pvdbow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package reuters to /Users/richard/nltk_data...\n",
      "[nltk_data]   Package reuters is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /Users/richard/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "import re\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import reuters\n",
    "from nltk.tokenize import word_tokenize\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "nltk.download('reuters')\n",
    "nltk.download('punkt')\n",
    "\n",
    "PERCENTAGE_DOCS = 100 # random subsample of Reuters training docs\n",
    "VOCAB_SIZE = 10000\n",
    "REMOVE_TOP_K_TERMS = 100\n",
    "MIN_TERM_FREQ = 5\n",
    "\n",
    "TEXT_WINDOW_SIZE = 8\n",
    "BATCH_SIZE = 10 * TEXT_WINDOW_SIZE\n",
    "EMBEDDING_SIZE = 128\n",
    "SHUFFLE_EVERY_X_EPOCH = 5\n",
    "PV_TEST_SET_PERCENTAGE = 5\n",
    "NUM_STEPS = 100001\n",
    "LEARNING_RATE = 0.1\n",
    "NUM_SAMPLED = 64\n",
    "REPORT_EVERY_X_STEPS = 2000\n",
    "\n",
    "END_TO_END_EVERY_X_STEPS = 30000\n",
    "E2E_TEST_SET_PERCENTAGE = 30\n",
    "TSNE_NUM_DOCS = 400\n",
    "\n",
    "# Token integer ids for special tokens\n",
    "UNK = 0\n",
    "NULL = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext line_profiler\n",
    "%load_ext memory_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Returns an eternal generator, periodically shuffling the order\n",
    "\n",
    "l_ is a list of integers; an internal copy of it is maintained.\n",
    "\"\"\"\n",
    "def repeater_shuffler(l_):\n",
    "    l = np.array(l_, dtype=np.int32)\n",
    "    epoch = 0\n",
    "    while epoch >= 0:\n",
    "        if epoch % SHUFFLE_EVERY_X_EPOCH == 0:\n",
    "            np.random.shuffle(l)\n",
    "        for i in l:\n",
    "            yield i\n",
    "        epoch += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accept_doc(fileid):\n",
    "    return fileid.startswith('training/') \\\n",
    "            and np.random.random() * 100 < PERCENTAGE_DOCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accept(word):\n",
    "    # Accept if not only Unicode non-word characters are present\n",
    "    return re.sub(r'\\W', '', word) != ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def normalize(word):\n",
    "    return word.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_dataset():\n",
    "    fileid2words = {fileid:\n",
    "            [normalize(word) for word in word_tokenize(\n",
    "                    reuters.raw(fileid)) if accept(word)] \\\n",
    "            for fileid in reuters.fileids() if accept_doc(fileid)}\n",
    "    count = [['__UNK__', 0], ['__NULL__', 0]]\n",
    "    count.extend([(word, count) for word, count in collections.Counter(\n",
    "            [word for words in fileid2words.values() \\\n",
    "            for word in words]).most_common(\n",
    "                    VOCAB_SIZE - 2 + REMOVE_TOP_K_TERMS)[\n",
    "                            REMOVE_TOP_K_TERMS:\n",
    "                    ] if count >= MIN_TERM_FREQ])\n",
    "    assert not set(['__UNK__', '__NULL__']) & set(next(zip(\n",
    "            *count[2:])))\n",
    "    dictionary = {}\n",
    "    for i, (word, _) in enumerate(count):\n",
    "        dictionary[word] = i\n",
    "    reverse_dictionary = dict(zip(dictionary.values(),\n",
    "                                  dictionary.keys()))\n",
    "    data = []\n",
    "    doclens = []\n",
    "    fileids = []\n",
    "    for docid, (fileid, words) in enumerate(fileid2words.items()):\n",
    "        for word in words:\n",
    "            if word in dictionary:\n",
    "                wordid = dictionary[word]\n",
    "            else:\n",
    "                wordid = UNK\n",
    "                count[UNK][1] += 1\n",
    "            data.append((docid, wordid))\n",
    "        # Pad with NULL values if necessary\n",
    "        doclen = len(words)\n",
    "        doclens.append(doclen)\n",
    "        fileids.append(fileid)\n",
    "        if doclen < TEXT_WINDOW_SIZE:\n",
    "            n_nulls = TEXT_WINDOW_SIZE - doclen\n",
    "            data.extend([(docid, NULL)] * n_nulls)\n",
    "            count[NULL][1] += n_nulls\n",
    "    return data, count, doclens, fileids, dictionary, reverse_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data, count, doclens, fileids, dictionary, reverse_dictionary = \\\n",
    "        build_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of documents: 7769\n",
      "Number of tokens: 1023352\n",
      "Number of unique tokens: 10000\n",
      "Most common words (+UNK and NULL): [['__UNK__', 524065], ['__NULL__', 244], ('dollar', 1112), ('foreign', 1101), ('rose', 1096)]\n",
      "Least common words: [('59.3', 5), ('catches', 5), ('125.2', 5), ('akzo.as', 5), ('narrower', 5)]\n",
      "Sample data: [(0, 1194), (0, 98), (0, 102), (0, 4915), (0, 302)]\n",
      "Effective vocab size: 10000\n"
     ]
    }
   ],
   "source": [
    "print('Number of documents:', len(set(next(zip(*data)))))\n",
    "print('Number of tokens:', len(data))\n",
    "print('Number of unique tokens:', len(count))\n",
    "assert len(data) == sum([i for _, i in count])\n",
    "print('Most common words (+UNK and NULL):', count[:5])\n",
    "print('Least common words:', count[-5:])\n",
    "print('Sample data:', data[:5])\n",
    "vocab_size = min(VOCAB_SIZE, len(count))\n",
    "print('Effective vocab size:', vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7769.000000\n",
       "mean      131.691080\n",
       "std       138.805498\n",
       "min         2.000000\n",
       "25%        46.000000\n",
       "50%        87.000000\n",
       "75%       165.000000\n",
