deirdre_corr_18214743_CA684.ipynb

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    "colab": {
      "name": "deirdre_corr_18214743_CA684.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "metadata": {
        "id": "1D2MT-_6NU7A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "import re\n",
        "import sys\n",
        "import random\n",
        "import os\n",
        "import torch\n",
        "import math\n",
        "import scipy.stats\n",
        "import torch.nn as nn\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from string import punctuation\n",
        "from pathlib import Path\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "from google.colab import drive\n",
        "from collections import Counter\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "!pip install --force https://github.com/chengs/tqdm/archive/colab.zip\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "sys.setrecursionlimit(1000000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VAMZNrA-CsdZ",
        "colab_type": "code",
        "outputId": "b5cea682-a6c1-4c08-b931-f6b142c275b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive/')\n",
        "os.chdir(\"/content/drive/My Drive/Memorability\")"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EhH8pWBYqbaw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Text Preprocessing\n",
        " The following cells demonstrate the text preprocessing techniques outlined in section 4.1 of the report"
      ]
    },
    {
      "metadata": {
        "id": "uMuKYaUeDHJt",
        "colab_type": "code",
        "outputId": "2c425246-7039-484e-b348-dde64e44a32b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "#load video captions\n",
        "video_captions = list(open('dev-set_video-captions.txt', 'r'))\n",
        "video_captions = [i.split('\\t') for i in video_captions]\n",
        "video_captions = [[a, b.strip()] for a, b in video_captions]\n",
        "video_captions = pd.DataFrame(video_captions, columns=['video', 'caption'])\n",
        "video_captions.head()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>video</th>\n",
              "      <th>caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>roulette-table-spinning-with-ball-in-closeup-shot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video6.webm</td>\n",
              "      <td>khr-gangsters</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>video8.webm</td>\n",
              "      <td>medical-helicopter-hovers-at-airport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video10.webm</td>\n",
              "      <td>couple-relaxing-on-picnic-crane-shot</td>\n",
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            "text/plain": [
              "          video                                            caption\n",
              "0   video3.webm                 blonde-woman-is-massaged-tilt-down\n",
              "1   video4.webm  roulette-table-spinning-with-ball-in-closeup-shot\n",
              "2   video6.webm                                      khr-gangsters\n",
              "3   video8.webm               medical-helicopter-hovers-at-airport\n",
              "4  video10.webm               couple-relaxing-on-picnic-crane-shot"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 127
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      ]
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    {
      "metadata": {
        "id": "ksAGm92kJrtZ",
        "colab_type": "code",
        "outputId": "eb816133-e26b-4479-c572-3a64d07dfa3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "#normalise and lemmatise captions\n",
        "wpt = nltk.WordPunctTokenizer()\n",
        "wordnet_lemmatizer = WordNetLemmatizer()\n",
        "stop_words = nltk.corpus.stopwords.words('english')\n",
        "\n",
        "def norm_lemm_caption(cap):\n",
        "    # replace hyphens\n",
        "    cap = cap.replace('-',' ')\n",
        "    cap = cap.strip()\n",
        "    # tokenize caption\n",
        "    tokens = wpt.tokenize(cap)\n",
        "    # filter stopwords out of caption\n",
        "    filtered_tokens = [token for token in tokens if token not in stop_words]\n",
        "    # lemmanise words in caption\n",
        "    lemm_tokens = [wordnet_lemmatizer.lemmatize(token, pos=\"v\") for token in filtered_tokens]\n",
        "    # re-create document from tokens\n",
        "    cap = ' '.join(lemm_tokens)\n",
        "    return cap\n",
        "\n",
        "#replace with normalised/lemmatised caption\n",
        "norm_lemm_corpus = np.vectorize(norm_lemm_caption)\n",
        "for index, row in video_captions.iterrows():\n",
        "  video_captions.at[index, 'caption'] = norm_lemm_corpus(row.loc['caption'])\n",
        "\n",
        "video_captions.head(5)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>video</th>\n",
              "      <th>caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>video3.webm</td>\n",
              "      <td>blonde woman massage tilt</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video4.webm</td>\n",
              "      <td>roulette table spin ball closeup shoot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video6.webm</td>\n",
              "      <td>khr gangsters</td>\n",
