnnniMSifDYY48N6ZxKpA0fc88b\nyBd+jvb5r4PDSVvcoKY9QU17nOq2BCcbo9g1jY2l/Quz/pAtjchXnwe3BzI7xVfXEsga0ymMpJTs\nfD1M2RxXr5im1madsyfiRMMm8xa5KCwdWKyNh0hLJKw555asHH6cWSRksHtbmDkVbmbNuzmEWnuL\nFQOUPd3GkpXeEYksaUqOHbQCjtdtGt+yD1JKkslkn8UKxO5+DdZAcq0gMwwDt9uNx+NJr3tuu1yu\nAa8x0HU1TcPpdKLrBrpu4nb6mDY9k6zs3kKmy6I0WFzWzUCw3fpfuX2Lv9+Yn9E8GEwEPe897S06\nxw7GsNlhyUpvv99nIExTcvxgjPZWKxh/JMLFNCWnj8W5Uptk1XrfgAknIyEWte6H6+7wkTXNjmEY\ndHR09MmWbGtrw+FwkJ2dTTKZJBwOM3v2bObMmUNxcXGvB5XTx2Ik4pLla0fn3kwlTeu3azPwBzTi\nMUkibpKIS5wugcut4faITvEmeok4j1fD6RRcupjkzPE4FYvdlM4Z3IraxfVE2qT3VI/Hg91uJx6P\n4/F4SCaT5OTk8Nxzz/HFL34RgC1btvDFL35xyCLtRkBKCdEIwje5lZMTuklNzRUuvnaQS3d+mpo3\nG6hpTyCBsiwXpVkuFuV6eXhBzpCEWU/EtFzEo380fo3vQXODTjIhKSzpLYRyptu5fYufliZLrJ0/\nlWDeIjeFJY5xj2uSUnJkb5TCEueI4i58ARvrt/rZ/UYEw5CUL7hx5y2UpvV0f+FcYtQlNYQmWLra\nw9kTcXa+Hua2zb5hVaDvSqXvsjZdG1/TUxTpuo7dbh/UyhUIBMjMzOwjyIbi7hoOibjB5Zo4tdVh\nEnHBstVZ5BfevC7xoZKRZWPRCg/7d1oW657uwq4Hg5zpdjbfF5gy1teByJpm5467/dRUJdm9LUzx\nLCfzFroHbXcqKTmwK4KmwYat/hF/T00TLFruIWe6jX07Isxb6KZs7uj7sZSSI/ssd2pWp/DrypbM\nyclhzpw5vY4Nh8PpjMqZM2cO+LBRPt/N6y8ECbYbwxK0PWlp0jm8J0LuTAeb7g1gt/f0DEkSCUk8\nZhKPWetE3KS91SAeS6X3pZKSQKY25tNWTbpI8/v9PPjgg3ziE5/A5XKxdOlSli5dSkdHB1lZWQBk\nZWXR0dExyS0dW+Su15C//inaP357wAm0x5q2mM6Zphg17Qmq2+PUtCdojuoUxpoprdhIWd40VmVZ\nRWGzr1OZf6ohpeTsiTgViwaeK3DaDDvr7/TT3JDi7Ik4505ZTzsFReMn1ipPJ0ilJPOXjFxceX2d\nQm1bGMOAeYuGJ5S7SCZNOlqtAp0FxY5xLzrak2jE5PDeCAK4457AmAScCyGYv8SDy6Wx87Uw6zZ1\nW1CSyWS/AqznPrfbnc5M67I8FRYW4vP5+giwofze4xmTZuiShvoUl2uStDTp5OY7mL84h9x8+4TV\nJrsRKCp10t6ic3hPlLV3+ECSfjBYstIzoTW1RovQBGVzXcwsdnDqSIw3Xgymg+z7nQovYrJvR5hp\nM+wsWuEZEzfuzCInGVk2DuyM0tKss2yNt8+k58Ph4rkEhi4pXzC4V0AIkbYKD4bDKZi30M3pYzHW\nbRqe0cM0reK+ly50F5bt0xZNpC1n1z2XIRHa2M9cM+kiraGhgd/85jd885vfxOv18vWvf50dO3b0\nOe5GEQxDQUqJfO3XoKeQ+3cgbr9zXK93qT3Bs6db2Xc5xIIZHkqz3KwvyeD9y1zk7/w1tsuH0T74\nD0MO0J9qNF3VSaUkBcWDW6um5zmYlmunuaHTsnYyzrzFbmYWjW3Ji+ZGnYvnE9xxT2DUN0yPV2ND\np1AzDcn8pdcvQ5FKSTradDpaDdpbDdrbDBJxk8xsG26PxvlTcRYt91BQMvSYl65aS11B4tfGLXXt\n74pf6jqmqSHJlVqdwhI/s+YGSOkmyaQHh2Nk8TbQHQwfDAaJJIOYjjZ+8fM2nJ4okUgQXdf7ZJcV\nFxf3qv803GDlwRjrqBFpSpobrSmcGup0MnNsFJU6WXGbb1QD5c3OwuUedm8Lc/JwjI52AwFsunfs\n4pUmGpdbY8VtPpobdY4fjHLpQpLFKz34emRBt7daMa+zK1zMnjeyh7iB8PltbLzbz8nDMXa8HGLV\nei/+IcwKcC3hoMH509YsAeMRB1g6x8mF8wmaGlLMyBvavTwSsmLHHE7Bpnv7lnEZLuP1wDTpIq2q\nqoqKigr8fksBr127lrNnz5KVlUV7e3t6nZmZ2e/nT548ycmTJ9OvH3nkkSGp78lEP3uCaCqJ5xN/\nQ+yH38J/94NjXgNMSsnR+hA/O3qV881R3rU4l09tmk2Gu/tPrl88R+S15wl8+dtomVljev2JQkrJ\nrtONLFuVTUbm0GMSMjJgVrnkyuU4xw4FqTqTYvkaN/mFo7+JxKIGR/Y2sH7LdHLzxsZFGQjAPe/w\n88Zvmzh30mDVbVkIIdBTJm0tKVqak7Q2JWltThKJGGTnOMiZ7qRktpcV050EMu3p79XSlGDPm600\nXpGs2ZCNx2vddA3DSMeEtLa20tLSkl4nk0k8Hk+vwPCeWYA9X9tsNgwd2loMhNCYNsNBW7CeHTui\n6fIEUsp0SQKfz9erREHX4na7CYVCtLe391oikQh+v5+srCyysrLIL8giLz+f6nOCux4spnzetAl5\nqAt2pKiriXH5UpzmxnY8HhsZmXYCWQ4yMu1kZFprr39oVmlrFoAU1VURaqqieH02ysp9rFnvTf+N\nFIOz+R4fr7/QyKy5fuYvGf1D0kTQ5TIfiEAASsqyOHsixM5XQ8xb5Gfh0gyu1MXZtyPI2g05FM8a\nv5ITG+7M4OL5CHu2t6OnzGF/XgjB2o3Z5M/0jUPrLFautXPyaJBZc7Kv+/8mpeTC+QhH9kVYvCKD\neQunxvRSzzzzTHp70aJFLFq0CJgCiQPV1dV84xvf4Ctf+QoOh4NvfvObzJkzh+bmZvx+Pw8//PBN\nlzhg/tvXoHQO4p6HMP/xz9De+T7EsrVjcm7DlOypDfHs6VYiSZN3Lcxhy6wMnLbeIlAmEpj/+Dji\nwUfR1m0ek2tPBg31KU4fjbH5/sDIa7JJSeMVnQtnU4RDKUrnuCie5RzS07eUkvb29nTskiY0juxL\nkDPdyfwlvrR4GStSSZO9b0YQmhWDEgmbZGTayMy2kZVjIyvHjj9DG3Bg6or1aG1t49zpJq5eacXp\niRBPdBAOh/H7/WRnZ6cFUNd2fxW1+21fynI919Ukmb/ETcns/t2FqVQqHVw/0JJIJPD5fGRmZpKR\nkUFGRgaZmZkDFs9sa7EsCguWeiieNfauLWlKWlsMGupTXK1LoackeQUO8gsdlM3Oork5aE3AnZ73\n0ZqMO5mU+P0avgxbet5Hf8B67XAIIiGDukuWO1NKKCp1UFjiHJHFQnFjMhx3eTRicvJwjPY2HWnC\nmo1jG9x/oyKlNcXUrHmuAbN8kwmTYwdihEMGK2/rv7DsZDClszsBnn/+ebZt24amaZSVlfGxj32M\neDzOE088QXNzMzNmzODxxx9P174ZjKks0mSwDfMLn0D78r8hfH7kgbcwX30e7a//eVRqPqGbvFrV\nwfNnWsly23nXwhzWFvkHnGjc/PG3IRJB+8ifj/iak016DrcFrjEp/BoIBLh8qZ2aqgT1tSmmzbBT\nWu5kRp69198mEolQW1vLpUuXqK2tRQiBy+XCMAziMR3d0NG07tpR/ZUk6CpjcG1sVM9lIJecnrKK\nlvozNDIybX3M7MlkkmAwSEdHB8FgsM+20+lMCzC3K4OWBg8ZgSxW356HPzAyt6+UkvraFKeOxJiR\n72DBUveExr11EQoa7N0exu3VCARs+DK6RZHXP7B4HQg9JWlqsERZ4xUdt0dYwqzAQWaPeS6vN8jq\nKUk4ZKRFWzjYLeC6ilsWljgoLB3e3JmKm4eRxDS2NOl4fdoN68odD1oadQ7vi3LnA4E+dRSbGzoL\nyxY7WbDUPaXqLE55kTbWTGWRZv7mGWhpRPvAnwDW/JfmFz6B9sFPIeYtGvb5OuI6L5xr47fn2pk/\nw8O7FuSwIPf6Zm95/ADmj76F9vdPIbyTm106Gq7WpTh7PMam+0ZuRetJzxulnpLUXUpSXZkkmUjh\nz24hoV+hrr6WcDhMYWEhJSUllJSUkJmZiRCCxqspju7rPU1Nd40pvd/6UPF4vN8A93A4nHaB9Jx+\npWvb5XKl47KuFWO6rveyPF277XT2FrSmYWVeXjyfYMFSq6bccH7PcNDg+KEYibjJ0lVecsZ47sPh\nkkpaMXk9rVnhoEk8ZuLxaWlrlq9z8mx/QMPp6i5MGouaNNSnaKhP0dqkkzXNTn6hg7wCx4BJDyMZ\nZKWU6fT+G8Elpxg/JrsY8s3Evh1hpuXamVNhhZqYhuRMp3V/2ZqpOTfylC7BcSshDQO5/UW0P/l8\nep/QbIj73o354i+wDUOkXQkl+dXpVnbUBFlfEuDL95ZQlDF41owMdWD+57+ifeTPb2iBJqWVlTNv\n8djP5SilpLWtiaa2S7RELnH16lW8LdNwiJkUFt7OoruKmJ7XO/A9FjU5sjfKytu8vQJQuyqRD3du\nOykl0Wi0j3C7cuUKoVCIRCKRzkrMzMxk9uzZ6W2PZ3jTcWk2wbxFbvILHRzZF6W+NsXS1d5BszB1\nXVJ5Ok51ZZK5C13Mmjs1ioM6nILpeQ6m5/XebxiSaNi0rFpBk9ZmndqLScIhEyT4AhpWZRyT3Jl2\nisucrLzNh8M5Pt9JCCtrTKFQjB0LlnnY9bpVuiQRlxzuLCy76d6RzRE62SiRNpEc3Qs50xEls3vt\nFrdvRf76J8jLFxFFs657isvBBD8+2syxhij3lWfxrw/OJtsztD+jlBLzv/4VcdsWRMWSEX+NqUBD\nvY6UkF/oIBKJ0NjYSENDA42NjUSj0V4uxZ7rwfZVVVVRW1uLx+OhpKSE5cuXU1RUhNPpJJk0uVyd\n4sThOBC3YtfKHNjsgkO7I5TNdTF9iJlFgyGEwOfz4fP5yM/PH5NzDkZGlpXJVXUmwY5XQtctyNhQ\nn+LEoRhZObYxrfY9nthsgkCmrU8NIymtacS6xFr2dNuUEJsKhWL4BDJs5Bc6OLAzSqjDGHZh2amG\ncndOIMbXPo/YeE+/gfrmS7+ESxcHjBFri+n89Hgzuy6FeHhBDg/My8LrGJ51xtzxMvKN36B99qsI\nx9Qz+Q6FaDRKQ0MD+3bVYnO20xFswjAMcnNz00sgEEi7E7vcjD3X/e0zDAOHw0Fubi7FxcXXzbSS\nUtLaZFBdlaDxSgp/wIbDKVi3yXfD3giuJdRhcGRfFJtdsGxNd8p/V9ByqMNgySoPM/JvzH401ih3\nlWI0qP4ztsRjJicOxahY4iZwAyTgKHfnFEBeqYX6S4hV6/t9X2y6H/NvP4JsuoqY0W05iaVMfnW6\nlf8928rW2Zn8v3fMJsM1/E4nW5qQv/wvtL/4pxtGoEWjURobG3stqVSKzMzpIHNYtnwBeXlbCATG\nPibtegghmJZrZ1qunUTcpL42NayaYzcCgUwbG+/yc+Fcgh2vWHOIdsWuzZ7nYuXtU2uCc4VCoejC\n7dFYvWH8yn1MJMqSNkGYP/4OeH1oD//ewMc8+0OIhtEe+zi6KXmlsp2fnWhhaZ6Xx5ZNJ88/sgxG\nKSXm019CzFmA9uCjI/0KE0JbWxtnz57l3LlzRKNRcnNzycvLY8aMGeTl5REIBHjz5TDzl3hGNNXS\n9VBPs/0TDhoc75xLcNGK3oU0FRaq7yhGg+o/tzbKkjbJyHgUuXc72t8/fd3jxF0PYnzhk+xd/TA/\nPBdjmsfO5zcXUT5tdAVR5e7XoaMNcf97RnWe8SIcDnP+/HnOnj1LOBxm3rx53HvvveTl5fWxTtXX\nJtE0QV6B6roThT/Dxu133rhJJgqFQnGjoka6CUDu3gbzlyBypl/3uDMJFz9Y+2liB6/wR1vmsWLm\n6GOcZHsr8uc/QPvMFydsjtChkEgkqKqq4uzZszQ2NjJ79mzWr19PUVHRgMVfpZScOxFnwbLhZS8q\nFAqFQnEjMnVG7ZsUKaUVrP/+jw54TF0wyQ+PNHK+Jc77F83kjv/8WxwPf2f0Ak1KzP/+FmLTfYiS\nOaM611ig6zo1NTWcOXOG2tpaioqKWLx4MbNmzRrSXIr1tSlsdkHuTNVtFQqFQnHzo0a78ebcCWvd\nT8mL9s6MzZ2dGZuPry/AZdcw969AvvniqN2T8sBb0FCP+OO/GtHnu0RVZWUlLS0tuFwu3G73gOuu\n7a5FCIGUksuXL3P27FkuXLjAtGnTqKio4K677sLtHrobV5pWXbRFy5UVTaFQKBS3BkqkjTPm679B\n3Pm2PsLiYF2YJ3Zf4c5ZGX0yNsUD78Z88kvIu96BcIwwWSDUgfzpv6F98nPDyuZMpVJUV1dTWVlJ\nTU0Nubm5lJeXs3z5cpLJJIlEgng8nl6HQqFer7vWqVQKp9OqTeP3+5k/fz7r1q27bmmL61Ffm8Lh\nEMzIV11WoVAoFLcGasQbR2RrM5w5hvjQp3rtP9sc46ndV/jCliIqpnv6fE4UzYKS2cjdryM23T+y\na//ku4jb7kTMrhj02GQySXV1NefPn6e2tpb8/HzKy8vZvHkzXu/1p5gaCNM0SSQSGIaB3z+6oHNp\nSs6ejLNkpbKiKRQKheLWQYm0cUTueAmxbhPC0y10Lnck+PL2y3z69pn9CrQutPvfg/mDp5Ab70Fo\nw5xS6PAeZE0V2jXisCeJRIKLFy9SWVlJbW0tBQUFzJ07l61bt+LxDNyu62EYkkjI7J5MOmiSSEBR\naZKCEseIq7jXXUrhdAmm56nuqlAoFIpbBzXqjRNSTyF3vIz2Z/8nva8lmuJLb9TywRW5rCocxLo0\ndyFkZCEP7kas2Wid05RcOJeg8kyCeQvdlM3tO9WFjIQxf/xttI/8JcLZey7PeDzOhQsXqKyspK6u\njqKiIsrLy7n77ruHHB8mpSQek4RDBpFgtyALh0wSMROvT8OXYU1gPS3Xjs0uqKlKcvp4jNnzXJTO\ndmF3DF2smZ2xaEtXKyuaQqFQKG4tlEgbJ+TBXZBfhCgoASCcMPjS65d5YG42W2dnDvp5IQTaA7+D\n+fyPkas3EA6ZHN0XRbMJVt3u5cThGB3t1tQ8PSu/y2e+j1hxO6JzsvZIJJIWZlevXqW4uJiKigru\nu+8+XK7BJ2QHaGnSqalMEAqaRMIGNpvA3ynE/AGNGfkOfAENr0/r11pWWOKkvVWn6kyC86eClM52\nMmueq9dE5ANRV5PC5bEq/CsUCoVCcSuhRr5xQm57Ae3uhwBI6Cb/tP0yS2d6edfCnKGfZMlqzF/+\niAvbL3KhfVqviWI33mXn8N4ou98Is3qDD7dHQ544iDx7nPCff5kLhw9TVVVFc3MzZWVlLF68mAcf\nfBDHMJIITFNy9kSc2otJ5i1yM2ueJcoczuFPpp2VY2fVejuRsMGFswm2/TZEfpGDORWuPhNe97z+\nuVNxlq1RVjSFQqFQ3HookTYOyNqL0NIEy9dhmJKv7axnutfBH6zMHZbYCAUlR5b/DY6aRu74nTK8\nvm5xZHcIVm/wcv5Ugh2vhJhbEabh2Z9zsWI9wWefY/bs2axatYri4uIh1SC7lnDI4NDuKC63YPN9\nAVzu4Quz/vD5bSxZ5aVisUl1ZZJdb4TJyrExZ76baTNsvX6fy9VJPF6N6bk3xlyjCoVCoVCMJUqk\njQPyjd8gNt0HmsZ39jWQ0E3+cmMh2hAFmmlKKk8nuHg+wfzF2RR+7wvYGv4KemRqSilpbm6mNVRF\nXct5zr8Spzi3gg1330VhYeGAVfsHbbuU1F5McvpYnHmL3JSV9417GwucLo15i9zMqXBRW53k2P4o\nDqdgznwXMwsdSOD8qQTL144su1ShUCgUihsdJdLGGBkJIw/uRPs/3+Qnx5upbI3zj3cX47ANTeh0\ntOkc2RfD7RFsujeAx6th3vsuzBd/AR/9a+rr66murqaqqgqA8vJy7l1QhueF33JozV8RbHFRWDgy\nUZVMmBw9ECMaMlh/p39AN+RYYrMLyspdlM52crU+RdWZBKePxsmebsPr11QsmkKhUChuWdQIOMbI\nXa8hFq/ixavwZnWQ/3tvKV7H4GLHNKz4q5qqJAuXeSgqcyCEIBQKUZ1TSHXHQeq++12yc3IoLS3l\n7W9/O9OnT4dkAvNLn0J79CPcMT+Dg7uj7NsRYeXtXpzDiB1rakhxZG+UgmInK2/z9kpGmAiEJphZ\n5GRmkZPWJp2LlQkWLB3dxPIKhUKhUNzIKJE2hkjTRG57gT0PfYZnTrTwlXtKyHIP/hO3t+oc2RfF\n69PYeLeX1rar7NxZQ01NDZFIhNLSUsrLStka68D36KO9Pms+9yPEnPmIZWtwAus2+Th9NM5br4RZ\ns9E3qDXMMCRnj8epu5Rk+VovM/InP/4rZ4adnBmqayoUCoXi1kaNhGPJqSOcyJzNdy7Z+eLWIvID\n15/SyTCsGmBV59rInNFIY8dlDv3oMtnZ2ZSWlrJ161by8vLQNA0ZCWH+7UeRrU2InBkAyMrTyP1v\nofY1w/UAACAASURBVH3x6fQ5NU2waIWHjCwbu94Is2yNl/zC/oVXKGglB3h8gk33BXC5xiY5QKFQ\nKBQKxehRIm0Mqdyxi6eK38Fn1sxkht1BW4uOrksMHfSURNdleh0KdXC+8hSReB1SxrD7SygvL2fr\n1q39TsUkfAHEhruQrzyPePQPkckE5n8+jfa+P0b4M/ocXzzLiT9D48DOCMF2F3MXutIJAFJKaqqS\nnD0RZ/4SNyWzxyc5QKFQKBQKxcgRUko52Y0Ya+rr6yfsWudOxak+nyCVNDEMEDZwOTXsDoHdLjrX\npLdNM0715cNcaaikfPZCFi8tT1vLBkO2NlvxZ//0beRLz0LTVbSP/fV1PxOPmex/K4LHq7F8rRfD\nkBzdHyUek6y8zYs/Y/yTA24UAoEAoVBospuhuAFRfUcxGlT/ubUpKCgY8D1lSRsFsajJhbMJlm7w\n8E+vnuJ+Ucc73vv2fo9NJpMcPnyYo0ePUlFRwb33//6wJy8XOdMRK27D/OE34fzJXm7OgXB7NNZv\n9XP8QIy3Xg2RTEqKZzlZvd6NNsHJAQqFQqFQKIaOEmmj4NzJOAVlDr5+sI7b6g/w4Afe1ucYwzA4\nefIk+/bto7i4mEcffZTMzMGnhRoIcd+7kX//ScQf/hkiI3tIn7HZBMvWeqi9mMTrtzFdlbX4/9m7\n7/Aoq7SP49+Z9EBISKMFZCEmmFANJYgUqUoNkLCCIrooFmyIiqxLV1RWOq64KCqwSpASFRsSmgKu\ngIiUlyAgJYTQ0iF95v0DmWVIAhNIyGT4fa7rvZw5z5kz9zM5q/d7znPOERERsXv6r/V1Op9VyMnE\nfHZ6Z1IvL4XBLokYAv83ZGk2mzl48CBbtmzB29ubvn37EhgYeMPfa6gVhHHSPKgZVLrPGQzUa2Db\nWZ0iIiJS8ZSkXacDe3LIrFZARkEhL+z+BKc+91uuHT9+nC1btmAymbjnnnuoV69emX63oVbdMm1P\nRERE7I+StOuQmV7IiRP5rDWk8WZ4Ac7rM6DJnZw5c4YtW7aQlpZG27Ztuf3227VqUkRERK6LkrTr\nsHPneX4tzGJ0cxeqfTKLzA738d/v13L8+HFatWpF48aNcXLSqkkRERG5fkrSSunk6TxOnSqgjW8y\nDd+dxa67erL9+FmaNq3NPffcg6vr1TewFREREbGFkrRSKDSZid+cDoUn6bzuPQ5Gj2DXgUMMHjwY\nLy+vig5PREREHIjOASqF2E2JuOeYGZj6NcmPjmHTvgT69u2rBE1ERETKnJI0G23+YSc5SUbCqxwh\nZ8jjfLN+A927d8fPz6+iQxMREREHpCTtGsyFhfyx/DNWHnGjtrsb9bo05ssvvyQyMpLbbrutosMT\nERERB2VTkvb111+TkZFR3rHYHXNaCukzp/DGhfp09q7JHc2r8M03X9OwYUMaN25c0eGJiIiIA7Np\n4cCePXv49NNPCQ8Pp0OHDrRq1QoXF5fyjq1Cmff/Rv77M5nZ8kna17oNzwtO7P99Ex4eHtx1110V\nHZ6IiIg4OIPZbDbbUjEzM5PNmzfzww8/kJSURJs2bejQoQNhYWHlHWOpJSUlXfdnzSYT5m+WY17/\nFYt7vMghvOlq8iHfuIdzKccZOHAgzs5aFOuIvLy8yMzMrOgwpBJS35Ebof5za6tdu3aJ12xO0i53\n9OhR5s2bx7Fjx/D396dLly707NkTd3f3Gwq0rFxvkmbOysD0wUzIucCW3s+y6PccXmlSh92/7Cft\n/K8MGjQIT0/PMo5W7IX+RSnXS31HboT6z63taklaqYaEdu/ezQ8//MC2bdto0KABI0eOJDAwkK++\n+oqpU6cyefLkGw62opgPJ2B6bxqGlndzpNMg/r0xiYn3BLFvwzFOpWwjOnqAEjQRERG5aWxK0hYt\nWsSWLVvw9PSkQ4cOTJ8+HV9fX8v14OBgHnnkkXIL8mYwLXgbw8BhZDVrx5vfHuGxljXITcrkePIG\neve5V1ttiIiIyE1lU5KWn5/Piy++SHBwcPGNODvz5ptvlmlgN5PZbIa0c5iaRfLPH0/Qtq4XEf7O\nLP76K1pGtKVevXoVHaKIiIjcYmxK0vr371/kTMqsrCzy8vIsI2p16tQp++hulgtZ4OrG4r1pGIEh\njauzLHYlfr5/oXVkk4qOTkRERG5BNu2T9s9//pOUlBSrspSUFN5+++1yCeqmS09lU1Abfjqeyeh2\ntYlfu5aCXA+6dm1X0ZGJiIjILcqmJC0pKanIlF+9evU4ceJEuQR1s5nTUvmoRkdeursOe3du4+zZ\nDO4I6UB1f8feC05ERETsl01JWrVq1UhOTrYqS05OdpiDxc+mZGAyGslNPkRCQgL+VTsR1tQx7k1E\nREQqJ5ueSbvnnnuYPn06999/PzVq1CA5OZnY2Fg6d+5c3vHdFIfS8ggtOMmWLcdp2bwP5oKqVPNx\nquiwRERE5BZmU5IWFRWFs7Mzixcv5ty5c/j5+dG5c2d69+5d3vHdFIcyC/HOOky3Xr1J2OVBuy72\nsSmviIiI3LpsStKMRiN9+/alb9++5RLEhQsXmD9/PsePH8dgMPDkk09Sq1YtZs2axZkzZwgMDGTU\nqFHltpls4vkL1PTwJCcrgBq1zVT10iiaiIiIVCybTxwoKCggKSmJjIwMq/LGjRvfcBAffvghLVq0\n4IUXXqCwsJDc3FxWrlxJkyZN6NevH3FxcaxatYoHHnjghr+rOObcFMKCb+PIwTw6dNezaCIiIlLx\nbErS9u/fz4wZM8jPzyc7OxsPDw9ycnLw8/Nj3rx5NxTAhQsX2L9/PyNHjgTAyckJT09Ptm/fzsSJ\nEwHo1KkTEydOLJck7fejJ3Ay5+Phfgd1/F3wrGLTWgoRERGRcmVTkvbxxx/Tt29fevfuzSOPPMKH\nH37I8uXLi2xwez1Onz6Nl5cX//rXvzh69CgNGjTg4YcfJj09HR8fHwB8fHxIT0+/4e8qzvadu8Dg\nw4lzHnRqrWfRRERExD7YlKQlJSXRs2dPq7KoqChGjhx5w8+pmUwm/vjjD4YPH07Dhg356KOPiIuL\nK1LPYDAU+/m9e/eyd+9ey/tBgwbZvDXI+fPnOZt0jPrubbijSTUCAr2v7ybEIbi6ujrMtjJyc6nv\nyI1Q/5Fly5ZZXoeHhxMeHg7YmKR5enqSnZ1NlSpV8PHxITExkapVq5KTk3PDgfn6+uLn50fDhg0B\niIyMJC4uDh8fH9LS0iz/9PYuPoG6/GYuyczMtOm7t2/fjtmzJp5OfgQ1MNr8OXFMXl5e6gNyXdR3\n5Eao/9zavLy8GDRoULHXbHoAq02bNuzcuRO4uGfapEmTeOWVV4iMjLzh4Hx8fPDz8yMpKQmA3bt3\nExQUREREBBs2bABgw4YNtGzZ8oa/63Imk4ndu3fj7XwHt6Wsx8mp+JE6ERERkYpg00jaww8/bHnd\nt29fQkJCyM7OplmzZmUSxCOPPMLcuXMpKCigRo0aPPXUU5hMJmbOnMn69esJCAhg1KhRZfJdl/zx\nxx8YjR7kOfsQajpcpm2LiIiI3CiD2Ww2X62CyWTiueeeY8aMGbi4VI6zLC+Nyl3NihWryM++jd/d\n3Xk1/TuMw8s2CZTKR1MOcr3Ud+RGqP/c2mrXrl3itWtOdxqNRoxGI/n5+WUaVEVKSUnhzOmzuAXU\npRap4O1T0SGJiIiIWLFpurNnz57MnDmT/v374+vra7XSskaNGuUWXHnZvm0XXh63c9g9jxYpZ8Gv\nekWHJCIiImLFpiRt4cKFAPz2229FrsXGxpZtROUsNzeX3w8mcE/7GOYcSWPg+ZPQ4I6KDktERETE\nik1JWmVLxK7m55/2UtWjFnVDvEn9v7PUTkvEUK1tRYclIiIiYuWWOgOpoMDEvn27iWjZjCPpudzm\n445TRipU03SniIiI2BebRtLGjx9f4o7/kyZNKtOAytP2//6B0clAeJPb+GJ/Kg193SBdCwdERETE\n/tiUpHXu3NnqfVpaGuvXr6d9+/blElR5yMm+OIrW4s5mGAwGDqXk0MTfDXJzwLNqRYcnIiIiYsWm\nJK1Tp05FyiIjI/nXv/5FdHR0WcdULnZuO0NO/imaNr14BumhlBz61TFANR8Mxltq1ldEREQqAZuS\ntOL4+vpy9OjRsoyl3KSeK+DwH/u4445GuLq6klNg4vT5fOoVFkA1TXWKiIiI/bEpSVu3bp3V+7y8\nPP773/8SEhJSLkGVJbPZzG/bM8nKPkjz5hdH/f5IyaGetxvOWacxKUkTERERO2RTkvbDDz9YvXdz\ncyM0NJRevXqVS1BlKfFIPmmZR6hRM4Dq1S+u4jyUmkNDX3fM6akYvLWyU0REROyPTUnahAkTyjuO\nclGQb2b/7myychNoe2drS/mhlFxC/d0hIU3bb4iIiIhdsumJ+Y0bNxZ5/uzIkSNs2rSpXIIqK7//\nXw5unqnk5WVTv359S/nhlIsjaWSk6pk0ERERsUs2JWmxsbH4+flZlfn7+7N06dJyCaosnM8q5Oih\nPC7kH6Bp06YY/1zBmVdoIikzj9t83DCnp2HQHmkiIiJih2xK0rKzs/H09LQq8/T05Pz58+USVFnY\n92sOQX8xcfTYH4SFhVnKj6TmUqeaK65Oxj9H0jTdKSIiIvbHpiQtKCiIn376yars559/JigoqFyC\nulFnkvPJSCvkQt5BGjRogIeHh+XaoZQcGlR3v/gmPRW0cEBERETskE0LBx544AHeeOMNtmzZQs2a\nNUlOTmb37t2MHTu2vOO7Lnt3ZnNHM1e+W7unyArUw3+u7AQgI11HQomIiIhdsmkkrVGjRkyfPp3g\n4GBycnIIDg5m+vTpNGrUqLzjuy5u7kay805QpUoVAgMDra4dSsm9uP1GTjaYC8HNo4RWRERERCqO\nTSNp+fn5+Pj4EBUVZSkrKCggPz8fFxeXcgvueoW38CB+/W80bdrUqjy/0Mzx9FzqV3eDlFNQrXqJ\nB8eLiIiIVCSbRtJee+01Dh8+bFV2+PBhXn/99XIJ6kYVmNI5e/YswcHBVuXH03OpUdUFd+c/Fw3o\neTQRERGxUzYlaceOHeP222+3KgsODrbbszt3795N48aNcXa2Hig8lJJDw0uLBjLStEeaiIiI2C2b\nkjRPT0/S09OtytLT03FzcyuXoG7U/v37ady4cZHyQyk5NPhz0YA5PQ2DkjQRERGxUzYlaW3atGH2\n7NkcO3aM3Nxcjh07xrx582jbtm15x3ddgoKC8PLyKlJ+ODWHYMvKTu2RJiIiIvbLpoUD999/P4sW\nLeLvf/87+fn5uLq60qlTJwYPHlze8V2XKxcMABSazBxJzeUvvn+O/qWnwm3BReqJiIiI2AObkjRX\nV1ceffRRhg8fTmZmJl5eXna9KrK4TXYTM/Lw83TG08UJAHNGGkbtkSYiIiJ2yqYk7ZKcnBxycnLI\nzs62lNWoUaPMg7pRxSWQlz+PBvy5cEDTnSIiImKfbErSEhMTmTNnTrGrOWNjY8s8qPJwOOWykwbg\n4nSnFg6IiIiInbJp4cD7779PeHg4CxcuxNPTkw8//JBu3boxcuTI8o6vzBy6LEkzm81/LhxQkiYi\nIiL2yaYk7ejRozzwwANUqVIFs9mMp6cnDz74YKUZRTOZzfyRmvu/g9UvnAcXNwyu9rmFiIiIiIhN\nSZqLiwuFhYUAeHl5cfbsWcxmM1lZWeUaXFlJyszDy80JL7eLiwYunjagUTQRERGxXzY9k9aoUSO2\nbt1Kp06diIyMZOrUqbi4uBAeHl7e8ZWJwym5NPS9bNRMpw2IiIiInbMpSXvhhRcsrwcPHkzdunXJ\nycmhQ4cO5RZYWTp0xaIBc3oqBq3sFBERETtWqi04AIxGY6VJzi45nJJD/zDf/xXocHURERGxczY9\nk1aZmc1mDqXm/G/RAEC6pjtFRETEvjl8knYqKx93JyM+HpcNGmakaSRNRERE7JrDJ2mHUq84aQAw\nZ6Ri0EiaiIiI2DGHT9IOp+QSfEWSdvG0AY2kiYiIiP0qceHA+PHjbTpEfdKkSWUaUFk7lJJDz5Ar\nRs0y0rRPmoiIiNi1EpO0zp0738w4yoXZbC5yZqfZVAhZGVDVuwIjExEREbm6EpO0Tp063cQwysfZ\nCwVgAN/LFw1kZYJHFQzOpd59REREROSmsSlTMZvNxMfHs3nzZjIzM3n77bfZt28faWlp3HXXXeUd\n43U7nJJDsK+79bSt9kgTERGRSsCmhQOxsbGsX7+erl27cvbsWQD8/Pz4/PPPyzW4G1VkfzTQHmki\nIiJSKdiUpG3cuJExY8bQrl07y6hUYGAgp0+fLtfgbtSVz6PBn0dCaSRNRERE7JxNSZrJZMLd3TrZ\nycnJKVJmbw6l5NLg8oPVATLTtP2GiIiI2D2bkrQWLVqwaNEi8vPzgYvPqMXGxhIREVGuwd2I1OwC\n8gtNBFZxsb6QnqrpThEREbF7NiVpDz30EKmpqTz88MNcuHCBhx56iDNnzvDAAw+Ud3zX7VDKxZMG\niuz1lq490kRERMT+2bS609PTk5deeom0tDTOnj2Lv78/Pj72negU9zwaXDwSyqjpThEREbFzJSZp\nJpOpSFm1atWoVq2a1XWjsWxOljKZTIwdOxZfX1/GjBlDVlYWs2bN4syZMwQGBjJq1Cg8PT1tbu9Q\nag5316tW9EK6tuAQERER+1dikjZ48GCbGoiNjS2TQL7++mvq1KlDdnY2AHFxcTRp0oR+/foRFxfH\nqlWrSjW9ejglh2HNA4teyNQWHCIiImL/SkzS5s2bZ3n9yy+/8NNPP9G/f3/8/f05e/Ysn3/+OW3a\ntCmTIM6dO8fOnTsZMGAAq1evBmD79u1MnDgRuHj6wcSJE21O0jJyC8nKM1HTy3rRgLkgH7KzoYpX\nmcQtIiIiUl5KTNICAgIsr1evXs2bb75JlSpVAKhduzYNGjRg7NixdO/e/YaD+Pjjjxk6dCgXLlyw\nlKWnp1uee/Px8SE9Pd3m9g6n5NCguhvGKxcNZKSDVzUMZTRFKyIiIlJebFo4cOHCBXJzcy1JGkBe\nXp5VUnW9fvnlF7y9valfvz579+4tsV6RVZp/2rt3r9XnBg0aROJ5M41qVsPLy3rErOB0EtnV/YqU\niwC4urqqb8h1Ud+RG6H+I8uWLbO8Dg8PJzw8HLAxSevYsSNTpkyhV69e+Pn5ce7cOb755hs6dux4\nw4Ht37+f7du3s3PnTvLy8sjOzmbu3Ln4+PiQlpZm+ae3t3exn7/8Zi7ZdzKd1kFVyczMtCo3nzyB\nqUq1IuUiAF5eXuobcl3Ud+RGqP/c2ry8vBg0aFCx12xK0h588EFq1qzJli1bSE1NxcfHhx49etC1\na9cbDm7IkCEMGTIEgH379vHll1/yzDPPsGTJEjZs2EBUVBQbNmygZcuWNrd5ODWH+5v6Fyk3Z6Ri\n0B5pIiIiUgnYlKQZjUa6d+9eJs+f2SoqKoqZM2eyfv16AgICGDVqlM2fTc0uoI6Xa9ELGToSSkRE\nRCoHm5I0gPXr17Np0yZSUlLw9fWlQ4cO3HPPPWUaTFhYGGFhYQBUrVqVcePGXVc79X3ccTIW8wxb\neirUqH0jIYqIiIjcFDYlaStXrmTjxo306dPHsgXHF198QWpqKgMGDCjvGEut4ZWHqv/JnJGK4fbw\nYq+JiIiI2BObkrT4+HgmTpxotS1Hs2bNmDBhgl0maQ2KOQ4KgIw0DNrIVkRERCoBmzYMy83NtRwH\ndYmXlxd5eXnlEtSNKu7MTkCHq4uIiEilYVOS1rx5c+bMmUNSUhJ5eXmcOHGCefPm0axZs/KO77rU\n9S5+upOMVC0cEBERkUrBpunOv/3tbyxcuJAXX3yRwsJCnJ2dadu2LY888kh5x3ddnItZNGDOzYHC\nQvCw/ZB2ERERkYpiU5Lm6enJ008/zVNPPUVmZiZeXl4YK9vRShkXD1Yv6eQCEREREXty1STt7Nmz\nxZanpKRYXvv7F9001i79maSJiIiIVAZXTdJGjhx5zQZiY2PLLJhylZ4K3noeTURERCqHqyZpt912\nG3l5eXTs2JH27dvj6+t7s+Iqc+aMVAxaNCAiIiKVxFWTtGnTpnHs2DE2btzIuHHjCAoKokOHDrRp\n0wZX12KOXbJn2n5DREREKpFrPv1fr149hg4dyjvvvEOvXr3YsWMHI0aM4PDhwzcjvrKjZ9JERESk\nErF5iWZycjL79u3j999/5y9/+QtVq1Ytz7jKnKY7RUREpDK56nRnVlYWP/74Ixs3biQnJ4f27dsz\nadKkyrOi83JaOCAiIiKVyFWTtMcff5zAwEDat29PSEgIcHFELTk52VKncePG5RthWdF0p4iIiFQi\nV03SfHx8yMvLIz4+nvj4+CLXDQYD8+bNK7fgyorZbL44kqbpThEREakkrpqkvfPOOzcrjvKVfQGc\nnTG4lXCmp4iIiIidqWRnO10nHawuIiIilcytkaRpjzQRERGpZG6JJE3bb4iIiEhlc0skaRcXDWgk\nTURERCqPWyNJy0jTHmkiIiJSqVx1dafDyEjFa+AwjF5eFR2J2DEnJye8KlkfMZvNZGVlVXQYIiJS\nDm6JJM2cnoahmjeZmZkVHYpImapsSaWIiNjuFpnuTAXnWyIfFREREQdxiyRpaeDkUtFRiIiIiNjM\n4ZM0s8kEmRng7FTRoYiIiIjYzOGTNM5ngocHBoPj3+olJpOJkJAQkpKSyrTuraJ///589tlnZdKW\nfl8REblejp+5VIKD1UNCQggNDSU0NJS6devSsGFDS1lcXFyp2zMajRw4cIDatWuXad2KtH//fgYP\nHkx4eDjh4eH06tWLTZs23XC706ZN44UXXiiDCC+6MsGrLL+viIjYH8d/mj7D/jeyPXDggOV127Zt\nefvtt2nXrl2J9QsLC3FyurWmb4cNG8aIESNYsmQJJpOJX3/9FYPBUNFhiYiIlBuHH0kzZ6RVqiOh\nzGYzZrPZqmzatGk8+eSTjBw5kkaNGrFy5Up27NhBnz59CAsLIyIigvHjx1NYWAhcTOKCgoI4ceIE\nAM888wzjx49n6NChhIaG0q9fPxITE0tdF2DdunW0b9+esLAwxo0bR1RUVIlTg7m5uYwbN44777yT\nli1bMmnSJAoKCgD44YcfiIyM5N1336Vp06ZERESwfPnyYts5c+YMSUlJDBkyBCcnJ1xcXGjVqhUt\nW7YEoGPHjmzYsMFSPy8vj/DwcBISEjhy5AhBQUEsX76cli1b0qxZM9555x0A1q5dy7vvvsuqVasI\nCQmhZ8+eljaOHTtGv379CA0NZejQoaSnp1uubdu2zfLb9+jRg//+978ATJ06lR07dvDKK68QGhrK\nxIkTi/y+2dnZTJgwgdatWxMWFkZ0dDT5+fkl9gcREbl1OXyS5iiHq3/33XcMGDCA/fv307dvX1xc\nXJgyZQp79+4lLi6ODRs2sHjxYkv9K0eZ4uLiGDNmDPv27aN27dpMmzat1HXPnj3Lk08+yfjx49m9\nezd169Zl165dJcY8Y8YMdu/eTXx8PGvWrGHbtm3MnTvXcv3kyZPk5uayc+dO3njjDcaOHcv58+eL\ntOPv70+9evUYOXIka9as4dy5c1bXY2JiWLFiheX9999/T926dQkNDbWU7dixg61bt/Kf//yH6dOn\nc+TIEbp27cqTTz5J//79OXDgAF9//bXVbzBnzhx27drF+fPn+fe//w3AiRMneOSRR3jppZfYt28f\nf//733n00UdJS0vj73//OxEREbz11lskJCQwceLEIr/vhAkTSEhI4Ouvv2bv3r2MGTMGo9Hx/2co\nIiKl5/j/dchIdYgjoVq1akWXLl0AcHNzo2nTpjRv3hyDwUDdunUZMmQIP/30k6X+laNxvXr1onHj\nxjg5OdG/f3/27dtX6rrx8fE0btyYbt264eTkxIgRI6heveTfNi4ujtGjR1O9enV8fX0ZNWqUVTLl\n7u7Oc889h5OTE927d8fV1ZXDhw8XacdgMLB8+XJq1arFpEmTuPPOOxk0aBBHjx4FLj4H9v3335Od\nnQ3AypUrGThwoNXnX3zxRVxcXGjcuDEhISFW91+cwYMHc9ttt+Hu7k7v3r3Zu3cvACtWrKBHjx50\n6NABuDiKFxYWZjWSd+Xveem9yWRi+fLlvPbaa/j7+2MwGGjVqtUtN3UtIiK2cfxn0tJToU59m6oW\nPta3TL7SacEXZdLO5a588PzgwYNMnjyZ3bt3k52dTWFhIS1atCjx84GBgZbXHh4exY5YXatucnJy\nkThq1apVYjunTp2iTp06lvdBQUEkJydb3vv6+lqNMl0trlq1avH6668DkJSUxIsvvsgLL7zAihUr\nqFOnDs2bN+ebb76hc+fObNy4kbfeesvq835+fjbfP0BAQECx9RMTE4mLi+Pbb78FLiZgBQUFdO7c\n+artwcVp2/z8fOrVq3fNuiIiIg6fpJkz0jDaOJJWHslVWblySvKVV14hIiKC9957Dw8PD+bPn098\nfHy5xlCjRo0iKyovT7quVLNmTRITE2nQoAFwMcGpWbPmDcdRu3Zthg0bZrUqMzo6mhUrVpCVlUXb\ntm3x9/e3qa3SLj6oXbs2f/3rX5k6dWqp2wsICMDV1ZWjR49y++23l+p7RUTk1nMLTHc6xjNpV8rK\nysLLywsPDw9+//13lixZUu7f2bVrV/bs2cPatWspLCxkwYIFpKSklFi/X79+zJo1i5SUFM6dO8fs\n2bOtpiFtlZKSwsyZMzl27BgA586dIzY2loiICEudXr16sXPnTj7++GOio6OtPn/l9OPlAgICOH78\nuM2xREdH8+2337Jp0yZMJhM5OTls2bKF06dPAxefn7sU55WMRiMxMTFMmDCBM2fOYDKZ2LZtm2XB\nh4iIyOUcP0mrBPukXc7WkZ3x48ezbNkyQkNDGTt2LP369SuxnWu1aWtdf39/3n33XSZOnEiTJk04\nfvw44eHhuLq6Flt/1KhRhIWF0aVLF7p3705ERARPP/20TXFczs3NjSNHjhATE0NISAjdunWjRMHg\nAgAAIABJREFUSpUqTJ8+3VLHw8OD7t27c+LECXr06HHVdi9/37dvX8tq0N69e1/zNwgKCuL9999n\n9uzZNGnShMjISP79739jMpkAeOyxx1i1ahXh4eFMnjy5SHsTJkwgODiYe++9l8aNG/PWW29dNYkU\nEZFbl8HsgP+FuLS7u7mgANPTMRj/tZxq3j5kZmZWcGSOxWQyceedd7JgwQJatWpV0eEwffp0Tpw4\nwYwZMyo6lJvGy8tL/doO6O8gN0L959Z2tc3OHXskLTMdqlbDYNTqubKyYcMGMjIyyM3NZebMmbi4\nuNC8efOKDouUlBSWLl3KAw88UNGhiIiIlAnHTtIy0uz+tIHK5ueff6Zt27Y0b96cTZs2sXDhQlxc\nXCo0pkWLFtGmTRvuu+8+q+fUREREKjPHnu7cvR3TutU4PTdRw8nikNSv7YP+DnIj1H9ubbfsdKc5\nPbVSHQklIiIicolDJ2kXV3ZqulNEREQqH8dO0jLTHXKPNBEREXF8jp2kVbI90kREREQucegkzZyR\nisEBDlcXERGRW49DJ2mkp90SI2mJiYkEBQVZdr0fOnQoy5cvt6luac2dO5eXX375umN1JNHR0Sxd\nurTM2gsJCSnVEVUiIuLYKvyA9XPnzjFv3jzS09MxGAx06dKFnj17kpWVxaxZszhz5gyBgYGMGjUK\nT0/P0jWeUTkWDjz44IO0aNGC0aNHW5V/9913vPLKK+zYsQOj8er59OVHDy1evNjmulezdetWnnnm\nGbZv324pe+aZZ2z6rKOZMWMGR44cYc6cOWXSXnR0NNHR0dx///2WsgMHDpRJ2yIi4hgqfCTNycmJ\nYcOGMWPGDF5//XW+++47Tpw4QVxcHE2aNGH27NmEh4ezatWqUrVrzsuF/DzwrFJOkZedmJgYVq5c\nWaR8xYoVDBw48JoJWnkxm802J3QiIiJStio8SfPx8aF+/foAuLu7U6dOHc6dO8f27dvp2LEjAJ06\ndWLbtm2lazjj4lRnZUgyevToQWpqKj///LOlLD09nfj4eKKjowGIj4+nR48eNGrUiNatW1/1fMrL\np+FMJhOTJ0+mSZMmtGvXjrVr11rVjY2NpVOnToSGhtKuXTuWLFkCQHZ2NkOHDuXUqVOEhIQQGhrK\n6dOnmTFjhtVo2po1a+jcuTPh4eHExMRw8OBBy7XIyEjmz59P165dCQsL46mnniIvL6/YmM1mM7Nm\nzaJNmzY0b96c559/3rK546Up2s8++4zWrVvTtGnTq45oxcfHc8899xAaGkrLli157733AOjSpYvV\n/RcUFNCkSRP27t171e/YsGEDc+fO5csvvyQkJITu3btb2jh+/DhRUVGEhobywAMPkJqaarm2Y8cO\n+vXrR1hYGN27d2fr1q0AvPXWW/z888+8+uqrhIaGMm7cOODi4e1Hjx4FICcnh0mTJtGmTRvCwsIY\nMGAAubm5Jd6ziIg4ngpP0i53+vRpjh49SkhICOnp6fj4XJyq9PHxIT09vXSNpadCJVk04O7uTu/e\nva2eI/viiy8IDg6mUaNGAFSpUoU5c+awf/9+Fi1axOLFi1mzZs01216yZAnr1q3j+++/5+uvv+ar\nr76yuh4QEMDixYtJSEhgxowZTJw4kT179uDh4cGSJUuoUaMGBw4cICEhgcDAQOB/06WHDh1i5MiR\nTJ48md9++43OnTszbNgwCgoKLO2vXr2aTz/9lK1bt7Jv3z6WLVtWbJyxsbEsX76cFStWsHXrVs6f\nP8+rr75qVWfbtm38+OOPLF26lFmzZlklhJd76aWXmDZtGgkJCaxbt4527doBF5PXFStWWOrFx8dT\no0YNwsPDr/odnTp14plnnqFPnz4cOHDA6nePi4tj1qxZ/Pbbb+Tm5jJ//nwATp48ybBhwxg1ahT7\n9u1j3LhxPPbYY6SkpDBmzBhat27N66+/TkJCAlOmTLH6XQEmT57Mnj17+PLLL9m7dy+vvvpqhY2o\niohIxbCbf+vn5OQwY8YMHn74Ydzd3YtcL/WIWCU7tzMmJobVq1dbRppWrFhBTEyM5XpkZCShoaEA\nNGrUiL59+1pGZq5m9erVPProo9SsWRNvb+8iz5R17tyZunXrAtCmTRs6duxoNaJ3NV9++SVdu3bl\n7rvvxsnJiSeeeIKcnByrZ9geffRRAgIC8Pb2plu3buzdu7fYtlatWsWIESMICgrCw8ODV155