sujitpal/12-regularization.ipynb

## 12-regularization.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularisation in NNs¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we start doing anything, I think it's important to understand for NLP, this is the intuitive process on what we are trying to do when we are processing our data in the IMDB dataset:\n",
    "1. Tokenization: break sentence into individual words\n",
    "    - Before: `\"PyTorch seems really easy to use!\"`\n",
    "    - After: `[\"PyTorch\", \"seems\", \"really\", \"easy\", \"to\", \"use\", \"!\"]`\n",
    "2. Building vocabulary: build an index of words associated with unique numbers\n",
    "    - Before: `[\"PyTorch\", \"seems\", \"really\", \"easy\", \"to\", \"use\", \"!\"]`\n",
    "    - After: `{\"Pytorch: 0, \"seems\": 1, \"really\": 2, ...}`\n",
    "3. Convert to numerals: map words to unique numbers (indices)\n",
    "    - Before: `{\"Pytorch: 0, \"seems\": 1, \"really\": 2, ...}`\n",
    "    - After: `[0, 1, 2, ...]`\n",
    "4. Embedding look-up: map sentences (indices now) to fixed matrices\n",
    "    - ```[[0.1, 0.4, 0.3],\n",
    "       [0.8, 0.1, 0.5],\n",
    "       ...]```\n",
    "       \n",
    "## Notes from class video\n",
    "\n",
    "Regularization is a loose family of methods to prevent _overfitting_.\n",
    "\n",
    "Neural Networks are typically over-parameterized and generally have more than enough capacity to represent the data it is trained on. The general strategy is to have an overfit model generalize by adding regularization strategies.\n",
    "\n",
    "## Taxonomy of Regularization Techniques\n",
    "\n",
    "* __Parametric__ -- adding prior knowledge to model\n",
    "  * Initializing weights with specific distributions.\n",
    "* __Functional__ -- restricting learnable function\n",
    "  * L1 and L2 (weight decay) regularization \n",
    "* __Algorithmic__ -- modifying learning algorithm to reduce generalization error but not training error\n",
    "  * Early Stopping\n",
    "  * Dropout\n",
    "* __Indirect__ -- not regularization but has same effect\n",
    "  * Batch Normalization\n",
    "  * Data Augmentation\n",
    "  * Transfer Learning / Fine Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Critical plotting imports\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Checnking for module version \n",
    "from cmp_version import cmp_version \n",
    "\n",
    "# PyTorch imports\n",
    "from torchtext import __version__ as ttver\n",
    "# https://github.com/pytorch/text/releases/tag/v0.9.0-rc5\n",
    "if cmp_version(ttver,\"0.9.0\")>=0: \n",
    "    from torchtext.legacy import data, datasets\n",
    "else:\n",
    "    from torchtext import data, datasets\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# Checking for iterable objects\n",
    "import collections\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set seed\n",
    "torch.manual_seed(1337)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(1337)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Set plotting style\n",
    "plt.style.use(('dark_background', 'bmh'))\n",
    "plt.rc('axes', facecolor='none')\n",
    "plt.rc('figure', figsize=(16, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create instances of fields\n",
    "# The important field here is fix_length: all examples using this field will be padded to, or None for flexible sequence lengths\n",
    "# We are fixing this because we will be using a FNN not an LSTM/RNN/GRU where we can go through uneven sequence lengths\n",
    "max_len = 80\n",
    "text = data.Field(sequential=True, fix_length=max_len, batch_first=True, lower=True, dtype=torch.long)\n",
    "label = data.LabelField(sequential=False, dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calling splits() class method of datasets.IMDB to return a torchtext.data.Dataset object\n",
    "datasets.IMDB.download('./')\n",
    "ds_train, ds_test = datasets.IMDB.splits(text, label, path='./imdb/aclImdb/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train :  25000\n",
      "test :  25000\n",
      "train.fields : {'text': <torchtext.legacy.data.field.Field object at 0x7fb5ebff7670>, 'label': <torchtext.legacy.data.field.LabelField object at 0x7fb5ebff79a0>}\n"
     ]
    }
   ],
   "source": [
    "# Training and test set each 25k samples\n",
    "# 2 fields due to the way we split above\n",
    "print('train : ', len(ds_train))\n",
    "print('test : ', len(ds_test))\n",
    "print('train.fields :', ds_train.fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get validation set\n",
    "seed_num = 1337\n",
    "ds_train, ds_valid = ds_train.split(random_state=random.seed(seed_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train :  17500\n",
      "valid :  7500\n",
      "valid :  25000\n"
     ]
    }
   ],
   "source": [
    "# Now we've training, validation and test set\n",
    "print('train : ', len(ds_train))\n",
    "print('valid : ', len(ds_valid))\n",
    "print('valid : ', len(ds_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build vocabulary\n",
    "# num_words = 25000\n",
    "num_words = 1000\n",
    "text.build_vocab(ds_train, max_size=num_words)\n",
    "label.build_vocab(ds_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 1002\n",
      "Label size: 2\n"
     ]
    }
   ],
   "source": [
    "# Print vocab size\n",
    "print('Vocabulary size: {}'.format(len(text.vocab)))\n",
    "print('Label size: {}'.format(len(label.vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('the', 225411), ('a', 111661), ('and', 110736), ('of', 101281), ('to', 93618), ('is', 73247), ('in', 63011), ('i', 49141), ('this', 49010), ('that', 46426)]\n"
     ]
    }
   ],
   "source": [
    "# Print most common vocabulary text\n",
    "most_common_samples = 10\n",
    "print(text.vocab.freqs.most_common(most_common_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('neg', 8835), ('pos', 8665)]\n"
     ]
    }
   ],
   "source": [
    "# Print most common labels\n",
    "print(label.vocab.freqs.most_common())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'neg'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample 0 label\n",
    "ds_train[0].label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['just',\n",
       " 'about',\n",
       " 'everything',\n",
       " 'in',\n",
       " 'this',\n",
       " 'movie',\n",
       " 'is',\n",
       " 'wrong,',\n",
       " 'wrong,',\n",
       " 'wrong.',\n",
       " 'take',\n",
       " 'mike',\n",
       " 'myers,',\n",
       " 'for',\n",
       " 'example.',\n",
       " \"he's\",\n",
       " 'reached',\n",
       " 'the',\n",
       " 'point',\n",
       " 'where',\n",
       " 'you',\n",
       " 'realize',\n",
       " 'that',\n",
       " 'his',\n",
       " 'shtick',\n",
       " \"hasn't\",\n",
       " 'changed',\n",
       " 'since',\n",
       " 'his',\n",
       " 'snl',\n",
       " 'days,',\n",
       " 'over',\n",
       " 'ten',\n",
       " 'years',\n",
       " 'ago.',\n",
       " \"he's\",\n",
       " 'doing',\n",
       " 'the',\n",
       " 'same',\n",
       " 'cutesy',\n",
       " 'stream-of-consciousness',\n",
       " 'jokes',\n",
       " 'and',\n",
       " 'the',\n",
       " 'same',\n",
       " 'voices.',\n",
       " 'his',\n",
       " 'cat',\n",
       " 'is',\n",
       " 'painfully',\n",
       " 'unfunny.',\n",
       " 'he',\n",
       " 'tries',\n",
       " 'way',\n",
       " 'to',\n",
       " 'hard.',\n",
       " \"he's\",\n",
       " 'some',\n",
       " 'weird',\n",
       " 'type',\n",
       " 'a',\n",
       " 'comedian,',\n",
       " 'not',\n",
       " 'the',\n",
       " 'cool',\n",
       " 'cat',\n",
       " \"he's\",\n",
       " 'supposed',\n",
       " 'to',\n",
       " 'be.',\n",
       " 'the',\n",
       " 'rest',\n",
       " 'of',\n",
       " 'the',\n",
       " 'movie',\n",
       " 'is',\n",
       " 'just',\n",
       " 'as',\n",
       " 'bad.',\n",
       " 'the',\n",
       " 'sets',\n",
       " 'are',\n",
       " 'unbelievably',\n",
       " 'ugly',\n",
       " '---',\n",
       " 'and',\n",
       " 'clearly',\n",
       " 'a',\n",
       " 'waste',\n",
       " 'of',\n",
       " 'millions',\n",
       " 'of',\n",
       " 'dollars.',\n",
       " '(cardboard',\n",
       " 'cut-outs',\n",
       " 'for',\n",
       " 'the',\n",
       " 'background',\n",
       " 'buildings',\n",
       " 'would',\n",
       " 'have',\n",
       " 'made',\n",
       " 'more',\n",
       " 'sense',\n",
       " 'than',\n",
       " 'constructing',\n",
       " 'an',\n",
       " 'entire',\n",
       " 'neighborhood',\n",
       " 'and',\n",
       " 'main',\n",
       " 'street.)',\n",
       " 'alec',\n",
       " 'balwin',\n",
       " 'tries',\n",
       " 'to',\n",
       " 'do',\n",
       " 'a',\n",
       " 'funny',\n",
       " 'great',\n",
       " 'santini',\n",
       " 'impression,',\n",
       " 'but',\n",
       " 'he',\n",
       " 'ends',\n",
       " 'up',\n",
       " 'looking',\n",
       " 'and',\n",
       " 'sounding',\n",
       " 'incoherent.',\n",
       " \"there's\",\n",
       " 'even',\n",
       " 'an',\n",
       " 'innapropriate',\n",
       " 'cheesecake',\n",
       " 'moment',\n",
       " 'with',\n",
       " 'faux',\n",
       " 'celebrity',\n",
       " 'paris',\n",
       " 'hilton',\n",
       " '---',\n",
       " 'that',\n",
       " 'sticks',\n",
       " 'in',\n",
       " 'the',\n",
       " 'mind',\n",
       " 'simply',\n",
       " 'because',\n",
       " 'this',\n",
       " 'is',\n",
       " 'supposed',\n",
       " 'to',\n",
       " 'be',\n",
       " 'a',\n",
       " 'dr.',\n",
       " 'seuss',\n",
       " 'story.',\n",
       " 'avoid',\n",
       " 'this',\n",
       " 'movie',\n",
       " 'at',\n",
       " 'all',\n",
       " 'costs,',\n",
       " 'folks.',\n",
       " \"it's\",\n",
       " 'not',\n",
       " 'even',\n",
       " 'an',\n",
       " 'interesting',\n",
       " 'train',\n",
       " 'wreck.',\n",
       " '(i',\n",
       " 'hope',\n",
       " \"they'll\",\n",
       " 'make',\n",
       " 'horton',\n",
       " 'hears',\n",
       " 'a',\n",
       " 'who',\n",
       " 'with',\n",
       " 'robin',\n",
       " 'williams.',\n",
       " 'then',\n",
       " \"we'll\",\n",
       " 'have',\n",
       " 'the',\n",
       " 'bad-seuss',\n",
       " 'movie-starring-spasitc-',\n",
       " 'comedian',\n",
       " 'trilogy.)']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample 0 text: broken down into individual portions\n",
    "ds_train[0].text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "just about everything in this movie is wrong, wrong, wrong. take mike myers, for example. he's reached the point where you realize that his shtick hasn't changed since his snl days, over ten years ago. he's doing the same cutesy stream-of-consciousness jokes and the same voices. his cat is painfully unfunny. he tries way to hard. he's some weird type a comedian, not the cool cat he's supposed to be. the rest of the movie is just as bad. the sets are unbelievably ugly --- and clearly a waste of millions of dollars. (cardboard cut-outs for the background buildings would have made more sense than constructing an entire neighborhood and main street.) alec balwin tries to do a funny great santini impression, but he ends up looking and sounding incoherent. there's even an innapropriate cheesecake moment with faux celebrity paris hilton --- that sticks in the mind simply because this is supposed to be a dr. seuss story. avoid this movie at all costs, folks. it's not even an interesting train wreck. (i hope they'll make horton hears a who with robin williams. then we'll have the bad-seuss movie-starring-spasitc- comedian trilogy.)\n"
     ]
    }
   ],
   "source": [
    "# Sample 0 text: human readeable sample\n",
    "def show_text(sample):\n",
    "    print(' '.join(word for word in sample))\n",
    "    \n",
    "show_text(ds_train[0].text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and iterable object for our training, validation and testing datasets\n",
    "# Batches examples of similar lengths together that minimizes amount of padding needed\n",
    "batch_size = 64  # Change batch size from 1 to bigger number once explanation is done\n",
    "train_loader, valid_loader, test_loader = data.BucketIterator.splits(\n",
    "    (ds_train, ds_valid, ds_test), batch_size=batch_size, sort_key=lambda x: len(x.text), repeat=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-d3991e923379>:2: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.9 it will stop working\n",
      "  isinstance(train_loader, collections.Iterable)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if iterator above is an iterable which should show True\n",
    "isinstance(train_loader, collections.Iterable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[torchtext.legacy.data.batch.Batch of size 64]\n",
       "\t[.text]:[torch.LongTensor of size 64x80]\n",
       "\t[.label]:[torch.FloatTensor of size 64]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What's inside this iteratable object? Our text and label although now everything is in machine format (not \"words\") but in numbers!\n",
    "# The text we saw above becomes a matrix of size 1 x 80 represented by the fixed length we defined before that\n",
    "list(train_loader)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[torchtext.legacy.data.batch.Batch of size 64]\n",
       "\t[.text]:[torch.LongTensor of size 64x80]\n",
       "\t[.label]:[torch.FloatTensor of size 64]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternative to above, this is much faster but the above code is easy to understand and implement\n",
    "next(train_loader.__iter__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_batch = next(train_loader.__iter__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['text', 'label'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What methods can we call on this batch object? Text and label\n",
    "test_batch.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[190,  10, 180,  ...,   2,  24,   7],\n",
       "        [  9, 178, 206,  ...,  97,   0,  29],\n",
       "        [ 52,   7,   3,  ...,  48,   0,  21],\n",
       "        ...,\n",
       "        [  0,   0,   4,  ...,   2,   0,   0],\n",
       "        [  2,   0, 654,  ...,   0,   0, 148],\n",
       "        [  9, 201,  10,  ...,  22,   0, 550]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's break this down to check what's in a batch\n",
    "test_batch.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 80])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 comment per batch, each comment is limited to a size of 80 as we've defined\n",
    "test_batch.text.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 0., 0., 1., 1., 0., 1., 1., 1., 1., 0., 0., 1., 0., 1., 0., 0., 0.,\n",
       "        1., 0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0., 1., 1., 1.,\n",
       "        0., 1., 1., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0.,\n",
       "        1., 1., 0., 0., 0., 1., 0., 1., 0., 1.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_batch.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extremely weird problem in torchtext where BucketIterator returns a Batch object versus just a simple tuple of tensors containing our text index and labels\n",
    "# So let's fix this with a new class FixBatchGenerator\n",
    "\n",
    "class FixBatchGenerator:\n",
    "    def __init__(self, dl, x_field, y_field):\n",
    "        self.dl, self.x_field, self.y_field = dl, x_field, y_field\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.dl)\n",
    "    \n",
    "    def __iter__(self):\n",
    "        for batch in self.dl:\n",
    "            X = getattr(batch, self.x_field)\n",
    "            y = getattr(batch, self.y_field)\n",
    "            yield (X,y)\n",
    "\n",
    "            \n",
    "train_loader = FixBatchGenerator(train_loader, 'text', 'label')\n",
    "valid_loader = FixBatchGenerator(valid_loader, 'text', 'label')\n",
    "test_loader = FixBatchGenerator(test_loader, 'text', 'label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[  9,   0,   0,  ...,  60,   0,  74],\n",
      "        [ 10,   7,   3,  ...,  39, 575,   2],\n",
      "        [  0,  10, 135,  ...,  12,   6, 206],\n",
      "        ...,\n",
      "        [  0,   9, 178,  ...,  28,   0,  12],\n",
      "        [  0,  17,   2,  ..., 101,   0,  38],\n",
      "        [  0,   0,  62,  ...,   0,   0, 177]])\n",
      "tensor([1., 0., 0., 0., 1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0., 1., 0., 1.,\n",
      "        0., 1., 1., 1., 0., 1., 1., 1., 1., 0., 0., 1., 1., 0., 1., 1., 0., 0.,\n",
      "        0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1., 1., 1., 0., 0., 1.,\n",
      "        0., 0., 0., 0., 1., 1., 1., 0., 1., 0.])\n"
     ]
    }
   ],
   "source": [
    "# Text index\n",
    "print(next(train_loader.__iter__())[0])\n",
    "\n",
    "# Text label\n",
    "print(next(train_loader.__iter__())[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline\n",
    "\n",
    "### Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeedforwardNeuralNetModel(nn.Module):\n",
    "    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim):\n",
    "        super(FeedforwardNeuralNetModel, self).__init__()\n",
    "        # Embedding layer\n",
    "        self.embedding = nn.Embedding(input_dim, embedding_dim)\n",
    "        \n",
    "        # Linear function\n",
    "        self.fc1 = nn.Linear(embedding_dim*embedding_dim, hidden_dim) \n",
    "\n",
    "        # Linear function (readout)\n",
    "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Embedding\n",
    "        embedded = self.embedding(x)\n",
    "        embedded = embedded.view(-1, embedding_dim*embedding_dim)\n",
    "        # Linear function\n",
    "        out = self.fc1(embedded)\n",
    "\n",
    "        # Non-linearity\n",
    "        out = torch.relu(out)\n",
    "        \n",
    "        # Toggle 3: Dropout\n",
    "        # out = torch.dropout(out, 0.8)\n",
    "\n",
    "        # Linear function (readout)\n",
    "        # Take note here use a final sigmoid function so your loss should not go through sigmoid again.\n",
    "        # BCELoss is the right class to use as it doesn't pass your output through a sigmoid function again.\n",
    "        # In multi-class problems you're used to softmax which can be simplified to a logistic,\n",
    "        # function when you have a two-class problem.\n",
    "        out = self.fc2(out)\n",
    "        out = torch.sigmoid(out)\n",
    "    \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = num_words + 2\n",
    "embedding_dim = max_len\n",
    "hidden_dim = 32\n",
    "output_dim = 1\n",
    "\n",
    "# Instantiate model class and assign to object\n",
    "model = FeedforwardNeuralNetModel(input_dim, embedding_dim, hidden_dim, output_dim)\n",
    "\n",
    "# Push model to CUDA device if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "# Loss function\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "# Optimizer\n",
    "# Toggle 2: L2 Norm option - this is called weight decay\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=0.005)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of groups of parameters 5\n",
      "--------------------------------------------------\n",
      "torch.Size([1002, 80])\n",
      "torch.Size([32, 6400])\n",
      "torch.Size([32])\n",
      "torch.Size([1, 32])\n",
      "torch.Size([1])\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Number of groups of parameters\n",
    "print('Number of groups of parameters {}'.format(len(list(model.parameters()))))\n",
    "print('-'*50)\n",
    "# Print parameters\n",
    "for i in range(len(list(model.parameters()))):\n",
    "    print(list(model.parameters())[i].size())\n",
    "print('-'*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 100 | Train Loss: 0.691243052482605 | Val Loss: 0.685560405254364 | Val Accuracy: 52.59\n",
      "Iter: 200 | Train Loss: 0.6649552583694458 | Val Loss: 0.6192490458488464 | Val Accuracy: 55.59\n",
      "Iter: 300 | Train Loss: 0.644988477230072 | Val Loss: 0.5516107678413391 | Val Accuracy: 58.43\n",
      "Iter: 400 | Train Loss: 0.5724755525588989 | Val Loss: 0.6089349389076233 | Val Accuracy: 58.57\n",
      "Iter: 500 | Train Loss: 0.558177649974823 | Val Loss: 0.4621509313583374 | Val Accuracy: 62.12\n",
      "Iter: 600 | Train Loss: 0.39767521619796753 | Val Loss: 0.4762592613697052 | Val Accuracy: 62.83\n",
      "Iter: 700 | Train Loss: 0.5065121650695801 | Val Loss: 0.5164039731025696 | Val Accuracy: 62.93\n",
      "Iter: 800 | Train Loss: 0.46617329120635986 | Val Loss: 0.4972568452358246 | Val Accuracy: 64.19\n",
      "Iter: 900 | Train Loss: 0.22484073042869568 | Val Loss: 0.6290019154548645 | Val Accuracy: 64.15\n",
      "Iter: 1000 | Train Loss: 0.18472328782081604 | Val Loss: 0.5441626906394958 | Val Accuracy: 64.09\n",
      "Iter: 1100 | Train Loss: 0.14298397302627563 | Val Loss: 0.5804673433303833 | Val Accuracy: 63.49\n",
      "Iter: 1200 | Train Loss: 0.14651983976364136 | Val Loss: 0.6106327176094055 | Val Accuracy: 64.36\n",
      "Iter: 1300 | Train Loss: 0.1230461448431015 | Val Loss: 0.5710459351539612 | Val Accuracy: 64.36\n",
      "Iter: 1400 | Train Loss: 0.0476459339261055 | Val Loss: 0.7127318978309631 | Val Accuracy: 64.53\n",
      "Iter: 1500 | Train Loss: 0.08026529848575592 | Val Loss: 0.6724677085876465 | Val Accuracy: 64.43\n",
      "Iter: 1600 | Train Loss: 0.06665916740894318 | Val Loss: 0.6913567185401917 | Val Accuracy: 64.47\n",
      "Iter: 1700 | Train Loss: 0.0453047901391983 | Val Loss: 0.8139302134513855 | Val Accuracy: 64.35\n",
      "Iter: 1800 | Train Loss: 0.017524056136608124 | Val Loss: 0.6624395251274109 | Val Accuracy: 64.24\n",
      "Iter: 1900 | Train Loss: 0.04578496143221855 | Val Loss: 0.7371417880058289 | Val Accuracy: 64.81\n",
      "Iter: 2000 | Train Loss: 0.003361660987138748 | Val Loss: 0.800719678401947 | Val Accuracy: 64.87\n",
      "Iter: 2100 | Train Loss: 0.011428197845816612 | Val Loss: 0.933302640914917 | Val Accuracy: 64.36\n",
      "Iter: 2200 | Train Loss: 0.007501623593270779 | Val Loss: 0.9014174938201904 | Val Accuracy: 64.48\n",
      "Iter: 2300 | Train Loss: 0.004618192557245493 | Val Loss: 0.8584492802619934 | Val Accuracy: 64.64\n",
      "Iter: 2400 | Train Loss: 0.016235534101724625 | Val Loss: 0.918178141117096 | Val Accuracy: 64.69\n",
      "Iter: 2500 | Train Loss: 0.0027656282763928175 | Val Loss: 0.9704509377479553 | Val Accuracy: 64.76\n",
      "Iter: 2600 | Train Loss: 0.002675929106771946 | Val Loss: 0.9840683937072754 | Val Accuracy: 64.39\n",
      "Iter: 2700 | Train Loss: 0.002459029434248805 | Val Loss: 0.9826191067695618 | Val Accuracy: 64.47\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "num_epochs = 10\n",
    "history_train_acc_0, history_val_acc_0, history_train_loss_0, history_val_loss_0 = [], [], [], []\n",
    "best_accuracy = 0\n",
    "for epoch in range(num_epochs):\n",
    "#     print('-'*50)\n",
    "    for i, (samples, labels) in enumerate(train_loader):\n",
    "        # Training mode\n",