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              "      <th>3</th>\n",
              "      <td>video8.webm</td>\n",
              "      <td>medical helicopter hover airport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video10.webm</td>\n",
              "      <td>couple relax picnic crane shoot</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "          video                                 caption\n",
              "0   video3.webm               blonde woman massage tilt\n",
              "1   video4.webm  roulette table spin ball closeup shoot\n",
              "2   video6.webm                           khr gangsters\n",
              "3   video8.webm        medical helicopter hover airport\n",
              "4  video10.webm         couple relax picnic crane shoot"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 128
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      "metadata": {
        "id": "ss0iyW0b8H-6",
        "colab_type": "code",
        "outputId": "7252ffac-92cf-47f0-dc47-7b90df34576a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "#load ground truth\n",
        "ground_truth_file = 'dev-set_ground-truth.csv'\n",
        "ground_truth = pd.read_csv(ground_truth_file)\n",
        "ground_truth.head()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>video</th>\n",
              "      <th>short-term_memorability</th>\n",
              "      <th>nb_short-term_annotations</th>\n",
              "      <th>long-term_memorability</th>\n",
              "      <th>nb_long-term_annotations</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video3.webm</td>\n",
              "      <td>0.924</td>\n",
              "      <td>34</td>\n",
              "      <td>0.846</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video4.webm</td>\n",
              "      <td>0.923</td>\n",
              "      <td>33</td>\n",
              "      <td>0.667</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video6.webm</td>\n",
              "      <td>0.863</td>\n",
              "      <td>33</td>\n",
              "      <td>0.700</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>video8.webm</td>\n",
              "      <td>0.922</td>\n",
              "      <td>33</td>\n",
              "      <td>0.818</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video10.webm</td>\n",
              "      <td>0.950</td>\n",
              "      <td>34</td>\n",
              "      <td>0.900</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "          video  short-term_memorability  nb_short-term_annotations  \\\n",
              "0   video3.webm                    0.924                         34   \n",
              "1   video4.webm                    0.923                         33   \n",
              "2   video6.webm                    0.863                         33   \n",
              "3   video8.webm                    0.922                         33   \n",
              "4  video10.webm                    0.950                         34   \n",
              "\n",
              "   long-term_memorability  nb_long-term_annotations  \n",
              "0                   0.846                        13  \n",
              "1                   0.667                        12  \n",
              "2                   0.700                        10  \n",
              "3                   0.818                        11  \n",
              "4                   0.900                        10  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "metadata": {
        "id": "qUz7TcrDaOf6",
        "colab_type": "code",
        "outputId": "d62983f2-675c-4926-f674-477bd0b31ae1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "cell_type": "code",
      "source": [
        "#get ground truth short and long term columns, convert to 2D list\n",
        "ground_truth = pd.DataFrame(ground_truth, columns=['short-term_memorability','long-term_memorability']).values\n",
        "ground_truth[0:5]"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.924, 0.846],\n",
              "       [0.923, 0.667],\n",
              "       [0.863, 0.7  ],\n",
              "       [0.922, 0.818],\n",
              "       [0.95 , 0.9  ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "metadata": {
        "id": "xnWvF_5p_9jc",
        "colab_type": "code",
        "outputId": "61bb7fb0-cf4d-4758-89c9-74ace74c5f10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        }
      },
      "cell_type": "code",
      "source": [
        "# convert captions to list\n",
        "captions_list = np.asarray(video_captions['caption'])\n",
        "\n",
        "#gets a count of all words in the whole dataset\n",
        "def get_counts(captions_list):\n",
        "  #create massive string\n",
        "  all_text = ' '.join(str(e) for e in captions_list)\n",
        "  # create a list of the words\n",
        "  words = all_text.split()\n",
        "  \n",
        "  # count all words using Counter and order\n",
        "  count_words = Counter(words)\n",
        "  total_words = len(words)\n",
        "  word_counts = count_words.most_common(total_words)\n",
        "  print('Counts of words in order:')\n",