       "max      1321.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(doclens).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_text_window_center_positions():\n",
    "    # If TEXT_WINDOW_SIZE is even, then define text_window_center\n",
    "    # as left-of-middle-pair\n",
    "    doc_start_indexes = [0]\n",
    "    last_docid = data[0][0]\n",
    "    for i, (d, _) in enumerate(data):\n",
    "        if d != last_docid:\n",
    "            doc_start_indexes.append(i)\n",
    "            last_docid = d\n",
    "    twcp = []\n",
    "    for i in range(len(doc_start_indexes) - 1):\n",
    "        twcp.extend(list(range(\n",
    "                doc_start_indexes[i] + (TEXT_WINDOW_SIZE - 1) // 2,\n",
    "                doc_start_indexes[i + 1] - TEXT_WINDOW_SIZE // 2\n",
    "                )))\n",
    "    return doc_start_indexes, twcp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "doc_start_indexes, twcp = get_text_window_center_positions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_train_test():\n",
    "    split_point = len(twcp) * (100 - PV_TEST_SET_PERCENTAGE) // 100\n",
    "    twcp_train = twcp[:split_point]\n",
    "\n",
    "    # Test set data must come from known documents\n",
    "    docids_train = set([data[i][0] for i in twcp_train])\n",
    "    twcp_test = []\n",
    "    for i in twcp[split_point:]:\n",
    "        if data[i][0] in docids_train:\n",
    "            twcp_test.append(i)\n",
    "        else:\n",
    "            twcp_train.append(i)\n",
    "    if len(twcp_test) < (BATCH_SIZE // TEXT_WINDOW_SIZE):\n",
    "        raise ValueError(\n",
    "            'Too little test data, try increasing PV_TEST_SET_PERCENTAGE')\n",
    "    return twcp_train, twcp_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.shuffle(twcp)\n",
    "twcp_train, twcp_test = get_train_test()\n",
    "twcp_train_gen = repeater_shuffler(twcp_train)\n",
    "del twcp # save some memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective test set percentage: 48442 out of 968919, 5.0%\n"
     ]
    }
   ],
   "source": [
    "print('Effective test set percentage: {} out of {}, {:.1f}%'.format(\n",
    "        len(twcp_test), len(twcp_test) + len(twcp_train),\n",
    "        100 * len(twcp_test) / (len(twcp_test) + len(twcp_train))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del twcp_train # save some memory, we use twcp_train_gen from now on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def generate_batch_single_twcp(twcp, i, batch, labels):\n",
    "    tw_start = twcp - (TEXT_WINDOW_SIZE - 1) // 2\n",
    "    tw_end = twcp + TEXT_WINDOW_SIZE // 2 + 1\n",
    "    docids, wordids = zip(*data[tw_start:tw_end])\n",
    "    batch_slice = slice(i * TEXT_WINDOW_SIZE,\n",
    "                        (i+1) * TEXT_WINDOW_SIZE)\n",
    "    batch[batch_slice] = docids\n",
    "    labels[batch_slice, 0] = wordids\n",
    "    \n",
    "def generate_batch(twcp_gen):\n",
    "    batch = np.ndarray(shape=(BATCH_SIZE,), dtype=np.int32)\n",
    "    labels = np.ndarray(shape=(BATCH_SIZE, 1), dtype=np.int32)\n",
    "    for i in range(BATCH_SIZE // TEXT_WINDOW_SIZE):\n",
    "        generate_batch_single_twcp(next(twcp_gen), i, batch, labels)\n",
    "    return batch, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Input data\n",
    "dataset = tf.placeholder(tf.int32, shape=[BATCH_SIZE])\n",
    "labels = tf.placeholder(tf.int32, shape=[BATCH_SIZE, 1])\n",
    "\n",
    "# Weights\n",
    "embeddings = tf.Variable(\n",
    "        tf.random_uniform([len(doclens), EMBEDDING_SIZE],\n",
    "                          -1.0, 1.0))\n",
    "softmax_weights = tf.Variable(\n",
    "        tf.truncated_normal(\n",
    "                [vocab_size, EMBEDDING_SIZE],\n",
    "                stddev=1.0 / np.sqrt(EMBEDDING_SIZE)))\n",
    "softmax_biases = tf.Variable(tf.zeros([vocab_size]))\n",
    "\n",
    "# Model\n",
    "# Look up embeddings for inputs\n",
    "embed = tf.nn.embedding_lookup(embeddings, dataset)\n",
    "# Compute the softmax loss, using a sample of the negative\n",
    "# labels each time\n",
    "loss = tf.reduce_mean(\n",
    "        tf.nn.sampled_softmax_loss(\n",
    "                softmax_weights, softmax_biases, embed,\n",
    "                labels, NUM_SAMPLED, vocab_size))\n",
    "\n",
    "# Optimizer\n",
    "optimizer = tf.train.AdagradOptimizer(LEARNING_RATE).minimize(\n",
    "        loss)\n",
    "\n",
    "# Test loss\n",
    "test_loss = tf.reduce_mean(\n",
    "        tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "                tf.matmul(embed, tf.transpose(\n",
    "                          softmax_weights)) + softmax_biases,\n",
    "                labels[:, 0]))\n",
    "\n",
    "# Normalized embeddings (to use cosine similarity later on)\n",
    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1,\n",
    "                              keep_dims=True))\n",
    "normalized_embeddings = embeddings / norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "session = tf.Session()\n",
    "session.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_test_loss():\n",
    "    # We do this in batches, too, to keep memory usage low.\n",
    "    # Since our graph works with a fixed batch size, we\n",
    "    # are lazy and just compute test loss on all batches that\n",
    "    # fit in the test set.\n",
    "    twcp_test_gen = (i for i in twcp_test)\n",
    "    n_batches = (len(twcp_test) * TEXT_WINDOW_SIZE) // BATCH_SIZE\n",
    "    test_l = 0.0\n",
    "    for _ in range(n_batches):\n",
    "        batch_data, batch_labels = generate_batch(twcp_test_gen)\n",
    "        test_l += session.run([test_loss], feed_dict={\n",