    "        model.train()\n",
    "        \n",
    "        # Load samples\n",
    "        samples = samples.view(-1, max_len).to(device)\n",
    "        labels = labels.view(-1, 1).to(device)\n",
    "\n",
    "        # Clear gradients w.r.t. parameters\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass to get output/logits\n",
    "        outputs = model(samples)\n",
    "\n",
    "        # Calculate Loss: softmax --> cross entropy loss\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Toggle 1: L1 norm, add to original loss\n",
    "        # fc1_params = torch.cat([x.view(-1) for x in model.fc1.parameters()])\n",
    "        # loss += 0.001 * torch.norm(fc1_params, 1)\n",
    "    \n",
    "        # Getting gradients w.r.t. parameters\n",
    "        loss.backward()\n",
    "\n",
    "        # Updating parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        iter += 1\n",
    "\n",
    "        if iter % 100 == 0:\n",
    "            # Get training statistics\n",
    "            train_loss = loss.data.item()\n",
    "            \n",
    "            # Testing mode\n",
    "            model.eval()\n",
    "            # Calculate Accuracy         \n",
    "            correct = 0\n",
    "            total = 0\n",
    "            # Iterate through test dataset\n",
    "            for samples, labels in valid_loader:\n",
    "                # Load samples\n",
    "                samples = samples.view(-1, max_len).to(device)\n",
    "                labels = labels.view(-1).to(device)\n",
    "\n",
    "                # Forward pass only to get logits/output\n",
    "                outputs = model(samples)\n",
    "                \n",
    "                # Val loss\n",
    "                val_loss = criterion(outputs.view(-1, 1), labels.view(-1, 1))\n",
    "                \n",
    "                # We use a threshold to define. \n",
    "                # There is another way to do this with one-hot label. Feel free to explore and understand what are the pros/cons of each.\n",
    "                # This opens up a whole topic on why it becomes problematic when we expand beyond 2 class to 10 classes.\n",
    "                # Why do we encode? Why can't we do 0, 1, 2, 3, 4 etc. without one-hot encoding?\n",
    "                predicted = outputs.ge(0.5).view(-1)\n",
    "\n",
    "                # Total number of labels\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total correct predictions\n",
    "                correct += (predicted.type(torch.FloatTensor).cpu() == labels.type(torch.FloatTensor)).sum().item()\n",
    "                # correct = (predicted == labels.byte()).int().sum().item()\n",
    "            \n",
    "            accuracy = 100. * correct / total\n",
    "        \n",
    "            # Print Loss\n",
    "            print('Iter: {} | Train Loss: {} | Val Loss: {} | Val Accuracy: {}'.format(iter, train_loss, val_loss.item(), round(accuracy, 2)))\n",
    "            \n",
    "            # Append to history\n",
    "            history_val_loss_0.append(val_loss.data.item())\n",
    "            history_val_acc_0.append(round(accuracy, 2))\n",
    "            history_train_loss_0.append(train_loss)\n",
    "            \n",
    "            # Save model when accuracy beats best accuracy\n",
    "            if accuracy > best_accuracy:\n",
    "                best_accuracy = accuracy\n",
    "                # We can load this best model on the validation set later\n",
    "                torch.save(model.state_dict(), 'best_model.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting loss graph\n",
    "plt.plot(history_train_loss_0, label='Train')\n",
    "plt.plot(history_val_loss_0, label='Validation')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation Accuracy')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting validation accuracy graph\n",
    "plt.plot(history_val_acc_0)\n",
    "plt.title('Validation Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parametric 1: Adding Prior Knowledge to Model\n",
    "\n",
    "Usually means that one initializes the weights to specific distributions instead of random.\n",
    "\n",
    "### Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeedforwardNeuralNetModel(nn.Module):\n",
    "    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim,\n",
    "                 initialize_weights=False):\n",
    "        super(FeedforwardNeuralNetModel, self).__init__()\n",
    "        # Embedding layer\n",
    "        self.embedding = nn.Embedding(input_dim, embedding_dim)\n",
    "        \n",
    "        # Linear function\n",
    "        self.fc1 = nn.Linear(embedding_dim*embedding_dim, hidden_dim) \n",
    "\n",
    "        # Linear function (readout)\n",
    "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "        if initialize_weights:\n",
    "            torch.nn.init.xavier_uniform_(self.embedding.weight)\n",
    "            torch.nn.init.xavier_uniform_(self.fc1.weight)\n",
    "            torch.nn.init.xavier_uniform_(self.fc2.weight)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Embedding\n",
    "        embedded = self.embedding(x)\n",
    "        embedded = embedded.view(-1, embedding_dim*embedding_dim)\n",
    "        # Linear function\n",
    "        out = self.fc1(embedded)\n",
    "\n",
    "        # Non-linearity\n",
    "        out = torch.relu(out)\n",
    "        \n",
    "        # Toggle 3: Dropout\n",
    "        # out = torch.dropout(out, 0.8)\n",
    "\n",
    "        # Linear function (readout)\n",
    "        # Take note here use a final sigmoid function so your loss should not go through sigmoid again.\n",
    "        # BCELoss is the right class to use as it doesn't pass your output through a sigmoid function again.\n",
    "        # In multi-class problems you're used to softmax which can be simplified to a logistic,\n",
    "        # function when you have a two-class problem.\n",
    "        out = self.fc2(out)\n",
    "        out = torch.sigmoid(out)\n",
    "    \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = num_words + 2\n",
    "embedding_dim = max_len\n",
    "hidden_dim = 32\n",
    "output_dim = 1\n",
    "\n",
    "# Instantiate model class and assign to object\n",
    "model = FeedforwardNeuralNetModel(input_dim, embedding_dim, hidden_dim, output_dim,\n",
    "                                  initialize_weights=True)\n",
    "\n",
    "# Push model to CUDA device if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "# Loss function\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "# Optimizer\n",
    "# Toggle 2: L2 Norm option - this is called weight decay\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=0.005)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 100 | Train Loss: 0.6434608697891235 | Val Loss: 0.6380903124809265 | Val Accuracy: 61.4\n",
      "Iter: 200 | Train Loss: 0.5745137929916382 | Val Loss: 0.4739680588245392 | Val Accuracy: 70.93\n",
      "Iter: 300 | Train Loss: 0.514424741268158 | Val Loss: 0.46899425983428955 | Val Accuracy: 73.17\n",
      "Iter: 400 | Train Loss: 0.40835675597190857 | Val Loss: 0.5148271322250366 | Val Accuracy: 72.77\n",
      "Iter: 500 | Train Loss: 0.38931983709335327 | Val Loss: 0.4465191662311554 | Val Accuracy: 72.47\n",
      "Iter: 600 | Train Loss: 0.2892666757106781 | Val Loss: 0.3578641414642334 | Val Accuracy: 71.4\n",
      "Iter: 700 | Train Loss: 0.23473544418811798 | Val Loss: 0.5575879216194153 | Val Accuracy: 70.13\n",
      "Iter: 800 | Train Loss: 0.30472612380981445 | Val Loss: 0.3725654184818268 | Val Accuracy: 69.03\n",
      "Iter: 900 | Train Loss: 0.07961718738079071 | Val Loss: 0.41416481137275696 | Val Accuracy: 69.27\n",
      "Iter: 1000 | Train Loss: 0.08179456740617752 | Val Loss: 0.3983740508556366 | Val Accuracy: 69.17\n",
      "Iter: 1100 | Train Loss: 0.023517802357673645 | Val Loss: 0.7242944240570068 | Val Accuracy: 68.45\n",
      "Iter: 1200 | Train Loss: 0.014340889640152454 | Val Loss: 0.7067967057228088 | Val Accuracy: 68.99\n",
      "Iter: 1300 | Train Loss: 0.009733382612466812 | Val Loss: 0.6368529200553894 | Val Accuracy: 68.4\n",
      "Iter: 1400 | Train Loss: 0.008463604375720024 | Val Loss: 0.6157438158988953 | Val Accuracy: 68.47\n",
      "Iter: 1500 | Train Loss: 0.00782714318484068 | Val Loss: 0.6451756358146667 | Val Accuracy: 69.0\n",
      "Iter: 1600 | Train Loss: 0.0037540767807513475 | Val Loss: 0.6817021369934082 | Val Accuracy: 69.0\n",
      "Iter: 1700 | Train Loss: 0.0022308961488306522 | Val Loss: 0.7286624908447266 | Val Accuracy: 68.93\n",
      "Iter: 1800 | Train Loss: 0.0022604791447520256 | Val Loss: 0.7519873976707458 | Val Accuracy: 69.11\n",
      "Iter: 1900 | Train Loss: 0.0023789687547832727 | Val Loss: 0.7365056872367859 | Val Accuracy: 68.93\n",
      "Iter: 2000 | Train Loss: 0.0017024491680786014 | Val Loss: 0.7291480898857117 | Val Accuracy: 69.09\n",
      "Iter: 2100 | Train Loss: 0.0016191734466701746 | Val Loss: 0.7497128844261169 | Val Accuracy: 69.19\n",
      "Iter: 2200 | Train Loss: 0.0009958603186532855 | Val Loss: 0.7735979557037354 | Val Accuracy: 69.13\n",
      "Iter: 2300 | Train Loss: 0.0009832768701016903 | Val Loss: 0.7816972136497498 | Val Accuracy: 69.21\n",
      "Iter: 2400 | Train Loss: 0.0007103491225279868 | Val Loss: 0.7805219292640686 | Val Accuracy: 69.21\n",
      "Iter: 2500 | Train Loss: 0.0007839307654649019 | Val Loss: 0.7890923619270325 | Val Accuracy: 69.17\n",
      "Iter: 2600 | Train Loss: 0.0007722758455201983 | Val Loss: 0.8050373196601868 | Val Accuracy: 69.25\n",
      "Iter: 2700 | Train Loss: 0.000850950600579381 | Val Loss: 0.8094838261604309 | Val Accuracy: 69.16\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "num_epochs = 10\n",
    "history_train_acc_1, history_val_acc_1, history_train_loss_1, history_val_loss_1 = [], [], [], []\n",
    "best_accuracy = 0\n",
    "for epoch in range(num_epochs):\n",
    "#     print('-'*50)\n",
    "    for i, (samples, labels) in enumerate(train_loader):\n",
    "        # Training mode\n",
    "        model.train()\n",
    "        \n",
    "        # Load samples\n",
    "        samples = samples.view(-1, max_len).to(device)\n",
    "        labels = labels.view(-1, 1).to(device)\n",
    "\n",
    "        # Clear gradients w.r.t. parameters\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass to get output/logits\n",
    "        outputs = model(samples)\n",
    "\n",
    "        # Calculate Loss: softmax --> cross entropy loss\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Toggle 1: L1 norm, add to original loss\n",
    "        # fc1_params = torch.cat([x.view(-1) for x in model.fc1.parameters()])\n",
    "        # loss += 0.001 * torch.norm(fc1_params, 1)\n",
    "    \n",
    "        # Getting gradients w.r.t. parameters\n",
    "        loss.backward()\n",
    "\n",
    "        # Updating parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        iter += 1\n",
    "\n",
    "        if iter % 100 == 0:\n",
    "            # Get training statistics\n",
    "            train_loss = loss.data.item()\n",
    "            \n",
    "            # Testing mode\n",
    "            model.eval()\n",
    "            # Calculate Accuracy         \n",
    "            correct = 0\n",
    "            total = 0\n",
    "            # Iterate through test dataset\n",
    "            for samples, labels in valid_loader:\n",
    "                # Load samples\n",
    "                samples = samples.view(-1, max_len).to(device)\n",
    "                labels = labels.view(-1).to(device)\n",
    "\n",
    "                # Forward pass only to get logits/output\n",
    "                outputs = model(samples)\n",
    "                \n",
    "                # Val loss\n",
    "                val_loss = criterion(outputs.view(-1, 1), labels.view(-1, 1))\n",
    "                \n",
    "                # We use a threshold to define. \n",
    "                # There is another way to do this with one-hot label. Feel free to explore and understand what are the pros/cons of each.\n",
    "                # This opens up a whole topic on why it becomes problematic when we expand beyond 2 class to 10 classes.\n",
    "                # Why do we encode? Why can't we do 0, 1, 2, 3, 4 etc. without one-hot encoding?\n",
    "                predicted = outputs.ge(0.5).view(-1)\n",
    "\n",
    "                # Total number of labels\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total correct predictions\n",
    "                correct += (predicted.type(torch.FloatTensor).cpu() == labels.type(torch.FloatTensor)).sum().item()\n",
    "                # correct = (predicted == labels.byte()).int().sum().item()\n",
    "            \n",
    "            accuracy = 100. * correct / total\n",
    "        \n",
    "            # Print Loss\n",
    "            print('Iter: {} | Train Loss: {} | Val Loss: {} | Val Accuracy: {}'.format(iter, train_loss, val_loss.item(), round(accuracy, 2)))\n",
    "            \n",
    "            # Append to history\n",
    "            history_val_loss_1.append(val_loss.data.item())\n",
    "            history_val_acc_1.append(round(accuracy, 2))\n",
    "            history_train_loss_1.append(train_loss)\n",
    "            \n",
    "            # Save model when accuracy beats best accuracy\n",
    "            if accuracy > best_accuracy:\n",
    "                best_accuracy = accuracy\n",
    "                # We can load this best model on the validation set later\n",
    "                torch.save(model.state_dict(), 'best_model_1.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting loss graph\n",
    "plt.plot(history_train_loss_1, label='Train')\n",
    "plt.plot(history_val_loss_1, label='Validation')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation Accuracy')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7n70H3UcO9J96W4HOw+VwtDd5fdsmGy3kjSYO5o1kYt5IJuaNZGLelNN8sxkfH4+Kigq1y/AanZ8Bc67/NQJNceg8XI6atx4d8zJsx+thO16PlpL8vmX6GxAcn+w+/TZkZhqCpsQjLOkshCWd5X6f3dKMrkPl7lNwu47sg8tu9di2TUa+njeaWJg3kol5I5mYN5KJeVNO881mbGys2iV41azv3IWw2RmwtzWh6sVfQfTax71M0evoax4PlwN9Z+PCPyTCfeQzZEYqQmakIiA8GgEZ5yMq4/y+97mc6Gk81Hfa7dGD6Dl2CNZjh+GwHB93TZOFr+eNJhbmjWRi3kgm5o1kYt6U03yz6cvPzYk9/ypEZ62C025F5Yu/hKPDe3fS6u1qR3vFDrRX7Oh7QadDUPT0IaffGqclIbh/DHlvTyesxw7DeuwQeo4dhrWx7197awMghNdqVoMv540mHuaNZGLeSCbmjWRi3pTTfLNpNpvx2muvqV2Gx4XPy8b0y34EAKh542F01x2QW4AQsDbVwtpUi+M7twIAdP4BCEmYi5CZqQiKmQVj7CwExcyEf3A4QmfNR+is+UMW4XLYYD12uK8BPXYYPY19R0Jtx+sgnL1yt8dDfDVvNDExbyQT80YyMW8kE/OmnOabzcZG33uGZFDMLCR9/xfQ6f1Qt/UFtJYWql0SAED02tF5aA86D+0Z8rp/aJS78QyKmQljzCwExc5EQHg0ghPmIjhh7pD5Xc5e2Jrr+o+GHnafjms9VguXY2JfE+qLeaOJi3kjmZg3kol5I5mYN+U032zW19erXYJH+QWHI/mGB+FvDEXL7n/haP7f1S5pRL2drejobEVH1a4hr/sFhfQ3oLNgjO3/N2YmAqLiYIztOzJ6MlvL0RNHQxsPuRtRZ0+npK0Znq/ljSY25o1kYt5IJuaNZGLelNN8s5mZmekzd5fS6f0w57r7EBSdgK4j+1Hz5iOT+ppHp7XrxI2IBtEbAhEYPX3Q0dC+JjRw6nQEmqYh0DQNEannDHmPo6PFfRrukGtLJfOlvNHEx7yRTMwbycS8kUzMm3KabzaLiorULsFjZlxxB8LnnA27pRmVL/4SLodN7ZK8wuWwoedoFXqODn24rk7vh8Ap8X2n4sbOQlDMiVNzDWEmGMJMCE/ORMyyNbAc2Ina/3sKPQ3VUmv3pbzRxMe8kUzMG8nEvJFMzJtymm82k5OTUVVVNfKME9zUZWsQs/TbcDnsqHrxV3C0N6tdknTC5XTflKhtzxcnJuh0CIiMQVDMLIRMT0Hs+VchfO5izL/7aTR9+SHqt76A3m6LlBp9JW80OTBvJBPzRjIxbyQT86acXu0C1GYymdQuYdzC5y7GzG/fBgCoeesP6Krdp3JFE4wQsLc2wrKvCEfz/47SR9ai8Yt3AQHELFuD9J++hJjl34FO7+f1UnwhbzR5MG8kE/NGMjFvJBPzppzmm83J/tycwOjpSLr2l9D5+aE+/+9o2fVPtUua8JzdFtS+/xfseXwd2vd/Bf/gMMxccxvm//hZhKdkeXXdkz1vNLkwbyQT80YyMW8kE/OmnOabTbPZrHYJivkZQzH3xt/APzgMrWWfo37rC2qXNKlYGw/hwHPrceCF/4G1+QiMsbOQ8p8PIfnG3yJw6nSvrHMy540mH+aNZGLeSCbmjWRi3pQb8ZrNlJQUvPHGG+7vk5KS8Ktf/QoJCQn49re/DbvdjqqqKtx4441ob2/3arHeUFdXp3YJyuj1SLr2lwiaOgPd9ZWofv33k/rOs2pq3/tvWPZ9hZjlV2Ba7g8QmbYU4SlLcOyL93D0k5fgtHZ5bF2TNm80KTFvJBPzRjIxbyQT86bciEc29+/fj8zMTGRmZmLx4sXo7u7Gu+++i23btiE9PR0LFy7E/v37ce+998qo1+NaWlrULkGRGZffgoiUJXB0tKLyhV/CZbeqXdKkJpwONBa+hbI/3ICmHf+ATqdHXM53kf7TlxB9zuWAzjMnAUzWvNHkxLyRTMwbycS8kUzMm3Jj+g06NzcXVVVVOHz4MLZt2wan0wkA+PLLLzF9undOO/S2jIwMtUsYs+hzLkfsed+Bq9eOypd+BXvbMbVL8hm9na049M6fsPfPt6Dj4DcwhEYi8aofY/5df0XYnLPHvfzJmDeavJg3kol5I5mYN5KJeVNuTI8+ueaaa/Daa6+d8vpNN9005FTbAfHx8aiqqkJnZyeMRiMKCgqwfv16mM1m1NTUwGq1IjU1FYWFhcjKyoLBYEBBQQFyc3NRWVkJoO9Ww/n5+VixYgUcDgeKi4uRk5ODiooKBAUFITExEVu2bIHZbIbFYkFZWRmWLVuG0tJSmEwmJCQkuKe3tLSgsrIS2dnZKCkpQXx8PCIiImAymWA2m9HY2Ij6+npkZmaiqKgIycnJMJlM7vfX1dWhpaUFGRkZ2L59O9LT0xEeHu6eLmObIuYugm3pdQCAaXX/xtTUGaj0tw3ZptjYWPf7J8M2jeZzUmOb5rQX4d8f/BPTzD9EcPwczPvRowhpq4al8DUEObsVbVNERARSU1P5OXGbpGxTREQEzGazT22TL35OvrJNNpsNeXl5PrVNvvg5+co2VVdXIy8vz6e2yRc/J1/ZprKyMuTl5fnUNnnycxqJGM0wGAyiqalJxMTEDHn95z//udi0adNp37Nhw4ZRLVvNcdlll6lew2hHoGmaOPu+d8WSR/LF9G/drHo9Whk6/wAx7aJrReZvPhRLHskXi373sUi49IdCH2gc87ImU944Jv9g3jhkDuaNQ+Zg3jhkDuZt+DFczzfq02hXrVqFr7/+GseOnThl8/rrr8fll1+Oa6+9drSLmXDCw8PVLmFU9IHBSL7xt/APCUfb3n/jyMfPqV2SZoheO47+8xWUPXIDmnduhd4/ANMu+j7S/9+LmLLYDOh0o17WZMkb+QbmjWRi3kgm5o1kYt6UG3WzmZeXN+QUWrPZjPXr12P16tXo6enxSnEyTIrn5uj0SPr+L2CMnYWehmocfO13gHCpXZXmOCzNqHnjYZT/5XZ0HtqLgPApmP29nyLt9r8gZNb8US1jUuSNfAbzRjIxbyQT80YyMW/KjarZNBqNWLlyJTZt2uR+7S9/+QvCwsKwbds2lJSU4KmnnvJakd40GZ6bM/1b6xCZthSOrnYceOGXcNm61S5J07oOl6PiyTtx8LXfw97ejJAZqUi77X8xO+/nMERMHfa9kyFv5DuYN5KJeSOZmDeSiXlTblQ3COrp6UF0dPSQ1+bOneuVgmSrqalRu4RhTVliRtyK/4DL2Yuql+6DveWo2iURAAiBlpJP0Lbnc8RdcA3iVnwPUzJzEblgORr+9ToaC96Ey2E75W0TPW/kW5g3kol5I5mYN5KJeVPOMw8PnMSs1on7fMrQxHTM+s6PAQCH330CndWlKldEJ3PZrajf+gLK/ngDWnb/C34BQUi45AYs+MlGRC288JT5J3LeyPcwbyQT80YyMW8kE/OmnOabzdTUVLVLOK2AqFjMuf7X0Psb0PjZ22gu+kjtkmgY9tZGHHzlQVQ8dTe66w4gMCoWc679H8y75XEET09xzzdR80a+iXkjmZg3kol5I5mYN+U032wWFhaqXcIp9IFGJN/wGxhCI9G+rwi1/3ha7ZJolDqrS7H3z7ei5u1H4ehsRdjsDKTdvgGJV/8E/qFREzJv5LuYN5KJeSOZmDeSiXlTTvPNZlZWltolDKXTYfY19yJ4WhJ6Gg/h4Cu/AVy88+ykIlxoLvoIZY+sRUPBGxAuJ6KzViFj/UtIXfMjQK/5/+xIkgm3fyOfxryRTMwbycS8Kaf533oNBoPaJQyRYL4JUQuWo7fbgsoXfwmntUvtkkghp7ULR/7xDPb86Ydo27sdfoHBaE9cjtRb/4zAqdPVLo80YKLt38i3MW8kE/NGMjFvymm+2SwoKFC7BDdT5sWYdtH3IZxOVL38a9ia69QuiTzA1lyHyhd+if3P/Qy9luMInZmGBXc/g5jl3wF0OrXLIx82kfZv5PuYN5KJeSOZmDflNN9s5ubmql0CACBkZhoSv3sPAODw+/+Ljqpd6hZEHmfZX4yUmn+g+aut0BsCMXPNbUhZ9wcERMaoXRr5qImyfyNtYN5IJuaNZGLelNN8s1lZWal2CTBETEXy2gegNwTg2Pb30fTl/6ldEnlJ9b69qHnzYVS++Cs4OlsRnpyJBf/9HKYsuVTt0sgHTYT9G2kH80YyMW8kE/OmnOabTbXpDUFIvuFBGMJMsBzYidoPNqhdEknQtucL7Hn0P9Fa+hn8gkIw+z/+H5Jv+A38Q6PULo2IiIiIyCM032wmJyert3KdDonfW4+QhLmwNh9B1d8fhHA51auHvG5w3nq72lD18v04+Nrv0dvTicj552LBPc8jKiNHxQrJl6i6fyPNYd5IJuaNZGLelNN8s5mfn6/auuMvvh6ms3LQ29OJyo3/A2dPh2q1kByny1tLySfY86cfon3/VzCERGDOdfdhdt7P4WcMU6FC8iVq7t9Ie5g3kol5I5mYN+U032yuWLFClfVGpp+P+JXXQ7icOPj3B2BtqlWlDpLrTHlztDfjwHPrcWjT43DaezAlMxcL7nkO4fOyJVdIvkSt/RtpE/NGMjFvJBPzppzmm02Hw6HKeqddlAcAOPKPZ2A5sFOVGki+kfLW9OX/Ye9jN6OzpgwB4dFI+eHvMes7P4Y+0CipQvIlau3fSJuYN5KJeSOZmDflNN9sFhcXS1+nzs8fxrgkAEBT0T+kr5/UM5q82Y7Xo+KpH+PIP56Bq9eOqUsvx4K7n0Ho7AwJFZIvUWP/RtrFvJFMzBvJxLwpp/lmMydH/s1YgmITofc3wNpUC5etR/r6ST2jzptwoaHgDZT/+VZ01x1A4JR4zPvRnzD98v+Czt/g3SLJZ6ixfyPtYt5IJuaNZGLelNN8s1lRUSF9nSEJcwEAXXUHpK+b1DXWvPU0VKP8L7ej/pOXASEQl3M15t/1NIKnp3ipQvIlauzfSLuYN5KJeSOZmDflNN9sBgUFSV9ncH+z2c1mU3OU5E04e1G/9QVUPHkHeo4dhjF2FtJu+wviV14Pnd7PC1WSr1Bj/0baxbyRTMwbycS8Kaf5ZjMxMVH6Otlsatd48tZVuw97H/8RGj97Gzo/P8SvXIvU2/8XQTGzPFcg+RQ19m+kXcwbycS8kUzMm3Kabza3bNkid4U6PYzT+m4O1F1XKXfdpLrx5k302lH7f09h31//G7aWBoRMn4f5d/0VsTlXAzrN/+dMJ5G+fyNNY95IJuaNZGLelNP8b6dms1nq+oKmzoBfQBBsLUfh7OmQum5Sn6fy1nFwN/Y8tg5NRR9BbwjAjMv/C/N+9CgCTNM8snzyDbL3b6RtzBvJxLyRTMybcppvNi0Wi9T18RRabfNk3ly2bhx6+1Ec2PgL2C3HEZZ0Fhb8+FlEn3OZx9ZBk5vs/RtpG/NGMjFvJBPzppzmm82ysjKp62OzqW3eyFt7+ZfY86f/RMvuT+EXaETiVf+NuTf9DobwKR5fF00usvdvpG3MG8nEvJFMzJtymm82ly1bJnV9IQnJAIAuXq+pSd7Km7PbgoOv/AZVr/wGvd0WRKSegwX//RxMZ1/olfXR5CB7/0baxryRTMwbycS8Kaf5ZrO0tFTeynQ6GOP7mk0e2dQmb+etdfen2POn/0R7xQ74B4cj6fv/g6Rrfwn/4HCvrpcmJqn7N9I85o1kYt5IJuZNuRGbzZSUFJSUlLhHe3s77rrrLkRFRWHr1q3Yv38/tm7disjISAnlep7JZJK2rkDTNPgbQ2G3NKO3s1XaemnikJE3h+U4Dvzt56h5+1E4bd0wLbwAC+55HhFp53p93TSxyNy/ETFvJBPzRjIxb8qN2Gzu378fmZmZyMzMxOLFi9Hd3Y13330XP/vZz5Cfn4+UlBTk5+fjZz/7mYx6PS4hIUHauk5cr8lTaLVKZt6aiz7Cnj+tQ8fB3TCEmTD3xt8g6Qf3ISAyRloNpC6ZeSNi3kgm5o1kYt6UG9NptLm5uaiqqsLhw4exZs0avPjiiwCAF198EVdccYU36vM6mc/N4c2BSPZzmuytDdj39D2o/b8n4bT1wHRWDhb8ZCOm5f4AOn+D1FpIPj4XjGRi3kgm5o1kYt6UG1Ozec011+C1114DAMTGxqKhoQEA0NDQgJiYyXm0ROZzc9hskirPaRICjZ+9g7I/3oDju/4Jv4AgJJhvRPo9f0PEfJ5a68v4XDCSiXkjmZg3kol5U85/tDMaDAasXr0a995776gXHh8fj6qqKnR2dsJoNKKgoADr16+H2WxGTU0NrFYrUlNTUVhYiKysLBgMBhQUFCA3NxeVlX2nmiYnJyM/Px8rVqyAw+FAcXExcnJyUFFRgaCgICQmJmLLli0wm82wWCwoKyvDsmXLUFpaCpPJhISEBPf0lpYWVFZWIjs7GyUlJYiPj8ecOXNgMplgNpvR2NiI+vp6ZGZmoqioCMnJyTCZTO7319XVoaWlBRkZGdi+fTvS09MRHh7unj7cNl2Um4vKWWkAgIsXp6Gw6YDXtik2NtY93ZvbJPNz8pVtmjNnDlJTU9XbJnEQn7/xOaZ/+zZgSjzm3vAbBFtq0VH4CoIcnfycfGyb5syZA7PZ7FPb5Iufk69s05QpU5CXl+dT2+SLn5OvbBMA5OXl+dQ2+eLn5CvbZLVakZeX51Pb5MnPaSRiNGP16tViy5Yt7u8rKipEXFycACDi4uJERUXFKe/ZsGHDqJat5pgzZ46U9QRExoglj+SLhfdtUn2bOdQbsvI24tDrRczyK8XZv35fLHkkXyz63WaRsOo/hT4gSP3aODw2JkzeODQxmDcOmYN545A5mLfhx3A936hPo83Ly3OfQgsAH3zwAdauXQsAWLt2Ld5///3RLmpCyc7OlrIenkJLgLy8jcjlwrEv3kXZI2vRVPQR9P4GTLswD+k/eYHP5vQhEyZvpAnMG8nEvJFMzJtyo2o2jUYjVq5ciU2bNrlfe+ihh7By5Urs378fK1euxEMPPeS1Ir2ppKREynrYbBIgL2+j1dvVhkNvP4ry/70VXYcrEBA5FUnf/x/M+68/wRiXpHZ5NE4TLW/k25g3kol5I5mYN+VG1Wz29PQgOjrafY48ALS0tODiiy9GSkoKLr74YrS2tnqtSG+Kj4+Xsp7g+GQAbDa1Tlbexqqrdh/KN9yOmrf+CEdnG8KSFmL+3X/FjNW3wc8YqnZ5pNBEzRv5JuaNZGLeSCbmTbkx3Y3WF8XGxkpZD49sEiAvb4oIgebij1H2h7Vo/LzvLIbY876D9P/3IqKzVgE6ncoF0lhN6LyRz2HeSCbmjWRi3pTTfLMp47k5/qFRCIiIRm9PJ2wtR72+Ppq4JsNzmpw9naj9YAP2Pv5f6Di4G4bQSCRe/ROk3fYXhMyYp3Z5NAaTIW/kO5g3kol5I5mYN+U032zKeG7OwFHNnvpKQAivr48mrsn0nKaehoPY99f/xsFXfwN7ezNCZqYi7Y4nMeu798A/JFLt8mgUJlPeaPJj3kgm5o1kYt6U03yzOZpnw4xXSELf9ZpddZVeXxdNbDLy5mktuz5F2R/W4ug/X4Wr14Gp2d9C+k9fRMzyKwG95nchE9pkzBtNXswbycS8kUzMm3Ka/02xvr7e6+vg9Zo0QEbevMFlt6Ju8/PY86f/RHtFEfyNoZi55nbMv+tphCadpXZ5dAaTNW80OTFvJBPzRjIxb8ppvtnMzMz0+jrczWY9m02tk5E3b7I1H8GBv92LAy/8D2zH6xE8LQmp//UYZn//FzBERKtdHp1ksueNJhfmjWRi3kgm5k05f7ULUFtRUZFXl+9nDEOgaRqcdiusTbVeXRdNfN7Omyzte/+Nsv07EbfiPzDtou9jytkXITLtXBzN/zsaP3sHwulQpS7/0CgEmuIQaIpDQNS0/n9jASHgtHXDZeuG09oNp60bTmsXnLYeuGxdfa9Zu+G09b9m7YLT1g3h7FVlOzzFV/JGkwPzRjIxb+QpekMQ9AEnhp/7a6P7+92dRkxZbIbL3tP/+0QPnPae/t8j+l4TvXa1N2VC0nyzmZycjKqqKq8tP7j/es2eowcBl8tr66HJwdt5k0n02nE0/+84vnMrpl9+C0xn5WD6t9YhOmsVDn+wAZZ9nv9FwC8oBAFRcf0N5TQE9P8bGBWHAFMs/AKMHl2fy2Hva0xt3e4G1Gnr6W9Uu+Gynmhanf1Nq8s20Mz2Na+9Ha0QLqdH6xotX8obTXzMG3maPiAIfkEh8AsMhj4wGH5BwfALDIZfUAimL8xEW9QuiF47XI6+IXrtcA1832uDcNjh6nX0z2NT/w+IOv1JzcxAc2M8tdkZ0gAZT7zHEAidivdLEEJAOHtPGS5nL4SrF6LXAeFy9v07eB5XL1y9A/OcNK/TAeF0wuV0nHbZ7iFE/8/BeJrGcPD3p/l5DmocT/7Zj9bskX42LueJ5tPe0/f7gN3a/3tBT3+jeqI57fu+f/rA/Dare5rL1qPa7w+epPlm02QyeXX5vF6TBvN23tRgbzuGg3//NZqSF2HmmtthjJ2FlB/+Hm17t6P2gyfH9Lgfnb8BAVGxCOw/Knmioez72j84fNj393ZbYGtpgK2lAfbWo/3/NkC4RN8vKe5fWozur/t+eQk5/WuGAOgNATCERir++fR2WXC85BM0F32MnoaDipejhC/mjSYureZNHxAEV6+df1Dup/Pz79vPDuxPB/apQcHQB4acaBgDB7120v5X3z99uKaqC8Ds1EvGVJtwueDqtfc3ofZBX9sGNauOvsb0lHnsQ+bRGwL6m78zNTynaYoMgeP86ZI3uBy2vqbQPXrgslv7X+v7ek5iIqoO1cIvMKgvnwHGvt8bAo19fwwIDIbeEAB/YyhgDPVgbXY47T1o2/MFDr39qMeWK5Pmm01vPzeHzSYN5svPaeqo/Bp7H78ZMcuvRPzF1yFy/jKEz12ChoI30PDpa3A5bIBOj4CI6NMclexvLEe47tNpt8Le2ghby1HYWxpgaznaP/qaSqe1y6PbpPMP6PsFaNAvR/r+RnQ0r/kbw2AIi0Lsed9B7HnfQVdtBZqLP0bLrk89Xuvp+HLeaOLxqbzpdPAPiYQhzARD+BQYwqJgCJsCQ7gJhrApCAg3wT/MBEOYCX4BQRAuJ3q72uGwHIejo6VvWFpg7zgOh6UFjo7jcHS0wmE5PnlOtdPp4R8cBv/QSBhCo/r+DYkc8r1/cNjQxjIoGHr/AI+V4Bw4AjRw6cPAZRC2bgTodbD3OqEzBEDv3/eHQb1/AHT+AdAbAqE39H/dP63vawP8AoKAgCCP1TgWwuWCy2FzNzRDGhyH9aSG5zTz9DdAUPNol14Pnd4fOr8TQ+83+HsDdH5+7n/1foYh855unPH9ej/o/Pv+1