malay_word2vec_tsne.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of malay_word2vec",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o3Kz6QmtX3ft"
      },
      "source": [
        "**Step 1: Mount Google Drive path**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qjc7bbx0Lm3T",
        "outputId": "aaf10c7d-3b5c-432c-abef-37d2525e5d6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7biRQ6dL6kz",
        "outputId": "8b20fdd9-e17c-48e1-d717-500d30a6f772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "source": [
        "%cd \"/content/gdrive/My Drive/thesis\"\n",
        "!ls \"./\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/thesis\n",
            "conv_wikidump_txt\t malay_word2vec.ipynb\n",
            "conv_wikidump_txt_test\t mswiki-latest-pages-articles.xml.bz2\n",
            "load_dataset.ipynb\t mswiki.model\n",
            "load_dataset_test.ipynb  mswiki.model.trainables.syn1neg.npy\n",
            "Malaya-Dataset-master\t mswiki.model.wv.vectors.npy\n",
            "Malaya-master\t\t ms-wiki-test.txt\n",
            "malay_word2vec\t\t ms-wiki.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CWjeNN1YlpT"
      },
      "source": [
        "**Step 2: Define function for loading the corpus**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVmpe4K2MLqQ"
      },
      "source": [
        "#check_corpus is used to print some lines from the .txt input file\n",
        "import sys, time\n",
        "\n",
        "def check_corpus(input_file):\n",
        "  \"\"\"Reads some lines of corpus from text file\"\"\"\n",
        "  while(1):\n",
        "    for lines in range(50):\n",
        "      print(input_file.readline())\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5x_-k1PINfb6"
      },
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import re\n",
        "from IPython.display import display\n",
        "\n",
        "#similar to check_corpus but place all text in a pandas dataframe instead\n",
        "def pandas_corpus(dataset_path):\n",
        "  title_list = list()\n",
        "  lines = list()\n",
        "  d = list()\n",
        "\n",
        "  with open(dataset_path, encoding='utf-8') as f:\n",
        "    for line in f:\n",
        "      lines.append(line.rstrip()) # All lines including the blank ones\n",
        "      # title_match = re.match(r\".*?'''(.*?)'''.*\", str(line))\n",
        "      # if title_match:\n",
        "      #   title = title_match.group(1)\n",
        "      # else:\n",
        "      #   title = \"N/A\"\n",
        "      # title_list.append(title)\n",
        "    #lines = list(line for line in lines if line) # Non-blank lines\n",
        "    d.append(pd.DataFrame({'title': \"N/A\", 'article': lines}))\n",
        "  df = pd.concat(d)\n",
        "  #print(df)\n",
        "  display(df)\n",
        "  #return temp_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hXQ7VT2NJjf"
      },
      "source": [
        "#load_corpus with processing time display\n",
        "def load_corpus(input_file):\n",
        "\n",
        "    \"\"\"Loads corpus from text file\"\"\"\n",
        "\n",
        "    print('Loading corpus...')\n",
        "    time1 = time.time()\n",
        "    corpus = input_file.read()\n",
        "    time2 = time.time()\n",
        "    total_time = time2-time1\n",
        "    print('It took %0.3f seconds to load corpus' %total_time)\n",
        "    return corpus"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8A6sBrByWtwJ"
      },
      "source": [
        "Call the functions above to load the corpus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTEqMTv9N_Bn",
        "outputId": "ed6acd24-3cc9-4e13-b70e-2c46e167dfd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "source": [
        "pandas_corpus(\"./ms-wiki.txt\")              #Converted Wiki Dump (.xml to .txt) obtained from [1] in README.md\n",
        "corpus_file = open(\"./ms-wiki.txt\",'r')\n",
        "#check_corpus(corpus_file)\n",
        "corpus = load_corpus(corpus_file)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>article</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>N/A</td>\n",
              "      <td>bahasa melayu tulisan jawi بهاس ملايو ialah se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>N/A</td>\n",
              "      <td>fail stilles mineralwasser jpg thumb air miner...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>N/A</td>\n",
              "      <td>kandungan terbuka dicipta dengan analogi dari ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>N/A</td>\n",
              "      <td>khalifah abdul malik bin marwan dilahirkan pad...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>N/A</td>\n",
              "      <td>penyu adalah daripada superkeluarga chelonioid...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131202</th>\n",
              "      <td>N/A</td>\n",
              "      <td>fail png thumb px layar tradisional suku bangs...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131203</th>\n",
              "      <td>N/A</td>\n",
              "      <td>dalam pelayaran istilah gunter digunakan untuk...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131204</th>\n",
              "      <td>N/A</td>\n",
              "      <td>fail jpg thumb px sandeq di majene sandeq adal...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131205</th>\n",
              "      <td>N/A</td>\n",
              "      <td>jiang chaoliang cina 蒋超良 pinyin jiǎng chāolián...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131206</th>\n",
              "      <td>N/A</td>\n",
              "      <td>anthony dominick benedetto lahir ogos atau jug...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131207 rows × 2 columns</p>\n",
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            ],
            "text/plain": [
              "       title                                            article\n",
              "0        N/A  bahasa melayu tulisan jawi بهاس ملايو ialah se...\n",
              "1        N/A  fail stilles mineralwasser jpg thumb air miner...\n",
              "2        N/A  kandungan terbuka dicipta dengan analogi dari ...\n",
              "3        N/A  khalifah abdul malik bin marwan dilahirkan pad...\n",
              "4        N/A  penyu adalah daripada superkeluarga chelonioid...\n",
              "...      ...                                                ...\n",
              "131202   N/A  fail png thumb px layar tradisional suku bangs...\n",
              "131203   N/A  dalam pelayaran istilah gunter digunakan untuk...\n",
              "131204   N/A  fail jpg thumb px sandeq di majene sandeq adal...\n",
              "131205   N/A  jiang chaoliang cina 蒋超良 pinyin jiǎng chāolián...\n",
              "131206   N/A  anthony dominick benedetto lahir ogos atau jug...\n",
              "\n",
              "[131207 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Loading corpus...\n",
            "It took 5.983 seconds to load corpus\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqCorVO4Zeyx"
      },
      "source": [
        "**Step 3: Word2Vec Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CABaYV1tWmGh"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')# ignore warning\n",
        "\n",
        "import logging\n",
        "import os.path\n",
        "import sys\n",
        "import multiprocessing\n",
        "\n",
        "from gensim.corpora import WikiCorpus\n",
        "from gensim.models import Word2Vec\n",
        "from gensim.models.word2vec import LineSentence\n",
        "from gensim.models.callbacks import CallbackAny2Vec"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MqbPbPMhialj"
      },
      "source": [
        "Define LossLogger to check the loss after each epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulbraMzGh9Dn"
      },
      "source": [
        "from gensim.models.callbacks import CallbackAny2Vec\n",
        "\n",
        "loss_list = list()\n",
        "\n",
        "class LossLogger(CallbackAny2Vec):\n",
        "    '''Callback to print loss after each epoch.'''\n",
        "\n",
        "    def __init__(self):\n",
        "        self.epoch = 0\n",
        "        self.loss_to_be_subbed = 0\n",
        "\n",
        "    def on_epoch_end(self, model):\n",
        "        loss = model.get_latest_training_loss()\n",
        "        loss_now = loss - self.loss_to_be_subbed\n",
        "        loss_list.append(loss_now)\n",
        "        self.loss_to_be_subbed = loss\n",
        "        print('Loss after epoch {}: {}'.format(self.epoch, loss_now))\n",
        "        self.epoch += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pg7Ac1ujZ9kY"
      },
      "source": [
        "The training uses Skip-gram model of Word2Vec.\n",
        "<br>Embedding size is set to 300, similar to English Word2Vec default.\n",
        "<br>Window size = 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwrq-afOZ0OO"
      },
      "source": [
        "def train_word2vec_model():\n",
        "  loss_list.append(25)\n",
        "  logger = logging.getLogger(\"mylog\")\n",
        "  logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s',level=logging.INFO)\n",
        "  logger.info(\"running %s\" % ' '.join(sys.argv))\n",
        "  \n",
        "  inp = \"./ms-wiki.txt\"\n",
        "  outp1 = \"mswiki.model\"\n",
        "  outp2 = \"mswiki.vector\"\n",
        "  \n",
        "  # train skip-gram model\n",
        "  model = Word2Vec(LineSentence(inp), iter=5, size=300, window=5, min_count=5, \n",
        "                   workers=multiprocessing.cpu_count(), compute_loss=True, callbacks=[LossLogger()])\n",
        "  \n",
        "  # save model\n",
        "  model.save(outp1)\n",
        "  model.wv.save_word2vec_format(outp2, binary=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlnjyccWbXwT",
        "outputId": "5b6e7e19-1221-4a72-a9b8-75fc22905352",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_word2vec_model()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-03-02 18:59:20,536: INFO: running /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py -f /root/.local/share/jupyter/runtime/kernel-00ce88dd-dee1-4923-a6cf-b52ae55fabe7.json\n",
            "2020-03-02 18:59:20,539: INFO: collecting all words and their counts\n",
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:402: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n",
            "2020-03-02 18:59:20,573: INFO: PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
            "2020-03-02 18:59:22,298: INFO: PROGRESS: at sentence #10000, processed 5610617 words, keeping 183706 word types\n",
            "2020-03-02 18:59:23,970: INFO: PROGRESS: at sentence #20000, processed 10469460 words, keeping 295486 word types\n",
            "2020-03-02 18:59:25,234: INFO: PROGRESS: at sentence #30000, processed 14066104 words, keeping 382606 word types\n",
            "2020-03-02 18:59:26,255: INFO: PROGRESS: at sentence #40000, processed 16964738 words, keeping 433052 word types\n",
            "2020-03-02 18:59:26,948: INFO: PROGRESS: at sentence #50000, processed 19035391 words, keeping 463467 word types\n",
            "2020-03-02 18:59:27,704: INFO: PROGRESS: at sentence #60000, processed 21389994 words, keeping 507635 word types\n",
            "2020-03-02 18:59:28,585: INFO: PROGRESS: at sentence #70000, processed 23908432 words, keeping 552989 word types\n",
            "2020-03-02 18:59:28,874: INFO: PROGRESS: at sentence #80000, processed 24742276 words, keeping 573093 word types\n",
            "2020-03-02 18:59:29,715: INFO: PROGRESS: at sentence #90000, processed 27047157 words, keeping 634792 word types\n",
            "2020-03-02 18:59:30,956: INFO: PROGRESS: at sentence #100000, processed 30742038 words, keeping 690871 word types\n",
            "2020-03-02 18:59:32,054: INFO: PROGRESS: at sentence #110000, processed 33836675 words, keeping 730013 word types\n",
            "2020-03-02 18:59:32,950: INFO: PROGRESS: at sentence #120000, processed 36343553 words, keeping 756088 word types\n",
            "2020-03-02 18:59:34,123: INFO: PROGRESS: at sentence #130000, processed 39534373 words, keeping 805787 word types\n",
            "2020-03-02 18:59:34,268: INFO: collected 814323 word types from a corpus of 39921798 raw words and 131266 sentences\n",
            "2020-03-02 18:59:34,269: INFO: Loading a fresh vocabulary\n",
            "2020-03-02 18:59:35,097: INFO: effective_min_count=5 retains 175214 unique words (21% of original 814323, drops 639109)\n",
            "2020-03-02 18:59:35,098: INFO: effective_min_count=5 leaves 38966354 word corpus (97% of original 39921798, drops 955444)\n",
            "2020-03-02 18:59:35,801: INFO: deleting the raw counts dictionary of 814323 items\n",
            "2020-03-02 18:59:35,837: INFO: sample=0.001 downsamples 32 most-common words\n",
            "2020-03-02 18:59:35,838: INFO: downsampling leaves estimated 35369915 word corpus (90.8% of prior 38966354)\n",
            "2020-03-02 18:59:36,600: INFO: estimated required memory for 175214 words and 300 dimensions: 508120600 bytes\n",