        "  print(word_counts)\n",
        "  return word_counts\n",
        "\n",
        "#assign an integer value to each word\n",
        "def assign_ints(word_counts):\n",
        "  #start from index 1 to facilitate 0 padding later\n",
        "  vocab_to_int = {w:i+1 for i, (w,c) in enumerate(word_counts)}\n",
        "  print()\n",
        "  print('Words with assigned ints:')\n",
        "  print(vocab_to_int)\n",
        "  return vocab_to_int\n",
        "\n",
        "#convert captions words to integers\n",
        "def caption_text_to_int(captions_list, vocab_to_int):\n",
        "  captions_int = []\n",
        "  for caption in (str(e) for e in captions_list):\n",
        "    r = [vocab_to_int[w] for w in caption.split()]\n",
        "    captions_int.append(r)\n",
        "  print()\n",
        "  print('First five captions:')\n",
        "  print(captions_int[0:5])\n",
        "  return captions_int\n",
        "\n",
        "counts = get_counts(captions_list)\n",
        "vocab_to_int = assign_ints(counts)\n",
        "captions_int = caption_text_to_int(captions_list,vocab_to_int)\n"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counts of words in order:\n",
            "[('woman', 899), ('young', 724), ('man', 563), ('slow', 495), ('motion', 452), ('sit', 345), ('couple', 319), ('walk', 298), ('girl', 291), ('happy', 279), ('beach', 270), ('park', 262), ('close', 249), ('use', 239), ('camera', 230), ('look', 229), ('shoot', 227), ('smile', 223), ('two', 191), ('people', 189), ('water', 187), ('work', 186), ('tablet', 184), ('tree', 167), ('hand', 156), ('play', 153), ('view', 153), ('talk', 152), ('female', 150), ('computer', 149), ('eat', 148), ('run', 144), ('laptop', 139), ('sunset', 137), ('beautiful', 133), ('videoblocks', 131), ('stand', 131), ('city', 128), ('mother', 128), ('little', 126), ('family', 125), ('drink', 125), ('background', 124), ('home', 123), ('phone', 118), ('sea', 113), ('national', 112), ('african', 111), ('attractive', 111), ('snow', 106), ('fish', 105), ('field', 103), ('steadicamof', 102), ('modern', 101), ('street', 99), ('business', 98), ('road', 98), ('american', 97), ('swim', 96), ('boy', 96), ('white', 95), ('new', 93), ('businessman', 92), ('portrait', 92), ('son', 91), ('table', 90), ('group', 87), ('closeup', 85), ('christmas', 85), ('take', 85), ('green', 84), ('businesswoman', 84), ('make', 82), ('kitchen', 82), ('father', 81), ('mountain', 79), ('steadicam', 77), ('outdoors', 77), ('black', 76), ('sun', 76), ('sunrise', 76), ('rock', 76), ('office', 75), ('grass', 74), ('relax', 73), ('cafe', 73), ('move', 73), ('fly', 73), ('farm', 70), ('near', 70), ('friends', 69), ('jog', 69), ('light', 68), ('cellphone', 68), ('doctor', 67), ('put', 67), ('red', 67), ('war', 67), ('dog', 66), ('summer', 66), ('read', 65), ('restaurant', 65), ('glass', 65), ('lake', 65), ('bridge', 65), ('hold', 65), ('africa', 64), ('train', 64), ('pan', 64), ('blue', 64), ('upof', 63), ('night', 63), ('construction', 63), ('coffee', 62), ('outside', 61), ('aerial', 61), ('steadycam', 61), ('build', 61), ('face', 60), ('around', 60), ('khr', 59), ('back', 59), ('wear', 59), ('food', 59), ('sunny', 59), ('south', 59), ('lie', 58), ('concept', 57), ('film', 57), ('zoom', 57), ('child', 57), ('civil', 57), ('time', 56), ('show', 56), ('watch', 56), ('fun', 55), ('cook', 54), ('women', 54), ('sky', 54), ('mountains', 53), ('sofa', 53), ('asian', 53), ('dance', 53), ('mobile', 52), ('book', 51), ('forest', 51), ('crowd', 51), ('bright', 51), ('cute', 50), ('together', 50), ('window', 50), ('airport', 49), ('daughter', 49), ('silhouette', 49), ('river', 49), ('listen', 49), ('landscape', 48), ('along', 48), ('rid', 47), ('laugh', 47), ('flower', 47), ('timelapse', 46), ('paint', 46), ('house', 46), ('men', 46), ('bicycle', 46), ('prepare', 46), ('garden', 46), ('type', 46), ('ball', 45), ('floor', 45), ('bike', 45), ('shop', 45), ('music', 45), ('baby', 44), ('drive', 44), ('open', 44), ('small', 44), ('bird', 43), ('front', 43), ('enjoy', 43), ('smartphone', 43), ('three', 42), ('feet', 42), ('healthy', 41), ('even', 41), ('dolly', 41), ('get', 40), ('pool', 40), ('eye', 39), ('canyon', 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            "\n",
            "Words with assigned ints:\n",
            "{'woman': 1, 'young': 2, 'man': 3, 'slow': 4, 'motion': 5, 'sit': 6, 'couple': 7, 'walk': 8, 'girl': 9, 'happy': 10, 'beach': 11, 'park': 12, 'close': 13, 'use': 14, 'camera': 15, 'look': 16, 'shoot': 17, 'smile': 18, 'two': 19, 'people': 20, 'water': 21, 'work': 22, 'tablet': 23, 'tree': 24, 'hand': 25, 'play': 26, 'view': 27, 'talk': 28, 'female': 29, 'computer': 30, 'eat': 31, 'run': 32, 'laptop': 33, 'sunset': 34, 'beautiful': 35, 'videoblocks': 36, 'stand': 37, 'city': 38, 'mother': 39, 'little': 40, 'family': 41, 'drink': 42, 'background': 43, 'home': 44, 'phone': 45, 'sea': 46, 'national': 47, 'african': 48, 'attractive': 49, 'snow': 50, 'fish': 51, 'field': 52, 'steadicamof': 53, 'modern': 54, 'street': 55, 'business': 56, 'road': 57, 'american': 58, 'swim': 59, 'boy': 60, 'white': 61, 'new': 62, 'businessman': 63, 'portrait': 64, 'son': 65, 'table': 66, 'group': 67, 'closeup': 68, 'christmas': 69, 'take': 70, 'green': 71, 'businesswoman': 