    "                dataset: batch_data, labels: batch_labels\n",
    "            })[0]\n",
    "    return test_l / n_batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train(num_steps):\n",
    "    avg_training_loss = 0\n",
    "    for step in range(num_steps):\n",
    "        batch_data, batch_labels = generate_batch(twcp_train_gen)\n",
    "        _, l = session.run(\n",
    "                [optimizer, loss],\n",
    "                feed_dict={dataset: batch_data, labels: batch_labels})\n",
    "        avg_training_loss += l\n",
    "        if step > 0 and step % REPORT_EVERY_X_STEPS == 0:\n",
    "            avg_training_loss = \\\n",
    "                    avg_training_loss / REPORT_EVERY_X_STEPS\n",
    "            # The average loss is an estimate of the loss over the\n",
    "            # last REPORT_EVERY_X_STEPS batches\n",
    "            print('Average loss at step {:d}: {:.1f}'.format(\n",
    "                    step, avg_training_loss))\n",
    "            avg_training_loss = 0\n",
    "            test_l = get_test_loss()\n",
    "            print('Test loss at step {:d}: {:.1f}'.format(\n",
    "                    step, test_l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_labels():\n",
    "    most_common_class = collections.Counter(\n",
    "            [c for cs in [reuters.categories(fileid) for fileid in fileids] \\\n",
    "            for c in cs]).most_common(1)[0][0]\n",
    "    print('Most common class in sampled documents:',\n",
    "          most_common_class)\n",
    "    return (\n",
    "            np.array(\n",
    "                    [1 if most_common_class in reuters.categories(\n",
    "                            fileid) else 0 for fileid in fileids], \n",
    "                     dtype=np.int32),\n",
    "            ('other', most_common_class)\n",
    "            )\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common class in sampled documents: earn\n"
     ]
    }
   ],
   "source": [
    "e2e_labels, target_names = get_labels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_two_d_embeddings(embeddings):\n",
    "    num_points = min(TSNE_NUM_DOCS, len(fileids))\n",
    "    tsne = TSNE(perplexity=30, n_components=2, init='pca', \n",
    "                n_iter=5000)\n",
    "    return tsne.fit_transform(embeddings[0:num_points, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot(embeddings):\n",
    "    fig = plt.figure(figsize=(13, 8))\n",
    "    class_1 = e2e_labels[:embeddings.shape[0]].astype('bool')\n",
    "    plt.plot(embeddings[class_1, 0], embeddings[class_1, 1], \n",
    "             'o', color='purple')\n",
    "    plt.plot(embeddings[~class_1, 0], embeddings[~class_1, 1],\n",
    "             'o', color='lightgrey')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_e2e_train_test():\n",
    "    indices = np.array(range(len(fileids)), dtype=np.int32)\n",
    "    np.random.shuffle(indices)\n",
    "    split_point = len(indices) * (100 - E2E_TEST_SET_PERCENTAGE) \\\n",
    "            // 100\n",
    "    e2e_train =np.array([True if i in indices[:split_point] \\\n",
    "                         else False for i in range(len(fileids))])\n",
    "    return e2e_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e2e_train = get_e2e_train_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def classification_experiment(embeddings):\n",
    "    X = embeddings[e2e_train, :]\n",
    "    y = e2e_labels[e2e_train]\n",
    "    clf = svm.SVC(kernel='linear', class_weight='balanced')\n",
    "    clf.fit(X, y)\n",
    "    predictions = clf.predict(\n",
    "            embeddings[~e2e_train, :])\n",
    "    print(classification_report(\n",
    "            e2e_labels[~e2e_train],\n",
    "            predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def end_to_end(num_steps):\n",
    "    train(num_steps)\n",
    "    current_embeddings = session.run(normalized_embeddings)\n",
    "    plot(get_two_d_embeddings(current_embeddings))\n",
    "    classification_experiment(current_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def run():\n",
    "    for i in range(NUM_STEPS // END_TO_END_EVERY_X_STEPS):\n",
    "        end_to_end(END_TO_END_EVERY_X_STEPS)\n",
    "    end_to_end(NUM_STEPS % END_TO_END_EVERY_X_STEPS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 2000: 3.3\n",
      "Test loss at step 2000: 5.1\n",
      "Average loss at step 4000: 3.1\n",
      "Test loss at step 4000: 5.0\n",
      "Average loss at step 6000: 3.0\n",
      "Test loss at step 6000: 5.0\n",
      "Average loss at step 8000: 2.9\n",
      "Test loss at step 8000: 4.9\n",
      "Average loss at step 10000: 2.9\n",
      "Test loss at step 10000: 4.8\n",
      "Average loss at step 12000: 2.8\n",
      "Test loss at step 12000: 4.8\n",
      "Average loss at step 14000: 2.8\n",
      "Test loss at step 14000: 4.7\n",
      "Average loss at step 16000: 2.7\n",
      "Test loss at step 16000: 4.7\n",
      "Average loss at step 18000: 2.7\n",
      "Test loss at step 18000: 4.7\n",
      "Average loss at step 20000: 2.7\n",
      "Test loss at step 20000: 4.6\n",
      "Average loss at step 22000: 2.7\n",
      "Test loss at step 22000: 4.6\n",
      "Average loss at step 24000: 2.6\n",
      "Test loss at step 24000: 4.6\n",
      "Average loss at step 26000: 2.6\n",
      "Test loss at step 26000: 4.6\n",
      "Average loss at step 28000: 2.6\n",
      "Test loss at step 28000: 4.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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k+YseDaAeBADIjHWl48ANFoCvZYCUBgP1IABAZims4lKn9lJbi8cWZS9amZ1GUwenavlb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101d8a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      other       0.66      0.52      0.58      1486\n",