hS++\n+MKywMFgMDB69GhcXV0JCwsjLCyMffv2FduWi4sLBw4cICsri2rVqtG4cWMABgwYQHx8POfPnwcu\n/saXRipL+x2X/PWvf6V+/fq4ubnRp08fS/1Vq1bRpUsXOnXqBED79u1p1qwZ69atK7GtSye0mc1m\nYmNjmTJlCoGBgRgMBiIiIir8jFQREbm5KnzhAEBhYSHTp0+nQ4cOtGrVCrg4epaWlmb5p7e3d7Gf\n3bt3r9V/+AcNGoSXlxe5udkU+gXg6eUFXHz27Vr6/Wd/GdwNfP5Ao1J/plWrVvj6+vLtt9/SrFkz\ndu3axQcffGC5vnPnTqZOnUpCQgL5+fnk5eXRu3fva7Z76tQpq3PB6tSpY3V93bp1zJw5k8OHD2M2\nm8nJyeGOO+6wKeZTp04RFBRkeW8wGKhduzbJycmWMn9/f8trDw8PTp06ZVNbQUFBFBQUcObMGUtZ\nQECA5bW7uzsXLlwotq0FCxYwa9Yspk6dyh133MHYsWOJiIigRo0atG7dmq+++op7772X9evXW0ax\nSvsdl1waXbx0f5cSwMTERFavXm2ZXjWbzRQUFHD33XdftT2AlJQU8vLyuO22265Z18nJCa8/+7hU\nHFdXV/0d5Lqp/8jls0zh4eGWGR67SNLeffddgoKC6Nmzp6UsIiKCDRs2EBUVxYYNG2jZsmWxn738\nZi7JzMzEdDoZPKtanmuy5X8A15NclaWBAwfy2WefcejQITp27Iifn5/l2tNPP83f/vY3PvnkE1xc\nXJgwYYLV808lCQwMtBw4D3DixAnL67y8PEaMGMHcuXPp0aMHRqOR4cOHW0Z0rjV6WaNGDRISEqzK\nkpKSqFWrlk33e2VbiYmJlveJiYm4uLgQEBBgFb8tmjZtysKFCyksLGThwoU88cQTlmcao6Oj+fTT\nTykoKKBly5bUqFHDpjZLO5Jbu3ZtBg4cyLRp00rdnq+vL25ubhw5cuSaCXNhYaEOZrYDOiBbboT6\nz63Ny8uLQYMGFXutwqc79+/fzw8//MCePXt4+eWXGTNmDL/++itRUVHs3r2b5557jj179hAVFVW6\nhjMq32kDMTEx/Pjjj3zyySdWU50A58+fx9vbGxcXF3bu3ElcXJzV9UuJ1ZX69OnDwoULOXnyJGlp\nabzzzjuWa/n5+eTn5+Pr64vRaGTdunVs3LjRct3f35/U1NQS/+XRp08f4uPj2bx5MwUFBcyfPx93\nd3ciIiJKfe9RUVEsWLCA48ePc/78ed566y369u1reQ6rpPu7Un5+PqtWrSIzMxMnJyeqVq1qNYp6\n7733smfPHhYuXGg11Xmt7/D39ycxMdHmOAYMGMDatWvZuHEjJpOJnJwctm7dahllDAgIsCwSuJLB\nYOCvf/0rkyZN4tSpU5hMJnbs2EF+fr5N3y0iIo6hwkfSGjVqRGxsbLHXLq16ux7m9DSMYZXnmTS4\nOMUXERHB/v37rVYQAkydOpVJkybxj3/8g8jISPr27Wu1mOLykZnLXz/wwAP88ccfdOvWjWrVqvH4\n44+zZcsW4OJihMmTJ/P444+Tn59P165d6dGjh+WzwcHBREVF0bZtW8xmM+vXr7eKqWHDhsydO5d/\n/OMfnDp1irCwMD766COcnZ2LxHEt999/P6dOnWLAgAHk5eXRqVMnq6nIK9u6WtsrVqzgH//4ByaT\niYYNGzJv3jzLNXd3d+677z6++OILq5Hba31H7969WblyJY0bN6ZevXp88803V42hdu3aLFy4kNde\ne42nnnoKZ2dnmjdvzhtvvAHA8OHDef7551m0aBEDBw5k8uTJVu2NGzeON998k549e5KdnU1YWBj/\n+c9/Svw+ERFxPAazrUMDlUhSUhKFb7yEMeYRDMFhgIaT5X9mzZrF4cOHy2xj2oqkfm0f9HeQG6H+\nc2u7/LnxK1X4dGe5ybg1joSS0klNTWXp0qU8+OCDFR2KiIjIVTlkkmY2myvNkVBy83zyySe0bt2a\nzp0707p164oOR0RE5Koq/Jm0cpGTDQYnDO4eFR2J2JEhQ4YwZMiQig5DRETEJg45kkZ6KlQrfl81\nERERkcrAMZO0jMpzJJSIiIhIcRw0SdOiAREREancHDJJM6enYfDWogERERGpvBwySauMpw2IiIiI\nXM4xk7T0W3v7DZPJREhIiE1nXpamroiIiNw8DpmkmTPSMFSihQMhISGEhoYSGhpK3bp1adiwoaXs\nyjM6bWE0Gjlw4MBVdzG+nroVaf/+/QwePJjw8HDCw8Pp1asXmzZt4sSJE9x2221WB8dfMmzYMN54\n4w0KCwsJCgoqUmfatGmMGjUKwFLn8mOxAN544w1efvnl8rsxERGREjhkklbZFg4cOHCAhIQEEhIS\nCAoKYtGiRZay4g6WLywsrIAoK9awYcPo2rUrv/32G7/++isTJ07E09OTOnXq0LZtW1asWGFVPyUl\nhU2bNjFo0CCg5LM+ryxPSkpi9erV5XMTIiIipeCYSVp6KlTShQNms5krj1OdNm0aTz75JCNHjqRR\no0asXLmSHTt20KdPH8LCwoiIiGD8+PGW5O3KkaNnnnmG8ePHM3ToUEJDQ+nXrx+JiYmlrguwbt06\n2rdvT1hYGOPGjSMqKorPPvus2HvJzc1l3Lhx3HnnnbRs2ZJJkyZRUFAAwA8//EBkZCTvvvsuTZs2\nJSIiguXLlxfbzpkzZ0hKSmLIkCE4OTnh4uJCq1ataNmyJQDR0dFFkrRVq1YRHh5Ow4YNLb+rLZ56\n6inefvttm+uLiIiUF8dM0jLTwatyJmkl+e677xgwYAD79++nb9++uLi4MGXKFPbu3UtcXBwbNmxg\n8eLFlvpXjhDFxcUxZswY9u3bR+3atZk2bVqp6549e5Ynn3yS8ePHs3v3burWrcuuXbtKjHnGjBns\n3r2b+Ph41qxZw7Zt25g7d67l+smTJ8nNzWXnzp288cYbjB07lvPnzxdpx9/fn3r16jFy5EjWrFnD\nuXPnrK736tWLU6dOsXPnTkvZypUriY6OvtpPWoTBYKBPnz64ubmVmDCKiIjcLI6ZpLm5Y3Bxqego\nylSrVq3o0qULAG5ubjRt2pTmzZtjMBioW7cuQ4YM4aeffrLUv3IkqFevXjRu3BgnJyf69+/Pvn37\nSl03Pj6exo0b061bN5ycnBgxYgTVq5c8rRwXF8fo0aOpXr06vr6+jBo1ymrEy93dneeeew4nJye6\nd++Oq6srhw8fLtKOwWBg+fLl1KpVi0mTJnHnnXcyaNAgjh49CoCHhwf33XefJbH6/fff2b9/f5Gp\n4q5du1qeaQsLC+O9996zum42mzEYDIwePZqZM2fektPKIiJiPxzz7M7rXDTwZWxamXx9n7+W/Sje\nlQ/2Hzx4kMmTJ7N7926ys7MpLCykRYsWJX4+MDDQ8trDw6PYEatr1U1OTi4SR61atUps59SpU9Sp\nU8fyPigoiOTkZMt7X19fq1G8q8VVq1YtXn/9deDic2MvvvgiL7zwgiXpi4mJ4fHHH2fSpEksX76c\nzp074+Nj/XdYu3atVTzTpk2ziueS7t27M3fuXP7zn/+UeG8iIiLlzTGTtOvcfqM8kquycuWU5Cuv\nvEJERATvvfceHh4ezJ8/n/j4+HKNoUaNGmzatMmqrLgk55KaNWuSmJhIgwYNAEhMTKRmzZo3HEft\n2rUZNmwYL7zwgqXsrrvuomrVqqxZs4a4uDimTJlyQ9/x8ssv8/zzz9O7d+8bDVdEROS6OOR0Z2Xa\nfuN6ZWVl4eXlhYeHB7///jtLliwp9+/s2rUre/bsYe3atRQWFrJgwQJSUlJKrN+vXz9mzZpFSkoK\n586dY/bs2QwcOLDU35uSksLMmTM5duwYAOfOnSM2NpaIiAiregMGDGDy5Mnk5ORYpoavV/v27WnQ\noEGRBQkiIiI3i0MmaZV5I9uStoq40vjx41m2bBmhoaGMHTuWfv36ldjOtdq0ta6/vz/vvvsuEydO\npEmTJhw/fpzw8HBcXV2LrT9q1CjCwsLo0qUL3bt3JyIigqefftqmOC7n5ubGkSNHiImJISQkhG7d\nulGlShWmT59uVS8mJoYTJ07Qr18/nJycbGr7anXGjBlDenr6NT8nIiJSHgxmB9xrIPGDuRjvsx6x\n8fLyIjMzs4Iickwmk4k777yTBQsW0KpVq4oO55akfm0f9HeQG6H+c2u72mbyjjmSVkn3SKsMNmzY\nQEZGBrm5ucycORMXFxeaN29e0WGJiIg4HIdcOGCoRKcNVDY///wzI0eOtJz5uXDhQlwcbLsTERER\ne+CQ050nfvoRQ70GVmUaThZHpH5tH/R3kBuh/nNruwWnOzWSJiIiIpWbYyZpXtUqOgIRERGRG+