72, 'make': 73, 'kitchen': 74, 'father': 75, 'mountain': 76, 'steadicam': 77, 'outdoors': 78, 'black': 79, 'sun': 80, 'sunrise': 81, 'rock': 82, 'office': 83, 'grass': 84, 'relax': 85, 'cafe': 86, 'move': 87, 'fly': 88, 'farm': 89, 'near': 90, 'friends': 91, 'jog': 92, 'light': 93, 'cellphone': 94, 'doctor': 95, 'put': 96, 'red': 97, 'war': 98, 'dog': 99, 'summer': 100, 'read': 101, 'restaurant': 102, 'glass': 103, 'lake': 104, 'bridge': 105, 'hold': 106, 'africa': 107, 'train': 108, 'pan': 109, 'blue': 110, 'upof': 111, 'night': 112, 'construction': 113, 'coffee': 114, 'outside': 115, 'aerial': 116, 'steadycam': 117, 'build': 118, 'face': 119, 'around': 120, 'khr': 121, 'back': 122, 'wear': 123, 'food': 124, 'sunny': 125, 'south': 126, 'lie': 127, 'concept': 128, 'film': 129, 'zoom': 130, 'child': 131, 'civil': 132, 'time': 133, 'show': 134, 'watch': 135, 'fun': 136, 'cook': 137, 'women': 138, 'sky': 139, 'mountains': 140, 'sofa': 141, 'asian': 142, 'dance': 143, 'mobile': 144, 'book': 145, 'forest': 146, 'crowd': 147, 'bright': 148, 'cute': 149, 'together': 150, 'window': 151, 'airport': 152, 'daughter': 153, 'silhouette': 154, 'river': 155, 'listen': 156, 'landscape': 157, 'along': 158, 'rid': 159, 'laugh': 160, 'flower': 161, 'timelapse': 162, 'paint': 163, 'house': 164, 'men': 165, 'bicycle': 166, 'prepare': 167, 'garden': 168, 'type': 169, 'ball': 170, 'floor': 171, 'bike': 172, 'shop': 173, 'music': 174, 'baby': 175, 'drive': 176, 'open': 177, 'small': 178, 'bird': 179, 'front': 180, 'enjoy': 181, 'smartphone': 182, 'three': 183, 'feet': 184, 'healthy': 185, 'even': 186, 'dolly': 187, 'get': 188, 'pool': 189, 'eye': 190, 'canyon': 191, 'nature': 192, 'winter': 193, 'elephant': 194, 'dress': 195, 'massage': 196, 'snowy': 197, 'behind': 198, 'moscow': 199, 'hair': 200, 'fitness': 201, 'school': 202, 'cat': 203, 'bed': 204, 'car': 205, 'runner': 206, 'cloud': 207, 'love': 208, 'russia': 209, 'room': 210, 'point': 211, 'large': 212, 'ocean': 213, 'sport': 214, 'horse': 215, 'boat': 216, 'pretty': 217, 'parent': 218, 'old': 219, 'wooden': 220, 'crane': 221, 'kid': 222, 'children': 223, 'brunette': 224, 'arch': 225, 'pour': 226, 'patient': 227, 'discuss': 228, 'wall': 229, 'spin': 230, 'chat': 231, 'eagle': 232, 'away': 233, 'leave': 234, 'day': 235, 'girls': 236, 'bag': 237, 'autumn': 238, 'decorate': 239, 'focus': 240, 'lifestyle': 241, 'fall': 242, 'ski': 243, 'suit': 244, 'blow': 245, 'shin': 246, 'yellow': 247, 'bench': 248, 'fire': 249, 'cut': 250, 'york': 251, 'caucasian': 252, 'jump': 253, 'skyline': 254, 'male': 255, 'wave': 256, 'kiss': 257, 'carry': 258, 'check': 259, 'person': 260, 'moose': 261, 'spring': 262, 'worker': 263, 'place': 264, 'station': 265, 'adult': 266, 'highway': 267, 'head': 268, 'panningof': 269, 'outdoor': 270, 'morning': 271, 'desert': 272, 'bubble': 273, 'space': 274, 'plant': 275, 'selfie': 276, 'traffic': 277, 'model': 278, 'across': 279, 'celebrate': 280, 'cycle': 281, 'soldier': 282, 'country': 283, 'cover': 284, 'apartment': 285, 'urban': 286, 'internet': 287, 'hat': 288, 'reef': 289, 'pass': 290, 'big': 291, 'bald': 292, 'cage': 293, 'art': 294, 'center': 295, 'top': 296, 'write': 297, 'machine': 298, 'student': 299, 'exercise': 300, 'gangsters': 301, 'tourists': 302, 'soccer': 303, 'workout': 304, 'flag': 305, 'hot': 306, 'mix': 307, 'line': 308, 'free': 309, 'headphones': 310, 'juice': 311, 'hotel': 312, 'gym': 313, 'orange': 314, 'ship': 315, 'wheat': 316, 'tilt': 317, 'sign': 318, 'hang': 319, 'animal': 320, 'shelter': 321, 'touchpad': 322, 'valley': 323, 'box': 324, 'meadow': 325, 'romantic': 326, 'motionof': 327, 'wash': 328, 'one': 329, 'high': 330, 'super': 331, 'professional': 332, 'meet': 333, 'wine': 334, 'video': 335, 'bear': 336, 'four': 337, 'ray': 338, 'digital': 339, 'lettuce': 340, 'artist': 341, 'pov': 342, 'travel': 343, 'engineer': 344, 'track': 345, 'mom': 346, 'tropical': 347, 'coral': 348, 'photo': 349, 'finger': 350, 'turn': 351, 'kruger': 352, 'market': 353, 'bryce': 354, 'chair': 355, 'nyc': 356, 'underwater': 357, 'surf': 358, 'warm': 359, 'ride': 360, 'message': 361, 'throw': 362, 'scientist': 363, 'busy': 364, 'swing': 365, 'workers': 366, 'something': 367, 'dollyof': 368, 'smoke': 369, 'dark': 370, 'rural': 371, 'help': 372, 'storm': 373, 'document': 374, 'texting': 375, 'woods': 376, 'hike': 377, 'hill': 378, 'colorful': 379, 'long': 380, 'cemetery': 381, 'pier': 382, 'parade': 383, 'toy': 384, 'aerialof': 385, 'scene': 386, 'terrace': 387, 'climb': 388, 'studio': 389, 'produce': 390, 'ice': 391, 'dc': 392, 'resort': 393, 'race': 394, 'call': 395, 'salad': 396, 'wait': 397, 'paper': 398, 'horizon': 399, 'press': 400, 'cruise': 401, 'flare': 402, 'give': 403, 'meal': 404, 'rest': 405, 'handsome': 406, 'truck': 407, 'cell': 408, 'sand': 409, 'tourist': 410, 'air': 411, 'guy': 412, 'arlington': 413, 'plan': 414, 'board': 415, 'shore': 416, 'cream': 417, 'smart': 418, 'executive': 419, 'rack': 420, 'wheel': 421, 'resolution': 422, 'trackingof': 423, 'peddle': 424, 'beauty': 425, 'spa': 426, 'chef': 427, 'san': 428, 'cross': 429, 'teach': 430, 'beer': 431, 'ultra': 432, 'womans': 433, 'pregnant': 434, 'style': 435, 'cliffs': 436, 'go': 437, 'memorial': 438, 'couch': 439, 'preparation': 440, 'helmet': 441, 'screen': 