      "       earn       0.38      0.51      0.44       845\n",
      "\n",
      "avg / total       0.56      0.52      0.53      2331\n",
      "\n",
      "Average loss at step 2000: 2.5\n",
      "Test loss at step 2000: 4.5\n",
      "Average loss at step 4000: 2.5\n",
      "Test loss at step 4000: 4.5\n",
      "Average loss at step 6000: 2.5\n",
      "Test loss at step 6000: 4.5\n",
      "Average loss at step 8000: 2.5\n",
      "Test loss at step 8000: 4.5\n",
      "Average loss at step 10000: 2.5\n",
      "Test loss at step 10000: 4.5\n",
      "Average loss at step 12000: 2.5\n",
      "Test loss at step 12000: 4.4\n",
      "Average loss at step 14000: 2.5\n",
      "Test loss at step 14000: 4.4\n",
      "Average loss at step 16000: 2.4\n",
      "Test loss at step 16000: 4.4\n",
      "Average loss at step 18000: 2.4\n",
      "Test loss at step 18000: 4.4\n",
      "Average loss at step 20000: 2.4\n",
      "Test loss at step 20000: 4.4\n",
      "Average loss at step 22000: 2.4\n",
      "Test loss at step 22000: 4.4\n",
      "Average loss at step 24000: 2.4\n",
      "Test loss at step 24000: 4.4\n",
      "Average loss at step 26000: 2.4\n",
      "Test loss at step 26000: 4.4\n",
      "Average loss at step 28000: 2.4\n",
      "Test loss at step 28000: 4.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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kH+NkLcBGdnVOBAHWEQCgcqxIU61BJnemlBWPIfuYymgM0kQiCAgnVwDgnHu1pN+T9ApJ\nXtK/995POedeJunjkr5f0lcl/bz3/pmc54oaYEWaMPqd3JlSVtx69jGl0Zg8CJLixogOEEbeEYAX\nJL3Xe//XzrnvlfRXzrmWpKaklvf+o86590m6c/E/JI4VaWyLISteFOvZx5RGY7IiSAKAbHIFAN77\nJyQ9sfjn/+ace0zSNZLeLumti992n6QTIgCAWJHGOutZ8aJZzj6mNBqTFUESAGRT2BwA59yQpO2S\nvijpKu/92cUvnZV0VVGvg7gNuiINqmU9K56SlEZjsiJIAoBsCgkAFst//pOkW733/9zZKHvvvXOu\n65PswIEDy38eHh7W8PBwEaeTNOsTbAdZkQZhWM6KpyS10ZgsCJIApOrEiRM6ceJE5p93eRtQ59zF\nkh6Q9Cfe+99aPPYVScPe+yecc6+U1PbeX7/q5zyNd7G6TbCd3zavPYf2mAoCWnOtFSvSjO4bNXV+\n6A+TL8vXbrc1Nze3/PdGo8E17tBrDkDdR6347AFYbbE96HtYNFcA4BZS/fdJ+qb3/r0dxz+6eOzX\nnXN3Srrce3/nqp8lAChYc6SpoeNDa46fGTmjow8erf6EUFupdrxgT2pBEp89AN0MGgDkLQF6i6R/\nJ+lvnHPzi8feL+keSZ9wzr1bi8uA5nwd9IEJtqgKky9hRWola3z2ymG9fBYoWt5VgP5vSb1mb/50\nnn8bg2OCLarC5EukwGKpDZ+94rE/DVLETsA1wgRbVCVU+R5ZOqxWVifd6h4DlM4Wj/1pkCICgBpZ\naqg6J9ju3beXBgyFC7FCTaxZOotZZMvnNYgyO+lWS21YHap4lM8iRQQANbOrsct0Z8gCssj5hdgv\nIMYsndUsstXzGlSZnXSrpTbs1VE8ymeRIgIAJCXWLLJFVU++jDFLZzWLbPW8BlVmJz1EqU2/ozKp\nTXwuG+WzSBEBAJISYxYZC2LM0lnNIls9r0GV2UmvutSmLqMyMekMuC674TKdeuUpbb1oK+WzSAIB\nAJKyURa5DnXRdRVjls7qhE2r5zWoMjvpVZfa1GVUJhbsp4DUEQBEjFr2wa2XRQ6ZgSPw2FiMk9yt\nTti0el6DKruTXmWpjaVRmRTaIwIupI4AIFLUsmezXhY51AOBof/+xTbJ3eqETavnlUVd6uGtjMqk\n0h5ZCriAEAgAIkUtezbrZZH/tP2nQc6JTFS9We2gWj2vVFkZlUmlPbIScAGhEABEKsYVUazolUUO\n9UAgEwXAyqhMKu2RlYALCIUAIFIxrohiXagHApkoAJKNUZlU2iMrARcQiguY9fR1bmjKnkTVbQ7A\nqW2ntPeQ7UmR1rXbbc3NzS3/vdFoVDIBmNUoAFhAewTEabG/2fcQHgFACapqQFtzrRW17KP7Run8\nRypE4IHBpLAySr+4FvVGewTEhwDAgImJCTWbzTXHZ2ZmNDk5GeCMqkcHAXVCVvRF612L898+X9jS\nxLQhANC/QQMA5gCUIJVJVL2ksowc0pHKyij96HUtJj82qWf+n2cKWZqYNgQAysWM0RLUdWSjX706\nCJ1DykBMUg/qO/W6Ft/8xjd7Lk08qBTakHa7rYmJCe3fv18TExNqt9uhTwlAQhgBKFi73dY3v/lN\nffCDH9RVV12lG264QTt27EhqeTE6S/lR/mBL6kF9p17X4vnvPN/1eJalievehjDCASA0AoACLTXq\n+/fvXz72y7/8y3rooYf07ne/O5mGnc5SPnQO7GHN8Bf1uhZb/Jau359laeK6tyGUlAEIjQCgQN0a\n9bvuuiu5Rp3OUj50DuxhzfAX9boW53/2fPelifftHfg16t6G1H2EI3atuVZhk9kBqwgACkSjvoDO\nUj68j2yysEmTFetdi86liffu23hfkvXK3erahtR9hCNm3fbYyTqZfaPXIchASAQABaJRfxGdpex4\nHyFWuxq7BurEbFTuVtc2pO4jHDGbnZrtOZm9qA56VUEGsB5WASrQUqPeaXp6Wo1GI9AZIUa8j5CK\nFFb76Wbnzp266aabNDMzs/xfnUY4emnNtdQcaWpseEzNkaZac63Qp7SGP9c9AZNlMnsv6wUZQFUY\nAShQ3YetUQ3eR0hFyuVudR7h6CaWrLfb0v09mWUyey9VBBnARggACpZao45y8D5CKFXWJlPuVjyr\nSwhXUVpThN3ju3XkdDGT2XupIsgANkIAAAB9SGHSXtVZ2jrVwlvoeFteQjhP1rvKz97SvzvoZPZB\nVBFkABshAACADcRSvpBX1VnaupS7Wel4W15COGvWO8Rnb9DJ7Fn+fancIAPYCAEAAGwglvKFvELU\nJteh3M1Kx9vynIqsWe+6fvbKDjKAjRAAABWxUCKAbNbrGNepNIja5GysdLwtz6nImvVmwixQDgIA\nJK+KDpyVEoG6qarz3atj/PQ/P12r0iBqk7Ox0vG2PqciS9aboBQoBwEAklZVfWnIEoG6jjxUWRvc\nq2N83p/Xm06/acX3xlyeQG1yNlY63nWZU9GJoBQoBwEAklZVfWmoEoE6jzxUWRvcq2P8Bx/7g67f\nH3N5ArXJg7PU8a7DnIpOBKVAOZIOAOpUu9uPumaC86iqvjRUiYCVyYllqLo2uFvHeHZqtuv3Up6Q\nnrp1vC0hKAWKl2wAkMqyfkvqnAnOo6r60lAlAlYmJ5bBQm0w5QkAgBglGwDUdWmxXuqcCc6jqg5c\nqBIBK5MTy2Ch8015Qr2kNiqM6jESDyuSDQBSW1qszpngPKrswIUoEcgz8mC9M2Sl8015Qj2kNiqM\n6jESD0uSDQAslA9Uqc6Z4Lzq3IHLOvIQS2eozvcO1UptVBjVYyQeliQbAFgoH6iSlWXqUL0sIw90\nhpCa1EaFUT1G4mFJsgGAlfKBqlhapm4j1ktPUkBnCKlJbVQY1WMkHpYkGwBI6ZUPxLBMXSylJ3VH\nZyg7Jvn1z1Kwn9qoMKrHSDwsSToAgD2UntgQU2fIUoe7ykl+ln7vLKwF+6mNCqN6MY3Eo/4IAGAK\npScLQmdGY+kMhVhVY72Od1WT/OqwmojFYD+1UWFUL4aReKSBAACmUHpiJzMaQ2eo6lU1Nup4VzXJ\nrw6riRDsA0A46fSqEIUfevMP6dObP73i2NzmOV3/49cHOqPqrZcZxUrf+ta3uh5/5plnSnm9Xh3v\nubk5SdVN8qvDaiIE+wAQDiMAMOWxP39Mrzv/On1Gn5GTk5fX68+/Xl/5wldCn1plisyMxl4nvpFv\nfOMbXY8/8cQTpbzeRh3vqib51WE1kZjmmdRN6BJDAOERAESsjp07f87rOl2nIQ2tOP74c4+HOaEA\nisqM1qFOfCNbt27V1NSUxsfHl48dOnRIl19+eSmvt1HHu6pJfnVYTSSWeSYx6eeZYKXEEEBYBACR\niqVzN2imibKA4jKjdagT38hVV12lN77xjTp8+LAuuugiffe739Vb3vIWPfLII6W8Xj8d7yom+dVl\nNZEY5pnEot9ngsXJ1wCqRwAQqRg6d1kyTZQFFJcZrUOd+EZuvPFG3X///dq3b9/ysTIz4ZY63qwm\ngk79PhN6lRg++sVH1ZprEQQAiSAAiFQMnbssmSbKAhYUkRmtQ534RkJ0yEN2vGMt+4v1vJfEcP79\nPhN6jbJueWaLjtxKKRCQCgKASMXQucs6mZWygGLUoU68H6lkwnuVeMz/5by+9GdfMjuhM5ZyxV5i\nOf9+nwndRlkf0kN6jV6jodNDlAIBiSAAiFQMnTvr9fx1XwnDUrkK8utV4jH+i+N661++dfmYtQmd\nMZQrrieW8+/3mbD0vvjAzR/Q9zz9PfLyC53/xYUX2IcBSAMBQKRi6NxZrudPZSWMVLLjKehV4vFy\n//IVf7c2oTOGcsX1xHL+gzwTdjV2afZHZzV0fGjN16wkaACUiwAgYtY7d5br+VkJA7HpVeLxwnde\nWHPMUhY3hnLF9cR0/oM8EywnaACUjwAApbJaz1/kZltAFbqVeBz80EFd+fiVa77XUhY3hnLF9cR+\n/r1YTtAAKB8BAJJkfX4CsFq3Eo83v/nN+uyXPqtrT1+7fMxaFjeGcsX1xH7+67GaoKn7/CzAAhdq\neNM558t+7RiWbkMY3eYAnNp2SnsPkQFDXFpzrRVZ3NF9o7yHEa1ubfP8tnntObSH9zWwjsW+bt+T\nlnIHAM65o5Iakp703r9x8djLJH1c0vdL+qqkn/feP7Pq50oNAHot3VaXzA3ys9BxIkgFgBc1R5pd\nJyc/sPUBveFH38BoANDDoAFAESVA/0HSYUm/13HsTkkt7/1HnXPvW/z7nQW8Vt9iWboN4YQe/o5l\nfXEAqEqv+Vnf8/T3aOj4UPDV2kjaoC5yBwDe+88554ZWHX67pKWFqe+TdEIVBwCxLN2GdBGkAtnR\nEXtRnWrme83P8loIDEKu1kbSBnVS1iTgq7z3Zxf/fFbSVSW9Tk/Wlm7jYdWflK4TQaoddepApYCO\n2IvqtqfJejsVLwm1WhtJG9RJ6asAee+9c65rb/zAgQPLfx4eHtbw8HBhr2tp6TYeVv1J7TpZC1JT\nVbcOVAroiL2obnuadC5P+ugXH9WWZ7as2KlYCrdaG0kbWHLixAmdOHEi88+XFQCcdc5d7b1/wjn3\nSklPdvumzgCgaJaWbuNh1Z/UrpOlIDVldetApaCKjlgso5F13NNkaX7WUnA+dHpo+Wshl7klaQNL\nVifODx48ONDPlxUAfErSuyT9+uL/P1nS66zLyk65ZA36k9p1shSkpqyOHai6q2IJ6VhGI+u8p4m1\nzcpCJm0oU0TRcgcAzrljWpjwe4Vz7h8lfVjSPZI+4Zx7txaXAc37OjEja9CfIq9TLNk7K0Fqyurc\ngaqrsjtiMY1GdquZt7YZXB6hV2vrFCppM0iZYizPPoRXxCpAoz2+9NN5/+26KPJhVecPd1HXKabs\nXR3E/p6seweql5jvW9kdsZhGI61lyesuRNKm3zJFnn0YROmTgFHcw6ruH+6irlNM2bvYFfGeDN0R\nTbEDZaEtyXvfy+yIxTZqaylLjuL1W6bIsw+DIACoSBEPqxQ+3EVcp5iyd7HL+5600BGV0utAhW5L\nrNz3XpigD0v6LVPk2YdBEABEpKgPd90nE8WWvYtZ3vdk6I5oqkJ3FKzfdybow5J+yxR59mEQBADG\ndQ6TP/bYY7n/vRTWPCd7V528D5zQHdFUhe4oxHDfmaAPK/otU+TZh0EQABi2epj85MmT+shHPqIP\nfehDy98z6Ic7hTXPyd5VJ+8DJ3RHNFVFTrjPUsfPfU9D6Pk9dbL5pZu19fVbl6/l5peu7b7x7MMg\nCAAMWz1MvmPHDknSnXfeqeuvv17S4B/uVNY8J3tXjbwPHDJWYRTRUchTx899rz/r8zxiMsi15NmH\nfhEAGNZtmHzHjh165JFHNDk5me3fNLDmOVmhesnzwCFjVY1e837yXOc8dfzc9/qzPs8jJlxLlIEA\nwLAyhslDr3lOVgirkbEqV1nzfvLW8XPf6y2GeR6x4FqiDGx1adjSMHmn6elpNRqNzP/mrsYu7Tm0\nR2dGzujxtz6uMyNntPdQdWue98pkzM3NVfL6QGrWm/eTB3X8WA/vj+JwLVGGZEYAYlz6sqxh8pBr\nnpPJqBblVihr3g91/FjPRu8P2qb+8VlDGZIIAGJe+rJuw+RVZzJSfshQbgWpvHk/1PFjPeu9P2ib\nBsNnDWVwoYaWnHO+qtdujjQ1dHxozfEzI2d09MGjlZwDFvRq+MtozKp8LYsmJibUbDbXHJ+Zmck8\niRzx6ZYAObXtVKWlf0An2iageIuJzr7LLJIYAUhl6csYVJnJSH3lBMqtIPW/iRBQFdomILwkAgAL\nS1/iRVWVNaX+kGHiGJaEnPdThRjneKWszLYp5bJPYBBJBAChl75EGKl3gJk4hhTEPMerH3UMbspq\nm5hbAPSvtnMAVmcBrrnqGj3ymUeWh8BH941G34hifanPAZAWrkHnEquNRiOZ3x1pqPMcr27Bzfy2\nee05tCf651cZbRNzC5Ay5gCod8fv5vfdTOenALFkpGJdOaHI61u3VaQsoMRggZXrUOc5Xuvt4WCx\nzR1EGW1T6mWfwCBqGQCkPvmzTLEMt1vpnAwqluubKkoMFli6DiHmeJWRBOnWZvUKbh794qMaGx4z\nnYAJIfXGAhBCAAAgAElEQVSyT4tifRanoJYBAFmA8sSQkbLUORlUDNc3ZSQXFli6DlXP8SojSO/V\nZj11/ildp+vWfP+WZ7bouoevK+S164R5T7bE/CxOQS2XwSELUJ4Yhtt7dU46602tiuH6pozkwgJL\n12FXY5f2HNqjMyNn9PhbH9eZkTOl7nGwXpCeVa8262XbXqb5bfMrjj+kh/QD+oHCXrtOdu7cqZtu\nukkzMzPL/8VQ9llXMT+LU1DLEYC6ZgEsDKXFsKSqpc7JoGK4vikjubDA2nUoapnTftrYMoL0Xm3W\nFVdeoX976N8u7+Hw5b/5st7w9Bs0pKHCXrtumPdkR8zP4hTUMgCIdfLneqwMpcWwpKq1zskgYri+\nKatrcqEfnXXvT51/Svc+da9um7ht+euxX4d+29gygvT12qzO4KbXikckCGBRzM/iFNR2GdC6sbS8\nWWuutWJXUWtLqsa+/Kf16xuShVGwFJdW7Vb3/vAPPKx/8T/8C11x5RWS4r8O/bax3a7FqW2ncpUd\n9dtmlfHaQFlifxbHhmVAa8rSUJr1XUVjHwEKdX0tdK7XY2UULMUSg25172/9h7fqzGvPaPK+eqyv\n3m8bu/TZ7AzS9+7L1wHvt80q47WBssT+LK47AoBIMFoymBQ7aXlY6Vyvx9LKMyGEDNBSmJw+SBtb\nRpDeb5tlPQEDdOJZbBcBQCRSrj1G+YroXJfdQbU0Cla10AFaCpPTaWMBpIQAIBIMpaFMeTvXVXRQ\nUx4FCz36kcLkdNpYACkhAIgIQ2koS97OdRUd1JQztKFHP1KpPaeNtcv6HCUgNgQAAHJ3rqvooFrK\n0FbdGbEw+kHteT50YLMLXQLXC/cUMSMAAJC7c11VB9VChjZEZyTl0Y86sNqBjUXoErhuuKeIHQEA\nAEn5OtcpdVBDdEYsjX5gcBY7sDEJXQLXDfcUsSMAAGok1JB0Sh3UUJ0RC6MfyMZiBzYmFkrgVuOe\nInYEAEBNhB6STqWDarEzAtt4z+RjcYSRe4rYEQAANcGQdDUsdkZilNIESt4z+VgcYcx7T1tzLc1O\nzcqf83JbnHaP765kkn1KnzusjwAAWCXWBpIh6WL1ekBb7IzEJvRoVdV4z+RnbYQxzz1tzbV05NaV\n+2ocOX1EkkoNAlL73GF9BABAh5gbSIaki7PRA9paZyQ2KY5W8Z6xJ28WPus9nZ2aXdG2SNL209t1\n7PCxUgOAFD936M1MABBr1jVldbxnMTeQsZcZhBoS7ybUAzoVjFYhtFBZeEny57onay48d6HU1+Vz\nh04mAoCYs66pqus9C9lA5g2oYi4zCPkw7ibUAzoVjFYhtJBBvtvS/Tmz6dJNpb4unzt0MhEAxJx1\nTVVd71mVDWRnh/+f/umf9Oyzz+ruu+9e/nqWgCrWMgNrGfdQD+hUxD5aZV0dR2eLFjLI3z2+W0dO\nr0x4nNp2Snv37S31dfncoZOJAIBhqfhYv2dZH4BVNZDdRlCmpqZ08uRJ7dixQ1I9Aqp+Wcu4h3pA\npyLm0Srr6jo6W7R+g/wyShOXfv7Y4WO68NwFbbp0k/bu21t6soPPHTqZCABSHZaqKktTxutYvmd5\nHoBVNZDdRlDGx8d1+PDh5QAgJdYy7qEe0CmJdbTKOsujs5ZGJvoJ8sssTVz6+dmpWV147oJmp2YL\n+Xc3wucOS0wEACkOS1WVpSnrdSzfs7wPwCoayF4jKBdddFGpr2uVxYz7rsYuOvyIjtXRWWsjE/0E\n+WWWJlqb94T0mAgAUhyWqipLU9brWL5nVh+AnXqNoHz3u99d/rOVgKoKZNwXWMqQIk5WR2ctjkxs\nFOSXWZpobd4T0mMiAJDSG5aqqpNa5utYvWdWH4Cduo2g3HvvvXr++eeXgyorAVVVrGTcQ3XCrWVI\nESero7MxJGZWK7M00dq8J6THTACQmqo6qTF0hotm9QHYqdsIys0330xHL7CQnXCLGVLEx+robIzP\nojJLE63Ne0J6CAACqaqTGkNnOK9uqzTcdNNN5h6Aq1kdQYlJ0St0hOyEx5ghhU39tC1VjHR1fj6f\nOv+U7n3qXt02cdvy160/i8osTbQ47wlpIQAIpKosjdVsUFF6TaTac2iPJicnA54ZylbGJLqQnfAY\nM6SIUxUjXas/n9fpOj38jYc1eWFSV1x5haQ4nkVllSZanffEPKR0uFAPHeec54GHvJojTQ0dH1pz\n/MzIGR198Gj1J9ShjPWji1CXBr6Mez8xMaFms7nm+MzMTOkBZa9OWQydJMSlive55bYZ3dEGxW3x\nmd53FosRgIpY7QzGzupEqiqXeBvkvVWniaZl3PuQJXODjNbVJYhDGFWMdFltm5fwGVqLeUhpIQCo\nAOv9lsfqRKqqlngb9L1Vpwa+jHsfumSu39rtKoI4khb1VcXou9W2WapXIqRIzENKS/hPYgLW6wwi\nn93juzW/bX7FsVPbTml032igM1pQVfZr0PdWnRr4su79zp07NTk5ufxfrw5Ba66l5khTY8Njao40\n1Zpr5XrdfvUK4ubm5gp7jaXAcuj4kK57+DoNHR/SkVuPVPY7olxLI12dpqen1Wg0CnsNq22zVM1n\nKEaUZacliRGA0EN91odCLRn0XlmdSFVV9mvQ91adGviQ9z7kqF4VQVwqmxSFfjaEUsVIl9W2WapX\nIqRIKawaiBfVPgCwMNRneSjUkqz3ysoGUp2qWuJt0PdW3Rr4UPc+ZAe5iiAuhaSFhWdDSFUsQ2yx\nbZbqlQgpUugSSFSr9gGAhZpn1vvtj4V7VZSqsl/9vrc6M53PPvus7rnnHl199dWSaOCzCNlBriKI\nSyFpUaf2BoOpWyKkSOxPk47SAgDn3Nsk/ZakiyT9jvf+18t6rQ3OI8TLrmB5KNQSC/eqSFVkv/p5\nb/XKdDYaDRr6jEJ2kKvI0qWQtKhbe4P+kekGSgoAnHMXSfptST8t6euS/tI59ynv/WNlvN56rAz1\nWR0KtaTXvfrKV76iiYmJZOpzB7XRe4tMZ/FCd5DLztLlSVpkrauvuh6/imdDqnMMYkCmG6krawTg\nTZL+3nv/VUlyzv1HSe+Q1HcAUFTDyVBfPLrdq0OHDummm27Sjh07kqrPLRKZzuKlMKqXJWmRta4+\nRD1+2c+G1OcYALCtrADgGkn/2PH3r0n6sX5/uMiGk6G+eHTeq9OnT+sVr3iF3vKWt2jHjh2SyFpn\nZWUUbCOxZUsZ1Vsr62hTiFGqsp8NjLwBsKysAKCvHseBAweW/zw8PKzh4WFJxTecDPXFY+le7d+/\nX2NjY6FPpxZiGAUjW1oPWUebQo1SlflsKPt3YqO27GJLNqSEe9O/EydO6MSJE5l/vqwA4OuSXt3x\n91drYRRghc4AoBMlC/2r64cllqx1DGIYBSNbWg9ZP7d1/LyX+Tuxu3x2JBvs4t4MpjNxLkkHDx4c\n6OfLWrLipKTXOueGnHOXSPoFSZ/q94fr+DAow9KHpdlsamxsTM1mU/fff7/a7XboU8utip0qU9Lv\n7rahEPTXQ9bPbR0/72X+Tuwunx27ANvFvalWKSMA3vvzzrm9kv5UC8uA/u4gKwDFULJgQd6sqeXR\ngxiy1ihOykF/FZ/Dqj7rWT+3dfy879y5U/N/Oa+JPRO65JJL9Pzzz2ukMVLI75TCRm1lIdlgF/em\nWqXtA+C9/xNJf5LlZ+v4MChDng9LDENtzN1IR6pBfxWfw6o/61k/t3X7vLfmWvrsv/+sbjh9w/Kx\nz379s3rj69+Yu0wnhY3aypJysqFMRSQZuDfVMrsTcN0eBmXI82Gh5hqWpBr0V/E55LO+oOoRz/XK\ndPIGAKH3oYhZqsmGMhWVZODeVMtsAICN5fmwMNQGa1IM+qv4HPJZDzPiWXSZzuoA5if+t5/QI595\npLb7UJQl1WRDmYpKMnBvqhV1AJD6Mmh5PixlDLWlfj+AQVUx5M2wephRkCLLdHoFMDe/72Y6Rxmk\nmGwoU5FJBu5NdaINAFgGbUHWD0vRQ23cD2BwVQx5M6weZhSkyDId62VclheUQPlIMsQp2gCgzPrK\nIlhvEIsearN+P2Jl/X1kVZbrFmIEq4ohb4bVw3RQlt47xw4fy12mY7mMK4YFJVAukgxxijYAsLwM\nWiwNYpFDbZbvR6xieR9Zk+W6hRzBqmLIO/Vh9VAdlF2NXYW8fyxnWK2PTqC7IhMeJBniFG0AYHkZ\ntBQbRMv3I1ZW3kexjUJkuW6MYNVb7B0UyxlWy6MT6K6MhEfqSYYYRRsAWF4GLcUG0fL9iJWF91GM\noxBZrhsjWPUXcwfFcgBjeXQC3ZHwgBRxAFBkfWXRUmwQLd+PWFl4H1kZhRhEluvGCBassxrAWB6d\nCMX6qCkJD0gRBwBScfWVRUu1QbR6P2Jl4X1kYRRiUFmuGyNYQDaWRydCiGHUlIQHpMgDAKtoEFEE\nC+8jC6MQg8py3RjBArKzOjoRQgyjpiQ8IEku1APeOedj7FwAKemVzSKgBYC19u/fr7GxsTXHZ2Zm\nNDk5Wf0J9dCaa61IeIzuGyXhEbnFkrO+h+0ZAaiA9XpAoBcLoxCwiZ2/kar13vuxJDYp2QUBQMli\nqAdEfRURfNZ1eJ/APDt2/kaqNnrvW5i7BfSDEqCSTUxMqNlsrjlubTgwLzpT9lC+0xvXJp/mSFND\nx4fWHD8zckZHHzxa/QkBFennvd9utzU3N7f8tUajQbuC0lECZEyMq6gMKqZRjpQClRgmo4XCtckn\nzzKClA6Vi+tbrn7e+3UdNUW9EACULIVRjlg6UzEFKkU4e/aspqamtHnzZp0/f1433HCDduzYEfq0\nTEghMC9T1mUEKR0qF9e3fCyhibrgHVuypXrATtPT02o0GoHOqHixdKZ6BSqdQ7VFaM211Bxpamx4\nTM2RplpzrUL//X60222dP39e4+Pjes973qPx8XF9/vOf18mTJys/F4tSCMzLtHt8t+a3za84dmrb\nKY3uG13359bbgRT5cX3Ll/W9D1jDCEDJUlhFJZbOVBWBipUM3AMPPKC77rprxbHx8XHdcccduv32\n2ys7D6uYqJdP1n0T2IG0XFzfbAYpm2LPENQFAUAF6l4PGEtnqopAZb0MXJUPiF7Bzqte9ap134up\nzJFIITAvW5ZlBIssn6DWfS3KUwaXJWkTaglN3vMoEgEAcoulM1VFoGIlA9cr2Ln88st7/kxqcyTq\nHphbVNQOpFZG2qxhh9fBWUnabIT3PIpGAIBCxNCZqiJQsZKByxLsxDKZu87qPgJTVPlELJ22TlVk\nbze/dLMuu+Eyndx2Uue+fU4v0Uu0907KU9ZjJWmzkRjf87CNAABJKTtQsZKByxLsxDKZu65SGYEp\nonwilk7bkiqyt0vvn9vveHGOz/T0tDa/lMf8eqwkbTYS23se9tEyZFT3TB2ysTRBbNBgJ5bJ3HXF\nCEz/Yum0Lakie8v7JxsrSZuNxPaeh30EABmkkqlDNqEmiOUVy2Tuugo9AhNTUiOWTtuSKrK3od8/\nsbKUtFlPbO952EcAkAGZFtRRLJO587La0Q05AhNbUiOWTtuSKrK3od4/dViZJoakTWzvedhHAJAB\nmRYUzUKn1MI5lC10R3e9axxyBCbGpEbITtugn5Uqsrch3j+sTFOd1YHW6L5RrjFyIQDIgFppZNGr\n0xC6U7p0bqHPoQohO7obXeOQIzAkNfqX5bNSRfY2xPuHlWmqQaCFMhAAZECtNPq11Ok/e/aszp8/\nv2J33qVOg4Xsq4VzqELIjm4/1zjUcrokNfqX9bNSxYhF1e8fVqapBoEWykAAkEEqtdLIpzNTODU1\npfHx8RVfX+o0WMi+WjiHKoTs6Fq+xhslNVIoD+uX5ftYNVamqQaBFspAAJBRDBtfIazOTOHmzb0/\nahayrxbOoQohR+8sX+P1khqplIf1y/J9rBor01SDQAtlIAAAStKZKTx//nzP77NQUmbhHKrQ7+hd\nGRlv69e4V1IjlfKwflm/j1ViZZpqEGihDAQAWIGh/uJ0ZgpvuOGGNWVAS50GCyVlFs6hKhuN3pWV\n8Y71GlPyslKs97EsMSyhGTsCLZTBhRrOdM752IdS69ZZ7tXxSfnhlsfq63ny5El94hOf0Kte9Spd\nfvnlajQaQa9rHdbvLsPExISazeaa4zMzM5qcnAxwRmFxPQDAvsW+aN8ZG0YAMoqpLrbfjh5D/cXq\nlim8/fbbTVxLlpXrjYz3SpS8oGp1S64BFhEAZBRLZ3mQjl7ZHZ8UG3Wrk8VZVq632Ecmi0bJC6oU\nU3INiBkBQEaxZAkH6eiV2fGpqlFPMcjIgmXleiPjvZbVQBb1E0tyDYgdAUBGsWQJB+noldnxqaJR\nJ3PUP5aV642Md/EIzNGvWJJrQOwIADKKJUs4SEevzI5PFY06maP+sazc+sh4F4fAHIOIJbkGxI4A\nIKNYsoSDdvTK6vhU0ainnDkaNMPKsnKoSq/AfPJjk+baS4QXS3INiB0BQA4xZAmtdPSqaNRTzRxl\nzbCyfjeq0CswP/v4WbXmWrwHRYlUp1iSa0DsogoAaCSzsdDRq6JRTzVzROkTLOsVmG95agurTokS\nqW5iSK4BsYsmAKCRjF8/jXqezalSzRylXPoE+2688UYd/NBB3f2Ru5eP/crEr+jKx6/UC1e8EPDM\nbCCABxBCNAEAjWT9FbE5VYqZo1RLn5BN1SOpO3fu1G9+6Dd11zvv0sUvuVgvfOcFXfn4lbr229fq\nzKVnSnvdWBDAAwghmgAgTyNJ6VAc2Jwqm1RLnzC4UCOp+96/T0duPaI3fPkNy8dYdWoBAXzc6F8g\nVtEEAFkbSUqH4sHmVNmkWvqUp1wsVaFGUq0sRmARAXy86F8gZtEEAFkbSUqH4sHmVNmlVvpURLlY\nikKWm1hYjMCiVAP4WKyX4ad/gZhFEwBkbSSpr4wHm1OhX5SLZUO5iU2pBfCx2CjDT/8CMYsmAJCy\nNZKxPfBSriekTAD9olwsG8pNgP5tlOGPrX8BdIoqAMgipgee9XrCKmquKRNAN6vfe//1//uvuk7X\nrfk+ysXWR7kJNpJyEmq1jTL8MfUvgNVqHwDE9MCzXE9IzTVC6fbe+/rVX9dnrv6MfvKJn1w+RrlY\nfzpHUltzLf3ePb+n+w7ex0RqmE9CVW2jDH9M/QtgtdoHAFI89ZWW6wmpuUYo3d57b3niLTr1L0/p\nzA+foVwsI4J6rGY5CRVCPxn+WPoXwGpJBACxsFxPSM01Qun13tv6fVt19MGjFZ9Nd9bLJrqV7xHU\nYzXLSagQyPCjzggADLFcT8gSnfUS0xr61t971ssmemX6n7/0eQ1paM33E9Sny3ISKhQy/KgrAgBD\nLGcbWKKzPmIr/bD+3rNeNtEr0z/38rmu328lsEqNhaC8yiSU9VEzoO4yBwDOuX8j6YCk6yX9qPf+\nVMfX3i/pFyV9V9K49/54zvNMhtVsA0t01kdspR/W33vWyyZ6lVBd/cqrNX/5vNnAKiVWgvKqklBV\nj5oRbABr5RkB+JKkn5P0f3YedM69TtIvSHqdpGskPeSc+0HvPePKkWOJznqIcT6H5fee9bKJXiVU\nV1xzhUb3jZoNrFJiKSivIglV5aiZ9RI9IJTMAYD3/itS1+zXOyQd896/IOmrzrm/l/QmSV/I+lqI\nA1mWlaxeD+s19bGxPHdHWr+Eqt/Ayup7uSpl//4xBuV5VDlqZr1EDwiljDkAr9LKzv7XtDASgBoj\ny7KS5ethvaY+q1CdVMtzd6T8JVSW38tVqOL3Ty0or3LUzHqJHhDKugGAc64l6eouX/qA9/6PB3id\nrp/2AwcOLP95eHhYw8PDA/yTsIQsy0q9rsdv/MZvBM+kWq+pzyJ0J9Xq3J0leUqoiv5sxzaaUEXb\nVtegvJcqR82sl+gBWZ04cUInTpzI/PPrBgDe+yxPjK9LenXH369dPLZGZwCAuJFlWanX9Xjqqac0\nMTGx/PdQmVTLNfVZEICWp8jPduhALYsq2rY6BuXrqXLUzHqJHpDV6sT5wYMHB/r5okqAOlvIT0ma\ndc7dq4XSn9dK+ouCXgdGkWVZqdf1uPrqlQNqdFKLQQDav0Ez8EV+tmMM1Kpq2+oWlG+kqlEz6yV6\nQCh5lgH9OUlTkq6QNOecm/fe/4z3/lHn3CckPSrpvKT3eHqHtUeWZaVu1+MjH/mIfuZnfibgWdUX\nTUx/smTgi/xsxxio1alti638qijWS/SAEPKsAvRHkv6ox9d+VdKvZv23ER+yLCt1ux6bN2/Wjh07\nAp1RvdWpk1amLBn4Ij/bMQZqdWnbYiy/AlAeF6pBds4xMICk9HoAx9iZsKjdbmtu7sXdbRuNBtd1\nlf3792tsbGzN8ZmZGU1OTpb++nwGwpmYmFCz2VxzvKp7D6BciyN7fQ+zlrEMKGR7qNXyudVZiExi\na66l2alZ+XNebovT7vHdta0ztjjMb+2zFjrpUpdseoxiLL/qZO2zBMSOAKAElodaLZ9bCqrspLbm\nWjpy68qlBY+cPiJJtQ0CLLn3o/fqz7/w57r7l+5ePrbRZ63sTo6FUimLgVoKQgd/efDcAopHCdAA\n+s2mWh5qtXxuKFZzpKmh40Nrjp8ZOaOjDx6t/oQS0ppr6dcO/pqmjk6t+Vqvz1pV5TExlEqR7S1e\nzOVXPLeAjVECVJJBsqmWh1otnxuK5c91D7AvPHeh4jNJz+zUrF7uXz7Qz1S1RKb1DDzZ3nLEXH7F\ncwsoHgFAn2anZld0/iVp++ntOnb42JoAwPLIhuVzW40sYD5uS/eH5qZLN1V8Junx57ye/87zA/0M\nnZwFMe4VEAvrwV8vMT23gFjQE+jTINnUpTrbTtPT02o0GqWc2yAsn1unpSxgs9nU2NiYms2m7r//\nfrXb7dCnFo3d47s1v21+xbFT205pdN9ooDNKh9vi9IrHX6Ff3b9yNeSDHzrY87NGJ2eB9UCo3W5r\nYmJC+/fv18TEBG1SBWJ5bgExYQSgT4NkUy0PtVo+t05kAfNbGpk6dviYLjx3QZsu3aS9+/YyAbgC\nu8d368jpI7ryc1fqrnfepYtfcrG+6b6pG2/qPYplYYKuBZYDIcqTwojluQXEhEnAfeo2B+DUtlPa\ne4gOVRlCr1cO5NWaa60Ivkb3jW7YVsQwQbdsliervut/eZduv+P2NcdplwCExiTgkpBNrVZMwWEv\nKa3Bb4G1672rsWvg14+1RrtIVrO9rbmWnvzqk0HPAQCKQgAwgCwPdGQTezkEa/BXq9v1/vDnPqz5\nO+Z1x4E7Ap4ZsrAYCM1OzeqSpy4JfRoAUAhKgCKS2qo4MZdDsAZ/tXpd70++5JP66P/1UYIu9K1X\nOzs2PKaL//JiPfs/PqsPTH5g+fsP3nVQ77n1PdG0TehPas9bxI8SoJpKcfKZxSxgv1iDv1q9rvdl\n37ms61K9QDfrtbNui9O1375WX/vc15Yndr/wnRfkt/po2yl0l+LzFulhGdBI9FoVpzNDDjtYg79a\nva63lyfoQt/Wa2eXltW99tvXavuXt+sNJ98g/5zX3jv3BjrbMFJYBpXnLVLACEAkrK+NHbMyhnqX\nloFcs2rUvrQ6C1XZPb5bH/7ch/W277xt+dhDekiv0WvkLuWzg/6s186yEEQ6mXGet0gBAUAk+p0v\nQd3iYMp6oNFZqNauxi7N3zGvYx89psu+c5m8vF6j1+ipbU8RdKFvG7WzoRaCsLLCVSr7szA/ESkg\nAIhEP6vipJKdKdKgD7RBAixWjarWHQfu0PYf3b4cdLlLHUEXBmJx9TFLK4rlyYzHlJyy+D4AikYA\nEIl+1sZOJTtTpEEeaARY9hF0IQ+LexDMTs2u6PxL0vbT24NMbs+aGY+t7bT4PgCKRgAQkY1WxaFu\ncXCDPNAIsID6s7b6mKUVxbJmxmNsO629D4CiEQDUCHWLgxvkgUaABaBqllYUy5oZj7HtjKlkCciC\nAKBGqFsc3CAPNEsBlpVJgUA3vD+LY21FsSyZcUttZz9iK1kCsiAAqBHqFrPp94FmJcCyNCkQWI33\nZ7HqsKKYlbazXzGWLAGDcqEic+ecjy0rALTb7RWbwTQajcofCM2RpoaOD605fmbkjI4+eFQSw9cI\np5/3J9Jjoe3s1/79+zU2Nrbm+MzMjCYnJ6s/IaAPi8/7vuvtGAFAlEKVGFiYGLbRpECGrxGSpUmr\nEuVIVlhoO/tFchIpIABAdFIvMdhoUmAMw9eMUNSXpUmrqbcVyCa2kiUgCwIAmLReB9HSutghbDQp\n0NKKG93uoyRGKGrM0qTV1NsKZMN8OqSAAADmbFTCYq3EoGobTQq0Mnzd6z4+++yz2r9//4rvtTZC\nUVd5R176+XlLk1ZTbyuQXUwlS0AWBAAwZ6MSFkslBqGst+OtleHrXvfxwx/+cKXngQV554YM8vNW\ndmSuuq1gvgGAWBAAwJyNSlgslRhYZGX4utd9PHfuXKXnsZFU5iPknRsSw9yS1apsK5hvUB+ptAlI\nGwEAzNmohMVSiYFVFoave93HrVu3mhihkNJaMSnv3BBLc0v6VWVbwXyDekipTUDaCACwoaqzIf2U\nsFgpMUBvve7ju9/9bknhRyikOLPaq/X7+cw7N8TK3JJBVdVWVD3fgHKjctShTQD6QQCQuI0eIiGy\nIVZKWJDPRvfRwv2MMavdaZDPZ965IVbmllhV5XwDyo3KE3ubAPSLACBh/TxEQmVDLJSw9EJ9aP8s\n30cp3qz2kkE+n3kDawLz9VU534Byo/LE3iYA/SIASFg/DxGyIStRH1ovsWe1B/185g3IrAd0IVU5\n3yDl5U3LLn2KvU0A+kUAkLB+HiJkQ1aiPrReYs9q8/m0par5BqkuhVxF6VPsbQLQLwKAhPXzECEb\nshIjIvUTc1abz2eaUl0KuarSp5jbBKBfBAAJ6+chQjZkJTKusITPZ5pSXQo55dInoGguVIfGOefp\nTIXXmmuteIiM7hut/UMkj15zALp1upgsXC6WQQTS0hxpauj40JrjZ0bO6OiDR6s/IcCQxb5G32UK\njO/wV64AABI9SURBVAAkjvX0B9NvxpXJwuViGUSgPFaTF2/8qTfqgacf0Mv9y/X8d57XKx5/hZ58\n5ZO1L30CysAIAFCCiYkJNZvNNcdnZmY0OTkZ4IzqhUwgUI5BRjlDn9fBDx/Um3/8zbrtjtuCnRdg\nxaAjAPVeMgBBteZaao40NTY8puZIU625VuhTqgyThctFLTBQjl4rnc3NzQU6owXdzuvuX7pb/+XJ\n/xLojIC4UQKEUqReosHoVrksLoPInIRiWC0/SYXV5IXV8wJiRQCAUqS+UyXLM5bL2jKIqQS8ZXfO\ns86dCRV8xRz09bqXVpMXVs8LiBUBAEqReomGpeUZ65hRtbYMYgoBbxUT27NstBcq+Io56FvvXlpN\nXlg9LyBWBAAohcUSjapZ2EymzqsRWVrBKoWAt4pdsLOUeYQKvmIO+ta7l0uLFFhIXnSylFQB6oAA\nAKWwVqKRqio6bUgj4K2iBjtLmUeo4CvmoG+je2khedGN1fMCYlSfpxNM2dXYpT2H9ujMyBk9/tbH\ndWbkjPYeqv9OldYwca4au8d3a37b/Ipjp7ad0ui+0UBnVLwqarCXyjw6TU9Pq9Fo9PyZUMFXzEEf\n9fQAGAFAaYoq0Yh5ol0V1rs+POirYW1OQhmqqMHOUuZR1GjjoO1MzKOc1NMDYCMwmNZtot38tnnt\nObSnVp2rrDa6PlY39UGc2u32ivXgG42GifdRa661Ivga3Tc6UPuQtZ3J+7ohWb2XALIZdCMwAgCY\nxo6v6+vn+vCgX8BIEnqhnQEQu0EDAEqAYFrME+2q0M/1YeJc3Es2ZlHHpV/LRDsDIDUEADAt5ol2\nVeD69CfmJRsHVeelX8vC5whAamjdYFosq6u0221NTExo//79mpiYULvdruR1Y7k+nVpzLTVHmhob\nHlNzpKnWXKv010wpw9tr6dfOMjCsFOPnCADyYAQApsWwukrIjGsM16dT0aU4/Za6pJThLXrp1xTK\niWL7HAFAXgQAMM/Sjq/dhN5sy/r16VRkKc4ggVfRSzZanlBc5OIKKZUTxfQ5AoC8Mqe/nHMfc849\n5pz7z865P3TOXdbxtfc75/7OOfcV59y/KuZUAZvYbKt/RZbiDFLqUuTGdEujGEPHh3Tdw9dp6PiQ\njtx6pJJSpn5k2UyrF8qJAKCe8owAHJf0Pu/9BefcPZLeL+lO59zrJP2CpNdJukbSQ865H/Te16/Y\nFhCbbW2ks4TkG/4buvilF+vab1+74nuylOIMGngVleG1PqE4y2ZavRDcAkA9ZQ4AvPed6a4vSvqf\nF//8DknHvPcvSPqqc+7vJb1J0hcynyVgGLtq9ramhGRMuvv9d0uf0XIQkLUUJ1TgFcOE4qKWfiW4\nDSOFeRcAwipqDsAvSjq2+OdXaWVn/2taGAkAaqnIjGvddCshOfhrBzWxd0Iv6IVcky1DBV4pTSgm\nuK1eSvMuAISzbgDgnGtJurrLlz7gvf/jxe/5oKTnvfez6/xTXdNIBw4cWP7z8PCwhoeHNzhdwCY2\n2+quVwnJG3e8UZOTk7n+7VCBV9ETii0juK1e6EUFsrI8MR6ooxMnTujEiROZf37dAMB7v+6n1zk3\nJulnJf1Ux+GvS3p1x9+vXTy2RmcAAKB+yi4hCRF4pbZkJMFttWKcd5HaTtuABasT5wcPHhzo5zOX\nADnn3ibpdklv9d4/1/GlT0madc7dq4XSn9dK+ousrwMgXnUtIWHJSJQlxnkX1ifGA1grzxyAw5Iu\nkdRazFj8uff+Pd77R51zn5D0qKTzkt7jY2zRAORGCQmWMLG1PzEGzTFMjAewUp5VgF67ztd+VdKv\nZv23AdQHJSRgYmv/YgyaU5oYD9SFC5Wcd84xMAAAEcmaxZ+YmFCz2VxzfGZmJvdkcITXbQ7AqW2n\nMm+2B2Bwi+1y35OIiloGFABQY3my+DFObEX/UpsYD9QBAQBQM9RaVyuV651neUpGe+uPifFAXAgA\ngBqh1rpaKV3vPFn8GCe2AkCdEQAANRLrJkKxSul658nixzixFQDqjAAAqBFqrauV0vXOm8VnNSgA\nsIMAAKgRaq2rVeT1bs21NDs1K3/Oy21x2j2+21RNNVn8tKUy1wVIBQEAUCPUWlerqOvdbRnFI6eP\nSJK5IIBOX3pSmusCpIJ9ABJW14xObL9XUee7lEH+5jPf1LlLz+nabddq68u2qtFomP79Y9dutzU3\nN7f89yzXuznS1NDxoTXHz4yc0dEHj+Y9RSAX9nEA7GMfAPSlrhmd2H6vos63M4M8pCFJ0vzX5/XO\nQ+80+XvXSRFZcX+uezLkwnMXcv27QBFSmusCpIJ9uhPVa/WSzkxmjGL7vYo639mp2RXlI5K0/fR2\nHTt8LPc5onxuS/cO1qZLy2ui2+22JiYmtH//fk1MTKjdbpf2Wogbo/VA/RAAJKquGZ3Yfq+izpcM\nctx2j+/W/Lb5FcdObTul0X2jpbze0shTs9nU2NiYms2m7r//foIAdLU016XT9PS0Go1GoDMCkBcl\nQImqa0Yntt+rqPMNkUFGcZYm+h47fEwXnrugTZdu0t59e0ubAJzS/gXIjxWggPohAEhUXVeLie33\nKup8d4/v1pHTK1eRObXtlPbu21vYuaJcuxq7KlvxJ7aRMoTHClBAvRAAJKquGZ3Yfq+izrfqDDLi\nFttIGQCgWCwDilLEthQnkJJeq09ZDpZjQvsHoGosA4rgYluKsw6s7yILW2IbKYsJ7R+AGDACgMIN\numkM2bJ8uu0iO79tXnsO7SEIADZQdPBcp02zaJuBeDACgOAGmWBItiy/9fYAIAAAeusWPB85fUSS\nMn926jLBmrYZqDfWCEThBhnZiW3jLovYA6BabKBVH2VsoFeXkW3aZqDeCABQuEE2jalLtiwk9gCo\nDhto1UsZwXNdNs2ibQbqjRIgFG6QCYZ1yZaFxB4A1WEDrXopI3jOMsHaYq09bTNQbwQAKEW/m8bE\ntnGXRewBUB1rWVFWf8qnrOB5kE2zyq61z/oeoW0G6o0AAEGxHGExqtxFNmWWsqJlTGBNjYXgucxR\npTzvEdpmoN5YBhQA+mRpA63mSFNDx4fWHD8zckZHHzxa6bkgu/3792tsbGzN8SKWDeU9AqSDZUAB\noCSWsqKs/lQPZSbCeI8A6IUAAAAGMEh9d5lY/akeyqy15z0CoBcCAACZWVy9JBWs/lQPZY4q8R4B\n0AtzAABkUlU9vJWVbqycx+pz6pzAOrpvNPg5wRbeI0AaBp0DQAAAIJOJiQk1m801x4uYvLik2yom\n89vmtefQnko7MVbOAwCAbgYNACgEBJBJFWviz07Nruh0S9L209t17PCx0l/b4nkAAFAE5gAAyKSK\nETwrq5hUcR7MpwAAVIUAAObQEYpDFTuFWlnFpOzzKHs3WAAAOhEAwBQ6QvGoYk18K6uYlH0eZe4G\nCwDAagQAMIWOUFzKXhN/aYJt5yome/ftrXzibdnnUcV8CgAAlhAAwJQUO0LdSp4kUQa1aFdjl4mV\ndso8D1ZEAwBUiQAApqTWEepW8nTw4EF93/d9n2677bblY5RB1VsV8ynqzOIeDQBgGQEATEmtI9St\n5Gnr1q0aHx9fcYwyqHqrYj5FXXXbo+HI6SOSRBAAAD0QAMCU1DpC3UqeNm/mY5misudT1NV6ezQQ\nAABAd/Q0YE5KHaFuJU/nz58PcCZAnKzsFQEAMWEnYCCgpZKnTk8//bTuvffeFcemp6fVaDSqPDUg\nClb2igCAmLhQky6dcz61CZ9AN+12W3Nzc8t/X+rorz5Wh1GR1Cdrssld8brNATi17ZT2Hqp+uVgA\nCGXxudL3UooEAAAq0a2jNr9tXnsO7Umio9Zrk7s6z3GpSmuutWKPhtF9o0m8pwBgCQEAAJOaI00N\nHR9ac/zMyBkdffBo9SdUsYmJCTWbzTXHZ2ZmNDk5GeCM6ouRFgCpGTQAYBIwgEqEnKxpoUOY4iZ3\nIfQaaZHYRwMAlhAAAKhEqMmaVjqEjHhWo9veGuyjAQArsUwCgErsHt+t+W3zK46d2nZKo/tGS33d\nXh3CzknWVei24lM/qzu1221NTExo//79mpiYULvdLvM0o8dICwBsjBEAAJVYmpTZOVlz777yV2qx\n0iHMssmdldGLmDDSAgAbIwAAUJldjV2Vr85iqUM46CZ3lLMMbmmkpdtqSwCABQQAAGot5g6hldGL\nmGQZaQGA1BAAAKi1mDuElkYvYjLoSAsApIZ9AADAKDYPAwD0g43AAKBG2u32ihWLGo0GnX8AwAoE\nAAAAAEBCBg0A2AcAAAAASEjmAMA59xHn3H92zs075/7UOffKjq+93zn3d865rzjn/lUxp4oqnThx\nIvQpYB3cH7u4N2G05lpqjjQ1Njym5khTrblW1+/j/tjG/bGLe1MveUYAPuq9/2Hv/XZJD0j6sCQ5\n514n6RckvU7S2yT9H845RhoiwwfdNu6PXdyb6rXmWjpy6xENHR/SdQ9fp6HjQzpy65GuQQD3xzbu\nj13cm3rJ3DH33v9zx1+/V9KFxT+/Q9Ix7/0L3vuvSvp7SW/KfIYAAKxjdmpW209vX3Fs++ntOnb4\nWKAzAgDbcu0D4Jz7FUk3S/qWpOHFw6+S9IWOb/uapGvyvA4AAL34c90XlLjw3IWuxwEgdeuuAuSc\na0m6usuXPuC9/+OO77tT0qXe+wPOucOSvuC9/4PFr/2OpE977/9w1b/NEkAAAABAAQZZBWjdEQDv\n/a4+/51ZSXOSDkj6uqRXd3zt2sVjq/9t9rgHAAAAKpZnFaDXdvz1HZIeW/zzpyS90zl3iXPuOkmv\nlfQX2U8RAAAAQFHyzAH4Nefcf6+Fyb9flfS/S5L3/lHn3CckPSrpvKT3sOMXAAAAYEOwnYABAAAA\nVK/y9fnZQMwu59zHnHOPLd6fP3TOXdbxNe5NYM65f+Oc+7Jz7rvOuX+56mvcHwOcc29bvAd/55x7\nX+jzSZ1z7qhz7qxz7ksdx17mnGs55/7WOXfcOXd5yHNMlXPu1c659mKb9ohzbnzxOPfHAOfcpc65\nLzrn/nrx/hxYPM79McI5d9FiX/qPF/8+0L0JsUEXG4jZdVzS6733PyzpbyW9X+LeGPIlST8n6bOd\nB7k/NjjnLpL021q4B6+TNOqc+6GwZ5W8/6CF+9HpTkkt7/0PSvqzxb+jei9Ieq/3/vWSflzSnsXP\nC/fHAO/9c5J2eu9/RNKPSHqbc+7HxP2x5FYtlNsvlfIMdG8q7ySwgZhd3vuW937pfnxRCys4Sdwb\nE7z3X/He/22XL3F/bHiTpL/33n/Ve/+CpP+ohXuDQLz3n5P09KrDb5d03+Kf75P0rys9KUiSvPdP\neO//evHP/00LC4lcI+6PGd77by/+8RJJF2uho8n9McA5d62kn5X0O5KWVtUc6N4EyRI6537FOff/\nStqtxREALWwg9rWOb2MDsbB+UdKnF//MvbGN+2PDNZL+sePv3AebrvLen13881lJV4U8GUjOuSFJ\n27WQeOL+GOGc2+Sc+2st3Ifj3vu/EPfHit+UdLteTKJLA96bUgKAxRqkL3X573+SJO/9B733/0LS\nH0jat84/xQzlgm10bxa/54OSnvfez67zT3FvStDP/ekT96d6XPPILK5Qx30LyDn3vZL+k6RbV1UI\ncH8C895fWCwBulbSjznn3rDq69yfAJxzN0p60ns/rxez/yv0c2/yLAPaU5kbiCGfje6Nc25MC8NK\nP9VxmHtTkQE+O524Pzasvg+v1sqRGdhw1jl3tff+icVFKJ4MfUKpcs5drIXO/+977z+5eJj7Y4z3\n/lvOubakEXF/LLhB0tudcz8r6VJJ/51z7vc14L0JsQoQG4gZ5Zx7mxaGlN6xOAFoCffGns6on/tj\nw0lJr3XODTnnLtHCxOxPBT4nrPUpSe9a/PO7JH1yne9FSZxzTtLvSnrUe/9bHV/i/hjgnLtiaRUZ\n59xLJO3SQn+N+xOY9/4D3vtXe++vk/ROSZ/x3t+sAe9NKSMAG2ADMbsOa2GyT2uhbdafe+/fw72x\nwTn3c5KmJF0hac45N++9/xnujw3e+/POub2S/lTSRZJ+13v/2AY/hhI5545JequkK5xz/6iFOWf3\nSPqEc+7dWngG/Xy4M0zaWyT9O0l/45ybXzz2fnF/rHilpPsWVzfbJOnj3vtPO+e+IO6PNUvP+4E+\nO2wEBgAAACSEtcIBAACAhBAAAAAAAAkhAAAAAAASQgAAAAAAJIQAAAAAAEgIAQAAAACQEAIAAAAA\nICH/P2wTU2cCo7RbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1232fd438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      other       0.70      0.57      0.62      1486\n",
      "       earn       0.43      0.57      0.49       845\n",
      "\n",
      "avg / total       0.60      0.57      0.57      2331\n",
      "\n",
      "Average loss at step 2000: 2.4\n",
      "Test loss at step 2000: 4.3\n",
      "Average loss at step 4000: 2.4\n",
      "Test loss at step 4000: 4.3\n",
      "Average loss at step 6000: 2.3\n",
      "Test loss at step 6000: 4.3\n",
      "Average loss at step 8000: 2.3\n",
      "Test loss at step 8000: 4.3\n",
      "Average loss at step 10000: 2.3\n",
      "Test loss at step 10000: 4.3\n",
      "Average loss at step 12000: 2.3\n",
      "Test loss at step 12000: 4.3\n",
      "Average loss at step 14000: 2.3\n",
      "Test loss at step 14000: 4.3\n",
      "Average loss at step 16000: 2.3\n",
      "Test loss at step 16000: 4.3\n",
      "Average loss at step 18000: 2.3\n",
      "Test loss at step 18000: 4.3\n",
      "Average loss at step 20000: 2.3\n",
      "Test loss at step 20000: 4.2\n",
      "Average loss at step 22000: 2.3\n",
      "Test loss at step 22000: 4.2\n",
      "Average loss at step 24000: 2.3\n",
      "Test loss at step 24000: 4.2\n",
      "Average loss at step 26000: 2.3\n",
      "Test loss at step 26000: 4.2\n",
      "Average loss at step 28000: 2.3\n",
      "Test loss at step 28000: 4.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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CfUgAACQpxXWne9ueHn30UX3+85/X6173Oq2tranb7Ub9t48TS/tM0WcYkCSGK+RRLEad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\nA66Mfp+TFbBVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1214e6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      other       0.71      0.60      0.65      1486\n",
      "       earn       0.45      0.58      0.50       845\n",
      "\n",
      "avg / total       0.62      0.59      0.60      2331\n",
      "\n",
      "Average loss at step 2000: 2.2\n",
      "Test loss at step 2000: 4.2\n",
      "Average loss at step 4000: 2.2\n",
      "Test loss at step 4000: 4.2\n",
      "Average loss at step 6000: 2.2\n",
      "Test loss at step 6000: 4.2\n",
      "Average loss at step 8000: 2.2\n",
      "Test loss at step 8000: 4.2\n",
      "Average loss at step 10000: 2.2\n",
      "Test loss at step 10000: 4.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZqJkyCy95S42jql6+ZTbSLR1PzLEQaA8r5BKckEtTKZlBmQgAUIgYMxWpsfCS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\nc6YBQB9DTG0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12250ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      other       0.72      0.61      0.66      1486\n",
      "       earn       0.46      0.59      0.52       845\n",
      "\n",
      "avg / total       0.63      0.61      0.61      2331\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#%mprun -f train run() # only works on physical file functions ;-(\n",
    "%lprun -f train run() # uncomment to use (time profiling)\n",
    "#%timeit -n 1 run() # uncomment to use (summary time profiling)\n",
    "#%memit -r 1 run() # uncomment to use (memory consumption)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}