            "2020-03-02 18:59:36,601: INFO: resetting layer weights\n",
            "2020-03-02 19:00:13,397: INFO: training model with 2 workers on 175214 vocabulary and 300 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
            "2020-03-02 19:00:14,411: INFO: EPOCH 1 - PROGRESS: at 0.31% examples, 275446 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:15,443: INFO: EPOCH 1 - PROGRESS: at 0.53% examples, 279478 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:16,452: INFO: EPOCH 1 - PROGRESS: at 0.82% examples, 281070 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:17,454: INFO: EPOCH 1 - PROGRESS: at 1.32% examples, 283735 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:18,507: INFO: EPOCH 1 - PROGRESS: at 1.67% examples, 282489 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:19,554: INFO: EPOCH 1 - PROGRESS: at 2.09% examples, 283393 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:20,568: INFO: EPOCH 1 - PROGRESS: at 2.55% examples, 285034 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:21,573: INFO: EPOCH 1 - PROGRESS: at 3.10% examples, 288161 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:22,573: INFO: EPOCH 1 - PROGRESS: at 3.75% examples, 287266 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:23,580: INFO: EPOCH 1 - PROGRESS: at 4.16% examples, 285808 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:24,618: INFO: EPOCH 1 - PROGRESS: at 4.70% examples, 285452 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:25,622: INFO: EPOCH 1 - PROGRESS: at 5.74% examples, 285397 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:26,665: INFO: EPOCH 1 - PROGRESS: at 6.13% examples, 284097 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:27,699: INFO: EPOCH 1 - PROGRESS: at 6.54% examples, 285105 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:28,729: INFO: EPOCH 1 - PROGRESS: at 6.87% examples, 284440 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:29,784: INFO: EPOCH 1 - PROGRESS: at 7.25% examples, 284392 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:30,799: INFO: EPOCH 1 - PROGRESS: at 7.61% examples, 285320 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:31,817: INFO: EPOCH 1 - PROGRESS: at 8.05% examples, 285912 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:32,823: INFO: EPOCH 1 - PROGRESS: at 8.38% examples, 285115 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:33,838: INFO: EPOCH 1 - PROGRESS: at 8.85% examples, 285033 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:34,839: INFO: EPOCH 1 - PROGRESS: at 9.47% examples, 285854 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:35,865: INFO: EPOCH 1 - PROGRESS: at 9.98% examples, 286104 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:36,882: INFO: EPOCH 1 - PROGRESS: at 10.40% examples, 285509 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:37,892: INFO: EPOCH 1 - PROGRESS: at 11.03% examples, 286178 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:38,922: INFO: EPOCH 1 - PROGRESS: at 11.49% examples, 286214 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:39,928: INFO: EPOCH 1 - PROGRESS: at 12.03% examples, 286375 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:40,951: INFO: EPOCH 1 - PROGRESS: at 12.53% examples, 286180 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:41,985: INFO: EPOCH 1 - PROGRESS: at 13.10% examples, 285983 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:43,009: INFO: EPOCH 1 - PROGRESS: at 13.61% examples, 285984 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:44,038: INFO: EPOCH 1 - PROGRESS: at 14.19% examples, 285625 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:45,055: INFO: EPOCH 1 - PROGRESS: at 14.71% examples, 285558 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:46,066: INFO: EPOCH 1 - PROGRESS: at 15.33% examples, 285637 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:47,082: INFO: EPOCH 1 - PROGRESS: at 15.81% examples, 286008 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:48,108: INFO: EPOCH 1 - PROGRESS: at 16.50% examples, 286508 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:49,136: INFO: EPOCH 1 - PROGRESS: at 17.04% examples, 286870 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:50,175: INFO: EPOCH 1 - PROGRESS: at 17.67% examples, 287334 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:51,206: INFO: EPOCH 1 - PROGRESS: at 18.22% examples, 287461 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:52,208: INFO: EPOCH 1 - PROGRESS: at 19.03% examples, 287736 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:53,226: INFO: EPOCH 1 - PROGRESS: at 19.79% examples, 287904 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:54,251: INFO: EPOCH 1 - PROGRESS: at 20.51% examples, 287702 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:55,253: INFO: EPOCH 1 - PROGRESS: at 21.42% examples, 287995 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:56,301: INFO: EPOCH 1 - PROGRESS: at 22.27% examples, 288104 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:00:57,324: INFO: EPOCH 1 - PROGRESS: at 23.85% examples, 288640 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:58,335: INFO: EPOCH 1 - PROGRESS: at 24.92% examples, 288629 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:00:59,366: INFO: EPOCH 1 - PROGRESS: at 26.19% examples, 288823 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:00,386: INFO: EPOCH 1 - PROGRESS: at 27.04% examples, 289109 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:01,395: INFO: EPOCH 1 - PROGRESS: at 27.74% examples, 289245 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:02,432: INFO: EPOCH 1 - PROGRESS: at 28.49% examples, 289105 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:03,435: INFO: EPOCH 1 - PROGRESS: at 29.01% examples, 289428 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:04,473: INFO: EPOCH 1 - PROGRESS: at 29.67% examples, 289391 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:05,509: INFO: EPOCH 1 - PROGRESS: at 30.76% examples, 289591 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:01:06,516: INFO: EPOCH 1 - PROGRESS: at 31.86% examples, 289818 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:07,563: INFO: EPOCH 1 - PROGRESS: at 33.20% examples, 290278 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:01:08,582: INFO: EPOCH 1 - PROGRESS: at 34.89% examples, 291113 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:09,593: INFO: EPOCH 1 - PROGRESS: at 36.23% examples, 291557 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:10,626: INFO: EPOCH 1 - PROGRESS: at 37.35% examples, 291580 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:01:11,631: INFO: EPOCH 1 - PROGRESS: at 38.49% examples, 291625 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:12,655: INFO: EPOCH 1 - PROGRESS: at 39.72% examples, 291811 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:13,657: INFO: EPOCH 1 - PROGRESS: at 41.07% examples, 292199 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:14,665: INFO: EPOCH 1 - PROGRESS: at 42.06% examples, 292066 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:15,672: INFO: EPOCH 1 - PROGRESS: at 43.64% examples, 292436 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:16,678: INFO: EPOCH 1 - PROGRESS: at 44.63% examples, 292453 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:17,693: INFO: EPOCH 1 - PROGRESS: at 45.25% examples, 292430 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:18,728: INFO: EPOCH 1 - PROGRESS: at 45.98% examples, 292405 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:19,747: INFO: EPOCH 1 - PROGRESS: at 46.69% examples, 292524 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:20,777: INFO: EPOCH 1 - PROGRESS: at 47.31% examples, 292659 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:21,808: INFO: EPOCH 1 - PROGRESS: at 47.96% examples, 292698 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:22,817: INFO: EPOCH 1 - PROGRESS: at 48.70% examples, 292648 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:23,836: INFO: EPOCH 1 - PROGRESS: at 49.76% examples, 292530 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:24,838: INFO: EPOCH 1 - PROGRESS: at 51.00% examples, 292596 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:25,844: INFO: EPOCH 1 - PROGRESS: at 52.84% examples, 292557 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:26,856: INFO: EPOCH 1 - PROGRESS: at 54.94% examples, 292328 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:27,875: INFO: EPOCH 1 - PROGRESS: at 58.78% examples, 292446 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:28,876: INFO: EPOCH 1 - PROGRESS: at 62.15% examples, 292411 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:29,882: INFO: EPOCH 1 - PROGRESS: at 63.12% examples, 291956 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:30,901: INFO: EPOCH 1 - PROGRESS: at 63.87% examples, 291907 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:31,932: INFO: EPOCH 1 - PROGRESS: at 64.97% examples, 292073 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:32,992: INFO: EPOCH 1 - PROGRESS: at 65.87% examples, 292069 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:01:34,005: INFO: EPOCH 1 - PROGRESS: at 66.58% examples, 292158 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:35,031: INFO: EPOCH 1 - PROGRESS: at 67.97% examples, 292371 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:01:36,061: INFO: EPOCH 1 - PROGRESS: at 69.19% examples, 292347 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:01:37,081: INFO: EPOCH 1 - PROGRESS: at 69.88% examples, 292424 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:01:38,101: INFO: EPOCH 1 - PROGRESS: at 70.64% examples, 292764 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:39,109: INFO: EPOCH 1 - PROGRESS: at 71.27% examples, 292807 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:40,120: INFO: EPOCH 1 - PROGRESS: at 71.92% examples, 292855 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:41,143: INFO: EPOCH 1 - PROGRESS: at 72.58% examples, 292791 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:42,173: INFO: EPOCH 1 - PROGRESS: at 73.32% examples, 292850 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:43,173: INFO: EPOCH 1 - PROGRESS: at 74.27% examples, 292990 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:44,180: INFO: EPOCH 1 - PROGRESS: at 74.99% examples, 293020 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:45,205: INFO: EPOCH 1 - PROGRESS: at 75.54% examples, 293093 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:46,232: INFO: EPOCH 1 - PROGRESS: at 76.07% examples, 293073 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:47,282: INFO: EPOCH 1 - PROGRESS: at 76.58% examples, 293047 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:48,303: INFO: EPOCH 1 - PROGRESS: at 77.30% examples, 293178 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:49,309: INFO: EPOCH 1 - PROGRESS: at 77.95% examples, 293333 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:50,346: INFO: EPOCH 1 - PROGRESS: at 78.73% examples, 293417 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:51,351: INFO: EPOCH 1 - PROGRESS: at 79.44% examples, 293336 words/s, in_qsize 3, out_qsize 1\n",
            "2020-03-02 19:01:52,393: INFO: EPOCH 1 - PROGRESS: at 80.22% examples, 293419 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:01:53,400: INFO: EPOCH 1 - PROGRESS: at 81.26% examples, 293534 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:54,402: INFO: EPOCH 1 - PROGRESS: at 81.81% examples, 293482 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:55,414: INFO: EPOCH 1 - PROGRESS: at 83.55% examples, 293814 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:56,438: INFO: EPOCH 1 - PROGRESS: at 85.32% examples, 294184 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:57,448: INFO: EPOCH 1 - PROGRESS: at 86.45% examples, 294278 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:58,459: INFO: EPOCH 1 - PROGRESS: at 87.46% examples, 294511 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:01:59,476: INFO: EPOCH 1 - PROGRESS: at 88.69% examples, 294700 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:00,499: INFO: EPOCH 1 - PROGRESS: at 89.50% examples, 294867 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:01,512: INFO: EPOCH 1 - PROGRESS: at 90.57% examples, 294949 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:02,552: INFO: EPOCH 1 - PROGRESS: at 91.35% examples, 294980 words/s, in_qsize 4, out_qsize 1\n",
            "2020-03-02 19:02:03,576: INFO: EPOCH 1 - PROGRESS: at 92.49% examples, 295192 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:04,584: INFO: EPOCH 1 - PROGRESS: at 93.45% examples, 295367 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:05,613: INFO: EPOCH 1 - PROGRESS: at 93.99% examples, 295469 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:06,662: INFO: EPOCH 1 - PROGRESS: at 94.58% examples, 295515 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:07,664: INFO: EPOCH 1 - PROGRESS: at 95.41% examples, 295514 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:08,669: INFO: EPOCH 1 - PROGRESS: at 96.36% examples, 295473 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:09,691: INFO: EPOCH 1 - PROGRESS: at 97.17% examples, 295480 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:10,696: INFO: EPOCH 1 - PROGRESS: at 98.00% examples, 295419 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:11,752: INFO: EPOCH 1 - PROGRESS: at 98.76% examples, 295234 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:12,771: INFO: EPOCH 1 - PROGRESS: at 99.63% examples, 295116 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:13,240: INFO: worker thread finished; awaiting finish of 1 more threads\n",