442, 'equipment': 443, 'wide': 444, 'industrial': 445, 'distance': 446, 'rain': 447, 'bar': 448, 'slave': 449, 'touch': 450, 'angle': 451, 'washington': 452, 'laboratory': 453, 'path': 454, 'sad': 455, 'notebook': 456, 'towards': 457, 'clothe': 458, 'milk': 459, 'aquarium': 460, 'may': 461, 'different': 462, 'colonial': 463, 'harvest': 464, 'addo': 465, 'lady': 466, 'kayak': 467, 'basketball': 468, 'float': 469, 'bottle': 470, 'hospital': 471, 'color': 472, 'monument': 473, 'branch': 474, 'power': 475, 'cheerful': 476, 'roll': 477, 'push': 478, 'shoe': 479, 'summertime': 480, 'childhood': 481, 'picture': 482, 'fry': 483, 'fresh': 484, 'hall': 485, 'rally': 486, 'vacation': 487, 'feed': 488, 'extreme': 489, 'game': 490, 'lift': 491, 'sunglasses': 492, 'picnic': 493, 'stadium': 494, 'real': 495, 'feel': 496, 'dci': 497, 'jellyfish': 498, 'hug': 499, 'welness': 500, 'stone': 501, 'students': 502, 'gallery': 503, 'serve': 504, 'draw': 505, 'dead': 506, 'campfire': 507, 'plane': 508, 'western': 509, 'multicultural': 510, 'casual': 511, 'technology': 512, 'cup': 513, 'scenic': 514, 'pose': 515, 'tap': 516, 'sleep': 517, 'alone': 518, 'wind': 519, 'cars': 520, 'strong': 521, 'print': 522, 'town': 523, 'utah': 524, 'gift': 525, 'pot': 526, 'foot': 527, 'flight': 528, 'vote': 529, 'wideof': 530, 'trail': 531, 'traditional': 532, 'shark': 533, 'sell': 534, 'funny': 535, 'balcony': 536, 'swan': 537, 'lay': 538, 'era': 539, 'exotic': 540, 'speed': 541, 'graffiti': 542, 'arm': 543, 'duck': 544, 'step': 545, 'paddle': 546, 'lab': 547, 'past': 548, 'buffet': 549, 'williamsburg': 550, 'explain': 551, 'climber': 552, 'australia': 553, 'friend': 554, 'sidewalk': 555, 'police': 556, 'bush': 557, 'mirror': 558, 'glow': 559, 'square': 560, 'delicious': 561, 'mesa': 562, 'pink': 563, 'team': 564, 'legs': 565, 'inside': 566, 'fence': 567, 'bay': 568, 'sunlight': 569, 'lapse': 570, 'state': 571, 'surface': 572, 'circus': 573, 'many': 574, 'grave': 575, 'balloon': 576, 'library': 577, 'natural': 578, 'golf': 579, 'freedom': 580, 'camp': 581, 'finish': 582, 'elephants': 583, 'plate': 584, 'trendy': 585, 'dry': 586, 'commute': 587, 'golden': 588, 'come': 589, 'brush': 590, 'cowboy': 591, 'fight': 592, 'grind': 593, 'martial': 594, 'arts': 595, 'fruit': 596, 'calf': 597, 'factory': 598, 'sunbed': 599, 'thumb': 600, 'shirt': 601, 'spoon': 602, 'set': 603, 'metal': 604, 'butterfly': 605, 'raw': 606, 'charm': 607, 'dirt': 608, 'earth': 609, 'basket': 610, 'lens': 611, 'tower': 612, 'tall': 613, 'rotate': 614, 'flour': 615, 'pond': 616, 'piano': 617, 'land': 618, 'island': 619, 'perform': 620, 'card': 621, 'slowmotion': 622, 'break': 623, 'tea': 624, 'nurse': 625, 'weight': 626, 'another': 627, 'baseball': 628, 'pad': 629, 'spain': 630, 'wild': 631, 'tennis': 632, 'headset': 633, 'area': 634, 'lion': 635, 'next': 636, 'kite': 637, 'dish': 638, 'groom': 639, 'cliff': 640, 'festival': 641, 'empty': 642, 'toward': 643, 'solar': 644, 'side': 645, 'mov': 646, 'movie': 647, 'decoration': 648, 'plat': 649, 'yoga': 650, 'vietnam': 651, 'wood': 652, 'band': 653, 'th': 654, 'century': 655, 'follow': 656, 'skier': 657, 'bull': 658, 'tattoo': 659, 'prescription': 660, 'straw': 661, 'cocktail': 662, 'football': 663, 'apple': 664, 'blond': 665, 'grandfather': 666, 'cherry': 667, 'wed': 668, 'virtual': 669, 'bake': 670, 'information': 671, 'architect': 672, 'development': 673, 'dive': 674, 'military': 675, 'partner': 676, 'fast': 677, 'blonde': 678, 'medical': 679, 'village': 680, 'stretch': 681, 'flat': 682, 'pull': 683, 'happiness': 684, 'barcelona': 685, 'umbrella': 686, 'rodeo': 687, 'zebras': 688, 'tool': 689, 'perch': 690, 'approach': 691, 'tire': 692, 'site': 693, 'tv': 694, 'rocky': 695, 'bikers': 696, 'countryside': 697, 'client': 698, 'shallow': 699, 'drift': 700, 'gun': 701, 'low': 702, 'mask': 703, 'casually': 704, 'airplane': 705, 'monitor': 706, 'tasty': 707, 'akvamarine': 708, 'latino': 709, 'note': 710, 'carnival': 711, 'early': 712, 'care': 713, 'cityscape': 714, 'breakfast': 715, 'flock': 716, 'club': 717, 'vintage': 718, 'overlook': 719, 'diverse': 720, 'try': 721, 'change': 722, 'bride': 723, 'skiers': 724, 'reality': 725, 'vietnamese': 726, 'start': 727, 'scientists': 728, 'umbrellas': 729, 'greece': 730, 'kick': 731, 'part': 732, 'consultant': 733, 'p': 734, 'bedroom': 735, 'shoulder': 736, 'snowshoe': 737, 'waterfall': 738, 'motorcycle': 739, 'potter': 740, 'grandmother': 741, 'wildlife': 742, 'boil': 743, 'key': 744, 'downtown': 745, 'norway': 746, 'supporters': 747, 'salon': 748, 'row': 749, 'soar': 750, 'windows': 751, 'lane': 752, 'retail': 753, 'rub': 754, 'international': 755, 'manager': 756, 'sail': 757, 'businesswomen': 758, 'clear': 759, 'grade': 760, 'uhd': 761, 'deep': 762, 'hd': 763, 'cake': 764, 'skin': 765, 'admire': 766, 'edge': 767, 'cinemagraph': 768, 'seamless': 769, 'petersburg': 770, 'event': 771, 'motionk': 772, 'escape': 773, 'x': 774, 'indoors': 775, 'wake': 776, 'piggyback': 777, 'heterosexual': 778, 'baker': 779, 'measure': 780, 'friendly': 781, 'safety': 782, 'seagulls': 783, 'swarm': 784, 'sunlit': 785, 'good': 786, 'roller': 787, 'march': 788, 'hip': 789, 'reenactors': 790, 'battlefield': 791, 'first': 792, 'harbor': 793, 'macro': 794, 'thoughtful': 795, 'health': 796, 'middle': 