fsbAOjG9XM7ZbrDCpfdBoiR/1BkMZnQ0tIy7Dw6vV/fHxcCg/v+gNLfiPY1pcH9TelJDWpg0IlpgxvY/mUM/NF7Mv+hQvPNptlsxmuvvea15Yf0N5tdbDYJ3s+b2oSzF42Fb6GlJB8Jq9YheskliL/4OkRnfwsuuxUBkTH9/8M40/udsLc19h2dbG2Avb+RtPU3lr2drRK3pu9U4d5O+7jWG5wwF9FZq2DKzEXIjFSEzEjF9MtvQes3hWgu/gid1aUerHgoVfOm0/G5wpIZwqcgIDJWtfWvWHkxNv/jw76jRHZb3y9yDhtErzrXcJ+Ozs/Q1ziGTxnUSJpO/To0Cjo/v1Et0+Ww9S+3770j6e3pHNqUdrSc+N7S35R2HIezp3O8m3sKfUAQ/EOjYAgd1DSGRA76PrJ/ehT8Q8Kh04/uZzCYy9kLp7Wr/9KDnkHXyPdfemAbdN38aS49cA1cqmDvGfZocV5eHl57a4z7N50een8DdIbAvibU39DXrPZ/725UB173HzRtUFOr8zP0HQHtP+rlbmYcwzc8LodtzD9PmhhG8/9T4XL257oLntrr6fwN8AsMntT/P9V8s1lXV+e1ZesDgxE0dQZcvQ5YG2u8th6aPLyZt4nE0dGCmjcfRvOODzHzijvcR/gBwG45PqiJPHFU0tZyFPb2Jp87Fa277gAO1x3AkX88jcj08xGdvQrhc85G9JJLEL3kElibjqC5+GMc37kVjo7h/2o6VjLzptP7IXR2BiJSsxEx7xwExcxE56E9aCv7HK1ln8Pe2iCtFq3Q6f0QmpiO8HnZiEjNRvC0JFXrqQWwYN6aU153H805editEA47nANNqaO/SXU3q0PnPfX9J74Wzl74h0YgYNDRR0O4CYZQ05DvRzoVfzBHVzt6T3uE8sSRS0fHcbhsPdDp/fqatMHrHmhcT/re3xgKf2MojLGzhl2/y2EbtJ6TmtJBR0rdR2HdjeJJjWP/0Uj/0Mi+o3pj0NtlgaOzFb1dbXB0tqG3oxWOrjb0drbC0dkGZ3cHet2NZXf/TVLk/HFB0f5N9GURDhsm/5VwJJNav7+JXgd6e9tVWbenaL7ZHOmQ+HgET5sDAOhpqFb/onSaELyZt4mo89Ae7P3zrQiZPhdOWw9sLQ2T5xQyD3M5bGgp+QQtJZ8gcEo8opdciilLzAiaOh3Tv7UOCeab0FbxJZqLP0Z7xQ6PNN3ezpshzITweVmISF2K8LmL+q5VGSRsdgbCZmdgxrdvQXfdAbTu+QJtZZ+jp6Haq3X5MkNENCLmZSNiXjbC5y6CX1CIe5rT1oOexhrV/gIeHBICq8PZdyQoILDvBif9R4MGTh1Tm3A6+5q1zhPN4smnufY1c61juqu2cDnhsDTDYWkecV6/4HAYwkwIGHJ0dehpuoZwE/wCg/suNTBNG88mD+G0W92NYm9nfwM5uJnsbOtrLjvb0NvVPqFvTqK1/5+Supg35TTfbGZkZKCsrMwryx64Ey2v16QB3szbhCVc6Krdp3YVE4rteD3qtvwNddteQERKFqKzv4WItKWIWrAcUQuWw245juM7t6K5+GPYmpX/NdXjedPpETJjHiJSz0FE6jkImZ4yZHJPQw3a9+1Ae0URuo8eRPjcRYhKPw8RqecgOGEughPmIuGSG2BtrnMf8eyqLZ/Upwd5m87PH6GJ6X3N5bysU45e9jQeQvu+IrRXFKGzulS1xw4B/ac1nu40M52+/5qjoP4mdGAEDbrJyqDXA4JOzG8I7Dvlccj7Tp1X52dAb7flpNNRBx8V7Gsqe7vbVc+bs9sCZ7dlxDOe9AFBJx0ZnXLaU4AhRH+DeFIT2dXfOHaceN3lsMrZSAk0+f9TUg3zppzmm83t27d7bdm8ORCdzJt5o0nI5UJ7xQ60V+yAf2gUpixeieisVTDGzMS0C/Mw7cI8dBzcjeaij9FaWjjm6308kTc/Yxgi5i1BxLxzED4va8ideV0OGyyVJf3bUHTKqbKtu/+F1t3/gs7fgPDkRYhMPw+R85chKDoBcRd8D3EXfA92SzPa9mxHW9nn6KjaNaGPpMhiiJjafzpyNsKTM085emk58DXa9xXBsq8I9rZjKlY61BnzJlzua9bg/fti+QyX3Qrb8XrYjterXcqExP+fkkzMm3KabzbT09Nx6NAhryybzSadzJt5o8mtt7MVjQVvorHgTYTOWoDo7FWIWngBwpIWIixpIWasuR0tuz9Fc9FH6D6yf1TLVJo347QkRKQuRURqNkJnzR9ykxDb8Xq09TeXHVW7RnVatOh1uJvqQ/rHEJqYjqgF5yEy/TwERsUi5tzViDl3NXq7O9Be/iVa93wOy76vfOoozHDcRy/7G0xj3NCnufU0VJ84ellTNmEvy+D+jWRi3kgm5k05zTeb4eGjv1nAWOj8A2CMmQXhdKL7qNxn69HE5a28kW/pPLQHnYf24PAHT8K08AJEZ61C6Kz5iFn6bcQs/Ta666v6bipUkg9nt+WMyxlt3vSBRoQnL3Lf3Ccgcqp7msvZi44DX7ubRWtT7fg2zuVC58Fv0HnwG9T+35MITpiLyPTzELXgPBjjEjFl8UpMWbwSTrsVlv1foa3sc7SVfwlnT8f41jvBBETG9N3YZ142wudm9t1tsJ/T1j3o6GXxhDp6ORzu30gm5o1kYt6U0wHw2sULGzZswG233eatxXuEaRTPzVEiZMY8pN3xJLqPHsTex9Z5fPk0OXkrb+T7gmJnITrrW5iy6GL3qayuXjvayr5Ac/HHsFR+fcq1aMPlLTB6OiLTzkHEvHMQmnTWkEfS2C3NaK8oQnvFDlgOfA2Xrdtr23VyTVHpfUc8Q2emuV8XTic6qnahdc/naNvzRd8dOCcZnZ8BobPT3Tf3McYlDpk+WY5eDof7N5KJeSOZmLfhDdfzaf7IpreeQ3fiFNpKjy+bJi9ff84meY+18RCOfPgU6j5+FpHzl/U9QmXuEpjOvhCmsy+EraUBzV9txvGvtriPhA3Om87fgLCkhe6b+wRFJ7iXLVxOdNaUob1iB9oqitBTr85+y9Z8BA3/eh0N/3odhohoRM5fjqj08xCWtBDhKYsRnrIYs668C52H9vbdYGjP5+O6gZK3BUTGICI1G+HzshGevGjI3Vid1i730cv2fcVwtDepWKlncP9GMjFvJBPzppzmm82amhqvLNfdbNbzek06wVt5I+0Qzl60lhaitbQQAZExmLLEjOgllyLQFIeES25A/MXXw3LgKzQXfYzqJgumLr0cEannICw5E34BJ5odR1c7LPuK+06P3f/VsKfjqsHR3oymf7+Ppn+/D7/gcESmLUXkgvMQMW8JQmfNR+is+Zh+2c3oaahGa/+dbaU0ySPdWTUwGKGJC/qOXp70HMXuowfRXtF3Y5/OQ3sm5dHL4XD/RjIxbyQT86ac5ptNq9U7N6DgzYHodLyVN9Ime9sxHP3kZRzN/zvC5mT23VQo/Tz3qZq9AAa3O111B9Be/iXaK4rQVVsBiPE/y1MGZ7cFx3duxfGdW6E3BCF8XlbfdqYthTFuNoxxsxF/8XWwtTSgbc/naNuzHb09HUMfj+F/mkdsDHoepK6/gfQL6H/Uxsnz+A/8GzD6uq1dsBzYifZ9xWjfVwRH+8jPYJzMuH8jmZg3kol5U07zzWZqaipKSko8ukydn7/7boLd9VUeXTZNbt7IGxGEQEfl1+io/BqHjWGYkpmL6KxLERo7A8fLi9BWvgOW/cWT8lrHk7kcVrSVfYa2ss+g8/NHWNJCRGacj8j5yxBoikPs+d9F7Pnf9dr6hcsFl8MGl8MKl8Pe90gPh23IsB6r9dmjl8Ph/o1kYt5IJuZNOc03m4WFhR5fZlDMLOj9A2BtOiLtxho0OXgjb0SDOXs6cGz7ezi2/T0kJCSgrm7iXtM4XsLZC8uBnbAc2InD7z6BkJlpiEo/D+FzFwM6/amNoN0GV2/fv8LdMA6aNmTe/mbypHmE06H2Zk9Y3L+RTMwbycS8Kaf5ZjMrK8vjv4zxFFo6E2/kjehMNJU3IdB1aC+6Du1VuxLN0lTeSHXMG8nEvCmnV7sAtRkMhpFnGqMQNpt0Bt7IG9GZMG8kE/NGMjFvJBPzptyoms2IiAi89dZbKC8vx969e7F06VIsXLgQ//73v1FSUoLi4mJkZWV5u1avKCgo8PgyB45sdrHZpJN4I29EZ8K8kUzMG8nEvJFMzJtyo2o2n3jiCWzevBlpaWlYuHAhysvL8cgjj+DXv/41MjMz8atf/QqPPPKIt2v1itzcXM8uUKeHMT4JANCt0rPqaOLyeN6IhsG8kUzMG8nEvJFMzJtyI16zGRYWhpycHNxwww0AAIfDgfb2dgghEB4eDqDvyGd9fb1XC/WWykrPNoRBU6fDL8AIW0vDhHtuHanP03kjGg7zRjIxbyQT80YyMW/KjdhsJiUloampCRs3bsTChQuxc+dO3HXXXbj77ruxZcsW/PGPf4Rer8eyZctk1Dvh8eZAREREREREo2g2/f39sWjRItxxxx0oKirC448/jp/97GeIiIjAj3/8Y2zatAlXX301nn/+eaxcuXLIe+Pj41FVVYXOzk4YjUYUFBRg/fr1MJvNqKmpgdVqRWpqKgoLC5GVlQWDwYCCggLk5ua6/4KQnJyM/Px8rFixAg6HA8XFxcjJyUFFRQWCgoKQmJiILVu2wGw2w2KxoKysDMuWLUNpaSlMJhMSEhLc01taWlBZWYns7GyUlJQgPj4eubm5qKqqgtlsRmNjI+rr65GZmYmioiIkJyfDZDK5319XV4eWlhZkZGRg+/btSE9PR3h4uHt6TU0NbGnL+35uHY244oorVNmm2NhY93RPbNNE+Jx8ZZuuuOIKdHR0+NQ2+eLn5CvbdMUVV8BkMvnUNvni5+Qr25Sbm4vk5GSf2iZf/Jx8ZZtmzJiB5ORkn9omX/ycfGWbjEYjkpOTfWqbPPk5jUQMN2JjY0V1dbX7+/POO098+OGHoq2tbch87e3tp7x3w4YNwy57IoyYmBiPLi/lR4+KJY/ki4jUbNW3jWPiDU/njYNjuMG8ccgczBuHzMG8ccgczNvwY7ieb8QbBDU2NqK2thYpKSkA+i6Q3bt3L+rr67FixQoAwEUXXYQDBybnaaMD2+AROh2C45MBAF1HJufPg7zLo3kjGgHzRjIxbyQT80YyMW/KjXgaLQDccccdeOWVVxAQEICDBw/ixhtvxPvvv48nnngC/v7+sFqtuPnmm71dq1c4HA6PLSswKg7+xlDYLc3o7Wz12HLJd3gyb0QjYd5IJuaNZGLeSCbmTblRNZu7d+8+5TmaX3zxBZYsWeKVomQqLi722LJO3ById6yi0/Nk3ohGwryRTMwbycS8kUzMm3Kjes6mL8vJyfHYsngnWhqJJ/NGNBLmjWRi3kgm5o1kYt6U03yzWVFR4bFlsdmkkXgyb0QjYd5IJuaNZGLeSCbmTTnNN5tBQUEeWxabTRqJJ/NGNBLmjWRi3kgm5o1kYt6U03yzmZiY6JHlGCKmwhAaid4uC+xtxzyyTPI9nsob0WgwbyQT80YyMW8kE/OmnOabzS1btnhkOSE8qkmj4Km8EY0G80YyMW8kE/NGMjFvymm+2TSbzR5ZzsAptF1sNmkYnsob0WgwbyQT80YyMW8kE/OmnOabTYvF4pHlBCckAwC669ls0pl5Km9Eo8G8kUzMG8nEvJFMzJtymm82y8rKPLKc4HieRksj81TeiEaDeSOZmDeSiXkjmZg35TTfbC5btmzcy/APjUJA5FQ4rV2wHa/3QFXkqzyRN6LRYt5IJuaNZGLeSCbmTTnNN5ulpaXjXkZw/MAptJWAEONeHvkuT+SNaLSYN5KJeSOZmDeSiXlTTvPNpslkGvcy+HxNGi1P5I1otJg3kol5I5mYN5KJeVNO881mQkLCuJfBO9HSaHkib0SjxbyRTMwbycS8kUzMm3KabzY98dyckIE70bLZpBHwOU0kE/NGMjFvJBPzRjIxb8ppvtkc73Nz/IyhCJwSD5fDBmtTrYeqIl/F5zSRTMwbycS8kUzMG8nEvCmn+WazpaVlXO8/cXOgKsDl8kRJ5MPGmzeisWDeSCbmjWRi3kgm5k05zTeblZWV43o/bw5EYzHevBGNBfNGMjFvJBPzRjIxb8ppvtnMzs4e1/vdzWY9Q0gjG2/eiMaCeSOZmDeSiXkjmZg35TTfbJaUlIzr/TyySWMx3rwRjQXzRjIxbyQT80YyMW/Kab7ZjI+PV/xefaARQdHT4ep1oKehxnNFkc8aT96Ixop5I5mYN5KJeSOZmDflNN9sxsbGKn5v8LQ50On16GmsgXA6PFgV+arx5I1orJg3kol5I5mYN5KJeVNO883meJ6b474TLU+hpVHic5pIJuaNZGLeSCbmjWRi3pTTfLM5nufm8HpNGis+p4lkYt5IJuaNZGLeSCbmTTnNN5uNjY2K38tmk8ZqPHkjGivmjWRi3kgm5o1kYt6U03yzWV9fr+h9On8DjLGJEC4neo4e9HBV5KuU5o1ICeaNZGLeSCbmjWRi3pTTfLOZmZmp6H3GuCTo/PxgPXYYLofNw1WRr1KaNyIlmDeSiXkjmZg3kol5U25UzWZERATeeustlJeXY+/evVi6dCkA4Pbbb0dFRQXKysrw8MMPe7VQbykqKlL0voFTaLt4Ci2NgdK8ESnBvJFMzBvJxLyRTMybcqNqNp944gls3rwZaWlpWLhwIcrLy3HBBRdgzZo1OOuss5Ceno4//vGP3q7VK5KTkxW9L4TXa5ICSvNGpATzRjIxbyQT80YyMW/K+Y80Q1hYGHJycnDDDTcAABwOB9rb23HLLbfgoYcegt1uBwA0NTV5tVBvMZlMit4XnMDHntDYKc0bkRLMG8nEvJFMzBvJxLwpN2KzmZSUhKamJmzcuBELFy7Ezp07cddddyElJQXnn38+fvvb38JqteInP/kJvvrqqyHvjY+PR1VVFTo7O2E0GlFQUID169fDbDajpqYGVqsVqampKCwsRFZWFgwGAwoKCpCbm4vKykoAfX9JyM/Px4oVK+BwOFBcXIycnBxUVFQgKCgIiYmJ2LJlC8xmMywWC8rKyrBs2TKUlpbCZDIhISHBPb2lpQWVlZXIzs5GSUkJ4uPjYTQaYTKZYDab0djYiPr6emRmZqKoqAjJyckwmUzu99fV1aGlpQXpGWfhQPwcAMC3lmXik/a6CbVNsbGx7umj3aaMjAxs374d6enpCA8Pd0/nNnl2m4xGI1JTU31qm3zxc/KVbTIajTCbzT61Tb74OfnKNrW3tyMvL8+ntskXPydf2aY9e/YgLy/Pp7bJFz8nX9mmHTt2IC8vz6e2yZOf00jEcGPx4sXC4XCI7OxsAUA8/vjj4oEHHhClpaXiiSeeEABEVlaWOHjw4Cnv3bBhw7DLnggjLy9vzO8xTksSSx7JF+k/fVH1+jkm11CSNw4OpYN545A5mDcOmYN545A5mLfhx3A934jXbB45cgRHjhxxXxj79ttvY9GiRThy5Ag2bdoEACguLobL5UJ0dPRIi5tw6urqxvwePl+TlFKSNyKlmDeSiXkjmZg3kol5U27EZrOxsRG1tbVISUkBAOTm5mLv3r147733cNFFFwEA5s6di4CAADQ3N3u3Wi9oaWkZ83tONJuVni6HfJySvBEpxbyRTMwbycS8kUzMm3KjuhvtHXfcgVdeeQW7d+/G2Wefjd/97nf429/+hqSkJJSWluL111/H2rVrvV2rV2RkZIz5PbwTLSmlJG9ESjFvJBPzRjIxbyQT86bciDcIAoDdu3cjKyvrlNevu+46jxck2/bt28f2Bp0exml9Nwdis0ljNea8EY0D80YyMW8kE/NGMjFvyo3qyKYvS09PH9P8QdEJ8As0wtbaiN5ui5eqIl811rwRjQfzRjIxbyQT80YyMW/Kab7ZDA8PH9P8vDkQjcdY80Y0HswbycS8kUzMG8nEvCmn+WZzy5YtY5qfzSaNx1jzRjQezBvJxLyRTMwbycS8Kaf5ZtNsNo9pfjabNB5jzRvReDBvJBPzRjIxbyQT86ac5pvNmpqaMc3PZpPGY6x5IxoP5o1kYt5IJuaNZGLelNN8s2m1Wkc9b4BpGvyNobBbjsPRweft0NiNJW9E48W8kUzMG8nEvJFMzJtymm82U1NTRz0vn69J4zWWvBGNF/NGMjFvJBPzRjIxb8ppvtksLCwc9bw8hZbGayx5Ixov5o1kYt5IJuaNZGLelNN8s5mVlTXqeYMTkgEA3fWV3iqHfNxY8kY0XswbycS8kUzMG8nEvCmn+WbTYDCMel4e2aTxGkveiMaLeSOZmDeSiXkjmZg35TTfbBYUFIxqPkNENAyhUejttsDe2ujlqshXjTZvRJ7AvJFMzBvJxLyRTMybcppvNnNzc0c1H49qkieMNm9EnsC8kUzMG8nEvJFMzJtymm82KytHd/3liTvR8npNUm60eSPyBOaNZGLeSCbmjWRi3pTTfLM5Wsb4vpsDdfHIJhERERER0Yg032wmJyePaj4+Y5M8YbR5I/IE5o1kYt5IJuaNZGLelNN8s5mfnz/iPP4hkQiIjIHT1g3b8ToJVZGvGk3eiDyFeSOZmDeSiXkjmZg35TTfbK5YsWLEedzP16yrBITwdknkw0aTNyJPYd5IJuaNZGLeSCbmTTnNN5sOh2PEeXgnWvKU0eSNyFOYN5KJeSOZmDeSiXlTTvPNZnFx8YjzsNkkTxlN3og8hXkjmZg3kol5I5mYN+U032zm5OSMOM9As8k70dJ4jSZvRJ7CvJFMzBvJxLyRTMybcppvNisqKoad7mcMRdCUeLgcNlibDkuqinzVSHkj8iTmjWRi3kgm5o1kYt6U03yzGRQUNOz04P7na3bXVwEul4ySyIeNlDciT2LeSCbmjWRi3kgm5k05zTebiYmJw053X69ZXymhGvJ1I+WNyJOYN5KJeSOZmDeSiXlTTvPN5pYtW4adfuKxJ7xek8ZvpLwReRLzRjIxbyQT80YyMW/KjarZjIiIwFtvvYXy8nLs3bsXS5cudU+75557IITAlClTvFakN5nN5mGnB8fzTrTkOSPljciTmDeSiXkjmZg3kol5U85/NDM98cQT2Lx5M66++moYDAYEBwcDAKZPn46VK1fi0KFDXi3SmywWyxmn6QOCEDR1BlzOXvQ01MgrinzWcHkj8jTmjWRi3kgm5o1kYt6UG/HIZlhYGHJycvD8888D6HuoaXt7OwDgsccew09/+lMIIbxbpReVlZWdcVrwtDnQ6fWwNlRDOPkwVxq/4fJG5GnMG8nEvJFMzBvJxLwpN2KzmZSUhKamJmzcuBFff/01nn32WQQHB+Pb3/426urq8M0338io02uWLVt2xmnG/us1u+p4cyDyjOHyRuRpzBvJxLyRTMwbycS8KTfiabT+/v5YtGgR7rjjDhQVFeHxxx/H/fffj5ycHFxyySXDvjc+Ph5VVVXo7OyE0WhEQUEB1q9fD7PZjJqaGlitVqSmpqKwsBBZWVkwGAwoKChAbm4uKiv7Grzk5GTk5+djxYoVcDgcKC4uRk5ODioqKhAUFITExERs2bIFZrMZFosFZWVlWLZsGUpLS2EymZCQkOCe3tLSgsrKSmRnZ6OkpATx8fGIiYmByWSC2WxGY2Mj6uvrkZmZiaKiIsRlXwAbAF1bHfLy8lBXV4eWlhZkZGRg+/btSE9PR3h4uHv5E2WbYmNj3dNP3qbk5GSYTCb3dG6T3G2KiYlBamqqT22TL35OvrJNMTExMJvNPrVNvvg5+co26fV65OXl+dQ2+eLn5CvbdOzYMeTl5fnUNvni5+Qr21RTU4O8vDyf2iZPfk4jEcON2NhYUV1d7f7+vPPOE5988olobGwU1dXVorq6WjgcDnHo0CERGxs75L0bNmwYdtkTYeTk5Jxx2vy7nxZLHskXITPnq14nh2+M4fLGweHpwbxxyBzMG4fMwbxxyBzM2/BjuJ5vxNNoGxsbUVtbi5SUFABAbm4uvv76a8TGxmL27NmYPXs2jhw5gkWLFo2qs51oEhISTvu6zt+AoNhECJcTPUcPSq6KfNWZ8kbkDcwbycS8kUzMG8nEvCk3qrvR3nHHHXjllVcQEBCAgwcP4sYbb/R2XdKc6bk5xrjZ0Pv5o6ehBi6HVXJV5Kv4nCaSiXkjmZg3kol5I5mYN+VG9ZzN3bt3IysrCwsXLsSVV16Jtra2IdNnz56N48ePe6M+rzvTc3OCE/h8TfI8PqeJZGLeSCbmjWRi3kgm5k25UTWbvqylpeW0r7ubzXo2m+Q5Z8obkTcwbyQT80YyMW8kE/OmnOabzYG7Np0sJL6v2ezikU3yoDPljcgbmDeSiXkjmZg3kol5U07zzWZ2dvYpr+n0fjBOSwIA9NRXyS6JfNjp8kbkLcwbycS8kUzMG8nEvCmn+WazpKTklNeCYmZBbwiAtbkOTmuXClWRrzpd3oi8hXkjmZg3kol5I5mYN+U032zGx8ef8hpvDkTecrq8EXkL80YyMW8kE/NGMjFvymm+2YyNjT3lteCEZABsNsnzTpc3Im9h3kgm5o1kYt5IJuZNOc03m6d7bg6PbJK38DlNJBPzRjIxbyQT80YyMW/Kab7ZPOW5OTodguP7j2zW885T5Fl8ThPJxLyRTMwbycS8kUzMm3KabzYbGxuHfB8YnQC/QCNsrY3o7WpXqSryVSfnjcibmDeSiXkjmZg3kol5U07zzWZ9ff2Q70Pcp9DyqCZ53sl5I/Im5o1kYt5IJuaNZGLelNN8s5mZmTnke/f1mvW8XpM87+S8EXkT80YyMW8kE/NGMjFvymm+2SwqKhryfXA8bw5E3nNy3oi8iXkjmZg3kol5I5mYN+U032wmJycP+Z53oiVvOjlvRN7EvJFMzBvJxLyRTMybcppvNk0mk/vrgKg4+AeHwdHRAofluIpVka8anDcib2PeSCbmjWRi3kgm5k05zTebg5+bw6Oa5G18ThPJxLyRTMwbycS8kUzMm3KabzYHPzdn4E60XWw2yUv4nCaSiXkjmZg3kol5I5mYN+U032zW1dW5v+aRTfK2wXkj8jbmjWRi3kgm5o1kYt6U03yz2dLS4v46eDqbTfKuwXkj8jbmjWRi3kgm5o1kYt6U03yzmZGRAQAwhEfDEBqF3m4L7K2NKldFvmogb0QyMG8kE/NGMjFvJBPzppzmm83t27cDGHwKbaWa5ZCPG8gbkQzMG8nEvJFMzBvJxLwpp/lmMz09HQAQnND3/Jzuep5CS94zkDciGZg3kol5I5mYN5KJeVNO881meHg4ACA4ntdrkvcN5I1IBuaNZGLeSCbmjWRi3pTTfLM58NycgSObfOwJeROf00QyMW8kE/NGMjFvJBPzppzmm02z2Qz/kAgERsXCaeuGrZm3Nibv4XOaSCbmjWRi3kgm5o1kYt6U03yzWVNTg+D4ges1qwAhVK6IfFlNTY3aJZCGMG8kE/NGMjFvJBPzptyoms2IiAi89dZbKC8vx969e7F06VI88sgjKC8vx+7du7Fp0yZERER4u1avsFqtg+5Ey1NoybusVqvaJZCGMG8kE/NGMjFvJBPzptyoms0nnngCmzdvRlpaGhYuXIjy8nJs27YN6enpWLhwIfbv3497773X27V6RWpqKptNkiY1NVXtEkhDmDeSiXkjmZg3kol5U27EZjMsLAw5OTl4/vnnAQAOhwPt7e3Ytm0bnE4nAODLL7/E9OnTvVuplxQWFrLZJGkKCwvVLoE0hHkjmZg3kol5I5mYN+X8R5ohKSkJTU1N2LhxIxYuXIidO3firrvuQnd3t3uem266CW+88cYp742Pj0dVVRU6OzthNBpRUFCA9evXw2w2o6amBlarFampqSgsLERWVhYMBgMKCgqQm5uLyspKAEBycjLy8/OxYsUKOBwOFBcXIycnBxUVFQgKCkJiYiK2bNkCs9kMi8WCsrIyLFu2DKWlpTCZTEhISHBPb2lpQWVlJbKzs1FSUoL4+HgsW3ERNhsToHP1YumCJByNDERmZiaKioqQnJwMk8nkfn9dXR1aWlqQkZGB7du3Iz09HeHh4e7pE2WbYmNj3dMbGxtRX1/PbZog23T99dfjtdde86lt8sXPyVe26eabb8b27dt9apt88XPylW265JJLcPToUZ/aJl/8nHxlm2JiYmC1Wn1qm3zxc/KVbRrgS9vkyc9pJGK4sXjxYuFwOER2drYAIB5//HHxwAMPuKf//Oc/F5s2bTrtezds2DDssifCuPymu8SSR/JF6u1/Ub0WDt8fV199teo1cGhnMG8cMgfzxiFzMG8cMgfzNvwYrucb8TTaI0eO4MiRIygqKgIAvP3221i0aBEA4Prrr8fll1+Oa6+9dqTFTFjlR9sB8BRakqOgoEDtEkhDmDeSiXkjmZg3kol5U27EZrOxsRG1tbVISUkBAOTm5mLv3r0wm81Yv349Vq9ejZ6eHq8X6i0zF54LgM0myZGbm6t2CaQhzBvJxLyRTMwbycS8KTfiNZsAcMcdd+CVV15BQEAADh48iBtvvBHFxcUIDAzEtm3bAPTdJOiWW27xarHe0OEfBgDorq9UuRLSgoFz6YlkYN5IJuaNZGLeSCbmTblRNZu7d+9GVlbWkNfmzp3rlYJk0huCgLCpcDl70dNQrXY5REREREREPmNUz9n0Vcb4JECnh7WxBqLXoXY5pAHJyclql0AawryRTMwbycS8kUzMm3KabjaD4/l8TZIrPz9f7RJIQ5g3kol5I5mYN5KJeVNO281mQl+z2cVmkyRZsWKF2iWQhjBvJBPzRjIxbyQT86acppvNkIS+Q+I8skmyOBw8XZvkYd5IJuaNZGLeSCbmTTnNNps6PwOC4mZDCBd66g+qXQ5pRHFxsdolkIYwbyQT80YyMW8kE/OmnGabTQCofvW3mFpfDJfDqnYppBE5OTlql0AawryRTMwbycS8kUzMm3KjevSJLxJOB1pLC9Ho36F2KaQhFRUVapdAGsK8kUzMG8nEvJFMzJtymj6yCQBBQUFql0AawryRTMwbycS8kUzMG8nEvCmn+WYzMTFR7RJIQ5g3kol5I5mYN5KJeSOZmDflNN9sbtmyRe0SSEOYN5KJeSOZmDeSiXkjmZg35TTfbJrNZrVLIA1h3kgm5o1kYt5IJuaNZGLelNN8s3nuueeqXQJpCPNGMjFvJBPzRjIxbyQT86ac5pvNFStWqF0CaQjzRjIxbyQT80YyMW8kE/OmnOabzdDQULVLIA1h3kgm5o1kYt5IJuaNZGLelNMBEN5a+DvvvIOGhgZvLd4jpk6diqamJrXLII1g3kgm5o1kYt5IJuaNZGLehhcXF4errrrqtNO82mwSERERERGRNmn+NFoiIiIiIiLyPDabRERERERE5HGabTbNZjMqKipw4MABrF+/Xu1ySAOqq6vxzTffoKSkBMXFxWqXQz7m+eefR2NjI0pLS92vRUVFYevWrdi/fz+2bt2KyMhI9Qokn3K6vN133304cuQISkpKUFJSglWrVqlYIfmS6dOn45///Cf27t2LsrIy3HnnnQC4jyPvOFPeuI9TTmht6PV6UVlZKWbPni0MBoPYtWuXSEtLU70uDt8e1dXVYsqUKarXweGb4/zzzxeZmZmitLTU/drDDz8s1q9fLwCI9evXi4ceekj1Ojl8Y5wub/fdd5+45557VK+Nw/dGXFycyMzMFABEaGio2Ldvn0hLS+M+jsMr40x54z5O2dDkkc3s7GxUVlaiuroaDocDr7/+OtasWaN2WUREin322WdoaWkZ8tqaNWvw4osvAgBefPFFXHHFFSpURr7odHkj8paGhgaUlJQAADo7O1FeXo6EhATu48grzpQ3UkaTzWZCQgJqa2vd3x85coQhIq8TQmDr1q346quvsG7dOrXLIQ2IjY11P36qoaEBMTExKldEvu7222/H7t278fzzz/OURvKKWbNmITMzEzt27OA+jrxucN4A7uOU0GSzqdPpTnlNCKFCJaQly5cvx+LFi7Fq1SrcdtttOP/889UuiYjIY5566inMmTMHZ599No4ePYpHH31U7ZLIx4SEhOCdd97B3XffjY6ODrXLIR93ct64j1NGk83mkSNHMGPGDPf306dPR319vYoVkRYcPXoUANDU1IR3330X2dnZKldEvq6xsRFxcXEA+h64fOzYMZUrIl927NgxuFwuCCHw7LPPch9HHuXv74933nkHr7zyCt59910A3MeR95wub9zHKaPJZrO4uBhz585FYmIiDAYDrrnmGnzwwQdql0U+LDg4GKGhoe6vL7nkEpSVlalcFfm6Dz74AGvXrgUArF27Fu+//77KFZEvG/ilHwCuvPJK7uPIo55//nmUl5fjsccec7/GfRx5y+nyxn2ccqrfpUiNsWrVKrFv3z5RWVkpfv7zn6teD4dvj9mzZ4tdu3aJXbt2ibKyMmaOw+Pj1VdfFfX19cJut4va2lpx0003CZPJJD755BOxf/9+8cknn4ioqCjV6+TwjXG6vL300kvim2++Ebt37xbvv/++iIuLU71ODt8Yy5cvF0IIsXv3blFSUiJKSkrEqlWruI/j8Mo4U964j1M2dP1fEBEREREREXmMJk+jJSIiIiIiIu9is0lEREREREQex2aTiIiIiIiIPI7NJhEREREREXkcm00iIiIiIiLyODabRERERERE5HFsNomIiIiIiMjj2GwSERERERGRx/1/aMe5maf427AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting validation accuracy graph\n",
    "plt.plot(history_val_acc_1)\n",
    "plt.title('Validation Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functional 2.1: Restricting Learnable Function (L1 regularization)\n",
    "\n",
    "### Network\n",
    "\n",
    "No changes in network, we can just call it with default parameters, ie, `initialize_weights=False`.\n",
    "\n",
    "### Training Loop\n",
    "\n",
    "Objective of L1 regularization is to restrict the weights of the network towards 0. This is done by adding a regularizer term to the loss function containing the 1-norm of the parameters of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = num_words + 2\n",
    "embedding_dim = max_len\n",
    "hidden_dim = 32\n",
    "output_dim = 1\n",
    "\n",
    "# Instantiate model class and assign to object\n",
    "model = FeedforwardNeuralNetModel(input_dim, embedding_dim, hidden_dim, output_dim)\n",
    "\n",
    "# Push model to CUDA device if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "# Loss function\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "# Optimizer\n",
    "# Toggle 2: L2 Norm option - this is called weight decay\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=0.005)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 100 | Train Loss: 0.8968726396560669 | Val Loss: 0.6933504939079285 | Val Accuracy: 52.97\n",
      "Iter: 200 | Train Loss: 0.8850858211517334 | Val Loss: 0.7012791633605957 | Val Accuracy: 52.61\n",
      "Iter: 300 | Train Loss: 0.8835084438323975 | Val Loss: 0.6849543452262878 | Val Accuracy: 54.36\n",
      "Iter: 400 | Train Loss: 0.7853503227233887 | Val Loss: 0.6128271222114563 | Val Accuracy: 55.89\n",
      "Iter: 500 | Train Loss: 0.8008366823196411 | Val Loss: 0.6662547588348389 | Val Accuracy: 57.49\n",
      "Iter: 600 | Train Loss: 0.7854050993919373 | Val Loss: 0.5761398673057556 | Val Accuracy: 58.91\n",
      "Iter: 700 | Train Loss: 0.7626585960388184 | Val Loss: 0.668764054775238 | Val Accuracy: 61.47\n",
      "Iter: 800 | Train Loss: 0.7500191330909729 | Val Loss: 0.563574492931366 | Val Accuracy: 62.71\n",
      "Iter: 900 | Train Loss: 0.7499464154243469 | Val Loss: 0.6150270104408264 | Val Accuracy: 65.52\n",
      "Iter: 1000 | Train Loss: 0.7351253032684326 | Val Loss: 0.5829649567604065 | Val Accuracy: 64.63\n",
      "Iter: 1100 | Train Loss: 0.7176674604415894 | Val Loss: 0.583477795124054 | Val Accuracy: 65.92\n",
      "Iter: 1200 | Train Loss: 0.6612926125526428 | Val Loss: 0.6827743053436279 | Val Accuracy: 66.84\n",
      "Iter: 1300 | Train Loss: 0.6319677829742432 | Val Loss: 0.4934847056865692 | Val Accuracy: 67.96\n",
      "Iter: 1400 | Train Loss: 0.6046731472015381 | Val Loss: 0.48394259810447693 | Val Accuracy: 66.47\n",
      "Iter: 1500 | Train Loss: 0.5914614200592041 | Val Loss: 0.5065696239471436 | Val Accuracy: 68.24\n",
      "Iter: 1600 | Train Loss: 0.6446681618690491 | Val Loss: 0.6317614316940308 | Val Accuracy: 68.45\n",
      "Iter: 1700 | Train Loss: 0.5322741866111755 | Val Loss: 0.510788083076477 | Val Accuracy: 68.59\n",
      "Iter: 1800 | Train Loss: 0.525399923324585 | Val Loss: 0.5228933095932007 | Val Accuracy: 68.11\n",
      "Iter: 1900 | Train Loss: 0.7607476711273193 | Val Loss: 0.75107741355896 | Val Accuracy: 68.17\n",
      "Iter: 2000 | Train Loss: 0.6492899656295776 | Val Loss: 0.6233732104301453 | Val Accuracy: 68.57\n",
      "Iter: 2100 | Train Loss: 0.6060168147087097 | Val Loss: 0.5488857626914978 | Val Accuracy: 68.04\n",
      "Iter: 2200 | Train Loss: 0.49364331364631653 | Val Loss: 0.6875171661376953 | Val Accuracy: 69.03\n",
      "Iter: 2300 | Train Loss: 0.5337584018707275 | Val Loss: 0.7134099006652832 | Val Accuracy: 68.55\n",
      "Iter: 2400 | Train Loss: 0.6568292379379272 | Val Loss: 0.6745195388793945 | Val Accuracy: 68.85\n",
      "Iter: 2500 | Train Loss: 0.466682493686676 | Val Loss: 0.6646616458892822 | Val Accuracy: 69.16\n",
      "Iter: 2600 | Train Loss: 0.4342079758644104 | Val Loss: 0.7456778883934021 | Val Accuracy: 68.76\n",
      "Iter: 2700 | Train Loss: 0.5624664425849915 | Val Loss: 0.7109711766242981 | Val Accuracy: 68.45\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "num_epochs = 10\n",
    "history_train_acc_2, history_val_acc_2, history_train_loss_2, history_val_loss_2 = [], [], [], []\n",
    "best_accuracy = 0\n",
    "for epoch in range(num_epochs):\n",
    "#     print('-'*50)\n",
    "    for i, (samples, labels) in enumerate(train_loader):\n",
    "        # Training mode\n",
    "        model.train()\n",
    "        \n",
    "        # Load samples\n",
    "        samples = samples.view(-1, max_len).to(device)\n",
    "        labels = labels.view(-1, 1).to(device)\n",
    "\n",
    "        # Clear gradients w.r.t. parameters\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass to get output/logits\n",
    "        outputs = model(samples)\n",
    "\n",
    "        # Calculate Loss: softmax --> cross entropy loss\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Toggle 1: L1 norm, add to original loss\n",
    "        # ####################################################################\n",
    "        fc1_params = torch.cat([x.view(-1) for x in model.fc1.parameters()])\n",
    "#         loss += 0.001 * torch.norm(fc1_params, 1)\n",
    "        fc2_params = torch.cat([x.view(-1) for x in model.fc2.parameters()])\n",
    "        loss += 0.001 * (torch.norm(fc1_params, 1) + torch.norm(fc2_params, 1))\n",
    "        # ####################################################################\n",
    "    \n",
    "        # Getting gradients w.r.t. parameters\n",
    "        loss.backward()\n",
    "\n",
    "        # Updating parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        iter += 1\n",
    "\n",
    "        if iter % 100 == 0:\n",
    "            # Get training statistics\n",
    "            train_loss = loss.data.item()\n",
    "            \n",
    "            # Testing mode\n",
    "            model.eval()\n",
    "            # Calculate Accuracy         \n",
    "            correct = 0\n",
    "            total = 0\n",
    "            # Iterate through test dataset\n",
    "            for samples, labels in valid_loader:\n",
    "                # Load samples\n",
    "                samples = samples.view(-1, max_len).to(device)\n",
    "                labels = labels.view(-1).to(device)\n",
    "\n",
    "                # Forward pass only to get logits/output\n",
    "                outputs = model(samples)\n",
    "                \n",
    "                # Val loss\n",
    "                val_loss = criterion(outputs.view(-1, 1), labels.view(-1, 1))\n",
    "                \n",
    "                # We use a threshold to define. \n",
    "                # There is another way to do this with one-hot label. Feel free to explore and understand what are the pros/cons of each.\n",
    "                # This opens up a whole topic on why it becomes problematic when we expand beyond 2 class to 10 classes.\n",
    "                # Why do we encode? Why can't we do 0, 1, 2, 3, 4 etc. without one-hot encoding?\n",
    "                predicted = outputs.ge(0.5).view(-1)\n",
    "\n",
    "                # Total number of labels\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total correct predictions\n",
    "                correct += (predicted.type(torch.FloatTensor).cpu() == labels.type(torch.FloatTensor)).sum().item()\n",
    "                # correct = (predicted == labels.byte()).int().sum().item()\n",
    "            \n",
    "            accuracy = 100. * correct / total\n",
    "        \n",
    "            # Print Loss\n",
    "            print('Iter: {} | Train Loss: {} | Val Loss: {} | Val Accuracy: {}'.format(iter, train_loss, val_loss.item(), round(accuracy, 2)))\n",
    "            \n",
    "            # Append to history\n",
    "            history_val_loss_2.append(val_loss.data.item())\n",
    "            history_val_acc_2.append(round(accuracy, 2))\n",
    "            history_train_loss_2.append(train_loss)\n",
    "            \n",
    "            # Save model when accuracy beats best accuracy\n",
    "            if accuracy > best_accuracy:\n",
    "                best_accuracy = accuracy\n",
    "                # We can load this best model on the validation set later\n",
    "                torch.save(model.state_dict(), 'best_model_2.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting loss graph\n",
    "plt.plot(history_train_loss_2, label='Train')\n",