            "2020-03-02 19:02:13,253: INFO: worker thread finished; awaiting finish of 0 more threads\n",
            "2020-03-02 19:02:13,254: INFO: EPOCH - 1 : training on 39921798 raw words (35369484 effective words) took 119.9s, 295114 effective words/s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss after epoch 0: 21949532.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-03-02 19:02:14,279: INFO: EPOCH 2 - PROGRESS: at 0.32% examples, 280207 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:15,285: INFO: EPOCH 2 - PROGRESS: at 0.54% examples, 285289 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:16,290: INFO: EPOCH 2 - PROGRESS: at 0.85% examples, 288155 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:17,318: INFO: EPOCH 2 - PROGRESS: at 1.36% examples, 288882 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:18,353: INFO: EPOCH 2 - PROGRESS: at 1.71% examples, 289668 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:19,390: INFO: EPOCH 2 - PROGRESS: at 2.17% examples, 292072 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:20,427: INFO: EPOCH 2 - PROGRESS: at 2.75% examples, 296759 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:21,444: INFO: EPOCH 2 - PROGRESS: at 3.30% examples, 300197 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:22,488: INFO: EPOCH 2 - PROGRESS: at 3.96% examples, 300407 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:23,508: INFO: EPOCH 2 - PROGRESS: at 4.38% examples, 300213 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:02:24,534: INFO: EPOCH 2 - PROGRESS: at 5.40% examples, 299096 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:25,567: INFO: EPOCH 2 - PROGRESS: at 5.98% examples, 299466 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:26,594: INFO: EPOCH 2 - PROGRESS: at 6.43% examples, 298395 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:27,600: INFO: EPOCH 2 - PROGRESS: at 6.78% examples, 299014 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:28,610: INFO: EPOCH 2 - PROGRESS: at 7.16% examples, 297999 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:29,622: INFO: EPOCH 2 - PROGRESS: at 7.49% examples, 297859 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:30,668: INFO: EPOCH 2 - PROGRESS: at 7.93% examples, 298498 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:31,669: INFO: EPOCH 2 - PROGRESS: at 8.34% examples, 299057 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:32,695: INFO: EPOCH 2 - PROGRESS: at 8.81% examples, 298808 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:33,708: INFO: EPOCH 2 - PROGRESS: at 9.49% examples, 300034 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:34,737: INFO: EPOCH 2 - PROGRESS: at 10.00% examples, 300233 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:35,782: INFO: EPOCH 2 - PROGRESS: at 10.57% examples, 300712 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:36,802: INFO: EPOCH 2 - PROGRESS: at 11.14% examples, 300330 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:37,849: INFO: EPOCH 2 - PROGRESS: at 11.66% examples, 300541 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:38,856: INFO: EPOCH 2 - PROGRESS: at 12.15% examples, 300196 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:39,887: INFO: EPOCH 2 - PROGRESS: at 12.71% examples, 299822 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:40,937: INFO: EPOCH 2 - PROGRESS: at 13.28% examples, 299429 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:41,963: INFO: EPOCH 2 - PROGRESS: at 13.88% examples, 299612 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:02:42,988: INFO: EPOCH 2 - PROGRESS: at 14.50% examples, 299632 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:44,008: INFO: EPOCH 2 - PROGRESS: at 15.06% examples, 299431 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:45,016: INFO: EPOCH 2 - PROGRESS: at 15.62% examples, 299607 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:46,035: INFO: EPOCH 2 - PROGRESS: at 16.22% examples, 299511 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:47,049: INFO: EPOCH 2 - PROGRESS: at 16.81% examples, 299571 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:48,049: INFO: EPOCH 2 - PROGRESS: at 17.38% examples, 299849 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:49,056: INFO: EPOCH 2 - PROGRESS: at 18.01% examples, 299741 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:50,063: INFO: EPOCH 2 - PROGRESS: at 18.58% examples, 300102 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:51,063: INFO: EPOCH 2 - PROGRESS: at 19.46% examples, 300291 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:52,069: INFO: EPOCH 2 - PROGRESS: at 20.34% examples, 300231 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:53,075: INFO: EPOCH 2 - PROGRESS: at 21.11% examples, 300531 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:54,125: INFO: EPOCH 2 - PROGRESS: at 22.00% examples, 300305 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:55,128: INFO: EPOCH 2 - PROGRESS: at 23.33% examples, 300702 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:02:56,150: INFO: EPOCH 2 - PROGRESS: at 24.71% examples, 300969 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:57,180: INFO: EPOCH 2 - PROGRESS: at 26.08% examples, 301368 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:58,193: INFO: EPOCH 2 - PROGRESS: at 26.99% examples, 301703 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:02:59,222: INFO: EPOCH 2 - PROGRESS: at 27.72% examples, 301808 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:00,232: INFO: EPOCH 2 - PROGRESS: at 28.49% examples, 301741 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:01,232: INFO: EPOCH 2 - PROGRESS: at 29.01% examples, 301834 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:02,266: INFO: EPOCH 2 - PROGRESS: at 29.67% examples, 301561 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:03,283: INFO: EPOCH 2 - PROGRESS: at 30.80% examples, 301812 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:04,302: INFO: EPOCH 2 - PROGRESS: at 31.94% examples, 302240 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:05,308: INFO: EPOCH 2 - PROGRESS: at 33.34% examples, 302865 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:06,310: INFO: EPOCH 2 - PROGRESS: at 34.95% examples, 303252 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:07,320: INFO: EPOCH 2 - PROGRESS: at 36.42% examples, 303808 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:08,347: INFO: EPOCH 2 - PROGRESS: at 37.51% examples, 304066 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:09,348: INFO: EPOCH 2 - PROGRESS: at 38.97% examples, 304572 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:10,352: INFO: EPOCH 2 - PROGRESS: at 40.09% examples, 304658 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:11,370: INFO: EPOCH 2 - PROGRESS: at 41.61% examples, 304721 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:03:12,394: INFO: EPOCH 2 - PROGRESS: at 42.82% examples, 304806 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:13,402: INFO: EPOCH 2 - PROGRESS: at 44.29% examples, 305159 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:14,445: INFO: EPOCH 2 - PROGRESS: at 44.91% examples, 304749 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:15,450: INFO: EPOCH 2 - PROGRESS: at 45.58% examples, 304868 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:16,457: INFO: EPOCH 2 - PROGRESS: at 46.35% examples, 304672 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:03:17,458: INFO: EPOCH 2 - PROGRESS: at 46.99% examples, 304612 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:18,461: INFO: EPOCH 2 - PROGRESS: at 47.52% examples, 304430 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:19,475: INFO: EPOCH 2 - PROGRESS: at 48.32% examples, 304403 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:20,494: INFO: EPOCH 2 - PROGRESS: at 48.97% examples, 304361 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:21,506: INFO: EPOCH 2 - PROGRESS: at 50.68% examples, 304764 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:22,554: INFO: EPOCH 2 - PROGRESS: at 52.35% examples, 304750 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:23,590: INFO: EPOCH 2 - PROGRESS: at 54.69% examples, 304814 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:24,610: INFO: EPOCH 2 - PROGRESS: at 58.78% examples, 305234 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:25,638: INFO: EPOCH 2 - PROGRESS: at 62.29% examples, 305483 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:26,640: INFO: EPOCH 2 - PROGRESS: at 63.44% examples, 305449 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:27,664: INFO: EPOCH 2 - PROGRESS: at 64.31% examples, 305562 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:28,686: INFO: EPOCH 2 - PROGRESS: at 65.26% examples, 305383 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:29,726: INFO: EPOCH 2 - PROGRESS: at 66.06% examples, 305350 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:03:30,766: INFO: EPOCH 2 - PROGRESS: at 67.01% examples, 305331 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:31,803: INFO: EPOCH 2 - PROGRESS: at 68.73% examples, 305427 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:32,811: INFO: EPOCH 2 - PROGRESS: at 69.49% examples, 305384 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:33,832: INFO: EPOCH 2 - PROGRESS: at 70.21% examples, 305588 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:34,845: INFO: EPOCH 2 - PROGRESS: at 70.95% examples, 305670 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:03:35,895: INFO: EPOCH 2 - PROGRESS: at 71.59% examples, 305529 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:36,906: INFO: EPOCH 2 - PROGRESS: at 72.26% examples, 305492 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:37,906: INFO: EPOCH 2 - PROGRESS: at 73.05% examples, 305615 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:38,928: INFO: EPOCH 2 - PROGRESS: at 73.87% examples, 305419 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:39,946: INFO: EPOCH 2 - PROGRESS: at 74.76% examples, 305362 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:40,961: INFO: EPOCH 2 - PROGRESS: at 75.29% examples, 305141 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:41,963: INFO: EPOCH 2 - PROGRESS: at 75.83% examples, 304984 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:42,979: INFO: EPOCH 2 - PROGRESS: at 76.34% examples, 304917 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:43,994: INFO: EPOCH 2 - PROGRESS: at 76.87% examples, 304786 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:44,999: INFO: EPOCH 2 - PROGRESS: at 77.53% examples, 304669 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:46,000: INFO: EPOCH 2 - PROGRESS: at 78.23% examples, 304527 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:47,025: INFO: EPOCH 2 - PROGRESS: at 78.92% examples, 304318 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:48,035: INFO: EPOCH 2 - PROGRESS: at 79.66% examples, 304337 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:49,037: INFO: EPOCH 2 - PROGRESS: at 80.71% examples, 304410 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:50,076: INFO: EPOCH 2 - PROGRESS: at 81.48% examples, 304384 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:03:51,101: INFO: EPOCH 2 - PROGRESS: at 82.22% examples, 304318 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:03:52,129: INFO: EPOCH 2 - PROGRESS: at 84.41% examples, 304604 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:53,137: INFO: EPOCH 2 - PROGRESS: at 85.83% examples, 304813 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:54,139: INFO: EPOCH 2 - PROGRESS: at 86.93% examples, 304777 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:55,167: INFO: EPOCH 2 - PROGRESS: at 87.99% examples, 304879 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:56,177: INFO: EPOCH 2 - PROGRESS: at 89.01% examples, 304983 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:57,178: INFO: EPOCH 2 - PROGRESS: at 89.84% examples, 305001 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:58,179: INFO: EPOCH 2 - PROGRESS: at 90.88% examples, 305102 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:03:59,236: INFO: EPOCH 2 - PROGRESS: at 91.89% examples, 305173 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:04:00,255: INFO: EPOCH 2 - PROGRESS: at 92.94% examples, 305201 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:01,259: INFO: EPOCH 2 - PROGRESS: at 93.79% examples, 305277 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:02,267: INFO: EPOCH 2 - PROGRESS: at 94.15% examples, 305124 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:03,275: INFO: EPOCH 2 - PROGRESS: at 94.90% examples, 305243 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:04,299: INFO: EPOCH 2 - PROGRESS: at 95.90% examples, 305312 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:05,318: INFO: EPOCH 2 - PROGRESS: at 96.74% examples, 305381 words/s, in_qsize 3, out_qsize 1\n",