797, 'clean': 798, 'oven': 799, 'money': 800, 'francisco': 801, 'vegetables': 802, 'dinner': 803, 'meat': 804, 'conduct': 805, 'guard': 806, 'buffalo': 807, 'apply': 808, 'stage': 809, 'luxury': 810, 'closeupof': 811, 'capitol': 812, 'ducklings': 813, 'mature': 814, 'church': 815, 'february': 816, 'chinese': 817, 'turtle': 818, 'grill': 819, 'hare': 820, 'passenger': 821, 'foreground': 822, 'flame': 823, 'patriotic': 824, 'brown': 825, 'alaska': 826, 'chop': 827, 'display': 828, 'instructor': 829, 'static': 830, 'convention': 831, 'reflection': 832, 'relationship': 833, 'canyonlands': 834, 'calm': 835, 'businessmen': 836, 'fireplace': 837, 'multiracial': 838, 'sing': 839, 'speak': 840, 'panel': 841, 'pc': 842, 'deck': 843, 'blizzard': 844, 'belly': 845, 'lock': 846, 'parenthood': 847, 'saint': 848, 'april': 849, 'estate': 850, 'right': 851, 'candle': 852, 'pizza': 853, 'sample': 854, 'snorkel': 855, 'warehouse': 856, 'shots': 857, 'clown': 858, 'unrecognizable': 859, 'cozy': 860, 'pathway': 861, 'seaside': 862, 'blacksmith': 863, 'confederate': 864, 'suitcase': 865, 'colorado': 866, 'panoramic': 867, 'hikers': 868, 'rise': 869, 'lean': 870, 'cape': 871, 'self': 872, 'crew': 873, 'adorable': 874, 'holiday': 875, 'crow': 876, 'stick': 877, 'hop': 878, 'scenery': 879, 'angry': 880, 'steam': 881, 'gear': 882, 'learn': 883, 'armchair': 884, 'strip': 885, 'colleagues': 886, 'dock': 887, 'daughters': 888, 'study': 889, 'architecture': 890, 'enter': 891, 'sauce': 892, 'beside': 893, 'reenactment': 894, 'stony': 895, 'dusk': 896, 'newspaper': 897, 'coaster': 898, 'cloudy': 899, 'project': 900, 'skyscrapers': 901, 'dessert': 902, 'photos': 903, 'dump': 904, 'log': 905, 'stylish': 906, 'life': 907, 'teacher': 908, 'crop': 909, 'dough': 910, 'cow': 911, 'party': 912, 'tomato': 913, 'heavy': 914, 'slo': 915, 'stir': 916, 'reach': 917, 'corn': 918, 'conference': 919, 'herd': 920, 'flip': 921, 'cannon': 922, 'quay': 923, 'helicopter': 924, 'bread': 925, 'burn': 926, 'process': 927, 'shadow': 928, 'serious': 929, 'iv': 930, 'remove': 931, 'fan': 932, 'runway': 933, 'rail': 934, 'salt': 935, 'juggle': 936, 'bowl': 937, 'texas': 938, 'ready': 939, 'hawaii': 940, 'islands': 941, 'sprawl': 942, 'late': 943, 'homosexual': 944, 'gay': 945, 'lesbian': 946, 'sex': 947, 'marriage': 948, 'company': 949, 'sushi': 950, 'sunbeds': 951, 'order': 952, 'lemon': 953, 'pill': 954, 'present': 955, 'treatment': 956, 'electricity': 957, 'vista': 958, 'siberian': 959, 'husky': 960, 'bikini': 961, 'handheld': 962, 'reflect': 963, 'bare': 964, 'smooth': 965, 'loop': 966, 'pills': 967, 'astronaut': 968, 'image': 969, 'year': 970, 'hipster': 971, 'receive': 972, 'purple': 973, 'creek': 974, 'leather': 975, 'passengers': 976, 'add': 977, 'sweet': 978, 'birthday': 979, 'live': 980, 'spot': 981, 'eel': 982, 'union': 983, 'tuscany': 984, 'salmon': 985, 'grassy': 986, 'browse': 987, 'shuttle': 988, 'strawberries': 989, 'practice': 990, 'dandelion': 991, 'pick': 992, 'jibof': 993, 'course': 994, 'seabed': 995, 'buy': 996, 'bus': 997, 'jeep': 998, 'seashore': 999, 'news': 1000, 'gull': 1001, 'fireworks': 1002, 'mouth': 1003, 'gather': 1004, 'slice': 1005, 'desk': 1006, 'oil': 1007, 'tomatoes': 1008, 'body': 1009, 'detail': 1010, 'elegant': 1011, 'test': 1012, 'research': 1013, 'sweaty': 1014, 'penguin': 1015, 'way': 1016, 'embrace': 1017, 'sugar': 1018, 'alley': 1019, 'lounge': 1020, 'gas': 1021, 'punch': 1022, 'concert': 1023, 'lions': 1024, 'shake': 1025, 'concrete': 1026, 'sailboat': 1027, 'ladies': 1028, 'delicate': 1029, 'gray': 1030, 'keyboard': 1031, 'blossom': 1032, 'policeman': 1033, 'treadmill': 1034, 'soap': 1035, 'repair': 1036, '#': 1037, 'NAME': 1038, '?': 1039, 'heron': 1040, 'apples': 1041, 'hover': 1042, 'release': 1043, 'grandson': 1044, 'stave': 1045, 'ingredients': 1046, 'seat': 1047, 'rainy': 1048, 'snake': 1049, 'proud': 1050, 'workshop': 1051, 'counter': 1052, 'rt': 1053, 'rope': 1054, 'diego': 1055, 'mill': 1056, 'surprise': 1057, 'electric': 1058, 'tie': 1059, 'full': 1060, 'skate': 1061, 'guitar': 1062, 'murky': 1063, 'arrive': 1064, 'therapist': 1065, 'salesman': 1066, 'owners': 1067, 'save': 1068, 'consumption': 1069, 'sort': 1070, 'exit': 1071, 'quill': 1072, 'chonchi': 1073, 'hood': 1074, 'domexpo': 1075, 'gostiny': 1076, 'dvor': 1077, 'surround': 1078, 'carve': 1079, 'photographer': 1080, 'moments': 1081, 'booths': 1082, 'vegetarian': 1083, 'say': 1084, 'chest': 1085, 'wilderness': 1086, 'touchscreen': 1087, 'container': 1088, 'rush': 1089, 'blur': 1090, 'rider': 1091, 'jet': 1092, 'panorama': 1093, 'customer': 1094, 'rocket': 1095, 'technician': 1096, 'world': 1097, 'gate': 1098, 'sift': 1099, 'motionfps': 1100, 'tee': 1101, 'launch': 1102, 'future': 1103, 'symbol': 1104, 'cigarette': 1105, 'reflective': 1106, 'shift': 1107, 'makeup': 1108, 'battle': 1109, 'businesspeople': 1110, 'snowfall': 1111, 'rabbit': 1112, 'waterpool': 1113, 'bustle': 1114, 'palm': 1115, 'tiltingof': 1116, 'statue': 1117, 'range': 1118, 'age': 1119, 'leopard': 1120, 'gum': 1121, 'dentist': 1122, 'wheelchair': 1123, 'bloom': 1124, 'girlfriends': 1125, 'toast': 1126, 'teenagers': 1127, 'glide': 1128, 'tomb': 1129, 'unknowns': 1130, 'conversation': 1131, 'tray': 1132, 'scan': 1133, 'designer': 1134, 'empire': 1135, 'port': 