HAN/HAN_final.ipynb

{
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    "# Hierarchical Attention for Document Classification <a name='top'></a> \n",
    "\n",
    "## Overview\n",
    "This notebook provides a **RAM-friendly** Keras implementation of the HAN model introduced in [Yang et al. (2016)](http://www.aclweb.org/anthology/N16-1174), with step-by-step explanations and links to relevant resources.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/Tixierae/deep_learning_NLP/master/HAN/han_architecture_illustration_small.bmp\" alt=\"\" width=\"550\"/>\n",
    "\n",
    "\n",
    "In my experiments on the **Amazon review dataset** (3,650,000 documents, 5 classes), I reach **62.6%** accuracy after 8 epochs, and **63.6%** accuracy (the accuracy reported in the paper) after 42 epochs. Each epoch takes about 20 mins on my TitanX GPU. I deployed the model as a [web app](https://safetyapp.shinyapps.io/DNLPvis/). As shown in the image below, you can paste your own review and visualize how the model pays attention to words and sentences, and where your review stands in the internal space of the model.\n",
    "<a href=\"https://safetyapp.shinyapps.io/DNLPvis/\" target=\"_blank\">\n",
    "<img src=\"https://raw.githubusercontent.com/Tixierae/deep_learning_NLP/master/HAN/dnlpvis_app_illustration_new.bmp\" alt=\"\" width=\"700\"/></a>\n",
    "\n",
    "## Concepts covered\n",
    "This notebook makes use of the following concepts:\n",
    "\n",
    "- **batch training**. Batches are loaded from disk and passed to the model one by one with a generator. This way, it's possible to train on datasets that are too big to fit on RAM. \n",
    "- **bucketing**. To have batches that are as dense as possible and make the most of each tensor product, the batches contain documents of similar sizes.\n",
    "- **cyclical learning rate and cyclical momentum schedules**, as in [Smith (2017)](https://arxiv.org/pdf/1506.01186.pdf) and [Smith (2018)](https://arxiv.org/pdf/1803.09820.pdf). The cyclical learning rate schedule is a new, promising approach to optimization in which the learning rate increases and decreases in a pre-defined interval rather than keeping decreasing. It worked better than Adam and SGD alone for me<sup>1</sup>.\n",
    "- **self-attention** (aka inner attention). We use the formulation of the original paper.\n",
    "- **bidirectional RNN**\n",
    "- **Gated Recurrent Unit (GRU)** \n",
    "\n",
    "<sup>1</sup>There is more and more evidence that adaptive optimizers like Adam, Adagrad, etc. converge faster but generalize poorly compared to SGD-based approaches. For example: [Wilson et al. (2018)](https://arxiv.org/pdf/1705.08292.pdf), this [blogpost]( https://shaoanlu.wordpress.com/2017/05/29/sgd-all-which-one-is-the-best-optimizer-dogs-vs-cats-toy-experiment/). Traditional SGD is very slow, but a cyclical learning rate schedule can bring a significant speedup, and even sometimes allow to reach better performance.\n",
    "\n",
    "## Other resources\n",
    "If you're interested in other Deep Learning architectures for NLP, check out my [Keras implementation](https://github.com/Tixierae/deep_learning_NLP/blob/master/CNN_IMDB/cnn_imdb.ipynb) of [(Kim 2014)'s 1D Convolutional Neural Net for short text classification](https://arxiv.org/abs/1408.5882). For a quick theoretical intro about Deep Learning for NLP, I encourage you to have a look at my [notes](https://arxiv.org/pdf/1808.09772.pdf).\n",
    "\n",
    "## Let's get started!\n",
    "\n",
    "### Requirements\n",
    "Given the size of the dataset and the complexity of the model, you should really use a GPU. To install CUDA and CUDNN (required for tensorflow GPU) on an Ubuntu 16.04 machine, I highly recommend this very simple [tutorial](https://gist.github.com/zhanwenchen/e520767a409325d9961072f666815bb8). It saved me a lot of time compared to the other methods I was following before. <br/> I developed and tested the code in this notebook for `Python 3.5.5/3.6.1`, `tensorflow-gpu 1.5.0`, `Keras 2.2.0`, and `gensim 3.2.0`.\n",
    "\n",
    "### Amazon review dataset\n",
    "The Amazon review dataset was introduced in [Zhang, Zhao, and LeCun (2015)](https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf). I'm grateful to Xiang Zhang for sending it to me. You can download it raw [here](https://drive.google.com/file/d/0Bz8a_Dbh9QhbZVhsUnRWRDhETzA/view?usp=sharing) (the compressed file is 614MB). <br/>Each line in the dataset is in format `score (1 to 5), title, review`:\n",
    "\n",
    "`\"1\",\"mens ultrasheer\",\"This model may be ok for sedentary types, but I'm active \n",
    "and get around alot in my job - consistently found these stockings rolled up down \n",
    "by my ankles! Not Good!! Solution: go with the standard compression stocking, \n",
    "20-30, stock #114622. Excellent support, stays up and gives me what I need. \n",
    "Both pair of these also tore as I struggled to pull them up all the time. \n",
    "Good riddance/bad investment!\"`\n",
    "\n",
    "### Preprocessing\n",
    "To make the entire pipeline **RAM-friendly**, I wrote a script [`preprocessing.py`\n",
    "](https://github.com/Tixierae/deep_learning_NLP/blob/master/HAN/preprocessing.py) that preprocesses the data **line-by-line**. That is, never more than one document is loaded to RAM at a time. With 8 cores @2.4GHz, it took me ~2.17 hours to preprocess the full dataset (3.65M docs, 1.6GB). If you want to get started immediately, you can download the output with all the necessary files [here](https://www.dropbox.com/s/hsyzjq6yr9j64q9/amazon_review_full_csv.tar.gz?dl=0) (3.6GB `.tar.gz` file).\n",
    "\n",
    "I tried to follow the steps described in the original paper as much as possible. So, among other things, the script:\n",
    "- splits the training set into training (90%) and validation (10%)\n",
    "- tokenizes docs into sentences and sentences into words\n",
    "- creates a vocabulary, and replaces the unfrequent words (<= 5 occurrences) with a special token\n",
    "- turn word docs into integer docs\n",
    "- write bacthes of docs and labels to disk, going from smaller docs to larger docs (**bucketing**)\n",
    "- learn word2vec embeddings from the training and validation sets\n",
    "\n",
    "### Environment\n",
    "Let's load all the libraries and functions we need and define our paths and constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from time import strftime\n",
    "from gensim.models import KeyedVectors\n",
    "from sklearn.decomposition import PCA\n",
    "from nltk import sent_tokenize, word_tokenize\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from keras import optimizers, backend as K\n",
    "from keras.backend.tensorflow_backend import _to_tensor\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers import Input, Embedding, Dropout, Dense, TimeDistributed\n",
    "\n",
    "dataset_name = 'amazon_review_full_csv'\n",
    "n_cats = 5 \n",
    "is_GPU = True # False\n",
    "n_units = 50\n",
    "max_doc_size_overall = 20 # max nb of sentences allowed per document\n",
    "max_sent_size_overall = 50 # max nb of words allowed per sentence\n",
    "drop_rate = 0.45\n",
    "my_loss = 'categorical_crossentropy'\n",
    "\n",
    "# replace with your own!\n",
    "path_root = '/home/antoine/Desktop/TextClassificationDatasets/' + dataset_name + '/'\n",
    "path_to_batches = path_root + '/batches/'\n",
    "path_to_save = '/home/antoine/Dropbox/deep_learning_NLP/HAN/results/' + dataset_name + '/'\n",
    "path_to_functions = '/home/antoine/Dropbox/deep_learning_NLP/HAN/'\n",
    "\n",
    "# custom classes and functions\n",
    "sys.path.insert(0, path_to_functions)\n",
    "from AttentionWithContext import AttentionWithContext\n",
    "from CyclicalLearningRate import CyclicLR\n",
    "from CyclicalMomentum import CyclicMT\n",
    "from han_my_functions import read_batches, bidir_gru, PerClassAccHistory, LossHistory, AccHistory, LRHistory, MTHistory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some notes about the above:\n",
    "- `drop_rate = 0.45` might seem a bit low. As explained in [A disciplined Approach to Neural Network Hyperparameters (Smith 2018)](https://arxiv.org/pdf/1803.09820.pdf) though, allowing the learning rate to increase during training, which is what our cyclical schedule will do, acts as a regularizer (large learning rates in general have a regularization effect). Therefore, other regularization techniques must be reduced to compensate.\n",
    "- I use the [`AttentionWithContext`](https://gist.github.com/cbaziotis/7ef97ccf71cbc14366835198c09809d2) class of `cbaziotis`, and the [`CyclicLR`](https://github.com/bckenstler/CLR) class of `bckenstler`.\n",
    "- I wrote the [`CyclicMT`](https://github.com/Tixierae/deep_learning_NLP/blob/master/HAN/CyclicalMomentum.py) class based on `CyclicLR`, following again the recommendation of [A disciplined Approach to Neural Network Hyperparameters (Smith 2018)](https://arxiv.org/pdf/1803.09820.pdf) (subsection 4.3) that *the optimal training procedure is a combination of an increasing cyclical learning rate and a decreasing cyclical momentum*\n",
    "- [`han_my_functions`](https://github.com/Tixierae/deep_learning_NLP/blob/master/HAN/han_my_functions.py) contains custom functions and callbacks that I wrote. In particular, `read_batches` is the generator that I use to load the batches from disk and pass them to the model on the fly, and `bidir_gru` is just a convenient wrapper for a bidirectional RNN with GRU units layer.\n",
    "\n",
    "## Defining the model\n",
    "\n",
    "### High level\n",
    "As shown in the Figure below, HAN is a pretty simple architecture. From a high level, there is:\n",
    "- a **sentence encoder** that takes as input a sequence of words and outputs a single vector (sentence vector or embedding). This sentence encoder is applied separately to each sentence in the document.\n",
    "- a **document encoder** that takes as input a sequence of sentences and outputs a single vector (document vector or embedding). \n",
    "- Finally, the document vector is passed to other layers to output a prediction (not shown in the Figure). The nature of the layers depend on the downstream task. For multiclass classification, we can add a single **dense layer with a softmax activation** to return a probability distribution over categories.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/Tixierae/deep_learning_NLP/master/HAN/han_architecture_illustration_small.bmp\" alt=\"\" width=\"700\"/>\n",
    "\n",
    "### Encoder\n",
    "- In the original HAN paper, the authors use the same encoder for both sentences and documents: a **bidirectional RNN with GRU units** (bidirectional GRU for short) topped by a **self-attention mechanism**.\n",
    "- The bidirectional GRU takes as input a sequence of vectors of length $T$ and outputs a sequence of annotations of same length $T$. It is made of two unidirectional RNNs, one which processes the input sequence from left to right, and one which processes it from right to left. The forward and backward annotations are concatenated at each time step $t$. The resulting annotation is biased towards a small window centered on the $t^{th}$ input vector, while with a unidirectional RNN, it would be biased towards the $t^{th}$ input vector and the input vectors immediately preceding it.\n",
    "- Rather than considering the last annotation (time $T$) as a comprehensive summary of the entire input sequence (which would force the bidirectional GRU to discard information by trying to make everything fit into the last annotation), a new hidden representation is then computed as a weighted sum of the annotations at all time steps by the **self-attention mechanism**. The weights of the sum, i.e., the *attention coefficients*, are determined by the similarity of each annotation with a trainable **context vector** initialized randomly. The context vector can be interpreted as a representation of the optimal word/sentence, on average. When faced with a new example, the model uses this knowledge to decide which word and sentence it should pay attention to. During training, through backpropagation, the model updates its word and sentence context vectors, that is, it adjusts its internal representations of what the optimal word and sentence look like.\n",
    "\n",
    "For more details, I encourage you to have a look at my [notes](https://arxiv.org/pdf/1808.09772.pdf).\n",
    "\n",
    "### Keras implementation\n",
    "Let's first load our pre-trained word vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gensim_obj = KeyedVectors.load(path_root + 'word_vectors.kv', mmap='r') # needs an absolute path!\n",
    "word_vecs = gensim_obj.wv.syn0\n",
    "# add Gaussian initialized vector on top of embedding matrix (for padding)\n",
    "pad_vec = np.random.normal(size=word_vecs.shape[1]) \n",
    "word_vecs = np.insert(word_vecs,0,pad_vec,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The HAN architecture itself can be written in a few lines of Keras code. First, we define the **sentence encoder**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, ?, 100)\n",
      "(?, 100)\n"
     ]
    }
   ],
   "source": [
    "sent_ints = Input(shape=(None,))\n",
    "sent_wv = Embedding(input_dim=word_vecs.shape[0],\n",
    "                    output_dim=word_vecs.shape[1],\n",
    "                    weights=[word_vecs],\n",
    "                    input_length=None, # sentence size vary from batch to batch\n",
    "                    trainable=True\n",
    "                    )(sent_ints)\n",
    "\n",
    "sent_wv_dr = Dropout(drop_rate)(sent_wv)\n",
    "sent_wa = bidir_gru(sent_wv_dr,n_units,is_GPU) # annotations for each word in the sentence\n",
    "sent_att_vec,sent_att_coeffs = AttentionWithContext(return_coefficients=True)(sent_wa) # attentional vector for the sentence\n",
    "sent_att_vec_dr = Dropout(drop_rate)(sent_att_vec)                      \n",
    "sent_encoder = Model(sent_ints,sent_att_vec_dr)\n",
    "\n",
    "print(sent_wa.shape)\n",
    "print(sent_att_vec_dr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some comments about the above snippet:\n",
    "\n",
    "- When defining the input shape, the tuple *does not include batch size*. Here, `(None,)` simply means a sequence of vectors of variable size, i.e., a sentence. Within a single batch, all sentences must have the same size. But, across batches there is no such restriction. This is why we let sentence size to be a variable, and fix it only at the batch level by padding sentences in our custom generator (see `read_batches` function in [`han_my_functions`](https://github.com/Tixierae/deep_learning_NLP/blob/master/HAN/han_my_functions.py)).\n",
    "\n",
    "- The output of the bidirectional GRU is of shape `(?, ?, 100)`. This time, batch size appears (index 0). The two other indexes correspond to sentence size (variable), and `n_units*2=100`, as the annotations of the forward and backward GRU (each being a vector of `n_units` elements) are concatenated at each time step. \n",
    "\n",
    "- Finally, after attention, we get a single vector of size 100 for our sentence. We still have index 0 corresponding to batch size, hence the shape `(?, 100)`. \n",
    "\n",
    "- `return_coefficients=True` is not necessary for training. We just want the attention coefficients to be exposed for a subsequent analysis (see 'Getting attention coefficients' section at the end of this notebook).\n",
    "\n",
    "We now define the **document encoder**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, ?, 100)\n",
      "(?, ?, 100)\n",
      "(?, 100)\n"
     ]
    }
   ],
   "source": [
    "doc_ints = Input(shape=(None,None,))        \n",
    "sent_att_vecs_dr = TimeDistributed(sent_encoder)(doc_ints)\n",
    "doc_sa = bidir_gru(sent_att_vecs_dr,n_units,is_GPU) # annotations for each sentence in the document\n",
    "doc_att_vec,doc_att_coeffs = AttentionWithContext(return_coefficients=True)(doc_sa) # attentional vector for the document\n",
    "doc_att_vec_dr = Dropout(drop_rate)(doc_att_vec)\n",
    "\n",
    "print(sent_att_vecs_dr.shape)\n",
    "print(doc_sa.shape)\n",
    "print(doc_att_vec_dr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Again, when defining input shape, the tuple *does not include batch size*. So here, `(None,None,)` means a sequence of sequences of variable-sized vectors, i.e., a **document**. And again, within a single batch, all documents must have the same size, but there is no restriction at the collection level. This is why we let document size to be a variable, and only fix it within each batch with our custom generator. This strategy allows us to use **bucketing**. By having documents sorted by size on disk, our generator is able to generate dense batches, which ensures that we make the most of each tensor product.\n",
    "\n",
    "- Next, we apply our **sentence encoder** to each sentence in the document with `TimeDistributed`. Recall that the sentence encoder takes a sequence of words as input and returns a single vector of size 100. So, the output of `TimeDistributed` is a sequence of as many 100-dimensional vectors as there are sentences in the document. So, in `(?, ?, 100)`, batch size appears at index 0, and `(?, 100)` means a sequence of undetermined size of 100-dimensional vectors.\n",
    "\n",
    "- The output of the attention layer is a single 100-dimensional vector. Index 0 in `(?, 100)` corresponds again to batch size. \n",
    "\n",
    "Finally, since we want to **predict the category** of the documents (number of stars of the review), we add a dense layer with a softmax and tie everything together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 5)\n"
     ]
    }
   ],
   "source": [
    "preds = Dense(units=n_cats,\n",
    "              activation='softmax')(doc_att_vec_dr)\n",
    "\n",
    "han = Model(doc_ints,preds)\n",
    "# so that we can just load the initial weights instead of redifining the model later on\n",
    "han.save_weights(path_to_save + 'han_init_weights')\n",
    "\n",
    "print(preds.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final output is a vector of size `n_cats=5`, as we have 5 categories. Again, index 0 corresponds to batch size.\n",
    "\n",
    "## Learning rate range test\n",
    "We will use the SGD optimizer with **Cyclical Learning Rate** (CLR) [Smith (2017)](https://arxiv.org/pdf/1506.01186.pdf) and Momentum (CLM) [Smith (2018)](https://arxiv.org/pdf/1803.09820.pdf) schedules. Instead of starting from an initial learning rate and decaying, in CLR the learning rate varies between two values `base_LR` and `max_LR`. For the first half of the cycle, the learning rate increases, and for the second half, it decreases. The cycle overspans multiple epochs, and the learning rate is **updated at the end of each batch**. There are different policies which control the way the learning rate increases/decreases, like cosine and others (see [`CyclicLR`](https://github.com/Tixierae/deep_learning_NLP/blob/master/HAN/CyclicalLearningRate.py) for details). The most basic but not least effective one is the **triangle policy** in which the changes are linear. This is the policy I'm going to use.\n",
