LSTM.ipynb

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    "## RNN for Human Activity Recognition - 2D Pose Input\n",
    "\n",
    "This experiment is the classification of human activities using a 2D pose time series dataset and an LSTM RNN.\n",
    "The idea is to prove the concept that using a series of 2D poses, rather than 3D poses or a raw 2D images, can produce an accurate estimation of the behaviour of a person or animal.\n",
    "This is a step towards creating a method of classifying an animal's current behaviour state and predicting it's likely next state, allowing for better interaction with an autonomous mobile robot.\n",
    "\n",
    "## Objectives\n",
    "\n",
    "The aims of this experiment are:\n",
    "\n",
    "-  To determine if 2D pose has comparable accuracy to 3D pose for use in activity recognition. This would allow the use of RGB only cameras for human and animal pose estimation, as opposed to RGBD or a large motion capture dataset.\n",
    "\n",
    "\n",
    "- To determine if  2D pose has comparable accuracy to using raw RGB images for use in activity recognition. This is based on the idea that limiting the input feature vector can help to deal with a limited dataset, as is likely to occur in animal activity recognition, by allowing for a smaller model to be used (citation required).\n",
    "\n",
    "\n",
    "- To verify the concept for use in future works involving behaviour prediction from motion in 2D images.\n",
    "\n",
    "The network used in this experiment is based on that of Guillaume Chevalier, 'LSTMs for Human Activity Recognition, 2016'  https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition, available under the MIT License.\n",
    "Notable changes that have been made (other than accounting for dataset sizes) are:\n",
    " - Adapting for use with a large dataset ordered by class, using random sampling without replacement for mini-batch.  \n",
    " This allows for use of smaller batch sizes when using a dataset ordered by class. \"It has been observed in practice that when using a larger batch there is a significant degradation in the quality of the model, as measured by its ability to generalize\"  \n",
    "      _N.S Keskar, D. Mudigere, et al, 'On Large-Batch Training for Deep Learning: Generalization Gap and Sharp \n",
    "      Minima', ICLR 2017_ https://arxiv.org/abs/1609.04836\n",
    "      \n",
    " - Exponentially decaying learning rate implemented\n",
    "\n",
    "\n",
    "\n",
    "## Dataset overview\n",
    "\n",
    "The dataset consists of pose estimations, made using the software OpenPose (https://github.com/CMU-Perceptual-Computing-Lab/openpose's) on a subset of the Berkeley Multimodal Human Action Database (MHAD) dataset http://tele-immersion.citris-uc.org/berkeley_mhad.\n",
    "\n",
    "This dataset is comprised of 12 subjects doing the following 6 actions for 5 repetitions, filmed from 4 angles, repeated 5 times each.  \n",
    "\n",
    "- JUMPING,\n",
    "- JUMPING_JACKS,\n",
    "- BOXING,\n",
    "- WAVING_2HANDS,\n",
    "- WAVING_1HAND,\n",
    "- CLAPPING_HANDS.\n",
    "\n",
    "In total, there are 1438 videos (2 were missing) made up of 211200 individual frames.\n",
    "\n",
    "The below image is an example of the 4 camera views during the 'boxing' action for subject 1\n",
    "\n",
    "![alt text](images/boxing_all_views.gif.png \"Title\")\n",
    "\n",
    "The input for the LSTM is the 2D position of 18 joints across a timeseries of frames numbering n_steps (window-width), with an associated class label for the frame series.  \n",
    "A single frame's input (where j refers to a joint) is stored as:\n",
    "\n",
    "[  j0_x,  j0_y, j1_x, j1_y , j2_x, j2_y, j3_x, j3_y, j4_x, j4_y, j5_x, j5_y, j6_x, j6_y, j7_x, j7_y, j8_x, j8_y, j9_x, j9_y, j10_x, j10_y, j11_x, j11_y, j12_x, j12_y, j13_x, j13_y, j14_x, j14_y, j15_x, j15_y, j16_x, j16_y, j17_x, j17_y ]\n",
    "\n",
    "For the following experiment, very little preprocessing has been done to the dataset.  \n",
    "The following steps were taken:\n",
    "1. openpose run on individual frames, for each subject, action and view, outputting JSON of 18 joint x and y position keypoints and accuracies per frame\n",
    "2. JSONs converted into txt format, keeping only x and y positions of each frame, action being performed during frame, and order of frames. This is used to create a database of associated activity class number and corresponding series of joint 2D positions\n",
    "3. No further prepossessing was performed.  \n",
    "\n",
    "In some cases, multiple people were detected in each frame, in which only the first detection was used.\n",
    "\n",
    "The data has not been normalised with regards to subject position in the frame, motion across frame (if any), size of the subject, speed of action etc. It is essentially the raw 2D position of each joint viewed from a stationary camera.  \n",
    "In many cases, individual joints were not located and a position of [0.0,0.0] was given for that joint\n",
    "\n",
    "A summary of the dataset used for input is:\n",
    "\n",
    " - 211200 individual images \n",
    " - n_steps = 32 frames (~=1.5s at 22Hz)\n",
    " - Images with noisy pose detection (detection of >=2 people) = 5132  \n",
    " - Training_split = 0.8\n",
    " - Overlap = 0.8125 (26 / 32) ie 26 frame overlap\n",
    "   - Length X_train = 22625 * 32 frames\n",
    "   - Length X_test = 5751 * 32 frames\n",
    "   \n",
    "Note that their is no overlap between test and train sets, which were seperated by activity repetition entirely, before creating the 26 of 32 frame overlap.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Training and Results below: \n",
    "Training took approximately 4 mins running on a single GTX1080Ti, and was run for 22,000,000ish iterations with a batch size of 5000  (600 epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf  # Version 1.0.0 (some previous versions are used in past commits)\n",
    "from sklearn import metrics\n",
    "import random\n",
    "from random import randint\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Useful Constants\n",
    "\n",
    "# Output classes to learn how to classify\n",
    "# LABELS = [    \n",
    "#     \"JUMPING\",\n",
    "#     \"JUMPING_JACKS\",\n",
    "# #     \"BOXING\",\n",
    "#     \"WAVING_2HANDS\",\n",
    "#     \"WAVING_1HAND\",\n",
    "#     \"CLAPPING_HANDS\"\n",
    "# ] \n",
    "\n",
    "# LABELS = [    \n",
    "#     \"GO_IN\",\n",
    "#     \"GO_OUT\",\n",
    "#     \"WALK_LEFT\",\n",
    "#     \"WALK_RIGHT\"\n",
    "# ] \n",
    "\n",
    "# LABELS = [    \n",
    "#     \"normal\", \"anomaly\"\n",
    "# ] \n",
    "\n",
    "# LABELS = [\n",
    "#     \"jalan_UL\", \"jalan_DR\", \"jalan_UR\", \"jalan_DL\",\n",
    "#     \"sapu_UL\", \"sapu_DR\", \"sapu_UR\", \"sapu_DL\",\n",
    "#     # \"barang_UL\", \"barang_DR\", \"barang_UR\", \"barang_DL\",\n",
    "#     # \"out_door_SE\", \"out_door_SW\", \"in_door_SE\", \"in_door_SW\",\n",
    "#     \"idle\"\n",
    "# ]\n",
    "\n",
    "LABELS = np.array([\n",
    "    \"jalan_DR\", \"jalan_UR\", \"jalan_DL\", \"jalan_UL\",\n",
    "    \"barang2_DR\", \"barang2_UR\", \"barang2_DL\", \"barang2_UL\",\n",
    "    \"barang1l_DR\", \"barang1l_UR\", \"barang1l_DL\", \"barang1l_UL\",\n",
    "    \"barang1r_DR\", \"barang1r_UR\", \"barang1r_DL\", \"barang1r_UL\",\n",
    "    \"diam_ND\"\n",
    "])\n",
    "\n",
    "# LABELS = np.array([    \n",
    "#     \"jalan_NE\", \"jalan_NW\", \"jalan_SE\", \"jalan_SW\",\n",
    "#     \"menyapu_NE\", \"menyapu_NW\", \"menyapu_SE\", \"menyapu_SW\",\n",
    "#     \"barang_NE\", \"barang_NW\", \"barang_SE\", \"barang_SW\",\n",
    "#     \"diam_NE\", \"diam_NW\", \"diam_SE\", \"diam_SW\"\n",
    "# ])\n",
    "\n",
    "\n",
    "load = False\n",
    "train = True\n",
    "update = True\n",
    "\n",
    "\n",
    "# DATASET_PATH = \"data/HAR_pose_activities/database/\"\n",
    "# DATASET_PATH = \"data/HAR_pose_activities/database/Training Default/\"\n",
    "\n",
    "# DATASET_PATH = \"data/Overlap_fixed4_separated/\"\n",
    "\n",
    "# DATASET_PATH = \"data/2a_Amplify/\"\n",
    "\n",
    "# DATASET_PATH = \"data/Direct2a/NormalizeOnce/\"\n",
    "# DATASET_PATH = \"data/Direct2/NoOverlap/NormalizeOnce/\"\n",
    "\n",
    "DATASET_PATH = \"data/Test/7/\"\n",
    "\n",
    "LAYER = 1  # 0 = Default 2 H.Layer, 1 = single H.Layer\n",
    "\n",
    "n_steps = 7\n",
    "# n_steps = 8\n",
    "\n",
    "\n",
    "X_train_path = DATASET_PATH + \"X_train.txt\"\n",
    "X_test_path = DATASET_PATH + \"X_test.txt\"\n",
    "# X_test_path = \"utilities/something/something.txt\"\n",
    "\n",
    "y_train_path = DATASET_PATH + \"Y_train.txt\"\n",
    "y_test_path = DATASET_PATH + \"Y_test.txt\"\n",
    "\n",
    "# n_steps = 32 # 32 timesteps per series\n",
    "# n_steps = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Load the networks inputs\n",
    "\n",
    "def load_X(X_path):\n",
    "    file = open(X_path, 'r')\n",
    "    X_ = np.array(\n",
    "        [elem for elem in [\n",
    "            row.split(',') for row in file\n",
    "        ]], \n",
    "        dtype=np.float32\n",
    "    )\n",
    "    file.close()\n",
    "    blocks = int(len(X_) / n_steps)\n",
    "    \n",
    "    X_ = np.array(np.split(X_,blocks))\n",
    "\n",
    "    return X_ \n",
    "\n",
    "# Load the networks outputs\n",
    "\n",
    "def load_y(y_path):\n",
    "    file = open(y_path, 'r')\n",
    "    y_ = np.array(\n",
    "        [elem for elem in [\n",
    "            row.replace('  ', ' ').strip().split(' ') for row in file\n",
    "        ]], \n",
    "        dtype=np.int32\n",
    "    )\n",
    "    file.close()\n",
    "    \n",
    "    # for 0-based indexing \n",
    "    return y_ - 1\n",
    "\n",
    "X_train = load_X(X_train_path)\n",
    "X_test = load_X(X_test_path)\n",
    "#print X_test\n",
    "\n",
    "y_train = load_y(y_train_path)\n",
    "y_test = load_y(y_test_path)\n",
    "# proof that it actually works for the skeptical: replace labelled classes with random classes to train on\n",
    "#for i in range(len(y_train)):\n",
    "#    y_train[i] = randint(0, 5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Parameters:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2088 2088 36 17\n",
      "(X shape, y shape, every X's mean, every X's standard deviation)\n",
      "(2088, 7, 36) (2088, 1) -1.8077502e-06 26.91086\n",
      "\n",
      "The dataset has not been preprocessed, is not normalised etc\n"
     ]
    }
   ],
   "source": [
    "# Input Data \n",
    "\n",
    "training_data_count = len(X_train)  # 4519 training series (with 50% overlap between each serie)\n",
    "test_data_count = len(X_test)  # 1197 test series\n",
    "n_input = len(X_train[0][0])  # num input parameters per timestep\n",
    "\n",
    "n_hidden = 36 # Hidden layer num of features\n",
    "# n_classes = 6 \n",
    "n_classes = len(LABELS)\n",
    "\n",
    "#updated for learning-rate decay\n",
    "# calculated as: decayed_learning_rate = init_learning_rate * decay_rate ^ (global_step / decay_steps)\n",
    "decaying_learning_rate = True\n",
    "learning_rate = 0.0025 #used if decaying_learning_rate set to False\n",
    "\n",
    "init_learning_rate = 0.005\n",
    "# init_learning_rate = 0.00015\n",
    "\n",
    "decay_rate = 0.96 #the base of the exponential in the decay\n",
    "decay_steps = 100000 #used in decay every 60000 steps with a base of 0.96\n",
    "\n",
    "global_step = tf.Variable(0, trainable=False)\n",
    "lambda_loss_amount = 0.0015\n",
    "\n",
    "# training_iters = training_data_count *300  # Loop 300 times on the dataset, ie 300 epochs\n",
    "# batch_size = 5\n",
    "batch_size = 64\n",
    "# batch_size = 512\n",
    "display_iter = batch_size*8  # To show test set accuracy during training\n",
    "\n",
    "print(training_data_count, test_data_count, n_input, n_classes)\n",
    "print(\"(X shape, y shape, every X's mean, every X's standard deviation)\")\n",
    "print(X_train.shape, y_test.shape, np.mean(X_test), np.std(X_test))\n",
    "print(\"\\nThe dataset has not been preprocessed, is not normalised etc\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions for training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def LSTM_RNN(_X, _weights, _biases):\n",
    "    # model architecture based on \"guillaume-chevalier\" and \"aymericdamien\" under the MIT license.\n",
    "\n",
    "    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size\n",
    "    _X = tf.reshape(_X, [-1, n_input])   \n",
    "    # Rectifies Linear Unit activation function used\n",
    "    _X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])\n",
    "    # Split data because rnn cell needs a list of inputs for the RNN inner loop\n",
    "    _X = tf.split(_X, n_steps, 0) \n",
    "\n",
    "    if LAYER == 0:\n",
    "      # Define two stacked LSTM cells (two recurrent layers deep) with tensorflow\n",
    "      lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "      lstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "      lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)\n",
    "\n",
    "    elif LAYER == 1:\n",
    "      # Single hidden layer\n",
    "      lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "      lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1], state_is_tuple=True)\n",
    "\n",
    "#     lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "#     lstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden*2, forget_bias=1.0, state_is_tuple=True)\n",
    "#     lstm_cell_3 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n",
    "#     lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1, lstm_cell_2, lstm_cell_3], state_is_tuple=True)\n",
    "    \n",
    "    outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)\n",
    "\n",
    "    # A single output is produced, in style of \"many to one\" classifier, refer to http://karpathy.github.io/2015/05/21/rnn-effectiveness/ for details\n",
    "    lstm_last_output = outputs[-1]\n",
    "    \n",
    "    # Linear activation\n",
    "    return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']\n",
    "\n",
    "\n",
    "def extract_batch_size(_train, _labels, _unsampled, batch_size):\n",
    "    # Fetch a \"batch_size\" amount of data and labels from \"(X|y)_train\" data. \n",
    "    # Elements of each batch are chosen randomly, without replacement, from X_train with corresponding label from Y_train\n",
    "    # unsampled_indices keeps track of sampled data ensuring non-replacement. Resets when remaining datapoints < batch_size    \n",
    "    \n",
    "    shape = list(_train.shape)\n",
    "    shape[0] = batch_size\n",
    "    batch_s = np.empty(shape)\n",
    "    batch_labels = np.empty((batch_size,1)) \n",
    "\n",
    "    for i in range(batch_size):\n",
    "        # Loop index\n",
    "        # index = random sample from _unsampled (indices)\n",
    "        index = random.choice(_unsampled)\n",
    "        batch_s[i] = _train[index] \n",
    "        batch_labels[i] = _labels[index]\n",
    "        \n",
    "        _unsampled = list(_unsampled)\n",
    "        \n",
    "        _unsampled.remove(index)\n",
    "\n",
    "\n",
    "    return batch_s, batch_labels, _unsampled\n",
    "\n",
    "\n",
    "def one_hot(y_):\n",
    "    # One hot encoding of the network outputs\n",
    "    # e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]\n",
    "    \n",
    "    y_ = y_.reshape(len(y_))\n",
    "    n_values = int(np.max(y_)) + 1\n",
    "    return np.eye(n_values)[np.array(y_, dtype=np.int32)]  # Returns FLOATS\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for _ in range(3):\n",
    "#     tf.reset_default_graph()\n",
    "#     var = tf.Variable(0)\n",
    "#     with tf.Session() as session:\n",
    "#         session.run(tf.global_variables_initializer())\n",
    "#         print(len(session.graph._nodes_by_name.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-55de171306ad>:19: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is deprecated, please use tf.nn.rnn_cell.LSTMCell, which supports all the feature this cell currently has. Please replace the existing code with tf.nn.rnn_cell.LSTMCell(name='basic_lstm_cell').\n",
      "WARNING:tensorflow:From <ipython-input-7-982ce1458cc4>:22: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Graph input/output\n",
    "x = tf.placeholder(tf.float32, [None, n_steps, n_input])\n",
    "y = tf.placeholder(tf.float32, [None, n_classes])\n",
    "\n",
    "# Graph weights\n",
    "weights = {\n",
    "    'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))\n",
    "}\n",
    "biases = {\n",
    "    'hidden': tf.Variable(tf.random_normal([n_hidden])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n",
    "\n",
    "pred = LSTM_RNN(x, weights, biases)\n",
    "\n",
    "# Loss, optimizer and evaluation\n",
    "l2 = lambda_loss_amount * sum(\n",
    "    tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()\n",
    ") # L2 loss prevents this overkill neural network to overfit the data\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) + l2 # Softmax loss\n",
    "if decaying_learning_rate:\n",
    "    learning_rate = tf.train.exponential_decay(init_learning_rate, global_step*batch_size, decay_steps, decay_rate, staircase=True)\n",
    "\n",
    "\n",
    "#decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps) #exponentially decayed learning rate\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost,global_step=global_step) # Adam Optimizer\n",
    "\n",
    "# correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
    "# accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'out': <tf.Variable 'Variable_4:0' shape=(17,) dtype=float32_ref>, 'hidden': <tf.Variable 'Variable_3:0' shape=(36,) dtype=float32_ref>}\n",
      "{'out': <tf.Variable 'Variable_2:0' shape=(36, 17) dtype=float32_ref>, 'hidden': <tf.Variable 'Variable_1:0' shape=(36, 36) dtype=float32_ref>}\n",
      "<tf.Variable 'Variable_1:0' shape=(36, 36) dtype=float32_ref>\n"