    "plt.plot(history_val_loss_2, label='Validation')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation Accuracy')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting validation accuracy graph\n",
    "plt.plot(history_val_acc_2)\n",
    "plt.title('Validation Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functional 2.2: Restricting Learnable Function (L2 regularization / weight decay)\n",
    "\n",
    "### Network\n",
    "\n",
    "No changes needed for the network.\n",
    "\n",
    "### Training Loop\n",
    "\n",
    "Can be done both ways, either by adding a `weight_decay` parameter to the optimizer, or by adding the L2 norm of the layer weights to the loss. Second approach similar to L1 regularization described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = num_words + 2\n",
    "embedding_dim = max_len\n",
    "hidden_dim = 32\n",
    "output_dim = 1\n",
    "\n",
    "# Instantiate model class and assign to object\n",
    "model = FeedforwardNeuralNetModel(input_dim, embedding_dim, hidden_dim, output_dim)\n",
    "\n",
    "# Push model to CUDA device if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "# Loss function\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "# Optimizer\n",
    "# Toggle 2: L2 Norm option - this is called weight decay\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=0.005)\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 100 | Train Loss: 0.6698310971260071 | Val Loss: 0.704235851764679 | Val Accuracy: 52.87\n",
      "Iter: 200 | Train Loss: 0.6776701211929321 | Val Loss: 0.6541276574134827 | Val Accuracy: 54.39\n",
      "Iter: 300 | Train Loss: 0.612124502658844 | Val Loss: 0.5352939963340759 | Val Accuracy: 55.35\n",
      "Iter: 400 | Train Loss: 0.6967016458511353 | Val Loss: 0.5992451310157776 | Val Accuracy: 56.03\n",
      "Iter: 500 | Train Loss: 0.7740015387535095 | Val Loss: 0.6086034774780273 | Val Accuracy: 56.13\n",
      "Iter: 600 | Train Loss: 0.6159039735794067 | Val Loss: 0.5599578619003296 | Val Accuracy: 55.53\n",
      "Iter: 700 | Train Loss: 0.5987639427185059 | Val Loss: 0.7038238644599915 | Val Accuracy: 56.44\n",
      "Iter: 800 | Train Loss: 0.6415882706642151 | Val Loss: 0.5606452226638794 | Val Accuracy: 58.21\n",
      "Iter: 900 | Train Loss: 0.5834193825721741 | Val Loss: 0.6676176190376282 | Val Accuracy: 56.55\n",
      "Iter: 1000 | Train Loss: 0.5698237419128418 | Val Loss: 0.5733292102813721 | Val Accuracy: 57.15\n",
      "Iter: 1100 | Train Loss: 0.4831635355949402 | Val Loss: 0.4939805567264557 | Val Accuracy: 57.95\n",
      "Iter: 1200 | Train Loss: 0.48864126205444336 | Val Loss: 0.5868851542472839 | Val Accuracy: 58.11\n",
      "Iter: 1300 | Train Loss: 0.5618135333061218 | Val Loss: 0.5428476929664612 | Val Accuracy: 57.83\n",
      "Iter: 1400 | Train Loss: 0.5025582313537598 | Val Loss: 0.5410205721855164 | Val Accuracy: 58.48\n",
      "Iter: 1500 | Train Loss: 0.47609743475914 | Val Loss: 0.5336862802505493 | Val Accuracy: 58.28\n",
      "Iter: 1600 | Train Loss: 0.6342171430587769 | Val Loss: 0.4959941804409027 | Val Accuracy: 58.8\n",
      "Iter: 1700 | Train Loss: 0.4169005751609802 | Val Loss: 0.4222002923488617 | Val Accuracy: 58.83\n",
      "Iter: 1800 | Train Loss: 0.5571115612983704 | Val Loss: 0.4647868871688843 | Val Accuracy: 58.89\n",
      "Iter: 1900 | Train Loss: 0.45954790711402893 | Val Loss: 0.4799288809299469 | Val Accuracy: 60.57\n",
      "Iter: 2000 | Train Loss: 0.4643167555332184 | Val Loss: 0.5598806738853455 | Val Accuracy: 60.83\n",
      "Iter: 2100 | Train Loss: 0.5494521260261536 | Val Loss: 0.4607630670070648 | Val Accuracy: 60.19\n",
      "Iter: 2200 | Train Loss: 0.3778795897960663 | Val Loss: 0.4404270648956299 | Val Accuracy: 61.61\n",
      "Iter: 2300 | Train Loss: 0.4605962634086609 | Val Loss: 0.44042620062828064 | Val Accuracy: 61.45\n",
      "Iter: 2400 | Train Loss: 0.5243778228759766 | Val Loss: 0.41823139786720276 | Val Accuracy: 62.21\n",
      "Iter: 2500 | Train Loss: 0.3514649271965027 | Val Loss: 0.5057516098022461 | Val Accuracy: 62.88\n",
      "Iter: 2600 | Train Loss: 0.4774780571460724 | Val Loss: 0.41545912623405457 | Val Accuracy: 62.29\n",
      "Iter: 2700 | Train Loss: 0.4740549623966217 | Val Loss: 0.4337567389011383 | Val Accuracy: 63.91\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "num_epochs = 10\n",
    "history_train_acc_3, history_val_acc_3, history_train_loss_3, history_val_loss_3 = [], [], [], []\n",
    "best_accuracy = 0\n",
    "for epoch in range(num_epochs):\n",
    "#     print('-'*50)\n",
    "    for i, (samples, labels) in enumerate(train_loader):\n",
    "        # Training mode\n",
    "        model.train()\n",
    "        \n",
    "        # Load samples\n",
    "        samples = samples.view(-1, max_len).to(device)\n",
    "        labels = labels.view(-1, 1).to(device)\n",
    "\n",
    "        # Clear gradients w.r.t. parameters\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass to get output/logits\n",
    "        outputs = model(samples)\n",
    "\n",
    "        # Calculate Loss: softmax --> cross entropy loss\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Toggle 1: L1 norm, add to original loss\n",
    "#         fc1_params = torch.cat([x.view(-1) for x in model.fc1.parameters()])\n",
    "#         loss += 0.001 * torch.norm(fc1_params, 1)\n",
    "    \n",
    "        # Getting gradients w.r.t. parameters\n",
    "        loss.backward()\n",
    "\n",
    "        # Updating parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        iter += 1\n",
    "\n",
    "        if iter % 100 == 0:\n",
    "            # Get training statistics\n",
    "            train_loss = loss.data.item()\n",
    "            \n",
    "            # Testing mode\n",
    "            model.eval()\n",
    "            # Calculate Accuracy         \n",
    "            correct = 0\n",
    "            total = 0\n",
    "            # Iterate through test dataset\n",
    "            for samples, labels in valid_loader:\n",
    "                # Load samples\n",
    "                samples = samples.view(-1, max_len).to(device)\n",
    "                labels = labels.view(-1).to(device)\n",
    "\n",
    "                # Forward pass only to get logits/output\n",
    "                outputs = model(samples)\n",
    "                \n",
    "                # Val loss\n",
    "                val_loss = criterion(outputs.view(-1, 1), labels.view(-1, 1))\n",
    "                \n",
    "                # We use a threshold to define. \n",
    "                # There is another way to do this with one-hot label. Feel free to explore and understand what are the pros/cons of each.\n",
    "                # This opens up a whole topic on why it becomes problematic when we expand beyond 2 class to 10 classes.\n",
    "                # Why do we encode? Why can't we do 0, 1, 2, 3, 4 etc. without one-hot encoding?\n",
    "                predicted = outputs.ge(0.5).view(-1)\n",
    "\n",
    "                # Total number of labels\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total correct predictions\n",
    "                correct += (predicted.type(torch.FloatTensor).cpu() == labels.type(torch.FloatTensor)).sum().item()\n",
    "                # correct = (predicted == labels.byte()).int().sum().item()\n",
    "            \n",
    "            accuracy = 100. * correct / total\n",
    "        \n",
    "            # Print Loss\n",
    "            print('Iter: {} | Train Loss: {} | Val Loss: {} | Val Accuracy: {}'.format(iter, train_loss, val_loss.item(), round(accuracy, 2)))\n",
    "            \n",
    "            # Append to history\n",
    "            history_val_loss_3.append(val_loss.data.item())\n",
    "            history_val_acc_3.append(round(accuracy, 2))\n",
    "            history_train_loss_3.append(train_loss)\n",
    "            \n",
    "            # Save model when accuracy beats best accuracy\n",
    "            if accuracy > best_accuracy:\n",
    "                best_accuracy = accuracy\n",
    "                # We can load this best model on the validation set later\n",
    "                torch.save(model.state_dict(), 'best_model_3.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting loss graph\n",
    "plt.plot(history_train_loss_3, label='Train')\n",
    "plt.plot(history_val_loss_3, label='Validation')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation Accuracy')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting validation accuracy graph\n",
    "plt.plot(history_val_acc_3)\n",
    "plt.title('Validation Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorithmic 3.1: Early Stopping\n",
    "\n",
    "Modifying learning algorithm to reduce generalization error but not training error.\n",
    "\n",
    "### Network\n",
    "\n",
    "No change in network structure.\n",
    "\n",
    "### Training Loop\n",
    "\n",
    "Training loop will now check for "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = num_words + 2\n",
    "embedding_dim = max_len\n",
    "hidden_dim = 32\n",
    "output_dim = 1\n",
    "\n",
    "# Instantiate model class and assign to object\n",
    "model = FeedforwardNeuralNetModel(input_dim, embedding_dim, hidden_dim, output_dim)\n",
    "\n",
    "# Push model to CUDA device if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "# Loss function\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "# Optimizer\n",
    "# Toggle 2: L2 Norm option - this is called weight decay\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=0.005)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 100 | Train Loss: 0.6910862922668457 | Val Loss: 0.692387044429779 | Val Accuracy: 54.44\n",
      "Iter: 200 | Train Loss: 0.6879244446754456 | Val Loss: 0.6597493290901184 | Val Accuracy: 55.96\n",
      "Iter: 300 | Train Loss: 0.622555136680603 | Val Loss: 0.6950249671936035 | Val Accuracy: 57.28\n",
      "Iter: 400 | Train Loss: 0.5822204351425171 | Val Loss: 0.5236734747886658 | Val Accuracy: 59.44\n",
      "Iter: 500 | Train Loss: 0.5852950215339661 | Val Loss: 0.569950520992279 | Val Accuracy: 61.63\n",
      "Iter: 600 | Train Loss: 0.4305810332298279 | Val Loss: 0.47103700041770935 | Val Accuracy: 62.63\n",
      "Iter: 700 | Train Loss: 0.46027034521102905 | Val Loss: 0.40290388464927673 | Val Accuracy: 62.75\n",
      "Iter: 800 | Train Loss: 0.5200461745262146 | Val Loss: 0.41819509863853455 | Val Accuracy: 63.32\n",
      "Iter: 900 | Train Loss: 0.28289297223091125 | Val Loss: 0.37377071380615234 | Val Accuracy: 63.87\n",
      "Iter: 1000 | Train Loss: 0.22161388397216797 | Val Loss: 0.36738845705986023 | Val Accuracy: 64.23\n",
      "Iter: 1100 | Train Loss: 0.357271671295166 | Val Loss: 0.5123420357704163 | Val Accuracy: 63.07\n",
      "Iter: 1200 | Train Loss: 0.1039721667766571 | Val Loss: 0.3886524438858032 | Val Accuracy: 64.52\n",
      "Iter: 1300 | Train Loss: 0.14719757437705994 | Val Loss: 0.5778351426124573 | Val Accuracy: 63.88\n",
      "Iter: 1400 | Train Loss: 0.051060836762189865 | Val Loss: 0.7673073410987854 | Val Accuracy: 63.88\n",
      "Iter: 1500 | Train Loss: 0.05219025909900665 | Val Loss: 0.6694402694702148 | Val Accuracy: 63.51\n",
      "Iter: 1600 | Train Loss: 0.019515611231327057 | Val Loss: 0.6408037543296814 | Val Accuracy: 64.03\n",
      "Iter: 1700 | Train Loss: 0.01628611423075199 | Val Loss: 0.5532904267311096 | Val Accuracy: 64.19\n",
      "Iter: 1800 | Train Loss: 0.02981315553188324 | Val Loss: 0.43895259499549866 | Val Accuracy: 63.89\n",
      "Iter: 1900 | Train Loss: 0.01712728850543499 | Val Loss: 0.5263328552246094 | Val Accuracy: 63.92\n",
      "Iter: 2000 | Train Loss: 0.006248848512768745 | Val Loss: 0.6418760418891907 | Val Accuracy: 63.97\n",
      "Iter: 2100 | Train Loss: 0.007783065550029278 | Val Loss: 0.5876957774162292 | Val Accuracy: 64.08\n",
      "Iter: 2200 | Train Loss: 0.011669144965708256 | Val Loss: 0.5767636895179749 | Val Accuracy: 64.28\n",
      "Iter: 2300 | Train Loss: 0.003585860598832369 | Val Loss: 0.5921313166618347 | Val Accuracy: 64.4\n",
      "Iter: 2400 | Train Loss: 0.003683378454297781 | Val Loss: 0.59865802526474 | Val Accuracy: 64.21\n",
      "Iter: 2500 | Train Loss: 0.00250599579885602 | Val Loss: 0.6094942092895508 | Val Accuracy: 64.6\n",
      "Iter: 2600 | Train Loss: 0.0030475356616079807 | Val Loss: 0.6050326228141785 | Val Accuracy: 64.43\n",
      "Iter: 2700 | Train Loss: 0.0015824350994080305 | Val Loss: 0.5935049653053284 | Val Accuracy: 64.32\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "num_epochs = 10\n",
    "history_train_acc_4, history_val_acc_4, history_train_loss_4, history_val_loss_4 = [], [], [], []\n",
    "best_accuracy = 0\n",
    "\n",
    "early_stop = False   # early stopping\n",
    "min_val_loss = None\n",
    "num_epochs_stop = 5\n",
    "epochs_no_improve = 0\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "#     print('-'*50)\n",
    "    for i, (samples, labels) in enumerate(train_loader):\n",
    "        # Training mode\n",
    "        model.train()\n",
    "        \n",
    "        # Load samples\n",
    "        samples = samples.view(-1, max_len).to(device)\n",
    "        labels = labels.view(-1, 1).to(device)\n",
    "\n",
    "        # Clear gradients w.r.t. parameters\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass to get output/logits\n",
    "        outputs = model(samples)\n",
    "\n",
    "        # Calculate Loss: softmax --> cross entropy loss\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Toggle 1: L1 norm, add to original loss\n",
    "#         fc1_params = torch.cat([x.view(-1) for x in model.fc1.parameters()])\n",
    "#         loss += 0.001 * torch.norm(fc1_params, 1)\n",
    "    \n",
    "        # Getting gradients w.r.t. parameters\n",
    "        loss.backward()\n",
    "\n",
    "        # Updating parameters\n",
    "        optimizer.step()\n",
    "        iter += 1\n",
    "\n",
    "        if iter % 100 == 0:\n",
    "            # Get training statistics\n",
    "            train_loss = loss.data.item()\n",
    "            \n",
    "            # Testing mode\n",
    "            model.eval()\n",
    "            # Calculate Accuracy         \n",
    "            correct = 0\n",
    "            total = 0\n",
    "            # Iterate through test dataset\n",
    "            for samples, labels in valid_loader:\n",
    "                # Load samples\n",
    "                samples = samples.view(-1, max_len).to(device)\n",
    "                labels = labels.view(-1).to(device)\n",
    "\n",
    "                # Forward pass only to get logits/output\n",
    "                outputs = model(samples)\n",
    "                \n",
    "                # Val loss\n",
    "                val_loss = criterion(outputs.view(-1, 1), labels.view(-1, 1))\n",
    "                \n",
    "                # We use a threshold to define. \n",
    "                # There is another way to do this with one-hot label. Feel free to explore and understand what are the pros/cons of each.\n",
    "                # This opens up a whole topic on why it becomes problematic when we expand beyond 2 class to 10 classes.\n",
    "                # Why do we encode? Why can't we do 0, 1, 2, 3, 4 etc. without one-hot encoding?\n",
    "                predicted = outputs.ge(0.5).view(-1)\n",
    "\n",
    "                # Total number of labels\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total correct predictions\n",
    "                correct += (predicted.type(torch.FloatTensor).cpu() == labels.type(torch.FloatTensor)).sum().item()\n",
    "                # correct = (predicted == labels.byte()).int().sum().item()\n",
    "            \n",
    "            accuracy = 100. * correct / total\n",
    "        \n",
    "            # Print Loss\n",
    "            print('Iter: {} | Train Loss: {} | Val Loss: {} | Val Accuracy: {}'.format(iter, train_loss, val_loss.item(), round(accuracy, 2)))\n",
    "\n",
    "            ### Early Stopping Check ###\n",
    "            ### https://www.kaggle.com/akhileshrai/tutorial-early-stopping-vanilla-rnn-pytorch\n",
    "            # If the validation loss is at a minimum\n",
    "            val_loss_np = val_loss.item()\n",
    "            if min_val_loss is None:\n",
    "                min_val_loss = val_loss_np\n",
    "            else:\n",
    "                if val_loss_np < min_val_loss:\n",
    "                    epochs_no_improve = 0\n",
    "                    min_val_loss = val_loss_np\n",
    "                else:\n",
    "                    epochs_no_improve += 1\n",
    "            iter += 1\n",
    "            if epoch > 5 and epochs_no_improve == num_epochs_stop:\n",
    "                print('Early stopping!' )\n",
    "                early_stop = True\n",
    "                break\n",
    "            ### end of Early Stopping check\n",
    "            \n",
    "            # Append to history\n",
    "            history_val_loss_4.append(val_loss.data.item())\n",
    "            history_val_acc_4.append(round(accuracy, 2))\n",
    "            history_train_loss_4.append(train_loss)\n",
    "            \n",
    "            # Save model when accuracy beats best accuracy\n",
    "            if accuracy > best_accuracy:\n",
    "                best_accuracy = accuracy\n",
    "                # We can load this best model on the validation set later\n",
    "                torch.save(model.state_dict(), 'best_model_4.pth')\n",
    "                \n",
    "    # Check early stopping condition\n",
    "    if early_stop:\n",
    "        print(\"Stopped\")\n",
    "        torch.save(model.state_dict(), 'best_model_4.pth')\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting loss graph\n",
    "plt.plot(history_train_loss_4, label='Train')\n",
    "plt.plot(history_val_loss_4, label='Validation')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation Accuracy')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting validation accuracy graph\n",
    "plt.plot(history_val_acc_4)\n",
    "plt.title('Validation Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indirect 4.1: Dropout\n",
    "\n",
    "### Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeedforwardNeuralNetModel(nn.Module):\n",
    "    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim,\n",
    "                 dropout_pct = 0.0,\n",
    "                 initialize_weights=False):\n",
    "        super(FeedforwardNeuralNetModel, self).__init__()\n",
    "        # Embedding layer\n",
    "        self.embedding = nn.Embedding(input_dim, embedding_dim)\n",
    "        \n",
    "        # Linear function\n",
    "        self.fc1 = nn.Linear(embedding_dim*embedding_dim, hidden_dim) \n",
    "\n",
    "        # Linear function (readout)\n",
    "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "        if initialize_weights:\n",
    "            torch.nn.init.xavier_uniform_(self.embedding.weight)\n",
    "            torch.nn.init.xavier_uniform_(self.fc1.weight)\n",
    "            torch.nn.init.xavier_uniform_(self.fc2.weight)\n",
    "        self.dropout_pct = dropout_pct\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Embedding\n",
    "        embedded = self.embedding(x)\n",
    "        embedded = embedded.view(-1, embedding_dim*embedding_dim)\n",
    "        # Linear function\n",
    "        out = self.fc1(embedded)\n",
    "\n",
    "        # Non-linearity\n",
    "        out = torch.relu(out)\n",
    "        \n",
    "        # Toggle 3: Dropout (default used in original code: 0.8)\n",
    "        if self.dropout_pct > 0:\n",
    "            out = torch.dropout(out, p=dropout_pct, train=self.training)\n",
    "\n",
    "        # Linear function (readout)\n",
    "        # Take note here use a final sigmoid function so your loss should not go through sigmoid again.\n",
    "        # BCELoss is the right class to use as it doesn't pass your output through a sigmoid function again.\n",
    "        # In multi-class problems you're used to softmax which can be simplified to a logistic,\n",
    "        # function when you have a two-class problem.\n",
    "        out = self.fc2(out)\n",
    "        out = torch.sigmoid(out)\n",
    "    \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = num_words + 2\n",
    "embedding_dim = max_len\n",
    "hidden_dim = 32\n",
    "output_dim = 1\n",
    "dropout_pct = 0.8   # this is what was in original code\n",
    "\n",
    "# Instantiate model class and assign to object\n",
    "model = FeedforwardNeuralNetModel(input_dim, embedding_dim, hidden_dim, output_dim,\n",
    "                                  dropout_pct=dropout_pct)\n",
    "\n",
    "# Push model to CUDA device if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "# Loss function\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "# Optimizer\n",
    "# Toggle 2: L2 Norm option - this is called weight decay\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=0.005)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 100 | Train Loss: 0.6960393190383911 | Val Loss: 0.7031258940696716 | Val Accuracy: 48.88\n",
      "Iter: 200 | Train Loss: 0.6892494559288025 | Val Loss: 0.6985400319099426 | Val Accuracy: 48.97\n",
      "Iter: 300 | Train Loss: 0.6821810007095337 | Val Loss: 0.6981098055839539 | Val Accuracy: 48.96\n",
      "Iter: 400 | Train Loss: 0.6968421339988708 | Val Loss: 0.69722980260849 | Val Accuracy: 48.71\n",
      "Iter: 500 | Train Loss: 0.6858571171760559 | Val Loss: 0.6904107928276062 | Val Accuracy: 48.8\n",
      "Iter: 600 | Train Loss: 0.6985050439834595 | Val Loss: 0.6840927600860596 | Val Accuracy: 51.76\n",
      "Iter: 700 | Train Loss: 0.7098813056945801 | Val Loss: 0.6936395168304443 | Val Accuracy: 53.57\n",
      "Iter: 800 | Train Loss: 0.6921101212501526 | Val Loss: 0.6846234798431396 | Val Accuracy: 54.2\n",
      "Iter: 900 | Train Loss: 0.675839900970459 | Val Loss: 0.6728613376617432 | Val Accuracy: 53.72\n",
      "Iter: 1000 | Train Loss: 0.6946123242378235 | Val Loss: 0.682217538356781 | Val Accuracy: 54.47\n",
      "Iter: 1100 | Train Loss: 0.6810269951820374 | Val Loss: 0.6664893627166748 | Val Accuracy: 55.01\n",
      "Iter: 1200 | Train Loss: 0.6708064079284668 | Val Loss: 0.6643191576004028 | Val Accuracy: 55.27\n",
      "Iter: 1300 | Train Loss: 0.6941815614700317 | Val Loss: 0.6740490794181824 | Val Accuracy: 55.19\n",
      "Iter: 1400 | Train Loss: 0.6902398467063904 | Val Loss: 0.6580645442008972 | Val Accuracy: 56.04\n",
      "Iter: 1500 | Train Loss: 0.6701032519340515 | Val Loss: 0.6677349209785461 | Val Accuracy: 56.61\n",
      "Iter: 1600 | Train Loss: 0.690679669380188 | Val Loss: 0.6624118089675903 | Val Accuracy: 57.01\n",
      "Iter: 1700 | Train Loss: 0.6901030540466309 | Val Loss: 0.6434675455093384 | Val Accuracy: 57.47\n",
      "Iter: 1800 | Train Loss: 0.6278012990951538 | Val Loss: 0.6636686325073242 | Val Accuracy: 57.19\n",
      "Iter: 1900 | Train Loss: 0.6611995100975037 | Val Loss: 0.6320900321006775 | Val Accuracy: 58.03\n",
      "Iter: 2000 | Train Loss: 0.6558763384819031 | Val Loss: 0.6276631951332092 | Val Accuracy: 58.41\n",
      "Iter: 2100 | Train Loss: 0.6250803470611572 | Val Loss: 0.6488019824028015 | Val Accuracy: 58.43\n",