    "\n",
    "- The motivation for CLR is that, even if it temporarily harms performance, **increasing the learning rate** during training is valuable in the long run, as it may allow the model, for instance, to **escape saddle points**. Having a large learning rate also plays a beneficial **regularization** role, and allows the model to **converge faster** than SGD with a traditional learning rate decay schedule (e.g., step or exponential decay). The author of CLR even showed in the very interesting [**super convergence** paper (Smith and Taupin 2017)](https://arxiv.org/pdf/1708.07120.pdf) that on some datasets and with some architectures, better performance can even be achieved in a single cycle where the learning rate only keeps increasing!\n",
    "\n",
    "- according to [Smith (2018)](https://arxiv.org/pdf/1803.09820.pdf) (section 4.3), CLM should be used in conjunction with CLR, but in the opposite direction: the momentum should decrease when the LR increases. No range test seems to be necessary for CLM, and `max_mt, base_mt = 0.95, 0.85` seem to be good default values. I wrote the [`CyclicMT`](https://github.com/Tixierae/deep_learning_NLP/blob/master/HAN/CyclicalMomentum.py) class to implement CLM.\n",
    "\n",
    "In the aforelisted papers, the author of CLR provides a simple way to determine the range in which we should let the learning rate vary: the **learning rate range test**. This test should be performed on the training set, for a small number of epochs. We set the half cycle size to be equal to the total number of iterations corresponding to the small number of epochs we selected. This way, the learning rate will keep increasing during the test. We set `base_lr` to a small number and `max_lr` to a high number. At some point during the test, the learning rate becomes too high and the model diverges, causing the accuracy/loss to plateau or even decrease/increase. The optimal `max_lr` should be selected as the point right before the plateau or divergence. So, let's do it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base_mt, max_mt = 0.85, 0.95\n",
    "batch_names = os.listdir(path_to_batches)\n",
    "batch_names_train = [elt for elt in batch_names if 'train_' in elt or 'val_' in elt]\n",
    "its_per_epoch_train = int(len(batch_names_train)/2) # /2 because there are batches for documents and labels\n",
    "\n",
    "nb_epochs = 6\n",
    "step_size = its_per_epoch_train*nb_epochs\n",
    "base_lr, max_lr = 0.001, 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_optimizer = optimizers.SGD(lr=base_lr,\n",
    "                              momentum=max_mt, # we decrease momentum when lr increases\n",
    "                              decay=1e-5,\n",
    "                              nesterov=True)\n",
    "\n",
    "han.compile(loss=my_loss,\n",
    "            optimizer=my_optimizer,\n",
    "            metrics=['accuracy'])\n",
    "\n",
    "lr_sch = CyclicLR(base_lr=base_lr, \n",
    "                  max_lr=max_lr, \n",
    "                  step_size=step_size, \n",
    "                  mode='triangular')\n",
    "\n",
    "mt_sch = CyclicMT(base_mt=base_mt, \n",
    "                  max_mt=max_mt, \n",
    "                  step_size=step_size, \n",
    "                  mode='triangular')\n",
    "\n",
    "# batch-based callbacks\n",
    "loss_hist = LossHistory()\n",
    "acc_hist = AccHistory()\n",
    "lr_hist = LRHistory()\n",
    "mt_hist = MTHistory() \n",
    "\n",
    "# ! the order of the callbacks matters! If lr_sch before lr_callback, the new learning rate will be saved (not the current epoch's)\n",
    "callback_list = [loss_hist, acc_hist, lr_hist, mt_hist, lr_sch, mt_sch]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's instantiate our generator. If you want to inspect the output, you can use `rd_train.__next__()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rd_train = read_batches(batch_names_train,\n",
    "                        path_to_batches,\n",
    "                        do_shuffle=True,\n",
    "                        do_train=True,\n",
    "                        my_max_doc_size_overall=max_doc_size_overall,\n",
    "                        my_max_sent_size_overall=max_sent_size_overall,\n",
    "                        my_n_cats=n_cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the test!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "23440/23440 [==============================] - 1196s 51ms/step - loss: 1.0719 - acc: 0.5310\n",
      "Epoch 2/6\n",
      "23440/23440 [==============================] - 1154s 49ms/step - loss: 0.9532 - acc: 0.5864\n",
      "Epoch 3/6\n",
      "23440/23440 [==============================] - 1155s 49ms/step - loss: 0.9473 - acc: 0.5891\n",
      "Epoch 4/6\n",
      "23440/23440 [==============================] - 1158s 49ms/step - loss: 0.9840 - acc: 0.5722\n",
      "Epoch 5/6\n",
      "23440/23440 [==============================] - 1161s 50ms/step - loss: 1.2259 - acc: 0.4566\n",
      "Epoch 6/6\n",
      "23440/23440 [==============================] - 1163s 50ms/step - loss: 1.1913 - acc: 0.4726\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7faedce0b8d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "han.fit_generator(rd_train, \n",
    "                  steps_per_epoch=its_per_epoch_train, \n",
    "                  epochs=nb_epochs,\n",
    "                  callbacks=callback_list,\n",
    "                  validation_data=None, \n",
    "                  validation_steps=None,\n",
    "                  use_multiprocessing=False, # only works with keras.utils.Sequence class (duplicate batches when used with generators)\n",
    "                  workers=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the results to disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hist = han.history.history\n",
    "hist['batch_loss'] = loss_hist.loss_avg\n",
    "hist['batch_acc'] = acc_hist.acc_avg\n",
    "hist['batch_lr'] = lr_hist.lrs\n",
    "hist['batch_mt'] = mt_hist.mts\n",
    "\n",
    "hist = {k: [str(elt) for elt in v] for k, v in hist.items()}\n",
    "with open(path_to_save + 'han_lr_range_test_trainval_history.json', 'w') as my_file:\n",
    "    json.dump(hist, my_file, sort_keys=False, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now, let's analyze the results. We plot the accuracy VS the learning rate every `n_its_plot` iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/Q6VFQa/DAnCAWhJO4YBP0ZDf61CAghAOpBNg8SQVYADeyczGSPqQpNO1jwSY\nmV0i6RJJqqmpUW1t7QF/biwW2+P7/alOrdm49aDOP9Tsbb7wr5iv/mG++of56h/mq3+8mi8SYAAA\neGTVtlZddt8Cratv1zfOmaovnDpZZuZ1WAAOQkvcqbokzH/LQI6EA+lkczxFBRiAf/FjSdc557r3\n9b3snLtT0p2SNGvWLDdnzpwD/tDa2lrt6f33rH1V21o6NWfOqQd8/qFmb/OFf8V89Q/z1T/MV/8w\nX/3j1XyRAAMAwAO/W7RJX3/0dUXDAc39/GydOLnK65AADICWhFNVScjrMICC0dMCkT3AAOzGLEkP\nZpJf1ZLOMbOUc+53XgVUEQlp5dZWrz4eAICCQwIMAIAciqe6dMMfV+iel9bp+IkVuu2CmRpRWuR1\nWAAGSEvC6ZBq9v8CcmVXBRgJMADv5Jyb1POzmf1G0h+8TH5JUkUkqIa2hJchAABQUEiAAQCQI5ub\nOnTF3IVavKFJXzh1kq49a6qCfp/XYQEYQK0Jp6ooFWBAruzaA4wWiEChMbMHJM2RVG1mGyV9S1JQ\nkpxzP/cwtD2qiIbUkexSZ7JLRUH2CwUAINtIgAEAkAPPv7lDVz+wSMkupzsunKmzp4/yOiQAA8w5\np5a4U1UJFWBArhTRAhEoWM65T/Tj2E9nMZT9VhFJ3yTT2J7QqLJij6MBAGDoIwEGAEAWdXc73frc\nav3omVU6bMQw3XHRTE0eXuJ1WACyoKUzpZSTqtkDDMgZWiACyCeV0aAkqbEtSQIMAIAcIAEGAECW\nNLUn9KWHFqt25Q598JjRuvHD0xUJ8dULDFX1sbgkqYoEGJAzPS0QO2mBCCAPlPeqAAMAANnHVTgA\nALLg9Y3Nuuy+Bdre2qnvfvAoXTR7vMzM67AAZFF9ZlP7alogAjlDBRiAfFKZ2Se0oY0EGAAAuUAC\nDACAAeSc04OvbtC3Hl+m6pKQfnvZyTpmXLnXYQHIgbrWTAVYlAQYkCvhnXuAUQEGYPArj6RbIDZR\nAQYAQE6QAAMAYIB0JLr0zceX6uEFG3XqlGr95OPH7rzLE8DQV7ezAoz/7oFc6WmBGE9SAQZg8KuI\n9FSAJT2OBACAwkACDACAAbC2rk2Xz12oFVtadPWZU3TNmVPk99HyECgkPXuAVZD4BnLGzBQK+GiB\nCCAvBP0+DQsH2AMMAIAcIQEGAMBBemrZVn113mvy+02//szxOv3wEV6HBMAD9bGESoLpi1sAcicc\n8NECEUB+l9EIAAAgAElEQVTeqIiGSIABAJAjJMAAADhAqa5u/fCpVfr5397S9DFluv3CmRpXGfE6\nLAAeqW+LqzRE5SeQa+GAnwowAHmjIhJUYzstEAEAyAUSYAAAHIAdrXFd9cBCvbymQRfMHq/r33+k\nioJ+r8MC4KG61oSGkQADci4c8LEHGIC8URENqT5GBRgAALlAAgwAgH56dW2Drpy7UM0dSf3wozP0\nkePGeh0SgEGgri2uqjAJMCDXwkGfOmmBCCBPVEZCWr095nUYAAAUBBJgAADsJ+ec7nrhbd30pzc0\nrqJYd3/2BB0xqtTrsAAMEvWxhCYNJwEG5Fo44KcCDEDeKI+E1NhGBRgAALlAAgwAgP0Qi6d07cOv\n6cnXt+q9R9boh+fPUGlR0OuwAAwSiVS3mjuSKg3x/wUg18IBn+JUgAHIE5XRoNoSXYqnuhQO0EId\nAIBsIgEGAMA+rNrWqsvuW6C1dW36+tlTdclpk2VGlQeAXRoyd3KXsgcYkHNFQZ/iKSrAAOSH8khI\nktTUnlRNKQkwAACyiQQYAAB78fjiTfraI68rGg5o7udP1EmHVHkdEoBBqC4WlySVsgcYkHPhgF9N\nHUmvwwCA/VIZTSfAGtsTqikt8jgaAACGNhJgAADsRiLVrRv+uFx3v7ROx0+s0K0XzOQvqAD2qJ4K\nMMAz4YBP8SQtEAHkh/JIul1yA/uAAQCQdSTAAADoY3NTh66Yu1CLNzTpC6dO0rVnTVXQ7/M6LACD\nWH1PBRgJMCDnwkG/ErRABJAneirAmtqpXAUAINtIgAEA0MsLb9bp6gcXKZHq1u0XztQ500d5HRKA\nPEALRMA74QB7gAHIHxWZPcCoAAMAIPtIgAEAIKm72+m251brlmdWacqIEt1x0XE6ZHiJ12EByBP1\nsYRCAZ+K2MseyLlwwKdOWiACyBM9LRAbSYABAJB1JMAAAAWvqT2hLz+0WM+t3KHzjhmtmz48XZEQ\nX5EA9l9dLKHqaEhmVIABuRYO+KkAA5A3wgG/oiG/GmmBCABA1nF1DwBQ0F7f2KzL5y7QtpZOffe8\nabroxAlcwAbQb/VtcVUPC0tKeR0KUHDCQZ/iKSrAAOSPimhIje1UgAEAkG0kwAAABck5pwf/uV7X\nP7FM1dGQ5l16ko4dX+F1WADyVF0sruElJMAALxQF/Ep2OXV1O/l93MQCYPCriJAAAwAgF0iAAQAK\nTkeiS3ctTeiFTa/r1CnV+snHj1VlNOR1WADyWH0soakjSyW1ex0KUHDCQZ8kKZHqVnGIjfgADH4V\n0RB7gAEAkAMkwAAABWVtXZsun7tQK7akdPWZU3TNmVO4WxzAQXHOqT6WUFUJiXTAC+FAOgEWT3WR\nAAOQFyojQa2ta/M6DAAAhjwSYACAgvHUsq366m9fk89MXz4urGv+7TCvQwIwBLTGU0p0dadbILIN\nEZBz4UA66RVPdXscCQDsn/IIFWAAAOSCz+sAAADItlRXt7735zd0yb0LNLEqqj9cdYpmDOceEAAD\noz6WvoBFBRjgjZ4KsM4kGWgA+aEyGlJrPKVkF4l7AACyiat/AIAhbUdrXFc/sEgvranXJ04Yr2+d\ne6SKgn695XVgAIaMulhcklQVDau72eNggALUswcYFWAA8kVFJChJamxPaMSwIo+jAQBg6MpaBZiZ\njTOz58xsuZktM7NrdnPMHDNrNrPFmcf12YoHAFB45q9t0Pt/9rwWrm/UDz5ytG768HQVBdkbBMDA\nqu9JgFEBBnhiZwvEJAkwAPmhIppeMzS1Jz2OBACAoS2bFWApSV91zi00s2GSFpjZ08655X2Oe945\n9/4sxgEAKDDOOf3qxbW66ckVGlNRrMeuOEFHji71OiwAQ1RdpgVidUlYOzyOBShERTsrwGiBCCA/\nVETSCbAG9gEDACCrspYAc85tkbQl83Orma2QNEZS3wQYAAADJhZP6bqHl+iPr2/Rvx1Zox9+dIbK\nioNehwX0m5mtldQqqUtSyjk3y8y+LekL0s48yzecc096EyF69OwBVhmlAgzwws4KMFogAsgTPQmw\npnYSYAAAZFNO9gAzs4mSjpX0ym5ePtnMlkjaJOk/nXPLdvP+SyRdIkk1NTWqra3NSpyxWCxr5x7s\nCnnsEuMv5PEX8tiloTf+TbFu/WxRp7a1OZ1/WFBnj2vVolde3O2xQ23s/VXo488jpzvn6vo89yPn\n3A89iQa7VReLqzwSVNCfte7iAPYiHKACDEB+qYimb9BraKMFIgAA2ZT1BJiZlUh6RNKXnHMtfV5e\nKGm8cy5mZudI+p2kKX3P4Zy7U9KdkjRr1iw3Z86crMRaW1urbJ17sCvksUuMv5DHX8hjl4bW+B9f\nvEk3PPu6IqGg7v/CTJ10SNVejx9KYz8QhT5+YCDVt8VVRfUX4JlwTwtE9gADkCd6KsAaqQADACCr\nspoAM7Og0smvuc65R/u+3jsh5px70sxuN7Pq3dzpDADAbiVS3brhj8t190vrNGtChW67cKZqSou8\nDgsYCE7SM2bWJekXmRuCJOkqM7tY0nyl91tt7PvGgayep1pw397a2KGA0oll5qt/mK/+Yb52b2tb\nOvG16PVlKq5fufN55qt/mK/+Yb5wMIqCfhUH/WpkDzAAALIqawkwMzNJd0la4Zy7ZQ/HjJS0zTnn\nzOwEST5J9dmKCQAwtGxu6tCV9y/UovVN+vwpk3Td2VNpQYah5BTn3CYzGyHpaTN7Q9Idkr6rdHLs\nu5JulvTZvm8cyOp5qgX37bsLajV1ZKnmzJnJfPUT89U/zNfubW7qkJ5/VpMPPUxzThi/83nmq3+Y\nr/5hvnCwKqMhNVABBgBAVmWzAuxdkj4p6XUzW5x57huSxkuSc+7nkj4i6XIzS0nqkPRx55zLYkwA\ngCHihTfrdPWDixRPdun2C2fqnOmjvA4JGFDOuU2ZP7eb2WOSTnDO/b3ndTP7paQ/eBUfdqmLJVRV\nQgtEwCu79gCjBSKA/FEeCaqpnT3AAADIpqwlwJxzL0iyfRxzq6RbsxUDAGDo6e52ur12tW5+epUO\nHV6in3/yOB0yvMTrsIABZWZRST7nXGvm5/dK+h8zG+Wc25I57EOSlnoWJCSl27A2dyRVFQ17HQpQ\nsMJBvyQpnuryOBIA2H+V0ZAaaIEIAEBWZXUPMAAABlJze1JfnrdYz76xXecdM1o3fmi6omG+yjAk\n1Uh6LN1RWgFJ9zvn/mxm95rZMUq3QFwr6VLvQoS0a/N6KsAA7xT1VIAlqQADkD/KIyFtaGj3OgwA\nAIY0rhoCAPLC0k3Nuuy+BdrW0qnvnjdNF504QZnkADDkOOfWSJqxm+c/6UE42Iu6WFySVF1CBRjg\nlYDfJ7/PaIEIIK9URoJUgAEAkGUkwAAAg95Dr67XNx9fpqpoSPMuPUnHjq/wOiQAkCTVx9IXrqqp\nAAM8FQ74aIEIIK9URENq6Uwp1dWtgN/ndTgAAAxJJMAAAINWZ7JL3/zdUv12wUadOqVaP/7YMaqi\nygLAINJTAcb/mwBvpRNgVIAByB8VkfTNM00dSSrJAQDIEhJgAIBBaV19my67b6FWbGnR1Wccqmve\nc5j8PloeAhhceirA2AMM8FY44GcPMAB5pSKaSYC1J0iAAQCQJSTAAACDztPLt+kr8xbLZ6ZffXqW\nzpha43VIALBbdW1xhfw+DQuzrAa8FA761EkLRAB5pCISlCQ1tCU9jgQAgKGLv6kDAAaNVFe3bnl6\nlW6vfUvTx5Tp9gtnalxlxOuwAGCP6mMJVZeEZEaFKuClcMBHBRiAvNLTArGxPeFxJAAADF0kwAAA\ng8KO1riufmCRXlpTr0+cMF7fOvdIFQX9XocFAHtVF4uz/xcwCIQDfsWpAAOQR3paIDa2kQADACBb\nSIABADw3f22Drrx/oZrak/rBR47WR2eN8zokANgv9bEE+38Bg0BR0Kd4igowAPmjcmcFGC0QAQDI\nFhJgAADPOOf06xfX6sYnV2hMRbEeveJ4TRtd5nVYALDf6mNxHVYzzOswgIIXDvjVkaQCDED+KA75\nFQ74aIEIAEAWkQADAHgiFk/pukeW6I9Ltug9R9To5vNnqKw46HVYALDfnHOqa0uoehgVYIDXwgGf\nmjq4iAwgv1RGQ7RABAAgi0iAAQBy7s1trbrsvgV6u65N1501VZeeNlk+n3kdFgD0S2s8pUSqW9VR\n9gADvBYO+hRP0gIRQH4pj4SoAAMAIItIgAEAcuqJ1zbra48sUSTk132fn62TD6n2OiQAOCD1sfQF\nK/YAA7wXDvjZAwxA3qmMBtVABRgAAFlDAgwAkBOJVLdufHKFfvOPtZo1oUK3XjBTI8uKvA4LAA5Y\nfSwuSaoqoQIM8Fo44FMne4AByDPlkZC2NLV4HQYAAEMWCTAAQNZtae7QFXMXatH6Jn3ulEn62tlT\nFfT7vA4LAA5KXaYCrJoKMMBz4YCPCjAAeacyElIDLRABAMgaEmAAgKx6cXWdrnpgkeLJLt12wUz9\n+9GjvA4JAAZEfVu6AqyaCjDAc+GgX/EUFWAA8ktFJKjmjqS6up387IkMAMCAIwEGAMiK7m6n22tX\n65anV+mQ4SW646LjdOiIEq/DAoABU9eavmO7IkIFGOC1okwFmHNOZlxEBpAfKqIhOSc1dyRVGWU9\nAQDAQCMBBgAYcM3tSX153mI9+8Z2fWDGaN304emKhvnKATC01LfFVVYcVChAS1fAa+GgX85JyS6n\nUIAEGID80JP0amxPkAADACALuBoJABhQSzc16/K5C7S1uVP/c940ffLECdyJDWBIqo8lVMX+X8Cg\nEM4kouOpLpLSAPJGeaaKvLEtIQ33OBgAAIYgEmAAgAHz0Kvr9c3Hl6kqGtJDl56kmeMrvA4JALKm\nLhZn/y9gkNiVAOvWMI9jAYD9VdmTAGtPehwJAABDEwkwAMBB60x26frHl2re/I065dBq/eTjx6iK\ni8IAhri6WFyHj+RSOzAYhAN+Sek1CQDki/JIUFKmAgwAAAw4EmAAgIOyrr5Nl9+3UMu3tOiqMw7V\nl95zmPw+Wh4CGPrq2xKqipLsBwaDcHBXBRgA5Iuefb/q2uIeRwIAwNBEAgwAcMCeWb5NX563WD4z\n/erTs3TG1BqvQwKAnEh2daupPckeYMAgsbMFYpIEGID8EQ0HVBkNaUNDu9ehAAAwJJEAAwD0W6qr\nW7c8vUq3176lo8aU6o4Lj9O4yojXYQFAzvS0KmIPMGBwCAfTLRDjKVogAsgvE6siWltHAgwAgGwg\nAQYA6Je6WFxXP7BI/3irXp84YZy+de40FWUuOgFAodgRS7cqqqYCDBgUdlaA0QIRQJ6ZWBXVS2vq\nvQ4DAIAhiQQYAGC/LVjXoCvmLlRTe1Lf/8jROn/WOK9DAgBP1MfSFWBVVIABg0I40FMBRgIMQH6Z\nWB3Vo4s2qSPRpeIQNxYCADCQfF4HAAAY/Jxz+vWLb+tjv3hZ4YBfj15xMskvAAWtPrNZfVWUCjBg\nMNi1BxgtEAHkl4nVUUnSuoY2jyMBAGDoIQEGANirtnhKVz2wSN/5/XLNOXyEfn/VKZo2uszrsADA\nU2t2pC9SUQEGDA5FQVogAoXGzH5lZtvNbOkeXj/PzJaY2WIzm29mp+Q6xv0xqSqdAGMfMAAABh4t\nEAEAe/TmtlZddt8CvV3XpuvOmqpLT5ssn8+8DgsAPPXcG9t1e+1bOu2w4SotYjkNDAY9LRA7qQAD\nCslvJN0q6Z49vP5XSU8455yZHS1pnqSpOYptv02ojkiS1tZTAQYAwEDjb+wAgN164rXN+tojSxQJ\n+XXf52fr5EOqvQ4JADy3cH2jLp+7QEeMGqbbLjhWZtwUAAwGO1sgUgEGFAzn3N/NbOJeXo/1+jUq\nyWU7pgNRWhRUVTSktXUkwAAAGGgkwAAA75BIdevGJ1foN/9Yq+MmVOi2C2ZqZFmR12EBgOdWb2/V\nZ3/zqmpKi/TrT5+gYUVBr0MCkNFTAUYCDEBvZvYhSTdJGiHp3/dy3CWSLpGkmpoa1dbWHvBnxmKx\nfr+/IpjS4rc2qba24YA/N18dyHwVMuarf5iv/mG++of56h+v5osEGABgpy3NHbpy7kItXN+kz75r\nkr5+zlQF/WwXCQCbmzp08V3/VMDn072fna3hw9j7CxhMwjv3AKMFIoBdnHOPSXrMzE6T9F1J79nD\ncXdKulOSZs2a5ebMmXPAn1lbW6v+vv+J7Yv1j9X1/X7fUHAg81XImK/+Yb76h/nqH+arf7yaLxJg\nAABJ0our63T1A4vUmezSrRccq/cfPdrrkABgUGhqT+hTv/qnWjpTeujSEzW+KuJ1SAD62NkCMUkF\nGIB/lWmXONnMqp1zdV7H09ekqqgeXbhJHYkuFYf8XocDAMCQwW39AFDgurudbntutT551yuqiIb0\n+BffRfILADI6El363N3zta6+XXdefJymjS7zOiQAu2FmCgV8tEAEsJOZHWqZzTrNbKaksKR6b6Pa\nvYnVUUnSugb2AQMAYCBRAQYABawt6fSFe+brr29s1wdmjNZNH56uaJivBgCQpK5up6seWKiF6xt1\n2wUzdfIh1V6HBGAvwgEfLRCBAmJmD0iaI6nazDZK+pakoCQ5534u6T8kXWxmSUkdkj7mnHMehbtX\nE6vSCbC1dW2aOrLU42gAABg6uMoJAAVq6aZmffsfHWpKdOg7H5imi0+aoMwNkgAAST9+ZpWeWbFd\n3/nANJ0zfZTX4QDYh3DATwUYUECcc5/Yx+vfk/S9HIVzUCZWp9srv13X7nEkAAAMLSTAAKAAzXt1\ng/778aWK+qUHLzlJx02o8DokABhUnlq2VT97drXOnzVWF580wetwAOyHcMCnziQVYADyz7CioKpL\nQlpXTwtEAAAGEgkwACggnckuXf/4Us2bv1HvOrRKHxvfQfILAPp4a0dMX5n3mo4eW6b/Oe8oqmOB\nPBEOsgcYgPw1oSqqt+tIgAEAMJB8XgcAAMiN9fXt+vDt/9C8+Rv1xdMP1T2fna3SEBd1AaC3WDyl\ny+5doFDApzsuOk5FQb/XIQHYT+GAX/EkCTAA+WliVVRrqQADAGBAUQEGAAXgmeXb9JV5iyVJv/r0\nLJ0xtcbjiABg8HHO6dqHX9NbO2K693OzNaa82OuQAPRDUdCneIoWiADy06TqiB5ZGFd7IqVIiMt1\nAAAMBL5RAWAI6+p2uuXplbrtubc0bXSpfn7RcRpXGfE6LAAYlO78+xo9+fpWff3sqXrXodVehwOg\nn8IBWiACyF8TqqKSpHX17TpiVKnH0QAAMDSQAAOAIaouFtc1Dy7Si6vr9fHjx+nbH5hGKy8A2IMX\nV9fpe39+Q+dMH6lLTpvsdTgADkA44FdTR9LrMADggEyqTifA1ta1kQADAGCAkAADgCFowboGXTl3\nkRrbE/r+R47W+bPGeR0SAAxam5o69MX7F+qQ4SX6/kdmyIz9EYF8FA74FE/SAhFAfprYkwCrb/c4\nEgAAhg4SYAAwhDjn9Jt/rNUNf1yh0eXFevSKkzVtdJnXYQHAoNXV7fTlBxcr2eX0808ep5Iwy2Mg\nX4WDfiVogQggT5WEA6ouCWttXZvXoQAAMGTwN3wAGCLa4ild98gS/WHJFr3niBG6+fxjVFYc9Dos\nABjU/t/za/TPtQ364Udn6JDhJV6HA+AghAM+dVIBBiCPTayK6O16EmAAAAwUEmAAMASs3t6qy+5b\nqDU7Yrr2rMN12WmHyOejhRcA7M2KLS26+alVOmvaSP3HzDFehwPgIIUDPsWpAAOQxyZWR/X8mzu8\nDgMAgCHD53UAAICD8/vXNusDt76oxraE7vvcbF0x51CSXwCwD/FUl7780GKVFgd144ens+8XMASE\nA34SYADy2qTqqLa1xNWeSHkdCgAAQwIVYACQpxKpbt30pxX69YtrddyECt12wUyNLCvyOiwAyAu3\nPLVKb2xt1a8+PUuV0ZDX4QAYAEVBn+IpWiACyF8TqiKSpLV17TpydKnH0QAAkP9IgAFAHtra3Kkr\n71+oBesa9dl3TdLXz5mqoJ+iXgDYHy+vqdedz6/RBbPH64ypNV6HA2CAhAN+Jbucurqd/FTDA8hD\nE6uikqS19W0kwAAAGABZu1pqZuPM7DkzW25my8zsmt0cY2b2UzNbbWZLzGxmtuIBgKHixdV1+vef\nPq8VW1p06wXH6vpzjyT5BQD7qbUzqa/Oe00TKiP6r3OO8DocAAMoHEyvhxK0QQSQpyZW70qAAQCA\ng5fNCrCUpK865xaa2TBJC8zsaefc8l7HnC1pSuYxW9IdmT8BAH10dzvd8be3dPNTKzV5eIkeumim\nDh0xzOuwACCvfOf3y7WluUMPX36yomGaIQBDSTiQToDFU10qDvk9jgYA+q8kHFB1SVhr60iAAci9\neMp5HQIw4LJWMuCc2+KcW5j5uVXSCklj+hx2nqR7XNrLksrNbFS2YgKAfNXcntQl987XD/6yUv9+\n9Gg9fuW7SH4BQD/9eekWPbxgo648/VDNHF/hdTgABlg4kE56dSapAAOQvyZVR7S2rt3rMAAUmMa2\nhK78a7t++tc3vQ4FGFA5ue3VzCZKOlbSK31eGiNpQ6/fN2ae29Ln/ZdIukSSampqVFtbm5U4Y7FY\n1s492BXy2CXGX8jjz4exr2vp0q2L4mrodLrwiJDeM7JJr770woCcOx/Gny2FPHaJ8aPwNLcn9V+P\nLdX0MWW6+swpXocDIAt6V4ABQL6aWBXV31bt8DoMAAWmvi2ulJNueXqVpowo0dnTqVHB0JD1BJiZ\nlUh6RNKXnHMtB3IO59ydku6UpFmzZrk5c+YMXIC91NbWKlvnHuwKeewS4y/k8Q/2sc+bv0E3PrNU\nFZGw5n16po6bMLAVC4N9/NlUyGOXGD8Kzy1Pr1Rje0L3fm42+yYCQ1TPHmBx9gADkMcmVkf12wUb\n1RZP0a4ZQM70VNAPCwf0lXmvaVxlREeNKfM4KuDgZfVv/2YWVDr5Ndc59+huDtkkaVyv38dmngOA\ngtaZ7NJ1Dy/RtQ8v0XETKvSHq08Z8OQXABSK5ZtbdO/L6/TJEyfoyNGlXocDIEuKMi0Q47RABJDH\nJlZFJUlr69kHDEDuJLrS66frzz1SFZGgLrlnvna0xj2OCjh4WUuAmZlJukvSCufcLXs47AlJF1va\niZKanXNb9nAsABSE9fXt+o87/qGH5m/QF08/VPd+braqS8JehwUAeck5p289sVTlkZC+8m+Hex0O\ngCzaVQFGC0QA+WtidUSStK6efcAA5E7PDURjKop158Wz1Nie1KX3zmddhbyXzQqwd0n6pKQzzGxx\n5nGOmV1mZpdljnlS0hpJqyX9UtIVWYwHAAa9v67Ypvf/7HltaGjXXZ+apf983+Hy+8zrsAAgbz2+\neLNeXduoa993uMoiQa/DAZBF4Z4KMFogAshjPRVgb9dRAQYgd3oSXeGAX0eNKdPN58/QwvVN+q/H\nlso553F0wIHLWjNh59wLkvZ61dal/+u5MlsxAEC+6Op2+tHTq3Trc6s1bXSp7rjwOI2vingdFgDk\ntVg8pRufXKEZY8t0/qxx+34DgLwWDlABBiD/RcMBDR8W1loSYAByqOcGop711DnTR+maM6foJ399\nU4fXDNMXTpvsZXjAAWM3TQDwWH0srqsfXKQXV9frY7PG6TvnTVNR0O91WACQ93721ze1vTWuOy+e\nJR/VtMCQt7MFInuAAchzk6qitEAEkFM9CbCi4K6GcdecOUWrtrXqpj+t0IjSsN575EgVh7hehfxC\nAgwAPLRgXaOunLtQje0Jff8/jtb5x1OhAAADYfX2mO564W19bNY4HTOu3OtwAORATwvETirAAOS5\nidURPbdyh9dhACgg8eSuFog9fD7TzefP0Pr/n707j4+yPPc//rlnyWTfSVhCCPu+oyKIAu6K2mqt\ntbW11qpF7XKs1u6L7a+tnp5z6lKr1tbWukvdrQuCETcUwiqb7CQsCSQhIQmZzHL//pgJUmVJIMkz\nM/m+X695TeaZzMw3Q0iePNdzXfd9TXz3iWW4DAzokc7wXpmM6JXJiN6R6x4ZWrdeYpcKYCIiDrDW\n8o/3tvCbl9fQOzuFf82ewqg+WU7HEhFJCNZafvnCKlKT3PzgnKFOxxGRLnJgBKI6wEQkzvXLS2P3\nvgoa/EHSfTp0JyKd79MjEFulJnl46rqTeWfDHlbvqGf1znqWbK3lxeU7DnzOSf1z+fJJxZwzqud/\nFNBEYoF+i4qIdLFGf5AfPrOSF5fv4IzhBfzPpePISvU6HUtEJGG8tmoX72zYw68uHEleus5GFOku\nPlkDTAUwEYlv/fPTANha3cjI3jpRUkQ6X+v+U9KnCmAQWZvw7JE9OXtkzwPb6poCrN5ZT9nWGp5a\nXMF3n1hGbloSl04s4vITiymJ/hwTcZoKYCIiXWhD1T6+9cgSNu1u4JazhzL7tIFal0ZEpAPtbwnx\n65fWMKxnBl85qdjpOCLShVrXUPVrBKKIxLmSvMiB4y17mlQAE5Eu0XKgA6xtHVxZqV5OHpjHyQPz\nuH76IN7ZsIfHPtjGg+9s5v4Fm5g6KI8rTy7hzBGFGKPjXuIcFcBERLrISyt28IM5K0jxuvnn1Scx\ndVC+05FERBLO397dzPa9+3ny2sl43J89e1FEEpdGIIpIouiXlwrAlupGh5OISHfRegLRoTrAjsbl\nMpw6pAenDulBVX0zTy0u5/EPy7n2n2WM7ZvNrecMZcpAHQMTZ+iogIhIJ2sJhvnVi6u48bGlDOuZ\nwcvfmabil4hIJ2hqCfLXdzYzY2gPThqQ53QcEeliHrcLt8toBKKIxL00n4eCDB9b9qgAJiJdwx8M\n4zbgPs4pRQWZydw4czBv3TKdOy4ZQ1V9M1/+ywd87W8f8tH2ug5KK9J26gATEelEu+qaueGxJZRt\nreWqqSX86Nzhx3Q2jYh0P8aYLcA+IAQErbWTjDG5wJNACbAF+KK1ttapjLHmsQ+2UdPYwo0zBzsd\nRcS6OfIAACAASURBVEQc4vO4NAJRRBJCSX6aOsBEpMv4A2G8HXi4yuN28cUT+nLhuN48/P4W/vTm\nRmbd/Q4XjO3NzWcNoV+e1giTrqECmIhIJ3lvwx6+88RSmlpC3H35eC4Y29vpSCISf2ZYa/ccdPuH\nwDxr7e+NMT+M3r7VmWixpTkQ4oEFmzh5QB4T++U4HUdEHBIpgKkDTETiX0leKvPXVjkdQ0S6CX8w\nhLdty3+1S7LXzbWnDuSyE4p5YMFG/vbOFl5ZuZMzRxRy2Ql9mTa4x3F3nYkcidoQREQ6WDhsubd0\nA1f89QOyUry8cONUFb9EpKNcBPwj+vE/gM85mCWmPF1WQdU+P9+eOcjpKCLiIJ/HTXNAHWAiEv+G\n98pkT0MLO+v2Ox1FRLoBfzCMtxMLUVkpXm45exhv3TKdq6aW8MHmGr7+0CKm3T6f/537MeU1TZ32\n2tK9qQNMRKQD1e0P8P2nlvPGmkpmjenF7y8ZQ7pPP2pFuitjTBnwN+CxYxhVaIE3jDEh4H5r7QNA\nobV2Z/T+XUDhYV73WuBagMLCQkpLS48lPgANDQ3H9fiuEAxb/rhgP4OyXfjLV1Ja4dwZhPHwfsUS\nvV/to/fr6MJBP9u276S0tFbvVzvp/WofvV/S2cYXRzral23bS6/RKQ6nEZFEFymAdf7rFGQm85Pz\nR3Dz2UN5Y3UVTy4u5+7567l7/npOGZTPpZP6ctaIQpI7ox1NuiUdlRUR6SCrdtQx+5El7Ni7n19e\nMIIrp5RgjNq4Rbq5y4CrgEXGmMXAQ8Dr1lrbhseeYq3dbowpAOYaY9YefKe11hpjDvk80WLZAwCT\nJk2y06dPP+YvoLS0lON5fFd4enE51c0r+MOXJjFjWIGjWeLh/Yoler/aR+/X0WUvfYvs3HSmT5+o\n96ud9H61j94v6WwjemWS5HGxtHwv547u5XQcEUlwLcFQlxTAWvk8bs4f04vzx/SioraJOWUVPL24\ngu88vpSMZA+zxvTi4glFTOqXo2NrclxUABMR6QBPLS7nZ899RE5qEk9eN5mJ/XKdjiQiMcBauwH4\niTHmZ8AsIt1gIWPMQ8Cd1tqaIzx2e/S6yhjzLHAiUGmM6WWt3WmM6QV0+4UhQmHLvaUbGdk7k+lD\nezgdR0Qclux14w9qBKKIxL8kj4uRvTNZtm2v01FEpBvo7BGIR1KUk8r3zhjCd2YOZuGmauYsqeD5\nZTt4/MNyinNTuXhCHy4eX0RxXqoj+SS+qQAmInIcmgMhfvnCKp5YVM6UgXncdfl48tN9TscSkRhi\njBlDpAvsPOBfwKPAKcB8YNxhHpMGuKy1+6IfnwXcBrwAXAn8Pnr9fKd/ATHu5ZU72bynkT9/ZYLO\nDBQRfB4X/mDY6RgiIh1ifN8cHvtwK4FQGK+7C1szRKTb8QfCOD110OUyTBmUz5RB+fz6oiCvfrSL\nZ5ZWcOe89fzxjfWMLcrivNG9OG90L/rmqhgmbaMCmIjIMSqvaWL2o2V8tL2eG2YM5KYzh+J26GwZ\nEYlN0TXA9gJ/BX5orfVH7/rAGDP1CA8tBJ6NFnQ8RNYQe9UYswh4yhhzNbAV+GLnpY994bDlT/M3\nMKggnbNH9nQ6jojEAJ/Hzf6AOsBEJDGML87mb+9uZt2ufYzqk+V0HBFJYP4uHoF4NGk+D5dMLOKS\niUVs37ufF5bt4JWPdvK7V9byu1fWMrpPFueO7sl5o3pRkp/mdFyJYSqAiYgcg/lrK/neE8uwwINf\nm8QZIwqdjiQiselSa+2mQ91hrb34cA+KPmbsIbZXA6d3XLz49saaStZV7uOPl43DpRMQRIRIB9je\n/S1OxxAR6RDj+mYDsHRbrQpgItKp/MEwnhj9m6pPdgqzpw9k9vSBlNc08cpHO/n3yl3c8eo67nh1\nHaP6ZHLx+CIuHNdbU5nkM2KorisiEvtCYcsfXlvHN/6+mKKcVF7+9jQVv0TkSL5pjMluvWGMyTHG\n/MbJQInCWss9b26gODeVWWO0MLyIRPi8LvwBjUAUkcRQlJNCfrqPpeVaB0xEOldkDTCnUxxd39xU\nrj11IM/dMJV3fziTn54/HIPhtpdWc9Jv53H13xfx8oqdNGsigESpA0xEpI2qG/x854mlvLuhmi9O\nKuK2i0aR7PSAZBGJdedaa3/cesNaW2uMOQ/4qYOZEsKC9XtYUVHH7y8ejUdrYohIlM/jpjmoAx4i\nkhiMMYwvzmbZNhXARKRz+YMhvMlOp2ifPtkpfHPaAL45bQAfV+7jmSXbeW7pduatXUJGsodZY3px\n1sienDwgT8fvujEVwERE2mDJtlpueHQJ1Y0t3H7JaC47odjpSCISH9zGGF/r2l/GmBRAMxk6wJ/m\nb6BXVjIXTyhyOoqIxBCfRx1gIpJYxhdnM3d1JXubWshOTXI6jogkKH8gjDc1NkcgtsWQwgx+eO4w\nbjl7KO9vrOaZJRU8v2wHj39YTorXzdRB+ZwxvIAZwwoozIyzSp8cFxXARESOwFrLw+9v5Tcvr6Zn\nVjLPzJ6i2esi0h6PAvOMMQ9Fb18F/MPBPAnh48p9fLilhp+cN5wkj7q/ROQTyV43/qAKYCKSOFrX\nAVtWvpfpQwscTiMiiaolFB8jEI/G7TKcMjifUwbn89tAiIWbqpm/top5a6p4Y00lAKP7ZDF9aA+m\nDspnfHE2Po+6wxKZCmAiIofR6A/yo2dW8sLyHZw+rID//eI4slK9TscSkThirb3dGLMCOD266dfW\n2teczJQInlxUjtdtuHhCH6ejiEiM8Xlc+DUCUUQSyJiibFwGlm5TAUxEOo8/EMbrit8OsENJ9rqZ\nPrSA6UML+NWFlo8rG5i3tpL5a6q4t3Qjd8/fQIrXzQn9czllUB5TB+UzvGcmrgR7H7q7NhfAjDGp\n1tqmzgwjIhIrNlQ1MPuRMjbubuCWs4cy+7SB+gUoIsfEWvsK8IrTORJFSzDMs0u3c8bwQvLSNU1S\nRP5TpAAWxlrrdBQRkQ6R7vMwpDCDpeVaB0xEOoe1NrIGmCtxT/o2xjC0ZwZDe2Zw/fRB1DcH+GBT\nDe9u2MM7G/bw23+vBSA3LYmT+ucyeUAekwfkMbggXccD49xRC2DGmCnAg0A6UGyMGQtcZ629vrPD\niYg44aUVO7h1zgqSvW7+efVJTB2U73QkEYlTxpjJwN3AcCAJcAON1tpMR4PFsTfWVFLT2MIXT+jr\ndBQRiUE+rxtrIRBSAUxEEsf44mxeXrGTcNjqQKyIdLhg2BK24O1GkwAzk72cOaKQM0cUAlBZ33yg\nGPbBphpe+WgXECmInViSy+QBuVwwtrdOwoxDbekA+z/gbOAFAGvtcmPMqZ2aSkTEAYFQmN/9ey1/\ne3czE4qz+dNXJtArK8XpWCIS3+4BvgQ8DUwCvgYMcTRRnHtyUTm9spI5dXAPp6OISAzyRdcF1BhE\nkfhhjLkD+A2wH3gVGAP8l7X2EUeDxZDxfXN4/MNyNlc3MrBHutNxRCTBtK6fmmgjENujMDOZiycU\ncfGEIgDKa5pYuKmahZtq+GBzNa+u2sVrqyp5/NrJDieV9mrTCERrbbkx//EfQH9NiEhC2VXXzI2P\nLWHx1lq+PqWEH583nCRPAqz+KSKOs9ZuMMa4rbUh4CFjzFLgR07nikc79u5nwfrd3DhjEO5u/MeZ\niBxeawGsORB2OImItMNZ1tofGGM+D2wBLgYWACqARY0vzgYi64CpACYiHc0fiBzq9+ow2AF9c1Pp\nm5vKpZMik0f+8No67i3dQE1jC7lpSQ6nk/Zoy7d1eXQMojXGeI0xNwNrOjmXiEiXeW/jHmbd/Tar\nd9Zz1+Xj+eWFI1X8EpGO0mSMSQKWGWPuMMb8F23b/5JDmFNWgbVw6USNPxSRQ/N5IrN71AEmElda\nT84+H3jaWlvnZJhYNLBHOhk+D0u31TodRUQS0CcdYA4HiWFnj+xJ2ELpuiqno0g7teXb+lvADUAf\nYDswDtD6XyIS98Jhy0ubWrjiwQ/ISvHy/A1TuXBsb6djiUhi+SqR/a0bgUagL3CJo4niVDhsebqs\nnCkD8yjOS3U6jojEKJ+3dQSiOsBE4shLxpi1wERgnjGmB9DscKaY4nIZxvbNZln5XqejiEgCat1v\n0rnghzeydyYFGT7mrVUBLN605dt6qLX2K9baQmttgbX2CiILuYuIxK26/QGu/WcZcz4OcN7oXjx/\n4ykMLsxwOpaIJBBjjBv4rbW22Vpbb639lbX2JmvtBqezxaOFm6opr9nPZSeo+0tEDu9AB5hGIIrE\nDWvtD4EpwCRrbYDISUMXOZsq9owvzmbtrn00tQSdjiIiCaaltQPMrTHzh+NyGWYOK2DBut0EQtrP\njCdtKYDd3cZtIiJxYdWOOi685x1K11XxlWFJ3H35eNJ9bVoSUUSkzaJrfvWLjkCU4/Tk4nIykz2c\nPbKn01FEJIZ90gGmEYgi8cIYcykQsNaGjDE/JbL2l0ZzfMr44mxCYcvKCk2IFJGO1brfpBGIRzZz\nWAH7/EEWbalxOoq0w2GP+BpjTiZyBk4PY8xNB92VCbg7O5iISGd4enE5P33uI7JTvTx53WT2bV6B\nMTrDRUQ6zSbgXWPMC0TOZgbAWvu/zkWKP3VNAV75aBeXTepLsle7oSJyeD6PRiCKxKGfWWufNsac\nApwB/DfwZ+AkZ2PFlnF9cwBYWr6XkwbkOZxGRBLJJ2uA6fjYkZwyOJ8kj4t5a6qYMjDf6TjSRkeq\n6yYB6USKZBkHXeqBL3R+NBGRjtMcCPGjZ1Zwy5wVTCjO4eXvTGNiv1ynY4lI4tsIvERkn+vg/Slp\nh+eXb6clGNb4QxE5qgMjEFUAE4knrS2b5wMPWGtfJnJMSg6Sm5ZEv7xUlm3TOmAi0rFaR0erA+zI\nUpM8TBmYx3ytAxZXDtsBZq19C3jLGPN3a+3WLswkItKhymuamP1oGR9tr+f66QO56cwheNz6rS4i\nnc9a+yunMySCJxeVM6JXJqP6ZDkdRURi3IEOsEBIR89F4sd2Y8z9wJnA7cYYH21bsqPbGd83m/c2\nVmOt1SQTEekwB0YgatjGUZ0+rICfPb+KTbsbnI4ibdSWRW+ajDH/DYwEkls3WmtndloqEZEO8uba\nKr735DLC1vLg1yZxxohCpyOJSDdijHkTsJ/erv2otvtoex2rdtTzqwtHOh1FROJAcvTU5eZgWAUw\nkfjxReAc4A/W2r3GmF7ALQ5niknji3N4btkOdtY10zs7xek4IpIgNAKx7WYMK4DnVzF/bRWDnA4j\nbdKWAtijwJPALOBbwJXA7s4MJSJyvEJhyx/f+Ji7529gRK9M/nzFBPrlpTkdS0S6n5sP+jgZuAQI\nOpQlLj21uJwkj4vPjevjdBQRiQMHRiAGQkf5TBGJFdbaJmPMRuBsY8zZwNvW2tedzhWLxvXNBmDp\ntr0qgIlIhznQAabe26MqykllWM8M3lhTyaAhTqeRtmjLt3WetfavQMBa+5a19huAzloWkZhV3eDn\nyr99yN3zN3DpxCKeuX6Kil8i4ghrbdlBl3ettTcB053OFS+aAyGeW7qdc0b2JCvV63QcEYkDB0Yg\nag0wkbhhjPkukZOvC6KXR4wx33Y2VWwa3iuTJI+LZeW1TkcRkQSiNcDa5/ThBSzaUktj4DPDXiQG\ntaUDLBC93mmMOR/YAeR2XiQRkWO3ZFstNzy6hOrGFm6/ZDSXnVDsdCQR6caMMQfvM7mAiYAWsmqj\nuasrqW8OctkJfZ2OIiJxwhddvEIFMJG4cjVwkrW2EcAYczvwPnC3o6liUJLHxeg+WSzdttfpKCKS\nQFpCGoHYHjOHFfKnNzfy0Z4Q5zsdRo6qLQWw3xhjsoDvE9n5yAT+q1NTiYi0k7WWfy7cyq9fWk3P\nrGSemT2FUX10jFlEHFdGZA0wQ2T04WYiB3mkDV5dtYv8dB8nD8hzOoqIxIlPOsA0AlEkjhjg4P+0\noeg2OYTxfbP558KtBEJhvG61a4jI8TvQAeZ2OEicGNc3m9y0JJZVaXWDeHDEApgxxg0Mtta+BNQB\nM7oklYhIOzT6g/zomZW8sHwHM4cV8H9fHKdRWSISE6y1/Z3OEK/8wRBvrdvNBWN74dKZiCLSRgcK\nYIEwaHdQJF48BHxgjHk2evtzwF8dzBPTxhVn8+A7m1m7cx+ji3TSp4gcP60B1j5ul2HG0AJeXVlB\nMBTGo5MRYtoR/3WstSHg8i7KIiLSbhuqGvjcn97lpRU7uOXsoTz4tUkqfolIzDDG3GCMyT7odo4x\n5nonM8WLhZtqaPAHOWN4odNRRCSOGGNI8rg0AlEkjlhr/xe4CqiJXq6y1v7R2VSxa3xxDgCLttQ4\nnEREEoU/GMYYcOu8wzY7fXgBjQFYWq6RtLGuLeXJd40x9xhjphljJrReOj2ZiMhRvLxiJxfd8w7V\njS08/I2TuGHGIHUJiEisucZae2CP2FpbC1zjYJ64MXf1LlK8bqYOync6iojEGZ/HpRGIInHAGJPb\negG2AI9EL1s/tY6qHKRPdgoD8tN4c12V01FEJEH4g2F8HhfG6JhaW00bnI/bwLw1+lkc69qyBti4\n6PVtB22zwMyOjyMicnSBUJjfv7KWv76zmfHF2dz7lQn0ykpxOpaIyKG4jTHGWmvhwHjpJIczxTxr\nLW+sruLUIfkkaxC9iLSTz+OmOaAOMJE4cPBaqUQ/JnrbAgOO9GBjzN+AWUCVtXbUIe7/CnBr9Pn2\nAbOttcs7JrqzzhhRyEPvbmZfc4CMZE1AEZHj4w+E8Hn0d1d7ZCR7GZrrYt6aSn547jCn48gRHLUA\nZq3Vul8iEjMq65u54dElLN5ay9enlPDj84aT5NGsXRGJWa8CTxpj7o/evi66TY5g5fY6dtU3c/OI\noU5HEZE4pA4wkfjQAWul/h24B3j4MPdvBk6z1tYaY84FHgBOOs7XjAmnDyvggQWbeHv9Hs4b3cvp\nOCIS5/zBsI6tHYNxPTw8traBbdVNFOelOh1HDkPf2SISN97fWM35d73Nqh313PmlcfzywpH6BS0i\nse5WYD4wO3qZB/zA0URxYO7qSlwGZg4rcDqKiMQhn1drgIl0B9baBUTWDDvc/e9Fx08DLASKuiRY\nF5jYL4esFC9vrKl0OoqIJICW6AhEaZ9xBZGuuflrD/2z2FpLdBiMOKgtIxBFRBxlreW+tzbx36+t\npX9+Go9fM5nBhRlOxxIRaYsU4C/W2vvgwAhEH9DkaKoYN3d1JZNKcslN07RIEWm/ZI8bv0Ygish/\nuhp45XB3GmOuBa4FKCwspLS09JhfqKGh4bge31YjssPMXbmd+T1qccXxuj1d9X4lCr1f7aP3q23K\ndzYTagnT0BDW+9UOqeEmeqW5mPPeOkoCW2kJWbbUh1lfG+Lj2jAb9oYIhCAn2ZDtM+QkG3KSXeT4\nDNnJhgyvISPJkO6F9CSDxxW/P8vbwqn/jyqAiUhMq9sf4OanlzN3dSXnj+nF7ZeMId2nH10iEjfm\nAWcADdHbKcDrwBTHEsW48pom1u7ax0/PH+50FBGJU5EOMI1AFJEIY8wMIgWwUw73OdbaB4iMSGTS\npEl2+vTpx/x6paWlHM/j26ohdwc3PraUzP5jmVSS2+mv11m66v1KFHq/2kfvV9s8snURjaaZ9PSQ\n3q92KC0t5YKJkTUZ716TxMqKOlpCkZOwBvRI4/yxOWQme9lV30xlfTPb65tZUuU/8DmfluHzkJ3m\npWdmMsW5aRTnplKclxK5zk0jPz0JE8cnPDj1//GoR5GNMRcfYnMdsNJaW9XxkUREIlbvqGf2o2Vs\nr93Pz2eN4KqpJXH9g15EuqVka21r8QtrbYMxRsPBj+D11ZHxEWeOKHQ4iYjEq8gaYOoAE4kXxphD\nVW/2WWsDHfDcY4AHgXOttdXH+3yx5NQhPfC4DHPXVMZ1AUxEnOcPhvF5XYBOIGqvC8f25slF5Vhr\n+frUEib1y2Fivxzy0n2H/HxrLbVNASrrm6ltbKG2KUBNU0v04xZqGlvYWdfMexv38K8lzf/xWJ/H\nRX66j9y0JHLTkshLTyIvLYncNB/pPjdJHhdet4skj4uk6HWy101Wipec1CSyU70ke91d8bbElLa0\nUVwNnAy8Gb09HSgD+htjbrPW/rOTsolIN/b04nJ++txHZKd6eeLaydqhF5F41WiMmWCtXQJgjJkI\n7Hc4U0x7Y3UlQwrT6ZeX5nQUEYlTPo+bvfuP+7i5iHSdJUBfoBYwQDawyxhTCVxjrS07lic1xhQD\nzwBftdZ+3FFhY0VmspeTBuQyb00VPzpXnfMicuz8Aa0BdqxG9cli+S/OavPnG2MOFLCOpjkQoqJ2\nP+U1TWyraaKitonqhhaqGyOFsg1VDVQ3+mlux+jvFK+bnFQv2dGCWGayl6wUL1mpXjKTPWSleMmM\nXrJSPrk/M8WDzxOfxbO2FMA8wHBrbSWAMaYQeBg4CVgAqAAmIh2mORDiVy+u4vEPyzl5QB53XT6e\nHhmHPmtCRCQOfA942hizg8gBnZ7AZc5Gil17m1r4cEsN1506wOkoIhLHfB4X/oDOYBaJI3OBOdba\n1wCMMWcBlwAPAfcSOf70GcaYx4mcpJ1vjKkAfgF4AaLrr/4cyAPujU4SCVprJ3XqV9LFTh9WyG0v\nrWZrdaNOHhKRY+YPhshO1frLsSbZ62ZQQTqDCtKP+HlNLUGaWkK0BMORSyhy7Q+GaQ6E2NsUYO/+\nFvY2BQ50ne1taqFuf4CNuxuobw5Qtz9w1EJastdFus9Lms9NapKH9Oh1ms9Nhs8b6UhL95GfnkSP\ndB/5GT7y031kp3hxObi+WVsKYH1bi19RVdFtNcYYnVYnIh2mvKaJ2Y+W8dH2eq6fPpCbzhyCx60z\nUEQkfllrFxljhgFDo5vWdcQ4n0T15roqQmGr8Yciclx8XrdGIIrEl8nW2mtab1hrXzfG/MFae50x\n5rBnQ1prLz/Sk1prvwl8swNzxpwzhkcKYG+sqeLqU/o7HUdE4pQ/qA6weJaa5CE1qS1lniPzB0PU\n7w9Stz9woChW33ppjmxv8Adp9Adp9Ido9AfZ29RCRW2Q+uYgNY0thML2M897xeRifvO50ced71i1\n5Z0pNca8BDwdvX1JdFsasPdwDzLG/A2YBVRZa0cd4v7pwPPA5uimZ6y1t7Uju4gkkDfXVvG9J5cR\ntpa/fG2SDn6KSCIZCowAkoEJxhistQ87nCkmzV1dSUGGj7FF2U5HEZE49kkHWHyOaZGuEQ5bmgIh\n9reEaA6E2B/9eH8gRFNLkNrGwIG1OFqvaxpbGFyYwW8/79xBnAS10xhzK/BE9PZlQKUxxg2omn0E\nxXmpDClMZ96aShXAROSY+YNhklQA6/Z8Hjc9MtzHPIkrHLbU7Q+wp8HP7gY/1Q0t7GnwM6Qwo4OT\ntk9bCmA3ECl6TY3efhj4l7XWAjOO8Li/A/dEP/9w3rbWzmpDBhFJUKGw5c43Puau+RsY3iuT+66Y\noNENIpIwjDG/IDKaZwTwb+Bc4B2OvH/ULfmDId5at5sLx/VxdDyCiMS/ZK8r2gGmAlgis9bS4I+c\nbVzd2EJdU+Rs5dazlOv3Bw6cwbyvOUiDPxi5jn7c4A+26XXcLkNOamSR+Zy0yDoY0uG+TGR84XPR\n2+9Gt7mBLzoVKl6cPryQvyzYRN3+gL4/ReSYtATDcbu+k8QOl8uQk5ZETloSgx0ueh3sqAWwaKFr\nTvTSZtbaBcaYkmOLJSLdQU1jC999Yilvr9/DpROL+PXnRpHs1S9cEUkoXwDGAkuttVdF11J9xOFM\nMem9jdU0toQ4Sx3AInKcfB6NQIxVgVCYrdWNfFzZwOY9jfgDIYJhS8haQqHoddgSDFsCwTCBUJhA\nyNISinzcEgxT2xSgptFPbWOAltDh/52TvS4yk71kJHvIiF73ykom3ech3eclPdlDWpKb1CQ3yV43\nKUluUryRS3KSm9zUyAGczGQP0fWjpJNYa/cA3z7M3Ru6Mks8OmN4AX8u3chbH+/mwrG9nY4jInHI\nHwzh86oDTBLTUQtgxpiLgduBAiKLtxsidbHMDnj9KcaYFcB24GZr7arDZLgWuBagsLCQ0tLSDnjp\nz2poaOi054513flrB339Tnz9G/eG+NMyP/UtlqtGJXFaj1oWvvt2l2YA/dt356+/O3/toK+/C+23\n1oaNMUFjTCbRtVSdDhWL5q6uJDXJzckD85yOIiJxzudx4Q+GnI7RbTT6g2zc3UCDP3hg8XX/Qde7\n9/n5uGof6yv3sXlPI4HQf64N4XEZXC6Dx2VwRy8elyHJ7cLrceF1Ry5JboPX7aJPdgqj+2SSm+aL\ndmVFurOyUiPdWZnJXjJTPDqTPY4YY4YANwMlHHScylo706lM8WRc3xxy05KYt6ZSBTAROSb+gNYA\nk8TVlhGIdwAXWGvXdPBrLwGKrbUNxpjziLS6Dz7UJ1prHwAeAJg0aZKdPn16B0eJKC0tpbOeO9Z1\n568d9PV35ddvreWRhVv5/aLVFGYm8/A1ExnVJ6tLXvtQ9G/ffb/+7vy1g77+LrTYGJMN/AUoAxqA\n952NFHvCYcsbqys5bUgPdQKLyHHzedwEQpaw/ewi3HLsWoJhttU0sXZXPet27WPtrn2s27WPbTVN\nR31s39wUhhRkMHNYIYML0hlSmMHAgrQOWbBdEsLTwH3Ag4Cq1+3kdhlmDivg9VW7CITCeN06iC0i\n7ePXCERJYG3Z26zshOIX1tr6gz7+tzHmXmNMfrT1XUQSUFNLkB89s5Lnl+1g5rAC/veLY8lOTXI6\nlohIp7HWXh/98D5jzKtAprV2hZOZYtGK7XVU7fNzpsYfikgHaB3hE9AUxGPyysqdfFzZwK76Zirr\nm9lVF7mubmw58DkuA/3z0xjdJ4svTCxiSGE6mSlefB43Po8Ln8dFkseFz+MmK8VLSpIOqskRBa21\nf3Y6RDw7Y3gBc8oqWLylVt30ItIu4XBk1LA6wCRRtaUAttgY8ySRDi1/60Zr7TPH88LGmJ5EpF12\negAAIABJREFUimvWGHMi4AKqj+c5RSR2bdzdwOxHylhf1cDNZw3h+umDcLk0S19Eug9r7RanM8Sq\nuat34XYZZgwtcDqKiCSA1gM4AfWRtNvy8r3MfnQJALlpSRRmJtMz08fYvlkUZiZTlJPKsJ4ZDCpI\nV8eudKQXjTHXA8/yn8edapyLFF+mDe5BktvFvDWVKoCJSLu0rqepNcAkUbWlAJYJNAFnHbTNAkcs\ngBljHgemA/nGmArgF4AXwFp7H5FF4WcbY4LAfuBL1mpGhUgi+vfKndzy9HJ8XjcPf+NEpg3u4XQk\nERGJIfPWVDGpXw45aeoKFpHj1zrCJxDWn5ft9ezS7SS5XSz88enk6meydJ0ro9e3HLTNAgMcyBKX\n0nweJg/MY97aKn46a4TTcUQkjvijLfM+jxuCDocR6QRHLYBZa686lie21l5+lPvvAe45lucWkfgQ\nCIW5/ZW1PPjOZsYXZ/OnL0+gd3aK07FERCSG1DUFWLtrH98/c4jTUUQkQRzoANMIxHYJhS0vrdjB\njGE9VPySLmWt7e90hkRwxvACfv78KjbubmBgj3Sn44hInPAHIy3zSR6XCmCSkA5bADPG/MBae4cx\n5m4iZ978B2vtdzo1mYjEtcr6Zm58bAmLttTy9Skl/Pi84ZFfpiIi3YgxJvcQm/dZawNdHiZGLS2v\nBWBCvxyHk4hIomgdzacRiO2zpibEnoYWPjeuj9NRpJswxsy01s43xlx8qPuPd+mN7ub04YX8/PlV\nzFtTqQKYiLSZP9jaAaZjdpKYjtQBtiZ6vbgrgohI4nh/YzXffnwJjf4Qd35pHBfpj2gR6b6WAH2B\nWsAA2cAuY0wlcI21tszJcLFg6ba9uAyM7ZvtdBQRSRCfdIBpBGJ7vL8jREayhxnDtB6jdJnTgPnA\nBYe476hLb8h/6pOdwvBembyxuoprTx3odBwRiRMqgEmiO2wBzFr7YvT6H10XR0TimbWW+xds4o5X\n11KSn8Zj10xmSGGG07FERJw0F5hjrX0NwBhzFnAJ8BBwL3CSg9liwpJttQwpzCDd15alaUVEjq51\nEXeNQGy7/S0hyiqDXDi+6EAHnUhns9b+Inp9TEtvyGedNaKQu+avZ2fdfnplafkBETm61hGIrWuo\niiSaox5pMMYMAW4GSg7+fGvtzM6LJSLxpr45wM1PLef11ZWcP7oXt39hjA5miojAZGvtNa03rLWv\nG2P+YK29zhjjczJYLAiHLcvK9zJrTG+no4hIAmk9gKMCWNu9saaS5hAafyiOiO4TXcJnjzvd5lSm\neHXxhD7cOW89zyzZzg0zBjkdR0TiwIEOMK86wCQxteXo9NPAfcCDgKaoi8hnrNlZz+xHyqio3c/P\nZo3gG1NLMMY4HUtEJBbsNMbcCjwRvX0ZUGmMcQPd/tDsxt0N7GsOMqFY4w9FpOO0jvBpCWkEYls9\nv2w72T7DSQPynI4i3dPzQB1QBvgdzhLX+uWlcWJJLv8qq+D66QP1d7mIHJU/8MkIxBaHs4h0hrYU\nwILW2j93ehIRiUtzyir46XMryUz28vi1kzmhJNfpSCIiseTLwC+A56K3341ucwNfdCpUrFiyrRaA\nCf1yHE4iIolEIxDbp7axhdJ1uzmj2I3bpYPl4ogia+05TodIFF+YWMQP/rWCpeV7mVCsfSwRObKD\nRyCqACaJqC29jS8aY643xvQyxuS2Xjo9mYjEtOZAiB89s5Kbn17O+L45vPydaSp+iYh8irV2j7X2\n29ba8dHLjdba3dbaFmvtBqfzOW3J1r1kpXjpn5fmdBQRSSAagdg+L6/cSTBsObm3xpeLY94zxox2\nOkSiOG9ML1K8buaUVTgdRUTiwIERiB6NQJTE1JY93Cuj17cctM0CAzo+jojEg/KaJq5/dAkrt9cx\ne/pAvn/mEDxu/aIUEfk0raV6ZEu21TK+OBuXOg5EpAO1HsAJaARim7ywbAeDCtIpzlDFUBxzCvB1\nY8xmIiMQDWCttWOcjRWf0n0ezhnVkxeX7+Dns0aQ7HU7HUlEYpgKYJLojlgAM8a4gCuste92UR4R\niXFvrqvie08sI2wtD3x1ImeN7Ol0JBGRWKa1VA+jbn+A9VUNXDC2t9NRRCTBtB7sVQfY0VXUNvHh\nlhpuPmsIxmx3Oo50X+c6HSDRfGFiEc8u3c7c1ZXa1xKRI2o5UABTsVwS0xELYNbasDHmHmB8F+UR\nkRgVClvufONj7n5zA0MLM7jviomU5GtklYjIUWgt1cNYXr4XgPHF2Q4nEZFEc6ADTAWwo3ph+Q4A\nLhrXh40rVACTrmWMybTW1gP7nM6SaE4ekEfvrGTmlFWoACYiR3RgDTCvOsAkMbVlBOI8Y8wlwDPW\nWs2QEOmGahpb+O4TS3l7/R6+MLGIX180ipQknRkiItIGLxpjrgeeJTLSBwBrbY1zkWLDkm21GAPj\n+qoAJiId65MCmP58PZrnl+5gYr8c+uamstHpMNIdPQbMAsqILLVx8ExkLb1xHFwuw8UTiri3dAOV\n9c0UZiY7HUlEYpQ/oBGIktjaUgC7DrgJCBpjmvlkFnNmpyYTkZiwdFstNzy6hD2NLfz+4tFcdkJf\njNFaLSIibaS1VA9j6ba9DCnIICPZ63QUEUkwHrcLt8sQ0ODZI1qzs551lfv49UUjnY4i3ZS1dlb0\nur/TWRLRJROLuOfNDTy7dDvfOm2g03FEJEb5NQJREtxRC2DW2oyuCCIiscVayyMLt3LbS6spzEzm\nX9+awuiiLKdjiYjEFR3QObRw2LJ0Wy3nje7ldBQRSVA+j0sdYEfx3LLteFyG88doPJo4zxiTAwwG\nDrQqWWsXOJco/vXPT2NSvxzmlFVw3akDdCKriBxS6wjEJHWASYJqSweYdkREupmmliA/fmYlzy3b\nwYyhPfi/y8aRnZrkdCwRkbhhjJlprZ1vjLn4UPdba5/p6kyxZNOeBuqbg0woznE6iogkKJ/HRYsK\nYIcVDlteXLaDU4f0IDdN+/niLGPMN4HvAkXAMmAy8D4w08lcieCSiUX86JmVrKioY6zGTovIIfiD\nYbxug9ulIrkkpqOWdqM7IguA14BfRa9/2bmxRMQpG3c38Lk/vcvzy3fw/TOH8NcrT1DxS0Sk/U6L\nXl9wiMssp0LFiiXb9gIwoZ8OxIhI5/B53BqBeAQfbqlhR10zF41T95fEhO8CJwBbrbUzgPHAXmcj\nJYbzx/TC53Exp6zC6SgiEqP8gTBJbnV/SeJqSwdY647IQmvtDGPMMOC3nRtLRJzwysqd3DJnBUke\nFw9/40SmDe7hdCQRkbhkrf1F9Poqp7PEoqXbaslM9jAgP93pKCKSoHxejUA82N6mFsq21rJoSy1l\nW2tYXlFHWpKbM0cUOh1NBKDZWttsjMEY47PWrjXGDHU6VCLITPZyzqievLB8Bz+dNVxr/IjIZ7SE\nQvi8+tkgiastBTDtiIgkuGDY8v9eXs1f3t7MuL7Z3PuVCfTOTnE6lohI3DPG+IBLgBIO2u+y1t7m\nVKZYsGTrXsYV5+DSmA0R6STJHjeBsNMpnFNZ38zCTdUs3FTD4i01rK9qAMDjMozqk8WVJ/fjwrF9\nSE1q06oIIp2twhiTDTwHzDXG1AJbHc6UMC6ZUMTzy3Ywb02V1l8Vkc/wB8L4tP6XJLC27O1qR0Qk\ngVXWN3PHomY+rt3MlSf34yfnj9DClyIiHed5oA4oA/wOZ4kJ+5oDfFy1j3NH93Q6iogkMJ/XRaAb\n/dQ9uOD1waZqNu1pBCDD52FiSQ4XjevNpJJcxhZlk5Kks7wltlhrPx/98JfGmDeBLOBVByMllKmD\n8umZmcycsgoVwETkM/xBFcAksR21AKYdEZHEtXBTNTc+tpT6/WHu/NI4LhrXx+lIIiKJpshae47T\nIWLJ8vI6rIXxxTlORxGRBObzuGhoStwRiIFQmLKttby5tor5a6sOdHhl+Dyc2D+Xy08sZvKAPEb0\nztSi9hLTjDFuYJW1dhiAtfYthyMlHLfLcPGEPty/YBNV+5opyEh2OpKIxBB/MKTxqJLQ2jTvwBhz\nCjDYWvuQMaYH0AfY3KnJRKTTWGt5YMEm7nhtHf3yUvmvsS4Vv0REOsd7xpjR1tqVTgeJFUu21QIw\nrm+2w0lEJJH5PG5qE2wEYnWDn9J1u5m/rooFH+9mX3MQr9twYv9cLp1UxMkD8lXwkrhjrQ0ZY9YZ\nY4qttduczpOoLplYxL2lG3lqUTk3zhzsdBwRiSH+YBifVx1gkriOWgAzxvwCmAQMBR4CvMAjwNTO\njSYinaG+OcDNTy3n9dWVnDe6J3d8YSyL33/H6VgiIonqFODrxpjNREYgGsBaa8c4G8s5S7bVMrgg\nnawUr9NRRCSB+TyuhFgDrKaxhVc+2snLK3aycFM1YQs9MnycO6onM4cVMHVQPhnJ+nkqcS8HWGWM\n+RBobN1orb3QuUiJZWCPdKYP7cFD727hm9MGkOxVt4eIRGgNMEl0bekA+zwwHlgCYK3dYYzJ6NRU\nItIp1uysZ/YjZVTU7udns0bwjaklGKMzREVEOtG5x/Pg6FigxcB2a+0sY8wvgWuA3dFP+bG19t/H\nF7HrWGtZum0vZ48sdDqKiCQ4n9dFIBSfIxD3NrXw2qpdvLRiJ+9trCYUtgzIT+PGGYM4a2RPRvTK\nxKUuL0ksP3M6QHcw+7SBXPbAQp5eXM5XTy5xOo6IxAh/MERqUpuGxInEpbZ8d7dYa60xxgIYY9I6\nOZOIdIJ/lVXwk+dWkpns5fFrJ3NCSa7TkUREEpYxJtNaWw/sO86n+i6wBsg8aNv/WWv/cJzP64hN\nexqp2x9ggtb/EpFO5vO446oDbFddM3PXVPL6ql28v7GaYNjSLy+Vb502gPNH92Z4rwyduCaJ7Dxr\n7a0HbzDG3A5oPbAOdGL/XCYUZ3P/gk1cfmIxHrc6PkQkMgIxO1U/DyRxtaUA9pQx5n4g2xhzDfAN\n4C+dG0tEOkpzIMRtL63msQ+2MXlALnddPl6L3oqIdL7HgFlAGWCJjD5sZYEBR3sCY0wRcD7w/4Cb\nOiFjl1uyNbL+14R+KoCJSOdK9sb2CERrLeurGpi7OlL0Wl5RB0D//DSuntafWaN7M6pPpope0l2c\nCdz6qW3nHmKbHAdjDLOnD+Kahxfz0oqdfG681gEXEWgJagSiJLajFsCstX8wxpwJ1BNZB+zn1tq5\nnZ5MRI5beU0TNzy2hBUVdXzrtIHcfNYQneUlItIFrLWzotf9j+Np/gj8APj06OlvG2O+RmQ04vet\ntbXH8Rpdamn5XjJ8Hgb1SHc6iogkuEgHWOyNQKysb2ZOWQX/Kqtg057IUkdj+2Zzy9lDOXtkIQN7\npKvoJd2GMWY2cD0wwBiz4qC7MoB3nUmV2E4fVsDggnT+XLqRi8b11s8bEcGvApgkuDYN+IwWvFT0\nEokjpeuq+N6TywiFLPd/dSJnj+zpdCQRkW7JGJMDDAYOtN9aaxcc5TGzgCprbZkxZvpBd/0Z+DWR\nLrJfA/9DpDv/04+/FrgWoLCwkNLS0mPO39DQcFyPP9jbq/dTnA4LFiTuRKOOfL+6A71f7aP3q+0q\nd7bQErIx8X4Fw5blu0MsqAiyYncICwzNcfG1EUmML3CTkxwAKqhYXUGFgzn1/dU+er86xGPAK8Dv\ngB8etH2ftbbGmUiJzeUyzJ4+kJueWs6b66qYOUzrsop0d/5gCJ/H7XQMkU5z2AKYMWYfkYMrn7kL\nsNbazEPcJyIOC4Utd85bz93z1zO0MIP7rphISb6W7hMRcYIx5ptE1vEqApYBk4H3gZlHeehU4EJj\nzHlECmeZxphHrLVXHPTcfwFeOtSDrbUPAA8ATJo0yU6fPv2Yv4bS0lKO5/GtGvxBtr/2Gp+fOZjp\n04cc9/PFqo56v7oLvV/to/er7Za0rOPVzRs47bTTHOlwsNaydtc+nllSwbNLt7OnoYXCTB/Xzyjh\n0ol9Y3L/XN9f7aP36/hZa+uAOuByp7N0JxeM7c3/vP4xfy7dqAKYiEQ6wLzqAJPEddgCmLX20+N2\nRCTG1TS28N0nlvL2+j1cMqGI33xuFClJOotDRMRB3wVOABZaa2cYY4YBvz3ag6y1PwJ+BBDtALvZ\nWnuFMaaXtXZn9NM+D3zUObE73oryvYQtjC/OdjqKiHQDPq8bCwRCliRP1xXANu1u4MXlO3lxxQ42\nVDXgcRnOGF7IZSf0ZdrgfI0jFxHHed0urpnWn1++uJpFW2o4oSTX6Ugi4iB/QCMQJbG1aQSiiMS+\nZeV7uf6RMvY0tPC7i0fzpRP6ap63iIjzmq21zcYYjDE+a+1aY8zQ43i+O4wx44h06W8BruuQlF1g\nza59AIzuk+VwEhHpDloP5DQHQyR18kGd7Xv38+LyHby4fAerdtRjDJzUP5erpo7inJE9yUv3derr\ni4i012UnFHPX/A3cV7qRE76uAphId2Wt1QhESXgqgInEOWstj3ywjdteXEVBRjJzZp/MmCKdXS8i\nEiMqjDHZwHPAXGNMLbC1PU9grS0FSqMff7WjA3aVLXsayUj2kJeW5HQUEekGWgtg/kD4oBUYO9bO\nuv3cNW89Ty2uIBS2jC/O5uezRnD+mF4UZnbSi4qIdICUJDdfn1LC/879mLW76hnWU6uciHRHwbAl\nbFEHmCQ0FcBE4lhTS5CfPPsRzy7dzvShPfjjZePITtWBRRGRWGGt/Xz0w18aY94EsoBXHYzkmC3V\njZTkpak7WUS6ROuZzP5gqMOfu7rBz59LN/Lwwq1g4auT+3H1Kf3pm5va4a8lItJZvnZyP+57ayP3\nlW7kj18a73QcEXGAPxgG6PRueREnqQAmEqc27W7gW4+Usb6qgZvOHMKNMwbhcumgoohIrDDGuIFV\n1tphANbatxyO5Kit1U2MKdL4QxHpGq2Lubce2OkI+5oDPPj2Zh58exP7AyEumVDEd88YTFGOCl8i\nEn+yU5P48onFPPTeFr5/1lAV8UW6oZbofpI6wCSRqQAmEodeWbmTW+aswOs2PPyNE5k2uIfTkURE\n5FOstSFjzDpjTLG1dpvTeZzUEgxTUdvEReN6Ox1FRLqJAx1ggeMrgNXtD7BwUzXvbdjDC8t3UNsU\n4NxRPfn+WUMYVJDREVFFRBxz9bT+/OP9Lfzl7U3cdtEop+OISBdr7ZT3ebUGmCQuFcBE4kggFOaO\nV9fyl7c3M7ZvNvd+ZQJ9slOcjiUiIoeXA6wyxnwINLZutNZe6FykrldR20TYQr+8NKejiEg38UkH\nWPtGIDYHQpRtreWdDXt4b8MeVm6vI2whxevmlMH5fHvmIK23KyIJo1dWChePL+KJD8u5ZtoAdYGJ\ndDOtJwqpA0wSmQpgInGiqr6ZGx9byodbavjayf34yfnDD5zZKiIiMetnTgeIBVurmwAoydNBFRHp\nGq0Hco42ArE5EGLJ1loWbq5h4aZqlpXvpSUYxuMyjOubzY0zBzN1YB7ji3O0PoaIJKTvnTmY55dv\n547X1nH35VoLTKQ78R8Ygajji5K4VAATiQMLN1Vz42NLafQHufNL47hoXB+nI4mISNucZ6299eAN\nxpjbgW61HtjmPZHmt5J8dYCJSNc4MALxEAWwPQ1+Hn5vCws31UQKXqEwLgOj+mRx5cn9mDIwnxP6\n55Lu05/LIpL4emWlcO20Adw1fwNXTS1hQnGO05FEpIscGIGok3wkgWmPXiSGWWt5YMEm7nhtHf1y\nU3nsmpMYUqi1BkRE4siZwK2f2nbuIbYltK3VjaT7POSlJTkdRUS6idYDOc2B/xyB2OgP8rW/fsja\nXfWM7pPFVVNLOGlALpNKcslM9joRVUTEcdedNpDHF5Xzm5dW86/ZUzDGOB1JRLrAgQ4wrwpgkrhU\nABOJUfXNAW55ejmvrarkvNE9uf2SMWToj3IRkbhgjJkNXA8MMMasOOiuDOBdZ1I5Z3N1EyX5qTqY\nIiJdJtn72RGI4bDle08uY+2uev769ROYMbTAqXgiIjElzefh5rOGcOu/VvLvlbs4f0wvpyOJSBdo\nXQMsya0CmCQuFcBEYtCanfXMfqSM8tr9/PT84Vx9Sn8dNBQRiS+PAa8AvwN+eND2fdbaGmciOWdr\ndSOj+mQ5HUNEupEDIxAP6gC7/bW1zF1dyS8vGKHil4jIp3xhYl8eencLv391DWeMKNCaQCLdwIER\niF79f5fEpfKuSIx5ZkkFn7/3XZpaQjx+zWS+OW2Ail8iInHGWltnrd1irb3cWrv1oEu3K34FQmEq\navdTkpfqdBQR6UZ8n+oAe2pROfe/tYmvTu7HlVNKHEwmIhKb3C7DT84fTnnNfv7x3han44hIF2hp\nHYGoNcAkgem7WyRG+IMhfvLsSm56ajlji7J56TuncGL/XKdjiYiIHJeK2v2EwpaSvDSno4hIN3Kg\nAywYZuGman787EqmDc7nFxeM0MllIiKHMW1wD2YM7cHd8zdQ09jidBwR6WR+FcCkG9B3t0gMqKht\n4tL73ufRD7bxrdMG8ug3T6IgI9npWCIiIsdtS3UjACX5KoCJSNdpPZDz8a59fOuRMkry07jnyxPw\naI0LEZEj+vF5w2lqCXHnGx87HUVEOplGIEp3oL1/EYeVrqti1t3vsHl3I/d/dSI/PHeY/jAXEZGE\nsXVPpADWTyMQRaQLtRbAnlxcjgH+euUkslK8zoYSEYkDgwszuPzEvjzywTY2VDU4HUdEOpE6wKQ7\n0He3iENCYcv/zf2Yq/6+iJ6Zybzw7VM4e2RPp2OJiIh0qC3VTaQluemR7nM6ioh0I8YYPC7wug33\nf3US/TSGVUSkzb53xhBSvG5+/8oap6OISCfyB1QAk8Sn724RB9Q0tnDV3xdx57z1fH58H569fir9\nNRpKREQS0JbqRvrlpWnNHRHpcqf39XDXl8ZrXV0RkXbKT/dxw4xBvLGminfW73E6joh0kgMjED0a\ngSiJSwUwkS62vHwvF9z9Dgs3VvPbz4/mfy4dS0qSftGIiEhi2lrdpJM8RMQRlw/3ce7oXk7HEBGJ\nS1dNLaEkL5UfP7uSppag03FEpBO0jkD0unWyoiQuFcBEuoi1lkcWbuXS+94HYM7sk/nyScU6I15E\nRBJWMBSmvKZJ63+JiIiIxJlkr5vbLxnDtpom/uf1j52OIyKdoCUYxudx6dikJDQVwES6QFNLkJue\nWs5Pn/uIkwfm8dK3T2FMUbbTsURERDrV9r37CYYtJVp7R0RERCTunDQgjysmF/O3dzezZFut03FE\npIP5owUwkUTWad/hxpi/GWOqjDEfHeZ+Y4y5yxizwRizwhgzobOyiDhp0+4GPv+n93hu2XZuOnMI\nD339BHLSkpyOJSIi0um2VDcBUKIRiCIiIiJx6dZzhtErM5kfzFlxYL0gEUkM/mAIn1fLskhi68wS\n79+Bc45w/7nA4OjlWuDPnZhFxBGvfrSTC+95l6p9zfzjqhP5zumDcbnUViwiIt3Dlj2NAJRoBKKI\niIhIXMpI9vLbi0ezoaqBe+ZvcDqOiHQgf0AdYJL4Ou073Fq7gP/f3p3HSVWd+R//Pr1U9d5Nd0OD\nLLKIC2pQRECNSmKMS5ygmSxGTTSJ464xv2yamckyZpsxu2scozERjWuMMWbUxHRcokgABQUFZEe2\nbui9u9bz+6Mu2GA3dDVddbvrft6vV73q1r23qp5z+lCcqueec6TtezlljqTfuJSXJVWZGSsUIyfE\nE0l9/8lluuzehZo0okxPXHOiTjp4uN9hAQCQVWsa21USytfw8rDfoQAAAKCfZh8yQh+bNlq31b+t\npe+0+B0OgAHCFIgIggIf33u0pPXdHm/w9m3a80Qzu0SpUWKqq6tTfX19RgJqa2vL2GsPdkEuuzSw\n5W/qSuq21yJ6a0dSHxxXoE8fGtWKV+dpxYC8emYE+e8f5LJLwS5/kMsuUX5kx9rGDh1YU8qiygAA\nAEPcN8+aoueWN+hrj7ymx644QQX5/GgODHWReELhAqZARG7zMwHWZ865OyTdIUnTp093s2fPzsj7\n1NfXK1OvPdgFuezSwJV/3qpGfeW+RWqPmH72qaN09tGj9z+4LAjy3z/IZZeCXf4gl12i/MiONQ3t\nOmRkud9hAAAAYD9VlYR0w5zDdfnchbrj+VW6YvZBfocEYD9F4kmFC0lmI7f52cI3Shrb7fEYbx8w\n5DjndMdzb+u8O+epoqhAj115wpBJfgEAkAnxRFLrd6RGgAEAAAw0M7vLzLaa2eu9HD/UzF4ys4iZ\nfSXb8eWiM44cpTOOGKmf/WWF3t7W5nc4APZTJJZUiNGcyHF+tvDHJX3WUmZJanbOvWf6Q2Cwa+mK\n6fJ7F+r7T76pD0+p0x+uOoGr3QEAgbepuUuxhNOE2hK/QwEAALnp15JO38vx7ZKukfSjrEQTEN+Z\nc7iKC/P11YdeUyyR9DscAPshkkgqXMgUiMhtGUuAmdn9kl6SdIiZbTCzL5jZZWZ2mXfKk5JWSVop\n6X8lXZGpWIBMeXNzi+bc/KKeWbZF//GRw3Tr+dNUXlTod1gAAPhudUO7JDECDAAAZIRz7jmlkly9\nHd/qnJsvKZa9qHLfiPIi3XD2EVq4rkk/evotv8MBsB8isYTCBYwAQ27L2BpgzrlP7+O4k3Rlpt4f\nyLRHF27QN36/RBVFhbr/32ZpxoRqv0MCAGDQWNuYSoBNqCUBBgAABjczu0TSJZJUV1en+vr6fr9W\nW1vbfj1/KKiQ9IGxBfrl31epuHWjjhrR/58Xg1BfA4n6Sg/1tXdNLR2qUMeuOqK+0kN9pcev+spY\nAgzIVZF4Qv/1x6WaO2+dZk6o1k3nHa0R5UV+hwUAwKCyprFDRYV5GlEe9jsUAACAvXLO3SHpDkma\nPn26mz17dr9fq76+Xvvz/KFi1gkJ/ett/9Ddyzr1p1NnaMyw/k17HZT6GijUV3qor73Lf/lZjT2g\nRrNnT5VEfaWL+kqPX/XFGEcgDRt2dOiTt7+kufPW6dKTJ2ruxTNJfgEA0IM1De0aX1PNe0Q7AAAg\nAElEQVQqM/M7FAAAAAywosJ83XLeNCWTTlfdt0jROOuBAUNNJJ5QuJD0AHIbLRzoo/q3tuqsm17Q\nqm3tuv2CY3T9GYepIJ9/QgAA9GRNYyoBBgAAgNw0vrZU//Px9+nV9U364Z/f9DscAGmKxJKsAYac\nxxSIwD4kk06/eHaFfv7XFTqkrly3XXAM65kAALAXiaTT+u2d+tCUOr9DAQAAOcrM7pc0W1KtmW2Q\n9C1JhZLknLvdzEZK+qdSS1YlzexaSVOccy0+hZyTzjhylC46frzuenG1Zkyo1ulHjPQ7JAB9FIkn\nFS7I9zsMIKNIgAF7saM9qmsfeFV/X75NH5s2Wt87+0gVh/iPAQCAvXmnqVPRRJIRYAAAIGOcc5/e\nx/HNksZkKZxA+8aZh2nRuh366sOvacqoCo2r6d96YACyJ5l0iiaSCjECDDmOFg704rX1TTrrphf0\n0tuN+v45R+rHn5hK8gsAgD5Y29ghSSTAAAAAAiBUkKebz5smk3TFfQvUFUv4HRKAfYgmUuv2MQUi\nch0tHNiDc073vrxWn7j9JUnSw5cfp/NmjpOZ+RwZAABDw+rGdknS+Fqu/gUAAAiCsdUl+vEnj9Lr\nG1t03SOL5ZzzOyQAexGJkwBDMDAFItBNZzShf//9Ej26aKNOPni4fvapozSsNOR3WAAADClrG9pV\nVJinuvIiv0MBAABAlpw6pU5fPe0Q3fjUWxozrERfOe0Qv0MC0ItIPDVSM1zIbFfIbSTAAM/qhnZd\nfu8CvbWlVV/60MG6+oMHKS+PUV8AAKRrTWOHDqwu5f9RAACAgLli9iSt396hm/+2UqOHFevTM8b5\nHRKAHkRijABDMJAAAyQt2BLX1X97Qfn5pl9/boZOPni43yEBADBkrWls18Ra1v8CAAAIGjPTDWcf\noU3NXfqPx17XqMoizT5khN9hAdgDUyAiKGjhCLR4IqnvP7lMNy2KaOLwUj1x9ftJfgEAsB8SSad1\njR2aQAIMAAAgkArz83TL+dN0SF25rpy7UK9vbPY7JAB72DUFYgFTICK3kQBDYG1t6dJ5d87THc+t\n0gfHFujBy47TmGElfocFAMCQtrmlS9FEUgfWkAADAAAIqrJwge7+3LGqLC7U5389XxubOv0OCUA3\njABDUNDCEUjzVjXqIze9oMUbmvTTT03VZw8Pc8UDAAADYE1DuyRpfA0XlQAAAARZXUWR7v7cDHVG\nE/rc3a+ouTPmd0gAPKwBhqCghSNQnHP63+dW6bw756ksXKDHrjxB5xw9xu+wAADIGWsavQQYUyAC\nAAAE3iEjy/XLzxyj1Q3tuu6RxX6HA8ATTXgJsELSA8htBX4HAGRLa1dMX31osf7vjc06/fCRuvET\n71N5UaHfYQEAkFPWNnYoVJCnkRVFfocCAACAQeD4g2r10amj9dyKbX6HAsATibEGGIKBBBgC4c3N\nLbr83oVat71D/37mYbr4xAkyM7/DAgAg56xuaNeB1SXKy+P/WQAAAKTUVYS1oz2qZNLRTwQGAdYA\nQ1CQAEPO+/2iDbr+0SUqLyrUfRfP1MyJNX6HBABAzlrX2KEDa5j+EAAAAO+qKQsrnnRq6YqpqiTk\ndzhA4L2bAGMEGHIbCTDkrEg8oRueWKp7X16nGROqdfN5R2tEOdMxAQCQSQ1tEU0fP8zvMAAAADCI\n1Jalkl4NbVESYMAgEIl7UyCyBhhyHAkw5KSNTZ264t4Fem1Dsy49aaK+etohKsjnAx0AgExyzqmp\nM6aqEtbYBAAAwLtqSsOSpMa2iA4aUeZzNAAiMaZARDCQAEPO+fvybbr2d4sUSzjdfsE0nX7EKL9D\nAgAgENoicSWSTlXFXNULAACAd9V4I8Aa26M+RwJAencKxBAJMOQ4EmDIGcmk0y+eXaGf/3WFDqkr\n163nT9PE4VxVBABAtjR3xiRJlcWMAAMAAMC7diXA2iI+RwJAkqI7E2DMmIUcRwIMOWFHe1TXPvCq\n/r58mz529Gh975wjVRxiEUcAALKpqcNLgDEFIgAAALqpLnl3DTAA/ovEEyrIM5aMQc4jAYYh77X1\nTbpi7kJta43oe+ccofNmjJOZ+R0WAACBs3MEWBUjwAAAANBNQX6eqkoK1djOCDBgMIjEk6z/hUAg\nAYYhyzmn+15Zp+88vlTDy8N66LLjNHVsld9hAQAQWDtHgFWVsAYYAAAAdldTGtJ21gADBoVIPKFw\nIbNnIfeRAMOQ1BlN6N8fW6JHF27USQcP188/dZSGlfJjGwAAfmrqTP2gUcUUiAAAANhDTVmYKRCB\nQSISYwQYgoEEGIac1Q3tuvzeBXprS6uu/dBkXf3BycrPY8pDAAD8tmsNMKZABAAAwB5qy0J6a3Or\n32EAEFMgIjhIgGFIeeqNzfrKg68pP9/068/N0MkHD/c7JAAA4GnujKmoME9FTKUBAACAPdSUhtXY\n3uh3GADkTYFYwPc25D4SYBgS4omkbnzqLf3yuVWaOqZSt5w/TWOGlfgdFgAA6KapI6qqYqYkBgAA\nwHvVlIXU1BFTLJFUYT4jTwA/ReJJhRgBhgAgAYZBb2trl666b5FeWb1dF8wap/88awpXKAAAMAg1\ndcRY/wsAAAA9qikLS5J2tEc1oqLI52iAYIsyBSICggQYBrVXVm/XlfctVGtXTD/55FR9bNoYv0MC\nAAC9aOqMsf4XAAAAelRbmpopoKGNBBjgt0g8qaJCEmDIfSTAMCg55/SrF1brB39+U+OqS/TbL8zQ\noSMr/A4LAADsRXNHTONrmaIYAAAA77VzBFhje8TnSABE4gkuXkQgkADDoNPaFdPXHl6sP7++Wacd\nXqcbPzFVFUV8IAMAMNg1dUZVVVzldxgAAAAYhGrKUiPAGtuiPkcCIBJjCkQEAwkwDCpvbW7VZfcu\n0LrtHfrGmYfq306cKDPzOywAANAHzZ0xVbIGGAAAAHpQ402B2NhOAgzwW4Q1wBAQJMAwaDy2aKOu\nf3SJyooKdN/FMzVzYo3fIQEAgD7qiiXUFUsyjQYAAAB6VFFUqII8U2MbUyACfovEEwoX5PsdBpBx\nJMDgu0g8oe8+sUy/fXmtZoyv1s3nHc1iqAAADDHNnTFJUhUjwAAAANCDvDxTdWmIKRCBQSASTypc\nyAgw5D4SYPDVxqZOXTF3oV5b36RLTpqor552iArz+fAFAGCoaerwEmDFIZ8jAQAAwGBVUxZWYzsj\nwAC/RWJJhfgNFgFAAgy+eW75Nn3xd4sUSzjdfsE0nX7EKL9DAgAA/dTUkbqSlxFgAAAA6E1tWUgN\njAADfBdNMAIMwUArR9Ylk04//8sKXXj3KxpRXqTHrzqB5BcAAD0ws3wzW2RmT3iPq83sGTNb4d0P\n8zvGnZq8KRBZAwwAAAC9qSkNMQIM8Fk8kVQi6VgDDIFAAgxZtaM9qs/fM18//ctynX3UaP3+yuM1\ncXiZ32EBADBYfVHSsm6Pr5P0V+fcZEl/9R4PCs0drAEGAACAvaspC7MGGOCzSDwpSQoXkBpA7qOV\nI2sWb2jSWTe9oH+sbNR3zz5CP/nkVJWEmIUTAICemNkYSR+RdGe33XMk3eNt3yPp7GzH1Zumzp1T\nILIGGAAAAHpWUxZSRzShzmjC71CAwCIBhiAh+4CMc87pvlfW6TuPL9Xw8rAevOw4HTW2yu+wAAAY\n7H4m6WuSyrvtq3PObfK2N0uq6+mJZnaJpEskqa6uTvX19f0Ooq2trU/PX7w8qnyT5v/jeZlZv99v\nqOtrfSGF+koP9ZUe6is91Fd6qC+gf2pLw5KkxvaIxoRKfI4GCKZIPJWADhcyBSJyHwkwZFRnNKF/\nf2yJHl24UScdPFw/+9RRqi7lynAAAPbGzM6StNU5t8DMZvd0jnPOmZnr5dgdku6QpOnTp7vZs3t8\niT6pr69XX57/9I4lGrZ1sz7wgQ/0+71yQV/rCynUV3qor/RQX+mhvtJDfQH9s/M3oca2qMYMIwEG\n+CESYwQYgoMEGDJmdUO7Lr93gd7a0qprPzRZV39wsvLzgntFOAAAaThB0kfN7ExJRZIqzOxeSVvM\nbJRzbpOZjZK01dcou2nuiKmymPW/AAAA0LuaMi8B1h7xORIguHZOgRgiAYYAoJUjI556Y7M+etML\n2tzSpbsvOlbXfuhgkl8AAPSRc+5659wY59x4SedKetY5d4GkxyVd6J12oaQ/+BTiezR1Rln/CwAA\nAHtVW5aaArGhLepzJEBwRXetAcYUiMh9jADDgIonkrrx6bf0y7+v0vvGVOrW86cxpB0AgIHzQ0kP\nmtkXJK2V9Emf49mluTOmEeVFfocBAACAQWzXCDASYIBvdq0BxggwBAAJMAyYra1duvq+RZq3ervO\nnzlO3/yXKVxJAADAfnLO1Uuq97YbJZ3iZzy9aeqI6eAR5X6HAQAAgEGsJFSg4sJ8NbYxBSLgl0ic\nNcAQHCTAMCDmr9muK+cuVEtXTD/55FR9bNoYv0MCAABZ1NwRU2UJa4ABAABg72rKQmpsZwQY4Jdd\nI8AKGbiA3EcCDPvFOadfvbBaP/jzmxo7rFi/+cIMHTqywu+wAABAFsUSSbVG4qoqZg0wAAAA7F1N\nWVgNjAADfBOJMQIMwUECDP3W2hXT1x9ZrCeXbNZph9fpxk9MVUURV34DABA0LZ0xSVIVI8AAAACw\nD7WlIW1u6fI7DCCwmAIRQZLRVm5mp5vZW2a20syu6+H4bDNrNrNXvds3MxkPBs5bm1s15+YX9dQb\nW/SNMw/V7RccQ/ILAICAaiIBBgAAgD6qKQupsY0pEAG/MAUigiRjI8DMLF/SLZJOlbRB0nwze9w5\nt3SPU593zp2VqTgw8B5btFHXP7pEpeECzb14pmZNrPE7JAAA4KOmjlQCrLKYBBgAAAD2rqYsrMb2\ni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      "text/plain": [
       "<matplotlib.figure.Figure at 0x61eb9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(path_to_save + 'han_lr_range_test_trainval_history.json' , 'r', encoding='utf8') as my_file:\n",
    "    hist = json.load(my_file)\n",
    "\n",
    "hist = {k:list(map(float,v)) for k,v in hist.items()} # convert strings to floats\n",
    "\n",
    "n_its_plot = 2000\n",
    "acc_plot = [elt for idx,elt in enumerate(hist['batch_acc'],1) if idx%n_its_plot==0]\n",
    "loss_plot = [elt for idx,elt in enumerate(hist['batch_loss'],1) if idx%n_its_plot==0]\n",
    "lr_plot = [elt for idx,elt in enumerate(hist['batch_lr'],1) if idx%n_its_plot==0]\n",
    "\n",
    "fig = plt.figure(figsize=(30,7))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.plot(range(len(hist['batch_lr'])),hist['batch_lr'])\n",
    "plt.xlabel('iterations')\n",
    "plt.ylabel('learning rate')\n",
    "plt.grid(True)\n",
    "plt.title('Learning Rate Evolution')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.plot(lr_plot,acc_plot)\n",
    "plt.xlabel('learning rate')\n",
    "plt.ylabel('training accuracy')\n",
    "plt.grid(True)\n",
    "plt.title('Learning Rate Range Test')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.plot(lr_plot,loss_plot)\n",
    "plt.xlabel('learning rate')\n",
    "plt.ylabel('training loss')\n",
    "plt.grid(True)\n",
    "plt.title('Learning Rate Range Test')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select `max_lr` as the learning rate right before the decrease, and `base_lr` as `max_lr/6`, following Smith's recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best accuracy: 59.1173\n",
      "for batch # 52000\n",
      "with learning rate: 1.109824\n",
      "base learning rate: 0.18497\n"
     ]
    },
    {
     "data": {
      "image/png": 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IRi3ClEoIXROWKisft24OU1kfJGSgj/YI67Re3/o6r2993e4YygFaFubrmjCl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\nAACABxRhAAAAHvw/eZ2pA4XoYhkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x614e5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_lr = float(lr_plot[acc_plot.index(max(acc_plot))])\n",
    "\n",
    "print('best accuracy:',max(acc_plot))\n",
    "print('for batch #',(acc_plot.index(max(acc_plot))+1)*n_its_plot)\n",
    "print('with learning rate:',max_lr)\n",
    "base_lr = round(max_lr/6,5)\n",
    "print('base learning rate:',base_lr)\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "plt.plot(lr_plot,acc_plot)\n",
    "plt.xlabel('learning rate')\n",
    "plt.ylabel('training accuracy')\n",
    "plt.grid(True)\n",
    "plt.title('Learning Rate Range Test')\n",
    "plt.axvline(x=base_lr,label='base LR',c='C1',ls='dashed')\n",
    "plt.axvline(x=max_lr,label='max LR',c='C2',ls='dotted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that it's better to record learning rate and accuracy *at the end of every batch* for the learning range test. Using the values at the end of each epoch only gives less precise results, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3VX1cRI6oamfnfQE+rX992rpzgDkAaWlpOfn5+S1uHEBFRQVJSUmtWjdUWZsj\nQ7C1WVW5892TpHYQ7hib6JfvCLY2B0Jb2pybm1vYoDfmjHw5R7BXRDoDC4E3ReRTYLePcZynqqUi\nkuqsu7nhm6qqItJoJlLVx4HHAcaMGaOTJk3y8Su/aMmSJbR23VBlbY4Mwdjma6q38KeCbQzNmeCX\ngjXB2GZ/C0Sbm+0aUtUrVPWIqt4D/BT4H3zszlHVUuexDFgAjAMOiEhPAOexrHWhG2OCTZ4VrAlJ\nTSYCEYlu+CteVZeq6mJVrWrug0Wko4h0qn8OTAHWA4v5vA7y1/CObmqMCQMNC9aY0NFkIlDVWmCL\niGS14rPTgPdEpBhYAbysqq8BDwCXiMhW4GLntTEmTOR50ikuOcKuQzZafajw5RzBWcAGEVlBgzoE\nqjqzqZVUdQeQ3cj8w8BFLYzTGBMiZmSn8+tXN7OoaB+3XDzQ7XCMD3xJBD/1exTGmLDRsGDNzRcN\nwHtxoAlmvtxHMM05N/DZBEzzd2DGmNCV58lgx8HjbNhX7nYoxge+JIJLGplnQ1MbY87osuE9iI0W\nFq6xgjWhoKlhqL8nIuuAs52CNPXTTsAK0xhjzqhzhzgrWBNCmhuGegbeyz1nNJhyVPX6AMRmjAlh\neZ50DpSfYvnOw26HYppxxpPFqnoU7/AQswMXjjEmXFw02ClYs2YfE/tb5bJg5ss5AmOMabHEOCtY\nEyosERhj/Ka+YM2SLVawJphZIjDG+E19wZpFRXb1UDCzRGCM8RsrWBMaLBEYY/yqvmDN6xsOuB2K\nOQNLBMYYvxqV2ZnMLonWPRTELBEYY/xKRMjLzuD9bYcoO1bpdjimEZYIjDF+d/kob8Gal9dawZpg\nZInAGON3A1I7MbRnMouKrGBNMLJEYIwJiDxPOkVWsCYoWSIwxgTETE86IrDYylgGHUsExpiA6JmS\nyLg+XVhYVIqqjUgaTCwRGGMCxgrWBCdLBMaYgJk2wluwxu4pCC5+TwQiEi0ia0TkJee1R0SWiUiR\niKwSkXH+jsEYExw6d4jjwkGpLC62gjXBJBBHBLcAmxq8fhC4V1U9wM+c18aYCGEFa4KPXxOBiPQC\nvgT8vcFsBZKd5ymAXUJgTAS5eIi3YM1iu6cgaPj7iOD3wJ1AXYN5PwAeEpES4GHgLj/HYIwJIolx\n0Vw6rAevrLOCNcFC/HUZl4hMB6ap6o0iMgm4XVWni8gfgaWq+oKIzALmqOrFjaw/B5gDkJaWlpOf\nn9+qOCpeSSjkAAANi0lEQVQqKkhKSmp1O0KRtTkyhHKb1x6s4XeFp5g7Kp6ctDNWzP0Podzm1mpL\nm3NzcwtVdUyzC6qqXybg18BeYBfwMXACeBJvHeT6BCRAeXOflZOTo61VUFDQ6nVDlbU5MoRym6tr\nanX0fW/ojU8Wtmi9UG5za7WlzcAq9WF/7beuIVW9S1V7qWof4BrgHVW9Hu85gQudxSYDW/0VgzEm\nOHkL1vTkrU0HrGBNEHDjPoJvA78VkWLgfpzuH2NMZJnpyeCUFawJCr53zrWBqi4BljjP3wNyAvG9\nxpjgNTrr84I1V+X0cjuciGZ3FhtjXNGwYM3BY6fcDieiWSIwxrgmz+MtWPPSWrunwE2WCIwxrhmY\nZgVrgoElAmOMq+oL1uw+bAVr3GKJwBjjqhnZ3oI1dlTgHksExhhXpXe2gjWnO1lVywuFe5n1tw8p\nO1HX/AptFJDLR40xpil5ngzuXrCODfvKGZ6R4nY4rtmw7yj5K0pYWFTKscoa+nbryKeV/k+OlgiM\nMa6bNqIHP1+8nkVFpRGXCI5VVrO4eB/5K0pYV3qU+Jgopo3oydVjMxnftwtLly71ewyWCIwxrmtY\nsOZHlw0hOkrcDsmvVJXVez7lmRUlvLx2PyeraxncoxP3zhzG5Z4MUjrEBjQeSwTGmKCQ50nnrU0H\nWLHzE87p39XtcPzik+NVzF+9l3krS9haVkHHuGguH5XBNWMzGdkrBRF3EqAlAmNMUKgvWLOoqDSs\nEkFdnfLB9sPkr9zDGxsOUFVbx+iszjz45ZF8aWRPOsa7vxt2PwJjjOGLBWvuzRtGfEy02yG1ycdH\nK3m+sIR5q0oo+eQknTvEcv2E3lw9NpOze3RyO7wvsERgjAkaMz3pzF9TytItB5kyrIfb4bRYTW0d\nBVsOkr9iDwVbyqhTmNi/K3dcOpgpQ9NIiA3O5GaJwBgTNM4b0I2uHeNYVLQvpBLB7sPHeXZVCc+t\n2kvZsVOkdorne5P6M2tMJr27dnQ7vGZZIjDGBI36gjX5K0s4VllNp4TAXj3TEpXVtbyx8QDzVu7h\n/W2HiRLIPTuVa8ZlkXt2d2KiQ+d+XUsExpigMtOTwT8+3M0bGw7w5SCsU/DRgWPkryhh/pq9HDlR\nTa+zErntkkF8ZUwmPVIS3A6vVSwRGGOCyuiszvQ6K5GFRaVBkwiOn6rh5bX7yV+5h9V7jhAbLUwZ\n1oPZY7OY2L8rUSF+34MlAmNMUBER8jzp/GXJdg4eO0X3TvGuxKGqrN17lPyVJbxYvI+KUzUMSE3i\nJ18awhWjMuia5E5c/mCJwBgTdC73ZPCngu28vHYfXz+3b0C/++iJahYWlZK/soRN+8tJiI1i+sh0\nZo/LZHTWWa7d9OVPlgiMMUFnYFonhvRMZmFRYBKBqrJ85yfMW1nCK+v2c6qmjhEZKfzy8uHM9KST\nHMQnrduDJQJjTFDK86TzwKub2X34uN8uwTx47BQvOEM+7Dx0nE4JMcwak8nVYzMjavA7vycCEYkG\nVgGlqjrdmTcX+D5QC7ysqnf6Ow5jTGiZme1NBIuL9jH3ooHt9rm1dcq7Ww8yb0UJb206QE2dMrbP\nWdyUO4BpI3qSGBecN335UyCOCG4BNgHJACKSC+QB2ap6SkRSAxCDMSbEpHdOZFxfb8GamyYPaHPf\nfOmRkzy7soTnVpWw72glXTvG8c3z+jJrTCYDUpPaKerQ5NdEICK9gC8BvwL+25n9PeABVT0FoKpl\n/ozBGBO68jzp/HjB+lYXrKmqqePtTQfIX1nCu1sPAnD+wO78ZPpQLh6SRlxM6Nz05U/+PiL4PXAn\n0HCEpUHA+SLyK6ASuF1VV/o5DmNMCJo2vCf3LN7A4uJ9LUoEOw5WMG9lCS+s3suhiip6piQwd/JA\nvpLTi8wuHfwYcWgSf9UIFZHpwDRVvVFEJuHd4U8XkfVAAXAzMBaYB/TT0wIRkTnAHIC0tLSc/Pz8\nVsVRUVFBUlJkHfZZmyNDpLT594WV7C6v47eTEjlx/PgZ21xVq6w6UMvSkmq2fFpHlICnezQXZsYw\nols0USF62WdbtnNubm6hqo5pbjl/HhGcC8wUkWlAApAsIk8Ce4H5zo5/hYjUAd2Agw1XVtXHgccB\nxowZo5MmTWpVEEuWLKG164Yqa3NkiJQ2l5+1j5ufWUNi1kiiStb9R5s37isnf+UeFqzx1vnt3bUD\nd07N5KqcXqR2Cs0hHxoKxHb2WyJQ1buAuwAaHBFcLyLfBXKBAhEZBMQBh/wVhzEmtF0yJI0OcdEs\nLi7l0i7eeccqq3mx2Dvkw9q9R4mLieKy4T24ZmwW4/t2CfkhHwLNjfsIngCecLqIqoCvnd4tZIwx\n9eoL1ry8dj99RsbwyvPFvFj8eZ3fe2YM5fJRGXTuEOd2qCErIIlAVZcAS5znVcD1gfheY0x4mOlJ\nZ8GaUn69ooYOcfvJ86Rzzbgssl2s8xtO7M5iY0zQO39AN75zQT+qDu/ltlm5JAVBnd9wYhfRGmOC\nXkx0FHdNG8KFmbGWBPzAEoExxkQ4SwTGGBPhLBEYY0yEs0RgjDERzhKBMcZEOEsExhgT4SwRGGNM\nhLNEYIwxEc5vw1C3JxE5COxu5erdiLxB7azNkcHaHBna0ubeqtq9uYVCIhG0hYis8mU87nBibY4M\n1ubIEIg2W9eQMcZEOEsExhgT4SIhETzudgAusDZHBmtzZPB7m8P+HIExxpimRcIRgTHGmCaETSIQ\nkakiskVEtonIjxp5f5KIHBWRImf6mRtxthcReUJEypySn429LyLyR+fvsVZERgc6xvbmQ5vDbRtn\nikiBiGwUkQ0icksjy4TVdvaxzeG2nRNEZIWIFDttvreRZfy7nVU15CcgGtgO9APigGJg6GnLTAJe\ncjvWdmzzBcBoYP0Z3p8GvAoIMAFY7nbMAWhzuG3jnsBo53kn4KNG/l2H1Xb2sc3htp0FSHKexwLL\ngQmB3M7hckQwDtimqjvUWxM5H8hzOSa/UtV3gU+aWCQP+Kd6LQM6i0jPwETnHz60Oayo6n5VXe08\nPwZsAjJOWyystrOPbQ4rzrarcF7GOtPpJ2/9up3DJRFkACUNXu+l8X88E53DqldFZFhgQnONr3+T\ncBOW21hE+gCj8P5abChst3MTbYYw284iEi0iRUAZ8KaqBnQ7R1Lxz9VAlqpWiMg0YCEw0OWYTPsK\ny20sIknAC8APVLXc7XgCoZk2h912VtVawCMinYEFIjJcVRs9F+YP4XJEUApkNnjdy5n3GVUtrz/8\nUtVXgFgR6Ra4EAOu2b9JuAnHbSwisXh3iE+p6vxGFgm77dxcm8NxO9dT1SNAATD1tLf8up3DJRGs\nBAaKSF8RiQOuARY3XEBEeoiIOM/H4W374YBHGjiLgf9yrjaYABxV1f1uB+VP4baNnbb8D7BJVX93\nhsXCajv70uYw3M7dnSMBRCQRuATYfNpift3OYdE1pKo1InIT8DreK4ieUNUNIvJd5/2/AlcB3xOR\nGuAkcI06p+NDkYg8g/fqiW4ishf4Od6TTPXtfQXvlQbbgBPAN9yJtP340Oaw2sbAucBXgXVO/zHA\n3UAWhO129qXN4badewL/EJFovEntWVV96bT9l1+3s91ZbIwxES5cuoaMMca0kiUCY4yJcJYIjDEm\nwlkiMMaYCGeJwBhjIpwlAhPyRKSi+aXa/B0zpZFRbf38nZNEZGIgv9NEprC4j8CY9iAi0c6t/v9B\nVRdz2k2K7fSdMapac4a3JwEVwAft/b3GNGRHBCasiMgdIrLSGZDs3gbzF4pIoTPe+5wG8ytE5Lci\nUgycIyK7ROReEVktIutEZLCz3NdF5DHn+f85Y8N/ICI7ROQqZ36UiPxZRDaLyJsi8kr9e6fFuERE\nfi8iq4BbRGSGiCwXkTUi8paIpDkDrn0XuFW8Y+6f79yB+oLTvpUicq4//5YmctgRgQkbIjIF7+Bj\n4/CO275YRC5whq/+pqp+4tzCv1JEXlDVw0BHvGO73+Z8BsAhVR0tIjcCtwM3NPJ1PYHzgMF4jxSe\nB64E+gBDgVS8Qyg/cYZw41R1jPOdZ+Edf15F5AbgTlW9TUT+ClSo6sPOck8Dj6jqeyKShfdO+iGt\n/oMZ47BEYMLJFGda47xOwpsY3gVuFpErnPmZzvzDQC3eAc4aqh/orBDvzr0xC1W1DtgoImnOvPOA\n55z5H4tIQROxzmvwvBcwT7zjy8cBO8+wzsXAUCdZASSLSFKDseyNaRVLBCacCPBrVf3bF2aKTMK7\nEz1HVU+IyBIgwXm7spHzAqecx1rO/H/kVIPncoZlmnK8wfNHgd+p6mIn1nvOsE4U3iOHylZ8nzFn\nZOcITDh5HfimeMeyR0QyRCQVSAE+dZLAYLyl/vzhfeDLzrmCNLwne32RwudDCn+twfxjeMs11nsD\nmFv/QkQ8rQ/VmM9ZIjBhQ1XfAJ4GPhSRdXj77TsBrwExIrIJeABY5qcQXsBbOWoj8CTeAipHfVjv\nHuA5ESkEDjWY/yJwRf3JYuBmYIxzInwj3pPJxrSZjT5qTDuq77MXka7ACuBcVf3Y7biMaYqdIzCm\nfb3kFBmJA35hScCEAjsiMMaYCGfnCIwxJsJZIjDGmAhnicAYYyKcJQJjjIlwlgiMMSbCWSIwxpgI\n9/8BExnciWWELRgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6147b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot([elt for idx,elt in enumerate(hist['batch_lr'],1) if idx%its_per_epoch_train==0],[round(float(elt)*100,4) for elt in hist['acc']],label='training')\n",
    "plt.xlabel('learning rate')\n",
    "plt.ylabel('training accuracy')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "We're now ready to train the model! First, we re-initialize it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "han.load_weights(path_to_save + 'han_init_weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we instantiate our train and test generators. We use a cycle of 12 epochs, which means that during 6 epochs, the learning rate will increase from `base_lr` to `max_lr`, and for the 6 following epochs, the learning rate will decrease from `max_lr` to `base_lr`. The momentum will do the opposite. Since CLR and CLM work at the granularity of a batch, we must convert our 6 epochs (half cycle) to a number of iterations, `step_size`. This is simply the number of batches per epoch times 6. This procedure will run for 50 epochs, and we use a patience of one cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23440 5080 140640\n"