     ]
    }
   ],
   "source": [
    "print(biases)\n",
    "print(weights)\n",
    "print(weights['hidden'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# if decaying_learning_rate:\n",
    "#     learning_rate = tf.train.exponential_decay(init_learning_rate, global_step*batch_size, decay_steps, decay_rate, staircase=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_losses = []\n",
    "test_accuracies = []\n",
    "train_losses = []\n",
    "train_accuracies = []\n",
    "sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training_iters = training_data_count *120\n",
    "# training_iters = training_data_count *5120\n",
    "# training_iters = training_data_count *2560\n",
    "\n",
    "training_iters = training_data_count *64\n",
    "# training_iters = training_data_count *128\n",
    "# training_iters = training_data_count *256\n",
    "# training_iters = training_data_count *512\n",
    "# training_iters = training_data_count *1024\n",
    "# training_iters = training_data_count *2048\n",
    "# training_iters = training_data_count *3072\n",
    "# training_iters = training_data_count *5120\n",
    "\n",
    "#create saver before training\n",
    "saver = tf.train.Saver()\n",
    "# saver = tf.train.Saver(var_list={'wh':weights['hidden'], 'wo':weights['out'], 'bh':biases['hidden'], 'bo':biases['out']})\n",
    "# load = False\n",
    "# train = True\n",
    "# update = True\n",
    "\n",
    "#check if you want to retrain or import a saved model\n",
    "if load:\n",
    "    saver.restore(sess, DATASET_PATH + \"model.ckpt\")\n",
    "    print(\"Model restored.\")\n",
    "\n",
    "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "if load:\n",
    "    # import the inspect_checkpoint library\n",
    "    from tensorflow.python.tools import inspect_checkpoint as chkp\n",
    "\n",
    "    # print all tensors in checkpoint file\n",
    "    chkp.print_tensors_in_checkpoint_file(DATASET_PATH + \"model.ckpt\", tensor_name='', all_tensors=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #64:  Learning rate = 0.005000:   Batch Loss = 7.128121, Accuracy = 0.078125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 6.616029262542725, Accuracy = 0.10057470947504044\n",
      "Iter #512:  Learning rate = 0.005000:   Batch Loss = 5.717802, Accuracy = 0.046875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 5.184216499328613, Accuracy = 0.10680076479911804\n",
      "Iter #1024:  Learning rate = 0.005000:   Batch Loss = 5.216472, Accuracy = 0.140625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 4.782445907592773, Accuracy = 0.14224137365818024\n",
      "Iter #1536:  Learning rate = 0.005000:   Batch Loss = 4.329869, Accuracy = 0.21875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 4.551438808441162, Accuracy = 0.18726053833961487\n",
      "Iter #2048:  Learning rate = 0.005000:   Batch Loss = 4.483626, Accuracy = 0.21875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 4.393509387969971, Accuracy = 0.18869732320308685\n",
      "Iter #2560:  Learning rate = 0.005000:   Batch Loss = 4.215524, Accuracy = 0.1875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 4.2276811599731445, Accuracy = 0.21503831446170807\n",
      "Iter #3072:  Learning rate = 0.005000:   Batch Loss = 4.287415, Accuracy = 0.234375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 4.115492820739746, Accuracy = 0.25431033968925476\n",
      "Iter #3584:  Learning rate = 0.005000:   Batch Loss = 4.182835, Accuracy = 0.265625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.9521026611328125, Accuracy = 0.30651339888572693\n",
      "Iter #4096:  Learning rate = 0.005000:   Batch Loss = 3.939018, Accuracy = 0.328125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.826676368713379, Accuracy = 0.3357279598712921\n",
      "Iter #4608:  Learning rate = 0.005000:   Batch Loss = 3.798097, Accuracy = 0.359375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.728804588317871, Accuracy = 0.38697317242622375\n",
      "Iter #5120:  Learning rate = 0.005000:   Batch Loss = 3.479088, Accuracy = 0.40625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.671064853668213, Accuracy = 0.4104406237602234\n",
      "Iter #5632:  Learning rate = 0.005000:   Batch Loss = 3.738040, Accuracy = 0.3125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.6364693641662598, Accuracy = 0.40325671434402466\n",
      "Iter #6144:  Learning rate = 0.005000:   Batch Loss = 3.523263, Accuracy = 0.40625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.5729024410247803, Accuracy = 0.39176246523857117\n",
      "Iter #6656:  Learning rate = 0.005000:   Batch Loss = 3.620688, Accuracy = 0.40625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.553097724914551, Accuracy = 0.42768198251724243\n",
      "Iter #7168:  Learning rate = 0.005000:   Batch Loss = 3.450549, Accuracy = 0.453125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.503946304321289, Accuracy = 0.4176245331764221\n",
      "Iter #7680:  Learning rate = 0.005000:   Batch Loss = 3.321179, Accuracy = 0.5625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.3957104682922363, Accuracy = 0.4573754668235779\n",
      "Iter #8192:  Learning rate = 0.005000:   Batch Loss = 3.416039, Accuracy = 0.390625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.2802841663360596, Accuracy = 0.48563218116760254\n",
      "Iter #8704:  Learning rate = 0.005000:   Batch Loss = 3.214815, Accuracy = 0.46875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.245483875274658, Accuracy = 0.49425286054611206\n",
      "Iter #9216:  Learning rate = 0.005000:   Batch Loss = 3.106053, Accuracy = 0.578125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.2002596855163574, Accuracy = 0.5081417560577393\n",
      "Iter #9728:  Learning rate = 0.005000:   Batch Loss = 3.160221, Accuracy = 0.546875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.1583251953125, Accuracy = 0.5287356376647949\n",
      "Iter #10240:  Learning rate = 0.005000:   Batch Loss = 3.287673, Accuracy = 0.4375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.1269025802612305, Accuracy = 0.49329501390457153\n",
      "Iter #10752:  Learning rate = 0.005000:   Batch Loss = 3.196078, Accuracy = 0.53125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 3.0933406352996826, Accuracy = 0.50957852602005\n",
      "Iter #11264:  Learning rate = 0.005000:   Batch Loss = 2.806655, Accuracy = 0.671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.991020441055298, Accuracy = 0.5660919547080994\n",
      "Iter #11776:  Learning rate = 0.005000:   Batch Loss = 2.914619, Accuracy = 0.5625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.994734048843384, Accuracy = 0.524904191493988\n",
      "Iter #12288:  Learning rate = 0.005000:   Batch Loss = 3.014144, Accuracy = 0.421875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.9351844787597656, Accuracy = 0.5296934843063354\n",
      "Iter #12800:  Learning rate = 0.005000:   Batch Loss = 3.056489, Accuracy = 0.46875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.905757188796997, Accuracy = 0.5397509336471558\n",
      "Iter #13312:  Learning rate = 0.005000:   Batch Loss = 2.805903, Accuracy = 0.625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.8523573875427246, Accuracy = 0.5641762614250183\n",
      "Iter #13824:  Learning rate = 0.005000:   Batch Loss = 2.687020, Accuracy = 0.5625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.814802646636963, Accuracy = 0.5579501986503601\n",
      "Iter #14336:  Learning rate = 0.005000:   Batch Loss = 2.824347, Accuracy = 0.578125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.770923614501953, Accuracy = 0.5986589789390564\n",
      "Iter #14848:  Learning rate = 0.005000:   Batch Loss = 2.674063, Accuracy = 0.609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.732942581176758, Accuracy = 0.5929118990898132\n",
      "Iter #15360:  Learning rate = 0.005000:   Batch Loss = 2.528544, Accuracy = 0.59375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.699118137359619, Accuracy = 0.5977011322975159\n",
      "Iter #15872:  Learning rate = 0.005000:   Batch Loss = 2.524259, Accuracy = 0.65625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.663540840148926, Accuracy = 0.5957854390144348\n",
      "Iter #16384:  Learning rate = 0.005000:   Batch Loss = 2.490851, Accuracy = 0.71875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.5904176235198975, Accuracy = 0.6350574493408203\n",
      "Iter #16896:  Learning rate = 0.005000:   Batch Loss = 2.607653, Accuracy = 0.609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.5760750770568848, Accuracy = 0.6393678188323975\n",
      "Iter #17408:  Learning rate = 0.005000:   Batch Loss = 2.467119, Accuracy = 0.59375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.5965514183044434, Accuracy = 0.6163793206214905\n",
      "Iter #17920:  Learning rate = 0.005000:   Batch Loss = 2.567468, Accuracy = 0.546875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.560197353363037, Accuracy = 0.5948275923728943\n",
      "Iter #18432:  Learning rate = 0.005000:   Batch Loss = 2.588521, Accuracy = 0.578125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.524402618408203, Accuracy = 0.6072797179222107\n",
      "Iter #18944:  Learning rate = 0.005000:   Batch Loss = 2.583121, Accuracy = 0.609375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.4832773208618164, Accuracy = 0.6446360349655151\n",
      "Iter #19456:  Learning rate = 0.005000:   Batch Loss = 2.339954, Accuracy = 0.75\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.436680316925049, Accuracy = 0.6508620977401733\n",
      "Iter #19968:  Learning rate = 0.005000:   Batch Loss = 2.405701, Accuracy = 0.671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.4349887371063232, Accuracy = 0.6398467421531677\n",
      "Iter #20480:  Learning rate = 0.005000:   Batch Loss = 2.384903, Accuracy = 0.65625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.419145345687866, Accuracy = 0.6546934843063354\n",
      "Iter #20992:  Learning rate = 0.005000:   Batch Loss = 2.144669, Accuracy = 0.765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.4000320434570312, Accuracy = 0.6503831148147583\n",
      "Iter #21504:  Learning rate = 0.005000:   Batch Loss = 2.393117, Accuracy = 0.703125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.367914915084839, Accuracy = 0.6317049860954285\n",
      "Iter #22016:  Learning rate = 0.005000:   Batch Loss = 2.284697, Accuracy = 0.65625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.323843002319336, Accuracy = 0.6642720103263855\n",
      "Iter #22528:  Learning rate = 0.005000:   Batch Loss = 2.388312, Accuracy = 0.671875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.307243824005127, Accuracy = 0.6934865713119507\n",
      "Iter #23040:  Learning rate = 0.005000:   Batch Loss = 2.166116, Accuracy = 0.78125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.3163528442382812, Accuracy = 0.6762452125549316\n",
      "Iter #23552:  Learning rate = 0.005000:   Batch Loss = 2.162857, Accuracy = 0.765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.222695827484131, Accuracy = 0.7227011322975159\n",
      "Iter #24064:  Learning rate = 0.005000:   Batch Loss = 2.153503, Accuracy = 0.734375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.1932828426361084, Accuracy = 0.7102490663528442\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #24576:  Learning rate = 0.005000:   Batch Loss = 2.366558, Accuracy = 0.703125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.171504259109497, Accuracy = 0.7140804529190063\n",
      "Iter #25088:  Learning rate = 0.005000:   Batch Loss = 2.289475, Accuracy = 0.625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.147657871246338, Accuracy = 0.7231800556182861\n",
      "Iter #25600:  Learning rate = 0.005000:   Batch Loss = 2.125134, Accuracy = 0.71875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.125875949859619, Accuracy = 0.7428160905838013\n",
      "Iter #26112:  Learning rate = 0.005000:   Batch Loss = 2.093380, Accuracy = 0.75\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.136894941329956, Accuracy = 0.7279693484306335\n",
      "Iter #26624:  Learning rate = 0.005000:   Batch Loss = 2.084898, Accuracy = 0.765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.0786967277526855, Accuracy = 0.7413793206214905\n",
      "Iter #27136:  Learning rate = 0.005000:   Batch Loss = 2.092527, Accuracy = 0.71875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.04349946975708, Accuracy = 0.7466475367546082\n",
      "Iter #27648:  Learning rate = 0.005000:   Batch Loss = 1.914174, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.052213668823242, Accuracy = 0.7452107071876526\n",
      "Iter #28160:  Learning rate = 0.005000:   Batch Loss = 2.053478, Accuracy = 0.703125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 2.0157511234283447, Accuracy = 0.7179118990898132\n",
      "Iter #28672:  Learning rate = 0.005000:   Batch Loss = 1.965601, Accuracy = 0.765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.9953298568725586, Accuracy = 0.7227011322975159\n",
      "Iter #29184:  Learning rate = 0.005000:   Batch Loss = 1.869186, Accuracy = 0.78125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.9675865173339844, Accuracy = 0.7485632300376892\n",
      "Iter #29696:  Learning rate = 0.005000:   Batch Loss = 1.837271, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.9159109592437744, Accuracy = 0.7710728049278259\n",
      "Iter #30208:  Learning rate = 0.005000:   Batch Loss = 1.791457, Accuracy = 0.8125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.9063334465026855, Accuracy = 0.7801724076271057\n",
      "Iter #30720:  Learning rate = 0.005000:   Batch Loss = 1.857687, Accuracy = 0.8125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.9077568054199219, Accuracy = 0.7696360349655151\n",
      "Iter #31232:  Learning rate = 0.005000:   Batch Loss = 1.928545, Accuracy = 0.765625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.8670611381530762, Accuracy = 0.7935823798179626\n",
      "Iter #31744:  Learning rate = 0.005000:   Batch Loss = 1.807008, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.8516287803649902, Accuracy = 0.8026819825172424\n",
      "Iter #32256:  Learning rate = 0.005000:   Batch Loss = 1.830468, Accuracy = 0.859375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.8550373315811157, Accuracy = 0.8084291219711304\n",
      "Iter #32768:  Learning rate = 0.005000:   Batch Loss = 1.690968, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.7981131076812744, Accuracy = 0.811302661895752\n",
      "Iter #33280:  Learning rate = 0.005000:   Batch Loss = 1.732868, Accuracy = 0.859375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.7944310903549194, Accuracy = 0.8117815852165222\n",
      "Iter #33792:  Learning rate = 0.005000:   Batch Loss = 1.645570, Accuracy = 0.875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.7861016988754272, Accuracy = 0.8103448152542114\n",
      "Iter #34304:  Learning rate = 0.005000:   Batch Loss = 1.761828, Accuracy = 0.84375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.7579760551452637, Accuracy = 0.8156130313873291\n",
      "Iter #34816:  Learning rate = 0.005000:   Batch Loss = 1.694028, Accuracy = 0.84375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.7335195541381836, Accuracy = 0.8218390941619873\n",
      "Iter #35328:  Learning rate = 0.005000:   Batch Loss = 1.746217, Accuracy = 0.75\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.7126727104187012, Accuracy = 0.8242337107658386\n",
      "Iter #35840:  Learning rate = 0.005000:   Batch Loss = 1.724317, Accuracy = 0.8125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.6939544677734375, Accuracy = 0.8232758641242981\n",
      "Iter #36352:  Learning rate = 0.005000:   Batch Loss = 1.674438, Accuracy = 0.796875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.6768701076507568, Accuracy = 0.8304597735404968\n",
      "Iter #36864:  Learning rate = 0.005000:   Batch Loss = 1.659842, Accuracy = 0.859375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.6699838638305664, Accuracy = 0.8338122367858887\n",
      "Iter #37376:  Learning rate = 0.005000:   Batch Loss = 1.719310, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.6534645557403564, Accuracy = 0.8419540524482727\n",
      "Iter #37888:  Learning rate = 0.005000:   Batch Loss = 1.643811, Accuracy = 0.84375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.6268422603607178, Accuracy = 0.8515325784683228\n",
      "Iter #38400:  Learning rate = 0.005000:   Batch Loss = 1.439323, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.617185115814209, Accuracy = 0.8510536551475525\n",
      "Iter #38912:  Learning rate = 0.005000:   Batch Loss = 1.592911, Accuracy = 0.8125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.6034682989120483, Accuracy = 0.8587164878845215\n",
      "Iter #39424:  Learning rate = 0.005000:   Batch Loss = 1.456410, Accuracy = 0.90625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5884202718734741, Accuracy = 0.8544061183929443\n",
      "Iter #39936:  Learning rate = 0.005000:   Batch Loss = 1.596128, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.6143090724945068, Accuracy = 0.8127394914627075\n",
      "Iter #40448:  Learning rate = 0.005000:   Batch Loss = 1.426139, Accuracy = 0.875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5931448936462402, Accuracy = 0.8314176201820374\n",
      "Iter #40960:  Learning rate = 0.005000:   Batch Loss = 1.575608, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5792934894561768, Accuracy = 0.8266283273696899\n",
      "Iter #41472:  Learning rate = 0.005000:   Batch Loss = 1.519090, Accuracy = 0.859375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5328527688980103, Accuracy = 0.8558428883552551\n",
      "Iter #41984:  Learning rate = 0.005000:   Batch Loss = 1.608394, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5433318614959717, Accuracy = 0.8505747318267822\n",
      "Iter #42496:  Learning rate = 0.005000:   Batch Loss = 1.463796, Accuracy = 0.890625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.543768048286438, Accuracy = 0.8491379022598267\n",