      "Iter: 2200 | Train Loss: 0.6640681028366089 | Val Loss: 0.632153332233429 | Val Accuracy: 59.05\n",
      "Iter: 2300 | Train Loss: 0.6157321333885193 | Val Loss: 0.6331596374511719 | Val Accuracy: 59.27\n",
      "Iter: 2400 | Train Loss: 0.6011375188827515 | Val Loss: 0.6419021487236023 | Val Accuracy: 60.07\n",
      "Iter: 2500 | Train Loss: 0.66325443983078 | Val Loss: 0.6044047474861145 | Val Accuracy: 60.85\n",
      "Iter: 2600 | Train Loss: 0.655142605304718 | Val Loss: 0.5861918330192566 | Val Accuracy: 61.12\n",
      "Iter: 2700 | Train Loss: 0.6300573945045471 | Val Loss: 0.607294499874115 | Val Accuracy: 61.25\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "num_epochs = 10\n",
    "history_train_acc_5, history_val_acc_5, history_train_loss_5, history_val_loss_5 = [], [], [], []\n",
    "best_accuracy = 0\n",
    "for epoch in range(num_epochs):\n",
    "#     print('-'*50)\n",
    "    for i, (samples, labels) in enumerate(train_loader):\n",
    "        # Training mode\n",
    "        model.train()\n",
    "        \n",
    "        # Load samples\n",
    "        samples = samples.view(-1, max_len).to(device)\n",
    "        labels = labels.view(-1, 1).to(device)\n",
    "\n",
    "        # Clear gradients w.r.t. parameters\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass to get output/logits\n",
    "        outputs = model(samples)\n",
    "\n",
    "        # Calculate Loss: softmax --> cross entropy loss\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Toggle 1: L1 norm, add to original loss\n",
    "#         fc1_params = torch.cat([x.view(-1) for x in model.fc1.parameters()])\n",
    "#         loss += 0.001 * torch.norm(fc1_params, 1)\n",
    "    \n",
    "        # Getting gradients w.r.t. parameters\n",
    "        loss.backward()\n",
    "\n",
    "        # Updating parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        iter += 1\n",
    "\n",
    "        if iter % 100 == 0:\n",
    "            # Get training statistics\n",
    "            train_loss = loss.data.item()\n",
    "            \n",
    "            # Testing mode\n",
    "            model.eval()\n",
    "            # Calculate Accuracy         \n",
    "            correct = 0\n",
    "            total = 0\n",
    "            # Iterate through test dataset\n",
    "            for samples, labels in valid_loader:\n",
    "                # Load samples\n",
    "                samples = samples.view(-1, max_len).to(device)\n",
    "                labels = labels.view(-1).to(device)\n",
    "\n",
    "                # Forward pass only to get logits/output\n",
    "                outputs = model(samples)\n",
    "                \n",
    "                # Val loss\n",
    "                val_loss = criterion(outputs.view(-1, 1), labels.view(-1, 1))\n",
    "                \n",
    "                # We use a threshold to define. \n",
    "                # There is another way to do this with one-hot label. Feel free to explore and understand what are the pros/cons of each.\n",
    "                # This opens up a whole topic on why it becomes problematic when we expand beyond 2 class to 10 classes.\n",
    "                # Why do we encode? Why can't we do 0, 1, 2, 3, 4 etc. without one-hot encoding?\n",
    "                predicted = outputs.ge(0.5).view(-1)\n",
    "\n",
    "                # Total number of labels\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total correct predictions\n",
    "                correct += (predicted.type(torch.FloatTensor).cpu() == labels.type(torch.FloatTensor)).sum().item()\n",
    "                # correct = (predicted == labels.byte()).int().sum().item()\n",
    "            \n",
    "            accuracy = 100. * correct / total\n",
    "        \n",
    "            # Print Loss\n",
    "            print('Iter: {} | Train Loss: {} | Val Loss: {} | Val Accuracy: {}'.format(iter, train_loss, val_loss.item(), round(accuracy, 2)))\n",
    "            \n",
    "            # Append to history\n",
    "            history_val_loss_5.append(val_loss.data.item())\n",
    "            history_val_acc_5.append(round(accuracy, 2))\n",
    "            history_train_loss_5.append(train_loss)\n",
    "            \n",
    "            # Save model when accuracy beats best accuracy\n",
    "            if accuracy > best_accuracy:\n",
    "                best_accuracy = accuracy\n",
    "                # We can load this best model on the validation set later\n",
    "                torch.save(model.state_dict(), 'best_model_5.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qIUb2IAYPphAAQJFuZo9Wwx69lj16LTl6PbqTYxY6v40bN7b4fsCgU4k66wbcAxpneSveuoqc79/DXF7oiPDQLWYyPn+S2PPvIGT02SRc8TgHvnmZ4j+XO+T6gm21lm+CYEsi3wRHEvkmOJLIt/azqqc0JSWFtLQ09u3bx+zZs497/95772Xr1q1s3bqVHTt2YLFYCAgIsOpYV5el1/OFWsgv43ryL/M+HjcfZIHlMFu0Kqp1lWDJyDjFj2sN4Txv7Mm7xkTuN0QxXQ6it2TCiOTsWxA6oYSEhBO+buoRT++bXib+sodxDwijJncfaW/dSeaCZxxWkDbTNQ4ufIW8nz5DUhR6Xnwf4afOdGwMgk2cLN8EwR5EvgmOJPJNcCSRb+3Xak+pLMvMnTuXKVOmkJOTw6ZNm1iyZAm7d+9u3ufFF1/kxRdfBODss8/mrrvuorS01KpjO4vAwEDM6M09oku1EiQgUnIjWfKkt2QiSfYkRDIyWPJmsOwNgFnX2K/XsUevZa9Wy169hirxXKrQisDAwGM+Nnj6EjH1WkJGnoUkK5iryshd8SFFG38A3bn5lLfyY8xVpcSceytRZ12P0SeA7O/eAV2MGegs/plvgmBPIt8ERxL5JjiSyLf2a7WndOTIkaSnp5OZmYnZbOaLL75g+vTpJ91/1qxZLFiwoF3HurITrTukAzl6Az9pZcxV87nDnMFtDem8YcllpVrKQa0OBYnesifnKkHca4ziXbck5hh6cp0SximyL8EYHX8zgstrzjdZJnTsefS//1NCR58DOhT8+g2pL1xF0R/LnF6QHlG4fjH75z+FZjETNn4GPWc+gKSIpwM6C7GOn+BIIt8ERxL5JjhSe/Jt9erVnHHGGce8dscddzB37tyT7j9s2DAAli1bhp+f33H7PProo9xzzz0tXnf69On06dOn+ePHH3+cSZMmtTV8m2m1KI2MjCQ7O7v545ycHCIjI0+4r8lkYurUqXzzzTdtPtbVpaSkWLVfMRY2aJV8rBbwf5YD3GDex/PmbL5Vi9it1dCga0TJ7kxSAvi3IYLX3eJ5wxjPbUoEZ8j+xEruYsCvQEpKCj4JQ+h357vEnHcbBk8fyvf+yc5Xrid76VuotVXODvE4pdvXsO/DB1HrawgaMpmEq59EdvNwdliCFaxt3wTBFkS+CY4k8k1wpPbk24IFC5g589jHn2bOnNncydeSadOmUV5e3uZrApx33nn07du3+eNHH32UVatWtetcttBqV4YkHV8i6ScZlnfOOeewbt06SktL23RsREQEGRkZVFVVYTKZWLNmDbNnzyYlJYUDBw5QV1dHcnIya9euZcSIERiNRtasWcOkSZNIT08HGsdwr1q1iokTJ2I2m9m0aRMTJkwgLS0NDw8P4uLiWLFiBSkpKVRUVJCamsrYsWPZsWMHgYGBREZGNr9fUlJCeno6I0eOZOvWrURERBAbG0tgYCApKSkUFBSQl5fHkCFD2LhxIwkJCQQGBjYfn5ubS0lJCQMGDGD9+vVE9e+P4uvL3BUrOHPyGGp27ye0rJbesie+h8oJwsgYxcgYfAGolyHfS6EuOpSfDu4hYewIGlSVzX9tY/S4sezetxc3kwexPXt26J7CwsKa32/rPfXv3x9fX9/m913l69QV7ml/QSll/c6h95B/AWCsryD7u7c5pVcQ0f3jycszufQ9eW78HMvoK/DrPZIx//cRnpu/ZP/uHV3u69SVci82NpaUlJQudU9d8evUVe7Jx8eHWbNmdal76opfp65yT/X19cyaNatL3VNX/Do5854iIiIYMmSITe6prKyMWbNmtemetm/fzvPPP8/q1atZtmwZl112GT179uSWW27hk08+QVVV/vjjD77//ntWrFhBaGgoQ4cOpb6+np9//pkzzjiDyMhIrrjiCoYNG0ZdXR35+fmkpqYya9YsoqKiuP766zEajaSmpvL111+jKArnn38+Z511Fo899hjz5s3j/PPP58svv6SyshJfX1/uvvtuPDw8+PXXX1mxYgUlJSW88847/PHHH/Tp0wcPDw/eeustPvrooxN+nZKTk49p61tbw1WClieLHT16NI899hhTp04F4IEHHgDgueeeO27fhQsX8tVXXzVX9tYeO3fuXG655ZYWA3W2/v37k5pq+7UYJSACN3rLJnpLnvSWTYRKblYdq+k6Fho3FR0LHPV/HYt+1P/RUZvet6Cj6vqx+57kfTM69WjUoVGva9Qd+Vhveq3pPdXmn5nuR5IVwiZcRMSUq5CNbqj1teT/PJ+CX79Ct5idHV6buAdHkvSv53EP7EHt4Sz2vT+bhrLDzg5LOAl7tW+CcCIi3wRHEvkmtOb8889n0aJFAMx3S7bLNS5tSGvx/e+++453332XJUuWMHv2bIKCgnj22Web5+hZtWoVt99+Ozt27GD16tXce++9bN68mczMTIYPH05sbCwff/wxo0aNwmAwsGXLFt555x1eeuklAgMDKSkpAeDJJ5+koKCAN998k48++ojvvvuueYTrkY+/++479u3bx6RJk9i3bx+ffPIJW7Zs4bXXXiMzM5OXXnqJN998k5tvvpmhQ4dy/fXXn/Cejv68HtFSzddqT+mmTZtITEwkLi6O3NxcZs6cyaWXXnrcfr6+vkycOJHLL7+8zcd2BgMGDLBLo6YDuTSQqzXwM+WgQiAGkpqK1CTJhLekYEDCgIQCzf+XJQk3JE5awjpwHLBFP6p4RaNe16k7ppjVqEc/5uN/7nt08VuPTg1qtyl2TREJxF10L16RiQD4lKTz21sPYa4ocnJk7VNflEvaW3eQeO2zeEbEk/zv19n7wWzqCg46OzThBOzVvgnCiYh8ExxJ5JvQGRwZwrtkyRJmzpzJtddey8UXX8wNN9yAwWCgR48e9O3blx07dpzw+PHjx7No0SJqa2sBWLJkSfN7/fv356mnnsLf3x9vb+9Wn3vt3bs3mZmZ7Nu3D4BPPvmEW265hddeew1o7IQE2Lx5MxdccEGH7/2IVotSVVW59dZbWbFiBYqi8OGHH7Jr1y5uvPFGAObNmwc0VsMrV66kpqam1WM7o/Xr1zvsWiVY+F2r5HcqW9xP4u8CtbFglTA0vaZI0gneO6qolf5+7ej3m/dpOt6IjDsSHsi4S3Ljv0h4SDLuHPlYbtpfwQvl7+A6yCzBNq2K9ZZytuhVmLvgCrCSwY2IyVcQPvESJEWhvuQQB795mYCGok5bkB5hrihmzzt3kXD1k/j0GkTyza+R/tFDVB3c6ezQhH9wZPsmCCLfBEcS+Sa0RWs9mq2JjY3l4MG2/wH+22+/5eWXX2bIkCGYTCZKS0u59957GTFiBGVlZXz00Ud4eLQ8T8fJHq/8+OOPOe+889i+fTtXXXUVp556aovnOdHjl0err68HGus8g8F2k1padaYffviBH3744ZjXjhSjR3zyySd88sknVh3bmUiygldcP/r1iW5XktmTDpibhtie8M3WDrYhBZoL1JYL2OML2saP/y58TUYTnkYPPOpqGSF5M8LoTa2u8qdWxXqtglS9ukv0oHrHDSDuonvwCIlG1zQKfltI7vIP0BrqGD9tmsvlW3uoddXsff8Bel36EAH9TyHphhfI+PwJynf/7uzQhKP079+/S+Sb0DmIfBMcSeSb4Ejtzbfq6mp++eUXPvzwQxYsWICvry/V1dWUl5cTGhrKmWeeyS+//HLS49euXcvHH3/Mc889h8Fg4Jxzzmmu1Xx8fMjPz8dgMHDZZZeRm5sLQGVlJT4+PsedKy0tjbi4OOLj48nIyOCKK65gzZo1bb6nthJrNrTCJ2EoSf96juKGaqKkGEq2/UxNzh5nh+VyVKAajeoja7AeXfRaWQBLipGos24g7JTGoQDuh7Lpu38fydv/pEfOQcYrfoxX/KjQLfyhVbJeq2CvXtvp+k9ld0+izvwXoWMbl0eqLTjIga9epDrr71EEvr6+zgrP5nRLAxmfP07s+XcSMmoaCVc+wYGvX6J4s5im31V0pXwTXJ/IN8GRRL4JjtSRfFuwYAGLFi1i5syZ7Nmzh61bt7Jz507279/PunXrWjx269atfPnll2zbto2DBw/y66+/Nr/38MMP88cff3Dw4MHmyeYAvvjiC9577z1uv/12ZsyY0bx/fX0911xzDV999RUGg4FNmzbxzjvvtPu+rNXqREeO4MoTHfn3H0/0tBtwD4pofq2uKIeSbasp2baausPir2+24BYQTvxlD+MVk4xmMZOz7F0Ob/iW4BFnEnnGNYQ0mOm9fTOJG9cQWvn3sOYi3cwGrYL1WgUH9Xon3oF1/JJHEnP+nbgHhKGpFg6tXkD+qv+iq8dOZHT0Q+ldSUTKNURManzuPGfZuxxa86WTIxKg6+ab4JpEvgmOJPJNaM2JJuRpL5Fvf2vrREetrlPa3ZWl/sqO568gas9iCn5bSENFMR7BUURMvoL+935I3zvfJfzUmbgFhDk71E7Lv984+t45D6+YZOpLDrHn7Ts4vG4haBpFfyxjx5wr2f3XT2w4ZRKf3/8sn9x4Dz/HRFKEhWDJyDlKEM8ae/KCsSfny0GEY3T2LR3H4OlLz5n/R+K1z+IeEEZ1dhq7X7uZvJUfH1eQQtddVy1vxUdkffsGAFHTbiDq7JuglWcXujqvmL70vvlVwiZcBJJzmuSumm8tkWQFo1+ws8PolrpjvgnOI/JNcCSRb+0nhu9aqWDXJrI3bCB76dv4xA8icPDpBPQfj2dEPJ4R8USddT1VB3ZSsu1nSravwVJV6uyQXZ6kGBqH646/EIDS1HUc+GoOam3VMftp9TXkLv+Awt+XEnnmv2DIJIpveJBNFcW4LXyHfnt2MUr2JlJy5yJDCBcRQoZWywatgg1aJaVYnHF7zQIHn0b0ubdi9PZHbagjb+XHFPz2DWjaSY85cOCA4wI8ShhGVKAI+y1Bc3j9t5iry+l5yWzCJ1yE0cufA1+9gK51hSeF28YnfjAJVz+F4m7Cp+cA/PuMJvPL5x2+fI6z8s1ZJMVA4rXP4ps4FEtNJTW5e6nO2UtNTuO/DaWHnB1il9bd8k1wLpFvgiOJfGs/UZRaqa6urvE/ukZl+lYq07eSteh1fHsPJ3Dw6fj3HYN3XD+84/oRfe6/qUzfRvG2nylL/RW1rtq5wbugY4brqhZyls3j8G8LWzymoewwmQue4fC6RUSffTPecf3Qr36Ijfn7+Xbp28Rk7GGM7MsI2Zt42US8bOJSPZTdeg0btAr+0Cr/fubVAYx+wcSefyf+fccAUJGxjYNfv0R9cV6rxzbnm4MkSB6crwQzRPYG4E+tkqVqCfv0Wrtcr/Sv1ag1FcRf+ThBw6Zg8PIj47PH0cyOvW9n8u09koQrH0M2ulO2awNe0b3xiR9M37veI3vxmxRv+dFhsTg635wt5rzb8E0ciq6pGDx98E0chm/isOb3LdUVVOf+XaTW5OwR6+zaUHfLN8G5RL4JjiTyrf1EUWql5ORktm7desxrumqmfNcGyndtQDZ64N9vDIGDT8c3aQS+ScPwTRqGdsEdlKdtouSvnynftQHN7PrPPdqbf79xxF18PwaTN/Ulh9j/3yepzrZ+Cu7qrN2kvXU7AQMnEnXW9Xj26EXCDS9QnvYHn3w3jw8PpzNY8mas4sMQyZt+shf9ZC+u1sPZrlezXqtgs1ZJvb0ep5YkgkdOI3raDSgeXlhqq8hZNo+ijd9bfYoT5Zs99JZMnK8EM1D2AqBO15CB4bIPw2Uf9mg1LFVL2KpX2fyzVbFvM3vm3UPitc/glzySpBteYN9HD6HWVNj4Sq7Hv984el32MLLByOHfl5K16DUMnr7EXnAXAQPG03PmA/j1HUvWwlewOODz4ah8cwWhp1xAyKiz0cz1pL19F+aKYryikvCMSmr+1+gdgF/ScPyShjcfZ64qO65H1Vxe6MQ76by6U74JzifyTWhNa0ugtIXIt7+19fMqJjqyUmRkZPMUyq1RTD4EDBhP4KDT8IkfjCQ3Piem1tdStmsdJdtWU7H3T3TVucNKHe244bo713Hgfy+g1ra8HmuL5zQYCRt3AeGnX4rB5I2uqhT+8R15P36CpbocEzLDZW/Gyb70k7xQmr5B6nSNLU1LzPylV9lsiRn34EjiLrwbn/jBQOM9Zi16vc1rjrYl39qjn+TJ+UowfWVPAGp0lR+1Ur5XS5GAFCWAM+QAvKTGdWdz9Xq+U0tYp1VgsXGT4R4SRdJ1z+MeGE5twUHSP3qI+pJ8m17DlQQOPo2el/wfkqJQ8OvXZC99+5j3g4alEDP9FhQPLxoqijnwvxeo2LvJrjHZO99chV/ySBKufgpJVsj4/AlKt594inujX8g/CtXeGL38jtvPXFlKdc4eanL3Nf6bsxdzRbG9b6PT6y75JrgGkW9Ca6ZOncquXbvIysrq8LlEvjWKiYmhb9++LF++/JjXW6r5RFFqpfPOO49vv/22zccZfQIJGHgqgYNPwzu2b/PrlpoKSnespWTbair3bwfdccNKneGfw3Vzv3+Xgl+/sdn5DV7+REy5kpDRZyPJCpbaKg79PJ+CdQvRLY3PR/qiMEr2YazsS++mYgygSlfZ2LTEzG69pn3fELJM+PiLiDjjKmSjO+bKUrIWv3HSX3pb0958a80gyYvzlSCSmu6/WldZrpWyXC05bmizBzKnyn6cpQQSLDVOHlWqm/lBLWWVVkatDYdCG32DSLzuOTx79MJcXU7GZ49RtX+7zc7vKoKGTyVuxj1Iskz+qv+Su+LDE+7nFhBOz0tm49NrIACH1y8mZ9m7dhvebK98cyUeYbH0ueUNFA8vcld+TP5Pn7XpeDf/UDyjeh9TrBo8j5/6v6GimJqcvc29qtXZe8QcA//QHfJNcB0i34TWSJJESkoKnp6e6HrHyqLBgwezbds22wTWSUmSRE1NDStWrDju8ymKUhu46KKL+Oqrrzp0DrfAHgQOOo3Awafh2aNX8+sNFUWU/rWGkm0/t2kYa2fh328ccRfdh8HTh/rSAvZ//oTd7tMjLJboaTfilzwKgPqSfHK+f++44jAYI2OaCtRY2aP59VLdzAatkg1aBRm6dQWAqUcv4i66D6+oJACK/lxJ9ndvd2gYqi3y7WjDJG/OU4KIl00AVOoq36slrNRKWy0uFWC07Ms5ciAxTZ+rGl3lJ62M5WopZTaaSEp29yT+sv/glzwKTbWQtfBVijb9YJNzu4KQMdOJPf92AHKXf0D+z/NbPkCSCZ9wEREp1yAbjNQV5pD5xbN2+d6xdb65GoOXH31ufRP3oAhK/lrN/v8+ZZPzugX2wCsysalYbfzXYPI+br+G8qLmntSa3L1UZ+/FUl1mkxg6m6DhKQyakMKeXTvRLA3oFvNJ/m1As5jRzQ1oqhnN3IB+5N+j9uluI46Etuvq7ZvgWkS+tUwUpTYQGhrK4cO2m+jCIyyOoMGnEzD4NDyOXgO1OI/Sbasp3vYzdQUHbHY9Z2gcrns9YeMbF+S1xXBda/kmDiPq7Juai/+qAzvJXvrWCX+hj5TcGCv7Mlb2JUxya369UDezSatko1bJPr32uG8UyWAkYtIVhJ16CbJioL60gIPfvGKToZa2yDcJGCH5cJ4SRFxTMVmuW1imlvCjVtquZ2oHSl6cowTSr+kZVIuu86tWzjK1hDwaOhRvY9AyUdNuJHxCY84cWvs1OcvmdfqRBGETLiL67JsAyF76VptGCZh69KLnzP/Ds0cvdFUl/+fPG9e2teFsxbZu31yJpBhJuuEFfHoOoDo7jT3v3G3XZ/vdgyKOGfbrGZFwwkK1vjiPgwtfpWLfZrvF4mqizrqB8FMvsfl5NXN9YwF7pJC1NPyjeG38V7OYqc1LJ//n/9o8BsF1deX2TXA9It9aJopSG5g1axYLFiywy7m9opMbl5gZdCpuvkHNr9fk76dk22pKtv3c6ZYocAsII/6yR+w2XNcqskzw8KlEplyL0ScAgOKtq8j94f2TzqQZL3kwVvZltOxDgPT3eqeluplNWhUbtUrS9Bo8Y/sRe9G9mEJj0DWNwxsWk7v8A7R628xW25F8k4Axsi/nyUFEye4AlOhmvlNL+Fkro8EG3/LxkgdnK4GMkHyQm57T/VOr5Du1hL02mLE3eMSZxJx/B7LBSHnaH+yf/3SnncW6x+QriDzjagAOLnyFwt+/a/M5JIORyJRrCRs/A0mWqc5KY/+Xz1JfmGOTGO3Zvjlb3EX3EjziTBrKi9j9xr8d/8ynJOEeFNnck+oVlYRnZCKKuyeauZ59Hz1EZXrXnxSjx6TLiUy5Bk21EHx4O9u3bUU2GJEMbo3/Gt2QDW5IBiPykdeO+ffv9ySDEdno1vi60vb5Gne/eSvVWbvtcJeCK+rK7ZvgekS+tUwUpTYwYsQINm2y72QjSDI+vQY2FqgDxh/zvFJl5g6Kt/xI6fY1x63j6WocOVzXGrK7Jz1OnUnYhIuQjW5o5gYKfv2a/NUL0OprTniMBCRIJkbKPoyUfQg5qkCtNhrZP2AY6f2GsMfHxP5Fr1F1cKdNY25PvinAONmP6UoQPZp6fIt0M0vUYtZo5Zjt8K0ehpFpSiATZD/cpMYJvWw1Y693zwHEX/k4Ri+/TjsBUuSZ/6LHabPQNZUDX71I8eaVHTqfT/xg4i6+H/eAMNSGOnKWzaNww5IOx+mQ9s0JjvRQqw117Hn7Tmpy9zk7pEaSTMx5txE65lzUhlr2vf9/VB3Y4eyo7Cb0lAuIOfcWdE1l/3+fIsGj1nb5JslNheuRgtXt+GJXafw3eNgZBAyYQP7qBeT+8L5tri+4vK7avgmuSeRby0RRagOOTjJJMeCbNILAwafh328cilvj8EvN0kD57t8p3vIT5Wkb0VWzw2JqjaQYiDzz+uahl44crmsNN/9QIs+8jqAhk4HGmTNzV37U+Nyi1vLw0J6SO6cE92ak7ENQ6d89LTW6yhatik1aJX/p1TbphYS25ZsCTGgqRkObitECvYHFajG/auU2m1m4Jb4opCgBTJED8LbhjL1ugT1IvPopTOFxWKorSP/s0c4xAZIkEX3Ovwk75QI01ULmgmfaPenVPykeXkRPv5XgYWcAUJ62kQNfvYC5sqTd5+yKP0T9+owh4aonkGSZjM8ep3THWmeHdCxJIm7GvQSPmIpaX8Pe9+7vkr13wSPPIm7GPQBkfjmH4s0rnJZvPvGD6X3jS9QezmLni9c4/PqCc3TF9k1wXSLfWtZSzSc7OJZOKyEhwaHX01UL5bs3kLngGf56cgaZXz5Pxd7NSLKBgAETSLjqCQY9/D9izr8Dr6Nm9XUWt4Awet/8KuETZqCpFrKXvk3GJ4+4TEEK0FB2mMwFz7L7jVuoOpCK0SeAuAvvpu8d8/BNHHbS4xRPX/SL72T3fa/wyd2P887Fl/GtycJBrQ5PSeEUxY+7jFG8Y0zkDkMEY2QfTB381rIm34xITJH9ecUYz/WGHoRKbuTp9bxtyeMe835+cVBBClCByldqEbeZ0/nUUkCRbiZScudGQw9eM/bibDmwXZ+ThpJ8ds+9jbLdv2Pw8iXp+hcIHnmWHe7AhiSZ2AvuaixILQ1kfPaYzQpSALWumgNfPk/GZ49jqa7AL3kk/e7+AP/+49t9Tke3b/ZmCu9Fr0sfRJJlcpd/6HoFKYCuc+Drlyje+hOKu2fj7NORic6OyqYCB59G7AV3AXDw29cp3rwCcF6+VWZux1JdgSk0Bo+QaKfEIDheV2vfBNcm8q39RE+plVzlwWWjbzCBQ04naMhkPCPim1+vK86jZMtPFG/9ifoix66P5N93LHEX3//3cN3/Ptkp/uIfMGACUdNuwD2wBwDlaX+Q/d086g4f/HufgROJOe82jN4BaOZ6cld+TMGvXzf3rIZhZETTEN+EppltAcy6xg69hk1aJZu1SqrauHxKS/nmhsQk2Z+zlcDm516ztXq+1Yr4Xat0/jc0J5+xd5VWxg/tmbFXkomadgPhEy4CaFzfc9m8Vnu4HU6W6XnxbIKGTkZtqCPjk0fsOpGN0TeIuIvuw6/3CKBp5uclb7b5+VtXad9sweAdQJ/b5uIeEEbx1lVkLnjG2SG1TJbpdenDBA6cgKW6gj3z7qH20H5nR9Vhfn3HkHDF40iKQs4P73No9d/PWDkz3+Iuvp/g4SnkfP8eh375wikxCI7Vldo3wfWJfGuZGL5rA644xbMpvBdBQycTOGQSbn7Bza9XHdxF8ZafKP1rNZYOLEvSmn8O1y3btZ7ML+e4VO9oaySDkdBx59Pj9MswmLzRVZXCjcso/P07IqZcRUD/cQBUZmzjwNcvUV+cd9JzBWJgpOzDCNmH3pKpeQIgVdfZpdewUavkT60Sa/ovT5RvHshMkf05SwnET2qc3OOAVscitZg/ddcoRk/EljP2HjsB0kb2z3/KZSZAkhQDPWc9RODACaj1tez76EGHDTUOGTOdqGk3oLh5UF9aQOaXz7Xp2q7YvrWHZDDS+4YX8Y7rT9XBXeyZdw+6xQazQtuZpBiIv+JR/PuOxVxVyp537qbucMcXcXcWn4ShJF77NLLBjfyf55O7/INj3ndmvvn3G0fCVU9QlbWbtDdvdUoMgmN1lfZN6BxEvrWspZqv7dPWdVNms+s8u3lE7aH95Hz/Ljk/vI9P/GCChk4mYMB4vGP74h3bl+hz/03Fno0Ub/6Rst2/2/SXM7eAMHpd9jDeMX2aZtd9r7EHsZPRLWYK1vyP4j9XEDHlKkJGnU3omHMJHXMuAJbaKnK+f5eijd9DKwsql2BhuVbKcq0UXxSGN/Wg9pM8GSB7MUD24ho9jL16LRu1SjZplRSfpMfw6HwzIZMiB3CmEohP0/OaGVoti9RituiuPekVwHa9mu2WanpJHpzTNGPvaYo/pyn+bZ6xt2jTD9QV5RB/5eP4JY8k+ZY3XGICJMlgJP6Kx/DvMxpLbRX7PnjAoaMFCjcspjJ9Cz1nPoBXdDK9b3iJgl+/Inf5R1Y9d+6K7Vt7xF54N95x/akvLSD9k0c6RUEKjY9rZHz2BAlXP4Ff75Ek3fACe9652+GjXmzBO7YfCVc/gWxwo+C3hccVpODcfKvY+ydqQx3eMX0w+gZjrihyWiyCY3SV9k3oHES+tZ/oKbVSZGQkubmu/wuCbPTAv98YgoZOwTdxOJLSWMRYaqso3bGWki0/UZm5vdUCqyXHD9d9iuqsXba6BafyCI0latoN+PcZTdmu9Rxc9Brm8o790uKFzDDZhxGyNwMkr+ZZaqGxuDxSoB7i74YsMjKSstx8zlQCSZED8GoqRvdoNSxSi9muu0bvYHvYYsbef06AlPHZY1Tu/8u+gZ+EbPQg4eon8E0chrm6nH3vz3baLK+SrNBj0uX0OP0yJEWhJn8/mQuebXU4aGdp31oSfupMos66HrWhlrS5d1Cbn+HskNpMMriReO0z+CYMoaHsMGlv39WplgPzjEwk6YYXMZi8Kdq0nANfv3jCnzXOzrf4K58goP84Di58lcLflzotDsExnJ1vQvci8q1lYviuDXTGdYcM3gEEDjqNoGGT8Yrq3fx6fWkBJVtXUbzlp2Oen2zN8cN1N5D55fOdariutRSTj13uywOZIbIXI2UfBkneeBxVoGZpdWzUKtmuV3NFv5FE7cnH1FSM7tSqWaQWs0s/8RI2ndGJZuzN1ur5VC1gpxX3Kbt70uvSh/DvMxpNtZC16LXGHm0Hkt09Sbz2GXx6DsBcWcKed++jruCAQ2M4Ea+YPvSc+QAewVFolgZyV3xEwdqvQT/xM7idsX072pEhmQDpnzxC2c51To6o/WSjB4n/eg6fngOoL8kn7e27MJcXOjusVnmExtL75lcwevlR8tcv7J//tMvmW9CwM+h5yWzK9/7JvvdnOy0OwTGcnW9C9yLyrWWiKLWBIUOGsHVr513g3CM0hqAhkwkcOhn3gLDm16tz9lKy9SeKt/6Mpar0pMe7+Yc2DteN7ds4XPeH9ylYK8bMd4QbEgMlL0bIPgyVvZt7Q4/2l1bFIrXY6uGtnZE7EqfJ/pypBDavB7tBreBz9TClrU2IJMlEnXU94RMvBhw7AZJi8iHpuufwikmmoewwe969j/qiHLtf11qy0YOos29sHopeuf8vMr98nobSguP27cztmykigeR/v4riZuoyk9fI7p4k/et5vGP7UleUw5537sZcUdz6gU7iHtiD3v9+FTffYMp2/07Gp4+iqyf/3nV2vimevgx+5Gt0Xeevxy9wmefSBftwdr4J3YvIt5aJJWFswMPDw9khdEjd4SxyV3zIjucuI+3tOyn8YxmW2iq8opKIPuffDPrPlyRe+yyBQyYhG4+9V7++Y+h757t4x/alvrSAPW/fJQpSG2hA50+9irfVfG4y7+N5czar1TIO6w3s9zPysPkAz1tyunRBClCPznKtlHvM+1lgOUydrjFG8eUlYy+myYEcX6ofRdfIWTaPzP+9gGYxEzZ+BolXP43i4WXXmA1e/vS+8SW8YpKpL84j7e27XKogBdDMdWQteo19Hz6IubIEn16D6HfXewQNnXLcvp21fTN4B5B49ZMobiaKNq/sEgUpgFZf0/hccs5ePIKjSLr+BQxe/s4O64SMfsEk3fAibr7BVKRvJeOzx1ssSMH5+abWVFCZuQNZMeCXPMqpsQj25+x8E7oXkW/tJ4pSK8XFxTk7BNvQdaoyd3Dwm5f568kZjYvK71yHruv4JY+k16wHGfTIV8RdPBvfxGFEnX0TiVc/hcHTh7Ldv7PrtZu6zPOjrkQF/tKreU89xJ3m/eyc3J8Mvc7ZYTmUBZ2lWgn3mffzh1aBhyRzmSGUZw096St5tnhs8Z/L2fvefZiryponQDqy1I+tGX2D6H3Ty3hGxFNXmE3aOx1/7k+yUWwnUp72Bztf/helO35F8fCi58wH6HX5oxg8fZv36Yztm2RwI+GqJ3DzD6XqQCoHv37Z2SHZlFpXzd73Z1OTvx9TWCxJN7yActTXzBUYvPxJuv4F3APDqTq4i/RPHrZqcilXyLey1MYh3v79xjk5EsHeXCHfhO5D5Fv7ieG7VgoMDKSkpMTZYdiNwdOXgEGnEjR0Ct6xfY95T1dVcn5oml23AxMkCdbr6vlmjYGSF1cbwgiX3ABYp5bzX7WwxTVO3QLCSbzmKUzhPe0yAZKbfyhJN7yIR3AkNfn72fve/S0Oe29NBG6cqwQxWvbhN62Cj9UCLHZskoOGpRAz/RYUDy8aKoo58NWLVOzZ2CnzreesBwkaMon60gJ2v/5vLNVlzg7JLgxe/vS+6WVMYbFU5+xl77v3usRwU8XkTe8bX8IzIoGavHT2zLsHtda62cBdId/c/EMZ+OAC1Poatj1+AbpFzJjZVblCvgndh8i3lnV4+G5KSgppaWns27eP2bNPPCnAxIkT2bp1K6mpqfzyyy/Nr995552kpqayY8cO5s+fj7u7e9vvwAWkpKQ4OwS7stRUULhhCWlzb2PHnCvJ+/ET6opyqCvOI+2dOxuH64qC1GG6er5ZY7tezf3mTL60FFKva4xT/HjR2JMz5YCTDultKD3E7rm3U7b7dwxeviReP4fgkWfZJB73oAh63/wqHsGRVOfsZc+8e9pdkMZJ7txhiGCOsScTlMZZiE9X/HnYEIO/HVfqKt68gp2v3EDl/u24+QaRdN2zxJx/B1POnGa3a9pDj9MvJWjIJNT6GtI/+k+XLUgBLNVl7H3vPuqKcvCKSiLxuueQ3VseOWBvsruJxGufxTMigdrDWex9b7bVBSm4RvvWUHaY6tx9KO6e+CYMdXY4gh25Qr4J3YfIt/ZrtSiVZZm5c+dy5pln0rdvX2bNmkWfPn2O2cfPz4+33nqLc889l/79+3PRRRcBEBERwe23387w4cMZMGAAiqIwc+ZM+9yJnVVUVDg7BIepL8ol78dPSZ1zFanPX0H1QTFc19G6U761xILOYq2Y+8yZbNIq8ZQUrjCE8bQhjmTJdMJjtPoa0j9+mENr/oesGIibcQ/R5/wb5PY/rXBkZlH3gDCqDuxs7K2qafvXqLdk4n5DFM8YezJK9kVF5ye1lBfM2RTqZhJlE08b40iU7PdMSkPpIfbMu4ecZe+iWcyEjjmXzOQLCR41DUlu8Qlel+DffzyRU69D1zT2z3+m1eVuugJzRTF75t1LfUk+3rF9Sbz2meOe/XcUyeBGwtVPNc4xUJLP3vfua/MfBVylfTsyS7MYwtu1uUq+Cd2DyLf2a/W3tJEjR5Kenk5mZiZms5kvvviC6dOnH7PPpZdeysKFC8nOzgagsPDv6esNBgMmkwlFUfD09CQvL8/Gt+AYqampzg5B6EZEvh2rCDOvWHKZY86mQG8gRvbgEWMsNys98DtRv2nzBEhzmiZAupDEa9o3AZKpRzy9b3q5cSKXjG3sff/+Ng+fHCh58bAhhkeNsQyWvanTNZapJdxh3s+HagFb9Wr+Yz7ATq2aAMnAw4ZYTpX92hyr1XSNQ2u+ZPcb/6Y6dx+auw9xF95Nv3s/InDw6SDZ8ynX9vOMTKTnzAcAyPnhPcp3b3ByRI5jLi9kz7x7aSg7jE/PASRc/SSSwc2hMUiKgfgrHsU3fjANFUXsffe+dq3j7CrtW1nqb0Dj2ttIYoqNrspV8k3oHkS+tV+r48QiIyObi02AnJwcRo06dra6pKQkjEYjq1evxsfHh9dee43PPvuMvLw8XnzxRbKysqitrWXlypX8+OOPx10jIiKCjIwMqqqqMJlMrFmzhtmzZ5OSksKBAweoq6sjOTmZtWvXMmLECIxGI2vWrGHSpEmkp6cDkJCQwKpVq5g4cSJms5lNmzYxYcIE0tLS8PDwIC4ujhUrVpCSkkJFRQWpqamMHTuWHTt2EBgYSGRkZPP7JSUlpKenM3LkSLZu3UpERASTJk3ipZdeIiUlhYKCAvLy8hgyZAgbN24kISGBwMDA5uNzc3MpKSlhwIABrF+/nv79++Pr69v8vqvcU1hYWPP74p5c656uvfZaPvvssy51T7b6Ov3cpzdDDtXSa1c+4xU/Rhr9+C3USO2Yfqz57ddj7ykxkH2/fgBjL8ev90j63/E2PXPXQFWxVfc0dPJ0suKmILl74lmRTeaiF7jkwvOtu6fcPM4M60nE5gwizY2/8NagkZMcwXovC3mVbpw5YNIxX6d5y1dwZ3gcvTIKucHQgym9BjCvIpOhI+34dfrsIS659yk21wfjERxJr0sfInbq1XhnriNSrmClq+TewTyUlDvAzYPK7as5LUKhctq0btZGnEbhrsWUDLoY38ShDPr3SwTsXEJ4aLD972ntr/S99kmqAnpBfRUJub9wyKAy8aKL2nxPZ599NgcPHnSJr5OxvgJ8AhiecgGJAUaXbfecn3ud956io6MpLy/vUvfUFb9OXeWeTCYTtbW1XeqebPl1ao3e0jZjxgz9vffea/748ssv119//fVj9nnjjTf0DRs26J6ennpQUJC+d+9ePTExUff399dXrVqlBwcH6waDQV+0aJF+2WWXHXeNuXPnthiDK2z9+/d3egxi6z6byLfWt1CM+j2GSH2+W7I+3y1Zf9YQp/eWTCfc1y0gXO939/v68Dmr9MGPLtJ94ge3en7vuAH6kCeW6MPnrNLjr3xclxSjVXHJoI+TffU5hp7Nsb1tTNDPkQN1E7JV55gg++kfG5P0+W7J+sOGGN0Xxf75Jst60PCp+oD/m68Pn7NKHz5nlZ58yxtWfa7svclGd73PbXP14XNW6b1vftXqr0VX3TxCY/VBj3ytD5+zSk+4+kldUgz2vaYk6XEX39/4/fP4Yt0zMrFD53Ol9i3q7Jv04XNW6VHTbnR6LGKzz+ZK+Sa2rr+JfGt5a6nma3W8Sk5ODtHR0c0fR0VFHTcENycnh+XLl1NTU0NxcTFr165l0KBBTJ48mczMTIqKirBYLCxcuJCxY8e2dkmXFBgY6OwQhG5E5FvrDmPmJUsuL5pzOKw3ECt78KgxlhuVHvj+Y0jvcRMg/et5gkedfHIfn4ShJP7rWRQPL4q3/cz+z59AV1uendOAxOmyPy8be3GLIYIo2Z0i3czHlgLuMGewVCuhFs2qe1urlfOkJYti3Uwf2ZOnjXH0tONzpoGBgaBpFP+5nNQXriJr8ZuYq0rxju1L7xtfIun6OXhFJ9vt+q2Ju/g+vKKTqS/JJ+PTR1v9WnR1dYcPsvfd+7FUV+Dfdyw9Zz3UoWemWxNz7q0ED09Bbahl34f/R03uvg6dz5XaN7E0TNfnSvkmdH0i39qv1Z9imzZtIjExkbi4OIxGIzNnzmTJkiXH7LN48WLGjx+PoiiYTCZGjRrF7t27ycrKYvTo0ZhMjROSTJo0id27d9vnTuwsMjLS2SEI3YjIN+tt0au4z5zJN2oRDbrGRMWPl4y9OEP2P6aB+3sCpC8bJ0C68G6iz73luF/m/ZJHNT5/6maiaNNyMhc8i66pJ72+OxJnyQG8auzFvwzhhEpu5OsNzLPkc5c5g5VaKQ3obb6vDL2O/5gPsEerIUgy8qghhvGyfdapPDrfdIuZw+sWseO5y8n54QMstVX4Jg6jz21zib/yCUzhPe0Sw8n0mHwFgYNOQ62rZt9H/8FSXe7Q67uq2kP72fv+/VhqqwgcOIGel8y2y3ORkVOvI3TceWiWBtI/fsQmE9+5UvtWdXAn5qpSPIIjHZ7bgmO4Ur4JXZ/It/Zr9ZlSVVW59dZbWbFiBYqi8OGHH7Jr1y5uvPFGAObNm0daWhrLly9n+/btaJrG+++/z86dOwH4+uuv2bJlCxaLha1bt/Luu+/a947sZMWKFc4OQehGRL61jRmdb9QiflPLucoQxmDZm6sN4Zyq+fOReoh9el3jjrpGzrJ3qS04SOwFdxF2ygV4hESx/79PodZVEzBgAj0vfQhZMXB4/WKyFr/ByZZC8kTmDDmAM5UAfKTGpvSgVsdirZg/tMp2lKHHK0flKUsWVylhTFYCuNkQQazqwXz1sJV9rtY5Ub5pDXUcWj2fwt+XED7xEkJPOZ+A/uPw7zuGkm0/k7fyY+pL8m0YxfECBp1K5BlXo2sq++c/RV3BAbter7Opyd3Hvg8eIOn6OQQNmYxuMXPg65dOmrNtFX7apfQ4/VJ0VSXj8yeoTN9ik/O6VPuma5Tt2kDIyLPw7zeO2kOZzo5IsDGXyjehyxP51n4S2OR3pw5paSFVVzFr1iwWLFjg7DCEbkLkW8cMk7y50hBGiGQEYLVaxhdqIZX83ePpHdef+Csfx+jtT23BQYo2/UDUWdcjyQqH1vyPnGXzTnhuXxTOVAKZIvvjKTUOE96n1bJYLWaLbv1ajW11uuzP1UoYBkkiVavmDUveMffTEdbkm8E7gB6nX0rI6HOQDUY01ULRph/I/+lzzBVtn4G1NZ5RvUm++RVkozvZS9+i4NdvbH6NrsI7bkDjcHM3E4c3LCFr0WsdPmfouPOJmX4ruqaR+cUzlGxbbYNIG7la++bXZzSJ1zxNdc5edr9+s7PDEWzM1fJN6NpEvrWspZpPzIFupZKSEmeHIHQjIt86ZrNexX3m/SxSizDrGqcpjc96Tpb9ObLYSdWBVHa/cQs1+fsxhcUSffZNSLJC3o+fnrAgDcTAlUoorxvjma4E4SkppGrVPGXO4lHLQbsWpAA/a2U8ZcmiTLfQX/biKWMcsZK7Tc5tTb5ZqkrJXjKX1BeuomjTD0iSROjocxgw+zOipt2IwdN2Q4uNfsEkXP0EstGdwj+WiYK0FVUHdpD+8cNo5npCx5zbuC5vBwQNn0rM9FsBOLjwFZsWpOB67VvFvs2o9bV4RSXh5h/q7HAEG3O1fBO6NpFv7SeKUisdmTpZEBxB5FvHNaDzlVrEbHMm27UqvCSFaw3hPGmIJb5p0qCG0kOkvXU7pTvXoWsaOd+/R96PnxxznnCMXK+E86oxnqlKIG6SzGatkkfMB3jGks0uvcZh97RXr+Uh8wHStVpCJCOPGWIZI/t0+LxtybeG0gIOfPUiO1+6jpK/fkE2uhE+8WIG/N9/iZhyJbK7Z4dikY0eJFz1JG6+wVRmbCPr29c7dL7uojJ9K+mfPIpmaSBs/IVEnnl9u84TMHAicTPuBiBryVyKNn5vyzAB12vfdIuZ8j0bATHhUVfkavkmdG0i39pPFKVWGjlypLNDELoRkW+2cwgzz1lyeMWcQ5Fuppds4nFDLP9SwvFBQauvJeOTR9j22Hkc+uWL5uOiJXduVSJ40diL05TGSZPWqxXMNmfykiWX9CPPqTpYKRaetGSxRi3DXZK5zRDJTCWkuQe4PdqTb3WF2ez/75Pseu0mytP+QHH3JGLKVQx84L+ETbwY2diOXlxJIu6S2XhFJVFXlEv6Z4+jq5a2n6ebqti7if2fP4mmWuhx2kwiplzVpuP9+oym56wHkWSF3BUfcfi3hXaJ0xXbt7KdYhbersoV803oukS+tZ8oSq20detWZ4cgdCMi32xvU9OQ3sVqMRpwuuLPi8ZenC77IQFqXTUA8ZIH9xgied7Yk7GKLxqNz6Tea97Pm2oe2Xq9M28DaJzYaZ56iI8th1B1nXOVIO43ROHVzia9I/lWk7uPfR8+SNpbd1KZuQODly/R026k/+xPCRl9DpLS6nx6zSKmXEXgwAlYaqtI//g/qDUV7Y6rJb0lEw8YonnDGM9Aycsu13CWsl3ryZz/NLqmEjHlSsJPu9Sq43ziBxN/+aPIioFDv3xJ/qrP7RajK7Zv5Wl/oKkWfHoORLHhUHTB+Vwx34SuS+Rb+4mi1EoRERHODkHoRkS