     ]
    }
   ],
   "source": [
    "batch_names_train = [elt for elt in batch_names if 'train_' in elt or 'val_' in elt]\n",
    "batch_names_val = [elt for elt in batch_names if 'test_' in elt]\n",
    "\n",
    "nb_epochs = 50\n",
    "half_cycle = 6 \n",
    "my_patience = half_cycle*2\n",
    "its_per_epoch_train = int(len(batch_names_train)/2)\n",
    "its_per_epoch_val = int(len(batch_names_val)/2)\n",
    "step_size = its_per_epoch_train*half_cycle\n",
    "\n",
    "print(its_per_epoch_train,its_per_epoch_val,step_size)\n",
    "\n",
    "rd_train = read_batches(batch_names_train,\n",
    "                        path_to_batches,\n",
    "                        do_shuffle=True,\n",
    "                        do_train=True,\n",
    "                        my_max_doc_size_overall=max_doc_size_overall,\n",
    "                        my_max_sent_size_overall=max_sent_size_overall,\n",
    "                        my_n_cats=n_cats)\n",
    "\n",
    "rd_val = read_batches(batch_names_val,\n",
    "                      path_to_batches,\n",
    "                      do_shuffle=False,\n",
    "                      do_train=True,\n",
    "                      my_max_doc_size_overall=max_doc_size_overall,\n",
    "                      my_max_sent_size_overall=max_sent_size_overall,\n",
    "                      my_n_cats=n_cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_optimizer = optimizers.SGD(lr=base_lr,\n",
    "                              momentum=max_mt, # we decrease momentum when lr increases\n",
    "                              decay=1e-5,\n",
    "                              nesterov=True)\n",
    "\n",
    "han.compile(loss=my_loss,\n",
    "            optimizer=my_optimizer,\n",
    "            metrics=['accuracy'])    \n",
    "\n",
    "lr_sch = CyclicLR(base_lr=base_lr, max_lr=max_lr, step_size=step_size, mode='triangular')\n",
    "mt_sch = CyclicMT(base_mt=base_mt, max_mt=max_mt, step_size=step_size, mode='triangular')\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_acc', # go through epochs as long as accuracy on validation set increases\n",
    "                               patience=my_patience,\n",
    "                               mode='max')\n",
    "\n",
    "# make sure that the model corresponding to the best epoch is saved\n",
    "checkpointer = ModelCheckpoint(filepath=path_to_save + 'han_trained_weights',\n",
    "                               monitor='val_acc',\n",
    "                               save_best_only=True,\n",
    "                               mode='max',\n",
    "                               verbose=0,\n",
    "                               save_weights_only=True) # so that we can train on GPU and load on CPU (for CUDNN GRU)\n",
    "\n",
    "# batch-based callbacks\n",
    "loss_hist = LossHistory()\n",
    "acc_hist = AccHistory()\n",
    "lr_hist = LRHistory()\n",
    "mt_hist = MTHistory() \n",
    "\n",
    "# epoch-based callbacks\n",
    "pcacc_hist = PerClassAccHistory(my_n_cats=n_cats, my_rd=rd_val, my_n_steps=its_per_epoch_val)\n",
    "\n",
    "callback_list = [loss_hist,acc_hist,lr_hist,mt_hist,lr_sch,mt_sch,early_stopping,checkpointer,pcacc_hist]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "23440/23440 [==============================] - 1298s 55ms/step - loss: 1.0170 - acc: 0.5573 - val_loss: 0.9090 - val_acc: 0.6053\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 2/50\n",
      "23440/23440 [==============================] - 1307s 56ms/step - loss: 0.9393 - acc: 0.5924 - val_loss: 0.8915 - val_acc: 0.6124\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 3/50\n",
      "23440/23440 [==============================] - 1309s 56ms/step - loss: 0.9222 - acc: 0.6005 - val_loss: 0.8828 - val_acc: 0.6176\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 4/50\n",
      "23440/23440 [==============================] - 1235s 53ms/step - loss: 0.9125 - acc: 0.6043 - val_loss: 0.8759 - val_acc: 0.6205\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 5/50\n",
      "23440/23440 [==============================] - 1235s 53ms/step - loss: 0.9058 - acc: 0.6076 - val_loss: 0.8698 - val_acc: 0.6228\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 6/50\n",
      "23440/23440 [==============================] - 1236s 53ms/step - loss: 0.9010 - acc: 0.6096 - val_loss: 0.8787 - val_acc: 0.6152\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 7/50\n",
      "23440/23440 [==============================] - 1236s 53ms/step - loss: 0.8948 - acc: 0.6124 - val_loss: 0.8649 - val_acc: 0.6241\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 8/50\n",
      "23440/23440 [==============================] - 1237s 53ms/step - loss: 0.8876 - acc: 0.6155 - val_loss: 0.8601 - val_acc: 0.6261\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 9/50\n",
      "23440/23440 [==============================] - 1238s 53ms/step - loss: 0.8818 - acc: 0.6179 - val_loss: 0.8582 - val_acc: 0.6276\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 10/50\n",
      "23440/23440 [==============================] - 1238s 53ms/step - loss: 0.8756 - acc: 0.6208 - val_loss: 0.8554 - val_acc: 0.6290\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 11/50\n",
      "23440/23440 [==============================] - 1237s 53ms/step - loss: 0.8704 - acc: 0.6232 - val_loss: 0.8507 - val_acc: 0.6300\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 12/50\n",
      "23440/23440 [==============================] - 1238s 53ms/step - loss: 0.8656 - acc: 0.6254 - val_loss: 0.8478 - val_acc: 0.6312\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 13/50\n",
      "23440/23440 [==============================] - 1238s 53ms/step - loss: 0.8634 - acc: 0.6261 - val_loss: 0.8479 - val_acc: 0.6317\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 14/50\n",
      "23440/23440 [==============================] - 1230s 52ms/step - loss: 0.8638 - acc: 0.6259 - val_loss: 0.8525 - val_acc: 0.6302\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 15/50\n",
      "23440/23440 [==============================] - 1231s 53ms/step - loss: 0.8642 - acc: 0.6259 - val_loss: 0.8466 - val_acc: 0.6319\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 16/50\n",
      "23440/23440 [==============================] - 1232s 53ms/step - loss: 0.8641 - acc: 0.6258 - val_loss: 0.8471 - val_acc: 0.6320\n",
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      "Epoch 18/50\n",
      "23440/23440 [==============================] - 1231s 53ms/step - loss: 0.8630 - acc: 0.6263 - val_loss: 0.8472 - val_acc: 0.6327\n",
      "0\n",
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      "Epoch 19/50\n",
      "23440/23440 [==============================] - 1235s 53ms/step - loss: 0.8620 - acc: 0.6266 - val_loss: 0.8492 - val_acc: 0.6302\n",
      "0\n",
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      "Epoch 20/50\n",
      "23440/23440 [==============================] - 1232s 53ms/step - loss: 0.8590 - acc: 0.6280 - val_loss: 0.8475 - val_acc: 0.6317\n",
      "0\n",
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      "Epoch 21/50\n",
      "23440/23440 [==============================] - 1232s 53ms/step - loss: 0.8561 - acc: 0.6292 - val_loss: 0.8454 - val_acc: 0.6319\n",
      "0\n",
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      "Epoch 22/50\n",
      "23440/23440 [==============================] - 1233s 53ms/step - loss: 0.8538 - acc: 0.6305 - val_loss: 0.8441 - val_acc: 0.6326\n",
      "0\n",
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      "Epoch 23/50\n",
      "23440/23440 [==============================] - 1232s 53ms/step - loss: 0.8510 - acc: 0.6317 - val_loss: 0.8435 - val_acc: 0.6341\n",
      "0\n",
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      "Epoch 24/50\n",
      "23440/23440 [==============================] - 1232s 53ms/step - loss: 0.8486 - acc: 0.6327 - val_loss: 0.8426 - val_acc: 0.6337\n",
      "0\n",
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      "Epoch 25/50\n",
      "23440/23440 [==============================] - 1234s 53ms/step - loss: 0.8472 - acc: 0.6332 - val_loss: 0.8456 - val_acc: 0.6327\n",
      "0\n",
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      "Epoch 26/50\n",
      "23440/23440 [==============================] - 1232s 53ms/step - loss: 0.8477 - acc: 0.6330 - val_loss: 0.8432 - val_acc: 0.6343\n",
      "0\n",
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      "Epoch 27/50\n",
      "23440/23440 [==============================] - 1232s 53ms/step - loss: 0.8483 - acc: 0.6329 - val_loss: 0.8441 - val_acc: 0.6330\n",
      "0\n",
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      "Epoch 28/50\n",
      "23440/23440 [==============================] - 1234s 53ms/step - loss: 0.8485 - acc: 0.6324 - val_loss: 0.8410 - val_acc: 0.6341\n",
      "0\n",
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      "Epoch 29/50\n",
      "23440/23440 [==============================] - 1234s 53ms/step - loss: 0.8488 - acc: 0.6325 - val_loss: 0.8418 - val_acc: 0.6339\n",
      "0\n",
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      "Epoch 30/50\n",
      "23440/23440 [==============================] - 1234s 53ms/step - loss: 0.8487 - acc: 0.6327 - val_loss: 0.8472 - val_acc: 0.6324\n",
      "0\n",
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      "Epoch 31/50\n",
      "23440/23440 [==============================] - 1233s 53ms/step - loss: 0.8479 - acc: 0.6329 - val_loss: 0.8431 - val_acc: 0.6334\n",
      "0\n",
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      "Epoch 32/50\n",
      "23440/23440 [==============================] - 1233s 53ms/step - loss: 0.8492 - acc: 0.6326 - val_loss: 0.8434 - val_acc: 0.6341\n",
      "0\n",
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      "Epoch 33/50\n",
      "23440/23440 [==============================] - 1234s 53ms/step - loss: 0.8461 - acc: 0.6338 - val_loss: 0.8425 - val_acc: 0.6337\n",
      "0\n",
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      "Epoch 34/50\n",
      "23440/23440 [==============================] - 1233s 53ms/step - loss: 0.8437 - acc: 0.6349 - val_loss: 0.8398 - val_acc: 0.6356\n",
      "0\n",
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      "Epoch 35/50\n",
      "23440/23440 [==============================] - 1234s 53ms/step - loss: 0.8420 - acc: 0.6357 - val_loss: 0.8416 - val_acc: 0.6345\n",
      "0\n",
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      "Epoch 36/50\n",
      "23440/23440 [==============================] - 1235s 53ms/step - loss: 0.8402 - acc: 0.6364 - val_loss: 0.8403 - val_acc: 0.6350\n",
      "0\n",
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      "Epoch 37/50\n",
      "23440/23440 [==============================] - 1236s 53ms/step - loss: 0.8393 - acc: 0.6370 - val_loss: 0.8391 - val_acc: 0.6354\n",
      "0\n",
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      "Epoch 38/50\n",
      "23440/23440 [==============================] - 1235s 53ms/step - loss: 0.8397 - acc: 0.6368 - val_loss: 0.8432 - val_acc: 0.6333\n",
      "0\n",
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      "Epoch 39/50\n",
      "23440/23440 [==============================] - 1236s 53ms/step - loss: 0.8409 - acc: 0.6361 - val_loss: 0.8417 - val_acc: 0.6347\n",
      "0\n",
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      "Epoch 40/50\n",
      "23440/23440 [==============================] - 1234s 53ms/step - loss: 0.8407 - acc: 0.6363 - val_loss: 0.8392 - val_acc: 0.6352\n",
      "0\n",
      "1016\n",
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      "Epoch 41/50\n",
      "23440/23440 [==============================] - 1236s 53ms/step - loss: 0.8408 - acc: 0.6362 - val_loss: 0.8422 - val_acc: 0.6343\n",
      "0\n",
      "1016\n",
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      "Epoch 42/50\n",
      "23440/23440 [==============================] - 1241s 53ms/step - loss: 0.8409 - acc: 0.6360 - val_loss: 0.8381 - val_acc: 0.6360\n",
      "0\n",
      "1016\n",
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      "Epoch 43/50\n",
      "23440/23440 [==============================] - 1250s 53ms/step - loss: 0.8400 - acc: 0.6363 - val_loss: 0.8449 - val_acc: 0.6343\n",
      "0\n",
      "1016\n",
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      "Epoch 44/50\n",
      "23440/23440 [==============================] - 1262s 54ms/step - loss: 0.8393 - acc: 0.6368 - val_loss: 0.8388 - val_acc: 0.6358\n",
      "0\n",
      "1016\n",
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      "Epoch 45/50\n",
      "23440/23440 [==============================] - 1250s 53ms/step - loss: 0.8377 - acc: 0.6376 - val_loss: 0.8380 - val_acc: 0.6356\n",
      "0\n",
      "1016\n",
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      "4064\n",
      "Epoch 46/50\n",
      "23440/23440 [==============================] - 1243s 53ms/step - loss: 0.8363 - acc: 0.6381 - val_loss: 0.8387 - val_acc: 0.6360\n",
      "0\n",
      "1016\n",
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      "4064\n",
      "Epoch 47/50\n",
      "23440/23440 [==============================] - 1252s 53ms/step - loss: 0.8348 - acc: 0.6387 - val_loss: 0.8393 - val_acc: 0.6358\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 48/50\n",
      "23440/23440 [==============================] - 1252s 53ms/step - loss: 0.8334 - acc: 0.6396 - val_loss: 0.8376 - val_acc: 0.6365\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 49/50\n",
      "23440/23440 [==============================] - 1254s 54ms/step - loss: 0.8326 - acc: 0.6399 - val_loss: 0.8377 - val_acc: 0.6366\n",
      "0\n",
      "1016\n",
      "2032\n",
      "3048\n",
      "4064\n",
      "Epoch 50/50\n",
      "23440/23440 [==============================] - 1255s 54ms/step - loss: 0.8331 - acc: 0.6395 - val_loss: 0.8397 - val_acc: 0.6352\n",
      "0\n",
      "1016\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7faecdeebba8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "han.fit_generator(rd_train, \n",
    "                  steps_per_epoch=its_per_epoch_train, \n",
    "                  epochs=nb_epochs,\n",
    "                  callbacks=callback_list,\n",
    "                  validation_data=rd_val, \n",
    "                  validation_steps=its_per_epoch_val,\n",
    "                  use_multiprocessing=False, \n",
    "                  workers=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for the learning rate range test, we save the model's history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'batch_lr', 'val_acc', 'batch_acc', 'acc', 'val_loss', 'batch_loss', 'batch_mt'])\n",
      "1172000\n"
     ]
    }
   ],
   "source": [
    "hist = han.history.history\n",
    "print(hist.keys())\n",
    "print(len(lr_hist.lrs))\n",
    "hist['batch_loss'] = loss_hist.loss_avg\n",
    "hist['batch_acc'] = acc_hist.acc_avg\n",
    "hist['batch_lr'] = lr_hist.lrs\n",
    "hist['batch_mt'] = mt_hist.mts\n",
    "hist['pcacc'] = pcacc_hist.per_class_accuracy\n",
    "\n",
    "hist = {k: [str(elt) for elt in v] for k, v in hist.items()}\n",
    "with open(path_to_save + 'han_history.json', 'w') as my_file:\n",
    "    json.dump(hist, my_file, sort_keys=False, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "We can immediately see that we reach **62.6%** accuracy after 8 epochs, and that we reproduce the results of the HAN paper (**63.6%** accuracy) after 42 epochs! We even slightly exceed this accuracy later on. Each epoch takes about 20 mins on my TitanX GPU. Let's now analyze the results in more details. First, a define a convenient function to generate some useful plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_results(my_results,loss_acc_lr,my_title):\n",
    "    assert loss_acc_lr in ['loss','acc','lr'], 'invalid 2nd argument!'\n",
    "    n_epochs = len(my_results['loss'])\n",
    "    if loss_acc_lr in ['loss','acc']:\n",
    "        plt.plot(range(1,n_epochs+1),my_results['val_' + loss_acc_lr],label='validation')\n",
    "        plt.plot(range(1,n_epochs+1),my_results[loss_acc_lr],label='training')\n",
    "    else:\n",
    "        plt.plot(range(1,n_epochs+1),my_results['lr'],label='learning rate')\n",
    "    plt.legend(loc=0)\n",
    "    plt.xlabel('epochs')\n",
    "    plt.ylabel(loss_acc_lr)\n",
    "    plt.xlim([1,n_epochs+1])\n",
    "    plt.grid(True)\n",
    "    plt.title(my_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n"
     ]
    }
   ],
   "source": [
    "with open(path_to_save + 'han_history.json' , 'r', encoding='utf8') as my_file:\n",
    "    histt = json.load(my_file)\n",
    "\n",
    "# convert strings to floats\n",
    "histt = {k:list(map(float,v)) for k,v in histt.items() if k!='pcacc'}\n",
    "\n",
    "lr_epoch = [elt for idx,elt in enumerate(histt['batch_lr'],1) if idx%its_per_epoch_train==0]\n",
    "print(len(lr_epoch))\n",
    "histt['lr'] = lr_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KoHYP7PwAit6AJT93Hq4MyFngBMvGnAZh0YffX1sr1JdBbQnUlxNfVQgliRCZ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\nlkQyhTEKPQsp2h6m+8AI+4fdC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XGcxI5jecWJ5C3p/P44lrMTD8mSnc/z8dBDsiQffe0T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6977fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(30,7))\n",
    "plt.subplot(1,3,1)\n",
    "plot_results(histt,'acc','accuracy')\n",
    "plt.subplot(1,3,2)\n",
    "plot_results(histt,'loss','loss')\n",
    "plt.subplot(1,3,3)\n",
    "plot_results(histt,'lr','learning rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good advice to interpret our basic plots can be found on Stanford's CS231n course page, [here](http://cs231n.github.io/neural-networks-3/#baby) (section \"Babysitting the learning process\").\n",
    "\n",
    "- We can observe on the first plot that we don't start overfitting before the 35th epoch or so, and that even then, we only slighlty overfit. This indicates that our regularization strategy (dropout and large learning rate at times) is effective, which is is a good thing.\n",
    "\n",
    "-  We can also see on the second plot that our loss VS epochs curve seems neither too linear nor too exponential, which indicates that the learning rate is adequate.\n",
    "\n",