      "Iter #43008:  Learning rate = 0.005000:   Batch Loss = 1.545187, Accuracy = 0.828125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5406603813171387, Accuracy = 0.8438697457313538\n",
      "Iter #43520:  Learning rate = 0.005000:   Batch Loss = 1.510318, Accuracy = 0.796875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5437473058700562, Accuracy = 0.8362069129943848\n",
      "Iter #44032:  Learning rate = 0.005000:   Batch Loss = 1.572634, Accuracy = 0.8125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5305618047714233, Accuracy = 0.834770143032074\n",
      "Iter #44544:  Learning rate = 0.005000:   Batch Loss = 1.509406, Accuracy = 0.859375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.5102436542510986, Accuracy = 0.8524904251098633\n",
      "Iter #45056:  Learning rate = 0.005000:   Batch Loss = 1.514125, Accuracy = 0.84375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.4759957790374756, Accuracy = 0.8529693484306335\n",
      "Iter #45568:  Learning rate = 0.005000:   Batch Loss = 1.375017, Accuracy = 0.875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.4869741201400757, Accuracy = 0.8486589789390564\n",
      "Iter #46080:  Learning rate = 0.005000:   Batch Loss = 1.411715, Accuracy = 0.890625\n",
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      "Iter #46592:  Learning rate = 0.005000:   Batch Loss = 1.384900, Accuracy = 0.859375\n",
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      "Iter #47104:  Learning rate = 0.005000:   Batch Loss = 1.372591, Accuracy = 0.90625\n",
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      "Iter #47616:  Learning rate = 0.005000:   Batch Loss = 1.660707, Accuracy = 0.8125\n",
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      "Iter #48128:  Learning rate = 0.005000:   Batch Loss = 1.298973, Accuracy = 0.90625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.4022610187530518, Accuracy = 0.8587164878845215\n",
      "Iter #48640:  Learning rate = 0.005000:   Batch Loss = 1.286846, Accuracy = 0.890625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.4109081029891968, Accuracy = 0.8572797179222107\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #49152:  Learning rate = 0.005000:   Batch Loss = 1.279489, Accuracy = 0.90625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 1.401212453842163, Accuracy = 0.8721264600753784\n",
      "Iter #49664:  Learning rate = 0.005000:   Batch Loss = 1.489454, Accuracy = 0.765625\n",
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      "Iter #50176:  Learning rate = 0.005000:   Batch Loss = 1.447151, Accuracy = 0.859375\n",
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      "Iter #50688:  Learning rate = 0.005000:   Batch Loss = 1.264660, Accuracy = 0.90625\n",
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      "Iter #51200:  Learning rate = 0.005000:   Batch Loss = 1.452172, Accuracy = 0.8125\n",
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      "Iter #51712:  Learning rate = 0.005000:   Batch Loss = 1.196169, Accuracy = 0.921875\n",
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      "Iter #52224:  Learning rate = 0.005000:   Batch Loss = 1.268198, Accuracy = 0.90625\n",
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      "Iter #52736:  Learning rate = 0.005000:   Batch Loss = 1.182090, Accuracy = 0.890625\n",
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      "Iter #53248:  Learning rate = 0.005000:   Batch Loss = 1.283271, Accuracy = 0.90625\n",
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      "Iter #53760:  Learning rate = 0.005000:   Batch Loss = 1.263555, Accuracy = 0.890625\n",
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      "Iter #54272:  Learning rate = 0.005000:   Batch Loss = 1.155997, Accuracy = 0.9375\n",
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      "Iter #54784:  Learning rate = 0.005000:   Batch Loss = 1.155146, Accuracy = 0.953125\n",
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      "Iter #55296:  Learning rate = 0.005000:   Batch Loss = 1.308331, Accuracy = 0.84375\n",
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      "Iter #55808:  Learning rate = 0.005000:   Batch Loss = 1.303267, Accuracy = 0.828125\n",
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      "Iter #56320:  Learning rate = 0.005000:   Batch Loss = 1.181633, Accuracy = 0.90625\n",
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      "Iter #56832:  Learning rate = 0.005000:   Batch Loss = 1.306479, Accuracy = 0.859375\n",
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      "Iter #57344:  Learning rate = 0.005000:   Batch Loss = 1.206895, Accuracy = 0.96875\n",
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      "Iter #57856:  Learning rate = 0.005000:   Batch Loss = 1.296278, Accuracy = 0.921875\n",
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      "Iter #58368:  Learning rate = 0.005000:   Batch Loss = 1.186169, Accuracy = 0.921875\n",
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      "Iter #58880:  Learning rate = 0.005000:   Batch Loss = 1.414612, Accuracy = 0.84375\n",
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      "Iter #59392:  Learning rate = 0.005000:   Batch Loss = 1.152058, Accuracy = 0.9375\n",
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      "Iter #59904:  Learning rate = 0.005000:   Batch Loss = 1.120015, Accuracy = 0.9375\n",
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      "Iter #60416:  Learning rate = 0.005000:   Batch Loss = 1.071859, Accuracy = 0.9375\n",
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      "Iter #60928:  Learning rate = 0.005000:   Batch Loss = 1.190029, Accuracy = 0.953125\n",
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      "Iter #61440:  Learning rate = 0.005000:   Batch Loss = 1.134891, Accuracy = 0.953125\n",
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      "Iter #61952:  Learning rate = 0.005000:   Batch Loss = 1.256201, Accuracy = 0.875\n",
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      "Iter #62464:  Learning rate = 0.005000:   Batch Loss = 1.119611, Accuracy = 0.9375\n",
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      "Iter #62976:  Learning rate = 0.005000:   Batch Loss = 1.078228, Accuracy = 0.953125\n",
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      "Iter #63488:  Learning rate = 0.005000:   Batch Loss = 1.183781, Accuracy = 0.9375\n",
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      "Iter #64000:  Learning rate = 0.005000:   Batch Loss = 1.017805, Accuracy = 0.953125\n",
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      "Iter #64512:  Learning rate = 0.005000:   Batch Loss = 1.266077, Accuracy = 0.875\n",
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      "Iter #65536:  Learning rate = 0.005000:   Batch Loss = 1.133278, Accuracy = 0.9375\n",
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      "Iter #66048:  Learning rate = 0.005000:   Batch Loss = 1.117328, Accuracy = 0.921875\n",
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      "Iter #67072:  Learning rate = 0.005000:   Batch Loss = 1.038305, Accuracy = 0.984375\n",
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      "Iter #67584:  Learning rate = 0.005000:   Batch Loss = 1.025730, Accuracy = 0.953125\n",
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      "Iter #68096:  Learning rate = 0.005000:   Batch Loss = 1.086188, Accuracy = 0.921875\n",
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      "Iter #68608:  Learning rate = 0.005000:   Batch Loss = 0.997979, Accuracy = 0.953125\n",
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      "Iter #73216:  Learning rate = 0.005000:   Batch Loss = 1.090819, Accuracy = 0.84375\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #73728:  Learning rate = 0.005000:   Batch Loss = 0.991408, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.9959101676940918, Accuracy = 0.9516283273696899\n",
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      "Iter #76288:  Learning rate = 0.005000:   Batch Loss = 0.986975, Accuracy = 0.953125\n",
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      "Iter #77312:  Learning rate = 0.005000:   Batch Loss = 1.002239, Accuracy = 0.921875\n",
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      "Iter #78336:  Learning rate = 0.005000:   Batch Loss = 0.937757, Accuracy = 0.921875\n",
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      "Iter #81920:  Learning rate = 0.005000:   Batch Loss = 1.001339, Accuracy = 0.921875\n",
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      "Iter #89600:  Learning rate = 0.005000:   Batch Loss = 0.821744, Accuracy = 0.984375\n",
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      "PERFORMANCE ON TEST SET:             Batch Loss = 0.783252477645874, Accuracy = 0.969348669052124\n",
      "Iter #97280:  Learning rate = 0.005000:   Batch Loss = 0.760770, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7892754077911377, Accuracy = 0.959770143032074\n",
      "Iter #97792:  Learning rate = 0.005000:   Batch Loss = 0.774118, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7989713549613953, Accuracy = 0.9636015295982361\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter #98304:  Learning rate = 0.005000:   Batch Loss = 0.831200, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.797653079032898, Accuracy = 0.9535440802574158\n",
      "Iter #98816:  Learning rate = 0.005000:   Batch Loss = 0.770075, Accuracy = 0.953125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7717423439025879, Accuracy = 0.9621647596359253\n",
      "Iter #99328:  Learning rate = 0.005000:   Batch Loss = 0.740212, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7693188190460205, Accuracy = 0.967432975769043\n",
      "Iter #99840:  Learning rate = 0.005000:   Batch Loss = 0.751632, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7530168890953064, Accuracy = 0.969348669052124\n",
      "Iter #100352:  Learning rate = 0.004800:   Batch Loss = 0.705948, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7468863129615784, Accuracy = 0.969348669052124\n",
      "Iter #100864:  Learning rate = 0.004800:   Batch Loss = 0.753899, Accuracy = 0.953125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7398058772087097, Accuracy = 0.9683908224105835\n",
      "Iter #101376:  Learning rate = 0.004800:   Batch Loss = 0.648552, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7467598915100098, Accuracy = 0.967432975769043\n",
      "Iter #101888:  Learning rate = 0.004800:   Batch Loss = 0.688424, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7369164824485779, Accuracy = 0.9655172228813171\n",
      "Iter #102400:  Learning rate = 0.004800:   Batch Loss = 0.833915, Accuracy = 0.90625\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.742978572845459, Accuracy = 0.9655172228813171\n",
      "Iter #102912:  Learning rate = 0.004800:   Batch Loss = 0.900267, Accuracy = 0.921875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.723395824432373, Accuracy = 0.9703065156936646\n",
      "Iter #103424:  Learning rate = 0.004800:   Batch Loss = 0.692292, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7061708569526672, Accuracy = 0.9798850417137146\n",
      "Iter #103936:  Learning rate = 0.004800:   Batch Loss = 0.730182, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7064144611358643, Accuracy = 0.9779693484306335\n",
      "Iter #104448:  Learning rate = 0.004800:   Batch Loss = 0.751045, Accuracy = 0.953125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.7100554704666138, Accuracy = 0.9755747318267822\n",
      "Iter #104960:  Learning rate = 0.004800:   Batch Loss = 0.660310, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6997123956680298, Accuracy = 0.9755747318267822\n",
      "Iter #105472:  Learning rate = 0.004800:   Batch Loss = 0.729833, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.697745144367218, Accuracy = 0.9765325784683228\n",
      "Iter #105984:  Learning rate = 0.004800:   Batch Loss = 0.697566, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6971502304077148, Accuracy = 0.9722222089767456\n",
      "Iter #106496:  Learning rate = 0.004800:   Batch Loss = 0.640382, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6890615820884705, Accuracy = 0.9727011322975159\n",
      "Iter #107008:  Learning rate = 0.004800:   Batch Loss = 0.612312, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6836671233177185, Accuracy = 0.9741379022598267\n",
      "Iter #107520:  Learning rate = 0.004800:   Batch Loss = 0.622281, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6817696690559387, Accuracy = 0.9746168851852417\n",
      "Iter #108032:  Learning rate = 0.004800:   Batch Loss = 0.694126, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6872013807296753, Accuracy = 0.9765325784683228\n",
      "Iter #108544:  Learning rate = 0.004800:   Batch Loss = 0.809812, Accuracy = 0.9375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6736462116241455, Accuracy = 0.9779693484306335\n",
      "Iter #109056:  Learning rate = 0.004800:   Batch Loss = 0.658914, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6867506504058838, Accuracy = 0.9736589789390564\n",
      "Iter #109568:  Learning rate = 0.004800:   Batch Loss = 0.638971, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6711495518684387, Accuracy = 0.9808428883552551\n",
      "Iter #110080:  Learning rate = 0.004800:   Batch Loss = 0.647433, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6702396869659424, Accuracy = 0.9779693484306335\n",
      "Iter #110592:  Learning rate = 0.004800:   Batch Loss = 0.630128, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6596091985702515, Accuracy = 0.9784482717514038\n",
      "Iter #111104:  Learning rate = 0.004800:   Batch Loss = 0.630913, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6601272821426392, Accuracy = 0.9736589789390564\n",
      "Iter #111616:  Learning rate = 0.004800:   Batch Loss = 0.677602, Accuracy = 0.9375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6636983156204224, Accuracy = 0.9736589789390564\n",
      "Iter #112128:  Learning rate = 0.004800:   Batch Loss = 0.687103, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6603795289993286, Accuracy = 0.9760536551475525\n",
      "Iter #112640:  Learning rate = 0.004800:   Batch Loss = 0.719061, Accuracy = 0.9375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6956730484962463, Accuracy = 0.9712643623352051\n",
      "Iter #113152:  Learning rate = 0.004800:   Batch Loss = 0.668462, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6962337493896484, Accuracy = 0.9722222089767456\n",
      "Iter #113664:  Learning rate = 0.004800:   Batch Loss = 0.639072, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6677800416946411, Accuracy = 0.9789271950721741\n",
      "Iter #114176:  Learning rate = 0.004800:   Batch Loss = 0.621097, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.662331759929657, Accuracy = 0.9746168851852417\n",
      "Iter #114688:  Learning rate = 0.004800:   Batch Loss = 0.635654, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6605595946311951, Accuracy = 0.9798850417137146\n",
      "Iter #115200:  Learning rate = 0.004800:   Batch Loss = 0.644732, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6516479253768921, Accuracy = 0.9774904251098633\n",
      "Iter #115712:  Learning rate = 0.004800:   Batch Loss = 0.621274, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6463708281517029, Accuracy = 0.9774904251098633\n",
      "Iter #116224:  Learning rate = 0.004800:   Batch Loss = 0.608012, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6418178081512451, Accuracy = 0.9765325784683228\n",
      "Iter #116736:  Learning rate = 0.004800:   Batch Loss = 0.632977, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6509612798690796, Accuracy = 0.9727011322975159\n",
      "Iter #117248:  Learning rate = 0.004800:   Batch Loss = 0.637293, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.665257453918457, Accuracy = 0.9736589789390564\n",
      "Iter #117760:  Learning rate = 0.004800:   Batch Loss = 0.707376, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6538852453231812, Accuracy = 0.9774904251098633\n",
      "Iter #118272:  Learning rate = 0.004800:   Batch Loss = 0.650459, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6725555062294006, Accuracy = 0.9722222089767456\n",
      "Iter #118784:  Learning rate = 0.004800:   Batch Loss = 0.624423, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6624670624732971, Accuracy = 0.975095808506012\n",
      "Iter #119296:  Learning rate = 0.004800:   Batch Loss = 0.641204, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6309430003166199, Accuracy = 0.9774904251098633\n",
      "Iter #119808:  Learning rate = 0.004800:   Batch Loss = 0.595201, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6210702657699585, Accuracy = 0.9818007946014404\n",
      "Iter #120320:  Learning rate = 0.004800:   Batch Loss = 0.605440, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6242057085037231, Accuracy = 0.9774904251098633\n",
      "Iter #120832:  Learning rate = 0.004800:   Batch Loss = 0.605368, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6486360430717468, Accuracy = 0.9746168851852417\n",
      "Iter #121344:  Learning rate = 0.004800:   Batch Loss = 0.639423, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.635444164276123, Accuracy = 0.977011501789093\n",
      "Iter #121856:  Learning rate = 0.004800:   Batch Loss = 0.670034, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6290382742881775, Accuracy = 0.9760536551475525\n",
      "Iter #122368:  Learning rate = 0.004800:   Batch Loss = 0.620471, Accuracy = 0.953125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6315211057662964, Accuracy = 0.9703065156936646\n",
      "Iter #122880:  Learning rate = 0.004800:   Batch Loss = 0.604255, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6115323305130005, Accuracy = 0.9803639650344849\n",
      "Iter #123392:  Learning rate = 0.004800:   Batch Loss = 0.589849, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6032994985580444, Accuracy = 0.982758641242981\n",
      "Iter #123904:  Learning rate = 0.004800:   Batch Loss = 0.631806, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6017096042633057, Accuracy = 0.9851532578468323\n",
      "Iter #124416:  Learning rate = 0.004800:   Batch Loss = 0.610409, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6124604940414429, Accuracy = 0.9822797179222107\n",
      "Iter #124928:  Learning rate = 0.004800:   Batch Loss = 0.595127, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.6075989007949829, Accuracy = 0.982758641242981\n",
      "Iter #125440:  Learning rate = 0.004800:   Batch Loss = 0.610757, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5942203402519226, Accuracy = 0.9822797179222107\n",
      "Iter #125952:  Learning rate = 0.004800:   Batch Loss = 0.630575, Accuracy = 0.953125\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5914614200592041, Accuracy = 0.9813218116760254\n",
      "Iter #126464:  Learning rate = 0.004800:   Batch Loss = 0.585450, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5932507514953613, Accuracy = 0.9837164878845215\n",
      "Iter #126976:  Learning rate = 0.004800:   Batch Loss = 0.630013, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5918149948120117, Accuracy = 0.982758641242981\n",
      "Iter #127488:  Learning rate = 0.004800:   Batch Loss = 0.576021, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5828436017036438, Accuracy = 0.9837164878845215\n",
      "Iter #128000:  Learning rate = 0.004800:   Batch Loss = 0.546618, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5853793025016785, Accuracy = 0.9808428883552551\n",
      "Iter #128512:  Learning rate = 0.004800:   Batch Loss = 0.568348, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5802171230316162, Accuracy = 0.984674334526062\n",
      "Iter #129024:  Learning rate = 0.004800:   Batch Loss = 0.531274, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5751498937606812, Accuracy = 0.9856321811676025\n",
      "Iter #129536:  Learning rate = 0.004800:   Batch Loss = 0.575446, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5688873529434204, Accuracy = 0.9861111044883728\n",
      "Iter #130048:  Learning rate = 0.004800:   Batch Loss = 0.615428, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5723927021026611, Accuracy = 0.9818007946014404\n",
      "Iter #130560:  Learning rate = 0.004800:   Batch Loss = 0.548812, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5641018748283386, Accuracy = 0.9875478744506836\n",
      "Iter #131072:  Learning rate = 0.004800:   Batch Loss = 0.535258, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5649733543395996, Accuracy = 0.9875478744506836\n",
      "Iter #131584:  Learning rate = 0.004800:   Batch Loss = 0.600433, Accuracy = 0.984375\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5601188540458679, Accuracy = 0.9875478744506836\n",
      "Iter #132096:  Learning rate = 0.004800:   Batch Loss = 0.552348, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5629166960716248, Accuracy = 0.9885057210922241\n",
      "Iter #132608:  Learning rate = 0.004800:   Batch Loss = 0.579235, Accuracy = 0.96875\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5488259196281433, Accuracy = 0.9899425506591797\n",
      "Iter #133120:  Learning rate = 0.004800:   Batch Loss = 0.554686, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5557687878608704, Accuracy = 0.9837164878845215\n",
      "Iter #133632:  Learning rate = 0.004800:   Batch Loss = 0.511436, Accuracy = 1.0\n",
      "PERFORMANCE ON TEST SET:             Batch Loss = 0.5541974306106567, Accuracy = 0.9861111044883728\n",
      "Optimization Finished!\n",
      "FINAL RESULT: Batch Loss = 0.5541974306106567, Accuracy = 0.9861111044883728\n",
      "TOTAL TIME:  27.5389986038208\n"
     ]
    }
   ],
   "source": [
    "# Perform Training steps with \"batch_size\" amount of data at each loop. \n",
    "# Elements of each batch are chosen randomly, without replacement, from X_train, \n",
    "# restarting when remaining datapoints < batch_size\n",
    "step = 1\n",
    "time_start = time.time()\n",
    "unsampled_indices = range(0,len(X_train))\n",
    "\n",
    "if not train:\n",
    "    \n",
    "    # only perform testing - on training set\n",
    "    loss, acc = sess.run(\n",
    "        [cost, accuracy], \n",
    "        feed_dict={\n",
    "            x: X_train,\n",
    "            y: one_hot(y_train)\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    print(\"PERFORMANCE ON TRAIN SET:             \" + \\\n",
    "          \"Batch Loss = {}\".format(loss) + \\\n",
    "          \", Accuracy = {}\".format(acc))\n",
    "    \n",
    "    # only perform testing - on test set\n",
    "    loss, acc = sess.run(\n",
    "        [cost, accuracy], \n",
    "        feed_dict={\n",
    "            x: X_test,\n",
    "            y: one_hot(y_test)\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    print(\"PERFORMANCE ON TEST SET:             \" + \\\n",
    "          \"Batch Loss = {}\".format(loss) + \\\n",
    "          \", Accuracy = {}\".format(acc))\n",
    "\n",
    "while train and (step * batch_size <= training_iters):\n",
    "    #print (sess.run(learning_rate)) #decaying learning rate\n",
    "    #print (sess.run(global_step)) # global number of iterations\n",
    "    if len(unsampled_indices) < batch_size:\n",
    "        unsampled_indices = range(0,len(X_train)) \n",
    "    batch_xs, raw_labels, unsampled_indicies = extract_batch_size(X_train, y_train, unsampled_indices, batch_size)\n",
    "    batch_ys = one_hot(raw_labels)\n",
    "    # check that encoded output is same length as num_classes, if not, pad it \n",
    "    if len(batch_ys[0]) < n_classes:\n",
    "        temp_ys = np.zeros((batch_size, n_classes))\n",
    "        temp_ys[:batch_ys.shape[0],:batch_ys.shape[1]] = batch_ys\n",
    "        batch_ys = temp_ys\n",
    "       \n",
    "    # Fit training using batch data\n",
    "    _, loss, acc = sess.run(\n",
    "        [optimizer, cost, accuracy],\n",
    "        feed_dict={\n",
    "            x: batch_xs, \n",
    "            y: batch_ys\n",
    "        }\n",
    "    )\n",
    "    train_losses.append(loss)\n",
    "    train_accuracies.append(acc)\n",
    "    \n",
    "    # Evaluate network only at some steps for faster training: \n",
    "    if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):\n",
    "        \n",
    "        # To not spam console, show training accuracy/loss in this \"if\"\n",
    "        print(\"Iter #\" + str(step*batch_size) + \\\n",
    "              \":  Learning rate = \" + \"{:.6f}\".format(sess.run(learning_rate)) + \\\n",
    "              \":   Batch Loss = \" + \"{:.6f}\".format(loss) + \\\n",
    "              \", Accuracy = {}\".format(acc))\n",
    "        \n",
    "        # Evaluation on the test set (no learning made here - just evaluation for diagnosis)\n",
    "        loss, acc = sess.run(\n",
    "            [cost, accuracy], \n",
    "            feed_dict={\n",
    "                x: X_test,\n",
    "                y: one_hot(y_test)\n",
    "            }\n",
    "        )\n",
    "        test_losses.append(loss)\n",
    "        test_accuracies.append(acc)\n",
    "        print(\"PERFORMANCE ON TEST SET:             \" + \\\n",
    "              \"Batch Loss = {}\".format(loss) + \\\n",
    "              \", Accuracy = {}\".format(acc))\n",
    "\n",
    "    step += 1\n",
    "\n",
    "print(\"Optimization Finished!\")\n",
    "\n",
    "# Accuracy for test data\n",
    "\n",
    "one_hot_predictions, accuracy_fin, final_loss = sess.run(\n",
    "    [pred, accuracy, cost],\n",
    "    feed_dict={\n",
    "        x: X_test,\n",
    "        y: one_hot(y_test)\n",
    "    }\n",
    ")\n",
    "\n",
    "test_losses.append(final_loss)\n",
    "test_accuracies.append(accuracy_fin)\n",
    "\n",
    "print(\"FINAL RESULT: \" + \\\n",
    "      \"Batch Loss = {}\".format(final_loss) + \\\n",
    "      \", Accuracy = {}\".format(accuracy_fin))\n",
    "time_stop = time.time()\n",
    "print(\"TOTAL TIME:  {}\".format(time_stop - time_start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved in file: data/Test/7/model.ckpt\n"
     ]
    }
   ],
   "source": [
    "##### Check if you want to save your current model\n",
    "if update:\n",
    "    save_path = saver.save(sess, DATASET_PATH + \"model.ckpt\")\n",
    "    print(\"Model saved in file: %s\" % save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 4.0127115 , -3.096812  ,  1.9507008 , ..., -2.8685133 ,\n",
      "        -2.4190218 ,  2.8185992 ],\n",
      "       [ 5.8267    , -0.5564997 ,  1.0594916 , ..., -2.5481293 ,\n",
      "        -3.606589  , -0.61002386],\n",
      "       [ 5.2107973 ,  1.617826  , -0.55898416, ..., -2.555879  ,\n",
      "        -1.7346652 ,  0.43418157],\n",
      "       ...,\n",
      "       [-1.1879985 ,  1.4092283 ,  0.83709145, ...,  2.2522497 ,\n",
      "         0.5549283 ,  6.676542  ],\n",
      "       [-0.9973055 ,  6.2251253 , -0.28018004, ...,  0.4483325 ,\n",
      "        -0.28341576,  8.914852  ],\n",
      "       [-1.8361615 ,  1.9449244 , -0.99618506, ..., -0.37941766,\n",
      "         0.03499174,  2.8120208 ]], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "##### Inferencing\n",
    "\n",
    "# X_infer_path = \"utilities/something/something.txt\"\n",
    "X_infer_path = DATASET_PATH + \"X_test.txt\"\n",
    "\n",
    "X_val = load_X(X_infer_path)\n",
    "\n",
    "preds = sess.run(\n",
    "    [pred],\n",
    "    feed_dict={\n",
    "        x: X_val\n",
    "   }\n",
    ")\n",
    "\n",
    "print(preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "263\n",
      "2088\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PF~4\\Python\\AN~3\\lib\\site-packages\\matplotlib-3.0.2-py3.5-win-amd64.egg\\matplotlib\\font_manager.py:1241: UserWarning: findfont: Font family ['Bitstream Vera Sans'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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/KADjOo9jcOPB3DvrXgqEFOCJJk9Q9+O6zvO/5+webhp3k1t/9j27eP9iOn3R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2w9h7Bt7Dv87+l/F5RJsR9IrvRXxIPOuz1gOwYO8C43hKixRje3DSYJSZCgMTB7L82uVu/kWE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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy: 98.61111044883728%\n",
      "\n",
      "Precision: 98.62660726873973%\n",
      "Recall: 98.61111111111111%\n",
      "f1_score: 98.61246467872995%\n",
      "\n",
      "Confusion Matrix:\n",
      "Created using test set of 2088 datapoints, normalised to % of each class in the test dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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A/zatR18CDqed7nXtzr7WRdnYHOyYDT+X5MfNWlzvLqRMWl8F/iXpd5ImkYKg\npUgZtQZ6E8MBi4hnSPfVeBvphqRXSvod6eLlJzQfJBQtAPwKeErSrUp3WT9f0u352K+TxvmX2vQm\n6WaP95LuRfKopMn5HF6llAHvbtKNU4sOJwVJ6wP/lvQHSReTEhjMIt3ItFF/zI/n5vpOzcuSua2X\nkoKaUcDNkq6QdF4+5r6kG3Xu2OAcKuCtb7v7u6fOkaSenFslXUQ6JyuQ7vtUr31IwdingYckXSjp\nklz2HlJ2slZ8BkpK5/Oq/Bk4VdKphfUnkjKxjQb+KenPki4gJfHYNj9+tVhhDtpuJ2XqukvS2bne\nUursS0k32FwReEDSn/L5eoQ0dPKMFr6/P5Eu0PeXdLmk03JbNshtnUXKCPc4cDQwLf/OnS/pWkmP\nk+75VUynvAmpF+4pSZdJOkfSX0gBzvtINzD9v8L2lwMvAp+XdJ2kM3IbtqnV+Jz+eh9Sj995kv4u\n6TxJd5B+h0+osuuR+fEoSbfl39UbSVkhj+/nkFU/Dy3421T6m35pje3MhhXP2TEbZiLiH/mie0tJ\nS+d/sK2o9wGlFMLfJ/VefJ50j5AbgZM7IdAp2I90Qf110sXfy6TAYBxpPspA71Ezm3ThP5Z0X6NP\nkb5ceoyUIevY8nH+ETEtD0fbizSk6cOkNM/PkC7yzqUsPXFEzJK0CSk98w6k8/4U6dv0I4A/N9H2\nX5OyPX2JdJFa+ib+SHJyhEg3cryRdEPMdYF3kC5mTwSOzheQzdifNDxpw/L7l+Tj/lbp/jrfIt1s\ncxrwrYio++I9Ip6WtD7pZ7w96Zy9DtxDyvw1oZ/MaM04lHQhvR3pd6LU6/m13J43Je1EumD/KulC\nfwQpMDkT+HlZ5r6Sz5Pmf21CGjY1P6nX9hd5kvwmwA/z+9uSNN/qEtLnYs9WvbmIuEPSjqTzuQFQ\nGsY6hTzkMSLulLQm6fPyOeCjpC8EppPSw/8auLBQ7UTS3LCNSb8HS5KGZt1NupHwmTH3vbyeUrrZ\n7RGkm+xuROrheIy+L3f6ew9nS3qCdNPedUlDfG8jnbfX6LtvTnGfC5XSYh9Bug/UaqS/KbtFxFmS\nDqlyuH4/DzT5t0nSfKQvnF4gZYmzXiY5QUED5J5Os+EnD4c6HTgwIjphaJmZmTVJfZnafhkRg3VD\nYesQ840aHSPHVso90j4vX7znrRFRbdhnWzksNBueziJ9k32Q0qRyMzPrXoeR0k0f3e6GmHUaBztm\nw1AepnMg6U7b+9bY3MzMOlTu1dkY+HFElN+Hy3pVuxMSdFGCAs/ZMRumIuIKhuamgmZmNkhySm7/\nLTerwj07ZmZmZmbWk9yzYx2i/+I1AAAgAElEQVRrgYUXjwWXXK5tx19lqfJ7Fw49f1XXdyfJdvHP\nwMzMAB599BGeffbZjvi3oA4fOtZJHOxYx1pwyeXY+NCz2nb8C8a1P6nIfPP5j9mbb7Y33PHPwMzM\nADZcr/3XBdY4D2MzMzMzM7Oe5J4dMzMzM7MuITyMrRHu2TEzMzMzs57kYMfMzMzMzHqSh7GZmZmZ\nmXUL4VShDXDPjpmZmZmZ9SQHO2ZmZmZmNqgk7S/p95IelhSFZVzZdgtI2lPSuZLulTRD0quSnpT0\nR0mbNHJcD2MzMzMzM+sa6tZsbOOBxerYbglgQoXyZYFtgW0l7RURv63noO7ZqZOk8YUIdGKTdYwr\n1DG5tS00MzMzM+tYdwOnA/sCT9ex/XV5208C3wCeKaz7laQF6zmoe3bsLbkb8YxCUQCvAjOBx4Cp\nwMSIuKmOfUteBh4HpgC/iIh/trbVZmZmZtbpImLj0nNJ3+tn0znAJhFxXaHsSklPARfl1wsDHwT+\nUeu4DnbqdzpwZX4+vZ0NGUICRgLL5GVtYC9J5wF7RcScOup4O7BqXraXtEFE3DVYDTYzMzPrdV06\njK0uETGL1KtT7v+VvZ5dT30OduoUEdOAae1uxxDbGBgBjAZ2BLbM5bsAi0vaOiKixr5jgKOB+YGF\ngG8Cew1im83MzMys9+xYeP4v4L56dvKcnTpVmrMjaVVJp0u6TdL0nCliTs4ccaykZeqse0lJJ0u6\nOWeaeFnSS5IelPRbSauUbT+20JZHJK0k6eycreIlSddLGjPQ9xwRUyLimog4IyK2Ag4vrP40sEMd\n+/4CmFRYtdJA22VmZmZmHWUpSVMLS0u/2Ja0E/CD/PI1YM9+vnCfi3t2BmY1YPeyshHAGnnZVtJa\nEfFCjXqWBr5eobw0/OsLksZExEMVthkF3EIaZlayEXCZpFVyV2Cr/BT4Cul9A+wGXNBgHY+1sD1m\nZmZmw04HDmN7NiIG/EV7JZIOAH5Jml7xCrBjRFxb7/7u2RmYR4FDgO2BLYCxwHb09WSMBvaso57n\ngSNI3XNb5Xo+C5yT148CvlNl30VJE7l2IQVeM3P5UrmsZXIEfVWhqOqHWtJGuQfqO/QNf3sV+E1/\nx5C0V+lbgVdn14oRzczMzKwXKfkV8CtSoDMT+FRE/KmRetyzMwARcV8eLrYvsCYpKJm/bLP166hn\nuqTbgH2AdUiBSvnPpr96doiIqQCS1gP2zuWr13wTjZtReL54P9tdX/Z6KnBARNzaX+URMYGcW33x\nldeoq3vSzMzMzHqHpJHA2cAXc9F/SIFOw1l9HewMgKQfM/c8lkpG1VHPHsBpTdYzqxToZMVgZIla\nx27C0oXnjXS9vB94V4vbYmZmZjbsdOAwtpokbQGU7o1TvEfO2pJK15RTSCOWJgEfz2UvAAcBoyRt\nVNjvgYioeb8eBztNkjQCOLBQdC5p2Nls0hC07+byeoYKHlJ4Pgk4iTS0bQxwbI16nit7/XqxmXUc\nu26S5gM2LxRNrbZtREjS0sD/kOb5LAicKemuiLi3le0yMzMzs443AVi5Qvl+eQHYFHiEvkAH0kii\nSnPEdwcm1jqo5+w0b0lSKuWSvSNiUkRMyesaUcxQdnBEXJrrWXigjWyxw4FiZrgz+9s4Ip4hpZl+\nOBctAPxscJpmZmZmZjY39+w0bzqpm60U8Bwl6VJgM+bN0FbLQ6TsbQCHSTqNNHfn0FY0tFm5q3AE\nKQrfmZSEoeSvwIW16oiIVyT9FDg1F302Z6i7vdXtNTMzM+t5osVjd4ZGRIxuYPOWvUP37DQpZyY7\noVC0H/A30pC0Snd97c/xhec75nqOBv4+kDa2wPXA1cAZzB3onEdKilBvAoGzmPuGrEe0pnlmZmZm\nZtW5Z6c5pYv8w0m9O7sDywMPAMeQhmuNrbuyiFOUZpp9m5Su+j/AicBdjdQzCIJ046YXSPfHmQpM\njIibGqok4jVJx5DeE8DnJK0ZEXe1tLVmZmZmPU6oKxMUtIuDnfotUnj+NEBEvA4cmZdyE8sLImJi\npfK87mTg5Aqr5vk0R8TkSuV53XhgfKV1tfTXvoHuGxEnkRIvmJmZmZkNCQc7NeQ83ysDWxeKuyqb\nWFmavopyQgQzMzMzs57hYKe2U4DdCq+nAee3qS3NKr/BZyXuDzUzMzPrAh7GVj8nKKjfbFIGsi0i\n4pV2N8bMzMzMzPrnnp0aImIcMK7NzRiQiHD4b2ZmZmbDjoMdMzMzM7Mu4mFs9fMwNjMzMzMz60nu\n2bGOtepSC/H7PdZt2/G/ePotbTt2STvfP0D9940dPPPN52+vzMzMrDkOdszMzMzMuoiHsdXPw9jM\nzMzMzKwnOdgxMzMzM7Oe5GFsZmZmZmbdQvhW8A1wz46ZmZmZmfUkBztmZmZmZtaTPIzNzMzMzKyL\nOBtb/dyzY2ZmZmZmPcnBjpmZmZmZ9SQPYzMzMzMz6xJCHsbWAPfsNEDSeEmRl4lN1jGuUMfk1rbQ\nzMzMzMxKHOzYXMqCsaiwvhjwPVK27pHivnl5XdIMSddL+qYk9yaamZmZ2ZDwhWdjTgeuzM+nt7Mh\nXWR+YAlgo7ysB3y5rS0yMzMz62IexlY/9+w0ICKmRcSUvPyr3e3pcGcAGwPbAjcWyneVtHx7mmRm\nZmZmw4mDnQZUmrMjaVVJp0u6TdJ0Sa9KmiPpXknHSlqmzrqXlHSypJslPSnpZUkvSXpQ0m8lrVK2\n/djicDJJK0k6Ow8ZeykPGxszCKehXqXA8E/At8vWrdSOBpmZmZnZ8OJhbAO3GrB7WdkIYI28bCtp\nrYh4oUY9SwNfr1C+al6+IGlMRDxUYZtRwC1AMbDaCLhM0ioRMauO9zGUHmt3A8zMzMy6lkex1c09\nOwP3KHAIsD2wBTAW2A6YlNePBvaso57ngSOAHYGtcj2fBc7J60cB36my76LAHGAXUuA1M5cvlcva\nYSVJG0naBji2UP5/EeFgx8zMzMwGnXt2Bigi7svDxfYF1iQFJfOXbbZ+HfVMl3QbsA+wDilQKf/5\n9FfPDhExFUDSesDeuXz1mm9icOzO3D1erwC/An7U306S9gL2AlhxJY92MzMzM7PmOdgZIEk/Bg6v\nsdmoOurZAzityXpmlQKdbEbh+RK1jl3mzbJ2KSKKKahVbdsaRgIfAxYgBT4VRcQEYALAOuuMmSf1\ntZmZmdmwJmdja4SHsQ2ApBHAgYWic4FPkbKQ/bxQXs95PqTwfBKwTa7ngDrqea7s9evFZtZx7KLy\n+T1Llb1euvB8JtX9iDR3aXOgNF9pLHBSg+0xMzMzM2uKg52BWRJYqPB674iYFBFT8rpGFMdsHRwR\nl+Z6Fh5oIxv0z7LXny49yTcE3bKw7t7+KoqI1yPiKuC7heJdJa0z4FaamZmZDVOSOmrpZA52BmY6\nKTFAyVGSPinpaObN0FZLMcvaYbmeQ4BDB9rIRkTEA6TMbiWnSJog6UjgZqCYAvsc6nMG8HDh9RED\na6WZmZmZWW0OdgYgz2U5oVC0H/A30pC06xqs7vjC8x1zPUcDfx9IG5u0B33zfkaSsskdCqxd2Obk\niLisnsoi4nXgmELRNpI+0oqGmpmZmZlV42CneaXJ84fn5SHgZeAu4EvAmQ1VFnEKKRPb/bmefwH7\nAz9uUXsbacs9pMxyvwTuAV4kzQN6GrgM2D4i9mmw2jOY+/46tZI6mJmZmVkF7R621k3D2JyNrTGL\nFJ4/DW/1WhyZl3ITywsiYmKl8rzuZODkCqvm+RRFxORK5XndeGB8pXX1iogngIPyUu8+o/tZ9yqw\n4kDaZGZmZmbWCAc7dZA0ElgZ2LpQ3O/k/E4jaaNa2+SECGZmZmZmPcHBTn1OAXYrvJ4GnN+mtjTr\n+jq26ex+SDMzM7NhTnT+0LFO4jk7jZkN/BXYIiKq3hjTzMzMzMzazz07dYiIccC4NjdjQCLCXwGY\nmZmZ2bDiYMfMzMzMrJv4K+y6eRibmZmZmZn1JAc7ZmZmZmbWkzyMzTpWABFRc7vB8vs91m3bsUt2\nnji1rcf/3bgxbT2+mZmZlRHOxtYA9+yYmZmZmVlPcrBjZmZmZmY9ycPYzMzMzMy6iIex1c89O2Zm\nZmZm1pMc7JiZmZmZWU/yMDYzMzMzsy7iYWz1c8+OmZmZmZn1JAc7ZmZmZmbWkzyMzczMzMysm3gU\nW93cs2NmZmZmZj3JwU4TJI2XFHmZ2GQd4wp1TG5tC83MzMzMzMGOVVQWjIWkNyW9LGm6pFslnSLp\nY/XsO9RtNzMzM+tlkjpq6WSes9Oc04Er8/Pp7WzIEBIwElgmL2sDe0k6D9grIua0s3FmZmZmZuUc\n7DQhIqYB09rdjiG2MTACGA3sCGyZy3cBFpe0dUS4F8fMzMzMOoaHsTWh0pwdSatKOl3SbXmo16uS\n5ki6V9Kxkpaps+4lJZ0s6WZJT+ahYy9JelDSbyWtUrb92EJbHpG0kqSzJc3I+10vacxA33NETImI\nayLijIjYCji8sPrTwA4DPYaZmZmZ9a/dQ9Y8jG34Wg3YvaxsBLBGXraVtFZEvFCjnqWBr1coXzUv\nX5A0JiIeqrDNKOAW0jCzko2AyyStEhGz6ngf9fop8BXS+wbYDbighfWbmZmZmQ2Ie3Za51HgEGB7\nYAtgLLAdMCmvHw3sWUc9zwNHkIaKbZXr+SxwTl4/CvhOlX0XBeaQhpbtDszM5UvlspbJQ9auKhQN\nuPcIQNJekqZKmvrss8+0okozMzOzntLunhz37AxDEXFfHi62L7AmKSiZv2yz9euoZ7qk24B9gHVI\ngUr5z6m/enaIiKkAktYD9s7lq9d8E42bUXi+eCsqjIgJwASAtdcZ4zlAZmZmZtY0BzstIunHzD2P\npZJRddSzB3Bak/XMKgU6WTEYWaLWsZuwdOF5reF5ZmZmZmZDysFOC0gaARxYKDqXNOxsNmkI2ndz\neT3DBg8pPJ8EnEQa2jYGOLZGPc+VvX692Mw6jl03SfMBmxeKplbb1szMzMxap9OHjnUSBzutsSSw\nUOH13hExG9INNhusa6XC84Mj4p5cz9iBNHAQHA4UM8Od2a6GmJmZmZlV4mCnNaaTEgOUAp6jJF0K\nbMa8GdpqeYiUvQ3gMEmnkebuHNqKhjZL0kak7HIrAzuTkjCU/BW4sJ99f1ah+KWI+FFLG2lmZmZm\nVuBgpwUiIiSdQN8QtP3yAjCZlFGtXscDJ+fnO+almXpa7foq5ecBe9W4oej3KpTNBBzsmJmZmTXK\no9jq5mBn4EoX+YeTend2B5YHHgCOARaggSAlIk5RGoj5bVK66v8AJwJ3NVLPIAjgNVIigsdIc3Qm\nRsRNbWyTmZmZmVlVDnaas0jh+dMAEfE6cGReyk0sL4iIiZXK87qT6evdKZonjo+IyZXK87rxwPhK\n62rpr32Dua+ZmZmZWas42GmApJGkOStbF4rvbVNzmpLn3vQrIqYMRVvMzMzMrHHOxlY/BzuNOQXY\nrfB6GnB+m9rSrGpzb4r8G2RmZmZmXa+e+77YvGaTMpBtERGvtLsxZmZmZmY2L/fsNCAixgHj2tyM\nAYkI99qYmZmZdSt5GFsj3LNjZmZmZmY9ycGOmZmZmZn1JA9js44l2ttN2/99UofG78aNaevxP33S\njW09PsBf992g3U0wMzPrGOn6qN2t6B7u2TEzMzMzs57kYMfMzMzMzHqSh7GZmZmZmXUNORtbA9yz\nY2ZmZmZmPcnBjpmZmZmZ9SQPYzMzMzMz6yIexVY/9+yYmZmZmVlPcrBjZmZmZmY9ycPYzMzMzMy6\niLOx1c89O2ZmZmZm1pMc7DRB0nhJkZeJTdYxrlDH5Na20MzMzMzMHOxYRWXBWFRYXwz4Hilb90hh\n3fiharOZmZlZz1PKxtZJSyfznJ3mnA5cmZ9Pb2dDzMzMzMysMgc7TYiIacC0drfDzMzMzIYXAfPN\n1+HdKR3Ew9iaUGnOjqRVJZ0u6TZJ0yW9KmmOpHslHStpmTrrXlLSyZJulvSkpJclvSTpQUm/lbRK\n2fZji8PJJK0k6WxJM/J+10saMwinwczMzMyso7lnp3VWA3YvKxsBrJGXbSWtFREv1KhnaeDrFcpX\nzcsXJI2JiIcqbDMKuAUoBlYbAZdJWiUiZtXxPszMzMzMeoJ7dlrnUeAQYHtgC2AssB0wKa8fDexZ\nRz3PA0cAOwJb5Xo+C5yT148CvlNl30WBOcAupMBrZi5fKpeZmZmZWZdrd0ICJygYhiLivjxcbF9g\nTVJQMn/ZZuvXUc90SbcB+wDrkAKV8p9Tf/XsEBFTASStB+ydy1ev+SY6gKS9gL0AVlxppTa3xszM\nzMxaQdL+wIbAGFInQMnuETGxwvZLAd8HtgFWBF4EbgWOj4g/13tcBzstIunHwOE1NhtVRz17AKc1\nWc+sUqCTzSg8X6LWscu8WdYuRUQxBbWqbTsQETEBmACwzjpj5kl5bWZmZmZdaTywWD0bSloZuA4o\nfvM9Etgc2FzSERHxk3rq8jC2FpA0AjiwUHQu8ClgY+DnhfJ6zvchheeTSNHsxsABddTzXNnr14vN\nrOPYReXze5Yqe7104flMzMzMzGxISOqopU53k27fsi/wdI1tT6Mv0LmZNDXk+/R9wf4jSRvWc1D3\n7LTGksBChdd7R8RsSDfnbLCuYgR7cETck+sZO5AGNuGfZa8/DZyZ2/I2YMvCunuHqlFmZmZm1n0i\nYuPSc0nfq7adpA8CnyjtBmwfEY/ldasCXyN9ib8/cEOt4zrYaY3ppMQApYDnKEmXApsxb4a2Wh4i\nZW8DOEzSaaS5O4e2oqH1iogHJN0CrJuLTskR9NOkXqtiCuxzyvcv2FzS2yuUXxoRNT+gZmZmZjas\nfKLw/NFSoJPdQAp2ADatpzIHOy0QESHpBPqGoO2XF4DJpIxq9ToeODk/3zEvzdTTCnvk4y5JGidZ\nKZvcyRFxWT91bJiXck9RRzRuZmZmZgWdmQFtKUnFeeMT8jzsZhS/UH+qbF3x9ZKSFq91WxcHOwNX\nmkR/OKl3Z3dgeeAB4BhgARoIUiLiFKXBj98mZar4D3AicFcj9bRCRNwjaU3SfKQtSR++BUhzg24F\nTouIi4ayTWZmZmbWcZ6NiFbdxL44NeTVsnXlrxcGHOwMgkUKz58GiIjXgSPzUm5ieUFOsTdPeV53\nMn29O0XzxPERMblSeV43npT5omkR8QRwUF7q3Wf0QI5pZmZmZsPWnMLzkWXryl/PrlWZg50GSBoJ\nrAxsXSjuqsn5kjaqtU1ETBmKtpiZmZlZYwSNZEDrRg8Vni9btm65wvMZtYawgYOdRp0C7FZ4PQ04\nv01tadb1dWzT079BZmZmZtaxri48X0nSShExLb/+eGHdNfVU5mCnObNJNzo6MCJeaXdjzMzMzMw6\nmaQtgAXzywULq9aWVOqhmRIRd0u6hpRtTcDvJR0NvB/4St4uSEm9anKw04CIGAeMa3MzBiQi3Gtj\nZmZm1rUaupFnJ5lAmg5SrpjFeFNSJuCvkjoW3gV8FPhj2T4/rnfaxXzNtNTMzMzMzGwwRMTDpPtM\nHgf8m5SF7QXSELfP5SRcdXHPjpmZmZmZDapGs/VGxNPAAXlpmoMdMzMzM7Mu0p2j2NrDwY5ZFV06\nHral/rrvBu1uAr++4aHaGw2ib264Su2NzMzMrCN5zo6ZmZmZmfUk9+yYmZmZmXURjz6pn3t2zMzM\nzMysJznYMTMzMzOznuRhbGZmZmZm3ULOxtYI9+yYmZmZmVlPcrBjZmZmZmY9ycPYzMzMzMy6hHA2\ntka4Z8fMzMzMzHqSe3bMzMzMzLqIO3bq1/E9O5LGS4q8TGx3e8zMzMzMrDt0fLBjlUlaTtLBkv4i\n6d+SZkl6OT+fIGmVJuudWAguQ9LrkuZIekzS9ZJ+Kundde4bkt7MbbtT0tGSlhzYOzczMzMzq4+H\nsXWvTYCfVyhfJS87SdokIm4f4HHmBxbMywrARsB3JX0/Iv6njv0FLAysmZftJK0dES8OsF1mZmZm\nw5ITFNTPPTv9kLRwu9tQw2vA+cCXgC2Ao4A38rpFgF8OsP47gI1z3QcA9+bytwG/kPTdOvbdEji9\nUP5eYNsBtsvMzMzMrKauC3YkrSfpKkmzJT0v6XxJKxbWbyHpQkn3S5oh6TVJMyXdLOlASSPK6isO\nvRovaVdJt0t6GTgnb7OzpD9JelDSC7nOGZKulbSHysJrSZMLdY7L29yZh5k9kYdzzV+2z/ySDpX0\ncN7unrzfuEJdkwu73AW8NyJ2jojzIuKKiDgUOKmwzfoDPN0zI2JKrvs4YG3gr4X1P5G0Uo19/wbs\nCfy3sK7aPmZmZmZmLdNtw9jWBXYCRhbKdgQ2lLRORDwNbAZ8sWy/RYGP5uXjVO9Z2AVYrUL554Bt\nysqWyHV9nDQ8a/8qdf6grM7lgEOAmcDPCuW/BXYvvP4AcBpwW6VKI+LeSuXA/ys8n11lm6ZExCuS\n9gEeIQ1PW4D086g0nK5cMSB8rJXtMjMzMxtOPIqtft3Ws/N+4HJga2A/+i7m3wX8ND+/DvgWKaD5\nBCn4+RLwYF7/OUnrVql/NeAGUgD1GdIQMYBLgL1JAc+mud6vAs/m9d+UtGw/dZ6Q6/tDofzbpSeS\nNmbuQOcM4NOkYGitKvXOI/cw7VAouqTefesVEdOYO6AaU2XTxSRtJGkL4BTSsDqA/wB/rFa/pL0k\nTZU09Zlnn2lJm83MzMxseOq2np0ngC9GxKsAkkYCpUny20v6OjCZFCAcTgo0FmHuXgVIw7tuqVD/\n48DmEfFyWfnlwMHAN0iT/xcsq3N+Uq/TpRXq/EtEfCu391Zg+1y+rKRFImIWcwcod0bEHvn5ZZLe\nU9inqhzoHE/qaYIUVBxaa78mzSg8X7zKNh8Bri8ruxA4KCLmVKs4IiYAEwDWWWdMDKSRZmZmZja8\ndVuwc3Mp0MmmFJ4vDiwNXEDKVNafUVXK/1oe6Eh6B6m3571N1nl14fmMsnVLALOYe5jbjWXbTKFG\nsJODvjNJPVKQhol9IiKm97ffACxdeP5CA/utByxGCsTMzMzMrFFyNrZGdNswtlreS1+g8wapd+cT\npKxgVxS2q/a+n6xQth19gc4c0hC5TXOdd9dR53OlJxHxetm60ie12IPRUG+GpMWBSfQFOvcAG0TE\nvxqpp4HjvZu5g7OpVTa9lnRO1gBK6a9XBi7OwZmZmZmZ2aDqtmDno2XZ1DYsPJ9JmrtTckdEHBkR\nVwM3UV8GsEqBRnG/SRFxQkRMJmVDe1eF7ZtRDEzKM6htXG2nnIVuCjA2F10NbBQRg9JzkoOU39AX\npL1K37ymeURyP2nOVCkl9qqk4YBmZmZmZoOq24axrQBcKOlUYDTww8K6P9CXhABgTUn7Ag8De1F7\nGFo1DxWef0LSl0mB1UFUH7rWqAtJCRcA1pZ0CnAxKdD5fKUdJK1OCm5WyEV3k5I0fKisa/OWiHil\nyXYtJmkj4B2k7HB7kXpqSg7PCQv6FRH3SfodsGsuOljSbyLipSbbZWZmZjYsCWdja0S3BTv/JmVi\nK08d/ThpMv4zpDkvGwAjgBPz+jmkhATVsrD158/5uKuS5gWdlcufAu4H3tdEnXOJiCmSTgdKiQn2\nygukm3N+pMJuG9AX6AB8CLiqwnbvJqWKbkalJAMArwM/iIhfNFDXUaTU3vMBy5Le3/FNtsvMzMzM\nrKZuG8Y2Bfgk6QL8RVIPy4XAhhExPSLeJN0TZyIwnRTkXEMa5lXtvjT9iogXSfN+/kiafzOTlNJ5\no3yMVtkLOAx4lDQ87L5cdmZhm6pZzAZRAC+RAsoppN6j1RoMdIiI+4CLCkXf9dwdMzMzMxtMHd+z\nExHjgfFlxR+fd8u3tn+Wue9ZUzIuL+XbVywv2+ZRKg8nG1tl+4rleV21jsc3I+Kn9N0vCABJxQDh\ngUI9E0lBXUvVcz6a3Tcidqi2zszMzMzqIWdja0DHBzvDyKGSFiMNm3uIlJb6S/QFWQGc3UzFud4P\n1dhsZkTcXWMbMzMzM7Ou4WCncyxMSnpwUIV1ARwSEbc1WfdapOF8/bmWKj1VZmZmZmbdyMFO57gS\neD8pKcBSpPlUT5JuaHpSRJTfbNTMzMzMhiGPYqufg50OERFXkgKewah7Mn33xjEzMzMzGxa6LRub\nmZmZmZlZXdyzY2ZmZmbWRZyNrX4Odsyso31zw1Xaevwr7mvl7bQa98k13tnW45uZmXUzD2MzMzMz\nM7Oe5J4dMzMzM7NuIWdja4R7dszMzMzMrCc52DEzMzMzs57kYWxmZmZmZl1COBtbI9yzY2ZmZmZm\nPcnBjpmZmZmZ9SQPYzMzMzMz6yIexlY/9+yYmZmZmVlPcs+OmZmZmVkXccdO/Tq+Z0fSeEmRl4nt\nbo+ZmZmZmXWHjg92rDJJy0k6WNJfJP1b0ixJL+fnEySt0mS9EwvB5eQK6ydXCj4ljS6UF5dXJT0u\n6VJJn2n+HZuZmZmZNcbD2LrXJsDPK5SvkpedJG0SEbcPbbPmMQJYPi9bS/paRJzW5jaZmZmZdS0n\nKKife3b6IWnhdrehhteA84EvAVsARwFv5HWLAL9sU7sAvgV8HBgHPFEo/15bWmNmZmZmw07XBTuS\n1pN0laTZkp6XdL6kFQvrt5B0oaT7Jc2Q9JqkmZJulnSgpBFl9RWHbY2XtKuk2yW9DJyTt9lZ0p8k\nPSjphVznDEnXStpDZeF12VCvcXmbO/MwsyckHS1p/rJ95pd0qKSH83b35P3GVRlWdhfw3ojYOSLO\ni4grIuJQ4KTCNuu35qz/f/buPMyuqsz3+PeXXIwgc4MGGVSEDkahgYShySAxgIpeglyCIg4B26jI\n3KA0AY3dIGhri80FLxEhgNoQoUGQITIFCbZpEmQSIkMgIkNIglSHxAAh7/1j7UPt2pxddU7VqTrn\nVH6ffvaTfdbae61VlbTPeVlrvatXHoyIuyLiUuAHufLtmjUgMzMzM1u3tNsytj2ATwHDcmWfBMZI\nGhURLwAfAiYX3tsY2KdAKw0AACAASURBVDO7xgMHl7T/aWDHKuWTgIMKZZtnbY0HdgFOKGnztEKb\nWwGnAh3AObnyHwNH5j6/H/gJcG+1RiPi4ZL+/pi7f7nkmWb6c7MHYGZmZta25Gxs9Wi3mZ2RwGzg\n48CxdH6Z3wY4K7v/DWkJ1cHARFLwcwTweFY/SdIeJe3vCNxNCqA+RloiBnAd8GVSwDMha/cLwLKs\n/hhJw7tp87ysvaty5cdXbiSNo2ugcwlwICkY2q2k3TfJZpgOyxVdV+u7/WBnSeMkfZaugeAPu3tJ\n0lRJ8yXNX7psaf+O0MzMzMwGtXab2XkWmBwRrwJIGgZ8L6s7VNKXgDmkAOEMUqCxEVCMf/cG7qnS\n/jPAfhGxulA+GzgF+Cpp8/8GhTaHkmadrq/S5g0RcVw23gXAoVn5cEkbRcQKugYo90fEUdn9TZJ2\nyL1TKgt0fkiaaQJ4GpjW03v96N8Ln58FTo+IS7p7KSJmADMARo0aHf00NjMzMzNbB7RbsDOvEuhk\n5ubuNwW2BK4kZSrrzmYl5TcWAx1J65Nme0b0ss3bc/fLC3WbAyvousztt4Vn5tJDsJMFfZeSZqQg\nLRWbGBFLunuvxNp809W6K3m2J8OBnXsxHjMzMzPLCDkbWx3abRlbT0bQGei8TprdmQiMA27JPVf2\ncz9XpewTdAY6K0lL5CZkbT5YQ5svVm4iYk2hrvIvNT+DUddshqRNgZvpDHQeAvaJiMfqaSdnRe5+\niyr1W+buO7ppZwLwNjqX6w0BTpT0hV6Oy8zMzMysLu0W7OxZyKY2JnffQdq7U3FfRJwZEbcD/0Vt\nWcCqBRr5926OiPMiYg4pG9o2VZ7vjXxgUsygNq7spSwL3Vxg36zodmBsRDzdh7H8IXc/In84abak\nLj/DVZYkAYCIWBUR/w78PFd8VjZbZmZmZmbWr9ptGdvWwCxJFwHvBr6Zq7uKziQEALtIOhp4EphK\nz8vQyizK3U/MNtx3ACdTvnStXrNICRcAdpd0IXAtKdA5pNoLkv6WFNxsnRU9SErSsHNhavOeiHil\njrFcQ0oVvQFpL9Jdkv4jq/s0nQHyquzZWnyLlEVvCPAOUrKHH3T7hpmZmZlV5VVstWu3YOcJUia2\nYuroZ0ib8ZeS9rzsA6wHnJ/VryQlJCjLwtadX2X9vpe0L+iyrPx5YCGwUy/a7CIi5kq6GKgkJpia\nXQD3AbtWeW0fOgMdSPthbqvy3HuAp+oYy9Is0cMlpH8f7wT+sfDYGmBqRCwrvl/S5qOSZpECHoBT\nJP2oSiIIMzMzM7OGabdlbHOB/YG7SDMLHaRZkTERsSQi1pLOxJkJLCEFOXeQlnl1u+SqTESsIu37\nuYa0/6aDlNJ5bNZHo0wFTgcWA68Cj2Rll+aeWdnA/kpFxE9Jy+kuJwVKr2TXU1nZ3hHxszqbPYvO\nZYJbAV9sxFjNzMzMzMq0/MxOREwHpheKx7/5yTeeX0bXM2sqpmRX8fmq5YVnFlN9Odm+Jc9XLc/q\nyiYe10bEWXSeFwSApKtzHx/NtTOTFNT1i4hYAHyujuefonr2tkr9Q7RfcG1mZmbWcoZ4HVvNWj7Y\nWYdMk7QJadncIlJa6iPoDLKCNKtSt6zdntI+d0TEgz08Y2ZmZmbWNhzstI4NSUkPTq5SF8CpEXFv\nL9vejbScrzt3UjJTZWZmZmbWjhzstI5bgZGkZARbkJZ8PUc60PSCiCgeNmpmZmZm6yCvYqudg50W\nERG3kgKe/mh7Dt3spzEzMzMzG4y8YdzMzMzMzAYlz+yYmZmZmbUJCeR1bDVzsGNm1o393/eOpvb/\n60eeb2r/AAe8b3izh2BmZtYrXsZmZmZmZmaDkmd2zMzMzMzayBCvYquZZ3bMzMzMzGxQcrBjZmZm\nZmaDkpexmZmZmZm1EWdjq51ndszMzMzMbFDyzI6ZmZmZWRvxxE7tPLNjZmZmZmaDkoMdMzMzMzMb\nlLyMzczMzMysTQgQXsdWq7aZ2ZE0XVJk18xmj8fMzMzMzFpb2wQ7Vp2krSSdIukGSU9IWiFpdXY/\nQ9L2vWx3Zi64DElrJK2U9GdJd0k6S9J7anh3Tp9+QDMzMzOzXvIytvb3QeC7Vcq3z65PSfpgRPy+\nj/0MBTbIrq2BscDXJP1TRHyvj22bmZmZWY2GeBVbzTyzUwNJGzZ7DD14DbgCOAI4APg28HpWtxHw\n/T62fx8wLmv7RODhrPx/Af8q6Wt9bN/MzMzMrOHaNtiRtJek2yS9LOkvkq6QtG2u/gBJsyQtlLRc\n0muSOiTNk3SSpPUK7eWXXk2X9BlJv5e0Gvhp9szhkn4p6XFJL2VtLpd0p6SjVDjOVtKcXJtTsmfu\nz5aZPSvpbElDC+8MlTRN0pPZcw9l700pWRr2ADAiIg6PiJ9HxC0RMQ24IPfM3n38dXdExNys7XOB\n3YEbc/X/Imm7PvZhZmZmZtZQ7bqMbQ/gU8CwXNkngTGSRkXEC8CHgMmF9zYG9syu8cDBJe1/Gtix\nSvkk4KBC2eZZW+OBXYATSto8rdDmVsCpQAdwTq78x8CRuc/vB34C3Fut0Yh4uFo58Mfc/cslz/RK\nRLwi6SvAU6SkIG8h/X1UW05nZmZmZo0iIZ8qWrN2ndkZCcwGPg4cS+eX+W2As7L73wDHkQKaiaTg\n5wjg8ax+kqQ9StrfEbibFEB9jLREDOA64MukgGdC1u4XgGVZ/TGShnfT5nlZe1flyo+v3EgaR9dA\n5xLgQFIwtFtJu2+SzTAdliu6rtZ3axURf6JrQDW60X2YmZmZmfVFu87sPAtMjohXASQNAyqb5A+V\n9CVgDilAOIMUaGwEb0pKvjdwT5X2nwH2i4jVhfLZwCnAV0mb/zcotDmUNOt0fZU2b4iI47LxLgAO\nzcqHS9ooIlbQNUC5PyKOyu5vkrRD7p1SWaDzQ9JME8DTwLSe3uul5bn7TRvRoKSpwFSAbbfzyjgz\nMzMz6712DXbmVQKdzNzc/abAlsCVpExl3dmspPzGYqAjaX3SbM+IXrZ5e+5+eaFuc2AFXZe5/bbw\nzFx6CHayoO9S0owUwJ+BiRGxpLv3+mDL3P1LjWgwImYAMwBGjRodjWjTzMzMbDDxKrbatesytp6M\noDPQeZ00uzORlFHsltxzZT//c1XKPkFnoLOStERuQtbmgzW0+WLlJiLWFOoq/2TzX+7r+qIvaVPg\nZjoDnYeAfSLisXraqaO/99A1OJvfH/2YmZmZmfVWu87s7ClpvYh4Lfs8JlfXQdq7U3FfRJwJKdMZ\nUMvaqGqBRv69myPivKzNjQv99cVjwEey+2IGtXFlL2VZ6G4iJTOANIt0SER0NGhcxf6GAT+iM0h7\nlc59TWZmZmZmLaFdg52tgVmSLgLeDXwzV3cVnUkIAHaRdDTwJGkvSE/L0Mosyt1PlPRZUmB1MuVL\n1+o1i5RwAWB3SRcC15ICnUOqvSDpb0nBzdZZ0YOkJA07FzJ13BMRr/RyXJtIGgusTwqopgLvy9Wf\nkSUsqGZ7SedUKf9DRFzey/GYmZmZrZMEDPE6tpq1a7DzBCkTWzF19DOkzfhLSXte9gHWA87P6leS\nEhKUZWHrzq+yft9L2hd0WVb+PLAQ2KkXbXYREXMlXQxUEhO8sVmfdLDnrlVe24fOQAdgZ+C2Ks+9\nh5Qqujd2Be6qUr4GOC0i/rWbd7cFvl6l/JeAgx0zMzMz6zftumdnLrA/6Qv4KtIMyyxgTEQsiYi1\npDNxZgJLSEHOHcC+QNm5NN2KiFWkfT/XkPbfdJBSOo/N+miUqcDpwGLS8rBHsrJLc8+sbGB/tQrg\nr6SAci5p9mjHHgIdMzMzM7OmUYQTXrUSSYoqfymSrqZzKdu5EXHiwI5s4I0aNTrunue8B7Zu+/Uj\nzzd7CBzwvrLjw8zM1h1j9hrNggXzm75+bLN3j4wPfaO1Fsf85xdGL4iIljxzsV2XsQ1m0yRtQlo2\nt4iUlvoIOgOdoJfLv7J2d+7hsY6IeLCHZ8zMzMzMaibpXaTzKvcjbXMYBvyFlEH4cuCSav/Bv68c\n7LSeDUlJD06uUhfAqRFxby/b3o20nK87d5KW+5mZmZmZ9VkW6NxL+o/4eVuQvnfuC4wGjm503+26\nZ2cwuxW4HniatEfmFVJigZ8BYyPiu80bmpmZmZk1m6SWumrwRToDnf8hJeP6MOk7b8VUSRs29jfl\nmZ2WExG3kgKe/mh7Dp1n45iZmZmZDYRNc/e3RMQlAJJeBP53Vj40uxrKMztmZmZmZtaffp2731/S\nkZL2B76RK78+Ijoa3bFndszMzMzM2oSUrnYSEddJOhE4g7Sc7eJc9avA94Az+6Nvz+yYmZmZmVlf\nbCFpfu6aWuWZP5POayx6C3AYKUFBw3lmx8yshbXCGTen37Swqf2f+dGdmtq/mZn1aFl35+xIOhz4\nefbxMeBQ4PHsz5nADsBNkkZERLWAqNc8s2NmZmZm1kaGSC111SCfUvqCiHggIlZFxGXA/Vn524CP\nN/x31egGzczMzMzMcrbM3W9cuVHKW71xrm6TRnfsZWxmZmZmZm2kzfITQJq9GZHdnyjpBWAR8H+A\n7XPP3dPojh3smJmZmZlZf5oO7A9sRjpz50dVnrk6Iu5odMdexmZmZmZmZv0mIh4BdgXOAx4GVgGv\nAy8CdwJfAj7ZH32XzuxIentvGoyIF3o/HDMzMzMz647a7aAdICL+BBw30P12t4zteSB60ebQXo7F\nzMzMzMysYboLdr5L74IdMzMzMzOzpisNdiLi1IEciJmZmZmZdU/AkPZbxdY0TlBgZmZmZmaDUl3B\njpLDJF0k6XpJu2Tlm2blw/tnmCBpuqTIrpn91Y+ZmZmZmQ0ONQc7kt4K3AZcAXwGOBDYIqt+mZRK\n7iuNHqB1T9JWkk6RdIOkJyStkLQ6u58hafueW6na7sxccDmnSv2casGnpHfnykPSvr3+4czMzMys\nKwm12NXK6pnZ+SYwBjgceBe5w1sjYg3wn8BHGjo6q8UHSckkDiSdQLshMCy7/yJwn6Tdmjc8MzMz\nM7PmqCfYOQy4KCKuBNZUqX8UeE9DRtViJG3Y7DH04DXSjNsRwAHAt0kHNQFsBHy/SeMyMzMzM2ua\neoKdbYDfd1O/Eti4b8OpnaS9JN0m6WVJf5F0haRtc/UHSJolaaGk5ZJek9QhaZ6kkyStV2gvv2xr\nuqTPSPq9pNXAT7NnDpf0S0mPS3opa3O5pDslHaXCPF5hqdeU7Jn7s2Vmz0o6W9LQwjtDJU2T9GT2\n3EPZe1NKlpU9AIyIiMMj4ucRcUtETAMuyD2zd2N+62ZmZmbWbFJrXa2su3N2iv4CdJeA4H3Ac30b\nTs32AD5FWq5V8UlgjKRREfEC8CFgcuG9jYE9s2s8cHBJ+58GdqxSPgk4qFC2edbWeGAX4ISSNk8r\ntLkVcCrQAZyTK/8xcGTu8/uBnwD3Vms0Ih4u6e+PufuXS54xMzMzMxu06pnZuR2YkiUq6ELSNsBR\nwK8bNbAejARmAx8HjqXzy/w2wFnZ/W+A40gBzURS8HME8HhWP0nSHiXt7wjcTQqgPkZaIgZwHfBl\nUsAzIWv3C8CyrP6YbjLS7UhK4vAx4Kpc+fGVG0nj6BroXELai3MOUPO+m2yG6bBc0XW1vttskqZK\nmi9p/tJlS5s9HDMzMzNrY/XM7Pwz8N/A74CfZWUfyr6gHwOsBc5u7PBKPQtMjohXASQNA76X1R0q\n6UvAHFKAcAYp0NiIXFKFzN7APVXafwbYLyJWF8pnA6cAXyUlANig0OZQ0qzT9VXavCEijsvGuwA4\nNCsfLmmjiFhB1wDl/og4Kru/SdIOuXdKZYHOD0kzTQBPA9N6eq9VRMQMYAbAqFGjo8nDMTMzM2s5\nrZ4BrZXUHOxExEJJB5BmG76TFZ+W/fko8JmIeKqxwys1rxLoZObm7jcFtgSuJGUq685mJeU3FgMd\nSeuTZntG9LLN23P3ywt1mwMr6LrM7beFZ+bSQ7CTBX2XkmakAP4MTIyIJd29V2Jtvulq3ZU8a2Zm\nZmbWEuqZ2SEifidpJDCKtEdHwGOk4KOVvvCOoDPQeR2YTgoeXgW+Aeyf1ZUt46u29+gTdAY6K4F/\nAh4kZaa7ANi5hzZfrNxExJpiLoNKVa6srlkNSZsC1wD7ZkUPAQdGxNP1tJOzIne/RZX6LXP3Hb3s\nw8zMzMys39QV7ABERADzs6tZ9pS0XkS8ln0ek6vrIO3dqbgvIs6ElOkM2K6G9qsFGvn3bo6I87I2\nNy701xeP0XlWUTGD2riyl7IsdDeRkhlAmkU6JCL6EoT8IXc/QtL2EbEo628Hus5wlSVJMDMzM7MG\nEjDEq9hqVnewI2kL0ib77bOiRaRlXwO5m3xrYJaki4B3kw48rbiKziQEALtIOhp4EphKz8vQyizK\n3U+U9FlSYHUy5UvX6jWLlHABYHdJFwLXkgKdQ6q9IOlvScHN1lnRg6QkDTsXZo/uiYhX6hjLNcAP\nSPuShgJ3SfqPrO7TdM5grcqeLTNVUrXDZn8YEQOVvc/MzMzM1kF1BTuSTiElKngLXfdsvCJpekR8\np/qbDfcEKRNbMXX0M6TN+EtJy9b2AdYDzs/qV5ISEpRlYevOr7J+30vaF3RZVv48sBDYqRdtdhER\ncyVdTMpsByk4m5rd3wfsWuW1fegMdCAtp7utynPvAZ6qYyxLs0QPl5D+nbwT+MfCY2uAqRGxrPh+\nzuEl5VcwcKnKzczMzGwdVHPq6eyL73dIX+y/QFpm9ffZ/R+Bb2fPDIS5pH03d5FmFjpIsyJjImJJ\ntn9oEjATWEIKcu4g7Wfp1ZKriFhFSjV9DWn/TQcppfPYrI9GmQqcDiwm7TF6JCu7NPfMygb2Vyoi\nfkr6e76cFCi9kl1PZWV7R8TPyt43MzMzs8aT1FJXK6tnZucEYAEpoMhnQpsn6eekmZQTgQsbOL43\nRMR0UqKBvPFvfvKN55fR9cyaiinZVXy+annhmcVUX062b8nzVcuzurJ/GWsj4iw6zwsCQNLVuY+P\n5tqZSQrq+kVELAA+V8fzT1E9e5uZmZmZ2YCqJ9h5D3BqIdABICJekfRT4NsNG9m6a5qkTUjL5haR\n0lIfQWeQFaRZlbpl7e7cw2MdEfFgb9o3MzMzM2sl9QQ7TwNv66Z+A9K5LtY3G5KSHpxcpS5IAee9\nvWx7N9Jyvu7cSclMlZmZmZk1n5fQ1K7mPTvAj4AvStqyWCHpHaR9JRc0amDrsFuB60nB5V/p3CPz\nM2BsRHy3eUMzMzMzM2sfpTM7kg4rFD0DLAP+KOkSUqKCAEYCnyctuXq2n8a5zoiIW0kBT3+0PQf/\nxwAzMzMzW0d0t4ztClIwU/lynL8/scrzo4CfA1c2bHRmZmZmZvYGCYa0eAa0VtJdsPPRARuFmZmZ\nmZlZg5UGOxExeyAHYmZmrenMj/b5zOQ+mf3w803tH+DDI4c3ewhmZm/wxE7t6klQYGZmZmZm1jbq\nST0NgKSdgT2BzXhzsBQR8a+NGJiZmZmZmVlf1BzsSBpGSlpwEClRQbXkBQE42DEzMzMz6yfyOraa\n1bOM7XRgEvB94COk4OaLwCHAfwP3ALs2eoBmZmZmZma9UU+wcxhwdUR8DViQlT0ZEdcCHwTWz54x\nMzMzMzNrunqCnXcBd2T3a7M/3wIQEa+Sztg5onFDMzMzMzOzIqm1rlZWT7Dzcu75FaSAJ5+L80Vg\nqwaNy8zMzMzMrE/qCXYWATsCRMQa4BHSfp2KScAzjRuamZmZmZlZ79WTevpW4HOSToyItcBFwA8k\nPUzKwrYTML3xQzQzMzMzMwAhhrT62rEWUk+w8x3gSmAosDYifijpbcBnSEva/hk4q/FDNDMzMzMz\nq1/Ny9gioiMi7o+I13Jl346IkRGxc0R8K5vxqZuk6ZIiu2b2pg1LJM3J/S6nNHs8ZmZmZmbNUs+e\nHWsCSQdJuljSA5LW9GdQWAg6Q9Lrkv4q6TlJv5N0rqQP1Phu5Vop6RFJ/1fSdo0es5mZmdk6pQWy\nr7VTNrbSZWyS9uxNgxHx370fjlVxFCn5QzMMAd5Kyro3HNgLOF7SvwFfzxJV9GQD0n6unYDJknaN\niOf6a8BmZmZmZhXd7dn5HSnxQK2UPT+0TyMaYJI2jIiXmz2ObjwN/AKYD3wEmDBA/T4PTAaGATsA\nR5KCHYCTSIfIHl3Du/sBp2blb8/a+Xb/DNnMzMzMrFN3wc5XBmwUBZL2In0h3gt4DZgNnBIRT2f1\nBwD/AOwCbAlsDKwCFpKSKJyX31uULfn6fPbxW8DjwD8C7wNuBg6WdDjwKeD9wBbA24D/AR4CLgUu\niYjItTkH+GD28UjSLMjxwAjSmUOXAqdHxOu5d4aSvvj/A+lMoseBfyMleLgke+zOiNi38k5EHJt7\nf2SNv8JGeCUi5mb3t0maAVwIfDEr+4qkyyLidzW8+79Jv1cAL2UzMzMz6wO1+tqxFlIa7ETEhQM5\nkJw9SEHHsFzZJ4ExkkZFxAvAh0gzB3kbA3tm13jg4JL2P012XlDBJOCgQtnmWVvjSYHVCSVtnlZo\ncytSUNMBnJMr/zEpMKp4P/AT4N6SdltGRISkk4DDgE2y4s+RZgDr8eeGDszMzMzMrEQrJigYSZrJ\n+ThwLFBZYrYNnamtfwMcRwpoJpKCnyNIMyUAkyTtUdL+jsDdpADqY8AVWfl1wJdJAc+ErN0vAMuy\n+mMkDe+mzfOy9q7KlR9fuZE0jq6BziXAgaRgaLeSdltKttzvv3JFo0seHSZprKSJks6ic1anA5jZ\nXR+SpkqaL2n+0mVL+zxmMzMzM1t31XPOzkB5FpgcEa8CSBoGfC+rO1TSl4A5pADhDFKgsRFpz1De\n3sA9Vdp/BtgvIlYXymcDpwBfBbYnbazPtzmUNOt0fZU2b4iI47LxLgAOzcqHS9ooIlaQZkQq7o+I\no7L7myTtkHun1S3P3W9a8sxw4K5C2a3ACRHR7cxORMwAZgCMGjW6nj1jZmZmZuuEVpytaFWtGOzM\nqwQ6mbm5+01Je3SupHO/TJnNSspvLAY6ktYnzfaM6GWbt+fulxfqNgdW0HWZ228Lz8ylfYKdLXP3\nL9Xx3m6Fd83MzMzM+lU7BoYj6Ax0XifN7kwExgG35J4r+9mqpT3+BJ2BzkrSErkJWZsP1tDmi5Wb\nKumYK7ND+VmKtpyxkLQR8Pe5ovkljy6OCAHvAn6dlf0N8J+SHPCYmZmZ2YBoxZmdPSWtl8umNiZX\n10Hau1NxX0ScCW9kOqsl01e1QCP/3s0RcV7W5saF/vriMVLqaEhL7PLGNaiPfiNpCHAuaclgxWXd\nvRMRf5JU2Uu1CWlm7AxSMGlmZmZmdRLOxlaPVgx2tgZmSboIeDfwzVzdVXQmIQDYRdLRwJPAVHpe\nhlZmUe5+oqTPkgKrkylfulavWaSECwC7S7oQuJYU6BxS9pKk3ekMxvJB2XaSKhnnFkbEwgaNs2KY\npLHAW0hL8PLn7AD8qCTtdBcRsUzSecDpWdEXJZ3tg0XNzMzMrL/1KtjJ/iv/ZkBHlWVbffUEKRNb\nMXX0M8A0YClpz8s+wHrA+Vn9SlJCgrIsbN35Vdbve0n7giozFs+Tzu7ZqRdtdhERcyVdDFQSE0zN\nLoD7gF1LXj2OzjOC8ibQecDot4DpfR1jQbUkAxU/AL5WR1vnktJ2bwi8NXv3xD6NzszMzMysB3Xt\n2ZG0s6QbSYHFEtL5M0h6u6QbJO3bgDHNBfYnfdFeRZphmQWMiYglEbGWdCbOzGwMK4E7gH2Bh3vT\nYUSsIu37uYa0/6aDlIp6bNZHo0wlzXAsBl4FHsnKLs09s7KB/fVVAK+Qgr55pKBl54g4qZ4gNyKW\nAz/KFX1J0tsbOlIzMzOzdcQQtdbVyhRR2155SR8gnbGyAriNdDjn/hFxe1Y/D3gkIqb0z1DbnyRF\nlV+4pKvpXMp2bkR41oOUevrueWU5EMxsXTH74eebPQQ+PLLsmDUzW1eM2Ws0CxbMb/pX+3fs8IE4\n/PtX9fzgAPrhwe9bEBFl5y82VT3L2P6FtIRs9+y9Iwr1twCTGzSuwWqapE1Iy+YWkdJSH0FnoBPA\n5Y3sMDunqKelfasjwlGFmZmZmQ0q9QQ744F/jYiXJP1Nlfo/Ae9szLAGrQ1JSQ9OrlIXwKkRcW+D\n+9yK8r03FYtJySDMzMzMrMW1+tKxVlJPsLMBufNkqtiwj2NZF9wKjCQlI9iCtGfqOdKBphdERPGw\nUTMzMzMz66V6gp1FwG7d1O9LylxmJSLiVlLAM5B9PkXnwaZmZmZm1sYkn7NTj3qysV0JfF7S+FxZ\nAEj6KvAx4GcNHJuZmZmZmVmv1TOz813gw6RMbA+SAp3vSNoCeBdwJ3Bew0doZmZmZmbWCzXP7ETE\natIhlt8A3gKsJWVmey0r+0hEvN4fgzQzMzMzs6TZ5+q00zk79czsEBGvAmdnV+m5MWZmZo3SCmfc\nPPzn/2lq/yO32bip/ZuZtat69uy8iQMdMzMzMzNrVTXP7Eg6rJbnImJW74djZmZmZmbdcTK22tWz\njO0KUlKC4q+3OLvjYMfMzMzMzJqunmDnoyXvvxf4MvAS8M+NGJSZmZmZmVlf1RzsRMTssjpJPwbm\nA38L3NyAcZmZmZmZWYGAIV7HVrM+JSioiIi/ApcBxzaiPTMzMzMzs75qSLCTWQVs28D2zMzMzMzM\neq2uc3bKSNoCmAosbkR7ZmZmZmZWXSNnKwa7elJP31hStTmwM7A+8A+NGJSZmZmZmVlf1RMY7g7s\nVrh2BTYEZgP7RcTFvR2IpOmSIrtm9rYdA0lzcr/LKc0ej5mZmZlZM9Qc7ETE8IjYqnC9MyI+EBGH\nRMTt/TnQdZmkgyRdLOkBSWv6MygsBJ1PVamfmaufU6iLKtdrkpZIukXSZxo9XjMzM7N1jdRaVyur\naRmbpA2AY4AFEXFb/w7JqjgKmNTsQfTS/wLeDuwH7CdpRESc0eQxmZmZmdk6oKaZnYhYBfwLsH3/\nDqc5JG3Y7DH0DpE6+QAAIABJREFU4GngF8DXgTuaPJZafRsYB3wSeCRXfqKk9ZozJDMzMzNbl9Sz\nZ2cR6b/QDwhJe0m6TdLLkv4i6QpJ2+bqD5A0S9JCScuz5VIdkuZJOqn4hbqw/Gq6pM9I+r2k1cBP\ns2cOl/RLSY9Leilrc7mkOyUdJXWdqCvujcmeuV/SaknPSjpb0tDCO0MlTZP0ZPbcQ9l7U8qWh0XE\nsRFxWER8F/hTg3/V/eWxiJgbEbOAb+TK3wb8TZPGZGZmZtbWJDGkxa5WVk/q6f8HHCfp/0ZER38N\nKLMH8ClgWK7sk8AYSaMi4gXgQ8DkwnsbA3tm13jg4JL2Pw3sWKV8EnBQoWzzrK3xwC7ACSVtnlZo\ncyvgVKADOCdX/mPgyNzn9wM/Ae4taXeweQVY1uxBmJmZmdngV0+w8zzwP8AfJf0EeIx0kGgX2X/J\n76uRwHXADOA9wNmkrG/bAGcBXwR+AzxDmulYAQQpwPgWsAMwSdIeEXFPlfZ3BO4G/h14mRQkkfV5\nB/Bs1uYQ4N3Ad4AtgGMknRMRz5e0eR5wMymYOTQrP54s2JE0jq6BziWk5WnjSUvUBpMdJY0l/Z18\nM1d+fkSsadKYzMzMzGwdUk+w8x+5+38qeSaARgQ7zwKTI+JVAEnDgO9ldYdK+hIwh5T++gxSoLER\nUJxH2xuoFuw8Q0qVvbpQPhs4BfgqaX/SBoU2h5Jmna6v0uYNEXFcNt4FdAY7wyVtFBErgMNyz98f\nEUdl9zdJ2iH3zmBwWnZVvETax/P97l6SNJV0QC3bbrddvw3OzMzMrF21+MqxllJPsPPRfhvFm82r\nBDqZubn7TYEtgSuBD/bQzmYl5TcWAx1J65Nme0b0ss186u3lhbrNSTNF+WVuvy08M5fWCHbW5u6r\n/b9SvmxtlfoymwKjSLNlpe9FxAzSjB6jRo2OOto3MzMzM+ui22BH0nbA0oj4a0TMHqAx1WIEnYHO\n68B0UvDwKmkz/P5ZXVkChueqlH2CzkBnJWn26kFgDXABsHMPbb5YuYmINcVcBpWqXFmrfpFfkbvf\nokr9lrn77vZuHUmaDTwYuAx4C2nf1WOk2TgzMzMzs37VUza2J0lBwEDbs5BNbUzuvoO0d6fivog4\nMzvU9L+AWtY+VQs08u/dHBHnRcQc4IFCf33xWO5+70LduAb10Vd/yN1vIOmN2TNJm9L17+Lh7hqK\niFci4ko6lyACnCzpnQ0ZqZmZmdk6aIha62plPS1ja9bwtwZmSbqIlCAgv8H9KuDx3OddJB1NCsym\n0vMytDKLcvcTJX2WFFidTPnStXrNAo7N7neXdCFwLSnQOaTsJUm70xmM5YOy7SRVMs4tjIiFDRjj\nHNKepq2zz9dKupyUjOIQOpM5BPDzGtv8HulQ2o2Bt5KSMRzfgLGamZmZmZWqZ8/OQHoC+DhvTh39\nDDANWEpatrYPsB5wfla/kpSQYI9e9PmrrN/3kvaXXJaVPw8sBHbqRZtdRMRcSRcDlcQEb2zGB+4D\ndi159Tjg81XKJ2QXpCx00xswxlclfY6UhGED0u/i2CqPnhYRf6hSXq3Nv0i6gJSKG+CLks4uyWpn