            "2020-03-02 19:04:06,339: INFO: EPOCH 2 - PROGRESS: at 97.67% examples, 305453 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:04:07,353: INFO: EPOCH 2 - PROGRESS: at 98.53% examples, 305489 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:08,360: INFO: EPOCH 2 - PROGRESS: at 99.46% examples, 305538 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:08,965: INFO: worker thread finished; awaiting finish of 1 more threads\n",
            "2020-03-02 19:04:08,967: INFO: worker thread finished; awaiting finish of 0 more threads\n",
            "2020-03-02 19:04:08,970: INFO: EPOCH - 2 : training on 39921798 raw words (35369945 effective words) took 115.7s, 305669 effective words/s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss after epoch 1: 13873472.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-03-02 19:04:10,003: INFO: EPOCH 3 - PROGRESS: at 0.36% examples, 303567 words/s, in_qsize 4, out_qsize 1\n",
            "2020-03-02 19:04:11,022: INFO: EPOCH 3 - PROGRESS: at 0.56% examples, 304852 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:12,049: INFO: EPOCH 3 - PROGRESS: at 0.92% examples, 301560 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:13,062: INFO: EPOCH 3 - PROGRESS: at 1.44% examples, 303114 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:14,075: INFO: EPOCH 3 - PROGRESS: at 1.79% examples, 304130 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:15,083: INFO: EPOCH 3 - PROGRESS: at 2.30% examples, 304136 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:16,102: INFO: EPOCH 3 - PROGRESS: at 2.85% examples, 305419 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:17,115: INFO: EPOCH 3 - PROGRESS: at 3.42% examples, 305347 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:18,141: INFO: EPOCH 3 - PROGRESS: at 3.98% examples, 304610 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:19,150: INFO: EPOCH 3 - PROGRESS: at 4.43% examples, 305122 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:20,156: INFO: EPOCH 3 - PROGRESS: at 5.62% examples, 305990 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:21,160: INFO: EPOCH 3 - PROGRESS: at 6.06% examples, 306663 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:22,165: INFO: EPOCH 3 - PROGRESS: at 6.51% examples, 306669 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:23,209: INFO: EPOCH 3 - PROGRESS: at 6.85% examples, 305196 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:24,222: INFO: EPOCH 3 - PROGRESS: at 7.24% examples, 305063 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:25,244: INFO: EPOCH 3 - PROGRESS: at 7.61% examples, 305174 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:26,247: INFO: EPOCH 3 - PROGRESS: at 8.05% examples, 304898 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:27,288: INFO: EPOCH 3 - PROGRESS: at 8.45% examples, 305089 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:04:28,347: INFO: EPOCH 3 - PROGRESS: at 9.01% examples, 304947 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:04:29,347: INFO: EPOCH 3 - PROGRESS: at 9.67% examples, 305477 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:30,361: INFO: EPOCH 3 - PROGRESS: at 10.14% examples, 305672 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:31,374: INFO: EPOCH 3 - PROGRESS: at 10.71% examples, 306168 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:32,426: INFO: EPOCH 3 - PROGRESS: at 11.30% examples, 305829 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:04:33,431: INFO: EPOCH 3 - PROGRESS: at 11.81% examples, 305765 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:34,484: INFO: EPOCH 3 - PROGRESS: at 12.39% examples, 305907 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:35,527: INFO: EPOCH 3 - PROGRESS: at 13.00% examples, 305981 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:36,545: INFO: EPOCH 3 - PROGRESS: at 13.53% examples, 305582 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:37,545: INFO: EPOCH 3 - PROGRESS: at 14.17% examples, 306035 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:38,547: INFO: EPOCH 3 - PROGRESS: at 14.71% examples, 305698 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:04:39,570: INFO: EPOCH 3 - PROGRESS: at 15.35% examples, 305541 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:40,573: INFO: EPOCH 3 - PROGRESS: at 15.86% examples, 305445 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:04:41,592: INFO: EPOCH 3 - PROGRESS: at 16.53% examples, 305414 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:42,608: INFO: EPOCH 3 - PROGRESS: at 17.10% examples, 305817 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:43,623: INFO: EPOCH 3 - PROGRESS: at 17.67% examples, 304972 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:44,628: INFO: EPOCH 3 - PROGRESS: at 18.22% examples, 304831 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:45,632: INFO: EPOCH 3 - PROGRESS: at 19.08% examples, 305112 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:46,636: INFO: EPOCH 3 - PROGRESS: at 19.86% examples, 305095 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:04:47,641: INFO: EPOCH 3 - PROGRESS: at 20.61% examples, 305060 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:48,654: INFO: EPOCH 3 - PROGRESS: at 21.62% examples, 305534 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:49,694: INFO: EPOCH 3 - PROGRESS: at 22.70% examples, 305683 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:04:50,705: INFO: EPOCH 3 - PROGRESS: at 24.33% examples, 305818 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:51,715: INFO: EPOCH 3 - PROGRESS: at 25.21% examples, 305478 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:52,738: INFO: EPOCH 3 - PROGRESS: at 26.56% examples, 305914 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:53,740: INFO: EPOCH 3 - PROGRESS: at 27.25% examples, 305860 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:54,741: INFO: EPOCH 3 - PROGRESS: at 28.07% examples, 305789 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:55,767: INFO: EPOCH 3 - PROGRESS: at 28.75% examples, 305794 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:56,774: INFO: EPOCH 3 - PROGRESS: at 29.23% examples, 305708 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:57,779: INFO: EPOCH 3 - PROGRESS: at 30.00% examples, 305771 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:04:58,820: INFO: EPOCH 3 - PROGRESS: at 31.52% examples, 305928 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:04:59,846: INFO: EPOCH 3 - PROGRESS: at 32.59% examples, 306341 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:05:00,846: INFO: EPOCH 3 - PROGRESS: at 34.22% examples, 306954 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:01,854: INFO: EPOCH 3 - PROGRESS: at 35.74% examples, 307215 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:02,859: INFO: EPOCH 3 - PROGRESS: at 37.09% examples, 307577 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:03,883: INFO: EPOCH 3 - PROGRESS: at 38.00% examples, 307800 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:04,890: INFO: EPOCH 3 - PROGRESS: at 39.47% examples, 308035 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:05,906: INFO: EPOCH 3 - PROGRESS: at 40.86% examples, 308438 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:06,909: INFO: EPOCH 3 - PROGRESS: at 41.98% examples, 308550 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:07,916: INFO: EPOCH 3 - PROGRESS: at 43.64% examples, 308958 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:08,921: INFO: EPOCH 3 - PROGRESS: at 44.67% examples, 309118 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:09,954: INFO: EPOCH 3 - PROGRESS: at 45.37% examples, 309147 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:05:10,981: INFO: EPOCH 3 - PROGRESS: at 46.11% examples, 309051 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:11,988: INFO: EPOCH 3 - PROGRESS: at 46.78% examples, 308838 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:13,004: INFO: EPOCH 3 - PROGRESS: at 47.37% examples, 308834 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:14,034: INFO: EPOCH 3 - PROGRESS: at 48.13% examples, 308794 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:15,044: INFO: EPOCH 3 - PROGRESS: at 48.83% examples, 308494 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:16,057: INFO: EPOCH 3 - PROGRESS: at 50.41% examples, 308750 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:17,100: INFO: EPOCH 3 - PROGRESS: at 51.47% examples, 308544 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:05:18,101: INFO: EPOCH 3 - PROGRESS: at 54.29% examples, 308947 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:19,115: INFO: EPOCH 3 - PROGRESS: at 57.60% examples, 309327 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:20,134: INFO: EPOCH 3 - PROGRESS: at 62.13% examples, 310045 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:21,177: INFO: EPOCH 3 - PROGRESS: at 63.34% examples, 310127 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:22,200: INFO: EPOCH 3 - PROGRESS: at 64.23% examples, 310037 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:23,237: INFO: EPOCH 3 - PROGRESS: at 65.15% examples, 309761 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:05:24,246: INFO: EPOCH 3 - PROGRESS: at 65.99% examples, 309656 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:25,254: INFO: EPOCH 3 - PROGRESS: at 66.78% examples, 309424 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:26,256: INFO: EPOCH 3 - PROGRESS: at 68.13% examples, 309391 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:05:27,275: INFO: EPOCH 3 - PROGRESS: at 69.30% examples, 309267 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:28,310: INFO: EPOCH 3 - PROGRESS: at 69.98% examples, 309101 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:29,320: INFO: EPOCH 3 - PROGRESS: at 70.68% examples, 308978 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:30,354: INFO: EPOCH 3 - PROGRESS: at 71.38% examples, 309048 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:05:31,360: INFO: EPOCH 3 - PROGRESS: at 72.05% examples, 309184 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:32,363: INFO: EPOCH 3 - PROGRESS: at 72.85% examples, 309100 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:33,371: INFO: EPOCH 3 - PROGRESS: at 73.55% examples, 309048 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:34,407: INFO: EPOCH 3 - PROGRESS: at 74.56% examples, 309175 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:35,438: INFO: EPOCH 3 - PROGRESS: at 75.22% examples, 309146 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:36,442: INFO: EPOCH 3 - PROGRESS: at 75.81% examples, 309098 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:37,466: INFO: EPOCH 3 - PROGRESS: at 76.34% examples, 309194 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:38,472: INFO: EPOCH 3 - PROGRESS: at 76.93% examples, 309232 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:39,479: INFO: EPOCH 3 - PROGRESS: at 77.60% examples, 309241 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:40,497: INFO: EPOCH 3 - PROGRESS: at 78.35% examples, 309169 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:05:41,506: INFO: EPOCH 3 - PROGRESS: at 79.07% examples, 309031 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:42,522: INFO: EPOCH 3 - PROGRESS: at 79.90% examples, 309175 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:43,548: INFO: EPOCH 3 - PROGRESS: at 80.98% examples, 309049 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:44,555: INFO: EPOCH 3 - PROGRESS: at 81.58% examples, 308878 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:05:45,572: INFO: EPOCH 3 - PROGRESS: at 82.64% examples, 308986 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:46,572: INFO: EPOCH 3 - PROGRESS: at 84.69% examples, 309222 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:47,611: INFO: EPOCH 3 - PROGRESS: at 86.05% examples, 309376 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:48,631: INFO: EPOCH 3 - PROGRESS: at 87.18% examples, 309538 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:49,631: INFO: EPOCH 3 - PROGRESS: at 88.37% examples, 309703 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:50,641: INFO: EPOCH 3 - PROGRESS: at 89.26% examples, 309731 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:05:51,657: INFO: EPOCH 3 - PROGRESS: at 90.26% examples, 309715 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:52,661: INFO: EPOCH 3 - PROGRESS: at 91.16% examples, 309873 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:53,666: INFO: EPOCH 3 - PROGRESS: at 92.16% examples, 309739 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:54,671: INFO: EPOCH 3 - PROGRESS: at 93.18% examples, 309703 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:55,674: INFO: EPOCH 3 - PROGRESS: at 93.83% examples, 309690 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:56,676: INFO: EPOCH 3 - PROGRESS: at 94.25% examples, 309664 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:57,713: INFO: EPOCH 3 - PROGRESS: at 95.18% examples, 309710 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:58,713: INFO: EPOCH 3 - PROGRESS: at 96.18% examples, 309752 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:05:59,727: INFO: EPOCH 3 - PROGRESS: at 97.01% examples, 309883 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:00,744: INFO: EPOCH 3 - PROGRESS: at 97.93% examples, 309825 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:01,750: INFO: EPOCH 3 - PROGRESS: at 98.74% examples, 309768 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:02,753: INFO: EPOCH 3 - PROGRESS: at 99.61% examples, 309566 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:03,203: INFO: worker thread finished; awaiting finish of 1 more threads\n",