+2Uc9Ol+qhcw2Z7JDq8ZHUviXoQdPGGI5RfblQUM0TxrjGCb7UK9rLFdLuNOcwXvqIQ7hemtjrtTKeMaSTYVuYZDszZPGOCIltzafxxb5VnVgB3vevpO97z9Ade4+3HyDib3gTvrf+xGBQye3ulxJ4ODTiZh8ReNMu/99krrDWR2O6Z+SJRMPGqJ51BjLQNmLIMnI/YYozpK71rpypTvWkvnl8+iaRtSZ1xE2fkaL+3vF9CXh6qeQjW4cXr+YnO/tO0u+K7Zvam0VVRl/ISkK/smjnB2OYEOumG9C1yXyrf1EUWqlsLAwZ4cgdCMi3+wrnwaetWTzmjmXYt1MvGzi34YI+ste1Ogqi9Vi7jBn8Kl6mBJce/jobr2G/5gPkKnVES658aQhjhGSd5vOYct8q9i7id2v30zGZ49TW3AQ96AIes38P/rd9d5Jh0Z6xfQh7qL7AMhe8hYVe/+0WTzQWIw+ZIjmEWMs/WUvqnWVb9QiFqlFyJLE5YZQblZ6YOzQIGjXUrJ1VePyMED0OTcTMmb6Cfcz9Ygn8bpnUdxNFG1e2bgMkp25avtWuvM3APz7n+LkSARbctV8E7omkW/tJ5aEsVJgYKCYUUtwGJFvjuOOxPlKMMNkb9apFazUSqmx6SqgjuGGxPVKOOMUPwAWqkV8oxZZ1cDbLd9kmaAhU4iYciXugeEAVGelkbviQyr2bW6M2z+UPrfNxegTaLPlTI7oI3lygRJEP7lxiG61rvKDWsLyo77GIyQfbjb0wEOSSddqedmSS5mL/yGiLUJGn0PsBXcCcODrl46ZuMgjJJreN7+C0TuA0h1ryfjvk6DZP/ddtX0z+gUz6KEvURvq+OvxC9DMzh+qL3Scq+ab0DWJfGuZWBLGBlJSUpwdgtCNiHxznHp0vlALuc+cybdacacsSKFxtuG5aj6fWw6j6ToXKMHcY4jCZEUzb7d80zSKN68g9YWrObjodcyVJXjFJJN0/RySbnwJn4QhJFz9JEafQCr2bSF78Zs2uWwfyZP/GGJ42BhDv6ae0a8shdxhzmDhP77Gm/RKHrMcpFA3kyCbeNr49+zMXUHh70vJWjIXgNgL7mocSg24BYSTdP0LGL0DKE/byP75zzikIAXXbd/M5UVUZ6WhuHngmzjM2eEINuKq+SZ0TSLf2s/6GSi6uYKCAmeHIHQjIt+E9vpeKyFLr+N2QyRDZW+eNMbykjmXfBpOeoy9801XzRRuWEzxnysIHXce4adegm/8YHzjBwNQV5RDxudPoGtqh67TV/LkQiWYPnLjsjTVusr3TT2jtS38sSFLr+c/5gPcaYikj+zJw4YY3lMPsU6zz0RLjnb4t4XIBjeizrqenhffj8HDm7DxF+LmH0Ll/r/I+OwxdNVxz0y7cvtWunMdXjHJ+Pc/hbJd650djmADrpxvQtcj8q39RE+plfLy8pwdgtCNiHwTOiK16TnTLK2OCMmdJ42xDGlhlllH5ZtmruPQL1+w47nLyfvpM9T6WszV5ez76CHU2sp2n7ev1FhI/scYQx/Zkypd5X+WQm43Z7BIK26xID2iEpVnLFn8pJbiJsncYohgVgeX2nElh375gtyVHyPJCjHn3YZ7UATVWWns++g/Dh+m6srtW9mR50r7jAFZ/IrUFbhyvgldj8i39hMtrpWGDBni7BCEbkTkm9BRhzHzqOUgf2gVeEoK9xiiOE8OOmGR5eh8U+uqyVv5MX89dTGpz19BfWFOu87TT/LkkX8Uo182DdP91spi9Ji4gA/VAj60HMKi65yjBHGfIQrPLvKjMv+nz8j/eT4ANfn72fvh/6HV1zg8Dldu3+oOZ1FXmI3ByxefuAHODkewAVfON6HrEfnWfmL4rpU2btzo7BCEbkTkm2AL9ei8ZsnjXLmOi5UQLjaEEKd58I4ln7qjCjZn5Vt7C6L+TcN0ezcN061sGqa7spVhutb6SSsjV2/gTkMEg2VvnjDG8qI5xyWXBWqr3OUfULJ9DfWF2U6byMfV27fS1HX0OG0m/v3GUbn/L2eHI3SQq+eb0LWIfGu/rvHnXwdISEhwdghCNyLyTbClJVoJL1pyqNZVRso+PG6IJRRj8/udJd/6S548aojhQWMMvWVPKpt6Ru80Z7C4HT2jLWlcaufgUUOg4xjYwhDozqQ2L92pM8u6er6ViaVhuhRXzzehaxH51n6ip9RKgYFda3F1wbWJfBNsbZtezcPmA9xtjCJadudpYxxvWPLYrle7fL4NaOoZTWruGbWwTC1hpVZ2TI+vrRU2DYG+2RDBSNmH+w1RzFcL+V7rnNP9G5E4XfZnouxHORZ2aDXs0KvJ1h1boLp6vlVnp9FQUYx7QBimiARq89KdHZLQAa6eb0LXIvKt/URRaqUVK1Y4OwShGxH5JtjDIcw8aj7IzYYeDG8qsr5QC1mx3DXzbaDkxYVKMImyCWgsRr9TS/jRzsXo0RqHQOdygRLMhUowlxtCiVHd+UA9hNn5y3xbxQ2JSbI/ZytBBEh//9gfJHsDUKqbmwvUHVo1FXRsFuTWuHz7puuU7VpP6OhzCOg3ThSlnZzL55vQpYh8az8xfNdKYt0hwZFEvgn2UovGK5ZcvlGLkCWJSw2hPE8U/2eI5kYlnIuUYE6X/RgseREtuePlhB8TAyUvHjfE8oAxmkTZRIVuYYHlMLebM1iqlTisID1CB75Ri3jFnEudrjFB8eM/hhj8Xfzvuu5InCUH8qoxnisMYQRIBjK1Ol415zLXkscatZxS3UyAZGSC4scthgjecUvkGUMcs5QQ+kueGO0w/3BnaN/KUtcB4N9vnJMjETqqM+Sb0HWIfGs/1/6J6kJyc3OdHYLQjYh8E+zpSJF1UKvjRkMPvKobGCCf/HnJOl2jBDMluoVi3UwJTf/qFoqxUKKbqbZBoTioqWc0oalntKK5Z7SUehfoldykV1JgaeAeQxSJsomnjLG8bMllv17n7NCO4Y7EFDmAaUogfk09oxlaLYvUYrboVY076bCOClAhSnJjoOTFANmLZMmTONmDODw4RwmiQddI02vYbsOhvp2hfavM2IqltgrPiHjcAnvQUJLv7JCEduoM+SZ0HSLf2k8UpVYqKemczxAJnZPIN8ER/tSr2G5OZ2xiX0rSDxAoGQiSjARiIEgyECgZCcKASVKIwJ0Iyf2k5+pI4Tq4qRiNbypGy5ueGXWVYvRoWXo9D5sPcIchkj5y45I076mHWKdVODs0PJCZIvszTQnEt6kYTddqWagWsU2vPulxOXoDOXoD32ulGJFIkkwMkL0YKHkRJ3swUPJmYPNQXwupWjXb9WpStWrK2zHUtzO0b7pqoXzPRoIGn05Av3EU/Pq1s0MS2qkz5JvQdYh8az9RlFppwIABpKamOjsMoZsQ+SY4SgM6PYYP5Jd9OzlZ/WdCbixYMdq0cC3FwgDJ65hi9Du1hJ9csBg9WgUqz1iyuEoJY7ISwC2GCGJUd75QC50StQmZM+QAzlIC8ZEUAPZptXyjFrG9hWL0RMzo7NRr2KnW8AWF+KLQX/ZigOTFQNmTAMnIeMWP8fgBcFCrY4dezXatmj16rVXP2XaW9q1s5zqCBp+OvyhKO7XOkm9C1yDyrf1EUWql9evXOzsEoRsR+SY4Umv5VotGrt5ALg12KVzLdQtL1RJWuXgxejQV+FAtIEuv50oljHOUIKIld9605FHjoGdeTchMlQM4UwnEu6kY3aPV8I1aRKrevjVg/6kClfVaBeubhvpGHjXUt4/kSazsQSwenH3UUN8dWg3bWxjq21nat/K0jWiWBrzj+mPw8sdSXdbuc3kh01PysNnXRbCes/It9oK78Esexe43b8FcUeyUGATH6yztmysSRamV+vfvz8GDB50dhtBNiHwTHMkW+dbewrVct7BWK6ehkxSj//STVkau3sCdhggGy948YYzlRXMOhzDb7ZpeyExVApkqB+DVVIzubipGd9m56MnVG8jVG/jhH0N9B0he9DxqqO9l/D3U98isvkeG+naW9k2rr6EyfSt+yaPw7zuGok0/tOs8/hh4xBhDuOTGfMthvuukSwp1Vs7IN1OPeEJGnw1AyKizyfvxE4dev9uSZKLO+hcgUfjHMuqLchweQmdp31yRKEqt5Ovr6+wQhG5E5JvgSI7KN2sK185ot17Df8wHuccQSYzswZNHrQFrS17InKUEkiIH4NlUjO7UqlmoFrPbCT1wLQ31HSB7EniSob7eutHhsbZX6c51jUVpv3HtKkp9UXjIGE245AbABUowv2sVFGGxdajCSTjj52nElCub/x888izyVn0GmmNnDe+OfOIHEz7xEgDCJ15M+d4/KdywhLLdGxz2+Re/v7WfKEqtJNYdEhxJ5JvgSCLfOq4QM49ZsrjZ0IMRTWvAzlcP871W2uFzezcVo2ccVYymatUsVItI02s7fH5bsXaob8NPu/ld8rJ50W4PZTvXo59/J76Jw5DdPNAarJ9p2RuZBw3RREruZGl1FGJmmOzDlYYwXraIGTodxdHtmykigYD+p6CZ6zFXluAe2AP/PmMo27nOoXF0RwH9TwGgJn8/7kER+CUNxy9pOPWlBRT98R2FG3/AUtXxNrkl4udp+1m1AF1KSgppaWns27eP2bNnn3CfiRMnsnXrVlJTU/nll1+aX/fz8+Orr75i9+7d7Nq1i9GjR9skcEcT6w4JjiTyTXAkkW+2UYfGq0etAXu5IYyblB7tXuvTB4WZSgivGeM5TwnGU1LYrlXzmPkgz1iyXaogPZEjw3znWHK4wbyPp8xZbFArcEPiHkMkQ6STL0PkKixVpVRn7UY2uuHXe6TVx3ki84AhmhjZg1y9nmcs2XxgKaBGVxku+zBc8rZj1MeSDEaCR5yJd9wAJFlx2HVdhaPbt4jJVwBweMNSDq9bBNA8lFewI0nCv+9YAA78bw7bn76ErCVzqSvMwT0gjMip1zHwwQX0vPQhvOMG2C0M8fO0/VrtKZVlmblz5zJlyhRycnLYtGkTS5YsYffu3c37+Pn58dZbbzF16lSys7MJCQlpfu+1115j+fLlXHTRRRiNRjw9Pe1zJ3Z24MABZ4cgdCMi3wRHEvlmO0fWgM3W6rnJ0IMJih89JDdeseRSZuWQTV8UpimBTJED8JAa/3a8TatikVrEPhdbE9VaZnR26TXsVmvwDA9hUGE9dxmieMOSxya90tnhtah05294x/XDv984SnesbXV/D2RmG6LpJZs4pDfwtDmbiqbnaf+nFnK1IZyrDGHsMFc7ZGKvqGk3EjbufADU+hoqM7ZRvvdPKvZudsozd47myPbtSC+p2lDHoV++QFctRE79F76Jw8V6t3bmFZWEm38I9aUF1OTuA+Dwbws5vG4RvglDCRlzLv59xxA0+HSCBp9OTf5+CjcspnjrKrR62/2BT/w8bb9Wi9KRI0eSnp5OZmYmAF988QXTp08/pii99NJLWbhwIdnZ2QAUFhYC4OPjw4QJE7j66qsBMJvNlJeX2/oeHKKurnP+IiB0TiLfBEcS+WZ7G/VKCiwN3G2IIlE28ZQxlpctuexvoaj0Q+FsJYjJsj/uTcXolqZiNKOTFqP/pAMrQySyDxVzthLE7YYI3lLz2KC5bmFatnMd0dNuxK/PaCRZQddOvjarGxL3NX3NC3UzT5uzjvljxI9aGeM1P+JlExcqwcxXC+0auym8J6FjzkVXVeqKcjCFxeLfd2xzj1J9ySEq9m2mYu+fVKRvRa113a9DezmyfYuY3PgsaeHvS5uHiZZs/4XgYWcQMvIscpd/4LBYuhv/fo1Dd48bJq3rjTm+bzNGvxBCRp9NyMhpePboRewFdxF11g0UbV5J4Yal1B3u+ARF4udp+7ValEZGRjYXmwA5OTmMGjXqmH2SkpIwGo2sXr0aHx8fXnvtNT777DN69epFYWEhH330EYMGDWLz5s3ccccd1NQcOyFDREQEGRkZVFVVYTKZWLNmDbNnzyYlJYUDBw5QV1dHcnIya9euZcSIERiNRtasWcOkSZNIT08HICEhgVWrVjFx4kTMZjObNm1iwoQJpKWl4eHhQVxcHCtWrCAlJYWKigpSU1MZO3YsO3bsIDAwkMjIyOb3S0pKSE9PZ+TIkWzdupWIiAgmTZrEwYMHSUlJoaCggLy8PIYMGcLGjRtJSEggMDCw+fjc3FxKSkoYMGAA69evp3///vj6+ja/7yr3FBYW1vy+uCfXuqcZM2ZQW1vbpe6pK36duso9zZgxg9DQ0C51T67ydfrGrHHRYQgqquZx955sGhDON3l7j7mn3mGRGH7cyETVC0Vr7Dk71MOX743VNET0YmTkeEpd6J46+nVKmTqVg8kHWL50HVPNXtxqiCQy0ILxtBEue09UFGDwDSNi0ClMSI44Ye5Fh/cgfukmklU3qt0VPgs0c8bYi467p9yRg+n58x7OUoLwOWMsP+1Ntcs96YBp6u3Uygq+BTuIzviFzd9lMGDyBRzCBzk8CffAcEJGTSNk1DTQdcyHMwnTy8nf9iv+ajlRET1c6vupPbkXHR1NcnKy3duIjXtzCOg/DkmzUP/XSmbNmtV4TyV7gDMIHT2N4aZS3AxKl2/3nHFPZUNPB8CzNJOUlJQW7ulnIo1FyNEDyTT0wBTdh7Bx5xM27nxMlXnkrf2G/oFyu79OJpOJ5ORk8XU6yT21RKKVORBnzJhBSkoK119/PQCXX345I0eO5Pbbb2/e54033mD48OFMmjQJk8nEhg0bmDZtGr6+vvz++++MGzeOjRs38uqrr1JRUcEjjzxyzDXmzp3LLbfc0mKgzhYZGUlurpiYQHAMkW+CI4l8sy8FuFoJY5ISAMBStZgv1EL8MXCOEsjpsj9uTT2jm7RKFqpFHDzJGp9dwdH5dp4cxMWGxkd+3rfk87PmmqOpIlKuIWLS5Rxev5isb18/7n0FuMsQxVDZmzLdwpPmLPJpOOn5rlRCmaoEsk+r5THLQbsM4g0YMIH4Kx7FXF1O6pwrUWurjt1BkvCMSMA3aTi+icPwjuuPbPh7ZuSuMtTXUe1b/JVPENB/HIfWfkXOd+8c817fu97Ds0cvMj5/gtLta+weS3fjERJN//s+xlJTwbYnLmzTTLum8F6EjDmHoKFTUNxNADRUFFH0xzIK/1jW5jVmxc/TlrVU87XaU5qTk0N0dHTzx1FRUeTl5R23T1FRETU1NdTU1LB27VoGDRrEr7/+Sk5ODhs3bgTg66+/5oEHHujIvTjNiBEjRJIJDiPyTXAkkW/2pQIfqAUc1Ou5SgnjHCWIAZIXkZIbxqZidKNWyaIuXowecXS+fasVY7HoXGoI5V+GHhgtMitsMGOxrZXtXEfEpMvx7zuWrMVvgP53GakAtxkiGSp7U6lbeMbSckEK8JVaxEjZh0TZxGmyPz9rZTaNVza6E3X2TQDkLv/w+IIUQNepyd1HTe4+Dq1egOzmgU+vgfgmNhappvC4Y4f6lhY0DvPtZEN9HdG+NT5LOq7pWdIvj3u/8PfviD3/dkJGnS2KUjvw7zcOgLJdv7d56ZfaQ/vJWvQauT+8T+DQyYSOmY4pLJaIKVfR4/TLKd35G4UbllCZsc2q84mfp+3XalG6adMmEhMTiYuLIzc3l5kzZ3LppZces8/ixYt58803URQFNzc3Ro0axSuvvEJBQQHZ2dkkJSWxd+9eJk2axK5du+x2M/ZkNHaeddWEzk/km+BIIt8c4yetjDy9gTsMEcTJHmi6zu9qBYu0YrK7QTF6xD/z7TuthAaLztWGMK4yhGG0SHynlTgpuhOrydlLQ1khbv4heEYmUZOzB2gcbnazEsFI2YdqXeUZSzY5essFKTSu2fup5TB3GiOZpYTwp1bZPBmSLYSfegnuAWHU5O6jaOP3Vh2jNdRRnraR8rTGjgSjXzC+icOae1LdA8Kah/rqmkZNzl7K9zUWqdVZu9FV11x71RHt25F1SQs3LDnhkiMlW38iatr1+CYOxT04qtP2Oruq5qJ052/tPodaV03h+sUUrl+MT/xgQsacS0C/UwgcOJHAgROpLTjI4Q2LKdnyE2rdyZezEj9P26/VolRVVW699VZWrFiBoih8+OGH7Nq1ixtvvBGAefPmkZaWxvLly9m+fTuapvH++++zc+dOAG677Tb++9//4ubmxv79+7nmmmvse0d2smaN+MuW4Dgi3wRHEvnmOLv0Gh4yH+QU2ZdNeiW5VhQwXc2J8m2lVorFonGtEs6lhlCMFolFWtuGzdlb2c51hI47j4D+p1CTswcJuEEJZ6ziS42u8pwlu0093Rv1SrZpVQyWvblcCeUt1TYzs7oFhBF+6kwAshbPBb1tPUdHmMuLKP5zBcV/rvh7qG9Tkeod1w+vmGS8YpKJmHR501Dfv6jY+yfl+/6kvtB1ii57t2+ekYkE9GvqJV3zvxPuo9ZVU7JtNSEjzyJk1DRyls2za0zdidE3GO/YvqgNdVTs/dMm56zM2EZlxjaMvsGEjDqL4FHTMIXFEnve7USdeT3FW3+icP1iag9lHnes+Hnafq0+U+oIneGZ0lmzZrFgwQJnhyF0EyLfBEcS+SY4Ukv5Nl725UalB7IksUgt4iu1yMHRnZxPwhB63/AitQUH2fnStVyrhDFZCaBO13jeks2edqwbG4KRF4w9cZNknjZnsVOvaf2gVvS6/FECB06geOsqMhc80+HznYhs9MC710D8kobhmzgcU3jcMe/XlxYcO6tvTYVd4rCGvdu3+KueIKDfOA6t+V+LxaZnVG/63v4W5upytj99CbrFbLeYupOQ0ecQe8GdlKb+Rsanj9rlGpKs4N9vHCFjp+MbP7j59crMHRRuWEzpjl+bRwqIn6ct69AzpUKjI7NUCYIjiHwTHEnkm+BILeXbr1oFFnT+rURwvhKMEcnuy6ZYq2r/diw1lZhCY7jauxeTG9xo0DVetOS0qyAFKMTMQrWImYZQrjWE84A5E3MH+gp84gcTOHACakMtOd+/2+7ztEYz11GxZyMVe44a6pswDN+kYX8P9R15FiEjz0LXNA6tXkDuig/tFk9L7Nm+HdNLeoJnSY9Wk7OH6tx9eEUmEjBgAiVbV9ktru7Ev/9JloKxIV1TKd2xltIda/EIjSVkzDkEDzsDn54D8Ok5AHNlKYUbl1H0xzLx87QDRFEqCIIgCILL2KBVYtFzuc0QydlKEEZkPlULnD6sS9dUyndtYHpJLSMb3DDrGi9bctnVwd7NZVoJp2h+RMnunCMHsrC9w5ZlmZjptwKQv2o+5nLH9TKby4so3ryC4s2NQ31NPeLxa3oW1afXIHpMuoyKfZup3P+Xw2JyhL+fJV2Mpbqs1f0Lf1+K14V3EzLqbFGU2oDi4YVP/GB0VaVs9+8OuWbd4YNkL36T3OUfEDRkEiFjpuPZoxcRky6nx2mz0LO2IW3e0uJ6xsKJyc4OoLNISEhwdghCNyLyTXAkkW+CI1mTb5v0Kl6x5GDWNVKUAK5TwpEcEFtrRv/6IyPXrkSVZF635LFdP/mEJ9ZSgQ/VQwBMV4IIp30TpYSOPhdTeE/qivMo+PWrDsfVbrpObV46h375gr3v3Ufeqs8AiL3wbiSD4yeBsVf75hmZiH/fsU29pCd+lvSfSrb9jFpfg0+vgXiExdolru7Er89oZMVAZeZ2hw8R1+prKfz9O3a9cj1pb91J8baf0TUN4oYRNe0Gh8bSVYii1EqrVom/aAmOI/JNcCSRb4IjWZtvW/VqXrTk0KBrnK74c6PSw6mF6dlyIOcU16BJEj9cfDXbvd1sdu40vZZf1DKMksy1hvA2H2/w8iPijKsByFn6tks9r3ho9QJqDx3AIySKHqdf5vDr26t9a2svKTQWMsVbfgIgZNTZdomrO2medTe1/bPu2kLVgR1kzn+aPfPuRlcthI2fQcCg05waU2ckilIrTZw40dkhCN2IyDfBkUS+CY7UlnzbodfwvCWHOl1jguLHLUoEih1jO5kUOYBLDaFous6i0aPZO2AYfk3rd9rKArWQSt1Cf9mLsbJvm46NSLkGg6cP5Xs2UbZrvU3j6ihdtXDwm5cBCD9tFh5hcQ69vj3aN8+opKZe0lqre0mPKPz9OwCChp2BbHS3eWzdhWRww6/3SACXyfnqg7sIztkAQNyMe/AIFb3hbSGKUiuZza7zV0eh6xP5JjiSyDfBkdqab7v1Gp6zZFOjq4xVfLnNEOnQwvR02Y+rDGEAfKAe4pfqxqVbApp6aWylErV5UqcrlFC8rPwVzRSRQMjIaWiqheylb9k0JlupOriTw+sXIysG4mbcA5Lj+rzt0b5FTG7qJV2/xOpe0iNq8zOoOrgLg8mbgIGn2jy27sI3cSiKu4nqnL00lB12djjNPHK3Ubz1JxR3E/FXPobs7unskDoNUZRaadOmTc4OQehGRL4JjiTyTXCk9uTbXr2WZy3ZVOsqI2Uf7jJEYXTAYN7xsi/XKo3DaT+2HGK1Vk757g3omopPwhAUDy+bXm+tVs5urQY/ycAlSohVx8RMvxVJljm87lvqDmfZNB5byl3+AQ0VRXjH9iVk9DkOu66t27fGXtIxjb2kJ1mXtDWFfzT2loaMFkN428u/X9Osu04euvtPf27axMGvX6Emfz+m0Bh6Xnyfs0PqNERRaqUJEyY4OwShGxH5JjiSyDfBkdqbbxl6HU9ZsqjULQyVvbnHEIWbHQvTMbJP85qp/7UcZqVWBoClupyqA6nIBiO+TcMHbUWncdIji65zuuxPguTR4v6Bg09rXpIi/6dPbRqLral11WR9+wYAUWf+C6NfsEOua+v2rSO9pEeU/vULltoqvGP7YurRy4bRdROSjH/fMQCU2nEpmPaYMGECmrmOjM8ew1JbRcCACYRNuMjZYXUKoii1UlpamrNDELoRkW+CI4l8ExypI/l2UK/nKUs25bqFgbIX9xuicbdDYTpc8ubfSgSyJPGVpZBlWskx7x9ZE9HWQ3gBcvUGlmnFyJLEtUr4SX9Rk908iDrrRgByfngfta7jMwHbW1nqb5Sm/obi4UXM9Nscck1btm+eUb2P6iVteV3Slmjmeoo3rwTEhEft4R3XD6O3P3VFOdQVHHB2OMc4km/1Rbkc+N8cAKLOvB6fXoOcGVanIIpSK3l4tPzXSkGwJZFvgiOJfBMcqaP5lq3X86Q5i1LdTF/ZkwcM0Zhs+OvMYMmL2w2RKJLEt2oRi06wbmjpzsaJVfySRyIptl/mZJFazGG9gTjZg6lywAn36XHapbj5h1Cdnda4PmgnkbX4DdS6agL6n4J//1Psfj1btm9HZtw9vG4xluryDp3ryBDeoKGTkd1EG9wWzUN3XayXFI7Nt7Kd68j/eT6SotDrsocx+jpmdEBnJYpSK8XFxTk7BKEbEfkmOJLIN8GRbJFveTTwhDmLIt1Mb9mTBw3RVk8M1JL+kid3GiIxSBLfqyX8Ty064X4NJfnU5GWgeHjhkzCkw9c97vzofGwpAGCGEkIghmPedw/sQdjExiGBWYvngq7bPAZ7MZcXkfPDBwDEnHebzZ/L/SdbtW+eUb3x7zMataGWgrXte5b0aHUFB6nM3IHi4UXg4NNtEGH3EdC/cYRCqYs9TwrH51vuyo+o2LcFo08A8Zc/jKQYTnygIIpSa61Y0Xn+Cil0fiLfBEcS+SY4kq3yrQAzT5izOKw3EC+beMgQg08H5uVNlkyNz6lKMj+qpXyutjyjZ9nOxl+I7TGEF2CbXs0fWgUektw8++8RUWffjGxwo+jPlVRn7bLL9e2p8PclVB3YiZtvMJFTr7PbdSbL/tQuXWuTAd627CU94sjyMI6c+KmzM/XohXtgD8yVJVRn7XZ2OMc5rn3TNPbPf5qGskK84/oTNe1G5wTWCYii1EopKSnODkHoRkS+CY4k8k1wJFvmW1FTYZrfNNT1P4YYfNtRmCZIHtxniMJdklmtlvGxWtDqMaWpjUMH/fuNtdsSJ59aDlOrq4yQfRgqeQPgmzScgP7jUOtryP3hPbtc1+50nQPfvIymWggdOx3v2H42v8SFSjDXGsK5uMGH2w0RHZoUyyu6qZe03ja9pEeU7liDpboCr6gkPKOSbHberiyg/5Ghu+tdcoTAido3S3UZGZ8/jmYxE3bKBQQOPs0Jkbk+UZRaqaKiwtkhCN2IyDfBkUS+CY5k63wrwcKT5oPkaPVEy+48YowhAOuHyMVJ7sw2RGOSFNap5byvHsKaX3Vr8zOoLzmE0ScQr5g+7b+BFpRi4aumIcRXGcLwUIxEn3sLAPk/fY65sqSlw11aXcEBDv3yBQCxM+6x6bO5KXIAFyrBaLqOWZEYJfvyH0MMfu3sSe/RNOPu4fXf2qyXFEC3mClqeh5Y9JZax79pZIKrzbp7xMnat+qs3c3rCMfOuAePsDgHRtU5iKLUSqmpqc4OQehGRL4JjiTyTXAke+RbGSpPWrI4oNURITUWpsFWFKbRkjv/Z4jBS1L4Q6vgbTXfqoK0+brNQ3jtN2HPSq2UTK2OEMnI5VGjMIXGUFeYQ8FvC+12TUfJX/U5dYU5mMJiCT/1Epucc5zs2zzc+T31EG8G1FKom0mQTTxhjCNKcmvT+Y7pJW3nuqQtOTLhUeDg0+z+fG1n5xYQjmdEAmpdNZXpW50dzgm11L4VblhC8eYfUdxMJFz5mPh6/4MoSq00duxYZ4cgdCMi3wRHEvkmOJK98q0SlactWezXagmT3HjYGEsoJ+99i8CNBw3R+EgKW7Qq3rTkobXxmkd6a/z72+e5UgAN+EA9hIbOqYdKCD6US/bSueiq2W7XdBTdYubgNy8D0GPSZXiERHfofEMkL25SegDwueUwa7RyEiaO4WHzAfZptYRIRh4zxDJQsr4YiJhyFQCH1y3CUmP7USX1hTlUpG9FcTMROGSyzc/flRyZ4Kg8baPL5n9r7dvBha9Qk5eBR0g0cRfd56CoOgdRlFppx44dzg5B6EZEvgmOJPJNcCR75ls1Gk9bstmr1RAiGXnYGEMPju8ZC8XIg8Zo/CQD27VqXrPkorbjelUHUjFXl+MRHIVHaGzHb+Ak9ut1/BEXh6xrnPrFe1SkbbTbtRytcv9fFG78HtngRuyFd7f7+dzekok7mpbyWawW8X3T2rI7duygApWnLFlsUCvwlBTuN0QxWfZv9Zxe0cn4JY9qepb0q3bFZY3C35cCEDJarFnakiNLwZTudL1Zd49orX3TzPVkfPYYltoqAgaMJ3yibUYIdAWiKLVSYGCgs0MQuhGRb4IjiXwTHMne+VaLxnOWHHZrNQQ1FaaRRw3ZDMbAQ8YYAiUju7QaXrbkYG7ToN2jaBrluzYAf/fi2INnVG82X34bVd6+xBQVcqrsZ7drOUPOsncxV5bi02sgwSPObPPxsZI79zXNnLxKLeXLo5byOZJvZnTeVPNYpBYhSxLXGsK5XAltcfqjv2fctU8v6RFlO9dhrizFs0cvvGL72u06nZnByx/vuH5olgbKXfiPMta0b/XFeWR++RwAkWdeh0/8YDtH1TmIotRKkZGRzg5B6EZEvgmOJPJNcCRH5FsdGs9bstmhVeMvGXjYEEOs5E5AU0EaIhnZq9XwgiWbhvYWpE2O9Nr422lpGCSJmOm30uBh4tu4CABmKaHtmmXYVam1lWQtmQtA1LQbMfpY/4eLcIw8YIjGs+m54A//MXPy0fmmA1+pRbxtycOi65ylBHKPIQr3E5Smf/eS1ti1lxRAVy0UbfoBgJBRYsKjE/HvOwZJVqhM34pWX+PscE7K2vatfNcG8lf9F0lW6HXZfzD6Bds5MtcnilIriXX8BEcS+SY4ksg3wZEclW8N6LxoyWGrVoWvZOA/hhj+Y4whTHIjQ6vleUsO9R0sSAEq9m5GbajFKzoZo1+IDSI/VtCQyXjH9qWhopjlqSvYrlXhLSlcqoTa/FrOVPrXasp2/47B5E30uf+26pgADDxw1DDsuZbjJ6o6Ub79qlXwjCWLKl1lqOzNo4ZYAv8xMdbfz5J+a9de0iMKN34PQOCgU1FMPna/Xmfj6rPuHtGW9i135cdU7N2M0TuA+MsfRVKsnzW8KxJFqZXEOn6CI4l8ExxJ5JvgSI7MNzM6r1hy2aRV4iUp9JDcOKjV8Zwlm9o2T2t0YrqlgYo9fwJNa5bakOzuSeRZ1wOQ+/17aA21fGgpoEHXmKD40VfytOn1nC1r0WuoDbUEDjoNvz6jW9zXC5kHDNGESm6ka7W8YsnBcoI/Mpws39L0Wh4xH2he4/YJYyxxknvjuWP64Jc80iG9pEc0lORTvmcTstGNoGFTHHLNzkJ2N+GbOAxd0xrXJ3VhbWrfdI39C56mvrQA79i+RJ19k/0C6wREUWqlkpLOuxaY0PmIfBMcSeSb4EiOzjcLOq9bclmplrJNq+JZSzbVNipIjyhr6r0JsPEQ3ohJl+PmG0TVwV0Ub/0JgMOY+VYtBuBaQxiGFp+K7Fwayg6Tt+IjAGLOux3Z3XTC/dyRuN8QTbTsTo5WzxxL9kl7vVvKt0OYedR8gN1aDYGSkUcMsQyTvIk4si6pg3pJj2ie8GiUmPDoaH5JI5CNblRn7cJSVerscFrU1vbNUl3O/s8fR7M0EDbufAKHTLJTZK5PFKVWSk9Pd3YIQjci8k1wJJFvgiM5I99U4GO1gDmWHCraNc9uy8p2/46uqvj0Goxi8rbJOd2Dowg95QJ0TSNr8Zug/110faeVkKfXEyG5c7bctSYqK/htEdXZabgHhBF5xjXHvW9A4i5DJImyiULdzLOWbKpa+CNDa/lWhcazlmzWquV4SDJ3GaM4rbgSta6aQw7qJT2ifPfvNFQUYQqLxbvXQIde21Z6Sh7MVEKOmVyso44suVSa6rqz7h7RnvatOnsP2U3PVMdeeDem8J62DqtTsKooTUlJIS0tjX379jF79uwT7jNx4kS2bt1Kamoqv/zyy7EXkWW2bNnC0qVLOxyws4wcOdLZIQjdiMg3wZFEvgmO1BXzTa2tpHL/X0iKgl9yy8NOrRV9zs3IBiNFf66gJmfPMe9Z0PnA0jihz3lKEGEtrMfa6egaB755BV1VCR13Pl7RvZvfkoCblR4MlL0p1y08a86iFEuLp7Mm3yzovKPm86WlEBk49YeFnPLhS+DAXlIAXVMp2nhkwqPO01sqAcMkbx42xPC0MY5zlSBuUyJs0ocvKYbm76kyF3+eFNrfvhX+/h1Ff65EcfMg/srHUDysX0u3q2i1KJVlmblz53LmmWfSt29fZs2aRZ8+fY7Zx8/Pj7feeotzzz2X/v37c9FFFx3z/h133MHu3bttG7mDbd261dkhCN2IyDfBkUS+CY7UVfOteQivDZaG8UsehX+f0Vhqq8hd/sEJ99mt17BWLcdNkrnGENbha7qS2rx0Cn79GkmWib3wHiS5cabha5Qwxii+1Ogqz1myOYS51XO1Jd9+igrju0uuxaIYGJNXwH2GKEwOHlRYtPF7dE0lYMAEDF6uvfSPGxKTZX9eNPbiHmMUfWRPqnWVSt1CjOzBGNm3w9fw6TUIg8mbmvz91Bfn2SBq++pI+5a16FVq8tLxCI4i7uLZ7V6zt7Nq9Ttt5MiRpKenk5mZidls5osvvmD69OnH7HPppZeycOFCsrOzASgsLGx+LzIykmnTpvH+++/bOHTHioiIcHYIQjci8k1wJJFvgiN11Xwr29U4AYtv7xFIhvYPXZQUI9HnNM4+m//Tpy0+QzdfPUyVrjJQ9ma03LVmbM378RPqi/PwjIgnbMJFXKQEM1kJoEHXeNGSw0G93qrztCXfIqZcwd4Bw3hnUB/KdQsDZW8eN8QS7MCe6Iayw5SnbUQ2GAka7pqT0PmjcJESzBvGBK41hNNDcqNQN/OppYDbzBnMVxvrgBlKcIcXLjoy625n6CWFjrVvmrmejE8fw1JbRUD/cYRPvMR2gXUCrc49HBkZ2VxsAuTk5DBq1Khj9klKSsJoNLJ69Wp8fHx47bXX+OyzzwB49dVXuf/++/HxOXljGRERQUZGBlVVVZhMJtasWcPs2bNJSUnhwIED1NXVkZyczNq1axkxYgRGo5E1a9YwadKk5rHbCQkJrFq1iokTJ2I2m9m0aRMTJkwgLS0NDw8P4uLiWLFiBSkpKVRUVJCamsrYsWPZsWMHgYGBREZGNr9fUlJCeno6I0eOZOvWrURERDBp0iS2bdtGSkoKBQUF5OXlMWTIEDZu3EhCQgKBgYHNx+fm5lJSUsKAAQNYv349/fv3x9fXt/l9V7mnsLCw5vfFPbnWPU2aNIm8vLwudU9d8evUVe5p0qRJGI3GLnVPXfHr1FXuaeTIkcfE3BXu6cjXKTU/A/ce8Zx6yb/I+n1Fu+5pxBX3URwShVZ2iNOi3NkQG9viPf2wYgMXVSnc4BVLvnshY6ec3mVyz1K0hUNBEZzrG8epSjAasG1sAiW7y7ho4tlW3VN0dDRhYWGt3tNfOWV49h6JrDawN+sPNp05nGFr9xJVAy/49OZjvzo8+sU7JPe2p2+AvmOImnAhPRty6BkX6xJfpxmjJxC6dT9JJQ0Ymh5xLvb3ID0hlC/3beO0yVMZkJ5Ora5Ttd9MeBXcPWIS+yJ92pl7K+kxYjIq4F1+kFmzZrl8G2EymZg1a1aHvk7ReevIj08hcup1jEqMJPXnb1263WtL7rVGb2mbMWOG/t577zV/fPnll+uvv/76Mfu88cYb+oYNG3RPT089KChI37t3r56YmKhPmzZNnzt3rg7oEydO1JcuXXrCaxzZx5W3wMBAp8cgtu6ziXwTmyM3kW9ic+TWlfOtx+mX6cPnrNJjZ9zbruONvkH6kCeX6sPnrNJ9E4dZdYwE+qOGGH2+W7J+tRLm9M+BrbfzR8/S57sl6/PdkvXxsm+bj7c23xKve04fPmeVHpFyTfNrnsj6A4Zofb5bsv6xMUkfLfs45r4lWR/wf/P14XNW6T4JQ53+NRgoeTV/Hua7JeufG3vrdxoi9UTJdNJjxsg++ny3ZP0NY7xuRGrXdb2ik/Xhc1bpA/5vvtM/B9ZutmrfIs64Wh8+Z5U+6JGvdaNfiNPvy1ZbSzVfq8N3c3JyiI6Obv44KiqKvLy84/ZZvnw5NTU1FBcXs3btWgYNGsS4ceM499xzyczM5IsvvuD0009v7kHtbMQ6foIjiXwTHEnkm+BIXTnfSpuGGPr3HQNy259FjDzzXyjunpSmrqNi32arjtGBD9UCLLrOZNmfXpJHm6/rqoZK3lywZQsAq8+6kF1D2j6JlDX55hXbF7/eI1Drqin49Zvm12vQeMGSzU9qKW6SzO2GSKbLQW2Ooc10jaKN3wMQMvoc+1/vBIxITJT9eN7QkweM0QyUvajTNZarJdxt3s+rllz26bUnPf53rZKDWh1BkpFJsn+7YvDvfwrQeYbugu3at7wfP6V8zyaM3gHEX/4IktKFJjM7iVZbzE2bNpGYmEhcXBxGo5GZM2eyZMmSY/ZZvHgx48ePR1EUTCYTo0aNYvfu3Tz44INER0fTs2dPZs6cyc8//8wVV1xht5uxJ2u6nAXBVkS+CY4k8k1wpK6cb3UFB6grysXo7Y93bL82HesV05fgYWegmRvI/u7tNh2brdfzvVaCLElcp4R3ifX++kie3G6IQEFieYgvW8eeTvTZN7d58h9r8u3IuqQF6xah/mPGXZXGov9zSwGarnOJIYQblR4dflayNUWbfkBXVfz7jcXo47hlf3xQOF8O4nVjPDcaehAtu1Oim1lgOcxt5nQ+VQ9z2IoJpnTgK7UIgOlKEO7tmIu3sz1PCjZs33SNzAXPUF9agHdsX6LPudk253VhrbZbqqpy6623smLFCnbv3s3//vc/du3axY033siNN94IQFpaGsuXL2f79u1s3LiR999/n507d9o9eEf6Z++wINiTyDfBkUS+CY7U1fOtbGfjWor+/U6x/iBJIua8WwE4tPZ/NJTkt/m6i9QiCnUzPWUPzpAD2ny8K4mT3LnHEImbJPOTWsqnuRup2LsZg5cv0We37Zfz1vLtmF7StV+fdL/vtVJeteRSp2tMVPz4P0MM3nYs/80VxZTtWo+sGAgeMdVu1zmiB25cq4TxhjGeiwwh+EkGDmh1zLXkcYc5g6VaCdUtrAd7Ilv0KvZptfhJBqa2cT1dj9AYTKExWKorqMzc3qZjncmW7ZulpoKMzx5HszQQOnY6gUMn2+zcrsiq76YffviB3r17k5CQwDPPPAPAvHnzmDdvXvM+L774Iv369WPAgAG89tprx51jzZo1nHOOc4Yg2MKQIUOcHYLQjYh8ExxJ5JvgSF0938pS2740TPDwqXhF9aahrJBDPy9o13Xr0fmkae3Si5RgAlqfy9IlhWNktiEaT0lhg1rBR2rjPR1c+AqauZ6gYVPwTRpu9flay7eIKVcBUPDbQtTayhb3/VOv4gnLQUp0M31lTx43xhFux5l5C//4DoDgkdNAsk8B3Efy5F5DFC+59WKyEoCbJLNFq+IpcxYPWg6wTqtA7cD5v2yaifdsJRCvNhTxzb2kuzeA1rZi2Jls3b7V5Owha/GbAMRecBem8F42Pb8r6QojPBxi48aNzg5B6EZEvgmOJPJNcKSunm9VWbsxV5biHtgDU4/Wf4FUPLyIPPM6AHK+n4dmrmv3tbfoVWzUKjFJClcaQtt9HmcJxMD/GWPwkwz8pVXxlpqH3vRefUk+eT9+CkDsBXciG617dralfPOO7Ydf0vDjniVtyQG9nkfMBzmg1dFDcuNxYxzJksmqY9uqYt9m6ovzcA8Mb1Mh3hoFGCf78rQhjoeNMQyVvWnQNVappdzbsJ8XLTns0mtscq1deg2pWjVeksI0xfre0oCmkQZlqb/ZJA5HsUf7VvTHMoo2/YDi5kH8lY+heHjZ/BquQBSlVkpISHB2CEI3IvJNcCSRb4Ijdfl80zXKdjVNeNSv9d7SHpOvxOgdQGXmDkq2re7w5T+1FFCna4ySfRksdZ5fXn1QeMAYTYhkZK9Ww6uW3ON66ArWfkVNXjrugT2IOONKq87bUr71mNL0LKkVvaRHK8HC45aDbNGq8JEUHjTEMF72tfp4q+k6hX8sAyBk9NkdPp0nMmfLgbxqjOcWQwQ9ZQ/KdQtfWQq5zZzBB2oBeTR0+Dr/9L+m3tKpciC+VjyNa/QLxismGbWhzuoJv1yFvdq3g4tepzp3Hx7BkfSc+QBIbX9G19WJotRKgYGOe8hcEES+CY4k8k1wpO6Qb0cmZglopSj1CI0ldNx56JrWPESvo0qw8HVTEXC1IQy3dkww42geyNxviCJKcidLq+MFSw71zX2kf9M1lQNfv4yuaYSNn4FnZGKr5z5Zvh3pJbXUVlndS3q0enResuTwvVqCQZK42RDBRUqwzT/bRX+uQLOY8e8zGqNfcLvOEYKRK5VQ3jQmcKkhlCDJSI5ez7uWfG43Z7BIK6ayQ4N0W5au17FZq8RDkpmutD57sX/fxu+bij2b0Mz1dovLHuzVvumWBjI+exxLTSX+fccSfuosu1zHmURRaqUVK1Y4OwShGxH5JjiSyDfBkbpDvlWkb0Wtr8EzMhG3gPCT7hd97r+RFQOFG5dRm5dus+sv10o5oNURKrlxvtK+QsZRDEjcbYgkXjZxWG/gOUtOixPq1OTs4fC6RUiyQuyFd7W69M7J8u3Is6SH1y1qUy/p0XTgc/UwH1oOoek65yvB3KpEYLRhaWqpKqVs529IskLIyLPadGyi5MEdhgheMfZiqhKIhySzQ6vmeXM2s82Z/KKVYz5B8W8PR2binSz7E9TK884BnXDW3SPs2b41lOST+UXj3D6RKVfjkzDUbtdyBlGUWqkrr6smuB6Rb4IjiXwTHKk75JtuMVOe1vhsmX+/sSfcx7/v2MaeuppK8pZ/aNPra8AHamOhNE0OJFJys+n5bUUCbjH0oL/sRZlu4VlzNmVYWj0ud8WH1JcW4BXVm7BxF7S474nyzTuuP75Jw9rdS/pPP2llzLHkUKOrjFF8ecgQbdUwVWsVbliKwdxATN8JhMruxEruJEsmBktejJF9OE324yw5gAvkIC5TQvmXEs7jhlgeN8YxSvZFA9ao5TxgzuRZSzZ/6dUOKkX/lqXXs0GtwCjJLf6hRDF54x0/CF1VKdv9uwMjtA17t2/laRvJ+/FTJFmh16UP4ebf+Z4dP5nOOTWbE+Tm5jo7BKEbEfkmOJLIN8GRuku+le38jcBBpxLQbxyHf1t4zHuSwdi87mDuyo+w/GNtTFvI0Ov4WStjshLAdUo4T1qyHF6ItOY6JZxRsi/VuspzlmwKrFj/EkBrqCNr0WskXvsMESlXU5r6Kw2lJ14f8kT5dmRd0o70kv7Tdr2axy1Z3GuIIkn25AljLK9YcqnQVUzImCQZD2RMyHhITf+e8HWl+b0j+5my61Aev6vxQoaeVsdUpaus0kpZoZZZVezb21dqISNlHybKfnynFnPoBF9vv+TRyIqBin1bbPa1cSRHtG95P32GV0wyfr1HEn/5o6S9fSe6at33jisTRamVSkpKnB2C0I2IfBMcSeSb4EjdJd/K0zaiWcx49xyAwdP3mMIzfMJFuAdFUJO/n8Lfl9othi/UQkbIPiTLnjxiiGG3XsNerZZ0vZaqNq45aWszlRBOV/xp0DVetOSQpbft2cHytD8o2baawMGnEXv+nez78P9OuN8/8+3YXtKTr0vaHtl6PY+YD3CvIYp42cSzRusLyNZYJIkGkxd1MlRWFFGHRi0adXrjv7Vo1B31caWu8pdedcJnc53lEGbWauWcpvhzoRLMXPX49XiPLKVUurNzzbp7hEPaN11j/4Jn6Xv723jFJBN97r/JWnT8cpydjShKrTRgwABSU1OdHYbQTYh8ExxJ5JvgSN0l39S6aioztuHXewR+fcdQ/Gfjs2ZGv2DCT78UgOwlc+26BmMNGh9YDnGbIYLesie98eTIqNJcvZ69Wi379Fr2arXk0+Cw8uVsOZBzlSAsus6rllz26LXtOk/Wkrn4Jg3HL3kkgYNPO+Hsxf/Mt4imGXcP/7YQtbaqfTfQgnJUnrRkcY0SznDZm4amgrFW144pJOvQqDnq/ycqMBuPUalFA09fBt33JZJiYMfzl5+0Z9jVLVKLGC/7MUb2ZYlWQvZRf4yQDG749h4BQNnO9c4KsUMc1b6pNRVkfPYYyf9+ndAx51KdtZvizSvtfl17EkWpldav75zfHELnJPJNcCSRb4Ijdad8K9u5Dr/eI/DvN665KI066wYUNxMl29dQmbHN7jH8qVdxqzmDRMlEomwiSTIRL3kQKbkTqbhzGv5A41DPIwXqPr2WDL3WLr1sE2U/Lm1aQ3Wems82vbrd57JUlZLz/bvEzbiH6HNvoXzPn8cN+Tw637zjBuCb2NRL+lvHnyU9mQZ05qn5zLPlhLY1FZTuWEvQ0MmEjJxG7grbPofsKEVY+EkrZaoSyEVKMC9b/h7u6ps0DMXNRHV2GubyQidG2X6ObN9qcveRtfgN4mbcQ+wFd1KTl0FtfobDrm9rYqIjK/Xv39/ZIQjdiMg3wZFEvgmO1J3yrWxX4y+ofknDkY0eeMcNIGjIJDRzPTnfveOwOCpR2aJX8aVayJOWLK4z7+U/5gN8ailgg1pBsW7GW1IYIntziSGE/xhjeN+YxNOGOK5Swhgj+xBsg36M4ZI31yuNsxF/bClgndbxZ2mLNn5PZcY2jN4BRJ9943HvH51vEVOuAOzXS2pvR4Z6B488E0m23URKjrZYLaZe1xgu+xAveTS/3pln3T3C0e1b0cbvKdz4PbLRnfgrH0MxeTv0+rYkilIr+fraYVFkQTgJkW+CI4l8ExypO+WbuaKYqoO7kI3u+CWPJOa8WwE49MsXNJQddlpcKrBfr2O5Vsr/t3fv0VHV997HP3vPTMidXAiJuZCgCQQMSqqAoBArHqe0WvTRtuLxUWtVakWr9emJx6dPbXsuRQ+KYK1SZLXqI4fagwq2B4IEBOUisQ6XkOvQBDKEhEDI/Taz9+/8EYhSQjJMZn47M/vzWuu3VpPM5TvOu7P8uWdmv6LV43H3ESzpc2Kl5zg2a804ove/nXaiGg67JR6PW9OwMiwbv7FdgR9bU7FAjUe2Eg7rJZz6ZKoSicetqVAVBeu1U9iin/Hb46l9bzl0Tx/GzViAmCumn/e3c72dd5TUz58llaWjthTdDbWwxSQgbphz4I5mrdBQdPb5/64lqf+XqoqxU/u/qfpMafBuSo14fTv2wUp0uqoQnpiKxK/dIv3+/YVv3/WSGc6rRqMHeyOZ2BvJZLbeWg7vQnTmVEy4/XHYYhLQe6YRDR//0eixLtAMD/bq7diL/re/joGCiUo4JikRmKRGIkeJQIJiwyzFhllq/7949wkdNaIHVaIb1Xo3qkQ32nDhe1YvV8LxtDUNNkVFkXYG68+es9JfeptcOFH8DtLs30fmnU/h8EsPQ3j6AHzZ25efJV0Prcf3twwbrWnvh5hw++NImnUrzhzaafQ4PvtQO42b1ThMU6MwRYlEXVY2bFFj0dPkQs/Jo0aP5zMjXt+Ex40jb/8CYyfPQNPeP0u/f3/hkVIvmeG8ajR6sDeSib2RTGbr7dxbEW0xCQAA159fh+6+tG+aNUIvBCpENzbqzVjmcWGxuxpP9/0NqzwnsE1rgUvvRZiiYrIaidssifiJLR2vh+XgJdvleNRyGearcchQxiBNCUOhNR0RigW7tFa8pQXmC3oaPl6H7oZahI9LR+r8ewd+b7fbET1xGmJzvua385Ia6fQXH0Hr60HspGswJjHV6HF81gkdf9H6v6n2u5ZxiJ969lt3S4PzW3fPMer1re9MY1BvSAEeKfVabW2t0SOQibA3kom9kUxm662nqQ7djUcRkZyJtiP7g/ro1gn04YTehx1oBQBEQUW2EoFJagRylAhkKxFIUcKQYgnDXIw977r79Q68rp0I2Df8Cs2D2vUvYspjryD5xu+h+cB2dDfUoLa29svzkgb5UVKg/1udzxzYjnEzFiBp1q1w/ffvjB7JZ5v0M7CLeExWI3FNZDJcCO7PkwLme33zJx4p9VJPT4/RI5CJsDeSib2RTGbs7cT2teg8Xo1j771s9Ch+1QkdB0Qn/qSdwr976vCQuwr/7K7B7z0N2KW14qTofwttud6Flz3HB3ljr5/nOVqGk7s3QLVYkXnnTwBFRU9MasgcJT3n3BGxxGvtUCw2g6fxXQ90bNROAwBu/OxT9LU2obOu3OCpRsaMr2/+wk2pl3Jzc40egUyEvZFM7I1kMmNvzV9sRfmKH6Knqc7oUQJKB3BU9OIjvQWvaifwpPtvWNxXjX/zHJN2FtTjm9egr/UUojOnImn2bYiedTsAoPGT/wr6o6TndNZVoOt4NWzRcYifdoPR44zIR3oLWsNsGH/ChYziDwAh62y5gWHG1zd/4abUSzt3Bu/bbSj4sDeSib2RTOzNXNqhQZd4f1pPJ459sBIAkPGtH6I7JhWe7g6c/PQ9iVMEXtNn/UdLk667zeBJRsYNgV2zCwAA8w8eCPqNCV/ffBfsz700M2bMMHoEMhH2RjKxN5KJvVGgtRzehTOln0K1hQEIraOk55x2FEPr7ULM5VcjfPwEo8fx2ZiEy1B107dxJi4BKR6BG9Sxw19pFOPrm++4KfWSzRa879mn4MPeSCb2RjKxN5Lh2AevwNPVBtXdHXJHSQFA7+1Gs2MbACBp1q0GT+O7uLwboFss2Jp9OQDgTsu4Szr/7WjD1zffcVPqpR07dhg9ApkIeyOZ2BvJxN5IBnfbKRx+6SEce+MnIXeU9Jxzb+FNvOYWKNYwg6fxTdyV/aeC2dF+DC69F0mKDV8P4qOlfH3zHTelXpo/f77RI5CJsDeSib2RTOyNZHG3ncbXr7vG6DECput4NTqPVcAaGYOEq280epxLZo2OR3TmldA9fWipKsGftCYAwO2WcQgL0qOlfH3zHTelXnI6nUaPQCbC3kgm9kYysTeSKdR7G/jCoyB8C2/c1NlQVBVt1V9A7+1GiejA3/RuxCtW3KLGGz2eT0K9t0DippSIiIiIKAg1798OT3cHorOuRETK5UaPc0nOvXW35fCugd+9q50CANxmSUQEtymm4tWzbbfbUVFRgerqahQWFg56mYKCAjgcDpSWluLjjz8GAKSnp2Pbtm0oKytDaWkpnnjiCb8NLlt2drbRI5CJsDeSib2RTOyNZAr13nR3D5q/2AoASLoueI6WqmMiEJvzNQhdR0vZnoHfHxSdKNe7EKNY8E1LgoET+ibUews0MdRSVVU4nU4xceJEYbPZxP79+8WUKVPOu8zYsWPF4cOHRUZGhgAgkpKSBACRkpIi8vPzBQARHR0tKisrL7guAPHqq68OOcNoWOPHjzd8Bi7zLPbGJXOxNy6Zi71xyVxm6C0iZaK49oVikf+rjUINCzd8Hm9W/FUF4toXisXkR1++4G+TlQixNixXrLHliBhYDJ/1UpYZehvJGmrPN+yR0pkzZ8LpdKKmpgZutxvr1q3DwoULz7vMPffcg/feew91dXUAgKam/g8qNzQ0wOFwAAA6OjpQXl6OtLS04e5yVCooKDB6BDIR9kYysTeSib2RTGborbuhBh21pbCERyFh+k1Gj+OVwd66e06l6MZ+vQMRigW3BdnRUjP0FijW4S6QlpY2sNkEAJfLhVmzZp13mUmTJsFms2H79u2IiYnBihUr8Pbbb593mczMTOTn5+Ozzz674D5SU1Nx5MgRdHR0ICIiAjt27EBhYSHsdjtqa2vR09OD3Nxc7Ny5EzNmzIDNZsOOHTswf/78gQ8UZ2dno7i4GAUFBXC73SgpKcG8efNQUVGB8PBwZGVloaioCHa7HW1tbSgtLcWcOXNw6NAhJCQkIC0tbeDvzc3NcDqdmDlzJhwOB1JTU5GXl4fi4mLY7XY0Njaivr4e+fn52LdvH7Kzs5GQkDBw/ePHj6O5uRnTpk3D7t27kZeXh9jY2IG/j5bHlJycPPB3PqbR9Zjy8vJw6NChkHpMofg8hcpjysvLQ1tbW0g9plB8nkLlMWVkZGDRokUh9ZhC8XkKlccUHR2NRYsWhdRjGux52nVoO6Kz8nDFLfdg7OnKUf2YxkREQb9qLnQAN0yMR0/Mty54TG2R8cC2SiywjUPX3KtwqK52VD+mc88TACxatChk//800sc0nCEPs951111i9erVAz/fe++9YuXKledd5pVXXhF79uwRkZGRIjExUVRVVYmcnJyBv0dFRYnPP/9c3HHHHZd8KHe0rLS0NMNn4DLPYm9cMhd745K52BuXzGWW3hRrmJj+i/fFtS8Ui8i0HMPnGWrFTrpWXPtCsZj61OohL/ekNU2sDcsV37ckGz6zt8ssvfm6RvT2XZfLhYyMjIGf09PTUV9ff8FlNm/ejK6uLpw+fRo7d+7E1VdfDQCwWq1Yv3493nnnHbz//vvD3d2oNW/ePKNHIBNhbyQTeyOZ2BvJZJbehKcPp/+6BcDo/8KjuLwbAAAtpZ8Oebk/eZqgC4Gvq3FIgk3GaCNmlt4CYdhNaUlJCXJycpCVlQWbzYa7774bGzduPO8yGzZswNy5c2GxWBAREYFZs2ahvLwcALBmzRqUl5dj+fLlgXkEklRUVBg9ApkIeyOZ2BvJxN5IJjP11rS3/5ylCdPnQx0TafA0F6EoiJs6BwBwZpDPk37VcfRhl94Gq6Lgf1nGyZhuxMzUm78NuynVNA1LlixBUVERysvL8e6776KsrAyLFy/G4sWLAfQ/AZs3b8bBgwexb98+vPHGGzh8+DCuv/563HfffbjpppvgcDjgcDiwYMGCgD+oQAgPDzd6BDIR9kYysTeSib2RTGbqraepDu1H9sMyJgKJ+fONHmdQURlTEBabiN7mBnTXO4e9/HrtFDxCYK4ai1SESZhwZMzUm78N+0VHALBp0yZs2rTpvN+tWrXqvJ+XLVuGZcuWnfe7Xbt2QVGUEY44OmRlZWHPnj3DX5DID9gbycTeSCb2RjKZrbeTe/+MmCumI+m629C090Ojx7nAUN+6O5iTcONjvQU3W+LxHes4rPDUD38lA5mtN38a9kgp9SsqKjJ6BDIR9kYysTeSib2RTGbrraX0U7g7WhCZegXGTp1t9DgXiD/7edIzh4f+POlXva+dRp/QMUuNRZYyJlCj+YXZevMnbkq9ZLfbjR6BTIS9kUzsjWRibyST2XoTmhtNe/q/+yX7vl/ispv/N6CMjn/dDx+fifCkdLg7W9FRW+r19c7Ag4/0FgDAdy1JAZrOP8zWmz+NjkqDQFtbm9EjkImwN5KJvZFM7I1kMmNv9cVvo37r2wAUpN3yACY99Dys0fFGj4X4vP637raW7QZ0/ZKuu1E7jW6hYboajUlKRCDG8wsz9uYv3JR6qbTU+/+iQzRS7I1kYm8kE3sjmUzZm66jfssfUL3mGbjbzyA252u48qnfISY739Cxzn2edLhv3R1MOzRs0s8AAL43io+WmrI3P+Gm1Etz5swxegQyEfZGMrE3kom9kUxm7q2t+q8oe/kRtDkdsMUkYNJDLyD1H+435O28trFJiMrIhdbXjbaqv/p0G3/RmtEhNExRIzFNGZ2nvDFzbyPFTamXDh06ZPQIZCLsjWRibyQTeyOZzN6bu70ZVav/CfUfvQkASP2H+zDpkf+ALTZR6hxxV/Zv1toqSyA8fT7dRjd0fKidBjB6P1tq9t5GgptSLyUkJBg9ApkIeyOZ2BvJxN5IJvYGQOio/+gtVK3+KdztzYi9YjqmPrkKsTnXSBsh/sqz37pbeulv3f2qLfoZtAgPrlAjcK0S7Y/R/Iq9+Y6bUi+lpaUZPQKZCHsjmdgbycTeSCb29qX2I/txePkjaKv+K2zR8cj5wVKk2r8PqIHdDlgiYhBz+dXQNQ9aK/aO6LZ6IfCBdgoA8B1LEhR/DOhH7M133JR6iecdIpnYG8nE3kgm9kYysbfzeTrOoOqNZ3C86PcABFLn34vJj7wIW+y4gN1n3JTroFgs6DhyAFp3x4hvb5veiibhRoY6BnPUWD9M6D/szXfclHqJ5x0imdgbycTeSCb2RjKxt0EIHSeK/z+qfvdT9LWdQszlV/W/nXfSjIDc3Ui+dXcwHgi8d/Zo6V2WcbD45Vb9g735jptSLzU3Nxs9ApkIeyOZ2BvJxN5IJvZ2ce1/O4Cy5YvRWvU5bNFxmPTQUqR94wd+fTuvahuD2Mn9m92WMv9sSgHgE70V9aIXyUoYCtSxfrvdkWJvvuOm1EtOp9PoEchE2BvJxN5IJvZGMrG3oXk6W1C95hm4Nq2B0DVcdtM9mLz4JdjG+ufbbWNzroElLBydxyrgbj3ll9sEAB3Af509WnqHZRxso+TTpezNd9yUemnmzJlGj0Amwt5IJvZGMrE3kom9eUEINGxfi8pVT6Ov9RRiJk7D1CdXYWzuyP/ZxeWd/dbdw5+O+Lb+3md6O2r1HiQqNtysxvn99n3B3nzHTamXHA6H0SOQibA3kom9kUzsjWRib97rqDmEspcfQWvFPtiixiLnwV8j/ZuPQFF9/NSmqiJuymwAQIufPk/6VQLAn7QmAMC3LYkYMwqOlhrVWzysuEWNN+S+/YWbUi+lpqYaPQKZCHsjmdgbycTeSCb2dmk8na2o/v2zcP33aghNQ8qN38PkHy5HWNz4S76tmIlXwRoVi+6Tx9Bz8lgApgUcohPVejfGKlZ8QzX+HKFG9BYFFc9YM/CANTmoN6bclHopOTnZ6BHIRNgbycTeSCb2RjKxNx8IgYaP16Hy9afQ13IS0VlX9r+d9+xRT2+d+9bdQBwl/ao/nj1aeqslAVEGb21k9zYGCv7JmoEMdQxcei92661S79+fuCn1Es87RDKxN5KJvZFM7I1kYm++6zh6GGUvL0ZL+V5YI2OR8/1/RfqtP4RisXp1/YFNaan/P0/6VWWiC6V6J6IUC261GHu0VGZvVih4ypqOHDUCTcKNX3vq0AFd2v37GzelXuJ5h0gm9kYysTeSib2RTOxtZDxdbXD+4Weo+/Pr0DUPUuZ9B5MffRlh8UMfEYxMy8GY+GT0tZ1Cp6sy4HOeO1pqVxMQa+CZS2X1pgD4keUyXKVGoVV48Gv3MZyBR8p9Bwo3pV5qbGw0egQyEfZGMrE3kom9kUzszQ+EQOPOP6HytafQe6YR0ROmYOqPVw0cCR3Ml0dJdwFCBHzEI6IHf9XbEa6ouMsyLuD3dzGyevuBJQXXWWLRJTQs9dShAW4p9xtI3JR6qb6+3ugRyETYG8nE3kgm9kYysTf/6TxW1v923rLdsEbGIPv+XyHjth8N+nZeWZ8n/ap3tVPQhcDNlnjcbfHPeVYvlYzevmdJwk2WOPQJHf/hceGo6A34fcrATamX8vPzjR6BTIS9kUzsjWRibyQTe/Mvrbsdzj/8P9R9+FvomgfJc+9E7qMrEBafMnCZMYmpiLzscni6O9D+twPSZqsTvXhNOwFNCHzbkogHLcnSTxIT6N6+pSZgoSURmhBY4TmOStEd0PuTiZtSL+3bt8/oEchE2BvJxN5IJvZGMrG3wGj8ZD0qf/tj9DY3IGpCLqY+uQpxeTcA+PIoaWvFXghN7uccd+ltWO45jj6h42ZLPJZYUqV+wjSQvRWoY/GP1v5T87yunYBDdAbsvozATamXsrOzjR6BTIS9kUzsjWRibyQTewuczroKlK1YjDOln8IaEY3s+36JjIVLEH9VAYCznyc1wBeiA0s9degSGmZbYvG0NR1hko6ZBqq3a5VoPGzpPxr9B08jdultAbkfI3m1KbXb7aioqEB1dTUKCwsHvUxBQQEcDgdKS0vx8ccfX9J1g0FCgvEn5CXzYG8kE3sjmdgbycTeAkvr7sCRt57DsQ2/ge5xI/n6OxA9YQp0dx9aK407Sl0huvFvnjq0Cw+mq9F4xpqBSAnH4gLR21QlEo9bU6EqCtZrp7BFP+P3+xgtxFBLVVXhdDrFxIkThc1mE/v37xdTpkw57zJjx44Vhw8fFhkZGQKASEpK8vq6AMSrr7465AyjYSUkJBg+A5d5FnvjkrnYG5fMxd64ZC72Jm9Fpk8W0wrfFte+UCyyH/gXw+cBIFIRJl6xXSHWhuWKf7dmiVhYAnp//u7tciVcrLHliLVhueJ+S7Lh/zxHuoba8w37nwxmzpwJp9OJmpoauN1urFu3DgsXLjzvMvfccw/ee+891NXVAQCampq8vm6w4HmuSCb2RjKxN5KJvZFM7E2eLlclylb8EHUfvoZjG141ehwAQD368Ev3UTSIPmSp4fi5bQISceG3BfuLP3tLRRgKremIUCzYpbXiLS20T2807LOSlpY2sNkEAJfLhVmzZp13mUmTJsFms2H79u2IiYnBihUr8Pbbb3t1XQBITU3FkSNH0NHRgYiICOzYsQOFhYWw2+2ora1FT08PcnNzsXPnTsyYMQM2mw07duzA/Pnz4XQ6AfS/h7u4uBgFBQVwu90oKSnBvHnzUFFRgfDwcGRlZaGoqAh2ux1tbW0oLS3FnDlzcOjQISQkJCAtLW3g783NzXA6nZg5cyYcDgdSU1ORmZmJhIQE2O12NDY2or6+Hvn5+di3bx+ys7ORkJAwcP3jx4+jubkZ06ZNw+7du5GXl4fY2NiBv4+Wx5ScnDzwdz6m0fWYMjMzkZubG1KPKRSfp1B5TJmZmbDb7SH1mELxeQqVxxQTE4NFixaF1GMKxecpVB5Tb28vFi1aFFKPadQ/Tz1HcdM3vj6qHtMfT57GA6fDkNoKLI2ZjH3zJmH9nh1+f55aWlqwaNGiET+mjqPH8U3HScRoQGW4jiOzpuHucTcGfXvDGfIw61133SVWr1498PO9994rVq5ced5lXnnlFbFnzx4RGRkpEhMTRVVVlcjJyfHquhjmUO5oWXl5eYbPwGWexd64ZC72xiVzsTcumYu9cZ1bkVDFc9YJYm1Yrnjdli2ylDF+vw9/9BYLi1hmmyjWhuWKn1sniDAohv+z89ca0dt3XS4XMjIyBn5OT0+/4MSwLpcLmzdvRldXF06fPo2dO3fi6quv9uq6wWLatGlGj0Amwt5IJvZGMrE3kom90Tld0LHUU4f9egdiFSt+Zp2AXCXCr/cx0t4ioKLQmoFUZQxq9R4s87jQB+Gn6Ua3YTelJSUlyMnJQVZWFmw2G+6++25s3LjxvMts2LABc+fOhcViQUREBGbNmoXy8nKvrhssdu/ebfQIZCLsjWRibyQTeyOZ2Bt9VS8EXvS4sEdrQ6RiwTPWDExXovx2+yPpzQYFT1vTMVENR4Pow/OeOnRB99tso92wm1JN07BkyRIUFRWhvLwc7777LsrKyrB48WIsXrwYAFBRUYHNmzfj4MGD2LdvH9544w0cPnz4otcNRnl5eUaPQCbC3kgm9kYysTeSib3R39MA/EarxzatBWGKip9Y0zFbjfHLbfvamwrgcWsqpqqRaBZu/Npdh1ZofpkpWHj19VObNm3Cpk2bzvvdqlWrzvt52bJlWLZsmVfXDUaxsbFGj0Amwt5IJvZGMrE3kom90WAEgDe0BnRAw7ctiXjMkoooNGKr3jKi2/WlNwXAI5bLcK0agw6hYamnDk1wj2iOYBT4s8iGiKKiIqNHIBNhbyQTeyOZ2BvJxN5oKOu0Jvyn5yRURcGD1hQsVBNHdHu+9PaPlvGYZxmLHqHjBU8dXKJvRDMEK25KvcTzXJFM7I1kYm8kE3sjmdgbDedDvRlveBqgC4HvWZNwjyXJ59u61N5uVxPxTUsCPELgJY8LTtHj830HO25KvVRbW2v0CGQi7I1kYm8kE3sjmdgbeWOb3oLfaPXwCIFbLYl42JICxYfbuZTeblbj8F1rEnQh8KqnHqWiy4d7DB3clHqpp8e8/+WC5GNvJBN7I5nYG8nE3shbe/V2vOhxoVfo+LolDk9YU2G9xK2pt73NVmPwgCUZALBGa8Bnov2S5w013JR6KTc31+gRyETYG8nE3kgm9kYysTe6FAdEJ5Z66tAlNMxSY/F/rGkYcwkbU296u1qJwqOWVKiKgv/0nMR2vXUkI4cMbkq9tHPnTqNHIBNhbyQTeyOZ2BvJxN7oUlWKbvyL5xhahQdXqdH4Z2sGorzcMg3X2yQlAk9a02BVFPxZO40P9WZ/jBwSuCn10owZM4wegUyEvZFM7I1kYm8kE3sjXxwVvfil+yhOCTcmqZH4mXUCxsIy7PWG6i1DGYOfWtMxRlGxXWvBWq3JnyMHPW5KvWSz2YwegUyEvZFM7I1kYm8kE3sjXzXAjV+4j6Je9CJTDcdztkyMw9A9Xay38bD1H3FVLNint2ON1hCIkYMaN6Ve2rFjh9EjkImwN5KJvZFM7I1kYm80Es3w4FfuY6jRe5CihOEXtglIQ9hFLz9Yb3Gw4llbBuIUK0r1TrzqqYceyKGDFDelXpo/f77RI5CJsDeSib2RTOyNZGJvNFJt0PCvnmMo17uQoNjwc9sEXK6ED3rZv+8tCiqesaZjvBKGI3o3XvIchxtCxthBh5tSLzmdTqNHIBNhbyQTeyOZ2BvJxN7IH7qhY6mnDl/oHYhRrPi/1gxMUSIvuNxXexsDBT+1pmOCGg6X6MULHhd6eIz0orgpJSIiIiIiGoIbAss9LuzSWhGhWFBoTcfXlOhBL2sB8JQ1DZPUSDQJN5a669AOTe7AQYabUi9lZ2cbPQKZCHsjmdgbycTeSCb2Rv6kAfitdgIfaWcQpqh4ypqG69XYgb9nZ2dDAfAjSyquUqPRKjz4tfsYmuExbOZgwU2pl4qLi40egUyEvZFM7I1kYm8kE3sjfxMAfq814gPtFCyKgsesqbhFjQMAFG8txvctyZhtiUWX0PC8pw4NcBs6b7DgptRLBQUFRo9AJsLeSCb2RjKxN5KJvVGgvKudwjuekwCAB6wpuENNxKPjJ+NmSzz6hI5lHhdqRa/BUwYPq9EDBAu3m/+Vg+RhbyQTeyOZ2BvJxN4okP6iN6PTo+EhSwq+Y00CqpugCYGVnnpUiG6jxwsqPFLqpZKSEqNHIBNhbyQTeyOZ2BvJxN4o0D7WW7HSUw+P6D/VyyrtBL4QHQZPFXy4KfXSvHnzjB6BTIS9kUzsjWRibyQTeyMZ9ol2POupwac35uBTvc3ocYISN6VeqqioMHoEMhH2RjKxN5KJvZFM7I1kcYk+7GlyGT1G0OKm1Evh4eFGj0Amwt5IJvZGMrE3kom9kUzszXfclHopKyvL6BHIRNgbycTeSCb2RjKxN5KJvfmOm1IvFRUVGT0CmQh7I5nYG8nE3kgm9kYysTffcVPqJbvdbvQIZCLsjWRibyQTeyOZ2BvJxN58x02pl2bPnm30CGQi7I1kYm8kE3sjmdgbycTefMdNqZcKCgqMHoFMhL2RTOyNZGJvJBN7I5nYm++4KfVSdHS00SOQibA3kom9kUzsjWRibyQTe/OdAkAYPcT69evR0NBg9BhDSkpKQlNTk9FjkEmwN5KJvZFM7I1kYm8kE3sbWkpKCu68885B/zYqNqVERERERERkTnz7LhERERERERmGm1IiIiIiIiIyDDelw7Db7aioqEB1dTUKCwuNHodCXE1NDQ4ePAiHw4GSkhKjx6EQtGbNGjQ2NuLQoUMDv4uPj8eWLVtQVVWFLVu2IC4uzrgBKaQM1ttzzz0Hl8sFh8MBh8OBBQsWGDghhYr09HRs27YNZWVlKC0txRNPPAGAr28UGBfrja9vIyO4Bl+qqgqn0ykmTpwobDab2L9/v5gyZYrhc3GF7qqpqRGJiYmGz8EVumvu3LkiPz9fHDp0aOB3zz//vCgsLBQARGFhoVi6dKnhc3KFxhqst+eee048/fTThs/GFVorJSVF5OfnCwAiOjpaVFZWiilTpvD1jSsg62K98fXN98UjpUOYOXMmnE4nampq4Ha7sW7dOixcuNDosYiIfPbJJ5+gubn5vN8tXLgQb775JgDgzTffxO23327AZBSKBuuNKBAaGhrgcDgAAB0dHSgvL0daWhpf3yggLtYb+Y6b0iGkpaWhrq5u4GeXy8XgKKCEENiyZQs+//xzPPzww0aPQyaRnJw8cFquhoYGjB8/3uCJKNQtWbIEBw4cwJo1a/h2SvK7zMxM5Ofn47PPPuPrGwXcV3sD+PrmK25Kh6AoygW/E0IYMAmZxfXXX49rrrkGCxYswGOPPYa5c+caPRIRkV+99tpruOKKKzB9+nScOHECL774otEjUQiJiorC+vXr8eSTT6K9vd3ocSjE/X1vfH3zHTelQ3C5XMjIyBj4OT09HfX19QZORKHuxIkTAICmpia8//77mDlzpsETkRk0NjYiJSUFQP+JrU+ePGnwRBTKTp48CV3XIYTA6tWr+TpHfmO1WrF+/Xq88847eP/99wHw9Y0CZ7De+PrmO25Kh1BSUoKcnBxkZWXBZrPh7rvvxsaNG40ei0JUZGQkoqOjB/73LbfcgtLSUoOnIjPYuHEj7r//fgDA/fffjw0bNhg8EYWycxsEALjjjjv4Okd+s2bNGpSXl2P58uUDv+PrGwXKYL3x9W1kDP+2pdG8FixYICorK4XT6RTPPvus4fNwhe6aOHGi2L9/v9i/f78oLS1lb1wBWWvXrhX19fWir69P1NXViQcffFAkJCSIrVu3iqqqKrF161YRHx9v+JxcobEG6+2tt94SBw8eFAcOHBAbNmwQKSkphs/JFfzr+uuvF0IIceDAAeFwOITD4RALFizg6xtXQNbFeuPrm+9LOfs/iIiIiIiIiKTj23eJiIiIiIjIMNyUEhERERERkWG4KSUiIiIiIiLDcFNKREREREREhuGmlIiIiIiIiAzDTSkREREREREZhptSIiIiIiIiMgw3pURERERERGSY/wHoGVbSz4d2bwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting loss graph\n",
    "plt.plot(history_train_loss_5, label='Train')\n",
    "plt.plot(history_val_loss_5, label='Validation')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation Accuracy')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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goflmMzExEeUMD0nCvJFMzBvJxLyRTMyb9oxkuevl+D1u9Dnr+kfLhUby/GPC67niazNvymm+2bTb7WqXQBrCvJFMzBvJxLyRTMybNuiMJkSn3YTYzNswaebCYZa7dgw0kYObSldLLTwdzYAQiutg3pTTfLPJ+zSRTMwbycS8kUzMG8nEvAU3i3064rJuQ8zi1TCGRQAY2XLXscK8Kaf5ZpP3aSKZmDeSiXkjmZg3kol5Cz56kwXRC3MQl3U7wpPnBx7vqTmJpsL34CzJ7//iHRUwb8ppvtlsaGhQuwTSEOaNZGLeSCbmjWRi3oKHNWkWYrNug23RysD9KX2ubrQc/Dua97+LnpqTKlfIvF0LzTebtbW1apdAGsK8kUzMG8nEvJFMzNvEZrCEwbZoJWKvvx1hSbMCj3dVH0FT4XtoPfwP+N0uFSscjHlTTvPNZkZGBr9diqRh3kgm5o1kYt5IJuZtYgqbPg9xWbcjemEODOb+b4/19nSg5cBuNBW+B1dDtboFXgbzppzmm83CwkK1SyANYd5IJuaNZGLeSCbmbeIwWCMQu+RWxGbehtCE5MDjHZUH0bz/XbSWfTTsrUfUxrwpp/lmMyUlBZWVlWqXQRrBvJFMzBvJxLyRTMzbOKfTYdLMhYi9/nZEp90EvdEMAPB0OtFcnIfmovfR11yjcpEjx7wpp/lm02azqV0CaQjzRjIxbyQT80YyMW/jk2mSDTFLHYjNXAtLbBKA/luWtJcXoqnwXbQf/QTC71O5yqvHvCmn+WaT980hmZg3kol5I5mYN5KJeRtHdHpEzlmK2KzbETX3RugMBgCAu60RzUXvo7loJ9xtjSoXeW2YN+X0ahegNofDoXYJpCHMG8nEvJFMzBvJxLypzxwVj8Rbv4H0R/8Hsx74DaLTbgIAtJZ9jJN/+SkO/+Y+1O5+ccI3mgDzdi1GdGUzMjISzz77LNLS0iCEwAMPPIDjx4/j1VdfRXJyMqqrq/GVr3wFbW1tY1zu6KupmTjrxWniY95IJuaNZGLeSCbmTR06vQGR825EXNZtiJidCZ2+/7qVq7kGzYXvoeXALng6nSpXOfqYN+VG1Gz+4Q9/wM6dO3HvvffCZDLBarXiZz/7GfLz8/HEE09g06ZNePTRR/Hoo4+Odb2jzukMvv9A0PjFvJFMzBvJxLyRTMybXGbbZMRl3YbYzDUwTer//KLf64bz8Mdo3v8uOk8dAoRQucqxw7wpN+wy2kmTJmH58uV47rnnAAAejwft7e1Yt24dXnjhBQDACy+8gLvuumtMCx0r6enpapdAGsK8kUzMG8nEvJFMzNvY0+kNiJqfjVnffhwLHv0fTF75TzBNsqG34TTO/t8/4vD/+Sqqtv0KnZUHg7rRBJi3azHslc2ZM2eiqakJzz//PBYuXIgDBw7gBz/4Aex2O+rr6wEA9fX1iI+PH/LcxMREVFZWoqurC6GhoSgoKMCmTZvgcDhQXV0Nl8uF1NRU7NmzB5mZmTCZTCgoKMCqVatQUVEBoP+rhvPz85GTkwOPx4OioiIsX74c5eXlsFgsSE5ORl5eHhwOBzo6OlBWVoZly5ahtLQUNpsNSUlJge1OpxMVFRXIyspCSUkJEhMTERkZCZvNBofDgYaGBtTW1iIjIwOFhYVISUmBzWYLPL+mpgZOpxPp6enYt28f0tLSEBEREdg+XuZkt9sD2zmn8TWnyMhIpKamBtWcgvF9CpY5RUZGwuFwBNWcgvF9CpY59fX1ITc3N6jmFIzvU7DMqaqqCrm5uUE1p/HyPn1ccgQpt+aixz4fPnNY/3+p93mAs4cxxV2LfW9v65/T6pUTZk7X+j6VlZUhNzc3qOY0mu/TcMSVxpIlS4TH4xFZWVkCgPjP//xP8dhjj4nW1tZB+zmdziHP3bp16xWPPR7G7bffrnoNHNoZzBuHzMG8ccgczBuHzMG8jfLQ6UXk3BtEyrd+JZY8vkss3ZIvlm7JF/M3/kXE33S3MIROUr9GFQfzduVxpZ5v2Cub586dw7lz51BYWAgA+Nvf/oZHH30UDQ0NSEhIQH19PRISEtDYODG/aSoiIkLtEkhDmDeSiXkjmZg3kol5Gx2miBjEZq5FbNZtCIm2AwD8Xg+ch/6Bpv3voOvUYZUrHB+YN+WGbTYbGhpw9uxZzJ49GydOnMCqVatw9OhRHD16FOvXr8cTTzyB9evX46233pJR76jjfXNIJuaNZGLeSCbmjWRi3q6BToeIWUsQd/0diJq3LHBfTFdzDZr2v4OW4jx4u9tVLnJ8Yd6UG9F9Nr///e/j5ZdfxqFDh7Bo0SL8+te/xuOPP45bb70VJ06cwK233orHH398rGsdE7xvDsnEvJFMzBvJxLyRTMzb1TOGRyPhlq8h/ScvYvZ3nkB0+s0QEHAeLsDxp/8VZb9dj4aC7Ww0L4F5U25Etz45dOgQMjMzhzz+hS98YdQLkq26ulrtEkhDmDeSiXkjmZg3kol5G7lJ1y1C3A13IGr+TdAbTQCAPmc9mva/i+ai9+HtalW5wvGPeVNuRM1mMHO5XGqXQBrCvJFMzBvJxLyRTMzblRmtEYhZ6kDc9bfDEjcVACD8PrQe2YumT99Bx4liQPhVrnLiYN6U03yzmZqaipKSErXLII1g3kgm5o1kYt5IJubt0sKT0xF3wx2IXrAceqMZAOBua0Jz0XtoKnwPnvZmlSucmJg35TTfbO7Zs0ftEkhDmDeSiXkjmZg3kol5u8AQGo6Yxbci7vo7EJqQDAAQfj/ay/ej6dN30Fb+KeDnVcxrwbwpp/lmMzMzEzU1NWqXQRrBvJFMzBvJxLyRTMwbEDZtLuKuvwPRC2+BwWwBAHg6nWgufB9Nhe/C3dqgcoXBg3lTTvPNpslkUrsE0hDmjWRi3kgm5o1k0mre9CFWxGSsQtwNd8CamBJ4vOPEATR++n/RfnQfhN+nYoXBSat5Gw2abzYLCgrULoE0hHkjmZg3kol5I5mCMW86gwmmyFiYI2NhjoqHOTJu4O94mKP6f5omRQf293S1oaU4D03730FfS62KlQe/YMybLJpvNletWoVXXnlF7TJII5g3kol5I5mYN5JpouXtahvJyxE+H7qqS9G0/x20ln4M4fNIqJ4mWt7GE803mxUVFWqXQBrCvJFMzBvJxLyRTOMpb6PZSLo7muFpb4K7vQnu9ma42xrhGfjpbm+Gp9PJW5aoYDzlbaLRfLNJRERERDQcndGEiJQMRMxaCnN0wrU1km39P9lIUrDTfLOZkpKCoqIitcsgjWDeSCbmjWRi3kgmWXkzWMIQmZqFqPnZiEy9HoYQ65B92EgGP57flNN8s5mfn692CaQhzBvJxLyRTMwbyTSWeTNFxCJq3o2Imp+NSdctgt544ZtIe2pOou3oJ+itr2IjqSE8vymn+WYzJycHr732mtplkEYwbyQT80YyMW8k02jnzRI3FVHzsxGVdhPCp80NPC78PnRUHkRb2V60Hd3Le1dqFM9vymm+2fR4+C1eJA/zRjIxbyQT80YyXXPedDqETU3tbzDnZyM0flpgk9/Th/bjxWg7shftxz6Bt6fjGquliY7nN+U032xy/TXJxLyRTMwbycS8kUxK8qYzGDHpukUDDeYymCNiA9u83R1oO/YJ2o7sRceJA/B7XKNZLk1wPL8pp1e7ALUtX75c7RJIQ5g3kol5I5mYN5JppHnTh1gRvfAWzPinn2Phf7yO2d95AvE33glzRCz6WhvQ8PEbOP7URhz85T2o3r4FbUf2stGkIXh+U07zVzbLy8vVLoE0hHkjmZg3kol5I5mulDdjeDSi5i9D9PxsTErJgN5oDmzrqTuFtrKP0XpkL3pree9EGhme35TTfLNpsVjULoE0hHkjmZg3kol5I5k+n7eQ2CmIHvj8Zdi0udDp+xfvCb8fnacOo+3IXrQd2Ys+Z50a5dIEx/ObcppvNpOTk/HJJ5+oXQZpBPNGMjFvJBPzRjJNT07GobPOQIMZmpAc2Ob3uNFefgCtZR+j/din8Ha3qVYnBQee35TTfLOZl5endgmkIcwbycS8kUzMmzboDEZY7MmwJqbAHBUHnU4P6PX9P3X6/iuKgZ+6QdsD23R6QK+DTmfo30evB3QG6PS6i7Z/7qdOB53+wv7lkXGYl/GdQF3enk60H/sUrUf2ouNEEfxufu6SRg/Pb8ppvtl0OBx45ZVX1C6DNIJ5I5mYN5KJeQs++pBQWCdfB2tiCqxJKbAmpsBiT4beaFK7NACAu60JbUf2ovXIx+g6dRjC71O7JApSPL8pp/lms6OD904ieZg3kol5I5mYt4nNGBY50FTOCjSXITFJgc8+nif8friazqKnpgKulhrA74Pw+yH8fkD4IYQfEKK/8fP7IYQAhK//55D9/Jd5rr//uEIM7OO78PjAfjffeD3yXntJpX8t0hqe35TTfLNZVlamdgmkIcwbycS8kUzM28RhjorvbyrPN5YDS2I/z+/1oKeuEj01FeipPYnemgr01FWOiyWq5YV9apdAGsLzm3IjajarqqrQ2dkJn88Hr9eLzMxMLFy4EH/+859hsVjg9Xrx8MMPT8gbni5btgynT59WuwzSCOaNZGLeSCbmbRzS6WGJmxpYAnv+iqXRGjFkV19fL3pqK/pHTf9PV0M1hM+rQuHDY95IJuZNuRFf2VyxYgVaWloCf2/ZsgX/+3//b+zcuRNr167Fli1bsGLFijEpciyVlpaqXQJpCPNGMjFvJBPzpi6d0YTQhBmDlsKGTp4Jg3noLRs8XW0XNZUn0VNTgb6WGkAIFSpXhnkjmZg35RQvoxVCICKi/38Zi4yMRG1t7agVJZPNZlO7BNIQ5o1kYt5IJuZNLr3JAtuiFQifsQDWpBSExk+HzmAYsl+fs/5zVyxPwtPerELFo4t5I5mYN+VG1GwKIbBr1y4IIfDUU0/hmWeewQ9/+EPk5eXhd7/7HfR6PZYtWzbWtY6JpKQktUsgDWHeSCbmjWRi3uQIiUlE3I3rEJu5BsbQ8MDjwu9Db8PpwJXK/gazEr6e4PxiE+aNZGLelBtRs5mdnY26ujrExcVh9+7dKC8vx5e//GX86Ec/whtvvIF7770Xzz33HG699dZBz0tMTERlZSW6uroQGhqKgoICbNq0CQ6HA9XV1XC5XEhNTcWePXuQmZkJk8mEgoICrFq1ChUVFQCAlJQU5OfnIycnBx6PB0VFRVi+fDnKy8thsViQnJyMvLw8OBwOdHR0oKysDMuWLUNpaSlsNhuSkpIC251OJyoqKpCVlYWSkhIkJiYiNDQUNpsNDocDDQ0NqK2tRUZGBgoLC5GSkgKbzRZ4fk1NDZxOJ9LT07Fv3z6kpaUhIiIisH28zMlutwe2c07ja06hoaFITU0NqjkF4/sULHMKDQ2Fw+EIqjkF4/sULHNqb29Hbm5uUM1p/LxPe3DDXd9AbVgykDAb0PV/Q2xIVz1CG8tRUVSA61On48TRMkScn1PZaXxl3e3jeE7X9j4dOXIEubm5QTWnYHyfgmVO+/fvR25ublDNaTTfp+GIqxmbN28WGzduFG1tbYMeb29vH7Lv1q1br+rYaozc3FzVa+DQzmDeOGQO5o1D5mDeRn8YLGEiPvtukfavL4ilW/LF0i35YvGv3hfJ9/6rsCbNUr0+NQfzxiFzMG9XHlfq+Ya9smm1WqHX69HV1QWr1YrVq1fjscceQ21tLXJyclBQUICVK1fi5MmTwx1qXHI6nWqXQBrCvJFMzBvJxLyNHkv8dMQvW4eYJathCAkFAPS1NqDpk7fRXPgevEG6NPZqMG8kE/Om3LDNpt1ux44dO/p3Nhqxbds25OXlYcOGDfjDH/4Ao9EIl8uFBx98cMyLHQvnLzcTycC8kUzMG8nEvF0jnR5R825E/LK7EDFrceDhjooSNO7dgbZjnwB+v4oFji/MG8nEvCk3bLNZVVWFRYsWDXl87969WLp06VjUJFVWVhYqKyvVLoM0gnkjmZg3kol5U8ZgjUBc1m2Iu/FOhETbAQA+dy9aDuxG47434Wrgvf0uhXkjmZg35RTf+iRYlJSUqF0CaQjzRjIxbyQT83Z1QhNTYM++C7ZFK6E3hQAAXM3n0LjvLbQU58Hn6la5wvGNeSOZmDflNN9sJiYmory8XO0ySCOYN5KJeSOZmLfh6QxGRKXdDHv2XQhPTgs83l6+Hw1730THiSJACBUrnDiYN5KJeVNO882m3W5XuwTSEOaNZGLeSCbZedMZTQibMgfhyWkItSfD0+mEq6UGfc016Guphbu9adw0bsbwaMTdcAfibrgD5ohYAIC3twstxTvRuO8t9LXUqlzhxMPzG8nEvCmn+WYzLy9P7RJIQ5g3kol5I5nGOm9GawTCk9MCwzplFvRG82X393vc6Gup7W9AW2oDTair+RzcbU2AGPsv2wmbNg/x2XchOn059EYTAKC3vgqN+95Ey2cfwO92jXkNwYrnN5KJeVNO882mw+HAK6+8onYZpBHMG8nEvJFMo523kNikQc1laPy0QduF34+e2kp0VZehp7YCxrAoWGITERI7BSExiTBHxCA0IRmhCclDju33etDnrAs0oa7mGvSdvyra1nBN3/qqM5pgW7gS8dl3IWzK7IFafWgt/QiN+95EZ+VBxcemC3h+I5mYN+U032w2NDSoXQJpCPNGMjFvJNO15E1nMMKaNOui5nI+TOHRg/bxuV3oPluOruoydFWVofvM0St+iY7ebEFITCIsA81nSGwSLDGJCIlJgjkqDqHx04Y0sADg93nhbq2/qAmtRV9LDVzNtXA76yD8vku+njkqHnE3fBGx198OU1gkAMDT3Y7m/e+i6dP/C3dbo+J/HxqK5zeSiXlTTvPNZm0tPydB8jBvJBPzRjJdTd4MoeEInz4P4dPTED4jDWFTUwPfyHqep7MVXdWl6Ko+gq7qUvTUVkL4vCN+Db/bhd66U+itOzVkm95kQUjM5AtNaGwSQmKS+v+OtsMSOwWW2CmI/NzzhM+HvraGwJLcvpYaeDpbEZ2+HFHzl0GnNwAAus+dQOPeN+E89CGE1z3immnkeH4jmZg35TTfbGZkZPDbpUga5o1kYt5IpivlzRyd0H/FckYawqfPR6g9GTq9ftA+vQ2n+69aDjSXY/mlOX6PC731VeitrxqyTWc0I8Q2ub8Bje1vQC0xSQiJTYQ5yg5LTCIsMYlDj+nzwlmSj8a9b6L7zNExq5368fxGMjFvymm+2SwsLFS7BNIQ5o1kYt5IpkDe9HpYJ1836POW5sjYQfv6vW50nzmBzqoydJ0uQ3f1EXh7OlSoeijhdcPVeBquxtNDtukMJoTYEi40obFTYI6KR/e542je/y48nU4VKtYmnt9IJuZNOc03mykpKaisrFS7DNII5o1kYt5IBmNYFKxTZiNxxR0wrLQgbNo8GEJCB+3j7e5A1+kjA5+3LEV3zQkIr0elipUTPg9cTWfhajqrdimax/MbycS8Kaf5ZtNms6ldAmkI80YyMW802ozWCFinzIY1aTbCpsyGdcpshET333+uF0DEwH6u5prActiu6iNwNZ0ZN/e8pODA8xvJxLwpp/lmk/fNIZmYN5KJeaNrYQgNH9RUhk2ZjRDb5CH7+fp60VNzEr7mM2guL0ZXdRm8Xa0qVExawvMbycS8Kaf5ZpP3zSGZmDeSiXmjkTJYwmBNmhVoKq1Js2GJTRqyn8/tQm9tBbrPnUD3uePoOXcCrqZzgPAjNzcXr5R9pEL1pEU8v5FMzJtymm82a2pq1C6BNIR5I5mYN7oUfUgorEmzEJZ0/orlHFjipgzZz+/pQ09t5UBTeRLd5473L4f1+y95XOaNZGLeSCbmTTnNN5tOJ785juRh3kgm5o30ZgusiSn9TeVAc2mJmzrktiN+rxu9tafQfe44us+dQE/NCbgaTkP4fSN+LeaNZGLeSCbmTTnNN5vp6ekoKytTuwzSCOaNZGLetEVnNMGaOGvgM5ZzEDZlNizxU6HTGwbt5/d60FN7MnC1svvcCbgaqiF83mt6feaNZGLeSCbmTTnNN5v79u1TuwTSEOaNZGLegp/ZNhmRc7IQmZqFSdctgsFsGbTd7/Oit+bkwGcs+69Y9tZVQfhG/7YjzBvJxLyRTMybcppvNtPS0nD69NAbNxONBeaNZGLego/OYMKkmQsQMdBghsZPG7S9p+4Ues4vhT13HD11p6Tdz5J5I5mYN5KJeVNO881mRETE8DsRjRLmjWRi3oKDOSo+0FxGpCyGISQ0sM3b24WOE8VoLy9E+/FCVW85wryRTMwbycS8Kaf5ZpP3zSGZmDeSiXmbmHQGI8KT0wLLY0MTZgza3lNbifby/Wg/Xoju00ev6kt8xhLzRjIxbyQT86ac5ptN3jeHZGLeSCbmbeIwRcQiMjULkXMyETFrCQyWsMA2X18POk4cQPvx/quXnvZmFSu9POaNZGLeSCbmTTnNN5vV1dVql0AawryRTMzbOKbXI3za/P4GMzUL1sSUQZt766vRfnw/2ssL0VVdds3fFCsD80YyMW8kE/OmnOabTZfLpXYJpCHMG8nEvI0vxvDowNLYiFlLYLROCmzzuXvRWVHSvzy2vBDutkYVK1WGeSOZmDeSiXlTbkTNZlVVFTo7O+Hz+eD1epGZmQkA+N73vofvfe978Hq9ePfdd7Fp06YxLXYspKamoqSkRO0ySCOYN5KJeVOZTo+wqakDVy+vR9iU2YM2u5rOBprLzqrD0r41dqwwbyQT80YyMW/KjfjK5ooVK9DS0hL4+5ZbbsG6deuwYMECuN1uxMXFjUmBY23Pnj1ql0AawryRTMybfEZrROCbYyNnZ8IYduEbDP2ePnRWHkRb+X50lBeiz1mnYqWjj3kjmZg3kol5U07xMtqHHnoIjz/+ONxuNwCgqalp1IqSKTMzEzU1NWqXQRrBvJFMzJs8YVPnID77bkQvyIHeaAo87mqpvXD1svIghNetYpVji3kjmZg3kol5U25EzaYQArt27YIQAk899RSeeeYZzJ49GzfffDN+9atfweVy4cc//jGKi4vHut5RZzKZht+JaJQwbyQT8za2dAYjotOXIz77SwifPg8AIPw+dJw4gLbyT9FeXoi+5nMqVykP80YyMW8kE/Om3IiazezsbNTV1SEuLg67d+9GeXk5jEYjoqOjccMNNyAzMxPbt2/HzJkzBz0vMTERlZWV6OrqQmhoKAoKCrBp0yY4HA5UV1fD5XIhNTUVe/bsQWZmJkwmEwoKCrBq1SpUVFQAAFJSUpCfn4+cnBx4PB4UFRVh+fLlKC8vh8ViQXJyMvLy8uBwONDR0YGysjIsW7YMpaWlsNlsSEpKCmx3Op2oqKhAVlYWSkpKkJiYiMjISNhsNjgcDjQ0NKC2thYZGRkoLCxESkoKbDZb4Pk1NTVwOp1IT0/Hvn37kJaWhoiIiMD28TInu90e2M45ja85RUZGIjU1NajmFIzvU7DMKTIyEg6HI6jmNB7ep5Nn64GZ18MwJxs+kxUAoPO40HkoH2mhnag8XIwYiwXJt948YeY0Gu9Tb28vcnNzg2pOwfg+BcucKioqkJubG1RzCsb3KVjmVFxcjNzc3KCa02i+T8MRVzM2b94sNm7cKN5//32Rk5MTeLyiokLExsYO2nfr1q1XdWw1Rm5uruo1cGhnMG8cMgfzNrrDOmWOmPHVR8XiX+8US7fki6Vb8sW8Hz0jYrNuE3pTiOr1qT2YNw6Zg3njkDmYtyuPK/V8w17ZtFqt0Ov16OrqgtVqxerVq/HYY4+hq6sLK1euREFBAWbNmgWz2Yzm5vF5o+krOf+/ABDJwLyRTMzbtetfKnsz4rPvHrRUtrXsYzTu3YHOyoPqFjiOMG8kE/NGMjFvyg3bbNrtduzYsaN/Z6MR27ZtQ15eHkwmE/7yl7+gtLQUbrcb69evH/NiiYiIZDCGRyPu+tsRd+MXYY6IBQB4ezrRXPQeGve9DXdrvcoVEhERjX/DNptVVVVYtGjRkMc9Hg/uv//+sahJqpSUFBQVFaldBmkE80YyMW9XzzplNuKzvwTbwlugN5oBAL31VWjY+yacn30Av4c39r4c5o1kYt5IJuZNOcW3PgkW+fn5apdAGsK8kUzM28jo9AZEpS+HPftLCE+eDwAQfj9ay/aice8bXCo7QswbycS8kUzMm3J6tQtQW05OjtolkIYwbyQT83ZlxrAoTF55H9J/ug3X3fdvCE+eD29vF+oLtqP0iftR+eJ/sNG8CswbycS8kUzMm3Kav7Lp8XjULoE0hHkjmZi3S7MmzUJ89t2wLbp4qWw1Gvbu4FLZa8C8kUzMG8nEvCmn+WaT669JJuaNZGLeLuhfKnvzwFLZNAADS2WP7O3/VtmKEpUrnPiYN5KJeSOZmDflNL+Mdvny5WqXQBrCvJFMzBtgDIvE5JX/NLBU9t8RnpzWv1R2z2so3fINVL7wH2w0RwnzRjIxbyQT86ac5q9slpeXq10CaQjzRjKNdd6M4dEIiZmMEFsiQmwJCLFNRkjMZJijE2CKiIHweuD3uOB3u+B39/X/9Ljg9/TBd/6x89s9A9vdF233DH3exfsIn/eytVmTZiF+2V2wLVoJvWlgqWzDaTTufQMtn30Av5tLZUcbz28kE/NGMjFvymm+2bRYLGqXQBrCvJFM15o3vdnS30DaJsMcaCYTERKdALMtAQbzMMc3GGEICb2mGq7E7/MOblQHfupNZlgTUwD0L5VtO7oPDR/vQGfFZ2NWC/H8RnIxbyQT86ac5pvN5ORkfPLJJ2qXQRrBvJFMw+ZNr4c5Mi7QUIbYJsMcMxkh0QkIiZkMU3j0FY/v7e5An7MWfc469Dnr0eesg9tZj76WWrg7mqEzGKE3WaA3W6A3hcBgvvB7/8+Bv80hA9tDB7ZdtP38voOeN7CvwQh9aDgQGj60tt4uNBftRNO+N9HnrLvWf0oaAZ7fSCbmjWRi3pTTfLOZl5endgmkIcwbyZSXlweDNeLCEtfPNZXmqHjoDZf/fwN+rxt9znq4L2om+1pq+x9rrYfP1X3F1xdeD/x9vaM9rYD+ZvZzzajJAp3RiJ5zJ7hUVjKe30gm5o1kYt6U03yz6XA48Morr6hdBmkE80ZjSqeDNTEFEbOWIGL2UkTNmA+/wXzFp7jbmweuTNb1N5UtFxpLT2cLIISk4q+e8Hnh83mHbXpJDp7fSCbmjWRi3pTTfLPZ0dGhdgmkIcwbjTZTZGyguYxIWQxTeFRgmx+Az9U9cEWyDn2t9ehrqYO79fzfDRBet2q1U3Dh+Y1kYt5IJuZNOc03m2VlZWqXQBrCvNG10pssmHTdAkTMWoqIWUsQmpA8aHtfawM6ThSj40QxIj2tqC4vVadQ0hye30gm5o1kYt6U03yzuWzZMpw+fVrtMkgjmDe6ap9bGhuePB9644Wlsb6+HnRWHkT7iWJ0nDyAvqZzgW1rcnPZbJI0PL+RTMwbycS8Kaf5ZrO0lP9FjORh3mgkrrQ0Vvj96DpzDB0nD6DjRDG6zxy77P0mmTeSiXkjmZg3kol5U07zzabNZlO7BNIQ5o0u5cLS2CWImLX0iktjOyoPwtczss+OMG8kE/NGMjFvJBPzppzmm82kpCS1SyANYd4IwNUtjT1xAH3N565wsMtj3kgm5o1kYt5IJuZNOc03m7xvDsnEvGnXiJbGDjSX3WcvvzT2ajBvJBPzRjIxbyQT86ac5ptN3jeHZGLeNESnx6TrFiFq7vVXWBpbhI4TB9BRUQJfb+eol8C8kUzMG8nEvJFMzJtymm82nU6n2iWQhjBvwS8kJhExSx2IWXwrQqLtgcdHa2ns1WDeSCbmjWRi3kgm5k05zTebFRUVapdAGsK8BSe92YLoBTmIXboGk2YuCDze11IL58EP0X68CN1njkL4fVLrYt5IJuaNZGLeSCbmTTnNN5tZWVmorKxUuwzSCOYtuITPSEfs0jWIXpADQ0goAMDn7kXr4Y/QUrwTnVWHASFUq495I5mYN5KJeSOZmDflNN9slpSUqF0CaYhW82awRvR/JlHFxmu0mCLjELtkNWKWOmCJvfDtdJ1VpWgpzoPz8D/g7+tVscILtJo3UgfzRjIxbyQT86bciJrNqqoqdHZ2wufzwev1IjMzM7Bt48aN+N3vfofY2Fi0tLSMWaFjJTExEeXl5WqXQRqhpbxZp8xBdNpNiEq7CaHx0+DpbkdXVSm6qg6j89Rh9NRVAn6/2mWOiM5oRnRaNmKWrkFEymLo9HoAgLutCS2f7UZzcZ6Uz2BeLS3ljdTHvJFMzBvJxLwpN+IrmytWrBjSTE6ZMgW33norTp8+PeqFyWK324ffiWiUBHXe9HpMmrEAUfOzEZ12E8xR8YFNfq8HprBIRKfdhOi0mwAAPlc3uqqPoLPqMDpPHULPuROjcruP0RQ2dQ5ilq6BbdFKGEPDAQB+jxuth/eiuTgPHScPAGL8NsxBnTcad5g3kol5I5mYN+WuaRnt73//e/zkJz/BW2+9NVr1SMf75pBMwZY3ndGEiFlLEZ2Wjch5y2AKiwxsc7c1ofXIx2gr+xidVYdhjrJj0owFmDQzHeEzFsASm4TI1CxEpmYBAPyePnSdPhq48tl95hj8nj7pczKGRyNm8RcQu3TNoNuVdJ8tR3NxHpwHPxyT25SMhWDLG41vzBvJxLyRTMybciNqNoUQ2LVrF4QQeOqpp/DMM8/gi1/8ImpqanD48OGxrnFM8b45JFMw5E0fYkVk6vWITrsJkalZMIRYA9tcTWfRWvYx2so+Qve5E4M+o+l21qHFWYeWA/0nbFNEbKDxnDRjAUITkhGRkoGIlAwAgN/nRc/Z4+isOoyuqsPoqj4Cn6t7TOakMxgROfcGxC5dg8g5WdAZDAAAT1crWj77AC3FeeitrxqT1x5LwZA3mjiYN5KJeSOZmDflRtRsZmdno66uDnFxcdi9ezfKy8vx85//HKtXr77i8xITE1FZWYmuri6EhoaioKAAmzZtgsPhQHV1NVwuF1JTU7Fnzx5kZmbCZDKhoKAAq1atCnzFcEpKCvLz85GTkwOPx4OioiIsX74c5eXlsFgsSE5ORl5eHhwOBzo6OlBWVoZly5ahtLQUNpsNSUlJge1OpxMVFRXIyspCSUkJEhMTMWPGDNhsNjgcDjQ0NKC2thYZGRkoLCxESkoKbDZb4Pk1NTVwOp1IT0/Hvn37kJaWhoiIiMD28TInu90e2M45ja85zZgxA6mpqRNvTierEDHnehiTF6E7bDJ0RlPgP+chPc3oOPYJbD21SAk3YNf+PNzpcKChIWkEc2pCuqUR+15/DNMyMmFMSEF5Sx/i59+AvlAbwpPnIzx5PrAiFxB+mHta0FxejDnRJtSVfgKdu/ua3qeo6XNhmXsTWsKnwWCNAAAInw9hbdVoLs5DvM+JefFxyHO3467c3PH/Pn0uezNmzIDD4Qja/zxxTuNrTlFRUcjNzQ2qOQXj+xQsc/J6vcjNzQ2qOQXj+xQsc+rq6kJubm5QzWk036cr0QG4qq+H3Lx5M3w+H77//e+jp6cHQP9nN2tra5GVlTXoBbdu3Yrvfve7V3N46VJTU/mBX5JmIuXNHBWPqIHPWIYnp0Gn77/aJ/x+dFWXobXsI7Qd2Qt365VPMkoZLGEImz4fk2akY9LMBbBOmQP9RU0uAPTWVweufHaeKoWno3n441ojELNoJWKWOhA2ZXbg8Z66U2gpzkNLST68Xa2jPh81TKS80cTHvJFMzBvJxLxd2ZV6vmGvbFqtVuj1enR1dcFqtWL16tV47LHHBn1QtqqqCkuXLp2Q30abkZHB8JA04z1vlvjpgW+QvbgR83s96DhR3L9E9ugnUpoxn6sbHccL0XG8EACgN4UgbNpchM9Ix6QZCxA2fR5CE5IRmpCM+BvvBAC4WmrRdepwfwN66jD6nHX9B9PrETl7KWKWrkHUvBuhN5oBAN6eTjgP/h3NxTvRc+7EmM9JtvGeNwouzBvJxLyRTMybcsM2m3a7HTt27Ojf2WjEtm3bgupDsoWFhWqXQBoyHvMWNnUOoubfhOj0m2GJmxp43NfXi/by/Wg78jHaywvH7POSI+X39KGz8iA6Kw+iDv2fs7QmzcakmQsQPiMd4clpsMQkwhKTiNjMNQAAd3szus8cQ9i0uTBHxgLovzLbfrwQzcV5aDuyF8LrUXFWY2s85o2CF/NGMjFvJBPzptywzWZVVRUWLVp0xX1mzJgxWvVIl5KSgsrKSrXLII0YF3k7f4uStJsQPT970C1KvN0daDu6F61le9Fx8gCE161ioVcmfF50nzmK7jNHgX/8FdDpETp5JibN7P/CofCZC2COjIU5/WYAgKv5HJqL8tDy2S542odfbhsMxkXeSDOYN5KJeSOZmDflrunWJ8HAZrOpXQJpiGp50+sROef6Ed2iBP7xe9/IKxJ+9NZWoLe2Ao0fvwGgf1lw2NRU9LXUoKu6TOUC5eP5jWRi3kgm5o1kYt6U03yzGUxLgmn8UyNvBksYUr75fzBp5oLAY66ms2gt/QhtRz4ecouSYOJqPA1X42m1y1ANz28kE/NGMjFvJBPzppxe7QLU5nA41C6BNER23kyTbJjz0H9i0swFcHc0o2bncyj73QMo++03UbPzOXSfPR60jSbx/EZyMW8kE/NGMjFvymn+ymZNTY3aJZCGyMxbSGwSZn/nCYTYJqO38QxOPrsJ7rZGaa9P6uP5jWRi3kgm5o1kYt6U03yz6XQ61S6BNERW3qxJszDrgd/ANCka3WfKcfIvP4W3p0PKa9P4wfMbycS8kUzMG8nEvCmn+WW06enpapdAGiIjb5OuW4Q5//wkTJOi0X6iGMef3shGU6N4fiOZmDeSiXkjmZg35TR/ZXPfvn1ql0AaMtZ5i0q7CTP/6efQG81wHvwQVa8+DuHzjulr0vjF8xvJxLyRTMwbycS8Kaf5K5tpaWlql0AaMpZ5i73+dlz39f+A3mhGw94dOPXKr9hoahzPbyQT80YyMW8kE/OmnOavbEZERKhdAmnIWOVt8sp/QtKabwMAavKeR13+/4zJ69DEwvMbycS8kUzMG8nEvCmn+WaT980hmUY9bzodpn7xYdhvuhvC78eZN/+Apk/fGd3XoAmL5zeSiXkjmZg3kol5U07zy2h53xySaTTzpjMYMeNrP4X9prvh97px6uVfstGkQXh+I5mYN5KJeSOZmDflNH9ls7q6Wu0SSENGK296kwXXfWMzIudkwdfXg4oX/gOdFSWjcmwKHjy/kUzMG8nEvJFMzJtymm82XS6X2iWQhoxG3gzWCMz61q8QPn0ePF2tOPncT9FTc3IUqqNgw/MbycS8kUzMG8nEvCmn+WW0qampapdAGnKteTNFxiH1of9E+PR56HPWofyPP2CjSZfF8xvJxLyRTMwbycS8Kaf5K5t79uxRuwTSkGvJmyV+GmZ/5wmYo+LRU3cKJ597FJ6OllGsjoINz28kE/NGMjFvJBPzppzmr2xmZmaqXQJpiNK8hU1NRepDf4A5Kh6dVaU4/ucfsdGkYfH8RjIxbyQT80YyMW/Kaf7KpslkUrsE0hAleYuYvRTXfeMXMJhD0Xb0E5x6+Zfwe/rGoDoKNjy/kUzMG8nEvJFMzJtymm82CwoK1C6BNORq82ZbtALJX30UeoMRzcV5OP23JyH8vjGqjoINz28kE/NGMjFvJBPzppzml9GuWrVK7RJIQ64mb/HZX8LMf/o36A1G1Be8iurtW9ho0lXh+Y1kYt5IJuaNZGLelNP8lc2Kigq1SyANGWneEld/E4lfuB8AcPbdp9BQsH0sy6IgxfMbycS8kUzMG8nEvCmn+WaTaFzR6TH9Sz9A3A13QPh8qH79SbQU56ldFRERERHRVdP8MtqUlBS1SyANuVLedEYTrvv6vyPuhjvg9/Sh4qXNbDTpmvD8RjIxbyQT80YyMW/Kaf7KZn5+vtolkIZcLm/6ECtS1j+GiJQMeHu7UPH8v6GrulRydRRseH4jmZg3kol5I5mYN+VGdGWzqqoKhw8fRklJCYqKigAAW7ZswbFjx3Do0CG88cYbiIyMHNNCx0pOTo7aJZCGXCpvxvBozPmX/w8RKRlwd7Tg+J9+xEaTRgXPbyQT80YyMW8kE/Om3IiX0a5YsQIZGRmBm5ru3r0baWlpWLhwIU6cOIGf/vSnY1bkWPJ4PGqXQBry+byZoxOQ+vB/IixpFlzN51C+9RH01p9SqToKNjy/kUzMG8nEvJFMzJtyij+zuXv3bvh8/bdh+PTTTzFlypRRK0qm81dqiWS4OG+hCTOR+t3/giV2CrprTqL8jz+Eu7Vexeoo2PD8RjIxbyQT80YyMW/KjajZFEJg165dKC4uxoYNG4Zsf+CBB/D++++PenEyLF++XO0SSEPO5y08OR1zHvo9zBEx6KgowfE//y94u1pVro6CDc9vJBPzRjIxbyQT86bciL4gKDs7G3V1dYiLi8Pu3btRXl6Ojz76CADws5/9DF6vFy+//PKQ5yUmJqKyshJdXV0IDQ1FQUEBNm3aBIfDgerqarhcLqSmpmLPnj3IzMyEyWRCQUEBVq1aFbifTUpKCvLz85GTkwOPx4OioiIsX74c5eXlsFgsSE5ORl5eHhwOBzo6OlBWVoZly5ahtLQUNpsNSUlJge1OpxMVFRXIyspCSUkJEhMTkZCQAJvNBofDgYaGBtTW1iIjIwOFhYVISUmBzWYLPL+mpgZOpxPp6enYt28f0tLSEBEREdg+XuZkt9sD2zmn8TWnhIQEzL/1ywhd+R3AYEL38U+R0XsMU7JvmLBzCsb3KVjmlJCQAIfDEVRzCsb3KVjmZDKZkJubG1RzCsb3KVjm5HQ6kZubG1RzCsb3KVjmVFNTg9zc3KCa02i+T8MRVzM2b94sNm7cKACIb3zjG2Lfvn0iNDT0kvtu3br1qo6txrjxxhtVr4FDO+P6rz4slvxml1i6JV9Mv/tHAjq96jVxBO/g+Y1D5mDeOGQO5o1D5mDerjyu1PMNu4zWarUiPDw88Pvq1atRVlYGh8OBTZs24c4770Rvb+9whxm3kpOT1S6BNCI++274ltwDncGA2g9ewuk3fg8Iv9plURDj+Y1kYt5IJuaNZGLelBt2Ga3dbseOHTv6dzYasW3bNuTl5eHkyZMICQnB7t27AfR/SdBDDz00ttWOgby8PLVLIA0Im5qKqV/8FwDAmbf+fzTu3aFyRaQFPL+RTMwbycS8kUzMm3LDXtmsqqrCokWLsGjRIqSlpeHXv/41AGDWrFmYNm0aMjIykJGRMSEbTQBwOBxql0BBTmc0IfkrP4FOb0BUw2E2miQNz28kE/NGMjFvJBPzppziW58Ei46ODrVLoCCXtPpbCLVPR2/DaZhP/EPtckhDeH4jmZg3kol5I5mYN+VG9G20waysrEztEiiIhU2bC/vyL0P4fajevgVd+on7+WaaeHh+I5mYN5KJeSOZmDflNH9lc9myZWqXQEFKZzQHls/WF7yG7rPlzBtJxbyRTMwbycS8kUzMm3KabzZLS0vVLoGCVJLjmwiNn4behtOo3f3fAJg3kot5I5mYN5KJeSOZmDflNN9s2mw2tUugIBQ2fR7sN987sHz2CQivBwDzRnIxbyQT80YyMW8kE/OmnOabzaSkJLVLoCCjM5ox4ys/gU6vR/0/XkX32eOBbcwbycS8kUzMG8nEvJFMzJtymm82ed8cGm1Jax6AJW4qeuurUbv7xUHbmDeSiXkjmZg3kol5I5mYN+U032zyvjk0msKnz4f9pnsgfD5Ubd8C4fMM2s68kUzMG8nEvJFMzBvJxLwpp/lm0+l0ql0CBQm9KQTJXz2/fPav6Dl3fMg+zBvJxLyRTMwbycS8kUzMm3KabzYrKirULoGCRNKaB2CJnYKeulOo/eClS+7DvJFMzBvJxLyRTMwbycS8Kaf5ZjMrK0vtEigIhCenIz77bgifD9WXWD57HvNGMjFvJBPzRjIxbyQT86ac5pvNkpIStUugCU5vsiD5K/8KnV6Pug+3oafm5GX3Zd5IJuaNZGLeSCbmjWRi3pTTfLOZmJiodgk0wSWt/TYssUnoqTuFuvz/ueK+zBvJxLyRTMwbycS8kUzMm3KabzbtdrvaJdAEFj4jHfabLl4+673i/swbycS8kUzMG8nEvJFMzJtymm82ed8cUqp/+exPAGDY5bPnMW8kE/NGMjFvJBPzRjIxb8ppvtnkfXNIqaTbvgNLTCJ6aiuHXT57HvNGMjFvJBPzRjIxbyQT86ac5pvNhoYGtUugCWjSzIWwZ38Jfp8XVa8+Mezy2fOYN5KJeSOZmDeSiXkjmZg35TTfbNbW1qpdAk0werMFyff+GABQ//eX0VtXOeLnMm8kE/NGMjFvJBPzRjIxb8ppvtnMyMhQuwSaYKas3YCQmET01JxE3d+3XdVzmTeSiXkjmZg3kol5I5mYN+U032wWFhaqXQJNIJOuW4T47Lv6l8+O4NtnP495I5mYN5KJeSOZmDeSiXlTTvPNZkpKitol0ARx8fLZuvz/QW/dqas+BvNGMjFvJBPzRjIxbyQT86ac5ptNm82mdgk0QUy57UGE2Caju+Yk6q9y+ex5zBvJxLyRTMwbycS8kUzMm3LGkexUVVWFzs5O+Hw+eL1eZGZmIjo6Gq+++iqSk5NRXV2Nr3zlK2hraxvjckcf75tDIzEpJQPxy9bB7/Wg+tUtEH6fouMwbyQT80YyMW8kE/NGMjFvyo34yuaKFSuQkZGBzMxMAMCjjz6K/Px8zJ49G/n5+Xj00UfHrMixxPvm0HD0IaFI/vLA8tkPXkJv/dUvnz2PeSOZmDeSiXkjmZg3kol5U07xMtp169bhhRdeAAC88MILuOuuu0arJqlqamrULoHGuSm3/zNCbAnoPncC9f/46zUdi3kjmZg3kol5I5mYN5KJeVNuRM2mEAK7du1CcXExNmzYAACw2+2or68HANTX1yM+Pn7sqhxDTqdT7RJoHIuYtQTxN3yxf/nsduXLZ89j3kgm5o1kYt5IJuaNZGLelBvRZzazs7NRV1eHuLg47N69G+Xl5SM6eGJiIiorK9HV1YXQ0FAUFBRg06ZNcDgcqK6uhsvlQmpqKvbs2YPMzEyYTCYUFBRg1apVqKioAND/7U/5+fnIycmBx+NBUVERli9fjvLyclgsFiQnJyMvLw8OhwMdHR0oKyvDsmXLUFpaCpvNhqSkpMB2p9OJiooKZGVloaSkBImJiVi1ahWefPJJOBwONDQ0oLa2FhkZGSgsLERKSgpsNlvg+TU1NXA6nUhPT8e+ffuQlpaGiIiIwPbxMie73R7Yzjkpn9OM2XNxYsbtAICommLYDG4sz829pjnl5ubipZde4vvEOUmZU25uLvbs2RNUcwrG9ylY5nTHHXcgPT09qOYUjO9TsMxp6tSpSE9PD6o5BeP7FCxzCg0NRXp6elDNaTTfp+GIqxmbN28WGzduFOXl5SIhIUEAEAkJCaK8vHzIvlu3br2qY6sxpk+frnoNHONzTL/nR2Lplnwx9/tbBfT60Tkm88YhcTBvHDIH88YhczBvHDIH83blcaWeb9hltFarFeHh4YHfV69ejbKyMrz99ttYv349AGD9+vV46623hjvUuJSWlqZ2CTQORcxagrjr74Df60bV9i2A3z8qx2XeSCbmjWRi3kgm5o1kYt6UG3YZrd1ux44dO/p3Nhqxbds25OXloaioCNu3b8e3v/1tnDlzBvfee++YFzsWIiIi1C6BxhmDJQzTv7wRAFC7+0W4Gk6P2rGZN5KJeSOZmDeSiXkjmZg35YZtNquqqrBo0aIhjzudTnzhC18Yi5qk4n1z6POm3PEvCIm2o/tMOeoLXh3VYzNvJBPzRjIxbyQT80YyMW/KKb71SbDgfXPoYhGzMxGXdduoL589j3kjmZg3kol5I5mYN5KJeVNO881mdXW12iXQOGGwhCH5y/8LAFC76wW4Gkdv+ex5zBvJxLyRTMwbycS8kUzMm3KabzZdLpfaJdA4MeWOh2COikfX6aOo37N9TF6DeSOZmDeSiXkjmZg3kol5U07zzWZqaqraJdA4EJmahbistfB73Kgeg+Wz5zFvJBPzRjIxbyQT80YyMW/Kab7Z3LNnj9olkMoMljBMv6f/22drdj0PV9PZMXst5o1kYt5IJuaNZGLeSCbmTTnNN5uZmZlql0Aqm/rFh2GOjEXX6aNo2PO3MX0t5o1kYt5IJuaNZGLeSCbmTTnNN5smk0ntEkhFkanXIzZzDfyePlRtfwIQY7N89jzmjWRi3kgm5o1kYt5IJuZNOc03mwUFBWqXQCoxhIZj+sC3z9bkPY++pnNj/prMG8nEvJFMzBvJxLyRTMybcppvNletWqV2CaSSqV/8LswRseiqLkPDR69LeU3mjWRi3kgm5o1kYt5IJuZNOc03mxUVFWqXQCqInHsjYpeuHlg++9sxXz57HvNGMjFvJBPzRjIxbyQT86acUe0CJjqd3gC92QJ9SCj0ZgsM5lDoQ0JhMFugNw9+7PO/641mQAgAYuCHH4Do/7/P/y4w8Le/f9+B3/t3Ef3HGRiD/oaAuPh3/8BPnxfu9ma4W+vR11oPd2sDhNej0r+iXIbQSZh+z48AADU7n0Nf89gvnyUiIiIi0hpNN5vhMxdg8uJbEN1nhd480CBeoikMNJCmz28faBiDhLujGW5nA/pa6wZ+1qPPWQ93az3cbY0QPq/aJY6KaXd+F+aIGHRWlaLh4x1SXzslJQVFRUVSX5O0i3kjmZg3kol5I5mYN+U03WzO3vBb1BqMuO66NYqPIfw++Pp64Xe74He74HP3wj/wt8898Hhf74Xf3b0X9ve6AQA63cBqZp0O0Omh0wFA/+/QAbqLfgd00Okv7K/T6Qb27f+p0130u/6ibTrdwHH6h95ogikyHiG2BIRE22GOssMcEQtzRCzCk+dfYp5+eDqa0dfaALezflAj2tfaAE9bI4Tfp/jfUZbIeTciZsmt8LldqH5N3vLZ8/Lz86W+Hmkb80YyMW8kE/NGMjFvymm62ew8+RkSEhJQc6bqoqawvyH0uwd+v7hRvMTvQbP0VK/vbzZtCQiJTkCILQHm6IFGNDoB5qg4mKPiYY6KB2akD3m68PsuLMs934QGrpLWw93RDPivrrHTGU1DrzAPXH0+/7shJHTwcuXPbR/0u9kCQ4gVwPnlszWj8k93NXJycvDaa69Jf13SJuaNZGLeSCbmjWRi3pTTdLN58i8/xfy77sJHb76pdinq8/vhbmuEu60RXTg8ZLNOb4ApMu5CE2pLgDnajhDbZIRE22GKiEVItB0h0XZMmrlw6OF9XnjaGvuvjLY1AsCllysHmsJQ6PSGMZlqy8G/o3Gv3OWz53k8QfI/TtCEwLyRTMwbycS8kUzMm3KabjYBcP31CAm/r/+zm631l9yuMxj7r3wONKIh0Qkw2+wDPxNgjohFSEwiQmISR/yafo/78kuRBx673BVov8cVWK7cv7T5/PN6r/oK62hi3kgm5o1kYt5IJuaNZGLelNN8s7l8+XK88sorapcx4QmfF30ttehrqUXnJbbrjCaYo+wDy3LtEH7f55Ytj6+mcKwwbyQT80YyMW8kE/NGMjFvymm+2SwvL1e7BE0QXg/6ms9p/jYjzBvJxLyRTMwbycS8kUzMm3J6tQtQm8ViUbsE0hDmjWRi3kgm5o1kYt5IJuZNOc03m8nJyWqXQBrCvJFMzBvJxLyRTMwbycS8Kaf5ZjMvL0/tEkhDmDeSiXkjmZg3kol5I5mYN+U032w6HA61SyANYd5IJuaNZGLeSCbmjWRi3pTTfLN54403ql0CaQjzRjIxbyQT80YyMW8kE/OmnOabzZycHLVLIA1h3kgm5o1kYt5IJuaNZGLelNN8sxkeHq52CaQhzBvJxLyRTMwbycS8kUzMm3I6AGKsDv7666+jvr5+rA4/KuLi4tDU1KR2GaQRzBvJxLyRTMwbycS8kUzM25UlJCTgnnvuueS2MW02iYiIiIiISJs0v4yWiIiIiIiIRh+bTSIiIiIiIhp1mm02HQ4HysvLcfLkSWzatEntckgDqqqqcPjwYZSUlKCoqEjtcijIPPfcc2hoaEBpaWngsejoaOzatQsnTpzArl27EBUVpV6BFFQulbfNmzfj3LlzKCkpQUlJCdauXatihRRMpkyZgr///e84evQoysrK8MgjjwDgOY7GxuXyxnOcckJrQ6/Xi4qKCjFjxgxhMpnEwYMHxdy5c1WviyO4R1VVlYiJiVG9Do7gHDfffLPIyMgQpaWlgceeeOIJsWnTJgFAbNq0STz++OOq18kRHONSedu8ebPYuHGj6rVxBN9ISEgQGRkZAoAIDw8Xx48fF3PnzuU5jmNMxuXyxnOcsqHJK5tZWVmoqKhAVVUVPB4P/vrXv2LdunVql0VEpNhHH30Ep9M56LF169bhhRdeAAC88MILuOuuu1SojILRpfJGNFbq6+tRUlICAOjq6sKxY8eQlJTEcxyNicvljZTRZLOZlJSEs2fPBv4+d+4cQ0RjTgiBXbt2obi4GBs2bFC7HNIAu90euP1UfX094uPjVa6Igt33vvc9HDp0CM899xyXNNKYmD59OjIyMrB//36e42jMXZw3gOc4JTTZbOp0uiGPCSFUqIS0JDs7G0uWLMHatWvx3e9+FzfffLPaJRERjZo//elPuO6667Bo0SLU1dXhySefVLskCjJhYWF4/fXX8cMf/hCdnZ1ql0NB7vN54zlOGU02m+fOncPUqVMDf0+ZMgW1tbUqVkRaUFdXBwBoamrCjh07kJWVpXJFFOwaGhqQkJAAoP+Gy42NjSpXRMGssbERfr8fQgg888wzPMfRqDIajXj99dfx8ssvY8eOHQB4jqOxc6m88RynjCabzaKiIsyaNQvJyckwmUz42te+hrffflvtsiiIWa1WhIeHB35fvXo1ysrKVK6Kgt3bb7+N9evXAwDWr1+Pt956S+WKKJid/y/9APClL32J5zgaVc899xyOHTuG3//+94HHeI6jsXKpvPEcp5zq31Kkxli7dq04fvy4qKioED/72c9Ur4cjuMeMGTPEwYMHxcGDB0VZWRkzxzHqY9u2baK2tla43W5x9uxZ8cADDwibzSY++OADceLECfHBBx+I6Oho1evkCI5xqby9+OKL4vDhw+LQoUPirbfeEgkJCarXyREcIzs7WwghxKFDh0RJSYkoKSkRa9eu5TmOY0zG5fLGc5yyoRv4hYiIiIiIiGjUaHIZLREREREREY0tNptEREREREQ06thsEhERERER0ahjs0lERERERESjjs0mERERERERjTo2m0RERERERDTq2GwSERERERHRqGOzSURERERERKPu/wHbwdvDTD+jcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting validation accuracy graph\n",
    "plt.plot(history_val_acc_5)\n",
    "plt.title('Validation Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indirect 4.2: BatchNorm\n",
    "\n",
    "### Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeedforwardNeuralNetModel(nn.Module):\n",
    "    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim,\n",
    "                 dropout_pct = 0.0,\n",
    "                 initialize_weights=False,\n",
    "                 use_batchnorm=False):\n",
    "        super(FeedforwardNeuralNetModel, self).__init__()\n",
    "        # Embedding layer\n",
    "        self.embedding = nn.Embedding(input_dim, embedding_dim)\n",
    "        \n",
    "        # Linear function\n",
    "        self.fc1 = nn.Linear(embedding_dim*embedding_dim, hidden_dim)\n",
    "\n",
    "        # optional batchnorm\n",
    "        if use_batchnorm:\n",
    "            self.bn1 = nn.BatchNorm1d(embedding_dim * embedding_dim)\n",
    "        else:\n",
    "            self.bn1 = None\n",
    "            \n",
    "        # Linear function (readout)\n",
    "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "        if initialize_weights:\n",
    "            torch.nn.init.xavier_uniform_(self.embedding.weight)\n",
    "            torch.nn.init.xavier_uniform_(self.fc1.weight)\n",
    "            torch.nn.init.xavier_uniform_(self.fc2.weight)\n",
    "        self.dropout_pct = dropout_pct\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Embedding\n",
    "        embedded = self.embedding(x)\n",
    "        embedded = embedded.view(-1, embedding_dim*embedding_dim)\n",
    "        # Linear function\n",
    "        out = self.fc1(embedded)\n",
    "\n",
    "        if self.bn1 is not None:\n",
    "            out = self.bn1(out)\n",
    "            \n",
    "        # Non-linearity\n",
    "        out = torch.relu(out)\n",
    "        \n",
    "        # Toggle 3: Dropout (default used in original code: 0.8)\n",
    "        if self.dropout_pct > 0:\n",
    "            out = torch.dropout(out, p=dropout_pct, train=self.training)\n",
    "\n",
    "        # Linear function (readout)\n",
    "        # Take note here use a final sigmoid function so your loss should not go through sigmoid again.\n",
    "        # BCELoss is the right class to use as it doesn't pass your output through a sigmoid function again.\n",
    "        # In multi-class problems you're used to softmax which can be simplified to a logistic,\n",
    "        # function when you have a two-class problem.\n",
    "        out = self.fc2(out)\n",
    "        out = torch.sigmoid(out)\n",
    "    \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = num_words + 2\n",
    "embedding_dim = max_len\n",
    "hidden_dim = 32\n",
    "output_dim = 1\n",
    "dropout_pct = 0.8   # this is what was in original code\n",
    "\n",
    "# Instantiate model class and assign to object\n",
    "model = FeedforwardNeuralNetModel(input_dim, embedding_dim, hidden_dim, output_dim,\n",
    "                                  dropout_pct=dropout_pct)\n",
    "\n",
    "# Push model to CUDA device if available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "# Loss function\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "# Optimizer\n",
    "# Toggle 2: L2 Norm option - this is called weight decay\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=0.005)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 100 | Train Loss: 0.7014918327331543 | Val Loss: 0.6985331177711487 | Val Accuracy: 50.36\n",
      "Iter: 200 | Train Loss: 0.6943099498748779 | Val Loss: 0.6990044116973877 | Val Accuracy: 49.12\n",
      "Iter: 300 | Train Loss: 0.6955150365829468 | Val Loss: 0.6972861289978027 | Val Accuracy: 48.83\n",
      "Iter: 400 | Train Loss: 0.7005130052566528 | Val Loss: 0.6891350150108337 | Val Accuracy: 49.96\n",
      "Iter: 500 | Train Loss: 0.6931066513061523 | Val Loss: 0.6954178214073181 | Val Accuracy: 49.05\n",
      "Iter: 600 | Train Loss: 0.706365704536438 | Val Loss: 0.7008278965950012 | Val Accuracy: 49.45\n",
      "Iter: 700 | Train Loss: 0.6912071108818054 | Val Loss: 0.6961744427680969 | Val Accuracy: 53.68\n",
      "Iter: 800 | Train Loss: 0.6855694055557251 | Val Loss: 0.6926569938659668 | Val Accuracy: 51.19\n",
      "Iter: 900 | Train Loss: 0.6670827865600586 | Val Loss: 0.6924158930778503 | Val Accuracy: 50.07\n",
      "Iter: 1000 | Train Loss: 0.6761055588722229 | Val Loss: 0.6950595378875732 | Val Accuracy: 50.61\n",
      "Iter: 1100 | Train Loss: 0.672704815864563 | Val Loss: 0.6849959492683411 | Val Accuracy: 51.68\n",
      "Iter: 1200 | Train Loss: 0.6780836582183838 | Val Loss: 0.6829615235328674 | Val Accuracy: 54.51\n",
      "Iter: 1300 | Train Loss: 0.6800839900970459 | Val Loss: 0.677135705947876 | Val Accuracy: 54.76\n",
      "Iter: 1400 | Train Loss: 0.6737532019615173 | Val Loss: 0.6925638318061829 | Val Accuracy: 55.07\n",
      "Iter: 1500 | Train Loss: 0.7042357921600342 | Val Loss: 0.6779385209083557 | Val Accuracy: 55.67\n",
      "Iter: 1600 | Train Loss: 0.6938480138778687 | Val Loss: 0.6779572367668152 | Val Accuracy: 56.49\n",
      "Iter: 1700 | Train Loss: 0.70582115650177 | Val Loss: 0.6762129664421082 | Val Accuracy: 56.41\n",
      "Iter: 1800 | Train Loss: 0.6545699238777161 | Val Loss: 0.6673324108123779 | Val Accuracy: 56.68\n",
      "Iter: 1900 | Train Loss: 0.6349267959594727 | Val Loss: 0.6664167046546936 | Val Accuracy: 57.32\n",
      "Iter: 2000 | Train Loss: 0.6501169204711914 | Val Loss: 0.6614956855773926 | Val Accuracy: 58.21\n",
      "Iter: 2100 | Train Loss: 0.6702625155448914 | Val Loss: 0.6181214451789856 | Val Accuracy: 58.51\n",
      "Iter: 2200 | Train Loss: 0.6169635653495789 | Val Loss: 0.6103445887565613 | Val Accuracy: 59.01\n",
      "Iter: 2300 | Train Loss: 0.6238602995872498 | Val Loss: 0.6049401164054871 | Val Accuracy: 59.55\n",
      "Iter: 2400 | Train Loss: 0.6343780159950256 | Val Loss: 0.6130780577659607 | Val Accuracy: 60.32\n",
      "Iter: 2500 | Train Loss: 0.6239176392555237 | Val Loss: 0.6115425825119019 | Val Accuracy: 60.47\n",
      "Iter: 2600 | Train Loss: 0.577979564666748 | Val Loss: 0.5672942996025085 | Val Accuracy: 61.07\n",
      "Iter: 2700 | Train Loss: 0.7061911225318909 | Val Loss: 0.6172072291374207 | Val Accuracy: 61.37\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "num_epochs = 10\n",
    "history_train_acc_6, history_val_acc_6, history_train_loss_6, history_val_loss_6 = [], [], [], []\n",
    "best_accuracy = 0\n",
    "for epoch in range(num_epochs):\n",
    "#     print('-'*50)\n",
    "    for i, (samples, labels) in enumerate(train_loader):\n",
    "        # Training mode\n",
    "        model.train()\n",
    "        \n",
    "        # Load samples\n",
    "        samples = samples.view(-1, max_len).to(device)\n",
    "        labels = labels.view(-1, 1).to(device)\n",
    "\n",
    "        # Clear gradients w.r.t. parameters\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass to get output/logits\n",
    "        outputs = model(samples)\n",
    "\n",
    "        # Calculate Loss: softmax --> cross entropy loss\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Toggle 1: L1 norm, add to original loss\n",
    "#         fc1_params = torch.cat([x.view(-1) for x in model.fc1.parameters()])\n",
    "#         loss += 0.001 * torch.norm(fc1_params, 1)\n",
    "    \n",
    "        # Getting gradients w.r.t. parameters\n",
    "        loss.backward()\n",
    "\n",
    "        # Updating parameters\n",
    "        optimizer.step()\n",
    "\n",
    "        iter += 1\n",
    "\n",
    "        if iter % 100 == 0:\n",
    "            # Get training statistics\n",
    "            train_loss = loss.data.item()\n",
    "            \n",
    "            # Testing mode\n",
    "            model.eval()\n",
    "            # Calculate Accuracy         \n",
    "            correct = 0\n",
    "            total = 0\n",
    "            # Iterate through test dataset\n",
    "            for samples, labels in valid_loader:\n",
    "                # Load samples\n",
    "                samples = samples.view(-1, max_len).to(device)\n",
    "                labels = labels.view(-1).to(device)\n",
    "\n",
    "                # Forward pass only to get logits/output\n",
    "                outputs = model(samples)\n",
    "                \n",
    "                # Val loss\n",
    "                val_loss = criterion(outputs.view(-1, 1), labels.view(-1, 1))\n",
    "                \n",
    "                # We use a threshold to define. \n",
    "                # There is another way to do this with one-hot label. Feel free to explore and understand what are the pros/cons of each.\n",
    "                # This opens up a whole topic on why it becomes problematic when we expand beyond 2 class to 10 classes.\n",
    "                # Why do we encode? Why can't we do 0, 1, 2, 3, 4 etc. without one-hot encoding?\n",
    "                predicted = outputs.ge(0.5).view(-1)\n",
    "\n",
    "                # Total number of labels\n",
    "                total += labels.size(0)\n",
    "\n",
    "                # Total correct predictions\n",
    "                correct += (predicted.type(torch.FloatTensor).cpu() == labels.type(torch.FloatTensor)).sum().item()\n",
    "                # correct = (predicted == labels.byte()).int().sum().item()\n",
    "            \n",
    "            accuracy = 100. * correct / total\n",
    "        \n",
    "            # Print Loss\n",
    "            print('Iter: {} | Train Loss: {} | Val Loss: {} | Val Accuracy: {}'.format(iter, train_loss, val_loss.item(), round(accuracy, 2)))\n",
    "            \n",
    "            # Append to history\n",
    "            history_val_loss_6.append(val_loss.data.item())\n",
    "            history_val_acc_6.append(round(accuracy, 2))\n",
    "            history_train_loss_6.append(train_loss)\n",
    "            \n",
    "            # Save model when accuracy beats best accuracy\n",
    "            if accuracy > best_accuracy:\n",
    "                best_accuracy = accuracy\n",
    "                # We can load this best model on the validation set later\n",
    "                torch.save(model.state_dict(), 'best_model_6.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting loss graph\n",
    "plt.plot(history_train_loss_6, label='Train')\n",
    "plt.plot(history_val_loss_6, label='Validation')\n",
    "plt.title('Loss Graph')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation Accuracy')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting validation accuracy graph\n",
    "plt.plot(history_val_acc_6)\n",
    "plt.title('Validation Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\n",
    "    \"baseline\",\n",
    "    \"weight_init\",\n",
    "    \"L1_reg\", \n",
    "    \"L2_reg\",\n",
    "    \"early_stopping\",\n",
    "    \"dropout\",\n",
    "    \"batch_norm\"\n",
    "]\n",
    "val_accs = [\n",
    "    history_val_acc_0[-1],\n",
    "    history_val_acc_1[-1],\n",
    "    history_val_acc_2[-1],\n",
    "    history_val_acc_3[-1],\n",
    "    history_val_acc_4[-1],\n",
    "    history_val_acc_5[-1],\n",
    "    history_val_acc_6[-1],\n",
    "]\n",
    "plt.bar(labels, val_accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weights = torch.Tensor().to(device)\n",
    "# for param_group in list(model.parameters()):\n",
    "#     weights = torch.cat((param_group.view(-1), weights))\n",
    "#     print(param_group.size())\n",
    "    \n",
    "# # Toggle 0: No regularization\n",
    "# weights_nothing = weights.cpu().detach().numpy()\n",
    "\n",
    "# # Toggle 1: L1 norm on FC1\n",
    "# # weights_L1 = weights.detach().numpy()\n",
    "\n",
    "# # Toggle 2: L2 norm\n",
    "# # weights_L2 = weights.detach().numpy()\n",
    "\n",
    "# # Toggle 3: dropout\n",
    "# # weights_dropout = weights.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.hist(weights_L1.reshape(-1), range=(-.5, .5), bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.hist(weights_nothing.reshape(-1), range=(-.5, .5), bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Show weight distribution\n",
    "# plt.hist((\n",
    "#     weights_nothing.reshape(-1),\n",
    "#     weights_L1.reshape(-1),\n",
    "#     weights_L2.reshape(-1),\n",
    "# ), 49, range=(-.5, .5), label=(\n",
    "#     'No-reg',\n",
    "#     'L1',\n",
    "#     'L2',\n",
    "# ))\n",
    "# plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pDL] *",
   "language": "python",
   "name": "conda-env-pDL-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}