    "- Of course, on the third plot, we can observe our triangular CLR policy. Looking more closely at the plot below (where the vertical dotted lines represent the end of each half cycle), we can clearly see that **when the learning rate increases** (odd half cycles), the performance of the model **plateaus or slightly decreases**, and that **when the learning rate decreases, performance increases**. This is especially obvious looking at the training accuracy curve (the orange one). This is consistent with Smith's observation that *CLR might harm short term performance, but is beneficial in the long run*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7face7642b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "plot_results(histt,'acc','accuracy')\n",
    "for ep_idx,foo in enumerate(range(nb_epochs),1):\n",
    "    if ep_idx % half_cycle == 0:\n",
    "        plt.axvline(x=ep_idx, c='C2',ls='dashed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of document embeddings\n",
    "### After training\n",
    "You can download the weights of the trained model [here](https://www.dropbox.com/s/tiehrbjq824kjlo/han_trained_weights?dl=0) (~135MB). We load `n_batch_show` batches with our generator in train mode, so that we can get both the documents and their labels. Then, we feed each document to an intermediate model corresponding to HAN without the last dense layer. This gives us the vectors of all the documents. Finally, we use PCA and the labels to visualize in two dimensions whether the internal representations of the **trained model** are correlated with the labels. We can clearly see that they do: the negative reviews are on one side, the positive on the other side, and the neutral in the middle. The fact that the model learned sensible representations was expected in light of the good accuracy that it reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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buw06+LH6J+hr/x/Q52tmwNN1O/SHvl9DX+/vQH+ImTH+fg66b+QvjLp/DHoQ1W/tBYnIaujM5P+NcawfAHivbSR+ut6LyNfTAPR5xKSUOgh9Pn8fZ7dPQn8A6Ib+IBLP+6AHoT0HPbvFN3D+Q2nWKKVOQT8P3wr9f+Yj0KP5gfP3kyhpohQ/mBARUfJEpBb6g8s7lVIP5Lo+lB4R+QvoWRGqFIMDShGbwomIyBERuR66afsQ9Ojiz0J3y3AyVyXlGRHZAz0yfBi6T+7fQU/lxaCSUsbAkoiInCqEbhLeAN238mnoOUSDOa0VpWoT9NyVNdBdHO6FzlgSpYxN4URERETkCg7eISIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIlcwsCQiIiIiVzCwJCIiIiJXMLAkIiIiIld4c12BWGpra9X69etzXQ0iIiKiC95zzz03opSqS7Rf3gaW69evx7PPPpvrahARERFd8ETklJP92BRORERERK5wJbAUkW+IyJCIHI7x99eLyLiIvGB8fdKN4xIRERFR/nCrKfw+AF8H8M04+/xWKfUWl45HRERERHnGlcBSKfW4iKx3oywiIiKilWh+fh69vb2YmZnJdVVi8vl8WLt2LQoLC1N6fDYH71wtIi8C6AfwMaXUkSwem4iIiCinent7UV5ejvXr10NEcl2dJZRSGB0dRW9vL1paWlIqI1uDdw4AWKeUehWArwH472g7icgHRORZEXl2eHg4S1UjIiIiyryZmRnU1NTkZVAJACKCmpqatDKqWQkslVIBpdSk8fODAApFpDbKfv+mlNqhlNpRV5dwqiQiIiKiZSVfg0pTuvXLSmApIg1i1FREdhrHHc3GsYmIiIgoO9yabuh7AH4HoE1EekXkDhG5U0TuNHb5IwCHjT6WXwVwq1JKuXFsIiIiInLu4YcfRltbGzZt2oQvfOELrpbt1qjwdyb4+9ehpyMiIiIiohwJh8PYs2cPHnvsMaxduxZXXnkl3vrWt+KSSy5xpXyuvENERER0gdi/fz82bdqEDRs2oKioCLfeeit+/OMfu1Y+A0siIiKiPNXRNYJ7HjmGjq4RV8rr6+vDRRddtLi9du1a9PX1uVI2wMCSiIiIKC91dI1gz95nsHdfN/bsfca14DKTGFgSERER5aEDx88CCmhcVQIoYztNTU1NOH369OJ2b28vmpqa0i7XxMCSiIiIKA9t31ANCDAwNg2IsZ2mK6+8El1dXThx4gTm5uZw//33461vfasLtdWyuaQjERFlSWd/AN2DE9jYUI62NRW5rg4RpaC9tRZ333ElDhw/i+0bqtHeumRtmaR5vV58/etfx0033YRwOIz3ve992Lp1qwu1Ncp3rSQiIsoLnf0B3PvYMSgFyKEB3HnDZgaXRMtUe2utKwGl1a5du7Br1y5XyzSxKZyIaIXpHpyAUkB1WTGU0ttERNnAwJKIaIXZ2FAOEeDs5CxE9DYRUTawKZyIaIVpW1OBO2/YzD6WRJR1DCyJiFagtjUVDCiJKOvYFE5ERERErmBgSURERESuYGBJREREdIF43/veh/r6elx66aUZKZ+BJREREdEF4vbbb8fDDz+csfIZWBIRERFdIF772teiujr9pSFj4ahwIiIiojy13JZnZWBJRERElIeW4/KsbAonIiIiykPLcXlWBpZEREREeWg5Ls/KwJKIiIgoD5nLs954WaNrzeDvfOc7cfXVV6OzsxNr167F3r17XajpeexjSURERJSn3F6e9Xvf+55rZUXDjCURERERuYKBJRERERG5goElEREREbmCgSURERERuYKBJRERERG5gqPCiYjIseW2vBwRZRczlkRE5Ii5vNyjBwdw72PH0NkfyHWViChJp0+fxhve8AZccskl2Lp1K77yla+4Wj4DSyIicmQ5Li9HRJG8Xi/+6Z/+CS+99BKefvpp3H333XjppZdcK5+BJRERObIcl5cjokiNjY3Yvn07AKC8vBxbtmxBX1+fa+WzjyURETliLi/HPpZEWRQM6i+/X3+56OTJk3j++efR3t7uWpkMLImIyDG3l5cjojiCQaCnR/88Ogo0N7sWXE5OTmL37t348pe/jIoK917TbAonIiIiykfBoP5eWBi5nab5+Xns3r0b73rXu3DLLbe4UqaJgSUR0QrT2R/Agwf6OGqbaLkzs5Pz85HbaVBK4Y477sCWLVvw0Y9+NO3y7BhYEhGtIJwSiGgF8ft183dNjWvN4E8++SS+9a1vYd++fbj88stx+eWX48EHH3Shshr7WBIRrSDWKYHOTs6ie3CCfSKJljOXB+1cd911UEq5Vp4dM5ZERCsIpwQiolxixpKIaAXhlEBElEsMLImIVhhOCUREucKmcCIiIiJyBQNLIiIiInIFA0siIiIicgUDSyIiIqILxMzMDHbu3IlXvepV2Lp1Kz71qU+5Wj4H7xCloLM/wFG3RES07BQXF2Pfvn0oKyvD/Pw8rrvuOrz5zW/GVVdd5Ur5DCyJkmSubKIUIIcGcOcNmxlcEhHRsiAiKCsrA6DXDJ+fn4eIuFY+m8KJkmRd2UQpvU1ERJQRJ04Av/mN/u6ScDiMyy+/HPX19bjhhhvQ3t7uWtkMLImSxJVNiIgoK06cAL77XeCJJ/R3l4JLj8eDF154Ab29vdi/fz8OHz7sSrkAA0uipJkrm9x4WSObwYmIKHN6evT3ysrIbZdUVVXhDW94Ax5++GHXymRgSZSCtjUV2LW9iUElERFlTnOz/j4+HrmdhuHhYZw7dw4AMD09jcceewwXX3xx2uWaOHiHiIiIKB+1tAC33aYzlc3NejtNAwMDeM973oNwOIyFhQW8/e1vx1ve8hYXKqsxsCQiIiLKVy0trgSUpssuuwzPP/+8a+XZMbAkIld1dI3gwPGz2L6hGu2ttbmuDhERZRH7WBKRazq6RrBn7zPYu68be/Y+g46ukVxXiYiIsoiBJRG55sDxs4ACGleVAMrYJld0dI3gnkeOMVgnorzGwJKIXLN9QzUgwMDYNCDGNqWNmWAiWi7Yx5KIXNPeWou777iSfSxdZs0ED4xN48Dxs7y2RJSXXMlYisg3RGRIRKJO3S7aV0XkFRE5KCLb3TguEeWf9tZa3HXTZgY+LmImmIiWC7cylvcB+DqAb8b4+5sBtBpf7QDuMb4TEVECzAQTkZvC4TB27NiBpqYm/OxnP3O1bFcCS6XU4yKyPs4ubwPwTaWUAvC0iFSJSKNSasCN4xMRrXTtrbUMKInIFV/5ylewZcsWBAIB18vO1uCdJgCnLdu9xu8iiMgHRORZEXl2eHg4S1UjIiIiujD09vbi5z//Od7//vdnpPy8GhWulPo3pdQOpdSOurq6XFeHiIiIKLc6OoB77tHfXfDhD38YX/rSl1BQkJkQMFuBZR+Aiyzba43fEREREVE0HR3Anj3A3r36e5rB5c9+9jPU19fjiiuucKmCS2UrsPwJgD81RodfBWCc/SuJKBWd/QE8eKAPnf3u9w0iIsorBw7o742NkdspevLJJ/GTn/wE69evx6233op9+/bh3e9+d5qVjOTWdEPfA/A7AG0i0isid4jInSJyp7HLgwCOA3gFwL8D+JAbxyWiC0tnfwD3PnYMjx4cwL2PHWNwSUQr23ZjdsaBgcjtFH3+859Hb28vTp48ifvvvx/XX389vv3tb6dZyUhujQp/Z4K/KwB73DgWEV24ugcnoBRQXVaMs5Oz6B6cQNuailxXi4goM9rbgbvv1pnK7dv1dp7jyjtEOdTRNcK5CZOwsaEccmgAZydnIaK3iYhWtPb2jASUr3/96/H617/e9XIZWBLliLn+MxSw91fduPuOKxlcJtC2pgJ33rAZ3YMT2NhQzmwlEVGeyavphoguJNb1n6GMbUqobU0Fdm1vYlBJRJSHGFgS5QjXf05fR9cI7nnkGDq6RnJdlcwIBoGhIf2diFYEPewkf6VbPzaFE+UI139Oj+tdCYJB/eX3669cCwaBnh798+go0NycH/UiopT5fD6Mjo6ipqYGIpLr6iyhlMLo6Ch8Pl/KZTCwJMqhlbb+czYHI1m7EgyMTePA8bOpHzMfgzgzS1lYCMzPnw96iWjZWrt2LXp7e5HPy1b7fD6sXbs25cczsCQiV2R7MNL2DdXY+6tud7oS5GMQ5/frIHd+/vx2DnT2BzhYisglhYWFaGlpyXU1MoqBJeUNTr2zvLmaQXTAza4Evzoxgdb5KfiLvVhVVpT7oBLQdWhuzmnzvDkhvVKAHBrAnTdsZnBJRHExsKS8wKl3lr9UM4jpZMTc6Erww44e/OsvTqGtogCbKj3Y1rYGb8iHwBLIeX9PTkhPRMliYEl5IdvZrpUuF9nfVDKI1ozY7HO92LmpFte01WU1eNnfNQoohRNBoHN8Dv0FAbwh/xe3yApOSE9EyWJgSXnB1f5yF7hcZn+TzSCaGbEibwFOj07h8ZfP4GDPWFabXHe21uD5U2OYC4UBEexsrcnKcZcDTkhPRMliYEkZk0zWjFPvpM7elLycsr9mRmw4MAsAqKvwYS60kLUm187+AHyFHtyycy1OnAliZ2sNbmlvzvhxl5O2NRUMKInIMQaWBg4ccVcqWbOVNvVONphNydNzYczNL+DWa9cvq+yvmRF7qnMY+18ZwVxoIWtNrhEDUwQcmEJE5AIGluDAkUxYTlmzfOT0g0734ASm58IYC84hFFb43pMn8L/+8NJllf01M2LXtNW51uTq5PpxYAoRkfu4pCO4ZnMmcLnC1JkfdPbu68aevc/EXa5wY0M55uYXEAoreAsExYUedA9OoL21FnfdtDnvg0ort9YAd3r9NjaUQwQcmEJE5CIGlmAQlAlmn8k7rt/IDHCSkvmg07amArdeux4VJXr+xZIiT9IB0kpbb9vp9TOb4W+8rDEnzeB5cd25FjkRuUzydTH0HTt2qGeffTZrx2Mfy/h4fZKTzvWyds2AICIwjzXnY6pzQcY71nK1HM4p1Tqmep+jPs66jCWQH8tYElHeEpHnlFI7Eu3HPpaGlTJwJBMBIPugJifd6xVrhHy8VVBSHbm7EvvCLocZBlK57qmughPzcfm4jCURLXtsCl9Bkumblwz2QdWcNl26cb2i9ZG0DjZRSm+nK5+6gZw6OYTDzx3DqZNDaZeVah/TbDVPp3LdU73/MR9nBpE5XouciFYWBpYrSKYCwHwKPtyQSvCQTNCeqeuVicEm+dIX9tTJIcjpHlRMBSCne1wJLpOV6gezzv4AHjzQh87+gONjxbru8cpK9f7HfJy5FnlNDZvBicg1bApfQTI1f+FyaFp0KtVm6mSaLjN1vVJZBSVR14jO/gBGJ2Zx/baGnE61MzF6DhUAZlAAHxYwMXoOWF+f1Tpks3kaWNr9JlFZqa6CE/dxOV6LnIhWHgaWK0gmA8CV0gc11T6FyQbtKV2vYPB8P7cYb/bJ9KVMFESnExS5rbymCpgKwIeF89tZlsoHs2Tmwkw08MZJWan2peXqOUSULQwsV5jlEADmcoR5qlndjGdtrSN0R0dTbpq0Xlt7EL3v0CBGJ2YXA5tEgUw279O69fU4BZ25LK+pwrosZiutAV+y99hckjJR87Q1iJ99rhc7N9Ximra6iOvttCxyyMEHNSJyH6cboqzKh6lg8nLqpKEhHVCaI3RraoD65IIr+7W968ZW3PNoF6CAQq9gW3MVSou9i8sXAoi5pGE+3KdscGNZRydTAD14oA+PHhxAkbcA3WcmUVFSiCp/4ZLjpTqdUCbk5evEKU6lROQ6TjdEeSkfprfJy6yu368DyzRG6Nqv7dz8wmIGTkHh2MBERHZy1/bbx3IwAAAgAElEQVQm3HnDZowMjGJjhQcNlZ6YZa2EaYiicWNZRyfNzGY2cjgwAwCoqyjGXGhhyfHypcl62U8xxqmUiHKGo8Ipq1baCHPXuDBCN9q1NafdeeO2xqgjg9sqPbi2dAYNISPDY7whL7f7lMrIbCB7yzqaA2heu2U16o2gMp+bu5f9FGOcSokoZ9gUTlm3rJvY8ly8axu1mTVOE/xyuU/pNmdnu/k5n5q7Y1kRXSHYx5LIVU6bwhlY5qHl8oaeb+JdN17TGPK5L5rDwMDsv2g2Z994WSN2bW9KWPxyek7koq7L6foQUeYxsFymVkSmIAfiXTde0wTyMbOTRMCbSsZyOT0n0q5rPt5fIlp2nAaW7GOZZ5Z936YciXfd8u2aZmvZQMf8fqC+Hp3j4ZT6KWaEdfCFdTuKc8E51Jf7sLmxPG5Qae2HmW/PiXjSqqsZoI+ORvShpSQFg7rbCK8fUUIcFZ5nMrV6zkoX7bqZTXlFhQVRB6I4bepzs0nQyWjbVI+XTj3dnCzdlT6EDkfJ27N5V2yoiVoH+/m95uL6hIOTopWRi76Raf1P4OjopES9xy7NMUt0oWBgmWdW0vKJ2WS/bgCWzOk4N7+weE2dTqeyd98r+NvvvgAlQLW/GPftuTqte5JoGp9Up3mxPu6bjx/Hn7y2BW/c1ug4AHJj2h3AxQDVHCWfoAk32vWs8hctqYP9/HyFnrivM/t5vPnyJjz0Ql/M88pk0JnW/wQXprHKhHwcwBTzucvgnCgpbArPQ+YUMQwqk2O9bvbmw7n5hYhr6qR5saNrBJ/47gsYmpjFSGAWJ4Ym8cDTPUv2S0aiaXxSbfa0Pq6itAhPdg7j3seOOW7WdmvaHWsAp5TeTlVH/zTuef4cOvqnY+4T7XpGq0O084v3OrOX8cLJszHPywxIHj04kNQ1T0bK/xNcmMbKbdm4XqmI+dzl1EVESWFgSStSogDOyTyNB46fhQAQY3vBhYFuZvbpjus3Rs1Gpjp/pPm4c1NzgFJYXVkSM7CL1sfTnGfxxssa02oGjxWgJtuv1MzA7t3XjT17n4n5uGjXM1odkj0/exmXr6+OGXg/1TmMc8F5FHkL0g6mo0l1js5FRh/afAmI3Pzw4aaYH67yMDgnymccFU4rhr2PYaI+h07+fvvdv8PJoUmElcKa6lJ8/8PXZTyTnE4fy32HBnFieDJi6cZzwbmYXQQyMRra3syZyqjmex45hr37uhebuO+4fiPuumlzzGMkqoMb5xGtzM7+AL7885cxFJgFANRXFOPDv7/FteZdN5aczDf2c/rw65qxrhR5MWo9H5voifIFl3SkvJSpf9zR+iYmkmhpx/bWWty35+rF5u/dVzVnpXtCqktOmo+zXuNzwbmI63L91tUZX6rRviyhvR/kA0/3JAyc4w1YMQOTyZkQpmZD2N3ejFvam+PWwQnrdQOw5HkarczuwQkUF3qwcXUZhgMz2Lmp9ny/vGAQKCgAFhZSDprc6vuaLU5e32b2uHtwAlurC7FuehSYRl4MjMmXJTWJljMGlpQ1bo48tjODF1+RBz0jQXztoU4c7Q+kvdZxXq4rnoD1zfGeR45FBHUAXFuq0emHBGuQGJwL4afP9cFf7I17X+INWOkenMDkTAgnh4IoEOCff34UTdWlKd0n8xy8noLFwTmzz/UCAIoLPQmfp1urCxGsBnqnFzDvL8I1bXXnRxGHw8DcHFBUBHg8KQVN5hrjmV5y0g3JvL4Xn6NDQzqo5MAYohWDgeUFLpura2Qy+7J9QzW++nAnXu4bBwA8dnAAq8qK0dpQnrHM3HJgz/ztvqoZu69qTvueJxNEWIPEE0OT2Hf4jKOMaaygfmNDOc5NziGsFuD1eBAKLaR0f63nEJiaR1FhAdasKsWJoQkAgsZVpfGfp8Eg1k2Pon6dD7OhMMZrGrHODJYA3c5r/R4jaIr3GrRm9/K9eTal13eejlonotQxsLyApTq1TaoymX1pb63FH2xvwv1PnkJznR+T0/OQAmAkMJOT+UDzZQm+WJm/dOuUbBBhBokdXSPYd+RMWhnTc8E5dA1OYGo2hInpEKrKilIqx3oOM/NhzM6HF6ciAhyMkDemoSnx+1AyP4+qUv3rw6PzqB+fQVlRAUoBwOzHbg+agkE8f6QX33iyDweH52K+Bheze+Yk3Sk2q2e6/2BKr2+H00oR0fLBwPIClmhORbdlOvuy+6pm7DtyBuHwAlavKkFdRTGUUtjdnp2+kaZsB+yJjplqc3684DjVDwnJzskYLRg6cPwsvAUFqK8swcxcEH+wvSml87OeQ0mRB7fsbEYovBCzj+USUbJtHV0j2PNfB7F1lRcXr/Li7desx8Z6/9KgKRjE9CsnUDk1hb98lR//etSLp3qnYr8GjeZ1s1/pUGU9Lt2SeD10Uya7oZiWvL4rPc4CYQaURCsKA8sLWC5W+clk53gzaPn3X3ZhPDiP+gofFpRazEBlS7YD9kwcM1FwnM6HBKeBbqxgyHzezsyFUV/lw+6rmhOWFU2ic3DUjGvLth043gco4KwqxAPd06haN4+7Lq1f+thgELOhMCbCAh8U1vkF+70FsV+DwSAmZ0I40DuBWl8BfnrwGILeYsf32JVuKA7WHI/IrnK1GqILEuexvIAlmlNxuTpyehyBmRCOnD6HqdlQ1gc8pDoXZT4d08lE7W1rKrBre5PjACXZ+Rh/eWgAQ+MzKBCJmO/Qzeetk3OIWm+zWRqImCPSfh9WV/min7Pfj2KvB6uKBMWFBRgJF+Ajv39x7HPx+zE1G0KtrwCFngIcHQsltWZ42hPgJ7vmeBJrvRPRysKM5QVuOY56jufA8bOYDykUeQpwbmoOLXVlrk9rlKgZN9vLcnb2BzA6MYtP3LwVZ87NOD5mvHNxO5sdryk2WnN3R9cIvvX4CVSUFmEkMIt1df6Iydbdvraxyoxa70oPpl85gdlQGMVeD0o2tSwGltZ7v7rKh98eHYre/Oz3o2RTC4r6R9EdCONdb6qJ/zz1+zFUWY+fHjyGo2MhHBkL4UNJ3JO0u6Eku6xhrgflOMiuLuvjEeUxBpa0otgDouu3NbhWdjJ9J60BeyYH8kSb0zHZtcWjnYvbwXGspthYAaf9A0JteTG6Bydw5PQ5fO5HR1ztvxrvWkSrd2UwjLnADAIhoMI7j6L+UTS0ng8mzHv/4IG++M3Pfj8aWv1w+gy9dEsTgt5iVB0/iw+lcE/S6oaSbKCYy0E52W6GZ7M/UQQGlrSiJBMQOQ34zP1ODE0m3Y8x2YE8yQahseZ0BBC3nAPHz2LrKi/am0rR0Rd90Ei0bHaqQbI5UOb0aBBTsyHMzIcX6x8t+DI/ILwyMAEFhdOjU3j04ACGAzMoLBDUVvhc678ar3/q+XkqZzFmNCF3D4ziIgDVxYJQWKE7EI4aHGZiFoSctTCkEijmKnuXbHZ1uR2PKM8xsKS0JRtsZHoqHidvvk4DPut+wbkQoOL3Y7SfWzKDaqzH+upDnfiDK5oSrvazsaEcU7MhFAhQ5PUgMDWPB57uwb4jZ+Ke22ua/XhdgQ50Xr+6HAtrE78RmktcBmdC8Pu8uG/P1Y7vX9uaCrzm4nr888+PIhRawOd+dARN1aUxg6/21lrcdWMrPvODQ2ioKsHQ+AzqKnwoLfbC6y1wtf9qzGb/WPNUAnjgiVE0+RT6ZoBd19XEPGc3ZkHIm2UGXQgUs3Iu2W6Gz3WzP1GeYWBJaUklI5fsVDyZCESdBnz2/a6/dDVa6sui1iXauSXqq2g9N+vqQS/3jeP+p07hpwf68AfbzweY9mvRtqYCu9ub8c8/P4rA1DzmF4w5ExOc26U1hZic8WNkJoxanwdlNYUJr9kDT/egZyQIr6cAo5OzeODpnqTux5lzM5ieDUfU666bNscMvubmF1BX4UN9pQ9z4QWcGZ9GfaUPH/n9i5PqS5qIvV/k6MQsOvsDaPPOAFg6T2XbmgrgOl3nXQkCpHRnQUi2b2rWBYM4cWIIB87MYG1zXdz7kY0pjwBkvxmec3ESRWBgSWlJdpqbZPfP1JyQTgenRFu5Jtbxo53bXTdtjtk0bz+3u25sBQToGQ4ivKAQVgonzkzi/idPYd+RM7jrxlbc82jXkmtxS3szmqpLF48BIPEk5H4/ynxelPm8i9tOieM9I0W75vGCI+v+hV7B23Y04Y3bGjMSjLS31qLKXxQR+Hz4dc1YB0TNRGVrTenuwQk0+4DNVV4cOxdK2Dc1q4JBTB47jumRILYA+OKPeoGbXx3z9ZHVdc+znV1lQEm0iIElpSXZ0cPJ7p+pOSGd9sVMps9mrHOL1TRvP7e5+QXcfceV+NpDnXigowfD4zMILSisKisCFPCLFwfPr4c+HIzIGNqPkbDOKWRZdl/VjJ8e6FtsCk92/kj7tbQHcvbgKNuj6+2Bz5Gz81jXlttM1NbqQly6BgBC2FYKqOrCqHV1K0hLJpga7B9FKDiHs7MLWF3qxcWV3rivz3T7nJ46OYSJ0XMor6nCuvVR5gZ1UV4E7kTLFAPLZSwXywbaJfvmn+z+mZzE3elAiGj7RXsDTufcCr0CBYUqfxGubavDs8fPIhReQO/oFMYm51Bf5cPvvaoBL5waW1wP/acH+mJmUB2dmxEsdfYH0N3ZlzCYaG+txX0fujqt55y1XglHTTs9D5dEC3w6+qdx4Pg5bN9QgPbW7AeW60qB6QqfMQIdKCmNXdd0mcFUsw8I9g7Ad1lzzACusz+AB58fws0NCvWlHoTCCzg6Hn8KJMd9TqNM3XPq5BDkdA8qAGAqgFNARoNLx4E7pxkiWkKUuY5tntmxY4d69tlnc12NvGVtRoVgRU1wbpcPAbRVRDZDkFY2o6NrBPsODeLE8CRKi70QAV5zcf3ilDrBuVBEH8u//tYB3P/UKTTX+jEzF8Yd12/EXTdtzotzSefYs/Nh7NxUi2va6nKaGbJ+YDgXnEv5NeZa/0djKpvpufD5AURGQOV2Bu/BA33o7BrAu5sFBUqhqNiL8otbowZMDx7ow6MHB7CtphDFczMYV4XY8SoXlk61Tt0DLE7d89QTL6ExHMS0KkCZRyFQWoFLr0j9eZ+Io9dFjLoSrVQi8pxSakei/ZixXKZysWxgriTKWmU78HSzGbK9tRajE7M4E5hZLM9X6ImZ+TTXQ5+ZC8fN4Dq9Jsmei5sDRswM1lOdw9j/yghePDWGgz1jMYPbZO5zqs8Ja9/Jex45ltJrzM1m1I7+aRztnodndhrDIQ96DvfgziIf2io9WDc9CpQCmB4FgulnzDY2lKP4zAAqPAoLCijGAnD2bNRyzYzpodF5iHhw5w2b3PlAEGXqns7xMH57agK71wiKEEZoQRD0FKd/rDgcZVc5zRBRVAwsl6lcrPOdjzI1uCcet5sho5XXtqYiZhN3oub2ZKZSevHkGKZmQwCQ8FycBkzJBHVtayrQPTiB4kJP3OA2mfvs1nMi1deYWx88zPMoKfKgtMiDrRdVQSmly/Mao/hdDGra1lSgdrwanqkxeKUABTDSddH2rfTg49fUozsQRm1jglWD7F56CejuBjZuBC65JPJvUabu6e48h4F5D/77zAL8C/M4MysYLxhGVf2qjGa3Ew7Q4jRDRFExsFymsj2wIV/lInMbL5uRSqYs2fkOE2VwnVwTa/BV6BX8yWtbEo64dhIwpRLUOQnUk7nPbj0nzNfYvkODqPQXospf5Ohxbn3wMM+jqrQoYrqljQ3lgN+TkaCmpnk1cHIaCIcBjwdYtWrpTkYTcAOAhuIwsFAIBD3O6vDSS8D3vqd/3r8feOc7I4PLKIPKNjaEIYcGcORsCIHpMNasKsFkcB5PdQ7nfl5PTjNEtAQDy2UsZ6tw5JF0M7duNJlay0o1U+bG9DXmuRQVFgASfyJ3e/A1HpxH9+DEYl2icTsANDkJrJO5z2ln8y0DMqr8RRiamMGZwAy6BiccNWu7NTF6wumWMhHU+P3A+vXxyzWbgEWAuTlgZAQYH8cpfx2OnJ2Pf87d3fp7eTkwMaG3o2UtbVM7mV0mnjg6hP6xaQDA/ldGct4nlwEl0VIMLGlZSydzmygQTDbozGW/V/tgrrtubMXc/ELMutuDlhPDkzgTmInbxO12AGgvO9GIdKf3ub21Fp+4eSv2d41iZ2tNcvfAtu7zyJQvpWZtNz4oJDrnzvEwugfnsbEhjLYYsU1KH5wSBUtmE/DMDKAUoBTCM7N45XQPOscTjCjfuFFnKicmzm87YL2ej798BnUVPsyFFjI7FyYRpcSVwFJE3gTgKwA8AP5DKfUF299vB/APAPqMX31dKfUfbhybKNXMbbxAMJXsoxv9XlPNoEabEzPeaHFr0KKgcGxgImrwZB+s42YAmCzzPnd0jeCeR47FLL+zP4DfHh2CUsBvjw5h60VVi3VOeH1tAzI2VniwrhRoKppFX2niZm23B5LFem476e+asf7HZhNwXx8wOQkAUFAo9wDvuKgAofACKkYHgLooAeoll+jm71h9LE0xpvG5pq0OB3vGMBdacG2aJSJyV9qBpYh4ANwN4AYAvQCeEZGfKKVesu36faXUn6d7PCK3xAsEU8k+phNUdfYH8MtDA/jW4ycwH1J4+sVTqN+1CS0t9Y6a2lIJas2gpbM/gK7BiSVN3KmObs5kFw0nwVKsvqCOAi3bgIyGVaW4s02vFV7s9aCk0pNW3dy6Bj/qOI3JmRAuqvHHzKRmNIPu9wNNTcCJE8DCAkLiwUR4DqHwAiZCQJ3XE3tA0SWXxA4ogSVZY9TVAQsLgN/vWjcDIsocNzKWOwG8opQ6DgAicj+AtwGwB5ZEEXI9P2WsQLCzPwAFhUKvJJ19TCWoMgO4ofEZVJQW4dJVXrx/cxF8gTGgZybm/Hj265dqUBvrzTqrS/A51PvsEeyeOY6RqtX4zbQ/arAUtS9oMIiZvkFsXeXFWVUYEWgteR5a+y4Ggygp8qDE70s4+jpTgVy0uTULCwRXrinBBhGUl3qiZu7ifdhIa9ooazaxpQUIBuHz+7GpKoiK0QHUeT0oKXI4mCdW+YDOGs/MAP39+ufRUaC5OWvLaRJRatwILJsAnLZs9wJoj7LfbhF5LYBjAD6ilDodZR+6QORimqBo7IGgNUu3rbkKLXVluH5bQ9J9N5MJ8MwAbnVlCUYnZrG+XOApEAxNh1HlD6MkSjAT6/qleg2jvVnHG6yT7DnG2j+pck6cwE0v/wZb5iaBoR6MlF6G7RuujHouEYFypQfo6cEVlQp128vxxQMTGDACrZjPQ+v1djj6OhNTgNmzxvXlPkAB16wtxftbi1Be4kVNeXHUTGq8D06pZKI7ukbQ2zOMm6pDeo15I9BDve5Luc7v183f6Q4osmaNFxaAgoK0plVyc+5VIkosW4N3fgrge0qpWRH5IID/AnC9fScR+QCADwBAc3Ny6xDT8pKvE7xbs3QA8Kr1q1wdEBSNGcAtKIWa8mKcmRP4Cr3wQWE2FEZJlDdSN69frOAuViYz2XO0728OLCoqLMA9j3Y5v1Y9PSgr9qBxXQPmz47h0zsq0RJj/4hAeWgIAFBWXoJmAO9rL4OvSX9YSDgJehJTymSif6k9a1zpLwQEqPWEoQD4y0tQUiQRGVYEgzoYW1hA+xo/2ls3xy1zMRMdZ3lC8x7ubvFhy4YSNNZXYJX1uNbrle4oaes1LygAhodTnlaJa34TZV+BC2X0AbjIsr0W5wfpAACUUqNKqVlj8z8AXBGtIKXUvymldiildtTV1blQNcpX2zdUJ5wSJxc2NpRDBCnPQWgN+KCM7QTMAO7GyxrxntdtRNBbjAf6FTrGgPGaxqhvpub1GwnMoKTYg9VVvqTqaTIDhr37urFn7zPo6BpZUrdd25si3oxjnWNnf0AvC9gfiCjDuv/kTAj/5weHsHdfNz7zg0MIToecXyvjw+aq0DTqK3xo2bHV2Uma129+HmU+L153Zcti0Ofoeej366ycg6Cmyl+EdXV+x3Ne2tmvof35+MZtjbj7jiuxeVMDmmv9Orgz62j2TRwaivxuNi0boj7HzceOjkZ9jHkPR8I6Mzo/bfw7LyjQx7HtnzbzmtcaXRNqalJaMtEaRCuFxSm1iChz3MhYPgOgVURaoAPKWwHcZt1BRBqVUgPG5lsBvOzCcWkZy9cJ3tMdHBCvOTRes681w7auzr94/HUxjm9OqfNARw9Ki71LRj87lUrmM9o5xssMWfefng3BX+TVQeZsCMHZ0NJrFStz1tIC3HabDnyam/W2E3Gyjm4+D9PNjkV7/LngHOrLfaj0F0bMYdneWrv0OhmZ2cXVcszvtqxi1Oe4+dgYTc7mPfzNqSBGJmfx6V2rgbpynU0EzjeLZ2JOxzSyoG6vkkVEiYlSKv1CRHYB+DL0dEPfUEp9VkQ+A+BZpdRPROTz0AFlCMBZAHcppY7GK3PHjh3q2WefTbtuRG5x2lcrWgBpn2fSjT6lDx7ow6MHBxabNG+8rBG7tjclVUaq9bKeIwD8qOM0RoOzi6OUF+ty4gTQ04PDUoHfTvsjmr+jzrdpHREMZC5YyYD//FU3Hn/5DLbXF2N1kUJTUw2uvcJh8Iul93NzYzm+8avjMe/NkueZee3CYT1xeVGRXj3HyTV0cN2XHG9oSAeUZjBaU7PY3zKfpNLH8oLvlxmnWwRduETkOaXUjkT7udLHUin1IIAHbb/7pOXnvwHwN24ciygXkslGRRtEk4k+pW5lY67fuhoAcNXmWoxOzKKzP+B4WUkzMC0sEFQYzb9lPq+uy4kTwHe/CwC4FMClt90GtLTg0ouqYmcIbfNIurEGdlJSfEPt7A9g/ysjWO1dwJtrwvB6BKu9waTqb7+f48H55OdZtfZNNKboWTy+eW4jIzrTaM36xutLanw4aG9uRrt1btRlslZ2sqPIL/h+mfbpnhJ9MGEQSjZceYfIgXSn3snEiOF0m+3t64Wfm5pDabE3qTdTM2CurfBhJDCDmnWr8N7rN+rH/uZ5vVNlJTA+rt+sWlrij17PZbCS7BuqRffgBIoLPdhZ64WnYAEFRUV6yp0kAkv7/TwXnMMD+08nN89qvGUYe3qAgQHgV7/S2cyCAuC229BZXGN5DtkyjpYPBwB0VwQnwahTeRCU2LOT+TjNVlYl8+EujdcMrVwMLIkcSDc7mKk+penM6WcGJ74iD4Kz8xibnMPaOBNuR2MPmG9uvyhyLWtAB5XW7XjcCFaSENG8W7mgf5lCttR8fpwKKuyoFNSVGNP/JFl/+/2M9ZxJ+oOKGSyMjp6v1/Q0Bg924t5ztbGzc2bQYPtwEBmMxWn+7ugADhwAtm8H2m2z0GUpKInXrB0tO3nB98tM5sNdrlsYKC+50scyE9jHkpzK1kTrK63fVUfXCG7/l9+hZziIipJCXNpchfX1fpT5vHjz5U0IhRccnWvc6280oyY12CZD7PW09y+97z2XYePc2PlVdja1JN0c3j04ga3VhVhXisjA2LwOdXV6pLMLQXNSz/sYGcsnt/8efnDGE7ufrj1jefPNGCwsw/eeH8KpKT0+KGZ2u6MD2LPn/Pbdd0cGl2700UyQ8YwIHKPUNVY/5ZX2Wk+a00zyMu4TTcnLah9LolzJ5kTry2nFDydBR5W/CNe21UItKNRW+DA5PY8afzGu39aAh17oc9zHLG7TdkvL+abW4sR9NzMl2vPE3pz805fH4AvNoMkH9M3MY1ddGG1JvEfGfH6YwdnCgh5U84Y3AI2NMd+EnQY1SU+IX14OlJUBDQ2LfSxri2sgjx2LnZ2zjsSvqwNE4AuMYXcj8Ni5QhwanV+S3Tbrv33fk2gA9LkODOjMpTWw9PuBw4d1gFlf7yyjbTUyolflKSiIOUgpUbN2rOzkcnqtZ4TTDz5ZbmGg5YGBJS1r+TrRei45CbbNTI6IYF19GQLBOcwvKNzcfhFGJ2Zd62OWiYEQqWSToj1P7M3Jlf5CHBuYwURBMc5Oudi3zszo+P06sBwd1cGWrdmwsz+ApzqHsf+VERQXetwbOGLPKq1bt7hWdxsQtZ9uRPbV7wd27NDljI7CW1wMTM2jXM0vCUat97tzuhp3zodRMmDMNLd9e2S9hoaAJ58EQiHg5ZeB0lJg2zZnwUkwqIPKUEgHlubvbI9N1Ky9LNcez4N+qRHypR6UNxhY0rKWiUExuZBKc36sxzgJts1MzkU1+g2hZt0q3Nx+Edpba9HZH3Ctj5nbAyGSClQtb8DRnif2fq9V/iJ0DU6437fOzMSZ/dFqavR3W1B572PHcC44j8D0PDauLsNcaMGd4DZBPzh7ds6sS7MPuHQNMF3h0wORjEUryjwKngofmkr9uHNHTcRjrff71IZLceCjn8G1U73R+1iawa7PB0xOAl1dOqPa4qALgjnyXURngs0R8DZOAkfXspPZCPg4WIaWAQaWtKzl60TryUilOT/eY+xBVFFhAe555FjE9bFmcsp83vMjuYGYk3Knwu2BEI4DVdsbcHtzc9Tnib05OVYQklafO3tzcpQ+luZ51VUUIzA9j+HADKr8RekHt8EgMDurAy+HI+3Numyu8gIIIRACSoqgyzCaPUv8flwbpRz7/a694XVApScy6DI1N+syg0EdINbVnd9OFCz5/br5G0a91qyJ+ZisNGtnK+DjYBlaBhhY0rKXdF+zPJNKc368x1iD7VjrcUfL5HT2B/DLQwP41uMnMB9SgABXbKhJ69zcbmp0HKhGeQNub61PeF2jBSFJN+dHG7DU0hJ38JJ5XnOhBdRXFGPnplpc01aX/PWyB3BmsKOU7mO5atXi32IFy2Zdjp0LYVspUGG+S5jBcJxAZsn9rvTEDrhaWoA//mPgxReBqirdz7KgIH/79tnWYV8yRyiwNOBzO4u5TOYOpQsbA0uiHEu2Ob+zPwAFhUKvxHyMGWzf88ixmAGoNYgyg6eh8RlUlBahyFPgWp9VNzNGjgNVF9+A7YuoylEAACAASURBVFnSpzqHYx8/3ryPcTg6r0RBij1rVlamfy4s1N+LiiKCyiXBspFZbKv0L9ZFVReixD7C3cG5LNY/wVKRuOQS3edzbEwHv9XVkX+Pd85uBWsOgr9TJ4dQOToAn0fgWwgtXdUo2vMtRhYzrew3B8vQMsDAkijHnDTnm/0pV1f58NujQ1AK2NZchZa6Mly/rSFm8Oc0aDWDp9WVJRgJzOLc1FxW+qym0rfUUaDq9wNnzugpb9rbgS1bUq6jNUs6Ox9eHFyz7pUBVL66Hg1rapZmCG3zPqZ9XpYgZXpgCAdCftQ2RvZvXJI1M9cKjxJc24PlkYFRtI3P6D+OjqKtuRlta5JbHnRJfc3s3sAAMDSEmepaPDflQ3BgIHIqK6eBciaal+3HMJvjLXXq7A/g4MEetK8CpgRYXVIAj4g+r1deATZu1N0b7I+NElR3jofTH8zGgJLyHANLojwQrznf2p+ypNiDi5sqFgfdvGr9KgBY0ofSWq6TPqhm8LSgFNbV+RMGrG7I2FRRwSDw+OPA3/6tDmx+8IOlcygmwZpNPDM+gxdPjWFbTSFuqJqHLzAGhILng55UJoV3ek4AJsOCs4EZ9I3N4P8eHo0MTOxZs1Wr9FeU7NbW6kIEq4He6VmMCbCxwgOEEDOzmFSWzRqsDQwATzyBufkwzk2H8NumaTy7UInaimKUFHkSD75C7Dq50sxsPcbMjB5pXlgYEch2D06gdxq4trYABWoBCkqf129+A3i9wP790aeQst4PY/32kTPBC3tVH7ogMLAkyhGn2Tprf8qRwAymZkOLfQxn5sMJgzMnfVBzMe1KRqaKMoOa3/1OT+3T3KwzR/Y5FJNkZhM7+wM42DOGcqWDN29xMQB1PsCxDtRxc1J4I0gJzc4CACakEErZ5pCM1UxqD7qCQaybHkX9Oh9mQ2GM1zSioc4P9ASjZjejNZsDiP1csQZrQ0OAUhjzlWMuOIb106N4prgS3oICKIX4gVW87gxOs5mJJui3HmNhQX8QsQWyGxvK8dihAXz/9ALWlgDXbalHY3//+a4FY2PRp5Ay78fZs0AgAExMYLs3jP2lAMKz2FKtA/yk5dt0Q0Q2DCyJciCZbJ29OXt3ezN8hR5sbCjHvkODrgVn2Z4U2kkzfdJN5WZQc9llwA9/CPT16UDBPoeiU8EgBvtH0R0ILzY933nDZowMjKLOG0SJx1i5zPoGn2CgTkqMIGWmfxQPdA/h1NTSOSQX93MyVQ+AEr8PJfPzqDL7UMbouxetj+nBnrHYzbnWYK2+HujqQsX8FIYBnCypgSwAoYUFFHo98Ue9x+tP6GR0tJP+rtZjFBToieNtgaz9Q1fjmgrAGwIOHow7hdTitll2YSFKAPzptmp4pib06k7To0AwiQCR0w3RMsDAki4Y2Vr60YlksnXRmrPNpsnVVT5AgK7BCUzPhlBUWJDV80jnmiZqpk+pqdzvx/TAEGZbWuH7X5+Ab2QIuPrqmNnKuE28wSCmXzmBucAMLgLwwBOjwHWbzwfg2c4c+f1oaPVjl78qvcxyrExgjPOwj8QHEL851xqsGZnCkp4eqJIaXOZdhe2egsg+lqkM0HEyOKun53yfx4kJvcpPfX3MY3T2BzAy5cPGCk9kv1lE+dDlYAqpWHWtKSsEUJTalEFmICuiM/JjYwwsKe8wsKQLQjaXfnTCSbbOGvRYm7Pt6x+/45pmfO2hY/AXeXHPo1249KKqtCdZd/rYdK9pvGb6VJrKO8fDeLDTWJaxaDN2vf0tMYOvhNMIBYOYDYURCAHVxYImn8qLPnFpZ5aTHFlsz9gBwMGesfhTPlnLNTK46wCss++XagbOyTnU1engy+g+MFtUjOmjr2C8phHr1keuSb50TfGqxMt5JspMWwNma10BPSF8KjMW+P06q2qcE86di5hCiigfMLCkC0K+Lf2YKFsXL+ixN02OBuZQX+FL+tzSDQwzfU1TWVWpe3ACp6aAiYJilB96DvPPPwr84RujZiwTTrbu96PY60GFdx6hsELfDLDLDKKWe5Okg4DSns21XhvX+uOmM+F3onOordWDas6cQbiqCqP+aoSm5rG/rwczRb6YKwa5Mqgm2vOj3hLMpjplkN8PVFToTGVRkU4dc5J0yjPZbTcjypHtG6oBQV4t/djeWou7btocNRizvtGZgxxMGxvKIYLFjNHO1pqUzs0aGEIZ20lIdE07ukZwzyPH0NE1klS5JjP4vuP6jY6DXvPalL/4HG751hex+oHvYvoDd+pph2x1e/HkWMRAqGj9FUs2taCooR6nS2uw6zpLRtMaEFm3Vwjzg82jBwdw72PH0NkfiPh725oK7NrelH72dmRET5BuXU/dLX6/HlCzbRtUfT0KBPB6CtA7Hfl6AvS9X1cKXFw0i3WlLiznaW2ynp0FensjnyN+f/RmeSeqq/XzTkXp30uUB5ixpAvCclv6Md4KM9FGcDdVlyZ9bumusx7vmrrV9SDZVZXMazP6/INQUDhTugrT50ZQtO9JNBhZS2vdCr2CP3ltS+ylK41+jQ1Rfp+xFVDyYNSv6xk8u2BQ93l86CEdfHV26lV43A4sjczg8Pgsnug7g97pBfRYM8+GtkoPmtv0KPlirwcllZ70jz00dL7JOhgETp4E1q9P/xw5STrlOQaWdMHI+tKPHR16mpvt25Oe6ibR9D/2pknz3MwsoZMA041gO9Y1zdhUQg7eTNvWVODJV78aBT++H9UTowiJoHtN62JwuO/QIEqKPKgqLcLA2DQE4jhoimgezsSbe4aa2JPtS+v2Gu8RzCDr2DEdmJeVAaGQ7jtokdYKNSbj3jT6g3gNFLoDYeyyTyxv1KmkyIMSv8+dNbj9fr1MpfWcwmH3mq0ZUFIeY2BJlAkdHcCePfrnvXtTmqA72UEa0bKEVf6iuG/ObgTb0YKWdLOhSwSD+poODekmxPb2uG+stTe8Dg90/zWaTh5F3/qLseuG1wHQwcqJ4UmUFnkwF15AoVcc1y16v9f6xA9MRjp9DmNIJXvctqYCb768CS+cPIvL11e7n60Mh/Xgms5OPZDF69XbhlMnh3DwYA96p4HHUl2hxnq8nh40AGgoBRAtG5lmBjpqELxqlR5cMzentz2e/AkG8yArTisXA0sig6vTER04oL83NupVOtKcoNvRIW1Zwn2HBjE0MZPe8nEJxApaXO96cPQo8Ktf6WbTI0f0kolXXBFz97Y1FcCdt6B7cAK7LG/23YMTKC32YutFVTgzPo237WhyXLeMNw8DGWliT2l0fX8AD73QB6WAgXN9WFfnd+9c/X4dZNXXA9deq7N6a9boe2sE1pWjA2hfpVe7+f7phfSutZNgPYXmZTOY9HoKFq9VxOvM79ejxs+c0RlZ+zroubLcB55R3uPgHSKcD5D27uvGnr3PpDzgZJE5IffAQOS2w7rEG/QS6+/2wTSV/sKYA4DcEm8AkHVwUroDeTA6qr+XlkZuxxFtgIk5uGdBKdRX+vDGbY2Oq2AfNJVq83BnfwAPHujDqZNDOgNrH9TR3Kwn3E7hDf/UySEcfu6YLtuQysC1eIPH0ub3676G9fV6ne3t2/U2sJhJK/Z6MBECQmG92k3Ca33ihF5i8cSJ6McDEgfr1gE1weDSe2NhHdx0/5MnMT0Xjn2tZmd1hnZ4ODuDvKLV3fq7o0f1gCmzmX6FDTyj3GPGkggZ6BPY3q6bv5PsY5mo2TLe3+1Zwip/EboGJzLTT87gdPWctAfytLbqpvDZWT3NSmtrSvVNZ+lKN5a9NAOSZh+wtVFhuNiLMl8hSja1JJyoPJFTJ4cgp3tQAQBTAZwCsG59fUrZ44z2sQTOn6OZPbMFfSVFHmyo1oNpdl7UiHXxrrWTFXbKy41Z3R1kDR1k9KyB98x8GLPz4ejXKgNdG5KuO3D+d4cPA088oety5Iiejsmt9eyJDAwsieBun8DzTeob0X5Xu+13ztcFjxbgJvq7vc9kptf/bm+txV03tuIXLw5iQ0PZYsbSXuetq7xobypFR99UakF7Swvw7ne7sgZ3OhOMpzs5uRmQrPML5sML6JsIoXoujKL+UTS0phdwTIyeQwWAGRTAhwVMjJ4DjInAUx1dn/G142M1QTc3oyQYRInfj6pEgZgZNFVWAuPjett8flgDLUAHlok4CAatgXdJkQe37GyOXE3Ien6jo8DMzPm1yDMpWt1NlrXbUVur+3+GQmwGJ9cxsCSCe9MRRcvOAUh5XXB7gJtsAJzp9b87ukZwz6NdCE6H8JPnenFRrR9lPm/EOb6m2Y/XFegszutXl2NhbYpvZJlYg9sht/rfmgHJkdE5XOb3oMZXAKX0aOUlUxolqbymCpgKwIeF89uGVEZYZ23t+GSWcozGzLiNj0duA6llDB30c3UcePv9elBSf//5tchLSjIXzMWqu23t9sUBUylm/oniYWBJZHBjhHS0jCKAtNYFT+bv2Waer9erF5Eu9MhiX0uzbpfWFGJyxo+RmTBqfR6U1RTmsspJc3M5UDMgeapzGA/0j6Cl3FjR57qa6A9IYvTuuvX1OAWduSyvqVpctjDh0pVJcGUKILdZ1+22Z7NTGQzlcCCP48B7YUEHttloDo+TAbau3e5G5p8oFgaWRC7avqEaX32oEy+eHIPf513MKCaTZUwU4GZ9Ps44zAxqKKRXAZkPKxQX2s7Rr7OYZT7v4vZy4nb/WzMg6eyvWzJqPUIKo3fXra9fbP42uTWa3c0A1XWxstmpTibu5jQ8mZxMP9bx4mWAzQFKy+x1SMsHA0sit4nlC7nJMrrRdBtRhnciapbDem5FhQWYm19YeswcrRTiVvO163NyGhJmvFwa+OHWQJysTLeUCbkOoszn/9mzekqlXOJUQ5QFDCyJXHTg+Fn4i7zYtLr8/7X37kFy1feZ9/Obq6TWSJorI4EuIywJhIAwyJ44yAhkMIpj4hDiBKec9Zto7UVhq9ZVb+2bbOHaym6tq4hf17vvupaV14lSjteb5fVG8YtxMAZWIIyNJ4hJEELoYtGg24jRXMRcJM1Fc/aP7/zUZ86c+727n0+VqnWme8759emWznOe722eu6WFjV1xS9zEEbo17+MXL7yOW5acxNLGucbSlqpbXw5qyhf3UOegWPQUz6mmH8TkdMVViHNLSz0mWoAzlycxklCXgdAk3fA7jv2Pj8vj2JijoHNLNYglDSHtKnVSlVBYEhIjTu5WnHl6XsQRujXv46bzF3FpchpLO1oWVt3mlMDnwKNlTSbpBzE6vZELcSYmsPbyEDrWSgugD1s9WgCliZcLF1UUTkzI90NXdXd1Bd+PD0Hnlmpgfm7tL/ux/I4OdK5qDb6OtMPypCphg3RCYkS7W7t23DhPPLo1Eo+bMA2x3fZxtG4FljTW21fd5pTA58Dcssa8nTXmpt1+sTTI1g3Zj50bDb+OuX0tLiyS5uVDF6PtL07Mos28rf9+6pSIqVOnwjUDHx6WsYxXr8rjcIh/uz6atLs1pdfP3dpaj4dXAotGR8K9n4gN+AnxAx1LQmLGzt1KKk/P6fhRQ7fz9/FRLHXIscwrgc+BW8uacsLi3r2/uBXfOnBqvgs2OeT/s9Ru31z/xcsTV3Bh9ApeOHcFpw6P5qOAx82FiyP0a82LDJMn6cN9dsuF1c81GfIe6xobARjh3k/WOad5gfPSE0MZhpH1GmzZunWrcfDgwayXQUhsxDqLPGFs87kq/T/iYlF6/LW2AjfdVJ7vcWBARNackDp8qRZ/+dbotYKbz113FXf1vVh6vd2UGo21uXh7Ow6/P4y3To9i2eI6HL84g00bVuLT3dcn+5784PTdtL6HMC7dxATw3nviWNbWyvjJhL4bXjmWg/1D6K6bwOKGuXxnuo7hiON7UYUopd4wDGOr1+voWBKSEnlqE+SGba7X8lr7PDaHgpeypKNDJqQApfcU58UmDWFuce+aWldAqdGSCzY1Iq+zm1Jjt16g5PbNzqKpdQXuujQKYAa3LgGMlpz0JDWfU+t5jpqnqmebp3BT5ZYLe+25Sr/BSwMWMSUKhSUhZB62bWXq5gSE+T/igQHvGc12hBSjiTu+SV5s0mrzYhFSawsFPNqw6JoL1jnZChx+w1/I3ybEvBYTuLxsEUZngGV1wOIl8b+FSDidZ5dz7avaOk8iLqu1VJKgZRFTolBYElJteFwgbHO9CrUL/yN+5x35ux/3S+NRfe2EU1V9rGIzyYtNmg6J5XOd74Itc55SY7cfG7dvcUMtFjeYXpMnAp7nXDd9zxOV1v8yo9661QKFJSHVhI8LhGPfQ+t/xAEKXrQA3Dl6HF2nTwMjI0Bzs+/WRU6jMmNt4ZRkI2uzaH3zTeD8eeDjHwd6euI9jh+CzFy3XnTzfkE2n+erV2Wdp08DLS2uldi3ttajyZjGYP9Q+sKyHJzASgwd5/l8lzkUloTkmNjDvz4vEJtWLcPFiSnsf+s8Lk5MybGt/xG7zWi2vActAAfOvIGvvvRD1Grh9sADvpZtV1UfpFdloObSPhpZB0YLsldeAb7+damy/t73gCefzEZcRiHPF2TzzcHFi3IDA8jNj03/yRs7m7D2l/24f4W41O11E+mKpnJxAhk6JgGgsCQkpyTSVN3lAmEWXxcnpvwd24f7ZRaADf94AaOLmtDc2SrC7fx5X8t2ah/kp4VToHBnks5MoSDVxTU1wMqVQH8/0NcH9PTEM1XFD729wGuvAZs2AXffHd97y1MRV6Egn5thlJzn2VkRmebPc2ICm5YXsPyODiwaHUFdYyMW1zq38EnkMyoXJzDvTjXJFRSWhOSURBw5hwuEVXx1NC0KNb3HzmE1u41HO29Eff+r0mi6sRHo7vZ9PqxV9X57VQaacZ20M9PdDezdK6Jybju1PL/eXmD3bjn3APDVrwIPPhj9PdrlzXZ0ZCtCCgVpDTQzI9tKiYNZWwtcuCCis1byhjvb24GZCQBG6XctJPYZlZMTSEFZHmkLOYDCkpCc4repeuCLns1/ilbxtbxQ72tyjVlIAvY5jwuarQ//mjh13d2Rw8B+Wji5NZ5eQNLOTE+PhL9N7/9k31n/wjcsExPAyy+L0GpvF3F15AiwY4fv9+h482KeWvThh9ILVLdt0uFdvYa0Lsi6RdDIiIhIQFIc6uuBS5dke9EiEXTvvy/rdOlfGujmJOg66QSWB+WStpADKCwJySlBHbkapfDBh5fxv97qD3zRs4qvT966Eneub3U9tjVUv+OW6xxdzvkCsC3VvELHYiQnkr7A9/TMe/+BhG8Y9AVx7VoJCQ8MiIO3eXMgUel482It4mptlUcd3h0ZkbQHIF2hae1tOT4u66mtFbE5PS3O8c9+dm2yEFpabEP5iX5Gfs9B2m5ZntIb8kC5pC3kAApLQnKMX0fu0uun8f6FCUAp/LdXirhzfWugfEwn8eW2D2uoHkDkGeVJ4dZ4OmsCC9+g6Avi1q0S/n73XeCOOwLlWLo6dtYiro4O+bsO72rHUF+Qh4dLBVJezk9cYsrqDOp9nz4totKjZVbin5EXabtlIduC2VIp4eNySlvIGApLQsqcTauWoat9Kd45O4pbzh5Fy/G38Q/1H6Dnz74YeD9BLpjWUP3Dv7oGD//qmrIZWxkLMV00ExW+5gvi7beHyqv0dOysRVxWEafdQqBUUOPl/ExMiMCZnRXxZ1PVHQi71kkbNgCvv+6rZVaUzyhy4U/abpk1vcFnW7AFVFL4mGkLvqGwJKQC2HFrJ/7p+8/hSy/8FygADcf34/CWTmz5HX/tfMKgQ/X7fnFq3s+qQlAC5XPRjOGCGDmdwCo0x8a8nZ/hYSk0UkpyQ4eHo59f642Az5ZZUY71/iXgWwdORSv8Sdsts+tRG+YmqtLCxxSUvqCwJKQC6NnQht9rHEJ9TQ3GmtuwaHQIFw68BiQoLDX73/4AMOQxlpZIWRAmn6ycLpoxXBDdHDvPfqtuQtNpXdYm9VGb1jvdCARpGB/iWMsvTWHNImCsNkLhT9pumVN6AxDsJorh46qkJusFEELiYeOD92Bp7SzWf3gOi3AVrds/nvgxzXmWixtqsf8tf30pc4XOJ3v1VXksFv39nr5IJnDRPHZuFM/2ncWxc6P2LygWgQMH/K81QXQR1979J/HY3tfRe2LQ+5cKBRErbuesuVlaUtXWymNzc7SFmm8EzNtJYDpWY10tbliM6IU/fs5ZnHR1Adu3y2PYc6cFcWtrfh39cmNiQgrwkvz+RoSOJSEVwpY7N+LSb9yHmdNnsHz1DVhy58bEj9m9vgXffeVdTF2dxZKGWhQvjOPYudFE8gVjn0KkCZtPlpCL5Nk+yqmwIkSo0jb3L+B+gvRbDbQO3TIorvMbt3vmdp5Mx1rcUIuP3bYGheHpbAp/4iDKuWP4OD7KJP2GwpKQSuHUKSxZvxa447ZoCfcB6NnQhj+4uws/O3YB1y1fjFnDSKQPYyJTiDQBZp4vIIGLpmfPRDshHCJUaStgl9cG3o/ffquB1mEWl3Gd3zhvBLwu8JZjrS0UsHZdpNVnCwtX8kGZpN9QWBJSKUQRSBH45K0rceL8GGYNwz3U19sbujG6X1cslKuZZAFHCDwrsJ0KK4BAFxxbAVtXH3g/fvutBlpHkhXycVyI/ZzvIMcqh56RFJTZUyY5q8rQPcZyxtatW42DBw9mvQxCyouMLlCe7VR6e4HHHittP/lkIHFpdiyhYOtYWl/znS/ehi2t9cEviDm4yHueT+sazQ4aEMixXLMIuGEx8LHb1mBteyHwfqJybR2jA7jh0iA+dm831n5sS6LHjEyI8+1InD0jSeWTYV9QpdQbhmFs9XodHUtCKgmbCtfEchNNePb46+uTx5UrZdpJX18gYenHFTO7mi1qGh0fDgBYFCwXKcb8xSh4nk/r5xwiVLlp1TJ8ZfsaLB/qR2NdLRZfHgIQcD8RXOh567h5EVqe7kVjDdDwk/eB9kJ+xZUW9e3tQFtb9O9EXD0jSXVQBs4xq8IJqWBCVeza4Fml7EV3tzz298/fDkDPhjbsfmCjozjuXt9ybfLPTc11WNJYF7yK1XyR19vanRoaKm3nkRBVw2uXACuWNGBxYZH8QAtKP/vRLvTevfLY21vaR8Cq1bUXz6GpZhYNhcXSu/LECd+/myrmDgI/+EE8OW7W1Ia6utxU/BMSBjqWhFQwcVXsfuuF4xi/MoNLkzN4uGcNfrsnYP5mT4+EvyO6W66HMLman1hTwFKMBs9Fiil/sWyIkrNl50Jv2RKualXPF790af522pidab1tdoiScBfNOb51dcDLL5eeY1iclCEUloRUMFErdgEprhi/MoP3BiZQo4D/+PdHAQCL6muDtU/p6UlEUM47hHnyz8SK4OFru0KeI0eAQ4fExVu5snJEJRCt2re7W9xKswsdVoTfdBNw773idHZ0yHbamPMmBwakIXtNzXyBbHfj4ZWT6yd0rlMbDhyQEZaFQmk9FJakzKCwJKSCiVqxC0hV8qXJGdQooKGuFpcmZ7Cv9xTaly0KP6IuJlzzR8PmIpnzF4tFCXnOzgLHjgGf+9zCfWaYTB8LYddt50JPTIRzQAuF0u9ndR6tohiQxuxmgWy98QDcC2906Hx2VkL8994rNydOTm57u7xuaqq0bV5fOX/PSNVAYUlIhRN1fvemVcvwcM8a/Me/P4rRS9O4rb0Bn7i+HpMN9XhraDrZ9jAuJNrbUqMdrOZmcakuXJj/fJk0LE4MqwsdxQE1h5/N22lhTgtQSsTglSsy+ce8FvONx/PPyxzzFSuA8fGFDqP+bhQKIhaHhkRYOjm5bW0iPoeGJB2gTbvvVf49I2VFLMJSKbUTwH8CUAvgLw3DeMLyfCOA7wK4E8AQgN8zDOO9OI5NCEkOsyP4jT/oxplTF7Bj2RTGrkwDmMbo5Qgj6iISR/7oNZyqm716g46MiBBpaAAMo7LyL8MS1lGziqf29lJYOI1zqkXxyAhw8aIIytlZoLPT/vgTE5ITOTMDDA7Kd8D6/dDbWizr3FGn91MoiPBcuXL+6yo5z5dUHJH7WCqlagEcB3A/gDMAXgfwecMwjphe88cAbjMM41Gl1CMAHjIM4/fc9ss+lqSaCN0SKMHwmG3vyOWzwNAQxq8qzExO4sqyZnRuSKcRu6/1hRGWXj02nXLoJibkOR22bGyUEYR+PgeGNRcyMCCCsr5enMLZ2VJVf5oOnXkd09MiBjs6nF934QJw7hywYQNw550LXxe0PZHddyPOvpmEhCTNPpYfA/BLwzDenTvwUwA+C+CI6TWfBfBnc3//WwD/WSmljLx2ZyckRUKHdBMOj/W9O4z6GoW2ZYswOHpFHMFt1wNDQ1haawBLGoBVGVXvIkD+qFevRa8emza9QQHI+a+tBZYsEXG5bJl/UWn93PTPK1xoujZ+N4eiZ2elcCaoQxdHc3uvSnmzUFRKHtvbnSddWb8/uh2T02dt93OOVCRlRBzC8noAp03bZwBY//e+9hrDMGaUUh8CaAUQrqkeIRVE6JBuwuGx61YswrJCA6ZmrmJZoQHXrViUuwucZ/6o2Y3cu9d+4o9ddbMFW0GkBYhhyGfQ4rPi3vq5jYwAY2PyM6cbhApwOF1nggPzv1s1NeIEBikCcmpu7yY27Z5z+45bj/HQQ84upJfzGPRmsIw/e1Jd5Kp4Ryn1ZQBfBoA1Kc05JiRrQrcESnhu7KL6WqxpW4LZWQM1NQqL6mtLx4l4rDSmAQHwN/HHo8emoyAKK7Ktn5sO3DjdIFRI4YavmeDm87h4cbBza+0xeeIEMDwMPPusCFVAxKZ+rVvPSKdjmo8xMgKcPAmsXWsvKu0+M+ZKkiogDmF5FsBq0/YNcz+ze80ZpVQdgOWQIp55GIbxbQDfBiTHMoa1EZJ7QrcEStg9vLGzCUsX1YmgUvEV6aRSza3x4UYCcO2x6SqIwpx36+cGSEWx0w1ChYiRGzuboN7qx/D43ukBAAAAIABJREFUJCanr+KDD6/g2LlR544CQc+tudBqdlaE44kTkqbQ1ibnuK8POCp9WDE+Lue0o8N/s3N9jJER2W+hUHI8rTcDwMLPLKmbwQpwtEnlEIewfB3ABqVUF0RAPgLg9y2v+SGALwJ4DcDvANjP/EpCSvhqCWRz8eg9dxl9715E9/oa9GyI94KyadUyPHr/RuecuJD4Dv3HMIc6jok/ZkEUm8C2CgC3GwSrGKmpkRy9wUEJF0fJJ4wB17xJE/r79PNjF/APvxzEm++P4OKFESy/owOdq1qjCyJzj8mlSyX3ta4OePttqfKuM13uli8HJidFHDpV/Lsd4/Dh0g2Cndh3EpBJ3AxWiKNNKofIVeEAoJT6NID/F9Ju6K8Mw/iaUurfAzhoGMYPlVKLAPw3AHcAGAbwiC72cYJV4YSYsKkK7T13OZ6q6JTxVc3tVamdMn7Fk2+COkz69Tr3sL8feOklaXFTU5PZ6L95aQIKvprlP9t3Fs8f6setrfW4f8U0li2px4olDfEKIvO/l/5+aQm0YYNsm3Mk77lHngsqzv1UaaflIvqtYickImlWhcMwjGcBPGv52b81/f0KgM/FcSxCqhKb0Frfuxfj6+OYIr5C/35yI1Nk06pl8TWBD+MwaXEyMFD6Pf3zy5czG/3nK2/SgnaAmwxx8+oaGwHE3APU7Axaz691ZKcdXqLQj/No7UFpDZXHJToTzrUmJCi5Kt4hhDhgc/HoXl8TeQ54VniF/g+3r0fH6BUsuXIaSxfVOedGliNRcib190A32tYuZkbFjmHSBHRIfLB/CO11E1hcOxc1i1sQOYk2p/ZRGr/C308/SqcCnjhD1znr1EAIhSUh5YDNxaNnQyHyHHAzUSq146zy7j0xiMcOzuKWnl246YOTePDLD2KLX7cyjj6GSRPFYdLfg9ZWmQiTcY5l2Dzcaw5wHotO4iqWctpPEsVYPs5f7OkchDhAYUlIuWBz8Yg6B1wTpVLb1+9aBYSLANTFPcO33IF9q27CiqYubPGzELs+hgMD0QuA/Bw3iJiN6jDp30kgjy6M+IiUJpCFoPQT5o4jtOxWwBN2/yFvnDx7iBISIxSWhJBIc7c9f9ca+jMM4Ac/KD0/V3iiXc+G+hpAIXiI39rH8LnnpNUQ4NwcPSpOTbm9yJNDN0ck8ZFH59EOP2HouELLTvsJs/9iUW6QDh2Sgi0gUMFWmFxYQsJCYUkICdWk3bcQtIb+Tp6UbS0AT51C70zTvErx3Z/agKnp2WChdXMfQ0Da8QDBCoCsjpCXYLKK2YyKaOIgtPjIS7sbP26e3zB0EEHpdlw3VzTI/v/mb2Q60/h4qcWRn+/a3Pf3lpZ6vKAQb8ssQhygsCSEBG7Sbm0Z5CoEraG/G28U58XUP7Dv+HzXc2p6Frsf2BjsTZj7GK5ZI2Hwp5/2bo6usRvXp5T83U4w9fYCr7wCnDXNg3AqoslD7qfHGkL368xDA3e/znHcFdRhHesgaNHe0iLndngYaGryLtgyCf61AL6yfQ3eHp5mjiVJHApLQgiAYPma1vC3qxC0C/0tXjxP5HTPDMZT4W6u+O3qCtYc3eo+njwJfOQj9oLJ3GdzagrYvRvYudNeVKQhPrzwsYbQDfHzME3Gr3McdwV1Eo619QZAC8jpaaC5Gbj9dvk++3ErgWvf37VLgLXrro+2NkJ8QGFJCAlM4NC59SJuafkSeqylFy6jGhdgDaXfeKM82gkma59NwPlCn4dwuc81hCrESXuajJ3zav3s3Ny8OPNAvY5rbmw/O+t9bKcbAD+9N62wvyXJCApLQkhgkhCC1xzTiQkJY6ddCGJ3AXdyzVxmkC9ovRRE9NgRRxg96hq8iPuzcgqvOwmvsOIrKm7H1eL46lVxtRsagNpa9xxUpxsAr96bdjgJ/nIptCJlSywjHZOAIx0JqUL8jMrLCzazzG3HVa5aDBw9Ku7Rhg3BBEKxCHzve+J21dQAX/hCeNGUhzxPvzh9Dw4cAF59tSS8tm0Dtm+P97hxiS49anF2VuaSNzbKZ+g2cjHptIly+vdFckeqIx0JIVVC0uIkD4UgfrEJs1tzT8+cuoCeGQBLlsgft96Tduf2xAlxu3Rj7RMnwp/3MK5XVji5bUk6r0lMxNHttYDSo9s+k3Zey+nfFylbKCwJIf5IowglSF6YjWOYNQtyT69bBOAKcP48cO4cMDpqv1anc6tHN166JI96uxqwcw2TEl7FInD4cEnQxiG6zOLYb44lkOwNAPMuSQpQWBJC5uMk2NIoQvFbCGKuynZqfp5B6Neae9q1ajHwi18AL70kjtXRo9IqZvPm+b/odG5vugm4914Jq3Z0yHa1E7fw0qJ+dlbc4XvvlaKsmproub55y2NMotCKEAsUloSQEm6CLekCEI3HBa/3xCCufO/HuPPKDJZ2rbZvfp5hi58FbZvGxuSxqUku6CdPLhSWTue2UJD3FZcQyKHLmzla1Dc3AyMjcq7b22UOO5BO03fdn1IpWUeSx6KgJAlDYUkIKdHXJ85NW5tMrjELtrBhyBidQ10cc8vF5WgfuoQ1OI2li+oWNj/PusWP+T1v3Ai88UYpv023MTLjdm7jEgJ+XN5yI45iG7Oor6kBtmyRfwOAfVV63C74xATw2mtyg9Q210Vg3TqKP1K2UFgSQkrcfLOEA8+dK22bCRqGjNk51MUxw7fcgT/HbvzRig+x/Qu/vlAgxeGuhhURdu/5858Xp/LGGxe6lZqki2usvTf9jLhMk6AiMa5iG6c2U9ZcxGIRl//6e5icnkFjfR0WfzFghb7T+zt6FHj5ZUmVUEpaErW3U1iSsoXCkhBSYvNm4KtfBY4dAzZtchZBfonZOTQXx/RffxMW7fooYNdDM2qRRxRBbPeet2+Pfi6j4tJ7M3PCiMSwFc52NwxWUW+Ti3j+0DFMjV7BaMMSLLt8CQ2HjqHT73fC7f0NDcnjkiVSpDU0RFFJyhoKS0JIiUJBRsbdfntpOwpr1mB88iounf4ASxrrsTRiXmagxuxRHMAogjitXNSg9PQEG3GZJmFEYpgK5yA3DBZn8WRDM1YDaJm5jJm57U7vIwpu72/DBklTmJqS52+/nc3MSVlDYUkIKRFz1WjvTBP+y6UbcdPMRRy9ugJ3F69i6vjxSNN6gsw0D00UcZjVFBg/BBlxmSZhRGKY72qEG4a222/GvpNDuH58EGeXtuHTt9/s/UvmtTq9v64uaXxv/b54ubgUnSSncPIOISQx9vzkOPbuP4mVzYtx4vwYLk3OoGPZomtTaVYUGnDy/Bhu7GwKPqM6KEEvxOU0qaYSSEMoRUlxmJjA+XNDODl6FW0rW4N/X4O+Pz25R7uc5ok9YSfo8DtNIsDJO4SQzDHnRF6enEGhoe7aVJr9b53HwNgVqVl4qx+P3r8xHnFpdwH3mcM3f853ypNqqr0VUBrOW1g3ee770wmgcwmA5bXBjx30/bm5nGFSBzJswUWqCwpLQkg8mITRsdU3X3MidU5kQ30N9jx/4tpUmuWFenwwegUtSxsxPD6Jk+fHogtLJwHp40JsnvO996WTMuc76ZD7tYNXYCugvBIm9zaLUYhuoX6z6NSzyL3WlHULLlI1UFgSQqJjEkaXv/VtPPvZf4X312+55kTufmAjAGDL6hXXHMEVhQacOD+G4fFJrB0fQPfpQaB5KtrFzkkA+Mjhs8757nt3OD1hGbUVEPPtkiWrUYhOn6cWnSMjwMWLwPi4/HELiee1qIxUHBSWhJDomITR5Mn3cf17RzF2250LnEhr4c2j92/E4JvvoPu9N7D4wxrgjZ9LVWx3dziB6SQAfBR6LJjzvb4l+PHD4qcVkJN4jKufY17Io0jO4yhE7cTX1vpzUvNcVEYqCgpLQogn83MPbVw8kzBqrK/F2XU3YXh8EkoBN3Y2Oe5306pl2HTiClBfIxfH8+eB11+XptF2OWBeeYhe4UMXQRColVHceLUCchOPWYRp9XHjFlp5Fsl5EZRmgjqpSTfhJwSsCieEeGDOPdTV3LaiyyHH0jNvUhcVjI2VwnnT08C2bdJY3Lx/nYcIxJuHmEeXzEwSFcJRMB/z6lVgxYp4Zly7vc9yIe3vUt6/u6RiYFU4ISQWfOcemnokbgL8F+LoEF1fH3DoUMl9seaAJTWSMM8umcbNmcoiTKtdUqWksffwsNwYRD13QR24PImqYhE4cQKoq5PvaFrfpTy8d0JMUFgSQlxJJfdQh+i6u51zwKLkIbqRVSg5CF7iMW1xoQXg1JRsNzTIrOuo5876PgcGnL8Peboh0K77zIz8uf9+mfedx++SA8fOjabXU5ZUNBSWhBBXUs09dMsBi5KH6IZflyzr5tJ5cqa0ABweBkZHRVTqn8ex70LBu+9inm4I9PduxQpgcBA4d06EZV4+Lw+OnRvFt144Hn9PWVKVUFgSQjxJZYyir4W4jCQMKzTs3ECriMx7c+ksQsL6WNZjx7UWr76LWbUAskOnbYyPi3u7YcP8G5usb0o8OHl+DIaBeHvKkqqFwpIQUhlEERpmEWQnIvPcXDrrkHCICUe+8Oq7WCiIKzg6Cixblq2wNLfyqasTgTkwUB43JZDODeqtfl+dHAjxgsKSEFIZxFXEYicigzaX9nDtYs1ny1NI2O9a/Dh4Xn0XJyaACxfk7xcuAIsXZy8ugfK6KZlj06plePT+jcyxJLFAYUkImU/Ow3auxBEKthORQZpLe7h2ceezHR6aRseHV7CkcQZLF9VlK678uMZBHDy3nNs8CWpNHDclSeNw07Np1TIKShILFJaEkBJxh+10b8vOTqClJbxY9WqMHidOItJvc2kPwWOXz6Z/HtQt6j0xiMf++hBuaa7DTc11ePATG7Ela2Hp5Rr7dfC8bnDylGOpCXNTkmZ+bNZpE6QqoLAkhJSIM2ynG5pPTkr18IMPAqtXBxer5sboe/fG2xhd798qWu1EpF8B4CF4rPlsdbU1oR1M3WN02KjHvpOXsWLtBLbc7PeNJ4TX+fHj4Pm5wcnjmMWgNyVpCz3rTc/Ro6WhBOUWnSC5pSbrBRBCckScYTvd0LxprhBgZEQezVNiguxn5cr523GgRevevfLY22v/Oi0AhobkUV+g7dCCp7XVVijofLZP3bYSj96/ETNXZ685mIaBaw6mH7rXtwAK8fYYLRaBAwfkMQm0+Nq2zfkmw3yDY962UijIZJ48iEpNV5dMjArqbpu3k0Kfp+lp6Qf77LPAq6+KiI/6eSf9vSFlAx1LQkiJILmEXuiG5mNzQqm5WR6DilU/jdHD4neaT9B8Pg8HzZrPFrYiN/Yeo2lVMHulFeQtLzEp0g7nm13e06eBmpp4ohNlUPlO0oPCkhAyH7+5hBqn/EdzQ/MoOZZejdGj4Fe0JigAolbkhu4xapfDaE2F6OvLppAr7A1OnkY8+iGLcL4+zoYNwOuvxyPey6DynaSHMvTEhJyxdetW4+DBg1kvgxDihjn/EYg//zEN/BYGlZtoccPJYTL/fHJSZoE3NMx/TV4x5ysCwfIVK+mzDUJcHSDoWFYFSqk3DMPY6vU6OpaEkPC4hZLjqOROo/WR2zQfM5UkOpwcJrNTODgoxR3l4kKFbT9UzZXSQaMTbvuJK4WGlD0UloRUK3EIP6dQcpRKbnOLoiNHSj+nCxIfbjmMWmwUiyIsyyHPsVgETpyQqTe6yCuIW/nmm8CxY8CmTVJ0VS3CMgrWm764RCopeygsCalG4mrh45T/6Lcoxm1dY2PAvfcCW7aUh2Pmh6TmagfFj8Pk5zV2jnLa78kchp2dBT79aZnCc/CgP/fsyBHgP/yH0nZnp1SaE2cY+iYuUFgSUo2EFX522IWSw1Zym9c1OQmcOye9L4EFjlnvicH51dBpNlEPgzXk2t5eGknoJwTrlRYQNG3Ay2Eyn0+n41nFRUdHuLByFDFqDeu/9544reZ1ub3Pd96RPNK2Ngn/v/MOcM89wdZQbbBYh7hAYUlINZJkCx8gfCW3eV2NjcAf/qFtNXnviUE8tvd1wAD2vnQS39lagy1PPC5PJtFEPQ6sOYCjo/O33XICvRyiJCYmeTnaduJCr//CBbkpuHQJuPNO92OFyXHUIrq9HVi6VJxKHbIH5Kaktha4etVb9HR3S9ud4WF5jPvfQiVSLe2gSCgoLAmpRpJs4WM+RtD9+lyXnjizsnkx+kcuY+jAa/JEHA6sH8IUFVlbFi1bJgLMTwsjL4cobgfJydE2v287cVEoAIcPAy+9JD87cUJuDLxmqwP+i260iJ6dBaamJF3irruAmRlpoXPuHPDyy5BRRkryLt1I499CpcFiHeIChSUh1UoY4ZcwvScG0XexGd33fc61N2P3+hbsfenktYkzrds/Drz2o+QcWDNh3UG7noWLF/sLAXs5RHE7SHaOtvV9b94s4q6tDdi5s3QOZmZEzK1YIeMCvURu0B6hWkQXCiIsh4YkD7e1tRSKb24Wx3J6WpxIL7Ea17+FampbxGId4gCFJSHVTpDcxATzGK3h7Sd3fdRRXFonzmzZ0AasXuF/bVHaGEVxB62Cw68AcXOI9Hu5556SqNNrDHvht3PxDhyQ55YvF1fyu98tjes052Hqxtvj47LtJXKDNgnX+9NOZ2traT/6+cZGEb1Kyc/1eUt6Dne1ti0ixASFJSGVjpsYDFIdHlcluQPW8Hbfu8OurmVP3Rh6FvUDdfUA2vy7TlHzEbPKL7NziKzv5Z57JAysiZJraT2f5vd99qw4gh0dwMDA/NSDMGHSIA6fef/t7eKYmn9fP3/4cEm0BulracWvCxm2jyYhFQaFJSGVjJcYDFIdrl+7fDlw5gzw/PPzXxvRzbSGt7vXtyx8kXbn6urCC6io+Yhp5pd5OavW96L7fiZRravf94kTEubes0f239CwMPUgbJjUj5Ps5zVdXaWweJQxnH5dSLs+mjU1IrqrISxOiAkKS0IqGS/hGKQ6vLtbxIRu5XL2rFxQu7oWCthvfENy8AJcVK3h7QVupdmdGx8XZ6ijQ5ypb34TeOQRf4I2DscxjfwyP86q9b1s3ixi5sMPcXl6Fn2XFqHt3Gjg+eOOdHXJ57l6NXDddcChQ8DHPx6Pc+3n/QZxm+OYw+3HhdRrOn1a/k38zu8Av/EbwVpJEVJBUFgSUsl4CccgFbE9PcDu3cCPfwxs3CgFEtoRMwvYs2eB/ftFeDhdVB3czZ4Nbc7hb7M7NzkphRuHDwPPPCOVxz/9qb/wfLlUtPpxVu3ey6pVOH/oGP7H6Vm8/0Et1AvH8ej9G+MTl7rY5pZb5I8Wt1HHb/p5v0Hd5qhuoZ/ColOnRFQ++6y0Nzp+XG541q1jWJxUJRSWhFQyfoRjkIrYnTulylajRYVZwM7OinPmdFF1C8+7hdPN7lxjI/DAA8Df/q2IynXrgrUZKoeKVr/OqvW9dHWhb6QB74/0o2VpI4bHJ3Hy/Fi8wtLqBMbRR9PP+007v9WP67lmjbQ4unpVipnq62U85Lp10cLwhJQpFJaEVDpxthUyO2TDw8Bzz5VEoBawN98sbqXTRdUpPO+VD2rnztXViVNp58j29koeqLUdTrng11m1KS65sbMJ6q1+DI9PQinZtiWsy2gVWUGdRLuCmLjGTMaNl+vZ1SWN/I8dE1HZ2CjpAVHD8ISUKRSWhJBgdHVJHt/XvibbZhGohaBbJa1TeN5PIZGd07hjhzw+/PB85/NLX5IwJgC8+aY4nOfPR2+VFDXkGwQvZ9WhuGTTqmV49P6NOHl+DDd2Ntm7lXFO6wniJLoVxPhxkvPoNj/0ELBq1UK3nYKSVCEUloSQ4HiJQDeXxik8H3TMpNnhBERYmtc3OSmhyUuXgLfflmkwTU3RWiXFPToxKi7FJZtWLXMPfwdxGb0q/oM4iZXalieHAwcIyYKarBdACClDtOgLO+lGFwKZL8RacO7a5U/4mcWteVuvp7ERGBuT3DdA2uLYvdYvxSLw4osiWJcvl59pcZYVWpCFyeXz6zJqAb93rzz29tq/rqsL2L7dW2hb1zw4KM3Xi0X/ayeE5BY6loQQIUgfSrPr2NBQEmpRHZsgro+bw9nTA/zFX5RyLDs7JXQfVghrp3JyEhgZkZ81NqbXHN2JKC11/LqM+rPt6JCK/1deKbUdCuM0mtc8OAj84Ael57J2gAkhkYkkLJVSLQD+PwDrALwH4HcNwxixed1VAG/NbZ4yDOM3oxyXEBIzYabq6OcTnMZji1kAu1W8W0WqXQ6cGbfcSe1MLl0qofXmZuC3fis7EWRda9hQsp98xe5uEemnTgGGUZq2U1sbvj+jFqXvvCPbSTR0J4RkQlTH8k8B/C/DMJ5QSv3p3Paf2LzusmEYvxLxWISQsHi5kUEm8MTxe2GxE8C7d/v7XbPQtJ4Pr9zJNWvErTx/XsTVxYvxvJ8wpJ3n2dMjbu9rr0nF/6ZNMoMbiJ4fmdV4zCRJs7iLkBwSNcfyswD+eu7vfw3gtyLujxASN35y5MLmTAb5vWIxei6dW16lX+zOh7mQBViYO9nVBdx+eymM29AQKL+y98Qg9vzkOHpPDAZfrxWvtSbB3XcDn/88cOutsm0Y8hi16EaH47dtq4wwuBb9r74qj8wbJVVIVMfyOsMw5q4oOA/gOofXLVJKHQQwA+AJwzD+/4jHJaSyiDhn2xU/rqJXI3Wn9fmd3BOXyxakctzJObI7Hzt3ys/cnLPubhlnqYtOfLprvScG8dje1wED2PvSSTy566PO04X8kIXLZ86LrKmRJvhx9WfMQ/uguFzGqHPoCakAPIWlUupFAJ02Tz1u3jAMw1BKGQ67WWsYxlml1HoA+5VSbxmGcdLmWF8G8GUAWFMJIRFC/BAmvzEIVjHW0CAzv+1Eot1xvdbnp+AmygXXKmqjClk7cZpgc+6+d4cBA1jZvBj9I5fR9+5wNGGZdJNwu5sIt76k5U5W/TwJqVA8haVhGPc5PaeU+kAptdIwjH6l1EoAAw77ODv3+K5S6mUAdwBYICwNw/g2gG8DwNatW51EKiGVRdJ5itYK7j175Od+RWyU9WmR0jl3bxr0guskaqMIWSdxmlBz7u71Ldj70kn0j1wGlGxHJimXz3y+9+yR/NXt20s5lU6z38uZOF3GcplDT0iCRA2F/xDAFwE8Mff4tPUFSqlmAJcMw5hUSrUBuAvA1yMel5DKIWhj8DBoMaZFZRCRGHZ91gbmjz8uc72DXHD9iFq7MKaXc5RiM+ueDW14ctdH0ffuMLrXtwR3K5NMkzBTLAJPPSVFSh0dEvb/8Y+lAvwTn5BzGLWheVKFLVH2G7fLaBb9aX12hOSIqMLyCQDfV0rtAvA+gN8FAKXUVgCPGobxzwHcDOC/KqVmIcVCTxiGcSTicQmpHPyGd+MgjEgMuz7z9JuxMamofuih+NarZ4GfPQusXi0/02HMrJwjh5Bxz4a2cOHvpNMkNDocfPmyzIC/dEl+vnGj5FQODJQKpqKIyiSq2c09Rqengc98BrjrrtLzXmH8pL4raX12hOSMSMLSMIwhAJ+0+flBAP987u8/B3BrlOMQUvGk5aCFFYlh1tfZKSJleLi0DQRzl5zWqy/aY2PA+LhULDc3zw9jpl0U4jYDOyxxpUl4nXO97i1b5HF2VgRlc7Ns33Yb0NoaPMfSLOqSKmw5dUpE5YcfypSlZ56RnqVdXf4/E7fvSlg31M9nV8m5q6Rq4eQdQqqNtERsSwvw4IMyqaa5WbbDuFZ269UX7aYm4IMPRGju3JltsURcM7DNQiaONAm7cw7MF0vmcPDq1favCYpV1LW3l44BxPdZ6RD91atAXV2pFZQWlkD4z8Tp++pHbHp9dvr89PeLI3zbbcDmzf7XRkhOobAkhPgniHuzZo2IFB2mXrMmPtequxv45jfl92dn5XHz5myLJQoFEVBh5nZr7IRMEIfZLqfPes77+iR/0nwMp3Cw+XwGddesoq6tLZmQc1eXhL+feUZEpXnUZtTPxO77Cvi7OfKKDkxMiKh8+WXpC3rsGPDP/hkLfkjZQ2FJCPFHULfRLXctqmvV0yNu6FNPyT6uXJEcziyJMrdbYydktm/35zA75fTV1Um6wORk6Ri6QMcs7t3CwWHC/HairqMjmHDyeyNz110S/ra+NupnYlfYE+TmyC06UCiIU2kY8vfpafa9JBUBhSUhxB9h3EarWImzUOLhh4H9+0VUAslU0wclaq6cnwrlII3fOzrEEauvl4KchgZJTRgZkdea3T03woSUo4q6n/0M+NGP5JiNjf5uZOyej/KZJHlzVChI+PvYMTmnNTXse0kqAgpLQoi/tihxtWXRAqC3F3juORE7U1PeoV7rGtOspk8LL+EdtPG7vhno6ADOnJG0gY4O+dnq1cB99/kT92FDykFFnRbNdXUS2h4fB2pr5WYmKzcvyZujzZsl/M2+l6SCUIaRzz7kW7duNQ4ePJj1MgipfKz9Jt3aohSLIgYHB4FPfSq8mNPHHB8HTp8uhVadjh1kjZXMgQMyh1q7xtu2SahcYxXfZiE6OSmNzhsaZDtou5+kK5jNax0fl8fLl4GZGSnS+vKX56+XPSIJSRWl1BuGYWz1eh0dS0KqnSAtbQYGxBUDgKefDi/w9DHr6+Wxrq70c7v9JT2dqFwI2vjd6q4B4d2xpFvimFMtJifFxV62TB4/85mFopI9IgnJJTVZL4AQkjE6N9FPSxuzwDNv+6FYFMetWCwdQ4dWZ2ZKx+7tlQlBvb32a5yYkH2Yn68WtFDcts2/49jVJa6mDunqv+cNs2hubJTirE9+EvgX/2J+w3Mg2veQEJIoDIUTUmmECRH6/Z2wIWm73MCBgdL8cp1jCTjvv7cX2LdPcu+0c+Z1fGuhS5hzY1csk9RoQsA55OwnFJ3kutLAnGM5M+P8PpgaQUjqMBROSDUSJkRYLEpl9c6d/ifh7NvnfHw74ea3jY7bLPOeHtkuFPyFxK3TGChqAAAa10lEQVRidvNm4Gtfk78HOTd2zcXjHk2oz9vNNwPXXSc/M7f18dPuJ6mRiWmi1+v1PiqxcIuQCoGhcEIqiaAhQi1GXn1VHotFf8fZv1/+PPZYKSStRe3evfN/DvivKPcKyzs9bxc+N4tZAHjlFXkMEj617uPUKfufRcF83r7yFeDNN0u5p7rNj7ndj3nbba19faXUg3LC7/nt6QF276aoJCRnUFgSUkkEyZcEwokkJ/HqJmr95gb29ADf+Abwuc/Jo1U0aKdq166S4+gkaOvqZJb4wIBs3323PAYZj2gniONqu6Qxn7eaGuDIkYVtffSjW7sf87qmpkSgBr1hsMNOtFuZmJDzbCd4gxL3+SWEpApD4YRUEkFDhGEu4k4zkL1mI7tNdtFMTEgo+Dd/s7RtFVHWyme3xuANDSLGHnigNJ0lSPjUqWdhnKMJzeetpgbYsQNobZ2fS+mn2bh5rYODMrbRq5m9V06mn9QKa5j+gw+Ad94JH6KOs08kISR1KCwJqTScxsjZiYgwF3E38bpjhzw+/HA4URFmwotXY/APPyxVnXd0SK6lbhLuBztB7CSS/RTPWF/j92bAT7sfva5iUYSl1wQfcy7jPfcsLJjx0+bJ/JkdPAg88YS4xVHaAPm5CSGE5BIKS0KqAbfCjjgu4tYq3YcfLh03iGgNM+HFTpjp0K9ZWCVd3OJn/3av0fOzt2yJr0+knxsGcxrEwIBU2zc1zV+7lwsNzP/MjhwR17Xa+40SUsVQWBJSDYSZ8+2EXXjUKRwdVMh5hXydqs69GoN3dUkhS1znwA4/59j6mhMnSrPOnaq9w+J1w2BOg5ielrQB69r9uKnmz2zHDuDZZ4PlsRJCKgoKS0KqgSgFEVbX0U5EuoWjgwo5p5CvVdA+/jjQ0uLsyFmFVQxFIcfOjeLk+THc2NmETauWzX/Sz/6tr2ltlccgof+4MIvvujrJSbVbu1NqhRn9mXV0sA0QIVUOG6QTUi2EaZ7t1Njcrjm125xq/btRHMI9e0RQrlwJvPdeyVELsu8IDcSPnRvFt144DsOQkduP3r9xobgMmmPZ0TG/Ej9OxzIo5d5cnRCSKGyQTgiZj11o1GsSjVNjcztXyk842u7Y5sk7bg5XQ0Oppc3srFR4B3VDI+STnjw/BsMAWpY2Ynh8EifPjy0Uln72b+ek2oX+4xJ6fvfDghlCSAxQWBJSrfhpJeMU3vUTHgWcxYo+tm5Vs3o1sHSpcxWx7qW4ZAlw6RLwx38sFcwp9jq8sbMJ6q1+DI9PQinZjgW70H9cbm8lTOMhhJQVFJaEVCvWXMnnn5dCkqjtiABvJ1Qfu27uvyA9Ucapivj556XZ+Q03iJhctkxGUKYYut20ahkevX+jc45lnPjJT/XjRMZZtEUIIT6gsCQkD3gJsSj71XO9rb0lzQU3U1PA2bMyqQWI1o7IjxOqj637S05PA42N9lXExaKsbXxcejO2tsrr/Kwr5rzBTauWJSsoNW6FQMWifFcOHZL0AMDZieQUG0JIyrB4h5CssfaADNtU2oqunL54EaitFVHxne/M37cWtAAwPFxytrZtk1zKMJiLbPr7pQWN7olod2yvHMsDB0TwjowAx48Dv/7rMiPai3INA2sxXFe3sGG5fk9jYyK016wRUe72ebEohxASAyzeIaRc8DPdJCi9vcC/+3ciFq9eFZEyMbFw3zpXUgsWO2crqJtqdkInJqTxdqEw370sFiXsvnOnt9jRa2lult/dudPfOfAKAyflEkfBSwyfOgWcPi1jE+vqJC+1qcndiWRRDiEkRSgsCckaP9NNgtLXJ2KuoUGKXaamZNtp3065lH7C2lbMTbWLRWD//miN0wcGpAq8rc2fENWYw8BTUzI/u1iU3w/zvtJAi+H6erkp6Oub/36Hh0WoA4BhyOf5yCPJCsc8CnBCSG6pyXoBhFQ9Wojt2hWfwOnuFiG5erUIud/93YVhcCtdXRJONYsUs5tq3jZTLEq4Wo9RBOQ4u3cDmzYBFy7IhBm9LrOTCMzv42hFC8CnnxYBODDg+rYXvJ/f/33gpptEhB09KoJW5yh6va8w2J2LIKxZA0xOiis5Pi55lOZ9nT8vTeHXrpVc0xUrSrPBoxzXCX3+9+6Vx97eePdPCKk46FgSkgf8tu+x4jbiMI4JKF5uqlvo1twiaGIC+Nf/2nmOtxNR0wS6ukS4NjbOD4kn4RKHyem05j92dQG33w68/roIyOnp+SH87m55L1NTpWKnKLmkXvmXSaRpVCPMcyVVBIUlqRhcx+1VIl7h3LBi1YxVoAIiFhsagHPnJMxeXy+uqDWPUYuSj3ykVHkOBGth5CQAg1yo7Sqju7riHz0YtLWPkyDs7hZ3dXp6/voB+xuGsDPQ/QjSJAR4tVGuRWSEhITCklQE88btvdVvP26v0gjrJgV1T7RANTc1f/99CbXX1wN33SWva2ycL4LcRInfghI7IRX0Qu0kZOMQ3maCtvZxEqJewtu67rAthfwI4bic72qGvURJlUFhSSoCX+P2Ko0wblIU98Tc1Hx2VloY6Wrz1auB++4r5fvpdjm7dknRzKc+FV6UmIVUsQi8+KLkIWqXtK/PWyhrwdbbCzz3HNDZKaHmOEOTToLQSci7CcIgldxhm9j7FaRxC/Bqg71ESZVBYUkqgsTG7eURc15lUDcpintibmpeUyNtjK5elQulWVT++Z/LfgsF4Oabxcns6Aj/fjVaFE9OSk9LAFAKePNNOQbgLpS14zo5KdXVDz4ogjjO0KRVELoJ+bCC0E6ohmkpFPb4SVGpeYh5O8+EJAyFJakIUh23lxZ2hTl2eZV+moVrorgn5rCozrG0tgB67jlph2MYIkBXrADWrYsn/KdFsRapq1eL63j0qLdQLhaBp54SUdnUJMJyZET2kWRo0kvIBxWEcefr5aXHZaXnIeblPBOSAhSWpGJIbdxeGjgV5sRRJR3FPfEKiw4OyuPSpdJm6Oc/L0390e8rbL6eWRQ3NopLCoiwdBPKWrRcviyCUhfFNDc7/05cxB0GrdR8vUp9X4RUIRSWhKSBm6CyCwE6Ccg4qnSTdE8+9Sngf/5PGSN5+bKMHvz5z6X/5MBAtKbkTqLYSyhr0bJlizwuXgzcfXf8OZZmzJ+p2/qsn71XOLhS8/Uq9X0RUoVQWBKSNG5tgdxaztgJyChVunFNUDGLH0D+PjwMvPaahMdvu01E5JIlwIYNJWGsidITsatL9v3cc6X34SWUzaIl7pxKO8yf6dSUnI/u7oXrtn7299wDvPxyadtunZWar1ep74uQKoTCkpCkcQtfO4UA3QRkmCrdICMM3VwzsxianJTimQ8+AP7u72T909OSf9nZKc6gnhdeLMoUHiCa2xpmFGPaosU8lrG/X5qdv/CCOLcNDaV1X7kir9Of/ZEj87edwsFxOc55K5ZhHiIhFQGFJSFJY3UfGxqkyXh3d7QQYBAH0m9upq7qPncOWLUK+JM/mX+xNwvhM2fk7yMjpebnSsmf2VmpugakmGf/fvmze7e8Nohran6fYXNM7URLUjOw9Wc4PCznoqVFROPkpIxi1OveuVNepz/7zZvF1UwjHFzpxTKEkMygsCQkSbR40YJKi0qg5FxZ3bTeXmDfPhFkhYK9MxfUufObm6mrumtrgTfekPGC5qpzsxBuaBDh1Nwsf798WarBDUPW/fDD8t4LhZIQ1KJSC0QvQWd9n3otdu8jiAPX2wt86Usi9hobgb/4i/jEpXZI+/qkFdL0tIj0996bv247J3XVqnRcxLDFMnlzOQkhuYPCkpCkMIsioBTaBuY7brt3z5+v/dhj4lwNDQF33CEhU6sz5+bc2V38/eZm6qruJUuk8EZva6xiCJC/P/BAKcdy1SoRlfoYVrc2iCC2vs+pKfv3EdSBe/55Ob9NTfL4/POl6UJxuJjaIe3uLp2rgYGF+7Y6qWmFg8M45UHOcd4EaN7WQ0gFQ2FJSFLYiT8v51D/zpo1InhOnZK+jdbXuc3Qdrr4O+Vm9vaKsGprk2Kb5culB2Vrq1R5W7ETQwDw0EMLX2sVtEFD2Xbv0+59BHXg2trk8dKl0naY/E0vzOdK587mgTB5p37Pcd7C7HlbDyEVDoUlIUnhJIrcnEP9O1euyAX/wQfnu3+anh7g8ceBV16R1jlexUBO6JDw0JBsP/CAiKsPPgB27IhHCFmFYJB2SX6d1qAO3M6dEqbWDuvOnZIGAPgTvRMT8qdQkD9pEHdOaFB31O85zltPyryth5AKh8KSkKRwEkVuVd1+hVSxKAUhbW3y+Cu/IhfLoAKrr680jWZiAjh/XhzSHTvEsYybMO2S/FTBB3XgurqkMMn8er95qBMTJbEyNCS/n7S4TMJNDYrfc5y3npR5Ww8hFY4yDCPrNdiydetW4+DBg1kvg5B8cuAA8OqrJRdm2zZg+3Z5TueTDQ+LUHQTcHaO5Re+IK6dVTBZHbOkqqqzxM970vmv9fVSmNPaGs8sdDf27BFBqd3UXbuCjfJMm7zlNOZtPYSUIUqpNwzD2Or5OgpLQsoQr7wxu8IhN3Gpcyy3b5dHHeLVQstczQ6IqDFvezloWYSOk8LsWALpO5ZANo4lIaSq8SssGQon1YOT49bZmex4vyTwCks6FcnYOXJuRT1azFy4IK2FmpqksOfFF+33b0cWoeMkMJ+7LVvSFcp+UgjoyhFCcgCFJakO7Poh7tkj+YXDw1Ikk8a4vzhxK77o7ga++U0pUCkUZDtonp45//LqVQkB19dL8/O1a0VY+SnCmZiQRx061oKsnMhDjqNbrikrnwkhOaEm6wUQkgpmBw8oOW5NTfI4MiKP5hBnVvT2iujt7Y339focLFokInHfPvfXj44Cp08DJ04A4+NAe7ucP8MojR/cscNbZGkROT09f9svQc8HIOJ1YKAkaqNi/f6YZ597EWb9QTFXPpu3K4ViUfKKi8WsV0II8YDCklQH2lHTDtt998nj2Jg8NjfLY9YVo9oZ27tXHr3EiNPr9cSb22+XRx1CnZgA/vEfJST9zDPO++/tBf7qr8RlvHpV9rN0qTQor60thbP99GYsFOT1ra3Bw+BBzwdQCr3rPqBHjkQXJdbvj98552HWH4ZyqXwOIxC1G/vqq/JIcUlIrmEonFQHdjlqW7bkL8cyaANxp9c79dB88EHgqafkvdpN9NF5hMWiFOy0tor4Xr4c+LVfA154QULhV66U9muHNZdT5yL+7Gci9DZvBu66K/7zAcwPvZ86Bfz0p0Dd3H91YUPEYdokhV1/GMI0PE+bsOF69qEkpKygsCTVgzVHzU9/xLQxC8KJCbkYm11Iq6hx6r3oJIQefhjYv99eGJrzCM3ibOlS4JZbgO9+V342NQXceSfwR3/kXfRjzkf82c9E1BoGcOiQPO8lLv32ljRTKIhbOT0t4fCaGhElhw9L3ukjj4T73MN8X8KsPyxpjYMMS1iBWC5uLCEEAIUlIflCC8J9+yRUvX+/PAIimLRQA0qi0clJsxNCbs6b1V3bsaPUOPypp+S5lhYpdlqyxFlkWffz2muyn0OHRFQuWiTC9h/+QSrM3Ry2ME6hDr1PTAC33QacPCmi8plnZP0//WkyxTdOFfdhnM5KJKxALAc3lhByDQpLUtmUYxPvnp5SjuTKlVLZDQAf+YgItX37RHACJaEZpFm2k/NmddfMoyTPnQO+/30RlYCMkXTCvJ/ZWUk1GBoqzee+ckUE5oULkjcHOIdFi0V5/c6dwQSFDr13dACLF4tT2dICrFuXTEjarWo8j854FkQRiHl3Ywkh16CwJJVLHlrEhMUsznSxixZ8Gre8PbOgBvyJazd37aGH5FHPJtfbXvtZt07+1NdLTuvSpcCZM1IA1N/vHhaNq4VOV5eEv3/603AhaT/9IdPKpSx3KBAJqXgoLEnlUs4Xe6vIA+b/ff9+Z5FkFtTf/KY8msPoXuLS6fmHHlooKJ0cYb0fXaGtWw3dfbesRYtGt7Bo0Jw881o6OuaLwbAhab/i1nwjMDVV+l2KKEJIlUFhSSqXNAsnksCu2EjjJpLMgtoaRo9TXPtxhM35juYpNX7CokFy8sxr2bNHKthXr5ZtLQbDhKT9ilstXJ9/Hjh7VlIG/uZv2KicEFJ1UFiSyqWSCyfcRJJbGF3P/G5oEGdNP4Y5P34dYaexh15h0SA5eea1HD8uOaFbtgSrPrYLeQcRtz09kg/66qtsjUMIqVooLEllU42FE05hdC0qdXi6vV0KaHTT8qA5qH4d4SgFVH5z8sxraWwEVq0KVn3sFPIOWnBSDa1xyrEgjhCSGsowjPC/rNTnAPwZgJsBfMwwjIMOr9sJ4D8BqAXwl4ZhPOG1761btxoHD9rujhAShj17RHxNTgLvvgusWAFcvAisXy9ibNeuYNXlgLfIMIeogWQLqNxyLL04cGC+07htG7B9e7h1+Cn2KVfMn+fUlHxfglbsE0LKEqXUG4ZhbPV6XVTH8jCA3wbwX10WUgvgSQD3AzgD4HWl1A8NwzgS8diElAd5cXi0qzczI9v19fI4MyPCMkwO6pYtIiqcxjSmWUBldaeDiJ04ncZKrnzWn+fy5cDRo8CPfyz5pMwlJYTMEUlYGobxDgAopdxe9jEAvzQM49251z4F4LMAKCxJ5ZOnlkfmEHkcOZY6pA5In0q7OeDlUkDFJtz+0J/nmTOyvXGjPDKXlBAyRxo5ltcDOG3aPgPA9gqmlPoygC8DwJpKzE0i1UfeWh7FmXNqHvs4PV2q/LYer1wKqII6jXlxotPEWv3e3Cw/5//XhJA5PIWlUupFAJ02Tz1uGMbTcS7GMIxvA/g2IDmWce6bkEyIy7HLo4gpFKSf5pEjwObNwIMP2r+u0gqoikXguedK1fVZO9Fpoz/PSs4lJYSExlNYGoZxX8RjnAWw2rR9w9zPCKl8rOFn7WAGESFZh9OdRO3hw8DXvw5cvQr86EelXpGVjK4e7+2V8P9NN0leZtZOdBZUci4pISQ0NSkc43UAG5RSXUqpBgCPAPhhCsclJB/09Igo01XZjz0mwsQv5nC6eTsJentlnXp9WtTarbuvD6ipEceqpsZ7XdZ9lyM6p1TnFupcw7zmjhJCSMpEEpZKqYeUUmcAfBzA3yulfjL381VKqWcBwDCMGQD/EsBPALwD4PuGYbwdbdmElBlRxKEWLUkXwNiJSLd1B1mXm0AtJ3QuYXOzhP6/8IXqCoOT8qRYlJZaxWLWKyFVQNSq8B8A+IHNz88B+LRp+1kAz0Y5FiFlTZRcyzAFMGFyMu0KjdzWrde1b1+4fedRjHmdN1aPk3LD77x7QmKCk3cISYOo1dE9PdIzcmLCvvraTNicTDsR6Wfd+/eXHp2O1dkJjI1Jc/awPTOTxu95Y24hKSf8zrsnJCYoLAlJiyjV0X56RmrCuoNOItJt3X6OVSxK5fi998oM7z/8w3y6leXiqhIShGoYM0pyRRrFO4SQqJh7Rpq37YiSk9nTI2P6/AoqP8fSgnjLFtlvS4v/9aRJWrmshKSJTt/Yto1hcJIKdCwJKQcKBXEqp6dL206k2ZTcz7HKxTEpp2buhASB6RskRZRh5LMP+datW42DBw9mvQxC8oM5v9JNWOYRNtMmhJCyRin1hmEYW71eR8eSkHKhHAWlho4JIYRUBcyxJIQQQgghsUBhSQghhBBCYoHCkhBCCCGExAKFJSGEEEIIiQUKS0IIIYQQEgsUloQQQgghJBYoLAkhhBBCSCxQWBJCCCGEkFigsCSEEEIIIbFAYUkIIYQQQmKBwpIQQgghhMQChSUhhBBCCImFuqwXQCqU3l6grw/o7gZ6erJeDSGEEEJSgI4liZ/eXuCxx4C9e+WxtzfrFRFCCCEkBSgsSfz09cnjypXztwkhhBBS0VBYkvjp7pbH/v7524QQQgipaJhjSeKnpwd48knmWBJCCCFVBoUlSYaeHgpKQgghpMpgKJwQQgghhMQChSUhhBBCCIkFCktCCCGEEBILFJaEEEIIISQWKCwJIYQQQkgsUFgSQgghhJBYoLAkhBBCCCGxQGFJCCGEEEJigcKSEEIIIYTEAoUlIYQQQgiJBQpLQgghhBASCxSWhBBCCCEkFigsCSGEEEJILFBYEkIIIYSQWKCwJIQQQgghsUBhSQghhBBCYkEZhpH1GmxRSl0A8H7W63CgDcBg1osguYDfBaLhd4Fo+F0gmkr6Lqw1DKPd60W5FZZ5Ril10DCMrVmvg2QPvwtEw+8C0fC7QDTV+F1gKJwQQgghhMQChSUhhBBCCIkFCstwfDvrBZDcwO8C0fC7QDT8LhBN1X0XmGNJCCGEEEJigY4lIYQQQgiJBQrLkCil/m+l1FGl1CGl1A+UUiuyXhPJBqXU55RSbyulZpVSVVX9RwSl1E6l1DGl1C+VUn+a9XpINiil/kopNaCUOpz1Wkh2KKVWK6VeUkodmbs2/Kus15QmFJbheQHAFsMwbgNwHMC/yXg9JDsOA/htAK9kvRCSPkqpWgBPAvh1AJsBfF4ptTnbVZGM+A6AnVkvgmTODID/0zCMzQB+FcBj1fR/AoVlSAzDeN4wjJm5zV8AuCHL9ZDsMAzjHcMwjmW9DpIZHwPwS8Mw3jUMYwrAUwA+m/GaSAYYhvEKgOGs10GyxTCMfsMw+ub+PgbgHQDXZ7uq9KCwjIc/AvDjrBdBCMmE6wGcNm2fQRVdRAghziil1gG4A0BvtitJj7qsF5BnlFIvAui0eepxwzCennvN4xDb+7+nuTaSLn6+C4QQQohGKbUUwD4AXzEMYzTr9aQFhaULhmHc5/a8Uur/APAZAJ802LepovH6LpCq5iyA1abtG+Z+RgipUpRS9RBR+d8Nw/i7rNeTJgyFh0QptRPA/wXgNw3DuJT1egghmfE6gA1KqS6lVAOARwD8MOM1EUIyQimlAOwF8I5hGP9P1utJGwrL8PxnAE0AXlBK/ZNS6ltZL4hkg1LqIaXUGQAfB/D3SqmfZL0mkh5zRXz/EsBPIEn63zcM4+1sV0WyQCn1PwC8BmCTUuqMUmpX1msimXAXgD8AsGNOH/yTUurTWS8qLTh5hxBCCCGExAIdS0IIIYQQEgsUloQQQgghJBYoLAkhhBBCSCxQWBJCCCGEkFigsCSEEEIIIbFAYUkIIYQQQmKBwpIQQgghhMQChSUhhBBCCImF/w2WLydD7hEQ5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff989ecc278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "han.load_weights(path_to_save + 'han_trained_weights')\n",
    "\n",
    "get_doc_embeddings = Model(doc_ints,doc_att_vec_dr)\n",
    "\n",
    "rd_inf = read_batches(batch_names_val,\n",
    "                      path_to_batches,\n",
    "                      do_shuffle=False,\n",
    "                      do_train=True,\n",
    "                      my_max_doc_size_overall=max_doc_size_overall,\n",
    "                      my_max_sent_size_overall=max_sent_size_overall,\n",
    "                      my_n_cats=n_cats)\n",
    "\n",
    "n_batch_show = 8\n",
    "\n",
    "docs = []\n",
    "labels = []\n",
    "for rep in range(n_batch_show):\n",
    "    batch_tmp = rd_inf.__next__()\n",
    "    docs.append(batch_tmp[0])\n",
    "    labels = labels + batch_tmp[1].tolist()\n",
    "\n",
    "labels = [elt.index(1.0) for elt in labels]\n",
    "\n",
    "doc_emb_dim = 100\n",
    "doc_embs = np.empty((0,doc_emb_dim))\n",
    "for doc in docs:\n",
    "    doc_emb = get_doc_embeddings.predict(doc)\n",
    "    doc_embs = np.append(doc_embs,doc_emb,axis=0)\n",
    "\n",
    "my_pca = PCA(n_components=2)\n",
    "doc_embs_pca = my_pca.fit_transform(doc_embs)\n",
    "\n",
    "my_colors = ['#0055a4','#4c88bf','#ffcccc','#ff6666','#ff0000']\n",
    "fig, ax = plt.subplots()\n",
    "for label in list(set(labels)):\n",
    "    idxs = [idx for idx,elt in enumerate(labels) if elt==label]\n",
    "    ax.scatter(doc_embs_pca[idxs,0], \n",
    "               doc_embs_pca[idxs,1], \n",
    "               c = my_colors[label],\n",
    "               label=str(label),\n",
    "               alpha=0.7,\n",
    "               s=10)\n",
    "\n",
    "ax.legend(scatterpoints=1)\n",
    "fig.suptitle('PCA visualization of doc embeddings AFTER training',fontsize=14)\n",
    "fig.set_size_inches(11,7)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before training\n",
    "Of course, if we go back to the model **before training**, the internal representations don't make any sense!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff978f7a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "han.load_weights(path_to_save + 'han_init_weights')\n",
    "\n",
    "doc_emb_dim = 100\n",
    "doc_embs = np.empty((0,doc_emb_dim))\n",
    "for doc in docs:\n",
    "    doc_emb = get_doc_embeddings.predict(doc)\n",
    "    doc_embs = np.append(doc_embs,doc_emb,axis=0)\n",
    "\n",
    "my_pca = PCA(n_components=2)\n",
    "doc_embs_pca = my_pca.fit_transform(doc_embs)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for label in list(set(labels)):\n",
    "    idxs = [idx for idx,elt in enumerate(labels) if elt==label]\n",
    "    ax.scatter(doc_embs_pca[idxs,0], \n",
    "               doc_embs_pca[idxs,1], \n",
    "               c = my_colors[label],\n",
    "               label=str(label),\n",
    "               alpha=0.7,\n",
    "               s=10)\n",
    "\n",
    "ax.legend(scatterpoints=1)\n",
    "fig.suptitle('PCA visualization of doc embeddings BEFORE training',fontsize=14)\n",
    "fig.set_size_inches(11,7)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting attention coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== attention over words ==\n",
      "[[('Excellent', 494.95), ('product', 40.84), ('.', 366.23)], [('This', 135.66), ('was', 104.97), ('exactly', 382.57), ('what', 133.47), ('I', 67.65), ('needed', 56.65), ('.', 118.71)], [('I', 270.04), ('highly', 367.26), ('recommend', 280.12), ('it', 35.22), ('!', 34.79)], [(':', 92.81), ('-', 60.18), (')', 707.43)]]\n",
      "== attention over sentences ==\n",
      "[('Excellent product.', 0.20189385), ('This was exactly what I needed.', 0.48167485), ('I highly recommend it!', 0.27260393), (':-)', 0.04382727)]\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "K.set_session(sess)\n",
    "\n",
    "han.load_weights(path_to_save + 'han_trained_weights')\n",
    "\n",
    "with open(path_root + 'vocab.json', 'r') as my_file:\n",
    "    word_to_index_han = json.load(my_file)\n",
    "    \n",
    "padding_idx = 0\n",
    "oov_idx = 1\n",
    "\n",
    "get_sent_att_coeffs = Model(sent_ints,sent_att_coeffs) # coeffs over the words in a sentence\n",
    "get_doc_attention_coeffs = Model(doc_ints,doc_att_coeffs) # coeffs over the sentences in a document \n",
    "\n",
    "# ===== toy example =====\n",
    "\n",
    "text = \"Excellent product. This was exactly what I needed. I highly recommend it! :-)\"\n",
    "\n",
    "# == preprocessing ==\n",
    "sents = sent_tokenize(text)\n",
    "sents_idxs = []\n",
    "sents_tokenized = []\n",
    "for sent in sents:\n",
    "    words = word_tokenize(sent)\n",
    "    sents_tokenized.append(words)\n",
    "    idxs = [word_to_index_han[elt] if elt in word_to_index_han else oov_idx for elt in words]\n",
    "    sents_idxs.append(idxs)\n",
    "\n",
    "max_sent_size = min(max([len(s) for s in sents_idxs]),max_sent_size_overall)\n",
    "sents_idxs_padded = [s+[padding_idx]*(max_sent_size-len(s)) if len(s)<max_sent_size else s[:max_sent_size] for s in sents_idxs]\n",
    "reshaped_sentences = np.reshape(np.array(sents_idxs_padded),(1,len(sents),max_sent_size))\n",
    "reshaped_sentences_tensor = _to_tensor(reshaped_sentences,dtype='float32') # a layer, unlike a model, requires tf tensor as input\n",
    "\n",
    "print('== attention over words ==')\n",
    "sents_att_coeffs = TimeDistributed(get_sent_att_coeffs)(reshaped_sentences_tensor)\n",
    "word_coeffs = sents_att_coeffs.eval(session=sess)\n",
    "word_coeffs = np.reshape(word_coeffs,(len(sents),max_sent_size))\n",
    "\n",
    "my_wcs = []\n",
    "for my_idx,wc in enumerate(word_coeffs):\n",
    "    my_keys = sents_tokenized[my_idx]\n",
    "    my_values = [round(elt*1e3,2) for elt in wc.tolist()[:len(my_keys)]]\n",
    "    my_wcs.append(list(zip(my_keys,my_values)))\n",
    "\n",
    "print(my_wcs)\n",
    "    \n",
    "print('== attention over sentences ==')\n",
    "sent_coeffs = get_doc_attention_coeffs.predict(reshaped_sentences)\n",
    "sent_coeffs = sent_coeffs.flatten()\n",
    "\n",
    "print(list(zip(sents,sent_coeffs)))"
   ]
  },
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   "metadata": {},
   "source": [
    ":star: # Thank you for your interest!\n",
    "If you found any error or have any suggestion for improvement, please file an issue on GitHub. You can also contact me at `antoine.tixier-1@colorado.edu`.\n",
    "If you use some of the code in this repository in your own work, please cite:\n",
    "* bibtex:\n",
    "```\n",
    "@article{tixier2018notes,\n",
    "  title={Notes on Deep Learning for NLP},\n",
    "  author={Tixier, Antoine J-P},\n",
    "  journal={arXiv preprint arXiv:1808.09772},\n",
    "  year={2018}\n",
    "}\n",
    "```\n",
    "* plain text:\n",
    "```\n",
    "Tixier, Antoine J-P. \"Notes on Deep Learning for NLP.\" arXiv preprint arXiv:1808.09772 (2018).\n",
    "```"
   ]
  }
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