\nZmZmZtYQ9RwqOpDmkvbd3EWaUeggzYqMiYglEbGWdCbOTGAJKci5A9iXHpZWlYmIVcBE4BrS/psO\nUirqsVkfjTIVOB1YTNpj9EhWdmnumZUN7K9u2ZLA3UhnKz0KrAZeIwWbVwMTI+Kc8haq+jc6U5Wv\nT8p6Z2ZmZmZ1EDT9ENHBdqjoOEk1zwBFxGU9P1X1vem8eWZifDfPL6PrmTUVU7Kr+HzV8sIzi6m+\nnGzfkuerlmd1ZX/zayPiLNJ5QW+QdHXu46OFtqbQw9hrHVcdbTwKfKXOd0r/tUfEUuBtfR2XmZmZ\nmVmtagli8kutuiPSPo5eBTvrkGmSNiEtm1tESkt9BJ1BVgCXN7LD7Jyinpb2rY6I+Y3s18zMzMwa\nr8UnU1pKLcHODOB3/T2QdciGpKQHJ1epC+DUiLi3wX1uRVoS2J3FpGQQZmZmZmaDQi3Bzl0RUWvW\nLevZrcBIUjKCLUj7pp4jHWh6QUQUDxs1MzMzM7NeaNVsbINWRNxKCngGss+naF4acTMzMzNrlDY4\n26aVtGo2NjMzMzMzsz5xsGNmZmZmZoNSt8vYIsLBkJmZmZlZC5F3J9TMe3bMzFpYRDR7CMg5Thm5\nzcZN7f/y+Yub2v9nR7+rqf2bmfWWZ27MzMzMzGxQ8syOmZmZmVmbEO2bjU3SW4EvA4cB7wPWB14A\nHgYujYj/aHSfDnbMzMzMzKxfSdoKuAn4u0LVttn1MuBgx8zMzMzM2ofS5s8r6Qx0HgTOB54ANgJG\nAmv6o28HO2ZmZmZmbaQNl7EdCIzL7h8B9o6IVbn6a/qrYycoMDMzMzOz/nRI7v5e4HJJz0laJWm+\npM/1V8ee2TEzMzMzs77YQtL83OcZETEj93mX3P0RhXdHAZdKGhkRpzZ6YA52zMzMzMzaSAuef7Ys\nIkZ3U79p4fMM0tK1TwBTs7KvSbosIh5u5MC8jM3MzMzMzPrT6tz9s8BXIuJm4CvAc1m5gI80umMH\nO2ZmZmZm1p8W5+7/FBFrAbI/83WbNLrjlgt2JE2XFNk1s9njaWeS5uR+l1OaPR4zMzMz65vKoaKt\ndNXgztz9dpKGAGR/bperW0yDtVywY+UkHSTpYkkPSFrTn0FhIegMSa9L+muWOeN3ks6V9IEa3n2q\n0WMzMzMzs7ZyKfA/2f07gfMlfZh01s47s/KXgesb3bGDnfZyFHAksDMwdID7HgK8FRgO7AUcDzwo\n6fuSnOjCzMzMzKqKiBdI32MrB4d+Gbg5+5Os/IsRsbTRfTvYyZG0YbPH0IOngV8AXwfuGMB+nycd\nBLUf6R/lvFzdScC/D+BYzMzMzNZdArXYVYuIuBrYG7gKeIEU4LyQff77iLiiP35dLR/sSNpL0m2S\nXpb0F0lXSNo2V3+ApFmSFkpaLuk1SR2S5kk6SdJ6hfZm5pZYTZf0GUm/l7Qa+Gn2zOGSfinpcUkv\nZW0ul3SnpKNUyPdX3BuTPXO/pNWSnpV0tqShhXeGSpom6cnsuYey96bk2pqTfycijo2IwyLiu8Cf\nGvyr7s4rETE3Im6LiAuBvwd+nKv/iqS9B3A8ZmZmZtZmImJBREyOiHdExHrZn5MjYn7Pb/dOqy8/\n2gP4FDAsV/ZJYIykUdmU2IeAyYX3Ngb2zK7xwMEl7X8a2LFK+STgoELZ5llb40kHI51Q0uZphTa3\nAk4FOoBzcuU/Ji1Jq3g/8BPSqbItLSJC0knAYXRmzfgc8LvmjcrMzMzMrKtWn9kZCcwGPg4cS9q4\nBLANcFZ2/xvgOFJAM5EU/BwBPJ7VT5K0R0n7OwJ3kwKojwGV6bPrSMu1DgImZO1+AViW1R8jaXg3\nbZ6XtXdVrvz4yo2kcXQNdC4BDiQFQ7uVtNtSIuJl4L9yRd0dJFUzSVMlzZc0f+myhi/bNDMzM2t7\nQ6SWulpZq8/sPAtMjohXASQNA76X1R0q6UvAHFKAcAYp0NiIlJUvb2/gnirtPwPsFxGrC+WzgVOA\nrwLbAxsU2hxKmnWqljHihog4LhvvAuDQrHy4pI0iYgVpRqTi/og4Kru/SdIOuXda3fLcffFk3F6J\niBmkU3UZNWp0NKJNMzMzM1s3tXqwM68S6GTm5u43BbYErgQ+2EM7m5WU31gMdCStT5rtGdHLNm/P\n3S8v1G0OrKDrMrffFp6ZS/sEO1vm7l9q2ijMzMzMzKpo9WVsPRlBZ6DzOml2ZyIpc9gtuefKfs7n\nqpR9gs5AZyVpidyErM0Ha2jzxcpNRKwp1FVmh/IzFm05eyFpI1Kigop+21hmZmZmZkmbHiraNK0+\ns7OnpPUi4rXs85hcXQdp707FfRFxJqRMZ3Q9jbVMtUAj/97NEXFe1ubGhf764jHgI9l9MYvZuAb1\n0W+y027PJS0ZrLisScMxMzMzM6uq1YOdrYFZki4C3g18M1d3FZ1JCAB2kXQ08CQwlZ6XoZVZlLuf\nKOmzpMDqZMqXrtVrFinhAsDuki4EriUFOoeUvSRpdzqDsXxQtp2kSsa5hRGxsEHjrBgmaSzwFtIS\nvCNJB4tW/CgiyjKxbSbpnCrlz0aEz+cxMzMzq1OL5wRoKa0e7DxBysRWTB39DDANWEra87IPsB5w\nfla/kpSQoCwLW3d+lfX7XtK+oMqMxfPAQmCnXrTZRUTMlXQx6SRZSMHZ1Oz+PmDXklePAz5fpXxC\ndgF8C5je1zEWDAfuKqn7AfC1bt7dmHQIatH9+DBSMzMzM+tHrb5nZy6wP+mL9irSDMssYExELImI\ntaQzcWYCS0hBzh3AvsDDvekwIlaR9v1cQ9p/00FKRT0266NRpgKnA4uBV4FHsrJLc8+sbGB/fRXA\nK6Sgbx5pGdvOEXFSlb1JZmZmZmZNp4i23B/f9iQpqvzyJV1N51K2cyPixIEdWesYNWp03D3PeQ9s\n3dYK/xstr5dousvnL25q/58d/a6m9m/WCsbsNZoFC+Y3/X8Qt9tpl/j6T65r9jC6OGbsexZEREPO\nXGy0Vl/GNphNk7QJadncIlJa6iPoDHQCuLyRHWbnFPW0tG91RDjCMDMzM7O252CneTYkJT04uUpd\nAKdGxL0N7nMryvfeVCwmJYMwMzMzM2trDnaa51ZgJCkZwRak/VPPkQ40vSAiioeNmpmZmdk6Tjgb\nWz0c7DRJRNxKCngGss+n6DzY1MzMzMxsUGv1bGxmZmZmZma94pkdMzMzM7N2IRjidTo1c7BjZtbC\nnPbZoPmpn3/54DNN7R9g0s5bN3sIZtaGvIzNzMzMzMwGJc/smJmZmZm1kSGe9a+ZZ3bMzMzMzGxQ\ncrBjZmZmZmaDkpexmZmZmZm1CR8qWh/P7JiZmZmZ2aDkYMfMzMzMzAYlL2MzMzMzM2sjzsZWO8/s\nmJmZmZnZoORgx8zMzMzMBqWWC3YkTZcU2TWz2eNpZ5Lm5H6XU5o9HjMzMzPrO6m1rlbWcsGOlZN0\nkKSLJT0gaU1/BoWFoPOpKvUzc/VzCnXhIMvMzMzMms0JCtrLUcCkZg/CzMzMzKwdONjJkbRhRLzc\n7HF042ngF8B84CPAhOYOx8zMzMwGkvDSrHq0/O9K0l6SbpP0sqS/SLpC0ra5+gMkzZK0UNJySa9J\n6pA0T9JJktYrtJdffjVd0mck/V7SauCn2TOHS/qlpMclvZS1uVzSnZKOkrquTizujcmeuV/SaknP\nSjpb0tDCO0MlTZP0ZPbcQ9l7U8qWh0XEsRFxWER8F/hTg3/VZmZmZmaDSqvP7OwBfAoYliv7JDBG\n0qiIeAH4EDC58N7GwJ7ZNR44uKT9TwM7VimfBBxUKNs8a2s8sAtwQkmbpxXa3Ao4FegAzsmV/xg4\nMvf5/cBPgHtL2jUzMzMzszq0+szOSGA28HHgWKCyxGwb4Kzs/jfAcaSAZiIp+DkCeDyrnyRpj5L2\ndwTuJgVQHwOuyMqvA75MCngmZO1+AViW1R8jaXg3bZ6XtXdVrvz4yo2kcXQNdC4BDiQFQ7uVtLtO\nkDRV0nxJ85cuW9rs4ZiZmZm1FoGklrpaWavP7DwLTI6IVwEkDQO+l9UdKulLwBxSgHAGKdDYiLSc\nMW9v4J4q7T8D7BcRqwvls4FTgK8C2wMbFNocSpp1ur5KmzdExHHZeBcAh2blwyVtFBErgMNyz98f\nEUdl9zdJ2iH3zjonImYAMwBGjRodTR6OmZmZmbWxVg925lUCnczc3P2mwJbAlcAHe2hns5LyG4uB\njqT1SbM9I3rZ5u25++WFus2BFXRd5vbbwjNzaY1gZ23uvlrIni9bW6XezMzMzPpBa8+ltJZWX8bW\nkxF0Bjqvk2Z3JgLjgFtyz5X9nM9VKfsEnYHOStISuQlZmw/W0OaLlZuIWFOoq/zbzM9YtOrsxYrc\n/RZV6rfM3Xf081jMzMzMzOrW6jM7e0paLyJeyz6PydV1kPbuVNwXEWdCynQGbFdD+9UCjfx7N0fE\neVmbGxf664vHSKmjIS2xyxvXoD766g+5+w0kfTAi7gSQtCld/y4eHtCRmZmZmZnVoNWDna2BWZIu\nAt4NfDNXdxWdSQgAdpF0NPAkMJWel6GVWZS7nyjps6TA6mTKl67VaxYp4QLA7pIuBK4lBTqHlL0k\naXc6g7F8ULadpErGuYURsbABY5xD2tO0dfb5WkmXA6uyMW6clQfw827amSxppyrll0WEgyQzMzOz\nOggY0uJJAVpJqwc7T5AysRVTRz8DTAOWkva87AOsB5yf1a8kJSQoy8LWnV9l/b6XtC/osqz8eWAh\nUO2Le10iYq6ki4FKYoKp2QVwH7BryavHAZ+vUj6BzgNGvwVMb8AYX5X0OVIShg1Iv4tjqzx6WkT8\noUp5xYHZVfQ7PCNkZmZmZv2o1ffszAX2B+4izSh0kGZFxkTEkohYSzoTZyawhBTk3AHsSy+/SEfE\nKtK+n2tI+286SKmox2Z9NMpU4HRgMfAq8EhWdmnumZUN7K9uEXE7KdPd/wMeBVYDr5GCzauBiRFx\nTnkLZmZmZmbN03IzOxExnTfPTIzv5vlldD2zpmJKdhWfr1peeGYx1ZeT7VvyfNXyrK5snnFtRJxF\n53lBAEi6Ovfx0UJbU+hh7LWOq442HgW+Uuc7nls1MzMz6yf+olW7lgt21iHTJG1CWja3iJSW+gg6\ng6wALm9kh9k5RT0t7VsdEfMb2a+ZmZmZWTM42GmeDUlJD06uUhfAqRFxb4P73Iq0JLA7i0nJIMzM\nzMzM2pqDnea5FRhJSkawBWn/1HOkA00viIjiYaNmZmZmZjgZW+0c7DRJRNxKCngGss+n8DJPMzMz\nM1tHtHo2NjMzMzMzs17xzI6ZmZmZWdsQ8jq2mnlmx8zMzMzMBiXP7JiZmVm3Ju28dbOHwJKO1U3t\n/x2bvLWp/ZtZ7zjYMTMzMzNrE8JLs+rh35WZmZmZmQ1KDnbMzMzMzGxQ8jI2MzMzM7M24mxstfPM\njpmZmZmZDUoOdszMzMzMbFDyMjYzMzMzszbiRWy188yOmZmZmZkNSg52zMzMzMxsUGpqsCNpuqTI\nrpnNHIuZmZmZWctTysbWSlcr88xOi5J0kKSLJT0gac1ABYWS5uT6iqzvlyUtlnS7pNMkvaPGd0PS\n65JekvTf2bsb9Of4zczMzMwqHOy0rqOAI4GdgaFNHMdQ4G3AdsAE4CzgCUlH1Pj+EGATYI/s3ZvU\n6v8JwMzMzMwGhXU22JG0YbPH0IOngV8AXwfu6EtDffhZbwLGAR8FTgP+lJW/Dbhc0qdqePdjwPW5\n8vHA3/dyPGZmZmbrNJG+wLfS1cpaanyS9pJ0W7Zs6i+SrpC0ba7+AEmzJC2UtFzSa5I6JM2TdJKk\n9Qrtzcwtp5ou6TOSfi9pNfDT7JnDJf1S0uPZcqvXsrbvlHRUcRaisFRrSvbM/ZJWS3pW0tmShhbe\nGSppmqQns+ceyt6bkmtrTv6diDg2Ig6LiO/SGWR097vLj+tISSdIekTSq8CZ9f5dZF6IiLkRcXNE\nnA3sAiyodAn8ezeBVOXdG0mzVHnb9XI8ZmZmZmY1a6VzdvYAPgUMy5V9EhgjaVREvAB8CJhceG9j\nYM/sGg8cXNL+p4Edq5RPAg4qlG2etTWe9AX/hJI2Tyu0uRVwKtABnJMr/zFpSVrF+4GfAPeWtNtX\n/0T1n7VPIqJD0gnAXVnRlsCBwKw6m/pzQwdmZmZmtg7xjoDatdLMzkhgNvBx4Fjg5ax8G9JeD4Df\nAMeRApqJpODnCODxrH6SpD1K2t8RuJsUQH0MuCIrvw74MingmZC1+wVgWVZ/jKTh3bR5XtbeVbny\n4ys3ksbRNdC5hBQgnAPsVtJuX+1I+rk+Qfpd3dLAtn8LrMp9Hl3y3NsljZX0UeCiXPl80t+DmZmZ\nmVm/aqWZnWeByRHxKoCkYcD3srpDJX0JmEMKEM4gfaHfiDcfIrs3cE+V9p8B9ouI1YXy2cApwFeB\n7YENCm0OJc06Xc+b3RARx2XjXQAcmpUPl7RRRKwADss9f39EVJZ03SRph9w7jbQgIib1Q7tExFpJ\nfyH9ngA2LXn0o9lVsRaYAZwWEVHWvqSpwFSAbbfzajczMzMz671WCnbmVQKdzNzc/aakJVNXAh/s\noZ3NSspvLAY6ktYnzTKM6GWbt+fulxfqNgdW0HU52W8Lz8ylf4Kd/+yHNoG0/4j0s1W8VOOrQ4B9\nSEHSX8oeiogZpKCIUaNGlwZFZmZmZusqL2KrXSstY+vJCDoDnddJszsTSRm/8su0yn6m56qUfYLO\nQGclaYnchKzNB2to88XKTUSsKdRV/h3mv7AP1Jf3aj9ro4wF1s99nl/y3KWkWbHRdCZY2IXO5YNm\nZmZmZv2qlYKdPQvZ1Mbk7jtIe3cq7ouIMyPiduC/qC27V7VAI//ezRFxXkTMAR4o9NcXj+Xu9y7U\njWtQH0X9ElRJ2hQ4N1e0DLixdBARayNiAfAPueKxkg7pj/GZmZmZmeW10jK2rYFZki4C3g18M1d3\nFZ1JCAB2kXQ08CRpf0dPy9DKLMrdT5T0WVJgdTLlS9fqNYuUcAFgd0kXAteSAp3SL/2SdqczGMsH\nZdtJqmScWxgRCxs0zmreLmks6Vyd3YGvAJVU4AEcGxEvl71cERG3SJpLmhUC+Iaka7rbu2NmZmZm\n1TkZW+1aKdh5gpSJrZg6+hlgGrCUtOdlH2A94PysfiUpIUFZFrbu/Crr972kfUGXZeXPAwuBnXrR\nZhcRMVfSxXSeNfPGBnzgPmDXklePAz5fpXxCdgF8C5je1zF2o5hkoGIl8OWIqGdJ2pnAzdn935Gy\n3/2yb8MzMzMzMyvXSsvY5gL7k85wWUWaYZkFjImIJRGxlnQmzkxgCekL9x3AvsDDvekwIlaR9v1c\nQ9p/00FK2Tw266NRpgKnA4uBV4FHsrJLc8+sbGB/jbSWNLY/kRIynAa8NyJ+Wk8jETGbrlnyzmjY\nCM3MzMzMqpBXEvU/Saq2ZEvS1XQuZTs3Ik4c2JG1tlGjRsfd88ryH5iZ2bpkSUfx5IiB9Y5N3trU\n/q35xuw1mgUL5jd9AdmO7/+7+Lcrft3sYXRx0C7DF0RE2dmLTdVKy9gGs2mSNiEtm1tESt18BJ2B\nTgCX91fnkt4O/G0Pjy2JiMd6eMbMzMzMrG042BkYG5KSHpxcpS6AUyPi3n7s/0Dgkh6euRSY0o9j\nMDMzMzMbUA52BsatwEhSMoItSHulniMdaHpBRBQPGzUzMzMzq8rZ2GrnYGcARMStpICnWf3PJCV2\nMDMzMzNbZ7RSNjYzMzMzM7OG8cyOmZmZmVnbEMLr2GrlmR0zMzMzMxuUPLNjZmZmLa/Z59w89HRH\nU/sH+MC2mzR7CGZtx8GOmZmZmVkbcTa22nkZm5mZmZmZDUoOdszMzMzMbFDyMjYzMzMzszYhYIiz\nsdXMMztmZmZmZjYoOdgxMzMzM7NBycvYzMzMzMzahZyNrR6e2TEzMzMzs0HJwY6ZmZmZmQ1KDnbM\nzMzMzNqI1FpX734GfVRS5K6nGvpLyjQ12JE0PfcDzmzmWMzMzMzMrP9J+hvg4oHoyzM7LUrSQZIu\nlvSApDUDFRRKmtNdX5KeytVPz5XvW4jOK9dqSYslzZK0T3+O3czMzGxdoBb7v164EBgOrG7sb+bN\nHOy0rqOAI4GdgaFNHktfDAO2AyYDv5F0QJPHY2ZmZmZNIulzwP8BOoCz+7u/dTbYkbRhs8fQg6eB\nXwBfB+7oS0NN+lknA/sCRwMrsrKh/H/27jzOkbra///rzci+I6BeYRwExB8iAjMoyuIArugFVETF\nhQEVVxAVNxABLwju29cFvLK4IoogKssVYcABRRgUN1A2gQuKsg2bXMA5vz8+lUl1Jt2dnq7Kp5K8\nn/PII0mlUudMd5LOqfOpT8EhGXIxMzMzs8wkzQS+UNx9J3Bz3TEbVexIepakn0u6X9Ldkk6VtGHp\n8RcUw6GukXSnpEckLZJ0maT3SFq+Y3snl4dcSXqdpN9Iegj4VrHOayT9SNJ1ku4ptnmnpIsk7S+N\nPeyqY5jXvGKdq4rhWrdJOlbSjI7nzJB0mKQbi/X+UDxvXmlb88vPiYgDI2LviPgEPbwQOvLaT9LB\nkq6W9DBw9FR/FxW4IiIuioivAN8oLZ+ZIRczMzOzoSBgOTXr0lPe6Tv1KcCawGkR8a36fkptTTqp\n6LbAq0nDnlpeBWwvaXZE/APYhdQxKFsDeGZx2QnYc5zt7wNs2mX5HsDuHcvWKba1E7AlcPA42zy0\nY5tPAD5IassdV1r+NdKQtJanAV8Hrhxnu9P1Ibr/X5vgf3MnYGZmZmaVWlfSFaX7J0TECR3rvJc0\n6uc24G39SqxJxc7mwFnACcBGpDF8qwEbAMcAbwYuBm4ldTruA4JUYBwFbALsIWnbiLi8y/Y3BS4h\ntc7uJxVJFDEvJP3g7yN1u2YBHwfWBd4p6biI+Ps42/wicC6pmNmrWP4uimJH0o6MLXROIg1P24k0\nRK0Om5L+XyeRfkaP1hRnInMkPQnYDHh9sSxoty67knQAcADAhjPdBDIzMzMbAHdExJzxHpT0RNJI\nowD2i4i7+pVYk4qd24BXRsTDAJJWBD5VPLaXpLcA84GtgcNJX+hXh6WmgNgO6Fbs3Ao8LyI6Z304\nD3gf8A7gycAqHducQeo6/bjLNn8aEQcV+S6kXew8XtLqEXEfsHdp/asiYv/i9jmSNik9p0oLI2KP\nGrY7Fd/vuP8X4JCI6PZzXKLYC3ACwOzZc6Km3MzMzMwG1jLOgJbTerRHb52n7ifneZKkAH4UEeON\n1JqyJhU7l7UKncKC0u21SD+k7wHPnWQ7a4+z/OzOQkfSyqRuz2bLuM0LSrfv7HhsHVKnqDyc7NKO\ndRZQT7Hzw2k8d3HpdrdXYnnZ4i6Pj2cjmju0zszMzMyGUJOKnclsRrvQ+TdwJKl4eBj4CPD84rHx\nJl34W5e6lWjlAAAgAElEQVRlL6Nd6DxAOtbl96RhX18mTfs80TaXtOAi4tHOuQxaD5WW9atT0e3/\n2qv7SrfX7fL4eqXbiybYzkbA3aSf6QeA5YFPS/pdRJw/jfzMzMzMbLDcCry7y/JnAq8pbt8NfBS4\nvsrATSp2nilp+Yh4pLi/femxRaRjd1p+GxFHQ5rpjN5m+OpWaJSfd25EfLHY5hod8abjWuBFxe3t\nOh7bsaIYnaZTVP2R9oQN20taIyLuBZC0C7Byad0/TZhExCLgg5J2oP37/LSkrSLCQ9TMzMzMlkH3\nUWDNFRH/BD7XuVzSPNrFzr0RsdQ609WkYueJwGmS/ps0QcARpcd+AFxXur+lpLcDN5IOZp9sGNp4\nbijd3lXS60mF1SGMP3Rtqk4DDixubyPpeOBMUqHz8vGeJGkb2sVYuSibKak1jvGaiLimojxbvkOa\nUU6kqQF/LelM0rFMbyit97/ART1u8wig1c3ZkjRj3hmVZGtmZmZmNo4mnWfneuClwE+A/0d7trRb\ngcOAK2gf87I88CXgbNLwtW4TEvTiJ7RbZWuRzgfzI9KxJZUUERGxADixtOgAUt4fAq6a4KkHkQqC\nM4CdS8t3Li1/dRU5lkXEH0g/75bNSMPQDiQVPwAPAvt2HGM10TZ/DvyytOjwClI1MzMzswEWESdH\nhIrLrDpiNKnYWUAqXH5B+jK9iNQV2T4ibo+IxaRz4pwM3E46xuZC0nzdEw6nGk9EPAjsSioc7ipi\nngXsUMSoygHAh4GbSMcYXV0sO6W0zgMVxpuWiDiW9HM5nVRsPgI8RJpR7avAVhFxwfhb6Kp8YtOt\nJf1nFbmamZmZjRo17F+TyYdO1E+Suh2jIul02kPZPhcR3Q7cGlmzZ8+JSy67YvIVzczMavaHWyaa\nk6c/tthwzclXstps/6w5LFx4RfZv9pttsVUcf/pU9znXa+enPnbhROfZyalJx+wMs8MkrUkaNncD\naVrq19IudAL4Zl3BJa0PPGWS1W6PiGvrysHMzMzMrN9c7PTHaqRJDw7p8lgAH4yIK2uMvxtw0iTr\nnALMqzEHMzMzM5smActl7y8NDhc7/XE+sDmwFencNcuRzoVzCfDliOg82aiZmZmZmU2Ti50+KE6i\nme1EmhFxMmliBzMzMzOzkeFix8zMzMxsYDR/BrQmadLU02ZmZmZmZpVxsWNmZmZmZkPJw9jMzMzM\nJtGEc9yc96e/Z43/ws0fnzW+FQTyKLaeubNjZmZmZmZDycWOmZmZmZkNJQ9jMzMzMzMbIB7F1jt3\ndszMzMzMbCi52DEzMzMzs6HkYWxmZmZmZgNCwHKejq1n7uyYmZmZmdlQcmfHzMzMzGyAuK/TO3d2\nzMzMzMxsKDWi2JF0pKQoLifnzsfMzMzMzAZfI4odG5+k3SWdKOl3kh7tV1EoaX4pVhSx75d0k6QL\nJB0q6XE9PLfWPM3MzMxGjhp2aTAXO823P7Af8HRgRsY8ZgCrAjOBnYFjgOslvTZjTmZmZmZm4xr5\nYkfSarlzmMQtwPeBDwAXTmdD0/i/ngPsCLwYOBS4uVi+KvBNSa+eTl5mZmZmZnVoZLEj6VmSfl4M\nm7pb0qmSNiw9/gJJp0m6RtKdkh6RtEjSZZLeI2n5ju2dXBpWdaSk10n6jaSHgG8V67xG0o8kXSfp\nnmKbd0q6SNL+0tgJzTuGas0r1rlK0kOSbpN0rKQZHc+ZIekwSTcW6/2heN680rbml58TEQdGxN4R\n8QnaRcZEP7tyXvtJOljS1ZIeBo6e6u+i8I+IWBAR50bEscCWwMJWSOALA1A0mpmZmQ0FNexfkzVx\n6ultgVcDK5aWvQrYXtLsiPgHsAvwyo7nrQE8s7jsBOw5zvb3ATbtsnwPYPeOZesU29qJ9AX/4HG2\neWjHNp8AfBBYBBxXWv410pC0lqcBXweuHGe70/Uhuv9fpyUiFkk6GPhFsWg9YDfgtKpjmZmZmZkt\nqyZ2djYHzgNeChwI3F8s34B0nAjAxcBBpIJmV1Lx81rguuLxPSRtO872NwUuIRVQLwFOLZafBbyV\nVPDsXGz3jcAdxePvlPT4Cbb5xWJ7Pygtf1frhqQdGVvonEQqEI4Dth5nu9O1Ken/9TLSz+pnFW77\nUuDB0v05FW7bzMzMzGzamtjZuQ14ZUQ8DCBpReBTxWN7SXoLMJ9UIBxO+kK/OkvPBbEdcHmX7d8K\nPC8iHupYfh7wPuAdwJOBVTq2OYPUdfpxl23+NCIOKvJdCOxVLH+8pNUj4j5g79L6V0XE/sXtcyRt\nUnpOlRZGxB41bJeIWCzpbtLPCWCtKrYr6QDgAIANZ86sYpNmZmZmQ0XNHjnWKE0sdi5rFTqFBaXb\na5GGTH0PeO4k21l7nOVndxY6klYmdXs2W8ZtXlC6fWfHY+sA9zF2ONmlHessoJ5i54c1bBNIxx+R\n/m8t91Sx3Yg4ATgBYPbsOVHFNs3MzMxsNDVxGNtkNqNd6Pyb1N3ZlTRbWHmY1nj/t791WfYy2oXO\nA6QhcjsX2/x9D9u8q3UjIh7teKxVe5e/uPfrS3y3/2tVdgBWLt2/osZYZmZmZmZT1sTOzjMlLR8R\njxT3ty89toh07E7LbyPiaFjSaehl3FO3QqP8vHMj4ovFNtfoiDcd1wIvKm5v1/HYjhXF6FRLUSVp\nLeBzpUV3AGfXEcvMzMzMxvIott41sdh5InCapP8GZgFHlB77Ae1JCAC2lPR24EbScR6TDUMbzw2l\n27tKej2psDqE8YeuTdVppAkXALaRdDxwJqnQefl4T5K0De1irFyUzZTUmnHumoi4pqI8u1lf0g6k\n8+psA7wNaE0FHsCBEXH/OM+dLem4LssXRMRPqk/VzMzMzCxpYrFzPWkmts6po28FDgP+STrm5TnA\n8sCXiscfIE1IMN4sbBP5SRF3Y9JxQd8olv8duAZ46jJsc4yIWCDpRKA1McGSA/GB3wJbjfPUg4B9\nuyzfubgAHAUcOd0cJ/Di4tLpAeCtEXFql8datigunVYi/dzNzMzMzGrRxGN2FgDPJ53D5UFSh+U0\nYPuIuD0iFpPOiXMycDvpC/eFwFzgT8sSMCIeJB33cwbp+JtFpCmbdyhiVOUA4MPATcDDwNXFslNK\n6zxQYbwqLSbldjNpQoZDgY0j4ltZszIzMzMbNWrYpcEa0dmJiCNZujOx0wTr38HYc9a0zCsunet3\nXd6xzk10H042d5z1uy4vHhvv1744Io6hfb4gACSdXrr7l45tzWOS3HvNayqms52qcjAzMzMzm45G\nFDsj5DBJa5KGb91Amrr5tbSLrAC+WVdwSesDT5lktdsj4tq6cjAzMzMz6xcXO/21GmnSg0O6PBbA\nByPiyhrj7wacNMk6pzCFTpKZmZmZ9U8aOdbwsWMN4mKnv84HNidNRrAu6Zipv5FOaPrliOg82aiZ\nmZmZmS0jFzt9FBHnkwqeXPFPJk3sYGZmZmY29FzsmJmZmZkNCoE8iq1nTZx62szMzMzMbNpc7JiZ\nmZmZ2VDyMDYzMzOzAfDCzR+fNf71t9+fNf7Gj1sta/wm8Si23rmzY2ZmZmZmQ8nFjpmZmZmZDSUP\nYzMzMzMzGyQex9Yzd3bMzMzMzGwoubNjZmZmZjYwhNza6Zk7O2ZmZmZmNpRc7JiZmZmZ2VDyMDYz\nMzMzswEij2LrmTs7ZmZmZmY2lFzsmJmZmZnZUGpMsSPpSElRXE7OnY+ZmZmZWdOogZcma0yxY+OT\ntLukEyX9TtKj/SoKJc2fKJakv5YeP7K0fG5peUiaVWeeZmZmZmbdeIKCwbA/sEfuJMzMzMzMBok7\nO4Ck1XLnMIlbgO8DHwAunM6GBuD/amZmZmYTyT1ubYDGsTW22JH0LEk/l3S/pLslnSppw9LjL5B0\nmqRrJN0p6RFJiyRdJuk9kpbv2N7J5SFXkl4n6TeSHgK+VazzGkk/knSdpHuKbd4p6SJJ+0tjJ/rr\nGOY1r1jnKkkPSbpN0rGSZnQ8Z4akwyTdWKz3h+J580rbml9+TkQcGBF7R8QngJt7+NmV89pP0sGS\nrpb0MHD0VH8XZmZmZmaDqKnD2LYFXg2sWFr2KmB7SbMj4h/ALsArO563BvDM4rITsOc4298H2LTL\n8j2A3TuWrVNsaydgS+DgcbZ5aMc2nwB8EFgEHFda/jVgv9L9pwFfB64cZ7vT9SG6/1/NzMzMzIZa\nU4udzYGzgBOAjYBjgdWADYBjgDcDFwO3kjod9wFBKjCOAjYB9pC0bURc3mX7mwKXAF8A7icVSRQx\nLwRuK7a5HDAL+DiwLvBOScdFxN/H2eYXgXNJxcxexfJ3URQ7knZkbKFzEml42k6kIWp12JT0/zqJ\n9DN6tKY4lZB0AHAAwIYzZ2bOxszMzKx51PSxYw3S1GLnNuCVEfEwgKQVgU8Vj+0l6S3AfGBr4HDS\nF/rVWXrU4HZAt2LnVuB5EfFQx/LzgPcB7wCeDKzSsc0ZpK7Tj7ts86cRcVCR70Laxc7jJa0eEfcB\ne5fWvyoi9i9unyNpk9JzqrQwIgZmcoOIOIFU5DJ79pzInI6ZmZmZDbCmFjuXtQqdwoLS7bWA9YDv\nAc+dZDtrj7P87M5CR9LKpG7PZsu4zQtKt+/seGwdUqeoPJzs0o51FlBPsfPDaTx3cel2t10I5WWL\nuzxuZmZmZpZNYycomMRmtAudf5O6O7sCOwI/K6033v/vb12WvYx2ofMAcBCwc7HN3/ewzbtaNyKi\nc6hYqygodyr61bXo9n/t1X2l2+t2eXy90u1F04hjZmZmZj2SmnVpsqZ2dp4pafmIeKS4v33psUWk\nY3dafhsRR0Oa6Qzo5UCPboVG+XnnRsQXi22u0RFvOq4FXlTc3q7jsR0ritFpOkXVH2lP2LC9pDUi\n4l4ASbsAK5fW/dM04piZmZmZVa6pxc4TgdMk/TdpgoAjSo/9ALiudH9LSW8HbiQd2D7ZMLTx3FC6\nvauk15MKq0MYf+jaVJ0GHFjc3kbS8cCZpELn5eM9SdI2tIuxclE2U1JrxrlrIuKaivJs+Q5pRjkB\nawK/lnQm6VimN5TW+1/gogm28wFJ3To/R3Y5bsrMzMzMrBJNLXauB17K0lNH3wocBvyTdMzLc4Dl\ngS8Vjz9AmpBg22WI+ZMi7sak44K+USz/O3AN8NRl2OYYEbFA0olAa2KCJTOPAb8FthrnqQcB+3ZZ\nvnNxgTQL3ZHTzbEsIv4g6TDgY8WizVh61rgHgX07jrHq9NZxlh8HuNgxMzMzm4KGjxxrlKYes7MA\neD7wC9KX6UWkrsj2EXF7RCwmnRPnZOB2UpFzITCXZRxOFREPko77OYN0/M0i0pTNOxQxqnIA8GHg\nJuBh4Opi2SmldR6oMN60RMSxpJ/L6aRi8xFSgfIX4KvAVhFxwfhbMDMzMzPLQxGe3befJCm6/NAl\nnU57KNvnIuLd/c2seWbPnhOXXHZF7jTMzMwMuP72+7PG3/hxq2WNv/2z5rBw4RXZmypP23Kb+N7Z\nF+dOY4ynb7j6woiYkzuPbpo6jG2YHSZpTdKwuRtI01K/lnahE8A36wouaX3gKZOsdntEXFtXDmZm\nZma2jITHsU2Bi53+W4006cEhXR4L4IMRcWWN8XcDTppknVOAeTXmYGZmZmZWOxc7/Xc+sDlpMoJ1\nScdN/Y10QtMvR0TnyUbNzMzMzGwZuNjps4g4n1Tw5Ip/MmliBzMzMzMbQPI4tp41dTY2MzMzMzOz\naXGxY2ZmZmZmQ8nD2MzMzMzMBoQAeRRbz1zsmJmZmdmkcp/nZu1t35k1/v/9+eas8W3ZuNgxMzMz\nMxsgbuz0zsfsmJmZmZnZUHKxY2ZmZmZmQ8nD2MzMzMzMBonHsfXMnR0zMzMzMxtKLnbMzMzMzGwo\neRibmZmZmdkAkcex9cydHTMzMzMzG0oudszMzMzMbCi52JkmSX+VFMVlbrFsfmnZvLwZmpmZmdkw\nkZp1aTIXO7ZER5EWkvbpeHxux+MrTfDcRyXdL+kmSRdIOlTS4/r/vzIzMzOzUeVipx4HAjsWl7Mz\n5zIdR0la1kksZgCrAjOBnYFjgOslvbaq5MzMzMzMJuLZ2GoQEb/PnUNFNgH2A742xeedA3wMWA3Y\nGngrqehZFfimpH9HxKlVJmpmZmY2Kho+cqxR3NnpkaRnSDpP0gOS7pL0bUlPHGfdrsfsSHqBpNMk\nXSPpTkmPSFok6TJJ75G0fMd2Ti5t50hJ+0r6k6R/SfqDpFcV621bDBV7QNI/JX1V0ioV/dc/ImnF\nKT7nHxGxICLOjYhjgS2Bha3/FvAFSatVlJ+ZmZmZWVfu7PRA0pbAAlKnAmAVYB9ge2D1KWxqF+CV\nHcvWAJ5ZXHYC9hznua8BnlK6/zTgVElPAQ4DWgXJKsBbittvnUJunX4FbAdsALwd+OyybigiFkk6\nGPhFsWg9YDfgtGnkZ2ZmZmY2IXd2evN52oXOrcAbgFcA9wLrTGE7FwMHkQqaXUnFz2uB64rH95C0\n7TjPfQrwJVKRcHFp+UeBq4ttHl1a/sZpdk8uAn5W3P5QBZ2YS4EHS/fndFtJ0gGSrpB0xT/v+Oc0\nQ5qZmZkNITXs0mDu7ExC0rrA3NKit0fEWcVjVwN/msLm5pOOYTkc2JTUFep8iWwHXN7lub+OiHcW\ncVcjdYFa9omIqyWdBbyr2O5jgI2A6Rw/dBjwfFIn5mBSd2uZRMRiSXeTOk8Aa42z3gnACQCzZ8+J\nZY1nZmZmZubOzuQ27rj/y9aNiLgauLuXjUgSaWa2o4HZpOFr3WrhtcfZxKWl23eWbi8q8iAiArir\n9NhUuk5LiYjLgTOLu4dMkNukJM3oyOeeaaRmZmZmZgNE0laSjpH0C0k3F8egPyDpKklH1HU8tzs7\nk5usOddr8+7ZwHOL2/8GjiQVMA8DHyF1UGD8AnRR6fbi0u2JioYqGouHA7sDawLvm8Z2dgBWLt2/\nYjpJmZmZmY2iNHKs4WPHunsr7ePKy7YsLntLenZE3FtlUHd2Jnddx/3tWjckPZVxhmN1MbN0+7cR\ncXREXEDqFM0c5znZRcQfgO8Wd5+9LNuQtBbwudKiOxjs8w+ZmZmZ2dTdRToWfk/gpcD3S49tTjps\nolLu7EwiIu6QdBHtrsyXJK1JOtj+I1PY1A2l21tKejtwI3AAsFklydbnCOBV9P56WV/SDqTz6mwD\nvA3YsHgsgAMj4v7KszQzMzOzpvoO8L6IuK+1QNLZpO/BWxaLtuv2xOlwsdOb1sH5q5K+tH+zWP4P\n0vCyNXvYxhWkYWvPAZYnzawG8ABpQoLxZmHLLiKul3QiqTDrxYuLS6cHgLf6hKJmZmZmy0igARzF\nFhEXd1kWkv5Cu9ipfGe4h7H1ICJ+C+wInE/q6CwCziAVLj0daB8Ri4E9gJOB20lf/C8kzfQ2lRnd\ncvko8NAU1l9M+j/eDFwAHApsHBHfqiE3MzMzM8tn3dapQ4pLTzvIJT2WdDqWlrOqTsydnR5FxG9o\nTyJQNqvLunPH2cYdwH5dHppXXDrXH2/5fMaZfCAilsqnV+PlXTx2K2MnGOj5uWZmZmY21O6IiK7n\nUBxPcVjIj2jP9nsuaahbpVzsDDlJ65NOSDqR2yPi2n7kY2ZmZmbTM4Cj2MaQtAFwDrBFsegC4BXF\nSKhKudgZfrsBJ02yzil06SCZmZmZmVVJ0hakQmeDYtFpwBsi4v/qiOdjdszMzMzMrHaSdiZN+tUq\ndD4NvLquQgfc2Rl6EXEyaVIEMzMzMxsGAziOTdLLgFOBFYpF3wXOBLZXe3q5hyKi0hPPu9gxMzMz\nM7O67UG70AF4TXEpu4kuk39Nh4sdMzMzM7OBITSIrZ1MfMyOmZmZmZnVKiLmRYQmucyqOq47O9ZY\nV1658I6Vl9dN09jEusAdVeXj+AOZQ+74Tchh1OM3IYdRj9+EHEY9fhNyyB2/ihyeVFUi1j8udqyx\nImK96Txf0hVTPcFVlUY9fhNyyB2/CTmMevwm5DDq8ZuQw6jHb0IOueM3JYeqyKPYeuZhbGZmZmZm\nNpRc7JiZmZmZ2VDyMDYbZic4fna5c8gdH/LnMOrxIX8Oox4f8ucw6vEhfw6540Mzcpg2MZCn2clG\nEZE7BzMzMzMz68GWW82Os86/JHcaY2y03soLm3o8lIexmZmZmZnZUPIwNjMzMzOzQeJxbD1zZ8fM\nzMzMzIaSix0bepK2yBh7S0nfzxj/I5LuknRnpvjPlnSQpINyxDczM7PR5mFsNrQkPQM4AvhPYPka\ntv94YCbwvxFxW8djWwMfAXavOu4UrQysBeSaiWR34ANF/C9kysHMzGyoyOPYeubOjg0kSVtI+qqk\nsyWdUBQXrceeKulM4EpgTyp+nUuaIekU4Fbgl8Atkk6TtLykVYvHriB90fenUcNIqrzwnULsFSS9\nI1Ps10n6oaTTc8Qvcnhc0e3cMlcORR7rZo6/vqRPZop9oKQrJS3MFP+pknaXlHVHUJHDrzPn8JJc\ncSV9RtKnc8QvclhB0hqS1qhp+6tI2kvSccV3heOK+6vUEc+azZ0dGziSNgcuBVYtLX69pF2BDYBT\ngBWor9B4K/D6jmWvAP4CPBuYW8RudVP+t6Y8bAokrQi8BXgfsGGG2G8tYj8B+FI/4xeeRir+c55v\n4GDg/UUOff/7I+lxpE7jAcBqNcWYATwWuCMiFnc89vgi/ptJXdf31ZHDJP4D2Ip8r4N9ST+DxdT0\nGpC0He2u+6Udj+1J6ro/o47YvZC0B3A4sDUwI0MKO5DeiwG8N0N8gKOo6bNA0t6kkQTrdXn4Dknv\njIhsw8ut/1zs2CB6H+mLStAuaFYAPgtsDqxYWvdm4OMVx39Vl2UC3lPEbuX01yL2SRXHtw6SViJ9\nkZ9JKi7PiIh/FY8tD7yD9Lp5fE3x9yN9gWvF/1xEfLn02DHA4xhbBI+qWnZCFIXMfrR/Bye3hpdK\neizpy+WbgZXqiF/EOQp4N2lHzP9J+n8R8X5Jy5GG1B5SxPfroIbXgaSVgbOBnUrLLgdeSPqdfJf0\nRb8Vv/LfgaTnkX7Prdfh5yPip8VjuwCfBrasK/6AqeM1sA/wDdrn3QzG/qzXA74r6TER8d2q4/eT\nPG6kZy52bBDtQPrgWgycWyx7ITCH9ofnjaQvmN+IiEcrjr95Ef9S0t4xAZ+h/Uf0HtIeq5Mi4t8V\nx7YOktYBFgCblRbfLGkH0vFKZwAbt1an4i8YknYDvk77j+omwBclPQA8nfTlt2zUv+BUTtKGwK+B\n9UuL31u8BtYHTgfWpv35UMeX3NeSCqqWlYocbgdmk3aSlF9/D1adg3Ew8Nziduv9uC3wMeA5pG5O\neSfZ5VUGl/Rs4BzaQ6efCuxSdHI2JRU6VhNJ6wPHM3bo+r+Au4F1SN3UKB4/XtL5EfHPvidqfedi\nxwbRE4vrD0bEpwEkvRf4JOmD7BvAWyLi4Zrir1lcfyIiFhbxP0m7CNsrIi6oKTZFvKxFlKSP9Ljq\n9rUmkhxC+lLR+hIp0l7VTwHPAmbR/oKzGPhBxfHf2mWZgKNJnaTWF6vFwPdIRbhV6/2kzln5i+xa\nwOdIQ4XWof36uB/4Sg057Ftcl3MQcGiRS8t9wJdJO0isWnuWbpd/BwfQHi4m0s6RoyPifyqOf3AR\np/M18Clgo9IySMd7Hl1x/FH3RlIHL0jH7B4MXBoRUXRXdySNANmqWG9/qh/5YQ3kYscG0UqkD7Mr\nSsvKtw+psdCB9h+z8nTOd5Vuz68xdku5PT+RuroIR9a47anarbgWcG9xvTqwN2MLje8CH4uIayqO\nvw3pZ/E30h9SSH9kn1ha5zvAURFxbcWxLdm1uBbwx+L204Dn0X4N3At8kTTE8C6qtyXpdXA1acga\npPfJ04rbD5O+9H4qIu6pIb7BU0i/gx+TvvgK+BqwR7H8FuBNEXF+TfGfVcS5n7TTDdLxnU+h/Tq8\nBDii7h1iI+p5xfXNwC4RcV/rgeL4uYuKoYRXkY7vfR4DXOx4FFvvXOzYICsXNEuGqkVEv84ps38x\nPhtSJ6Hlw+oYTBsRH60hfi+fdXV/HuYqtsqeXMQ5kbQHF+AE0pedAH4DvK6GIqeldRDshyLimwCS\n/kGaKCOAgyPiizXFRlKvX5qeXGMOO02+FjD2fVKlmaSf9XERcViR0zHAh4rl/0N6DdT52bBOcX1k\nRJxe5CDgtCKHN0TEaXUFl3RDj6uuXVP8N/S46tPriF9Yvbj+aut3Lel4UrED8LKI+E2N8R9XXL8/\nIo4v4v8e+CrpNfDxiDi0ruCSTuxx1dk15tDre3zNyVeZso1JP+evlgudsoi4R9JXSEMbN6khB2sg\nFzs2yBZ0FhWk7xedQ7wiIup4re/XGae4PqJzRaDqYqczdr/dTHM6O61Z+b4XEQEg6TRSsQOwT0T8\npcb4y5N+FuUvmzeWbn+txtiQZv/L/buYnzmHVYr45T32PycVOwBv7sNOkMcUOZTPufW30u0zao4/\ni7zd3pNr3HavlityuLe0rLx3v85CB9IENQH8qbSsvJPl2JrjzyP/7+CvGXN4bHE92e/5yo71bci5\n2LFB1vlHPcZZnjt+5R/8EXFK1ducYvxZOeN3aA3pKx/wveR2zYVO2VbFtMMwdu/1tuqoyiPi4opj\nN2VEQ+5O30Ol2/+3JGhEP6d/f7Gk1h7jTUvLX93ldfANqtWEbm8THFp0V6E0aUWXzkdExBup3rql\nDkd5+uO1JI3prEXEzTXEb8LvOMdnQWumxfsnWe+B4nrlGnLoD3k2tqlwsWODqtvbvF9v/SZ1Nazt\ni5IWFbeXDJHoMswrImJXqveFLsvE0sdwVX1eiayFb0kTvmj/QFKryFkyBX2XIV4RERtTj27DlETq\nfHSqstg5qsJtLaumfP16ccf91uf1vqVlrZ0kdRQ73SZBEanrUVb1Z8HFNONvU67PgtbxtOVit5tW\nAVzpCcetuVzs2CDKOoSrYV0NJL2S9Ed8DmnWp3tIEzacHBFVzzzWZFt13G/90X9uaVnd57YoT23c\n2dKXQBkAACAASURBVOnrZYjRlEVE7iGNkGaaaoLy7Hfl38Gs0rJ+n9+kW6zKc4iIrMVORDTpi2Pu\noivLqIOImFvn9nu0c+4EWLrYtRHnYscGTu4hXE0haRXSVMbl2cgg7bV6MWk4zU+BV7VOsFlh7IkO\nhl4MLCKNW/9WRJxXZezxUupDjKnEz9l57LuIuCl3DjTjdzC0v+MB0YTOxki99ztFxEW5c2Bkft4j\n8t+sgIsds8F1AvASxu8YqHj8eKDXmZJ6NWuCuBSPbQXsI+krEfHOiuOX5S5+m9LVQNIGwKtZust3\nap+PW+m37EO4mtTZkPRM0nu+83XwjYj4dc7c6tSAzkYTuhrAkp1hu7H0a+DsiBjWE9o2odi1BlIx\neZHZQMoxhKsJXQ1J2wGX0i44riXNPnUn6YDY59GeajiA50TEZRXGX9zDauUhQ6+IiDOrim9Lk3QI\n8F/ACl0efhj4cOskvDXEnmjK2/J74oyaznFjQDFBxueBt5UXM/YL4JeBdxXnHaky9kRToJdfA9+J\niKurjG1jSdqbdAzhel0evgN4Z0R8v6bYE51wuvw6uCgiHp1gXZvAllvPjrMv+GXuNMbYcJ0VF0bE\nnNx5dONixwbSBEO4oP2Hva4hXIuZvKvRUktXQ9JXSeeUCeAjEXFMx+MCPkKaBjuAEyLibUttaNnj\nd5tee8nDpD+yL6R9DpxzIuKlVcW3sSS9F/hkcbfztVkuOt8XEZ+pIX7rPTGZB0nTQJ9adQ4Gkj4F\nvKe4O9Hr4DMR8b6KY/f6GgjSOak+Oema08tnFDsbSNqHNPGFGHvy6fJxQ4uB10fEd2uI3+vr4BZg\n34YMexs4z9h6dpx9YbOKnQ3WdrFjVilJ3wL2oXvRUf5w/3ZEVDqEqwldDUlXAFsD50XEbhOsdy7w\nAmBhRGxbZQ6TkfQY4FfANsDtEfGEPsTs+zCu3F0NSbOAv5BmImq95v5Mu8u3Ke33yCPAplVPd9vD\nDgBKjz8CbFfXOU9yDeHK3dmQ9DTg97R/zouAX9J+HTyb9kk3A3h6RPypy6aWNf5UPxd3qeuLbq7O\nRu6uhqT1getpn3sM0g6Gu0knvV2Z9u/gfmDjiPhnxTn0sjOw9dgDwFYRcX1Fsbt1tScUEQ9Pvlbz\nuNiZGh+zYwOnGMJVLnTGG8Il4LWSvlTlEC4mPj6gs6sB8Cag6iFcGxTX35tkve+Sip0NJlmvchHx\nqKRzSMXOOpOtP10TDON6JfBfkuoaxjWP3vZkfk5SHV2NN9E+oeXZwNsi4pbWg5I2Ar5Ceh08plh/\noi9ly2KisfKt98Rmxe3HAAdR8ayKkwzh2hZ4m6RahnAV5jL562BP4EOS6uhsvKl0+2vAeyKidT4R\nJK1G+vm0fu5vBN5bYfyJjp1rvQa2B9Yolr0DqLzY6aGzsR7wXUmPqaGzcSQ9djUk1dHVeCOp0AnS\niTMPBi6NiJC0HLAj8FnS8ZSrAvsDH684h4lOzdB6HbTOb7MK8G6gqtEPUx3FUfXU333l6Ql6N7C/\nZBtp80q3D59kCFdr/cqKnV6meO3oasyuKnZJ6zwyf51kvdYsWWvVkEMvWuczqHXvWTGM6xPF3W7D\nd1YEPiEp6hjG1UpjgseC9OXiFEl/rrirsVNx/SfgZRHxyJjAETdK2p10VvGnMnYq7kr0cmB4caLN\ni4An0M65Sh8H3t5Kifbvo/yF9+2kE49WOoSrpJcTKS4HHCfp1xV/2d2uuL4sIt6yVOCI+yW9Cdgc\neBap01OZXqZAl7QW6bN4U9r5VqbobBzP2POn/IulOxvLAcdLOr/qzkYrlXGWt16HM4GfSKqsq1F4\nXnF9M6lzdt+SwKnAv0jSLsBVpB1gz6PiYqeXUzMUOZxOKnyrPOdZ+b1utkRjZo8xm4I5pA+08zoL\nHUhnCywKkv8hfej1va1aDFE4p7hbR1ej1b2YrIhoffFdvoYcxiXpsZJeTxpOFMBEkzpMN9Ys4FjG\n7k28BriENLxryarAsaUzm1fl4uJyUZfLxcDVpdxaXY0qbVRs/2udhU5LMVTjBNLPIMvscRFxHfCd\n4m6lQxqLIVzvof1zvhc4F/g26XOgdUZ1Ae+RtHmV8QunTHD5Bunz4L7S+u+oOP6TSP//k8ZbIdK4\n9ZNK6/dVRNwD/LC4u/5E6y6jcmdjIamTsXpEbEgawrcz6Ys+tDsbVbq5uNzU5XIzqdBuaXU1qrQx\n6f/+1XKhU1b8Dr5Cei9sUnH8nkTEBbS7b1V/Hveyw8HHb4wYd3ZsEDV+CFehzq5Gaw/WgtTI6mn9\n6oJL/+51VYrCtMr4HbIO42pAV2Pt4vqPk6zXerz2IYUZ5B7C1YTORqt7e90k611bXK894Vr1WaW4\n7vUzZCqydjYydzUAHltcT9Y5vrJj/Rxak0TMqHCbE039vTapo/ts2sXOQ+Ov3ny9/ek3cGfHBlOj\nh3D1s6tBe1z6RJe64k4Wv+VOoK6hY7D0MK5byg9GxI3A7qQOC9QwjGsydXY1SEP0YPLx6q3HV6o4\nfk+KDtxrirt/q3jzY4ZwlQsdSEO4SAXRZaTXZqVDuHpVc2ej1e2d7Atca+fLlA/mni5JOwKvJ30u\nVjpJRqHxnY2auxqt9/b9E66VJgaA9rEzfVXsfNizuPuPqrYbERd1XoDLSceKnUj7c+LfpE73plXF\ntmZzZ8cGUdYhXA3qauTerzNZfJH+0OwfEX+vMY+ehnFJOoF0cG6WYVw1mkH6/39R0qIJ1mvtJKj8\ndTPJTGQC1iVNUNDqwP2i4hR6GsIl6STS8Sp9H8JVUldnYznSz+AHkv5vgvVaxXHV3d6Jduq0XgOr\n0P5cnOg1s6wGpbNRR1ejtb0ADpU0URHRKrQr3+E9yeyUrdfB9qTPoyAd21o5ScuTjtH7EGlShNaU\n298BjoiIOndCWsO42LFBlHUIVyn+ZAekQ31djUpnsqo4/mLSsQl/jIhrJ1ivKo0fxlVzV6Nlq5q2\n24u5TD4OvvV+eZQ0pKxKAzGEqw+dDYDH09tnU9Vm0fvB4Q+SdjxUrfGdjbq6Gh1eXNN2ezGP3j8L\ngnSS28oUs87tRxqqvEEp1pmkCY0m+zsxMJR9f+fgcLFjgyznOz1rVyMiJprmtXa543fIOoyrAV2N\nVpzcev2S+5YazrGTfQhXQzobvfwO6nyt9LLtW4E3FEM7q5a1s9GUrgaD8XkQwGERMb+ygNKrSaeG\n2KQU/3+KOAurimODx8WODaqcH+ZN6mpMiaTtKc6JEhF9f//XFD/3MK655O1qNKHwnCiHJe8J0olV\n76ghftYhXIVZ5O1sHFXx9qqMX34NzB9vuGmFcnU25pGxq8HE57vql4lyKL8OvlNDl+U7jD2v0q9I\nQxZfIekV3Z4QEYdWnIM1kIsdG0RZh3A1rKuxLHLv9asrfs5hXNm6Gr3MAla3JuRQyDWEqyVbZ6OX\n83/VKXf8Dk3/jKu8qwG9zQxZtybkQPt9vh2Tz3o4uMVO7lf5AHGxYwNnUIuN3F2VIZfzYz93V2OZ\nSNoC+ALp9Vj1FLg5csg9hKtJnY2eSZoNfJ/0O9h4wOPn7mzk7Goss2Jq/ENJv4M3DkEOvb7Pc3fB\nrE/8hctGRkOKDe+LqV7u45ea0tWYqjXpbQjeIOSQvavQsM7GVKxEewjeQMfP3VXIHX8aHkd7CF6W\nYqeiHHIXu9ZQLnZs1LjYGDKDWmw0obMyLAa40MjeWbH8nY3c8YfFABeby8RfZnrnk4qa2UiStIWk\nCyT9PFMKra7G3EzxR56k2ZJukHR9xjRanY1ZGXMYda2uwrwRjT/SJG0i6URJX8+di9XDnR0zG1VN\nGMZleeUewmVm+TVhGJ/VyMWOmZmZmdmAkNLFeuNhbGZmZmZmNpTc2TEbIRFxCRl3cuSOb2ZmZqPF\nxY7ZEJC0KrAP8BRgHZaeqKXWWX5yxzczMxsl8nxsPXOxY9YndXU1iqlrzwbWnWTVWoqN3PFtmf0e\n2Nk5jLzLgY1GOP7QKXY+/Vdx92cRcc4kT/kLUOkU/k3IwazFxY6NjBqLjdxdjc8C602yTp2zTeWO\nbz2QtDzwBICIuDki7gUuGrUchpGkNyzL8yLiGxHxMHDTIMe3sSLiAUnvIH3Hu7CH9f9JxSdnbkIO\nZi0udmzg5Sw2GtLVmEMqJu4FvgvcCTxaY7ymxR9UlXU1JD0D+DSwHXA/8CPgw8UXiJZnAr8AFlPD\nZ38TchhQVXQ2TmbqOxQC+MY04zYl/hi5uwq54xduIP1NzDnWqQk5DC//VHvmPzY20BpQbDShq/Eg\nsCLwroio5ctDw+Mvq74OoaqrqyFpI+BiYDXSn79VgDcBL5C0a0Tc0PmU6cZsYg7LqJIhVA3pbOT+\nmeaOv0TurkLu+IWvkv4+zQPOqnjbg5RDLzyEbsi52LFBl7vYaEJX4xxSZ+uhPsdtSvye1DmEKnNX\n44PA6ox9nQt4EnCBpOdExG0VxmtUDg0pNE4mb2fj4mWIX6Xc8bvJ3VXIHX8RcC2wh6RfAz8E/kbH\n76nmHVTZc5C0CmmHRrdRH0TExR5CN/wU0bTPJ7PeSWp1FSYsNiLiqJri3wGsDeyXq6shaUPSHur7\ngVdGxG9GKX6Rw6TFhqTtKYqNiKis2Ci6Gr+l3dWA9Mf8ZmBJV6MUPyJiRoXxrwOeTHrdfwX4M7A3\nsFOxyu+AHYEt64ifOwdJi1mGQqPi18DiZXha5b8Ha5P0LtLOsDMj4uUjGL/1vhDjvz8qfR80KQdJ\nKwGfIY3qGG/7tf7/67TVNrPj/Isvy53GGOutvvzCiJiTO49uBvKXbFaSewhVE7oaF5F+BusDV0i6\nD7irY52IiI2HMX4DhlDl7qw8sYj98Yg4vFj2ZUlfAN4JPB04HfjYEOeQewhVEzsboy53VyF3/LLc\n7w/ofw6fBd7S55jWUC52bNDlLjYOBZ4PfEzStTm6GsAs0h/Q1h60NUhfvlsm2qs2DPFzFxu7Ftf/\nZumuxkzgbEk71hj/EWAFYH55YUQcJGn9IpddScXoMOaQvdCIiLk540+FpM2AVwFExEeHOP6JtD+T\nZheXTrVNktCA+NnfF5lzeAXtn3+3HXA2Qlzs2KDLXWzk7qq0dO416/detJzxcxcbubsa/yCNSe92\n7NobgMcBzy3yqEu2HAap0GiIpwJHkl6zfS92MsXP3dnoe/wmvC8y57BKcf1fEXFExjxqo9yv6gHi\nYscGXe5iYxZ5uxqQ/6SMuePnLjZyd1auIRUaLwVO7cjhYUl7ApcA/19N8ZuSw0DI3VkZIbk7G7nj\nj7pfk3awXJE7EcvPxY4NulnkLzaydlUiIutJGXPHJ3+xkbuzMh/YDdhL0mERMWZ2sYhYJOnFwKWk\nwnBYc5hUQwqNWjobkk7scdWZVcVsUvxOuTsbueNPpAnvgz7k8AFSwXmEpD9HxF9qiJGRUPaG5eBw\nsWPDIGexkburYfmLjdxdjW8BtxS3V+62QkTcImkX4DlDnEMvcg/hqtM88nYScse33jXhfVBrDhFx\nuaRjim1fLWkRcM/Sq9U+xNwawMWODbqsxUYDuhoASJoFvJc09fLawHIdq9T6oZ45fu5iYz4ZuxoR\n8Xfgez2sdy1pdqjKNSGH3BrU2ci9uzd3/Enl7mzkjj8KJO0NHEV71MdawJrlVXBxPjJc7NhAa0qx\nkVMx9fLlpCJjvC8atX2o545P/iFUg9LVGFoNKTTmkffL079JOxkuJJ3jaTwzqWcnUe74U5G7szGU\nQxmbkkPhcPJP3FMb4QkKpsLFjtk05e6qAB8inR16STzGntyy7o/E3PGzFhtN6mpIuqCH1R4A/gJ8\nMyJ+OyQ5zKM5e2lzfQX5E7AFcE5EfGq8lSTtQT3FRu741oz3QRNyANiYlMcvgP9HOuH4spz814aA\nix0beDmLjQZ0NSB9cQjg28DrimXvJk3UcCjwS+rde5k1fpOKjQaYS2+vt92Ad0n6QER8eohyyLmv\nM3dn4wrScWm5zmCeO372rkLu+CVN2OefO4e/ApsBx0bEeZlzscxc7NhAa0CxkburAe2hWd+jXWxc\nHhGXSnoUOAY4gzRN9zDGb4wmdFZoj0XvfO11LhPwcUmXRMSvBjyH3IUG5O9snEn6LLp9kvVuBk4Z\nwviQv6uQO34T3gdNyAHgOOBk0k4VFzsjzsWODbrcxUburgq0/4/3Ao8CM0jFH6S9rQLeQWrlD2P8\ndiL5i4255O2s7AccCGwN/ABYUCzfEXg58BvSGdtb9wW8Haiy2MmRQ+5CAzJ3NiLix8CPe1jvN6Tf\n0VDF75C7qzCqQxmbkgOkSWv+ArxT0jNJx2wu6lzJE0SMBhc7NuhyFxtN6GrcTZpeeSXgjuL22yTd\nCLyxWKfOYRO545fNJf8wrpydleVIRcZxEXFYafkXJH2MdO6JL0XEXpI+ARwCbF9R7Jw5ZB9CRTM6\nG6Mud1chd/wmvA+akAPAEbQ/c59ZXLpxsTMCXOzYoMtdbDShq3EzqcBYE1gIvAR4cXGB9IH/1yGO\n3ylnsZG7s/Ke4vriLo9dDHyQtDPgRNLEDocAT6gods4cshcaDetsjKrcXYXc8bO/DxqSQ4s6rjs1\nYSKFZebZ2HrnYscGXe5iowldjd+S9lptBnyFVGzA2A/4zw9x/LLcxUbuzsomxfVeLD1Ofc+Ode4q\nrqueoajvObjQGCv3cM6M8XN3FUZ6KGNTcii4e2pLuNixQZe72GhCV+NQ4LPA3RHxD0lvIn2pngnc\nAHw+Ik4Y4vhluYuN3J2VvwFPAvaXNJs0Tj1IMxVuU1oH4MnF9R0Vxm9KDqNuLnmHc+aKn7urkDu+\nFSJi6HdqWO9c7Nigy11sZO9qRMRdtPeQExEnkr5M90Xu+B1yFxu5OytfB/6L9Lp/RnFpaQ3v+1px\n/6XFddWTNDQhh6xyd1ZaaZB3Vr6+x8/dVcgd30aLss/DMThc7Nigy11sNKmrYfmLjdxdjWNJJ9Ob\nN87jJ5GmZIU0M9HHgfMrjJ89h4YUGnMZ7Vn5cscfeU14HzQkh9mMfw4+z8Y2IhQx0Mdn2YiTtA6w\nHu1iY3+GvNjo8Q9Ip4iIXYch/kQk3UAqNgCuYuliQ8BfI+LJknYC5gM3R8SsiuIfRrursdTDxfIP\nR8SxpWF0Z0XEnl3Wn04eOwB7A5sWMa8FTouIS6qM08QcJC2m9wOPA6h8Rr4ih9b2J+tsQCq4d6iq\nsyJpP+C/WXo4J6XhnG+OiBNLr8Mbqzr5cu741qj3QZYcJD0GOA3YY8KgETOqiNdvW28zJ+Zf8uvc\naYyx1iozFkZE7ln4unKxYzZgpvgHBIov2VV9qOeOP2GgzMWGpOVIX/LmjbPKiaQveVHkuhpwfkT8\nvIr4lr/QKHLYl3Zn43SW7mz8lrGdjQC+HRFvqCj+74HNgd06zx4v6UXA2cAfI+LpkrYs8nkoIlYZ\nhvhFnFGdpKEVvwnvg2w5SHo3MF7h1Irdl79Lddh69py4qGHFzporN7fY8TA2syloUFcj92Dd3PHH\nk3UIVUQsJg1hO5FJuhoRcUxVcbuRtCrpYOmlflcRMdE5QAY9hyYMoco9UUbu4Zy540P+oYS54zfh\nfZAzh1eRfv7Xk15rQfqsn0ka9v5r4OoK4tgAcLFjA6UBxcZclqGrUVHsltwHtuaOP66mFBsRsYD2\nH/a+kSTgfcC7gMePs1pQ42d/A3LIXWhA/okych87ljt+y8hN0lDShPdBzhyeWlwfQTrpOMBRpELq\ndGBXUmFlI8DFjg2aueQvNrJ2NSIi65SlueP3IlexUZapq3EM6QsEXeJ2+9I1jDnkLjQgf2cj94x4\nueND/s5G7vhNeB/kzKE1JPI22t8BVoiIxZKOJx3L83Hg+RXF6yvR3OEVTeRixwZRzvd4Y7saNlaO\nYqMBXY19Gf/90a/3Te4cchcakL+zkXtWvtzxIX9nI3f8JrwPcuZwL2kGNgH3AasDzyZNSrNRsc6z\nKoplDedixwZN1mKjqV0NSWsAb2b8KTZrnQ0td/xSHrmLjdxdjXWKOD8g7VW+MyL+XXPMpuWQu9CA\nzJ2N3MM5c8cv5O5s5I7fhPdBzhz+SfpbtDrpdTcbOErSy4Ati3XuryiWNZyLHRsoTS02cpK0Hmno\nw6zxVqH6oXyNid8hd7GRu6vxF2AL4KSI+Ecf4jUxhyYMoWpCZyP7cM7M8XN3NnLHb8L7IGcOvwee\nQpqQ4AxSsTOjuG7FPqeiWHl4HFvPXOyYTVMDuhofot2W79SPIiN3/LLcxUbursbnSVNfvxA4t49x\nm5RD9kKjIZ0NIP+sfBnj5+5s5I6f/X2QOYevkWZiux64CHgBsFPp8V+SRgHYCHCxYwMvZ7HRkK7G\nC4sY5wEvKm5/EngsadjffNKBsMMavyx3sZG7q/Fv0hfqgyRtDFxAe6/xEhFR5+8jaw5NKjRGdVa+\n3PELuTsbIz2UMXcOEfEz4GelRXMlPZv2CcevCJ9ocmT4pKI20HotNuo6cZikzwAHj/NwX05cJul+\nYGXSjD5nFHF3jIhLJR0LvB/YJyK+N4zxO3K5ilRsvCQi+t5VkLQ/qavx+Yh4d4b4rRO+TlRkR0TU\nOfV09hyaJNNEGa0D4OkSt/bPpdzxixyynuA3d3wbbtvMnhMXX3p57jTGWH2l5XxSUbOa5B5C1YSu\nxvLF9V2kverLkf5wAvyc9KXjA0BdxUbu+GW5h1A1obPS0oQR3VlzyDWEqgGdjdzDOXPHz97ZyB2/\nLPdQxn7kIOkNxc2zI+KO0v0J9emz2DJzsWODLnex8aTi+vgiPsBZRVfjDlJX44Qa4wPcDawHrFDc\nfizwWkkLgP8s1tlsiOOX5S42TqK95/olxWWp8NT3mryY/h8n1agcGlBoQP6JMnIP58wdf4lRnaSh\nCe+DPudwcrGtHUnHPrXuT6TOz2JrEBc7NuhyFxtN6GrcSio21gB+B+wCvK64tNw2xPHLchcbZX3v\nakTE3H7HbGAOuQsNyN/ZyH3sWO74Y+TubPgEw9lyaEJ3uzYa6v9dtVzs2KDLXWw0oatxFenkdZuQ\n9mbtUixvfRQGqQgY1vjjyfGnoAmdlVGXu9CA/J2N3MM5c8fP3tnIHZ9mvA/6mUPrs3dRx30zFzs2\n8HIXG03oahxNKuZuiohrJD2FdLK6VYEHgC+SpvQc1vhlWf/ANaCrAYCk9YFt6T47YV/GqWfMIXeh\nAfk7G7mHc+aOD/m7CrnjN+F90LccOj97m/JZbM3g2dhsoElaCGxFOnHbO0jFRvlFLeD6iNi0pvgn\nkmbb+QDpnAnfYOwfsgAOj4iP1RF/grweAzw2Im7vZ9ymxB9lxQyB7ySdQK+b2mdCy5lD7hn5ihxG\nela+3PGLHG4FnjDJanXOSJc7fhPeB33LQdJOk6+1tIi4uOpc+mGb2XNiwS+bNRvbqit6NjazuuQe\nQtX3rkZRYAEcExHXl+53W7d1M4B/AH8CTo2IRwY1/iDI1dWQtB/jT4XeFw3IIfsQKprR2WjJPbI/\nV/zcnY3c8ZvwPuhnDvOZele/7olKrCHc2bGBJunJpCk9W8XGR1m62Di8n39k6u5qlPaats5l07rf\nq5uAF0bEtYMYvxc5h3Fl7mrMJ50l/A5gXdLv5Q/ABsBawJ+B2yNi5zriNyEHSfsCh5J2gPyUDIVG\n7s5G8TuY9D1Z4+8ga/wih9zn3Modvwnvg77l0PGe61Wt53qq0zaz58SCXzWss7NCczs7LnZs6NRZ\nbEylq1FSaVdjnGJjqn4SEbsPYvzJZC429iOdOX0idQ5duYNU4L2pyKM1FevvSV82NgZ2iIgb64jf\nhBxyFxpdchjPwH7RGgQNGEqYO37T3ge15iDpr11irE7qsEE6plekHS4B3AvcHRFPnm7sHFzsTI3b\ndzZQGjCEal6xvf8Gri/d79VRkqbb1TiquL654/5kVgdeCjwFeO4Axx9XA4ZQ7VtcT9jVqDH+GsX1\njbRfl4+JiPskfRL4EfBp4OVDnkNLriFUngkqv9xDCXPHL8s9lBFqziEiZo0JJv0HcAnwv8CrI+Ka\nYvlTge+SZsjbtc6crDnc2bGBknsIVdO7GpMpjim6hkx7leuO34AhVLm7GneTio2di3irAAdGxJcl\nvY40gcY9EbHOBJsZ6ByaMISqKXLPypd5OGnuoYS548+fIG45gbqHtGbJQdK3gNcAr4iIMzseexlw\nOnB6RLyy6tj9sM3sOXHJr67IncYYq6ygnjs7knYnHcs2m/Q34hbgLOBjEXFn1bm5s2PDYCp7jGaR\n9iova7HR2K5Gj/5Ke8a4YYy/RbHtD9AeTvY2xhYb3U40WpXcXY07ihxWB24Ang4cJ2ku7b2YdR+/\nljUHTzmb9DKckxpPrps7fmc6fYrTmPhNeB9kzuHFxfUqXR5rLXNnJwNJRwEf6Vi8CfAe4OWSdoqI\nW6qM6WLHBk3WYiMijpro/kQknUDqaqw22bp1iYiHSUPvhjV+7mLjgSKHxcCDpD+qW5CGNa1ZrDO3\nptgAfwSeDPwHcDap0FgVeAXtPcwX1Ri/KTk0wqjOypc7fiH3UMLc8UfdisX1cZIWAb8q7m9HOgcS\ntE9Kbn0iaUfg8OLuYuDDwNWkHZTbkXZIt2bwq4yLHRsoA15s/JW8XZVRkLvYyN1ZORX4F3Af8Eng\nZaQCv+UG0myFdWpCDk0YwpWzs5H72LHc8XN3FbLHb8n9PsiYwyXA84EnkoZHjUmJ9Jq8tIa4fSFA\nufuVy+Zg2p3OEyPiWFhyzsSbisdeIOlpEfHHqoK62LFR8lcyFhu5uyojInexkbWrERGnkooNACRt\nRSo2ZpJ+Hj+JiH/VFb8pOeQeQtWAzkbu4Zy54xv53weZc3g/8AvS5y+MPfcepB1j768hrk1sbun2\ngtaNiLhF0s3Ak4pFu5D+nlbCxY6NDBcbIyH3EKpsXQ1JqwLfbOUREadFxEOkmYf6oiE55C40IH9n\nI/dwztzxl8jd2RjVoYy5c4iIqyTtQDqxaXnoukh/Aw6OiKty5DaqJK1NeypwgL93rPJ32sXO5v2Q\nBwAAEfVJREFUxlXGdrFjZsMk6xCqnF2NiHhA0ouBFYAT6ogxCDmQv9CA/J2N3MM5c8cH8nc2Rnwo\nY/YcIuJ3wM6SHkt6zwm4ro7ZvvrtyisXnrfy8lo3dx4dVpJUniLuhIgo/x1YtWP9hye4X+nhBi52\nzGxo5Cw2mtDVIB2TtiUZJ8FoQA65Cw3I39nIPZwzd/zsnY3c8WnG+6AJOVAUNwNf4JRFxIty57AM\nHui4v+IE9++vMvBSLVUzs0EkaVVJPywuewNExEMR8d2I+HhEfL/OY0Ui4gHSdKd7kM7OncOnSHsv\nD5S0wojmMG6hQer2PYFUaNSp9Ue91dmA9MUP+jcrH7SHc0J7OOfa1D+cM3d8GNtVoIj5e+Du4vY1\npE7TsMZvwvugCTlYQ0TE3aTXf8vjO1Z5Qun29VXGdmfHzIZCQ4ZQ5e5qbEI6a/sOwHWSzgb+Rsek\nHBHx0SHOoQlDqHJ3NnLPiJc7PuTvKuSO34T3QRNysGa5kHZHe0fgZABJGwEbdqxXGUV4FlwzGw6S\nfkMqNl4VET/IEP+1pKFsvwCeX0yK0c/4rbO2wwRnbo+I8Y4hGPgcJF1LmqRid+BjpELjfuBcUqGx\nNnBnRKxXR/wihzOB/wTeWuTyAZb+mZwREXvVlUNHPivR5xnxcseX9DDpWJldgZ8Xi3eOiIsl/Sdp\nKOGZEVHLUMIGxG/C+yB7DtYskp4LzC/uLiadc+dPwKGkiTwAzo+I51cZ150dMxsmnyIVGwdKOqvf\nxQb5uxow9mzt3c7E0I89XDlzyD0jH4zwrHy545fk7irkjt+E90ETcrAGiYiLJH2MVNwsR/sEry03\nA2+qOq6LHTMbJrmLjSNKsTYA3jzOenXFP6Wm7U5F7hyyD6Ea5Vn5cscvyT2UMHf87O+DhuRgDRMR\nh0m6HDgQ2Ia0I+AW0slfj42If1Yd08PYzGxoNGAI1eIeVos6h5HZWP0eQtWts1FXrAlyyD2cM2v8\nIoesQwlzx++ST9ahjE3JwUaTix0zGxq5iw1JJ/WyXkTsV0f8UdeEQqPI41+kzsZLIuLcDPFzHzuW\nNX6Rw6tJMyP+CPgf4FLGdhWuB3aJiFuGLX4T3gdNyMGsxcWOmQ0NFxuJpNnAdox/1vY6jxnKmkPu\nQqPIIXdn5QhgH9KwzltJx0v0bThn7vjj5DRSkzQ05H2QPQczcLFjZjY0JD0GOI20R3lcNc/GljWH\n3IVGkUPuzkoThnPmjJ+1q5A7fpFDE94H2XMwAxc7ZmaVy9jVeDfjn6QvKL541lzsZM0hd6FR5JC7\ns5J7OGf2Y9dydxUaEL8J74PsOZiBZ2MzsyGUsdjoqatBfbOxvYr0hfp60hftAM4nDZ3ZDPg1cHVN\nsZuSQ+4Z+cCz8uWOD/lP8Js7fhPeB03IwcydHTMbHg0YQpW7q3EPaarb1wHfLmLuCPwKOJ005e3c\niLiyjvhNyCH3EKpSDpPxrHw1yt1VaED8prwPsuZgBu7smNlwORDYc5zHlhQbNcbP3dVYpbi+jfb/\nc4WIWCzpeFIR+HGg0rNTNzCH3CdWbUJnY9Tl7irkjg/53wdNycFGnIsdMxsmuYuNpxbXR5C6GgBH\nMbar8fYa499LGron0on8VgeeDcwHNirWeVaN8ZuQQ/ZCoymz/eWelS9z/NxDCXPHz/4+oBk5mHkY\nm5kNjwYMoXoYmAHsAlxQLN41IuZLehHpQPWfR0QtXQ1JV5PO5bEn8BFgNvAo8FvS8QMrAH+PiP+o\nI35Tchh1DRjO2YRZAUd+kgYzS9zZMbNhknsIVe6uxu9JhcZM4AxSoTGjuG4N4TunxvhNyaERMnY2\ncg/nzB0f8ncVcsc3s4KLHTMbJrmLjX8W8Vcnjdefzf/f3p0Hy1FVcRz//kgBCgpRoxYFKAUoFRYt\nN0QFiUgQcSOy/AElplgUQQQRUDZNVFRQNpU1ChRCoeEPAS234AKyRqDYZMeEIhQgCAQJEASPf9w7\nvs6k52VeyOT26/l9ql7NezM9fU5P0nl9cu69DTMlTSN1NQCeGWD8WaQhfPcDVwA7AB+svH4tcPgA\n4zclh6JDqLwqX/H4xYcSlo7fUXooY1NysOHmYWxm1hqlh1BJmg3sAnwJWBv4NkuvRnReROwziPg1\n+byeNI9oPeA+4IZYyf/or+wcGjKEaqhX5Ssd3xpzHhTPwQzc2TGzdik9hKp4V0PSJOA4YFdgYn56\nIXAxMJ/UfRqowjk0YQhV6c5G6eGcpeP/X+muwhAPZWxKDmYudsysVYoWGxExB5jT+VnSbqzcrsZ6\nwNU5XnWZ14nAvsCOkraOiAdbnEPpQgO8Kl/p+MWHEpaOTzPOgybkYLb0/zKYmY1XETEnIo6MiN9F\nxHPAbsC2wJ6ki6ttIuKJQeYgaZKksyT9C3iEdIF3GulCf9IgYwPfB9bvlRqpADm+5TlUC42OmcBm\nwKXApsCPBhgfRulsAGcBr2Kwn0Gnc9aZOybS3LG5wMn5tUHOHSsdH0a6Cqr5Amrv+dKm+E04D5qQ\ng5mLHTNrl5LFRu5q3Jhjdf5nW4x0NW6Q1KsQWBE+Qrq4XgQcRho+s1X+/umcy44DjN+EHEoXGpCO\nE0Y6G5A6G7DyVuWDkeGcMDKcczUGP5yzdHwY6Srcl38OUtf1btKfy1zg/BbHb8J50IQczFzsmFl7\nNKDYKN3VWDU/fi0iToqIufnrJODorm3amkPpQgPKdzZmASeQhg+dDFzJkl2FQc8dKx0fyncVSsdv\nwnnQhBzMXOyYWauULjZKdzXm5sd5Na91nrtxgPGbkEPpQgMKdzZKD+csHT8r3VUoHb8J50ETcjBz\nsWNmrVK62Cjd1TgSeAHYL0+QBkDSBFJn6z+kJbkHqXQOTRhCVbyzUXjuWPH4lO8qlI7fhPOgCTmY\neTU2M2uVarFxeuX5uZIWk4aNDLLYmAtMYSV1NSSdU/P0/aQVoOZJuj4/tyWwbn7ts6SL79bk0KX4\n8t/Dvipf6fhZ6Rv8lo5f/DxoSA5mvqmombWHpD+Sio2PR8Rvu17bCfg1cGVETBlQ/C1Jv9R/C+we\nES/m5yeQ7jHzMWBqRKyQC31J/6X3fSqq97DofL/Cb2bZhBxGU+rGqsu419AxETGwew1JuoiRCfJ1\nq34F8IuI2KON8XMORW/wWzp+TT5Dd4Nhsw4XO2bWGgWKjbquxpbAZNJY/bquxl9X1AVOLjTGahDF\nTtEcupUsNHL8Xp0NSBe5C4BBdlaeIF1gLwJmAFfll7YmDSFcC3gqIl7bxvg5h6nAdqR/Dzr/JlS7\nCtcAnxzU3KHS8XMORc+DpuRg5mLHzMathhQbxboakr6x7K2WFhEzV0T8puRQVbrQyDmU7qz8mzRB\n/qCu4ZxIOpA0nHNRRLy6jfF75FS0q7Cy4zfkPCiegxl4zo6ZjW/T6V1srAtMy993io2NgY2AFTl0\nZLSbA6rH9yvEoAqG8ZZDl86KfHV/L6or8g1sCBVLLpQxg/rOxqBX5ZtCuRXxSscHltlVmM/IamFt\njN+E86AJOZi5s2Nm41fpIVRN62pYY4ZQle6srNThnE2Ln2OVHkpYOn4TzoPiOZiBOztmNr4VLRpc\ntDRS6RX5YMhW5Ssdv4fSXYXS8ZtwHjQhBzN3dszMrD1Kr8iX4wzVqnyl4/fIaagXaWjIeVA8BzNw\nZ8fMzNrlSFKhsZ+kOV2FxkBuatqQzkaxuWMNid+tdFehdPyVfh40NAczFztmZjZ+NaTQmE7ZhTJK\nD6csHb9O6UUShmooY1NyMKvjYWxmZjZuNWEIVemFMmxppRdJGLahjE3JwayOOztmZjbelR5C1cTO\nxlAp3VUoHb+TRp+vDXIoYRNyMFuCOztmZjZueflvg/JdhQbEL34eNCEHszoudszMzGxcKz2UsHR8\nM+vNw9jMzMxsvCvdHSgd38x6cGfHzMzMzMxaaZXSCZiZmZmZmQ2Cix0zMzMzM2slFztmZjZwkqZL\nCklTRnuuSSTNl/SXPrbbIB/HjJcRKySdt7zvH2W/U/K+p6/ofZuZjQcudszMWqhykVv9ekbSjZIO\nzjc3HLfy8c2QNLF0LmZm1lwudszM2u0i4DPAXsC3gDWAU4AzSiaV/Qx4Jct3Y8UpwDcAFztmZtaT\nl542M2u3myLigs4Pks4A7gT2lXRsRDxa9yZJqwITIuL5QSUWES8BLw1q/2ZmZu7smJkNkYh4GriW\ndAf3DQHycLCQtJmkkyQtAJ4Htuq8T9L2kv4g6SlJz0u6VdL+dTEk7SvpLkmLJd0n6eAcr3u72jk7\nklaTdISkmyU9K2mhpBskfTG/fh6pqwMwrzJMb0ZlH2tLOj7HXyzpMUkXSdqwJo/1Jc3OcZ6W9CtJ\nG43hY60l6YD8mT0k6QVJD0u6QNIGo7xne0nX5eN+RNKpktas2a7v4zMzG2bu7JiZDRFJAjbOPz7e\n9fKFwHPAiUAAD+f3fA44E7gOOA5YBEwFzpC0UUQcXtn/IcDJwC3AUaRhc4cD/+wzv9WA35OGqf0B\nuIBUeG0BfBr4MXAWsBYwDfhy5ThuzftYG7gGeBNwDvB3YB3gAOB6Se+OiAfythNJw+jWz8d4B7At\n8GfSELuX4zDSZ/ZD4Algc2BfYDtJW0TEv7q2fyewKzALOB/4EPAlYHNJUyPiv2M9PjOzYedix8ys\n3daQNInUWVkHOAh4O3BdRNzbte1TwPYR8WLnCUnrkC7Wfx4Re1S2PV3SqcChks6MiPtz4XAcaZjc\n+yPi2byPc4G7+sz3EFKh892IOKr6gqRVACLiWkm3koqdSyJiftc+vknqWm0VEbdU3n8ecBvpbvfT\n89NHABsAe0fEuZVjOwU4uM+ce9kiIhZ1HcNlwOXAPsAJ3dsD0yLikkoep5IKnt2Bny/H8ZmZDTUP\nYzMza7eZwGOkzsotwN7AZcDONdueUi10sl2B1YGfSppU/QJ+Rfo98uG87Q6kTs5pnUIHICIWkLpG\n/dgTeJJ0Qb+ETmdjNLlztSepW/NQV76LSJ2WHSpv2Rl4lNRJqTq+z3x76hQ6klbJw84mkf4MFgLv\nrXnL3ZVCp+N7+XFa3tdYj8/MbKi5s2Nm1m5nAxeThqUtAu6JiCd6bHtPzXOT8+Plo8R4Y37szBep\n6+LcsYw8O94C3PwyFkZ4PfA60gX/Yz22qRZNGwJ/y4sl/F9EPCzpqeXMAQBJ2wFfJxU2r+h6+TU1\nb7mz+4lKHp3PdqzHZ2Y21FzsmJm1270RMVqhUvVszXOdhQX2Is/hqfGPrm1jlP30o+79/erEuZz+\nuzO94o0l5yXfKL2HNOfoPuBrwDzSfKggDUerG1nRTx7Lc3xmZkPLxY6ZmY2mM6/n8T6Kpvvz42Tg\nT12vTaY/9wCTJa0eEYtH2a5XYfAYae7RWn0Wef8A3ippQrW7k+cqrd1nznX2ACYAH42IeZX9rkl9\nVwdg0+4nKnl0CsqxHp+Z2VDznB0zMxvNbGAxMFPSUquT5bkoq+cf55C6FwdKWqOyzXqki/9+XEgq\nBo6piVXtcDyTH19b3SbP67kQ2FLSrnUBJL2h8uOlpGF4e3Vt9tU+8+2lUzh1d4eOovfv3k0kdc+l\n6uRxCSzX8ZmZDTV3dszMrKeIWCDpC8BPgDsl/Qx4gDR3ZAvSBP9NgfkR8aSkY4EfANdIOp+0YMH+\npA7RO/oIeSrwCeCYylCw54HNgE2A7fN21+XH4yVdmLe5PSJuB44GPgDMljQ7b/sC8GZgJ+BGRlYr\nO4FUiM2S9C7SMs5TgPex9NLcY/FL0rLYv5F0do4/FXjbKPu9DbhA0izS5/Uh0gIRVwC/qGw3luMz\nMxtqLnbMzGxUEXGupHtI9435PDCRdMF+N3As8Ehl2xMlPQMcCnwXeJBU/Cwk3RNmWbFekLQD8BVS\nEfIdUiFzL3BuZburJX2VVEjNIv0+m0kqeBZK+kDex+7Ap4AXgQXAVaTCrbOfJyVtA5xE6u4I+Aup\n0PjjWD6nruO4WtIupM/nW6SO1+Wke/hc2eNtN5E+t+PycT1Nuq/QUdWV6MZyfGZmw04RL2ceqJmZ\nmZmZWTN5zo6ZmZmZmbWSix0zMzMzM2slFztmZmZmZtZKLnbMzMzMzKyVXOyYmZmZmVkrudgxMzMz\nM7NWcrFjZmZmZmat5GLHzMzMzMxaycWOmZmZmZm1kosdMzMzMzNrpf8B3cXyRRO1SK0AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[174   0   3   0   3]\n",