1136, 'fountains': 1137, 'mechanic': 1138, 'short': 1139, 'tent': 1140, 'divers': 1141, 'foam': 1142, 'players': 1143, 'problems': 1144, 'excite': 1145, 'drip': 1146, 'ask': 1147, 'question': 1148, 'owner': 1149, 'graze': 1150, 'poppies': 1151, 'device': 1152, 'giraffes': 1153, 'pensive': 1154, 'knife': 1155, 'letter': 1156, 'choose': 1157, 'ok': 1158, 'dental': 1159, 'hole': 1160, 'cookies': 1161, 'unit': 1162, 'sheet': 1163, 'television': 1164, 'yard': 1165, 'terminal': 1166, 'lipstick': 1167, 'condition': 1168, 'lunch': 1169, 'catch': 1170, 'huge': 1171, 'cucumber': 1172, 'multi': 1173, 'accident': 1174, 'manta': 1175, 'downhill': 1176, 'senior': 1177, 'pencil': 1178, 'neck': 1179, 'formal': 1180, 'successful': 1181, 'memories': 1182, 'door': 1183, 'hallway': 1184, 'festive': 1185, 'fashionable': 1186, 'round': 1187, 'metro': 1188, 'puppy': 1189, 'binoculars': 1190, 'goose': 1191, 'up': 1192, 'santa': 1193, 'gourmet': 1194, 'homework': 1195, 'others': 1196, 'macroof': 1197, 'outer': 1198, 'backdrop': 1199, 'planet': 1200, 'nasa': 1201, 'raise': 1202, 'dribble': 1203, 'bourbon': 1204, 'zip': 1205, 'dollyyoung': 1206, 'spinach': 1207, 'foodk': 1208, 'sunbathe': 1209, 'patients': 1210, 'refresh': 1211, 'pipette': 1212, 'transfer': 1213, 'st': 1214, 'technologies': 1215, 'fuel': 1216, 'figurines': 1217, 'cap': 1218, 'hour': 1219, 'horizontal': 1220, 'operate': 1221, 'hide': 1222, 'pigeons': 1223, 'cnc': 1224, 'bite': 1225, 'alaskan': 1226, 'puerto': 1227, 'toss': 1228, 'drug': 1229, 'leaf': 1230, 'boulders': 1231, 'peninsula': 1232, 'teenager': 1233, 'dip': 1234, 'chocolate': 1235, 'college': 1236, 'via': 1237, 'staticof': 1238, 'piece': 1239, 'plastic': 1240, 'consult': 1241, 'gettysburg': 1242, 'pole': 1243, 'tae': 1244, 'kwon': 1245, 'copenhagen': 1246, 'smartphones': 1247, 'mobilephone': 1248, 'corridor': 1249, 'creative': 1250, 'kenya': 1251, 'industry': 1252, 'nighttime': 1253, 'giraffe': 1254, 'hluhluwe': 1255, 'imfolozi': 1256, 'freeze': 1257, 'traveler': 1258, 'fix': 1259, 'brain': 1260, 'hoop': 1261, 'colony': 1262, 'argentina': 1263, 'class': 1264, 'oculus': 1265, 'rift': 1266, 'french': 1267, 'hammock': 1268, 'string': 1269, 'chaise': 1270, 'grey': 1271, 'raft': 1272, 'lobsters': 1273, 'inspect': 1274, 'jeans': 1275, 'banjo': 1276, 'strum': 1277, 'tundra': 1278, 'november': 1279, 'antique': 1280, 'auto': 1281, 'spread': 1282, 'f': 1283, 'stack': 1284, 'scuba': 1285, 'shower': 1286, 'cotton': 1287, 'search': 1288, 'afro': 1289, 'lawn': 1290, 'clinic': 1291, 'pet': 1292, 'angleof': 1293, 'playpen': 1294, 'boys': 1295, 'animals': 1296, 'examine': 1297, 'hard': 1298, 'athlete': 1299, 'alongside': 1300, 'blueprint': 1301, 'pareo': 1302, 'store': 1303, 'hamburger': 1304, 'great': 1305, 'clock': 1306, 'riverbank': 1307, 'performance': 1308, 'soup': 1309, 'text': 1310, 'complex': 1311, 'lead': 1312, 'menu': 1313, 'ceremony': 1314, 'star': 1315, 'desktop': 1316, 'older': 1317, 'interest': 1318, 'flash': 1319, 'wife': 1320, 'emotions': 1321, 'cambodia': 1322, 'medium': 1323, 'scale': 1324, 'nativity': 1325, 'dramatic': 1326, 'backpack': 1327, 'stop': 1328, 'woodshed': 1329, 'los': 1330, 'angeles': 1331, 'chain': 1332, 'several': 1333, 'gallop': 1334, 'strike': 1335, 'canvas': 1336, 'spruce': 1337, 'briefcase': 1338, 'llanquihue': 1339, 'cement': 1340, 'crash': 1341, 'scratch': 1342, 'shimmer': 1343, 'grandpa': 1344, 'london': 1345, 'bathroom': 1346, 'farmer': 1347, 'unpack': 1348, 'wheelbarrow': 1349, 'trailer': 1350, 'aim': 1351, 'rifle': 1352, 'champagne': 1353, 'commuters': 1354, 'july': 1355, 'streets': 1356, 'japanese': 1357, 'story': 1358, 'army': 1359, 'laser': 1360, 'economy': 1361, 'glacier': 1362, 'pepper': 1363, 'airlock': 1364, 'shovel': 1365, 'pedestrian': 1366, 'lionfish': 1367, 'find': 1368, 'captain': 1369, 'circle': 1370, 'footage': 1371, 'bad': 1372, 'chainsaw': 1373, 'instrument': 1374, 'elements': 1375, 'furnish': 1376, 'windsurf': 1377, 'satellite': 1378, 'fair': 1379, 'porch': 1380, 'gymnasium': 1381, 'jacket': 1382, 'security': 1383, 'presentation': 1384, 'pile': 1385, 'bun': 1386, 'comb': 1387, 'wad': 1388, 'statuettes': 1389, 'tombstones': 1390, 'architects': 1391, 'buck': 1392, 'boyfriend': 1393, 'giant': 1394, 'lightning': 1395, 'archway': 1396, 'file': 1397, 'cavalry': 1398, 'among': 1399, 'someone': 1400, 'uhdx': 1401, 'boston': 1402, 'engine': 1403, 'sue': 1404, 'sexy': 1405, 'roof': 1406, 'flow': 1407, 'pharmacist': 1408, 'sleepy': 1409, 'ethnic': 1410, 'whitewater': 1411, 'crab': 1412, 'courtyard': 1413, 'slowmo': 1414, 'joyful': 1415, 'design': 1416, 'cattle': 1417, 'dancers': 1418, 'plaid': 1419, 'public': 1420, 'fisherman': 1421, 'exam': 1422, 'charge': 1423, 'shuffle': 1424, 'deal': 1425, 'onto': 1426, 'stroke': 1427, 'spend': 1428, 'denmark': 1429, 'stairs': 1430, 'spider': 1431, 'potatoes': 1432, 'uniform': 1433, 'descend': 1434, 'cosmetic': 1435, 'mulatto': 1436, 'sniff': 1437, 'claim': 1438, 'dj': 1439, 'bean': 1440, 'rim': 1441, 'musician': 1442, 'drum': 1443, 'revolution': 1444, 'easel': 1445, 'multiple': 1446, 'pant': 1447, 'veterans': 1448, 'recreation': 1449, 'iron': 1450, 'omelet': 1451, 'sick': 1452, 'garbage': 1453, 'tractor': 1454, 'main': 1455, 'bill': 1456, 'chicken': 1457, 'rowboat': 1458, 'golfer': 1459, 'rapids': 1460, 'cao': 