            "2020-03-02 19:06:03,216: INFO: worker thread finished; awaiting finish of 0 more threads\n",
            "2020-03-02 19:06:03,217: INFO: EPOCH - 3 : training on 39921798 raw words (35370894 effective words) took 114.2s, 309624 effective words/s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss after epoch 2: 8968648.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-03-02 19:06:04,220: INFO: EPOCH 4 - PROGRESS: at 0.35% examples, 307596 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:05,229: INFO: EPOCH 4 - PROGRESS: at 0.56% examples, 314046 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:06,240: INFO: EPOCH 4 - PROGRESS: at 0.95% examples, 315351 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:07,287: INFO: EPOCH 4 - PROGRESS: at 1.46% examples, 308496 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:08,297: INFO: EPOCH 4 - PROGRESS: at 1.82% examples, 309895 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:09,312: INFO: EPOCH 4 - PROGRESS: at 2.34% examples, 311803 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:10,317: INFO: EPOCH 4 - PROGRESS: at 2.91% examples, 311313 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:11,345: INFO: EPOCH 4 - PROGRESS: at 3.55% examples, 310977 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:12,348: INFO: EPOCH 4 - PROGRESS: at 4.06% examples, 310416 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:13,357: INFO: EPOCH 4 - PROGRESS: at 4.53% examples, 310454 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:14,364: INFO: EPOCH 4 - PROGRESS: at 5.69% examples, 309909 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:15,364: INFO: EPOCH 4 - PROGRESS: at 6.12% examples, 309590 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:06:16,388: INFO: EPOCH 4 - PROGRESS: at 6.53% examples, 308812 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:17,397: INFO: EPOCH 4 - PROGRESS: at 6.87% examples, 307467 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:18,420: INFO: EPOCH 4 - PROGRESS: at 7.27% examples, 307497 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:19,426: INFO: EPOCH 4 - PROGRESS: at 7.63% examples, 307794 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:20,443: INFO: EPOCH 4 - PROGRESS: at 8.10% examples, 308362 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:21,472: INFO: EPOCH 4 - PROGRESS: at 8.51% examples, 308562 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:22,487: INFO: EPOCH 4 - PROGRESS: at 9.06% examples, 308228 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:23,517: INFO: EPOCH 4 - PROGRESS: at 9.73% examples, 308062 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:24,554: INFO: EPOCH 4 - PROGRESS: at 10.16% examples, 307126 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:25,560: INFO: EPOCH 4 - PROGRESS: at 10.72% examples, 307250 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:26,597: INFO: EPOCH 4 - PROGRESS: at 11.30% examples, 306740 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:27,606: INFO: EPOCH 4 - PROGRESS: at 11.82% examples, 306770 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:28,656: INFO: EPOCH 4 - PROGRESS: at 12.35% examples, 306069 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:29,677: INFO: EPOCH 4 - PROGRESS: at 12.91% examples, 305816 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:30,687: INFO: EPOCH 4 - PROGRESS: at 13.52% examples, 306356 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:31,720: INFO: EPOCH 4 - PROGRESS: at 14.16% examples, 306410 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:32,744: INFO: EPOCH 4 - PROGRESS: at 14.71% examples, 306145 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:33,756: INFO: EPOCH 4 - PROGRESS: at 15.37% examples, 306379 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:34,759: INFO: EPOCH 4 - PROGRESS: at 15.92% examples, 306968 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:35,784: INFO: EPOCH 4 - PROGRESS: at 16.59% examples, 306932 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:36,812: INFO: EPOCH 4 - PROGRESS: at 17.13% examples, 306661 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:37,850: INFO: EPOCH 4 - PROGRESS: at 17.78% examples, 306742 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:38,872: INFO: EPOCH 4 - PROGRESS: at 18.32% examples, 306526 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:39,904: INFO: EPOCH 4 - PROGRESS: at 19.21% examples, 306385 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:40,945: INFO: EPOCH 4 - PROGRESS: at 20.01% examples, 306100 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:41,987: INFO: EPOCH 4 - PROGRESS: at 20.82% examples, 306235 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:43,044: INFO: EPOCH 4 - PROGRESS: at 21.79% examples, 306042 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:44,057: INFO: EPOCH 4 - PROGRESS: at 22.95% examples, 306016 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:45,087: INFO: EPOCH 4 - PROGRESS: at 24.44% examples, 306027 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:46,101: INFO: EPOCH 4 - PROGRESS: at 25.64% examples, 306207 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:47,118: INFO: EPOCH 4 - PROGRESS: at 26.72% examples, 306573 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:48,149: INFO: EPOCH 4 - PROGRESS: at 27.42% examples, 306492 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:49,173: INFO: EPOCH 4 - PROGRESS: at 28.33% examples, 306799 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:50,210: INFO: EPOCH 4 - PROGRESS: at 28.95% examples, 307069 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:51,250: INFO: EPOCH 4 - PROGRESS: at 29.60% examples, 307026 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:52,256: INFO: EPOCH 4 - PROGRESS: at 30.54% examples, 307184 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:53,280: INFO: EPOCH 4 - PROGRESS: at 31.86% examples, 307489 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:54,311: INFO: EPOCH 4 - PROGRESS: at 33.18% examples, 307548 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:55,321: INFO: EPOCH 4 - PROGRESS: at 34.86% examples, 308145 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:06:56,335: INFO: EPOCH 4 - PROGRESS: at 36.29% examples, 308598 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:57,361: INFO: EPOCH 4 - PROGRESS: at 37.45% examples, 308810 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:06:58,375: INFO: EPOCH 4 - PROGRESS: at 38.77% examples, 309136 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:06:59,400: INFO: EPOCH 4 - PROGRESS: at 40.09% examples, 309613 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:00,416: INFO: EPOCH 4 - PROGRESS: at 41.63% examples, 309897 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:01,418: INFO: EPOCH 4 - PROGRESS: at 42.93% examples, 310162 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:02,434: INFO: EPOCH 4 - PROGRESS: at 44.30% examples, 310103 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:03,443: INFO: EPOCH 4 - PROGRESS: at 45.00% examples, 310209 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:04,499: INFO: EPOCH 4 - PROGRESS: at 45.72% examples, 310271 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:05,511: INFO: EPOCH 4 - PROGRESS: at 46.55% examples, 310472 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:06,541: INFO: EPOCH 4 - PROGRESS: at 47.18% examples, 310360 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:07,573: INFO: EPOCH 4 - PROGRESS: at 47.84% examples, 310335 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:08,613: INFO: EPOCH 4 - PROGRESS: at 48.65% examples, 310387 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:09,639: INFO: EPOCH 4 - PROGRESS: at 49.97% examples, 310730 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:10,660: INFO: EPOCH 4 - PROGRESS: at 51.21% examples, 310785 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:11,673: INFO: EPOCH 4 - PROGRESS: at 53.70% examples, 310971 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:12,683: INFO: EPOCH 4 - PROGRESS: at 56.57% examples, 311233 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:13,691: INFO: EPOCH 4 - PROGRESS: at 61.32% examples, 311912 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:14,728: INFO: EPOCH 4 - PROGRESS: at 62.95% examples, 311769 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:15,736: INFO: EPOCH 4 - PROGRESS: at 63.87% examples, 311850 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:16,738: INFO: EPOCH 4 - PROGRESS: at 64.85% examples, 311522 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:17,745: INFO: EPOCH 4 - PROGRESS: at 65.78% examples, 311468 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:18,767: INFO: EPOCH 4 - PROGRESS: at 66.51% examples, 311358 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:19,768: INFO: EPOCH 4 - PROGRESS: at 67.86% examples, 311266 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:20,797: INFO: EPOCH 4 - PROGRESS: at 69.15% examples, 311278 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:21,813: INFO: EPOCH 4 - PROGRESS: at 69.89% examples, 311355 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:22,828: INFO: EPOCH 4 - PROGRESS: at 70.59% examples, 311211 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:23,854: INFO: EPOCH 4 - PROGRESS: at 71.23% examples, 311021 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:24,906: INFO: EPOCH 4 - PROGRESS: at 71.90% examples, 310797 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:25,925: INFO: EPOCH 4 - PROGRESS: at 72.58% examples, 310621 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:26,938: INFO: EPOCH 4 - PROGRESS: at 73.32% examples, 310527 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:27,951: INFO: EPOCH 4 - PROGRESS: at 74.33% examples, 310630 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:28,973: INFO: EPOCH 4 - PROGRESS: at 75.02% examples, 310492 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:29,977: INFO: EPOCH 4 - PROGRESS: at 75.60% examples, 310453 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:30,985: INFO: EPOCH 4 - PROGRESS: at 76.16% examples, 310568 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:32,031: INFO: EPOCH 4 - PROGRESS: at 76.70% examples, 310545 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:33,046: INFO: EPOCH 4 - PROGRESS: at 77.43% examples, 310568 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:34,073: INFO: EPOCH 4 - PROGRESS: at 78.17% examples, 310678 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:35,081: INFO: EPOCH 4 - PROGRESS: at 78.95% examples, 310801 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:36,109: INFO: EPOCH 4 - PROGRESS: at 79.70% examples, 310710 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:37,126: INFO: EPOCH 4 - PROGRESS: at 80.78% examples, 310669 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:38,177: INFO: EPOCH 4 - PROGRESS: at 81.55% examples, 310723 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:39,192: INFO: EPOCH 4 - PROGRESS: at 82.35% examples, 310623 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:40,226: INFO: EPOCH 4 - PROGRESS: at 84.50% examples, 310736 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:41,254: INFO: EPOCH 4 - PROGRESS: at 85.86% examples, 310727 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:42,289: INFO: EPOCH 4 - PROGRESS: at 87.01% examples, 310681 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:43,299: INFO: EPOCH 4 - PROGRESS: at 88.01% examples, 310555 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:44,304: INFO: EPOCH 4 - PROGRESS: at 89.01% examples, 310532 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:45,321: INFO: EPOCH 4 - PROGRESS: at 89.84% examples, 310447 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:46,326: INFO: EPOCH 4 - PROGRESS: at 90.86% examples, 310402 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:47,371: INFO: EPOCH 4 - PROGRESS: at 91.81% examples, 310368 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:48,404: INFO: EPOCH 4 - PROGRESS: at 92.90% examples, 310336 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:49,417: INFO: EPOCH 4 - PROGRESS: at 93.78% examples, 310395 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:50,444: INFO: EPOCH 4 - PROGRESS: at 94.15% examples, 310430 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:51,446: INFO: EPOCH 4 - PROGRESS: at 94.87% examples, 310224 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:52,449: INFO: EPOCH 4 - PROGRESS: at 95.88% examples, 310310 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:53,469: INFO: EPOCH 4 - PROGRESS: at 96.66% examples, 310169 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:54,485: INFO: EPOCH 4 - PROGRESS: at 97.55% examples, 309991 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:55,531: INFO: EPOCH 4 - PROGRESS: at 98.41% examples, 309923 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:07:56,546: INFO: EPOCH 4 - PROGRESS: at 99.33% examples, 309894 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:57,314: INFO: worker thread finished; awaiting finish of 1 more threads\n",