      " [  0  94   0   0   0]\n",
      " [  1   0 124   0   0]\n",
      " [  0   0   1  34   0]\n",
      " [  0   0   0   0   0]]\n",
      "Group Testing Accuracy: 98.15668202764977%\n"
     ]
    },
    {
     "data": {
      "image/png": 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AhFzZTwJvA88BAzPplrZti7Q7JuVdTwp+ndIPTOnTgOGZ9MXwwd/LcvUsUKTu1ZPsZ+fS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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[174   0   0   0   3]\n",
      " [  0  66   0   0   0]\n",
      " [  0   1  91   0   0]\n",
      " [  0   1   0  63   0]\n",
      " [  0   0   0   0   0]]\n",
      "Group Testing Accuracy: 98.74686716791979%\n"
     ]
    },
    {
     "data": {
      "image/png": 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QaEEQBH2U2fByfN3MhldR7lTg78CCwDrAz4DBwCHAYsAewPRM+YG58/PH75Zr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\nVoiEFkIIoRX+H77mp0bMIXRvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[170   0   0   0   3]\n",
      " [  0 113   2   0   0]\n",
      " [  0   0  92   0   0]\n",
      " [  0   1   1  94   0]\n",
      " [  0   0   0   0   0]]\n",
      "Group Testing Accuracy: 98.52941176470588%\n"
     ]
    },
    {
     "data": {
      "image/png": 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D4RQSBEEQdA1dpM8i2n4QBEHQHYSFFgRB0MN0U5djW1hoksZJsrRNaLU8nYyk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\nCI0QCS2EEEIj/D+xhLf3BfLB/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100   1   0   1   0]\n",
      " [  0 184   0   0   3]\n",
      " [  0   0  51   1   1]\n",
      " [  0   0   0 141   0]\n",
      " [  0   0   0   0   0]]\n",
      "Group Testing Accuracy: 98.55072463768117%\n"
     ]
    },
    {
     "data": {
      "image/png": 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HUSOxnpfgPb5DkfjpzDmTyL6ECTOWI8z2dH0q/QnlxAxc0OrNQgRHkyMy4gz4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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Group Testing Accuracy: 98.49592139948915%\n"
     ]
    }
   ],
   "source": [
    "# (Inline plots: )\n",
    "%matplotlib inline\n",
    "\n",
    "font = {\n",
    "    'family' : 'Bitstream Vera Sans',\n",
    "    'weight' : 'bold',\n",
    "    'size'   : 18\n",
    "}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "width = 12\n",
    "height = 12\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))\n",
    "#plt.plot(indep_train_axis, np.array(train_losses),     \"b--\", label=\"Train losses\")\n",
    "plt.plot(indep_train_axis, np.array(train_accuracies), \"g--\", label=\"Train accuracies\")\n",
    "\n",
    "indep_test_axis = np.append(\n",
    "    np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1]),\n",
    "    [training_iters]\n",
    ")\n",
    "#plt.plot(indep_test_axis, np.array(test_losses), \"b-\", linewidth=2.0, label=\"Test losses\")\n",
    "plt.plot(indep_test_axis, np.array(test_accuracies), \"b-\", linewidth=2.0, label=\"Test accuracies\")\n",
    "print(len(test_accuracies))\n",
    "print(len(train_accuracies))\n",
    "\n",
    "plt.title(\"Training session's Accuracy over Iterations\")\n",
    "plt.legend(loc='lower right', shadow=True)\n",
    "plt.ylabel('Training Accuracy')\n",
    "plt.xlabel('Training Iteration')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Results\n",
    "\n",
    "predictions = one_hot_predictions.argmax(1)\n",
    "\n",
    "print(\"Testing Accuracy: {}%\".format(100*accuracy_fin))\n",
    "\n",
    "print(\"\")\n",
    "print(\"Precision: {}%\".format(100*metrics.precision_score(y_test, predictions, average=\"weighted\")))\n",
    "print(\"Recall: {}%\".format(100*metrics.recall_score(y_test, predictions, average=\"weighted\")))\n",
    "print(\"f1_score: {}%\".format(100*metrics.f1_score(y_test, predictions, average=\"weighted\")))\n",
    "\n",
    "print(\"\")\n",
    "print(\"Confusion Matrix:\")\n",
    "print(\"Created using test set of {} datapoints, normalised to % of each class in the test dataset\".format(len(y_test)))\n",
    "confusion_matrix = metrics.confusion_matrix(y_test, predictions)\n",
    "\n",
    "#print(confusion_matrix)\n",
    "normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100\n",
    "\n",
    "\n",
    "# Plot Results: \n",
    "width = 12\n",
    "height = 12\n",
    "plt.figure(figsize=(width, height))\n",
    "plt.imshow(\n",
    "    normalised_confusion_matrix, \n",
    "    interpolation='nearest', \n",
    "    cmap=plt.cm.Blues\n",
    ")\n",
    "plt.title(\"Confusion matrix \\n(normalised to % of total test data)\")\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(n_classes)\n",
    "plt.xticks(tick_marks, LABELS, rotation=90)\n",
    "plt.yticks(tick_marks, LABELS)\n",
    "plt.tight_layout()\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Grouped confusion matrix\n",
    "LABGRO = [  {0,4,8,12}, \n",
    "            {1,5,9,13},  \n",
    "            {2,6,10,14},  \n",
    "            {3,7,11,15},  \n",
    "            {16}]\n",
    "\n",
    "avg_acc_gr = 0\n",
    "for gr in range(4):\n",
    "    y_gr = []\n",
    "    pred_gr = []\n",
    "\n",
    "    for y, pred in zip(y_test, predictions):\n",
    "        s = LABGRO[gr]\n",
    "        l = list(s)\n",
    "        # Only get the ones in the same group on both sides\n",
    "        if y[0] in s:\n",
    "            # Rearrage the numbers to 0, 1, 2, etc\n",
    "            y_gr.append(l.index(y[0]))\n",
    "            if pred in s:\n",
    "                pred_gr.append(l.index(pred))\n",
    "            else: # Assign to \"Others\"\n",
    "                pred_gr.append(len(l)+1)\n",
    "\n",
    "    LABGR = LABELS[list(LABGRO[gr])]\n",
    "    LABGR = np.append(LABGR, \"Lainnya\")\n",
    "\n",
    "    confusion_matrix = metrics.confusion_matrix(y_gr, pred_gr)\n",
    "    normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100\n",
    "\n",
    "    print(confusion_matrix)\n",
    "\n",
    "    acc_gr = 0\n",
    "    for x in range(len(LABGR)):\n",
    "        acc_gr += confusion_matrix[x,x]\n",
    "    acc_gr /= sum(sum(confusion_matrix))\n",
    "    avg_acc_gr += acc_gr\n",
    "    print(\"Group Testing Accuracy: {}%\".format(acc_gr*100))\n",
    "\n",
    "\n",
    "    # Plot Results: \n",
    "    width = 6\n",
    "    height = 6\n",
    "    plt.figure(figsize=(width, height))\n",
    "    plt.imshow(\n",
    "        normalised_confusion_matrix, \n",
    "        interpolation='nearest', \n",
    "        cmap=plt.cm.Blues\n",
    "    )\n",
    "    plt.rcParams.update({'axes.titlesize': 'medium'})\n",
    "    plt.title(\"Confusion matrix \\n(normalised to % of total test data)\")\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(LABGR))\n",
    "    plt.xticks(tick_marks, LABGR, rotation=90)\n",
    "    plt.yticks(tick_marks, LABGR)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()\n",
    "\n",
    "print(\"Average Group Testing Accuracy: {}%\".format(avg_acc_gr*100/4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#\n",
    "#X_val_path = DATASET_PATH + \"X_val.txt\"\n",
    "#X_val = load_X(X_val_path)\n",
    "#print X_val\n",
    "#\n",
    "#preds = sess.run(\n",
    "#    [pred],\n",
    "#    feed_dict={\n",
    "#        x: X_val\n",
    "#   }\n",
    "#)\n",
    "#\n",
    "#print preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.10057471, 0.106800765, 0.14224137, 0.18726054, 0.18869732, 0.21503831, 0.25431034, 0.3065134, 0.33572796, 0.38697317, 0.41044062, 0.4032567, 0.39176247, 0.42768198, 0.41762453, 0.45737547, 0.48563218, 0.49425286, 0.50814176, 0.52873564, 0.493295, 0.5095785, 0.56609195, 0.5249042, 0.5296935, 0.53975093, 0.56417626, 0.5579502, 0.598659, 0.5929119, 0.59770113, 0.59578544, 0.63505745, 0.6393678, 0.6163793, 0.5948276, 0.6072797, 0.64463603, 0.6508621, 0.63984674, 0.6546935, 0.6503831, 0.631705, 0.664272, 0.6934866, 0.6762452, 0.72270113, 0.71024907, 0.71408045, 0.72318006, 0.7428161, 0.72796935, 0.7413793, 0.74664754, 0.7452107, 0.7179119, 0.72270113, 0.74856323, 0.7710728, 0.7801724, 0.76963603, 0.7935824, 0.802682, 0.8084291, 0.81130266, 0.8117816, 0.8103448, 0.81561303, 0.8218391, 0.8242337, 0.82327586, 0.8304598, 0.83381224, 0.84195405, 0.8515326, 0.85105366, 0.8587165, 0.8544061, 0.8127395, 0.8314176, 0.8266283, 0.8558429, 0.85057473, 0.8491379, 0.84386975, 0.8362069, 0.83477014, 0.8524904, 0.85296935, 0.848659, 0.84818006, 0.8386015, 0.88314176, 0.87835246, 0.8587165, 0.8572797, 0.87212646, 0.8702107, 0.8726054, 0.87116855, 0.88122606, 0.881705, 0.8965517, 0.8965517, 0.8893678, 0.8874521, 0.87116855, 0.8955939, 0.8979885, 0.8702107, 0.8534483, 0.8663793, 0.91331416, 0.9104406, 0.88841, 0.9094828, 0.9295977, 0.91283524, 0.9190613, 0.93582374, 0.90900385, 0.9300766, 0.9367816, 0.9334291, 0.9329502, 0.9377395, 0.9310345, 0.94779694, 0.9468391, 0.94061303, 0.9468391, 0.95019156, 0.947318, 0.9468391, 0.9454023, 0.9463602, 0.9506705, 0.9348659, 0.9487548, 0.9511494, 0.9492337, 0.9487548, 0.9008621, 0.94300765, 0.9516283, 0.9439655, 0.95258623, 0.9559387, 0.947318, 0.94588125, 0.94588125, 0.95019156, 0.9506705, 0.95689654, 0.95258623, 0.9492337, 0.9468391, 0.9334291, 0.9420498, 0.954023, 0.9554598, 0.947318, 0.954023, 0.9545019, 0.9511494, 0.95689654, 0.96695405, 0.960728, 0.95977014, 0.9583333, 0.9650383, 0.95689654, 0.960728, 0.95977014, 0.96216476, 0.9626437, 0.96695405, 0.9683908, 0.9703065, 0.97078544, 0.96599615, 0.9650383, 0.9631226, 0.9703065, 0.96216476, 0.96647507, 0.96934867, 0.95977014, 0.95737547, 0.96934867, 0.95977014, 0.9636015, 0.9535441, 0.96216476, 0.967433, 0.96934867, 0.96934867, 0.9683908, 0.967433, 0.9655172, 0.9655172, 0.9703065, 0.97988504, 0.97796935, 0.97557473, 0.97557473, 0.9765326, 0.9722222, 0.97270113, 0.9741379, 0.9746169, 0.9765326, 0.97796935, 0.973659, 0.9808429, 0.97796935, 0.9784483, 0.973659, 0.973659, 0.97605366, 0.97126436, 0.9722222, 0.9789272, 0.9746169, 0.97988504, 0.9774904, 0.9774904, 0.9765326, 0.97270113, 0.973659, 0.9774904, 0.9722222, 0.9750958, 0.9774904, 0.9818008, 0.9774904, 0.9746169, 0.9770115, 0.97605366, 0.9703065, 0.98036397, 0.98275864, 0.98515326, 0.9822797, 0.98275864, 0.9822797, 0.9813218, 0.9837165, 0.98275864, 0.9837165, 0.9808429, 0.98467433, 0.9856322, 0.9861111, 0.9818008, 0.9875479, 0.9875479, 0.9875479, 0.9885057, 0.98994255, 0.9837165, 0.9861111, 0.9861111]\n"
     ]
    }
   ],
   "source": [
    "#sess.close()\n",
    "print(test_accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Final accuracy of >90% is pretty good, considering that training takes about 7 minutes.\n",
    "\n",
    "Noticeable confusion between activities of Clapping Hands and Boxing, and between Jumping Jacks and Waving Two Hands which is understandable.\n",
    "\n",
    "In terms of the applicability of this to a wider dataset, I would imagine that it would be able to work for any activities in which the training included a views from all angles to be tested on. It would be interesting to see it's applicability to camera angles in between the 4 used in this dataset, without training on them specifically.\n",
    "\n",
    " Overall, this experiment validates the idea that 2D pose can be used for at least human activity recognition, and provides verification to continue onto use of 2D pose for behaviour estimation in both people and animals\n",
    " \n",
    "\n",
    " ### With regards to Using LSTM-RNNs\n",
    " - Batch sampling\n",
    "     - It is neccessary to ensure you are not just sampling classes one at a time! (ie y_train is ordered by class and batch chosen in order)The use of random sampling of batches without replacement from the training data resolves this.    \n",
    " \n",
    " - Architecture\n",
    "     - Testing has been run using a variety of hidden units per LSTM cell, with results showing that testing accuracy achieves a higher score when using a number of hidden cells approximately equal to that of the input, ie 34. The following figure displays the final accuracy achieved on the testing dataset for a variety of hidden units, all using a batch size of 4096 and 300 epochs (a total of 1657 iterations, with testing performed every 8th iteration).\n",
    "   \n",
    " \n",
    " \n",
    "\n",
    "## Future Works\n",
    "\n",
    "Inclusion of :\n",
    "\n",
    " - A pipeline for qualitative results\n",
    " - A validation dataset\n",
    " - Momentum     \n",
    " - Normalise input data (each point with respect to distribution of itself only)\n",
    " - Dropout\n",
    " - Comparison of effect of changing batch size\n",
    " \n",
    "\n",
    "Further research will be made into the use on more subtle activity classes, such as walking versus running, agitated movement versus calm movement, and perhaps normal versus abnormal behaviour, based on a baseline of normal motion.\n",
    "\n",
    "\n",
    "## References\n",
    "\n",
    "The dataset can be found at http://tele-immersion.citris-uc.org/berkeley_mhad released under the BSD-2 license\n",
    ">Copyright (c) 2013, Regents of the University of California All rights reserved.\n",
    "\n",
    "The network used in this experiment is based on the following, available under the [MIT License](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/blob/master/LICENSE). :\n",
    "> Guillaume Chevalier, LSTMs for Human Activity Recognition, 2016\n",
    "> https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's convert this notebook to a README for the GitHub project's title page:\n",
    "# !jupyter nbconvert --to markdown LSTM.ipynb\n",
    "# !mv LSTM.md README.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### Camera\n",
    "\n",
    "# import argparse\n",
    "# import logging\n",
    "# import time\n",
    "\n",
    "# import cv2\n",
    "# import numpy as np\n",
    "\n",
    "# from tf_pose.estimator import TfPoseEstimator\n",
    "# from tf_pose.networks import get_graph_path, model_wh\n",
    "\n",
    "# logger = logging.getLogger('TfPoseEstimator-WebCam')\n",
    "# logger.setLevel(logging.DEBUG)\n",
    "# ch = logging.StreamHandler()\n",
    "# ch.setLevel(logging.DEBUG)\n",
    "# formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s')\n",
    "# ch.setFormatter(formatter)\n",
    "# logger.addHandler(ch)\n",
    "\n",
    "# class openpose:\n",
    "#     def __init__(self, camera=0,resize='0x0',resize_out_ratio=4.0,model='mobilenet_thin',show_process=False):\n",
    "\n",
    "#         logger.debug('initialization %s : %s' % (model, get_graph_path(model)))\n",
    "#         w, h = model_wh(resize)\n",
    "#         if w > 0 and h > 0:\n",
    "#             e = TfPoseEstimator(get_graph_path(model), target_size=(w, h))\n",
    "#         else:\n",
    "#             e = TfPoseEstimator(get_graph_path(model), target_size=(432, 368))\n",
    "#         logger.debug('cam read+')\n",
    "#         cam = cv2.VideoCapture(camera)\n",
    "#         ret_val, image = cam.read()\n",
    "#         logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0]))\n",
    "        \n",
    "#         fps_time = 0\n",
    "\n",
    "#         while True:\n",
    "#             ret_val, image = cam.read()\n",
    "\n",
    "#             logger.debug('image process+')\n",
    "#             humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=resize_out_ratio)\n",
    "\n",
    "#             logger.debug('postprocess+')\n",
    "#             image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)\n",
    "\n",
    "#             logger.debug('show+')\n",
    "#             cv2.putText(image,\n",
    "#                         \"FPS: %f\" % (1.0 / (time.time() - fps_time)),\n",
    "#                         (10, 10),  cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n",
    "#                         (0, 255, 0), 2)\n",
    "#             cv2.imshow('tf-pose-estimation result', image)\n",
    "#             fps_time = time.time()\n",
    "#             if cv2.waitKey(1) == 27:\n",
    "#                 break\n",
    "#             logger.debug('finished+')\n",
    "\n",
    "#         cv2.destroyAllWindows()\n",
    "\n",
    "# if __name__ == '__main__':\n",
    "#     openpose()\n"
   ]
  }
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