KQ\nSZrB6HTtSiIiIiJ2zCGTNBEREZHKTkmaiIiIiB1SkuYAEhMTCQoKwmQyATB06FCWL19uU93Smjt3\nLi+//PJ1xyoiIiK2UZJmBx588MEiRxwBfPfdd7Ro0cKmhOryI40WL15MdHS0TXWvZuvWrbRs2dKq\n7JlnnmHatGk2fb6inDx5kscee4wmTZoQFhZG165d+eyzz8jNzSUsLIwtW7YU+cyECRN4/PHHAYiM\njOTHH3+0ur5s2TL69+9ved+mTRuaNWtGdna2pezTTz+96u8uIiJSGkrS7EBMTAwrV64sUr5ixQoG\nDhyI0Vgxfyaz2WxzQmdPnn32WYKCgti2bRt79uxh9uzZBAQE4ObmRp8+fYqMMppMJr744gsGDRp0\n1XavPOPUZDLx/vvvl1hHRETkRihJswM9evQgNTWVn3/+2VKWnp5OfHy8ZWQmPj6eHj160KhRI1q3\nbs2MGTNKbC86OpqlS5cCFxOQyZMn06RJE9q1a8fatWut6sbGxtKpUydCQ0Np164dS5YsASA7O5uh\nQ4dy6tQpQkJCCA0N5fTp08yYMYNnnnnG8vk1a9bQuXNnwsPDiYmJ4eDBg5ZrkZGRzJ8/n65duxIW\nFsZTTz1FXl5esTGbzWZmzZpFmzZtaN68Oc8//7xlc8dLU7SfffYZrVu3pmnTpsyZM6fE+9+1axcx\nMTG4u7tjNBoJDw+nU6dOwMWE+OuvvyYnJ8dSf/369ZjNZu65554S2yzOk08+yXvvvadNKEVEpFwo\nSbMD7u7u9O7d22qE54svviA4OJhGjRoBUKVKFebMmcP+/ftZtGgRixcvZs2aNddse8mSJaxbt47v\nv/+er7/+mq+++srqekBAAIsXLyYhIYEZM2YwceJE9uzZg4eHB0uWLKFGjRocOHCAhIQEAgMDgf+N\nFh06dIiRI0cyefJkfvvtNzp37sywYcMoKCiwtL969Wo+/fRTtm7dyr59+1i2bFmxccbGxrJ8+XJW\nrFjB1q1bOX/+PK+++qpVnW3btvHjjz+ydOlSZs2aZZUQXi4iIoK///3vfP7555w4ccLqWsuWLQkM\nDOSbb76xlK1cuZKoqKirjlgWdzBH06ZNadu2Le+++26JnxMREbleStLsRExMDKtXr7aMNK1YsYKY\nmBjL9cjISEJDQwFo1KgRffv2ZevWrddsd/Xq1Tz66KPUrFkTb29vq1EwgM6dO1O3bl3g4nNWHTt2\ntBrRu5ovv/ySrl27cvfdd+Pk5MQTTzxBTk4O27dvt9R59NFHCQgIwNvbm27durF3795i21q1ahUj\nRowgKCgIDw8PXnnlFb744gvL83gGg4HRo0fj6upKWFgYYWFh7Nu3r9i23nvvPdq0acOcOXO46667\n6NGjB7t27bJcHzhwIJ999hkAmZmZfPfdd1a/NcDw4cMJDw+3/N+VCeMlL774Ih999BEpKSk2/WYi\nIiK2csgD1q/X1abQSuPZZ58t9WdatWqFr68v3377Lc2aNWPXrl188MEHlus7d+5k6tSpJCQkkJ+f\nT15eHr17975mu6dOnbI6F6xOnTpW19etW8fMmTM5fPgwZrOZnJwc7rjjDptiPnXqFEFBQZb3BoOB\n2rVrk5ycbCnz9/e3vPbw8ODUqVM2tRUUFERBQQFnzpyxlAUEBFheu7u7c+HChWLbqlatGmPHjmXs\n2LGkpqYyefJkhg8fbkkeo6OjmTVrFqdPn2bdunXUr1+f8PBwqzYWLlxIu3btLO+XLVtmmUK+XGho\nKF26dGHevHncfvvtxcYjIiJyPZSkXeZ6kquydGmE59ChQ3Ts2BE/Pz/Ltaeffpq//e1vfPLJJ7i4\nuDBhwgRSU1Ov2WZgYCBJSUmW95dP/+Xl5TFixAjmzp1Ljx49MBqNDB8+3DK1d62H4GvUqEFCQoJV\nWVJSErVq1bLpfq9sKzEx0fI+MTERFxcXAgICrOIvrerVq/PEE0+wfPly0tLS8PHxoU6dOrRu3ZoV\nK1awbt26IqNoUPz0ZklGjx7Nvffea1kdKiIiUhY03WlHYmJi+PHHH/nkk0+KJA7nz5/H29sbFxcX\ndu7cSVxcnNX1kpKKPn36sHDhQk6ePElaWhrvvPOO5Vp+fj75+fn4+vpiNBpZt24dGzdutFz39/cn\nNTW1xAfj+/TpQ3x8PJs3b6agoID58+fj7u5OREREqe89KiqKBQsWcPz4cc6fP89bb71F3759Lc+J\nlSZpujTiWFhYSFZWFh9//DH169fHx8fHUic6OpoPP/yQHTt2MGDAgFLHe7n69evTt29fq5FPERGR\nG6UkzY4EBQURERFBdnY23bt3t7o2depU/vnPf9KoUSNmz55N3759ra5fuT3EJQ888AAdO3akW7du\n9OzZk549e1quValShcmTJ/P4448THh7O559/To8ePSzXg4ODiYqKom3btoSHh3P69Gmr72zYsCFz\n587lH//4B02bNmXt2rV89NFHODs7F4njWu6//34GDhzIgAEDuOuuu/Dw8GDKlCnF3tO12s7Ozmb4\n8OGEhYXRrl07kpKS+PDDD63q9OrVi/T0dO6++26raVRb476yzvPPP092dra24BARkTJjMJdmiKKS\nKG56zMvLS1sliMNRv7YP+jvIjVD/ubVd/tz4lTSSJiIiImKHlKSJiIiI2CElaSIiIiJ2SEmaiIiI\niB1SkiYiIiJih5SkiYiIiNghJWkiIiIiduiWORbKbDbj5eVV0WGIHXNycqKwsLCiwygVB9zmUERE\n/mT3Sdqvv/7KRx99hNls5p577iEqKuq62snKyirjyMTRaENJERGxJ3Y93Wkymfjggw949dVXmT59\nOps3b7Y6IFxERETEUdl1knbw4EFq1apFQEAAzs7OtGvXjm3btlV0WCIiIiLlzq6TtJSUFPz8/Czv\nfX19SUlJqcCIRERERG4Ou07SRERERG5Vdr1wwNfXl7Nnz1rep6Sk4Ovra1Vn79697N271/J+0KBB\nVz1RXuRqtAJYrpf6jtwI9Z9b27Jlyyyvw8PDCQ8PB+w8SQsODiY5OZkzZ85QvXp1Nm/ezHPPPWdV\n5/KbgYs3OmjQoJsdqjgA9R25Xuo7ciPUf6Skv79dJ2lGo5Hhw4fz2muvYTab6dy5M0FBQRUdloiI\niEi5s+skDaB58+bMnj27osMQERERuakcbuHA5VOfIqWhviPXS31HboT6j5TEYNa5MiIiIiJ2x+FG\n0kREREQcgZI0ERERETtk9wsHSqOsDmMXxzFy5Eg8PT0xGAw4OTnxxhtvkJWVxaxZszhz5gyBgYGM\nGjUKT09PAFatWsX69etxcnLi4YcfplmzZgAcPnyYf/3rX+Tn59OiRQsefvjhCrwrKS/vvvsuv/zy\nC97e3rz99tsAZdpfCgoKmDdvHocPH8bLy4tRo0bh7+9fIfcqZau4vvPZZ58RHx+Pt7c3AIMHD6Z5\n8+aA+o7YyOwgCgsLzU8//bT59OnT5vz8fPOLL75oTkxMrOiwpIKNHDnSnJmZaVW2ePFic1xcnNls\nNptXrVplXrJkidlsNpuPHz9ufumll8wFBQXmU6dOmZ9++mmzyWQym81m89ixY82///672Ww2m6dO\nnWreuXPnTbwLuVn+7//+z/zHH3+YR48ebSkry/7y3XffmRcsWGA2m83mzZs3m2fOnHnT7k3KV3F9\nZ9myZeYvv/yySF31HbGVw0x36jB2KY7ZbMZ8xdqY7du307FjRwA6depk6Sfbt2/nrrvuwsnJicDA\nQGrVqsXBgwdJS0sjOzub4OBgADp06KC+5aAaNWpElSpVrMrKsr9s27bN0lZkZCS7d+++Wbcm5ay4\nvgMU+fcPqO+I7RxmurO4w9gPHjxYgRGJPTAYDLz22msYjUa6du1Kly5dSE9Px8fHBwAfHx/S09OB\ni30oJCTE8llfX19SUlJwcnKy6lt+fn6kpKTc3BuRClOW/eXyf08ZjUaqVKlCVlYWVatWvVm3IzfZ\nt99+y6ZNm2jYsCEPPfQQnp6e6jtiM4dJ0kSKM2XKFKpXr05GRgavvfZasee6GgyGCohMKquy7C/F\njbKI4+jRowfR0dEYDAaWLl3KokWLeOKJJ8qkbfWdW4PDTHfachi73HqqV68OQLVq1WjVqhUHDx7E\nx8eHtLQ0ANLS0iwP9V7Zh86dO4evry++vr6cO3euSLncGsqyv1x+zWQykZ2drZEQB1atWjVLUt+l\nSxfL7I76jtjKYZK0yw9jLygoYPPmzbRs2bKiw5IKlJubS05ODgA5OTn89ttv1KtXj4iICDZs2ADA\nhg0bLP2kZcuWbNmyhYKCAk6fPk1ycjLBwcH4+Pjg6enJwYMHMZvNbNq0iVatWlXUbUk5u/I5xrLs\nLy1btmTjxo0AbN26lcaNG9/cm5NydWXfuZTcA/z3v/+lbt26gPqO2M6hThz49ddf+fCFkW59AAAG\nY0lEQVTDDy2HsWsLjlvb6dOn+ec//4nBYKCwsJD27dsTFRVFVlYWM2fO5OzZswQEBDBq1CjLA7+r\nVq1i3bp1ODs7F1kW/84771iWxT/yyCMVeWtSTmbPns2+ffvIzMzE29ubQYMG0apVqzLrL/n5+cyd\nO5cjR47g5eXFc889R2BgYIXdr5Sd4vrO3r17OXLkCAaDgYCAAEaMGGF5vlF9R2zhUEmaiIiIiKNw\nmOlOEREREUeiJE1ERETEDilJExEREbFDStJERERE7JCSNBERERE7pCRNRERExA4pSRMRh2EymXjo\noYesdm0vi7oiIhVB+6SJSIV56KGHLMfm5OTk4OLigtFoxGAw8Nhjj3H33XdXcIRlKzk5meeee47Y\n2NiKDkVEKgEdsC4iFWbRokWW108//TRPPPHEVY+7MZlMGI2aABCRW4OSNBGxC8UN6i9dupTk5GQM\nBgO//PILf/vb36hVqxYff/wxSUlJuLq6EhkZybBhwzAajZhMJgYPHsw777yDv78/c+fOpWrVqiQn\nJ7N//37q1avHs88+S0BAQKnqAuzcuZOPPvqIjIwM2rdvzx9//EHXrl3p2LFjkbh///133n//fZKT\nk3Fzc6NDhw48+OCDTJgwAfjfCOKECRNo0KAB8fHxrF69mvT0dG6//XZGjBiBn5+fJcZHHnmE1atX\nk5OTQ+fOnRkyZAgAJ0+eZP78+Rw9ehRnZ2eaNm3Ks88+W45/JRG5mfT/koqIXdu2bRvt27fn448/\n5q677sLZ2ZlHHnmEhQsXMmXKFHbt2sX3339f4uc3b97M/fffz4cffoifn99VpxpLqpuens6sWbN4\n6KGH+OCDDwgMDOTQoUMltrNw4UL69evHxx9/zJw5c4iMjARg0qRJwMURxI8//pgGDRrw008/sXr1\nasaMGcP777/P7bffzpw5c6za2759O9OmTePNN9/kp59+shy0/emnnxIREcFHH33E/Pnzuffee237\nUUWkUlCSJiJ2rVGjRtx5550AuLi40KBBA4KDgzEYDAQGBtKlSxf27dtX4ucjIyP5y1/+gtFo5O67\n7+bIkSOlrvvLL79Qv359IiIiMBqN9O7dGy8vrxLbcXZ25uTJk2RlZeHu7k5wcHCJddeuXUv//v2p\nWbMmRqOR/v37c/DgQVJTUy11oqKi8PT0xN/fn/vuu4/Nmzdbvuf06dOkpKTg7OxMSEhIid8jIpWP\npjtFxK75+flZvU9KSmLRokUcPnyY3NxcTCYTt99+e4mf9/Hxsbx2c3MjJyen1HVTU1OLxOHr61ti\nO0899RSxsbE899xz1KxZk+joaFq0aFFs3TNnzvDBBx/w4YcfWsqMRiPnzp3D29sbsP4NAgICLAnc\nQw89xNKlSxk7dizVqlWjd+/exU6/ikjlpCRNRCqVf//734SEhPDCCy/g6urKl19+yS+//FKu3+nj\n48OuXbusylJSUkqsX6tWLZ5//nkAtmzZwvTp0/noo48sK1kv5+/vz/3330/btm2LXDOZTACcO3eO\nWrVqAXD27FmqV69uieuJJ54A4P/+7/947bXXCAsLszxHJyKVm6Y7RaRSyc7OxtPT8//buV9XRcIo\njOPP4I9wDWIwGqbZDBYxiCB2/wKTYFXEIvgvGDRaxCyKQSwmiyY1TjRoEU3iqIjDhsvKLuvu3uUi\nzO5+P3UO79uGh3M4r/x+vzabjSaTycvvjMfjWq/XWiwWchxHo9FIx+Pxp/XT6fTx/e3tTYZhyDAM\nBYNBGYah3W73qM1ms+r3+9put5Kk0+mk+Xz+3XnD4VC2bWu/32s8HiuZTEqSZrPZIyx+vYftV+Df\nQScNgCs86zI9k8/n1W63NRgMZJqmksmkLMv61Jm/qw0GgyqVSup0Omq1WkqlUjJNUz6f72n9crlU\nt9vV7XZTOBxWuVyWx+ORx+NRLpdTrVbT/X5XvV5XIpHQ9XpVo9HQ4XBQIBBQLBZ7LBtI7yGxWq3q\ncrkok8konU5Let8i7XQ6Op/PCoVCKhQKP4xlAfy9eMwWAP6Q4zgqFouqVCqKRqMvvefbZ0IA/F/o\niwPAB6xWK9m2rdvtpl6vJ6/X+8utTQD4LMadAPABlmWp2WzKcRxFIhFVq1V5vfxCAbwO404AAAAX\nYtwJAADgQoQ0AAAAFyKkAQAAuBAhDQAAwIUIaQAAAC5ESAMAAHChL+RbWefkfEP0AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff8aa324e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "data = np.column_stack((acc_synthetic_batch, acc_synthetic_valid, acc_SVHN_batch, acc_SVHN_valid))\n",
    "df1 = pd.DataFrame(data, index = np.arange(0,20000,500), columns=['Training on Synthetic', 'Validation on synthetic', 'Training on SVHN', 'Validation on SVHN'])\n",
    "df1.index.name = 'Training steps'\n",
    "f = plt.figure(1, figsize = (10,5))\n",
    "ax = df1.plot(ax=f.gca())\n",
    "ax.set_ylabel(\"Model accuracy\")\n",
    "axes = plt.gca()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# These are previously saved performance values using 3 different optimization methods\n",
    "file_name1 = 'assets/Accuracy_Momentum.pickle'\n",
    "file_name2 = 'assets/Accuracy_RMS.pickle'\n",
    "file_name3 = 'assets/Accuracy_Adagrad.pickle'\n",
    "with open(file_name1, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    data_Momentum = save['data_acc']\n",
    "    del save\n",
    "with open(file_name2, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    data_RMS = save['data_acc']\n",
    "    del save\n",
    "with open(file_name3, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    data_Adagrad = save['data_acc']\n",
    "    del save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C2P8h5K//H0g+/6ifM6Ea2PxREL/b0wuvXcGKORW4sMnL0xIRERGVEIaxEiDU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\n4xrOMIEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff8d9ec0290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_compare = np.column_stack((data_Adagrad[:,1], data_Momentum[:,1], data_RMS[:,1]))\n",
    "df2 = pd.DataFrame(data_compare, index = np.arange(0,20000,500), columns=['Adagrad', 'Momentum', 'RMS'])\n",
    "df2.index.name = 'Training steps'\n",
    "f = plt.figure(1, figsize = (10,5))\n",
    "#plt.title('Number of digits in the different dataset images (%)', color='black')\n",
    "ax = df2.plot(ax=f.gca())\n",
    "ax.set_ylabel(\"Model accuracy\")\n",
    "axes = plt.gca()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}