            "2020-03-02 19:07:57,317: INFO: worker thread finished; awaiting finish of 0 more threads\n",
            "2020-03-02 19:07:57,318: INFO: EPOCH - 4 : training on 39921798 raw words (35370081 effective words) took 114.1s, 309994 effective words/s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss after epoch 3: 8253628.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-03-02 19:07:58,332: INFO: EPOCH 5 - PROGRESS: at 0.36% examples, 308065 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:07:59,353: INFO: EPOCH 5 - PROGRESS: at 0.57% examples, 317763 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:00,385: INFO: EPOCH 5 - PROGRESS: at 0.99% examples, 318263 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:01,404: INFO: EPOCH 5 - PROGRESS: at 1.51% examples, 315516 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:02,427: INFO: EPOCH 5 - PROGRESS: at 1.87% examples, 315011 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:03,435: INFO: EPOCH 5 - PROGRESS: at 2.38% examples, 316891 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:04,440: INFO: EPOCH 5 - PROGRESS: at 2.97% examples, 317622 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:05,463: INFO: EPOCH 5 - PROGRESS: at 3.69% examples, 318167 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:06,504: INFO: EPOCH 5 - PROGRESS: at 4.16% examples, 316708 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:07,510: INFO: EPOCH 5 - PROGRESS: at 4.70% examples, 314219 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:08,543: INFO: EPOCH 5 - PROGRESS: at 5.81% examples, 314447 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:09,555: INFO: EPOCH 5 - PROGRESS: at 6.24% examples, 314598 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:10,584: INFO: EPOCH 5 - PROGRESS: at 6.63% examples, 313712 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:11,591: INFO: EPOCH 5 - PROGRESS: at 7.03% examples, 313033 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:12,596: INFO: EPOCH 5 - PROGRESS: at 7.39% examples, 312568 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:13,597: INFO: EPOCH 5 - PROGRESS: at 7.78% examples, 312551 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:14,606: INFO: EPOCH 5 - PROGRESS: at 8.18% examples, 311951 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:15,619: INFO: EPOCH 5 - PROGRESS: at 8.63% examples, 312063 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:16,667: INFO: EPOCH 5 - PROGRESS: at 9.24% examples, 311189 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:17,692: INFO: EPOCH 5 - PROGRESS: at 9.84% examples, 311447 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:18,713: INFO: EPOCH 5 - PROGRESS: at 10.32% examples, 311017 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:19,740: INFO: EPOCH 5 - PROGRESS: at 10.97% examples, 311089 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:20,748: INFO: EPOCH 5 - PROGRESS: at 11.46% examples, 311129 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:21,789: INFO: EPOCH 5 - PROGRESS: at 12.04% examples, 310830 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:22,812: INFO: EPOCH 5 - PROGRESS: at 12.60% examples, 310602 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:23,818: INFO: EPOCH 5 - PROGRESS: at 13.14% examples, 309765 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:24,852: INFO: EPOCH 5 - PROGRESS: at 13.73% examples, 309636 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:25,867: INFO: EPOCH 5 - PROGRESS: at 14.30% examples, 308900 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:26,901: INFO: EPOCH 5 - PROGRESS: at 14.91% examples, 308945 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:27,919: INFO: EPOCH 5 - PROGRESS: at 15.53% examples, 309334 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:28,939: INFO: EPOCH 5 - PROGRESS: at 16.12% examples, 309118 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:29,967: INFO: EPOCH 5 - PROGRESS: at 16.77% examples, 309286 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:30,974: INFO: EPOCH 5 - PROGRESS: at 17.36% examples, 309526 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:31,974: INFO: EPOCH 5 - PROGRESS: at 17.99% examples, 309261 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:32,982: INFO: EPOCH 5 - PROGRESS: at 18.53% examples, 309046 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:34,005: INFO: EPOCH 5 - PROGRESS: at 19.44% examples, 309246 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:35,023: INFO: EPOCH 5 - PROGRESS: at 20.38% examples, 309518 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:36,051: INFO: EPOCH 5 - PROGRESS: at 21.16% examples, 309415 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:37,070: INFO: EPOCH 5 - PROGRESS: at 22.07% examples, 309183 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:38,072: INFO: EPOCH 5 - PROGRESS: at 23.42% examples, 309590 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:39,092: INFO: EPOCH 5 - PROGRESS: at 24.80% examples, 309360 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:40,099: INFO: EPOCH 5 - PROGRESS: at 26.08% examples, 309425 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:41,124: INFO: EPOCH 5 - PROGRESS: at 26.97% examples, 309338 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:42,156: INFO: EPOCH 5 - PROGRESS: at 27.69% examples, 309022 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:43,173: INFO: EPOCH 5 - PROGRESS: at 28.45% examples, 308751 words/s, in_qsize 3, out_qsize 1\n",
            "2020-03-02 19:08:44,184: INFO: EPOCH 5 - PROGRESS: at 28.98% examples, 308617 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:45,220: INFO: EPOCH 5 - PROGRESS: at 29.67% examples, 308540 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:46,234: INFO: EPOCH 5 - PROGRESS: at 30.76% examples, 308492 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:47,261: INFO: EPOCH 5 - PROGRESS: at 31.91% examples, 308577 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:48,277: INFO: EPOCH 5 - PROGRESS: at 33.26% examples, 309032 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:49,283: INFO: EPOCH 5 - PROGRESS: at 34.95% examples, 309625 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:50,315: INFO: EPOCH 5 - PROGRESS: at 36.43% examples, 310042 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:51,328: INFO: EPOCH 5 - PROGRESS: at 37.48% examples, 309896 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:52,348: INFO: EPOCH 5 - PROGRESS: at 38.85% examples, 310163 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:53,401: INFO: EPOCH 5 - PROGRESS: at 40.09% examples, 310165 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:54,409: INFO: EPOCH 5 - PROGRESS: at 41.61% examples, 310192 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:55,447: INFO: EPOCH 5 - PROGRESS: at 42.84% examples, 310108 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:08:56,467: INFO: EPOCH 5 - PROGRESS: at 44.29% examples, 310316 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:57,481: INFO: EPOCH 5 - PROGRESS: at 44.95% examples, 310245 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:58,487: INFO: EPOCH 5 - PROGRESS: at 45.62% examples, 310272 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:08:59,494: INFO: EPOCH 5 - PROGRESS: at 46.42% examples, 310410 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:00,528: INFO: EPOCH 5 - PROGRESS: at 47.11% examples, 310385 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:01,552: INFO: EPOCH 5 - PROGRESS: at 47.73% examples, 310251 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:02,589: INFO: EPOCH 5 - PROGRESS: at 48.53% examples, 309994 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:03,613: INFO: EPOCH 5 - PROGRESS: at 49.50% examples, 310150 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:04,617: INFO: EPOCH 5 - PROGRESS: at 50.98% examples, 310469 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:09:05,620: INFO: EPOCH 5 - PROGRESS: at 53.15% examples, 310793 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:06,633: INFO: EPOCH 5 - PROGRESS: at 55.73% examples, 310996 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:07,644: INFO: EPOCH 5 - PROGRESS: at 60.36% examples, 311617 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:08,646: INFO: EPOCH 5 - PROGRESS: at 62.82% examples, 311870 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:09,662: INFO: EPOCH 5 - PROGRESS: at 63.75% examples, 311944 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:10,711: INFO: EPOCH 5 - PROGRESS: at 64.81% examples, 311833 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:11,750: INFO: EPOCH 5 - PROGRESS: at 65.73% examples, 311640 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:12,783: INFO: EPOCH 5 - PROGRESS: at 66.45% examples, 311474 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:13,814: INFO: EPOCH 5 - PROGRESS: at 67.82% examples, 311359 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:14,863: INFO: EPOCH 5 - PROGRESS: at 69.10% examples, 311191 words/s, in_qsize 4, out_qsize 1\n",
            "2020-03-02 19:09:15,871: INFO: EPOCH 5 - PROGRESS: at 69.86% examples, 311289 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:16,875: INFO: EPOCH 5 - PROGRESS: at 70.53% examples, 311206 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:17,884: INFO: EPOCH 5 - PROGRESS: at 71.21% examples, 311175 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:18,906: INFO: EPOCH 5 - PROGRESS: at 71.86% examples, 310967 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:19,911: INFO: EPOCH 5 - PROGRESS: at 72.58% examples, 311038 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:20,937: INFO: EPOCH 5 - PROGRESS: at 73.36% examples, 311093 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:21,938: INFO: EPOCH 5 - PROGRESS: at 74.30% examples, 311021 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:22,951: INFO: EPOCH 5 - PROGRESS: at 75.01% examples, 310830 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:23,967: INFO: EPOCH 5 - PROGRESS: at 75.56% examples, 310632 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:24,976: INFO: EPOCH 5 - PROGRESS: at 76.12% examples, 310641 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:26,001: INFO: EPOCH 5 - PROGRESS: at 76.61% examples, 310419 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:27,002: INFO: EPOCH 5 - PROGRESS: at 77.32% examples, 310377 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:28,026: INFO: EPOCH 5 - PROGRESS: at 78.00% examples, 310415 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:29,059: INFO: EPOCH 5 - PROGRESS: at 78.76% examples, 310244 words/s, in_qsize 4, out_qsize 1\n",
            "2020-03-02 19:09:30,071: INFO: EPOCH 5 - PROGRESS: at 79.53% examples, 310262 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:31,093: INFO: EPOCH 5 - PROGRESS: at 80.45% examples, 310386 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:32,120: INFO: EPOCH 5 - PROGRESS: at 81.39% examples, 310400 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:33,157: INFO: EPOCH 5 - PROGRESS: at 82.06% examples, 310331 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:34,164: INFO: EPOCH 5 - PROGRESS: at 84.26% examples, 310628 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:35,182: INFO: EPOCH 5 - PROGRESS: at 85.76% examples, 310915 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:36,189: INFO: EPOCH 5 - PROGRESS: at 86.91% examples, 310894 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:37,198: INFO: EPOCH 5 - PROGRESS: at 87.87% examples, 310815 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:38,224: INFO: EPOCH 5 - PROGRESS: at 88.98% examples, 310931 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:39,243: INFO: EPOCH 5 - PROGRESS: at 89.78% examples, 310809 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:40,250: INFO: EPOCH 5 - PROGRESS: at 90.83% examples, 310668 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:41,282: INFO: EPOCH 5 - PROGRESS: at 91.64% examples, 310587 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:42,290: INFO: EPOCH 5 - PROGRESS: at 92.71% examples, 310461 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:43,338: INFO: EPOCH 5 - PROGRESS: at 93.64% examples, 310229 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:44,353: INFO: EPOCH 5 - PROGRESS: at 94.07% examples, 310146 words/s, in_qsize 4, out_qsize 0\n",
            "2020-03-02 19:09:45,374: INFO: EPOCH 5 - PROGRESS: at 94.69% examples, 310061 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:46,403: INFO: EPOCH 5 - PROGRESS: at 95.65% examples, 310007 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:47,456: INFO: EPOCH 5 - PROGRESS: at 96.54% examples, 309924 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:48,481: INFO: EPOCH 5 - PROGRESS: at 97.46% examples, 309961 words/s, in_qsize 3, out_qsize 0\n",
            "2020-03-02 19:09:49,511: INFO: EPOCH 5 - PROGRESS: at 98.33% examples, 309958 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:50,564: INFO: EPOCH 5 - PROGRESS: at 99.26% examples, 309955 words/s, in_qsize 2, out_qsize 1\n",
            "2020-03-02 19:09:51,417: INFO: worker thread finished; awaiting finish of 1 more threads\n",
            "2020-03-02 19:09:51,432: INFO: worker thread finished; awaiting finish of 0 more threads\n",
            "2020-03-02 19:09:51,435: INFO: EPOCH - 5 : training on 39921798 raw words (35368095 effective words) took 114.1s, 309939 effective words/s\n",
            "2020-03-02 19:09:51,436: INFO: training on a 199608990 raw words (176848499 effective words) took 578.0s, 305947 effective words/s\n",
            "2020-03-02 19:09:51,437: INFO: saving Word2Vec object under mswiki.model, separately None\n",
            "2020-03-02 19:09:51,439: INFO: storing np array 'vectors' to mswiki.model.wv.vectors.npy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss after epoch 4: 7485888.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2020-03-02 19:09:52,255: INFO: not storing attribute vectors_norm\n",
            "2020-03-02 19:09:52,261: INFO: storing np array 'syn1neg' to mswiki.model.trainables.syn1neg.npy\n",
            "2020-03-02 19:09:53,160: INFO: not storing attribute cum_table\n",
            "2020-03-02 19:09:53,654: INFO: saved mswiki.model\n",
            "2020-03-02 19:09:53,658: INFO: storing 175214x300 projection weights into mswiki.vector\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jS6optLtDjOZ"
      },
      "source": [
        "**Step 4: Plot the loss for Word2Vec Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QzQhowBv-B02",
        "outputId": "aba894d4-38e7-4bcb-87df-9c767fb906ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print (loss_list)\n",
        "loss = loss_list    #backup list\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "# \"b--\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "#plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Word2Vec Training Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "# Customize the major grid\n",
        "plt.grid(which='major', linestyle=':', linewidth='0.5', color='black')\n",
        "plt.rcParams['figure.figsize'] = [20, 10]\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[21949532.0, 13873472.0, 8968648.0, 8253628.0, 7485888.0]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l1bz4IrS2BstaXHwxfPGF78hSg+bcg25/c9eLb38rwkJo\n26hUI+aul/r6eg45BObNgwsvhFtugbFj4aWXfEcWPZZ7vf7mrhff/laEGYaxCUOGwM03w6xZkJMD\nhxwC550HTU2+IzMMw8gerAgLYeTIkb5D8Ia562Vj/wMOCPac/N734I47YNdd4YUXPAUXMZZ7vf7m\nrhff/laEhdCk+E9+c9dLZ/6DB8MNN8Arr0BubjCJ/9xz4dNPPQQYIZZ7vf7mrhff/laEheB7U0+f\nmLteuvLfbz94+2247DK4556gV+zZZ1MYXMRY7vX6m7tefPtbEWYYRrfJzYVrr4XXXoMttwwWfD3z\nTFi50ndkhmEYmYcVYSGUlZX5DsEb5q6X7vrvvTe89RZccUWwMfiYMfDkkxEHFzGWe73+5q4X3/5W\nhIXQ0tLiOwRvmLteeuI/cGCwCfi//w3Dhwebg59+OmTqskOWe73+5q4X3/5WhIWwZMkS3yF4w9z1\n0hv/ceNgzhy46ip4+OGgV+yxxyIILmIs93r9zV0vvv2tCDMMo8/EYnD11UExts02cPzxcNJJsHy5\n78gMwzDSFyvCQigtLfUdgjfMXS999d9992B48he/gMcfh9Gjg94x55ITX5RY7kt9h+ANc9eLb38r\nwgzDSCoDBsCVVwYT90tL4eST4YQToK7Od2SGYRjphRVhIVRXV/sOwRvmrpdk+u+6a7CUxW9+A08/\nHfSKPfhg+vaKWe6rfYfgDXPXi29/K8IMw4iM/v3h8suDRV5Hjgyenjz6aFi61HdkhmEY/rEiLISS\nkhLfIXjD3PUSlf+oUcG2RzfcEOw9OWYM3H9/evWKWe71+pu7Xnz7WxEWQiwW8x2CN8xdL1H65+QE\nG4G/804wVHnmmTB1KtTURHbLHmG51+tv7nrx7W9FWAhVVVW+Q/CGueslFf4jR8LLL8NNNwVfx4yB\nu+7y3ytmudfrb+568e1vRZhhGCmnXz+4+GKYNw/22gvOPRcmTYKPPvIdmWEYRuqIrAgTkXtEpF5E\nFoQcFxG5WUTeF5F5IrJXVLH0hqKiIt8heMPc9ZJq/7IyePFFuO224EnKXXeF22+H9etTGgZgudfs\nb+568e0fZU/YfcDkLo5PAXZOvKYBt0cYS4+Jx+O+Q/CGuevFh3+/fnDeeTB/Puy7L5x/Pnz96/DB\nB6mNw3Kv19/c9eLbP7IizDk3C1jRRZOjgT+5gNeBPBFJm5J88eLFvkPwhrnrxad/aSn84x9w553B\n9kdjx8Itt6SuV8xyr9ff3PXi27+/x3tvC3TcObMm8Vntxg1FZBpBbxmFhYXU1dXR0NBAfX09ACNH\njqSpqYna2uDUsrIyWlpa2jfmbNuWoG1RtpKSEmKxWPuEvKKiIuLxeHsyCgoKWLFiBTNnzgQgPz+f\nwsJCKisraW1tJS8vj6KiIqqqqmhpaSEej1NcXEx1dTXNzc3k5uZSWlpKTU0NTU1NxGIxysrKqK2t\npbGxkZycHMrLy9s9AEaNGhW5U35+PosWLdqsU0VFRdY5dTdPK1asoKKiIqucepKniooK70577hln\n5sztuOCCAVx8cR733PMZt93WQiz2UaS/e8uWLWv/N5/ueYrid6+6upq6urqscupunhYsCGbNZJNT\nd/O0YMECRo0alVVOPclTRUVFSpzCEBfhI0kiUgo85ZzbtZNjTwG/cc69knj/InC5c25OV9ccP368\nmzOnyyZJYeHChYwePTry+6Qj5q7THdLL37lgLbHvfhfWrIFf/hIuuSRY6iIK0sndB5r9zV2nO6TG\nX0TmOufGd3bM59ORHwMdV0krTnyWFuTn5/sOwRvmrpd08hcJ1hJbuDCYI3bppXDAAfCf/0Rzv3Ry\n94Fmf3PXi29/n0XYDOCMxFOS+wKfOuc2GYr0xea6ELMZc9dLOvpvsw3MmAEPPBAUYHvsAdddB+vW\nJfc+6eieSjT7m7tefPtHuUTFQ8BrQLmI1IjI2SIyXUSmJ5o8A3wAvA/cCZwfVSyGYWQ2IsG+kwsX\nwuGHB/tR7rcfvPuu78gMwzB6T2QT851zp2zmuAMuiOr+fcV3F6VPzF0v6e6/9dbw6KPw17/ChRcG\nC73+9Kdw2WUwYEDfrp3u7lGj2d/c9eLb31bMD6GwsNB3CN4wd71kgr8InHRS0Ct2zDHw4x/DPvsE\nq+/3hUxwjxLN/uauF9/+VoSFUFlZ6TsEb5i7XjLJf8QIePhheOQR+PhjGDcOrr4aWlp6d71Mco8C\nzf7mrhff/laEhdDa2uo7BG+Yu14y0f/444NesRNPhJ/9DL7yFXjrrZ5fJxPdk4lmf3PXi29/K8JC\nyMvL8x2CN8xdL5nqn58PDz4ITzwB9fWw997BMOWaNd2/Rqa6JwvN/uauF9/+VoSF4HtTT5+Yu14y\n3f+oo4JesdNPDxZ3HTcO3nyze+dmuntf0exv7nrx7W9FWAhtWxFoxNz1kg3+w4bBfffB009DY2Ow\nKfgPfwirV3d9Xja49wXN/uauF9/+VoSF0NLb2b1ZgLnrJZv8Dz88WEfsrLPg2mthzz3htdfC22eT\ne2/Q7G/uevHtb0VY5cJMPQAAENdJREFUCPF43HcI3jB3vWSb/9ChcNdd8PzzsGoV7L9/sP3RqlWb\nts02956i2d/c9eLb34qwEIqLi32H4A1z10u2+k+cCPPnw3e+A7/9Ley+O8yevWGbbHXvLpr9zV0v\nvv2tCAuhurradwjeMHe9ZLP/llvC7bfDiy9CayscdBBcfDF88UVwPJvdu4Nmf3PXi29/K8JCaG5u\n9h2CN8xdLxr8DzkkWF3/wgvhlltg7Fh46SUd7l2h2d/c9eLbP7K9IzOd3Nxc3yF4w9z1osV/yBC4\n+WY44QT49reDwuxrX9uN//f/oKgo2KOyqOjL7zX8WLTkvjPMXS++/a0IC6G0tNR3CN4wd71o8z/w\nwKBX7Kc/hQcfHMqsWcFQ5cYMHbppYdbZ98OGBXtbZiLact8Rc9eLb38rwkKoqanxvrGnL8xdpzvo\n9B88GP73f+GUUyrYY49xNDRAbW3wWrZs0+/feCP42tkTlrHYl0VZV0VbYSEMGJB6167QmPs2zF2n\nO/j3tyIshKamJt8heMPc9aLZv6mpiZwcKCgIXrvvvrn24YVabS188AH861/wySebnisCw4d3Xqht\n/FmqnqDXnnutaHYH//5WhIUQi8V8h+ANc9eLZv+eusfjwWvkyK7brV0LdXVdF2z/+U/wde3aTc/f\nYovN96wVFQVFXb8+PGpludeJZnfw729FWAhlZWW+Q/CGuetFs39U7gMGQHFx8OoK52DFivBCbdmy\nYP7a88/DZ59ten5OTjDMubl5a4WFMGjQpudb7nWi2R38+1sRFkJtba3acXJz1+kOuv19u4tAfn7w\nGjOm67arVn1ZoHVWtNXUwJw5UF8P69dvev6wYZsOga5fv47x4zcs2IYOzdwHDXqC79z7RLM7+Pe3\nIiyExsZG3yF4w9z1otk/k9wHD4YddwxeXbFuHSxf3nXv2quvBt+vXr3tJucPGtS9Bw0KCqB/Bv/f\nJJNyn2w0u4N//wz+ZxMtOTk5vkPwhrnrRbN/Nrr37/9lsbTnnuHtnIPnnnuVHXbYL7RgW7wYXn45\nGDLdGJGgEOvOgwZbbBGdb2/Jxtx3F83u4N/firAQysvLfYfgDXPXi2Z/ze4iMG7cThQUwC67dN12\nzZovHzQIGw6dPz9os27dpufH49170CA/P3VDoZpzr9kd/PtbERZCXV0dBQUFvsPwgrnrdAfd/prd\nofv+AwfCdtsFr65Yv572NdfCetfeeiv4+vnnm54/YMDmHzTYeuvg1dcH3DTnXrM7+Pe3IiyEhoYG\n3yF4w9z1otlfszsk379fPxgxInjttlvXbT//vOsHDaqr4fXXg/ltzm16fn5+93Y0iMc7713TnHvN\n7uDf34owwzAMwytDhsBOOwWvrli7Nnjis6sHDWbNCr5vadn0/NzczouzTz/dmpYW2Gab4JXJ208Z\nmYUVYSGMGjXKdwjeMHe9aPbX7A6Z4T9gAGy7bfDqCuegsbHreWvvvgsvvhi0g1247rovzx80KCjG\ntt2266/ZsPd1JuQ9Snz7R1qEichk4CYgB7jLOfebjY5vB9wP5CXa/NA590yUMXWXhoYGtWunmLtO\nd9Dtr9kdsstfJOjNGjYMRo/uum1zM8ye/R6DB+/M0qXw8cds8HXuXJgxI2i3McOGbb5YS/flO7Ip\n773Bt39kvxoikgPcChwG1ABvisgM59zCDs1+DPzVOXe7iIwGngFKo4qpJ9TX1zN6c/96sxRz1+kO\nuv01u4Ne/9xciMU+ZsKEnUPbOAeffkqnRVrb13ffDXrZWls3PLdfv2Dos2Nx1lnB5msIVGve2/Dt\nH2V9vjfwvnPuAwAR+QtwNNCxCHPAlonvhwJLI4zHMAzDMHqMCOTlBa+u/n/d2hrMWQsr1j74AGbP\n7nytNU1DoMaXRFmEbQss6fC+BthnozZXA/8QkYuALYCvRxhPjxi5uV15sxhz14tmf83uoNs/We45\nOV9O9h83Lrzd6tXBvLSwXrVUDoFqzjv49/c9Un0KcJ9z7gYR+SrwgIjs6pzbYLczEZkGTAMoLCyk\nrq6OhoYG6uvrgeCH2NTURG1tLRBsyNnS0sKSJUENWFpaCkB1dTUAJSUlxGIxqqqqACgqKiIej7N4\n8WIACgoKWLlyZfv7/Px8CgsLqayspLW1lby8PIqKiqiqqqKlpYV4PE5xcTHV1dU0NzeTm5tLaWkp\nNTU1NDU1EYvFKCsro7a2lsbGRnJycigvL2/3gGByYNRO+fn5LFq0aLNO77//PmPGjMkqp+7myTlH\nfX19Vjn1JE///e9/+frXv55VTt3NU1VVVXvM2eLUkzx98cUX5OTkZJVTd/P08ssvt5+baqe99+7c\n6f33q/jii/6IbEtT05ZUVNTzyScDWbUqj8bGwXz4YQtvvTWQlStjtLZuOI7Zr59jq61ayM9fw9Zb\nr6e0NMbAgQ3k5X3BNtvA7rsPZ/36GkQaWbaslkMOOSQj8hTF796cOXPYbrvtIncKQ1xni64kgURR\ndbVzblLi/Y8AnHO/7tDmXWCyc25J4v0HwL7Oufqw644fP97NmTMnkpg7MnPmTA4++ODI75OOmPvB\nvsPwhmZ/ze6g2z+T3Tc3BLp0afDqbDmsQYNgq62aKSvLVTsEmorci8hc59z4zo5F2RP2JrCziOwA\nfAyc/P/bu9+YK+s6juPvj/wJpoQJcscEux3yoNk/yZFFa4aLVTp8oBtUVDhbk2VSc6X1wJbrST1o\nDnU1NQPT0qbByCHJlJVZakAggY2xvOvW0QANiGUY9O3B+d1xdjoX9yHuc3431+/z2s7u61zXtXO+\nH3/u4nuuv8AnW9b5C3A5sFLS24EJwL4u1mRmZlYbp3oIdMuWQxw7NrH2V4GOVl37TxYRRyXdAPyC\nxu0n7ouIHZJuAzZFxFrgJuAeSV+mcZL+0ujWrrmTNGvWrNwlZOPs5So5f8nZoez8JWSfMAEuuKDx\najY4+AYzZzamT/Uq0L6+4S8sGG03ws099l3tW9M9v9a1zLu1aXonMK+bNfy/3mh3u+VCOHu5Ss5f\ncnYoO7+zN5zKVaDN0y+9BM88U30IdDRdBZp77L3zsMLg4GD2DjkXZy8zO5Sdv+TsUHZ+Zz+57Dmu\nAm3XqPX1nfoh0Nxj7ybMzMzMRlzVIdBmJR4CbeYmrMLQ5aglcvZylZy/5OxQdn5nzyf3IdBzzql+\nUkIvuAkzMzOzUa1bh0CXLJnEggW9ydCOm7AKAwMD2X8h5OLs/bnLyKbk/CVnh7LzO3t/7jJGzMke\nAt22bSdwac/qa+UmzMzMzIrRfAh0795/Zq3ljKzfPorNHLpxSoGcvVwl5y85O5Sd39nLlTu/m7AK\n48ePz11CNs5erpLzl5wdys7v7OXKnd9NWIWhh3KWyNnLVXL+krND2fmdvVy587sJMzMzM8vATViF\n6dOn5y4hG2cvV8n5S84OZed39nLlzu8mrMKkSZNyl5CNs5er5PwlZ4ey8zt7uXLndxNWYdeuXblL\nyMbZy1Vy/pKzQ9n5nb1cufO7CTMzMzPLwE1YhWnTpuUuIRtnL1fJ+UvODmXnd/Zy5c7vJqzClClT\ncpeQjbOXq+T8JWeHsvM7e7ly51dEZC3gZEnaB/y5B181Fdjfg+8ZjZy9XCXnLzk7lJ3f2cvVi/xv\ni4hz2y047ZqwXpG0KSIuyV1HDs5eZnYoO3/J2aHs/M5eZnbIn9+HI83MzMwycBNmZmZmloGbsGp3\n5y4gI2cvV8n5S84OZed39nJlze9zwszMzMwy8J4wMzMzswyKbsIk3Sdpr6Q/VCyXpBWSdkt6QdKc\nXtfYLR1kv0zSQUlb0+vWXtfYLZJmStooaaekHZKWt1mnzmPfSf5ajr+kCZKel7QtZf9mm3XeJOnh\nNPbPServfaUjr8PsSyXtaxr3z+WotZskjZH0e0mPtVlWy7EfMkz2Wo+9pAFJ21O2TW2WZ9nmj+3F\nl4xiK4E7gfsrln8MmJ1e7wO+l/7WwUpOnB3g6Yi4sjfl9NRR4KaI2CJpErBZ0oaI2Nm0Tp3HvpP8\nUM/xPwLMj4jDksYBv5b0eEQ827TOdcDfIuJCSYuBbwOLchQ7wjrJDvBwRNyQob5eWQ68CLy5zbK6\njv2QE2WH+o/9hyOi6p5gWbb5Re8Ji4hfAa+dYJWrgPuj4VngbEnTe1Ndd3WQvbYiYk9EbEnTf6ex\nUTqvZbU6j30n+Wspjefh9HZcerWeGHsVsCpNPwJcLkk9KrFrOsxea5JmAFcA91asUsuxh46yly7L\nNr/oJqwD5wGDTe9fppB/rJL3p0MXj0u6KHcx3ZAON1wMPNeyqIixP0F+qOn4p0MyW4G9wIaIqBz7\niDgKHARq8WyXDrIDXJ0OxzwiaWaPS+y224GvAv+uWF7bsWf47FDvsQ/gCUmbJX2+zfIs23w3YVZl\nC41HLbwbuANYk7meESfpLOBR4EsRcSh3Pb02TP7ajn9EHIuI9wAzgLmS3pG7pl7pIPvPgf6IeBew\ngeN7hU57kq4E9kbE5ty19FqH2Ws79skHI2IOjcOOX5D0odwFgZuw4bwCNP8amJHm1V5EHBo6dBER\n64BxkqZmLmvEpHNiHgUejIiftVml1mM/XP66jz9ARBwANgIfbVn037GXNBaYDLza2+q6qyp7RLwa\nEUfS23uB9/a6ti6aByyUNAA8BMyX9EDLOnUd+2Gz13zsiYhX0t+9wGpgbssqWbb5bsJObC3wmXTV\nxKXAwYjYk7uoXpD01qFzISTNpfH/Sh02RqRcPwBejIjvVqxW27HvJH9dx1/SuZLOTtMTgY8Af2xZ\nbS3w2TR9DfBU1OCGip1kbzkHZiGN8wVrISK+FhEzIqIfWExjXJe0rFbLse8ke53HXtKZ6SIkJJ0J\nLABa7wyQZZtf9NWRkn4CXAZMlfQy8A0aJ6sSEd8H1gEfB3YD/wCuzVPpyOsg+zXAMklHgdeBxXXY\nGCXzgE8D29P5MQBfB86H+o89neWv6/hPB1ZJGkOjsfxpRDwm6TZgU0SspdGg/kjSbhoXryzOV+6I\n6iT7jZIW0riC9jVgabZqe6SQsW+roLHvA1an35VjgR9HxHpJ10Pebb7vmG9mZmaWgQ9HmpmZmWXg\nJszMzMwsAzdhZmZmZhm4CTMzMzPLwE2YmZmZWQZuwszstCfpmKStTa9bRvCz+yW13lPIzOyUFX2f\nMDOrjdfT43jMzE4b3hNmZrUlaUDSdyRtl/S8pAvT/H5JT6WHFT8p6fw0v0/S6vTg8m2SPpA+aoyk\neyTtkPREuuM8km6UtDN9zkOZYprZacpNmJnVwcSWw5GLmpYdjIh3AncCt6d5dwCr0sOKHwRWpPkr\ngF+mB5fPAXak+bOBuyLiIuAAcHWafwtwcfqc67sVzszqyXfMN7PTnqTDEXFWm/kDwPyI+FN6aPlf\nI2KKpP3A9Ij4V5q/JyKmStoHzGh6kDGS+oENETE7vb8ZGBcR35K0HjgMrAHWDD303MysE94TZmZ1\nFxXTJ+NI0/Qxjp9PewVwF429Zr+T5PNszaxjbsLMrO4WNf39bZr+Dccfzvwp4Ok0/SSwDEDSGEmT\nqz5U0hnAzIjYCNwMTAb+Z2+cmVkV/2ozszqYKGlr0/v1ETF0m4q3SHqBxt6sT6R5XwR+KOkrwD7g\n2jR/OXC3pOto7PFaBuyp+M4xwAOpUROwIiIOjFgiM6s9nxNmZrWVzgm7JCL2567FzKyVD0eamZmZ\nZeA9YWZmZmYZeE+YmZmZWQZuwszMzMwycBNmZmZmloGbMDMzM7MM3ISZmZmZZeAmzMzMzCyD/wDO\nDxB3+nP2+QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mh4Cv06HFghL"
      },
      "source": [
        "**Step 5: Load Trained Word2Vec**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7sve8OCs_V1"
      },
      "source": [
        "!pip3 install --upgrade gensim --user"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWIjCbnpEUnQ"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')# ignore error\n",
        "import gensim\n",
        "model = gensim.models.Word2Vec.load (\"./mswiki.model\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQkraakQGBHJ"
      },
      "source": [
        "**Step 6: Define Functions for Testing Word2Vec**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x50KzbhqFvZA"
      },
      "source": [
        "#Print most similar words to the input word\n",
        "def test_most_similar (input_text):\n",
        "   word = model.wv.most_similar(input_text)\n",
        "   for t in word:\n",
        "        print (t[0],t[1])\n",
        "\n",
        "#Test the similarity between two input words\n",
        "def test_similarity (input_text1, input_text2):\n",
        "   print (model.wv.similarity (input_text1, input_text2))\n",
        "\n",
        "#Find the least similar word (odd-one-out) among 5 words\n",
        "def test_doesnt_match (input_text1, input_text2, input_text3, input_text4, input_text5):\n",
        "    list5 = [input_text1, input_text2, input_text3, input_text4, input_text5]\n",
        "    print (model.wv.doesnt_match(list5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBMg6EnxIuQ9"
      },
      "source": [
        "a. Try to list the most similar words to see whether if the resutls are sensible"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kk1BZo2tG1ek",
        "outputId": "dcb0e4ef-bec7-4da4-9ae6-a6d480f5bd7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "test_most_similar(\"basikal\")\n",
        "#word: bicycle\n",
        "#similars: motorcycle, racing, riders, BMX, racers, cycling etc."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "motosikal 0.7236130237579346\n",
            "motorsikal 0.6834418773651123\n",
            "lumba 0.6807292103767395\n",
            "berbasikal 0.6282558441162109\n",
            "penunggang 0.6212975978851318\n",
            "gegancu 0.6155469417572021\n",
            "bmx 0.6085044741630554\n",
            "menunggang 0.5766348838806152\n",
            "pelumba 0.5760899186134338\n",
            "mengayuh 0.5740960240364075\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oC33NoKRHV6i",
        "outputId": "3d865171-4c77-45b8-d00c-6c98f43d0037",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "test_most_similar(\"sabah\")\n",
        "#word: Sabah (a state in East Malaysia)\n",
        "#similars: Sarawak (its neighbouring states) and cities/places near Sabah"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sarawak 0.7705913782119751\n",
            "tawau 0.6347787380218506\n",
            "sandakan 0.634151816368103\n",
            "kudat 0.5683799982070923\n",
            "kuching 0.5637481212615967\n",
            "papar 0.563594400882721\n",
            "semporna 0.5613182783126831\n",
            "keningau 0.5437761545181274\n",
            "penampang 0.5365309715270996\n",
            "malaysia 0.5328742861747742\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlPHyZSbI4_H"
      },
      "source": [
        "b. Check similarity between 2 words. As an example, consider the first case with Perak & Selangor (2 neighbouring states) and the second case with Perak & Sarawak (far from each other)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTgqmmAtIB0f",
        "outputId": "bb10ba88-c784-46cc-b987-9c63a5f803c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_similarity (\"perak\",\"selangor\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.59728384\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MY90cYvcIOr8",
        "outputId": "d6183744-e7c8-4cf6-ff86-2e12c8103fb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_similarity (\"perak\",\"sarawak\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.37876657\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZ3XdawfKEGF"
      },
      "source": [
        "c. Print odd-one-out between: cup, bowl, plate, pan, elephant. Ideally the animal should be printed out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DD8L7jjvJeVC",
        "outputId": "334968d6-0773-47fb-e391-a977a2e286de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "test_doesnt_match(\"cawan\",\"mangkuk\",\"pinggan\",\"kuali\", \"gajah\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gajah\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/root/.local/lib/python3.6/site-packages/gensim/models/keyedvectors.py:877: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7AkXLBgKl6I"
      },
      "source": [
        "**Step 7: Visualization after dimensionality reduction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6Ox8C_WNfTJ",
        "outputId": "cdb5dba7-8118-4d1b-c04b-d003ef193ab3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.decomposition import PCA\n",
        "import numpy as np\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "def display_closestwords_tsnescatterplot(model, word, antnm):\n",
        "    \n",
        "    arr = np.empty((0,300), dtype='f')\n",
        "    word_labels = [word]\n",
        "    antnm_labels = [antnm]\n",
        "\n",
        "    # get close words\n",
        "    close_words = model.wv.similar_by_word(word)\n",
        "    close_words += model.wv.similar_by_word(antnm)\n",
        "    temp = np.array(model.wv.similar_by_word(antnm))\n",
        "    \n",
        "    # add the vector for each of the closest words to the array\n",
        "    arr = np.append(arr, np.array([model[word]]), axis=0)\n",
        "    for wrd_score in close_words:\n",
        "        wrd_vector = model[wrd_score[0]]\n",
        "        word_labels.append(wrd_score[0])\n",
        "        arr = np.append(arr, np.array([wrd_vector]), axis=0)\n",
        "    \n",
        "    # find tsne coords for 2 dimensions\n",
        "    tsne = TSNE(n_components=2, random_state=0, perplexity=2, n_iter=1000)\n",
        "    np.set_printoptions(suppress=True)\n",
        "    Y = tsne.fit_transform(arr)\n",
        "    \n",
        "    target_ids = range(len(close_words))\n",
        "    for i in target_ids:\n",
        "        if (close_words[i][0] in temp[:,0]):\n",
        "            plt.scatter(Y[i, 0], Y[i, 1], c='blue')\n",
        "        else:\n",
        "            plt.scatter(Y[i, 0], Y[i, 1], c='red')\n",
        "        plt.annotate(close_words[i][0], xy=(Y[i, 0], Y[i, 1]), xytext=(0, 0), textcoords='offset points')\n",
        "    plt.rcParams['figure.figsize'] = [10, 5]\n",
        "    plt.show()\n",
        "    \n",
        "display_closestwords_tsnescatterplot(model, 'benci', 'suka')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gK3Bn4aclMKPwr4iISKWxfPly+vTpQ8OGDQH41a9+xbJly8jMzHTlOX36NGfOnAGga9eu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\n+BvgCNYiyhj8BgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3elM4l6zEhK",
        "outputId": "99ef38a8-a9e4-4ca3-bcaa-caf8c5dacd92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "topics = ['Pengangkutan (Transports)', 'Sukan (Sports)', 'Makanan (Foods)', 'Tumbuhan (Plants)',\n",
        "        'Haiwan (Animals)', 'Angkasa (Space)', 'Eropah (Europe)', 'Asia (Asia)',\n",
        "        'Afrika (Africa)', 'Filosofi (Philosophy)', 'Pekerjaan (Professions)']\n",
        "\n",
        "keys = ['kenderaan', 'sukan', 'makanan', 'tumbuhan',\n",
        "        'haiwan', 'planet', 'bulgaria', 'thailand',\n",
        "        'nigeria', 'aristotle', 'jurutera']\n",
        "\n",
        "embedding_clusters = []\n",
        "word_clusters = []\n",
        "for word in keys:\n",
        "    embeddings = []\n",
        "    words = []\n",
        "    for similar_word, _ in model.wv.most_similar(word, topn=30):\n",
        "        words.append(similar_word)\n",
        "        embeddings.append(model[similar_word])\n",
        "    embedding_clusters.append(embeddings)\n",
        "    word_clusters.append(words)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
            "  app.launch_new_instance()\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5msjKWjcH9Br",
        "outputId": "6e68d23e-ac2c-4eb6-cd36-ff28339aea7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        }
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import numpy as np\n",
        "\n",
        "embedding_clusters = np.array(embedding_clusters)\n",
        "n, m, k = embedding_clusters.shape\n",
        "tsne_model_2d = TSNE(perplexity=15, n_components=2, init='pca', n_iter=3500, random_state=32)\n",
        "embeddings_2d = np.array(tsne_model_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.cm as cm\n",
        "% matplotlib inline\n",
        "\n",
        "\n",
        "def tsne_plot_2d(title, labels, embedding_clusters, word_clusters, a, filename=None):\n",
        "    plt.figure(figsize=(16, 9))\n",
        "    colors = cm.rainbow(np.linspace(0, 1, len(labels)))\n",
        "    for label, embeddings, words, color in zip(labels, embedding_clusters, word_clusters, colors):\n",
        "        x = embeddings[:, 0]\n",
        "        y = embeddings[:, 1]\n",
        "        plt.scatter(x, y, c=color, alpha=a, label=label)\n",
        "        for i, word in enumerate(words):\n",
        "            plt.annotate(word, alpha=0.75, xy=(x[i], y[i]), xytext=(5, 2),\n",
        "                         textcoords='offset points', ha='right', va='bottom', size=8)\n",
        "    plt.legend(loc=1)\n",
        "    plt.title(title)\n",
        "    plt.grid(True)\n",
        "    if filename:\n",
        "        plt.savefig(filename, format='png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "tsne_plot_2d('T-SNE on Word2Vec Representation, 11 Topics', topics, embeddings_2d, word_clusters, 1,\n",
        "                        'word2vec_tsne2d.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MICoqiri4OHx8fPj00085fPhw/p0AIbKxJfg2M2dqLlwAreHCBZg5U+cpQH3h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AoFc+bMobi4mJUrV2pMyleV5ORkEhMTcbToRYoOIepoof+cVBf1FdLffvtNw6wzPj6e2bNn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tOXfuHKASPdTT08PU1JT09HSNAKuoPZp6PRMTE1577TUkScLExIS4uDgKCwv59ttvuXHj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\nnrIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlXJJ16eW3Mo",
        "outputId": "524302cc-4287-442c-8084-c7b82cb5b57e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "embedding_3d_clusters = []\n",
        "word_3d_clusters = []\n",
        "for word in keys:\n",
        "    embeddings = []\n",
        "    words = []\n",
        "    for similar_word, _ in model.wv.most_similar(word, topn=30):\n",
        "        #word_3d_clusters.append(similar_word)\n",
        "        embedding_3d_clusters.append(model[similar_word])\n",
        "        words.append(similar_word)\n",
        "        # embeddings.append(model[similar_word])\n",
        "    # embedding_3d_clusters.append(embeddings)\n",
        "    word_3d_clusters.append(words)\n",
        "\n",
        "embedding_3d_clusters = np.array(embedding_3d_clusters)\n",
        "f, g = embedding_3d_clusters.shape\n",
        "h = len(topics)\n",
        "j = f//h\n",
        "\n",
        "tsne_model_3d = TSNE(perplexity=30, n_components=3, init='pca', n_iter=3500, random_state=12)\n",
        "embeddings_3d = np.array(tsne_model_3d.fit_transform(embedding_3d_clusters)).reshape(h, j, 3)\n",
        "# # embeddings_3d = tsne_model_3d.fit_transform(embedding_3d_clusters)\n",
        "# # embeddings_3d = np.array(tsne_model_3d.fit_transform(embedding_3d_clusters.reshape(f * g, h))).reshape(f, g, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfCbB1l99STB",
        "outputId": "5e402cf6-52e2-447f-b672-a7b711a9e865",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        }
      },
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "def tsne_plot_3d(title, labels, embedding_clusters, word_clusters, a, filename=None):\n",
        "    fig = plt.figure(figsize=(10, 10))\n",
        "    ax = Axes3D(fig)\n",
        "    #ax = fig.add_subplot(111, projection='3d')\n",
        "    colors = cm.rainbow(np.linspace(0, 1, len(labels)))\n",
        "    for label, embeddings, words, color in zip(labels, embedding_clusters, word_clusters, colors):\n",
        "        x = embeddings[:, 0]\n",
        "        y = embeddings[:, 1]\n",
        "        z = embeddings[:, 2]\n",
        "        ax.scatter(x, y, z, c=color, alpha=a, label=label)\n",
        "        for i, word in enumerate(words):\n",
        "          ax.text(x[i], y[i], z[i], word, alpha=0.7, c=color)\n",
        "          # plt.annotate(word, alpha=0.75, xyz=(x[i], y[i], z[i]), xytext=(5, 2),\n",
        "          #                textcoords='offset points', ha='right', va='bottom', size=8)\n",
        "\n",
        "    \n",
        "    #plt.legend(loc=4)\n",
        "    plt.title(title)\n",
        "    plt.grid(True)\n",
        "    if filename:\n",
        "        plt.savefig(filename, format='png', dpi='figure', bbox_inches='tight')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "#tsne_plot_3d('Visualizing Embeddings using t-SNE', topics, embeddings_wp_3d, a=0.1)\n",
        "tsne_plot_3d(' ', topics, embeddings_3d, word_3d_clusters, 1,\n",
        "                        'word2vec_tsne3d.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AwHGcOtCHgjghRwcFb0I6gCzL6iTJ06dP11QPW8/Yd4ZhIElSI0smpCE2mw3hcBihUAg8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\nCIXA4YA9b6osLjaJi4emJkFyop+ClMOsWbuNoqKhx/J6J9a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ebRB0x0qy+2C5De65zmB3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lwcCry/oPisCw93H9M0TTo7O9m4cSPjx48flEr6oqO/v58NGzYQCAQYN24cWHGMp6+DZC9C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\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vtlQTHsUdIxa8trQZT2MEMXkK40vfQtkESIUyBXpkMsXFAU665+cEZ80i870V1PUJjKvO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\nZK/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D370FRAiQ3e/rnJ5TQyDfcEPy/n6+n8/7XS5YsykUihm/ZjiORQX3BSz4MbyqEw2I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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}