1461, 'dai': 1462, 'manicure': 1463, 'activity': 1464, 'attach': 1465, 'wire': 1466, 'operator': 1467, 'lovely': 1468, 'canal': 1469, 'sheep': 1470, 'heart': 1471, 'brooklyn': 1472, 'koala': 1473, 'sprinkle': 1474, 'powder': 1475, 'combine': 1476, 'boutique': 1477, 'celebration': 1478, 'bunny': 1479, 'ostrich': 1480, 'feather': 1481, 'stabilizedsportswoman': 1482, 'tracker': 1483, 'bracelet': 1484, 'zebra': 1485, 'luggage': 1486, 'magazine': 1487, 'lobby': 1488, 'grow': 1489, 'k': 1490, 'cub': 1491, 'shape': 1492, 'canyons': 1493, 'jewish': 1494, 'lynx': 1495, 'slowly': 1496, 'seed': 1497, 'indian': 1498, 'ethnicity': 1499, 'vehicle': 1500, 'spear': 1501, 'mud': 1502, 'shoreline': 1503, 'whip': 1504, 'driver': 1505, 'la': 1506, 'builder': 1507, 'john': 1508, 'goats': 1509, 'needle': 1510, 'shelf': 1511, 'tiltof': 1512, 'meditate': 1513, 'egg': 1514, 'moon': 1515, 'artists': 1516, 'grinder': 1517, 'wildflowers': 1518, 'mail': 1519, 'narrow': 1520, 'rollercoaster': 1521, 'mediumof': 1522, 'aircraft': 1523, 'carrier': 1524, 'opinion': 1525, 'journalists': 1526, 'ab': 1527, 'twist': 1528, 'vivacious': 1529, 'squeeze': 1530, 'takeaway': 1531, 'kudu': 1532, 'service': 1533, 'hiker': 1534, 'begin': 1535, 'rhino': 1536, 'seal': 1537, 'journey': 1538, 'outdoorsk': 1539, 'lovers': 1540, 'lips': 1541, 'electronic': 1542, 'chemical': 1543, 'experiment': 1544, 'snowboarder': 1545, 'end': 1546, 'microscope': 1547, 'energy': 1548, 'california': 1549, 'calve': 1550, 'officer': 1551, 'nearby': 1552, 'vegetable': 1553, 'lifestylek': 1554, 'exterior': 1555, 'think': 1556, 'seagull': 1557, 'spectators': 1558, 'dragon': 1559, 'stock': 1560, 'tell': 1561, 'chart': 1562, 'olive': 1563, 'penguins': 1564, 'aunt': 1565, 'nieces': 1566, 'marshland': 1567, 'muffins': 1568, 'thomas': 1569, 'gorgeous': 1570, 'trip': 1571, 'grimace': 1572, 'riders': 1573, 'multicolored': 1574, 'thessaloniki': 1575, 'august': 1576, 'otter': 1577, 'dig': 1578, 'railway': 1579, 'coca': 1580, 'panningshowing': 1581, 'cookie': 1582, 'pasta': 1583, 'map': 1584, 'unhappy': 1585, 'overdue': 1586, 'crockery': 1587, 'creation': 1588, 'pottery': 1589, 'report': 1590, 'asia': 1591, 'castle': 1592, 'potink': 1593, 'dollies': 1594, 'pollution': 1595, 'firefighters': 1596, 'silver': 1597, 'select': 1598, 'puppies': 1599, 'cockatoo': 1600, 'texts': 1601, 'tierra': 1602, 'del': 1603, 'fuego': 1604, 'racial': 1605, 'grenades': 1606, 'earphones': 1607, 'location': 1608, 'ballerina': 1609, 'garter': 1610, 'coil': 1611, 'staff': 1612, 'module': 1613, 'parachute': 1614, 'oahu': 1615, 'online': 1616, 'haircut': 1617, 'poker': 1618, 'steer': 1619, 'millennium': 1620, 'startup': 1621, 'stuff': 1622, 'firewood': 1623, 'atmosphere': 1624, 'swat': 1625, 'orchard': 1626, 'manhattan': 1627, 'sled': 1628, 'historical': 1629, 'epoch': 1630, 'russian': 1631, 'waitress': 1632, 'dream': 1633, 'playground': 1634, 'smili': 1635, 'incoming': 1636, 'drop': 1637, 'canon': 1638, 'edit': 1639, 'yacht': 1640, 'everglades': 1641, 'beautician': 1642, 'squad': 1643, 'gangster': 1644, 'brick': 1645, 'answer': 1646, 'spectacular': 1647, 'vivid': 1648, 'escalator': 1649, 'trade': 1650, 'grate': 1651, 'pomegranate': 1652, 'campaign': 1653, 'rooftop': 1654, 'bore': 1655, 'agent': 1656, 'sculpture': 1657, 'european': 1658, 'capture': 1659, 'atv': 1660, 'snack': 1661, 'steadicamthrough': 1662, 'curly': 1663, 'magpie': 1664, 'button': 1665, 'protective': 1666, 'popcorn': 1667, 'jam': 1668, 'seminar': 1669, 'motherhood': 1670, 'sparkler': 1671, 'insert': 1672, 'transparent': 1673, 'loopwith': 1674, 'blackmagic': 1675, 'ursa': 1676, 'mini': 1677, 'beard': 1678, 'tiltingshowing': 1679, 'luscious': 1680, 'ants': 1681, 'promenade': 1682, 'depth': 1683, 'grain': 1684, 'juan': 1685, 'rico': 1686, 'din': 1687, 'ancient': 1688, 'muskets': 1689, 'impatient': 1690, 'albuquerque': 1691, 'fiesta': 1692, 'gesture': 1693, 'retrieve': 1694, 'munch': 1695, 'india': 1696, 'trunk': 1697, 'number': 1698, 'page': 1699, 'pen': 1700, 'niagara': 1701, 'b': 1702, 'luxurious': 1703, 'grand': 1704, 'sieve': 1705, 'shelve': 1706, 'bouquet': 1707, 'mall': 1708, 'seahorse': 1709, 'hawk': 1710, 'cloth': 1711, 'years': 1712, 'stream': 1713, 'gently': 1714, 'blurry': 1715, 'interior': 1716, 'coast': 1717, 'tour': 1718, 'cheese': 1719, 'nol': 1720, 'dealer': 1721, 'museum': 1722, 'dirty': 1723, 'frenzy': 1724, 'web': 1725, 'pay': 1726, 'castro': 1727, 'quiet': 1728, 'respirator': 1729, 'fjord': 1730, 'baggage': 1731, 'could': 1732, 'member': 1733, 'pain': 1734, 'property': 1735, 'pedal': 1736, 'sandwich': 1737, 'korean': 1738, 'wrack': 1739, 'hen': 1740, 'chicks': 1741, 'rarotongo': 1742, 'upper': 1743, 'billow': 1744, 'duty': 1745, 'zone': 1746, 'credit': 1747, 'code': 1748, 'bank': 1749, 'flyfish': 1750, 'mixer': 1751, 'load': 1752, 'classroom': 1753, 'hammer': 1754, 'magic': 1755, 'viewwater': 1756, 'lakesailboats': 1757, 'departure': 1758, 'chase': 1759, 'tube': 1760, 'virginia': 1761, 'basketballin': 1762, 'coat': 1763, 'toddler': 1764, 'venice': 1765, 'sunsetk': 1766, 'create': 1767, 'app': 1768, 'telephone': 1769, 'crete': 1770, 'goggle': 1771, 'france': 1772, 'fossil': 1773, 'bloomy': 1774, 'sunshine': 1775, 'luzhniki': 1776, 'underside': 1777, 'harrier': 1778, 'elk': 1779, 'hillside': 1780, 'spray': 1781, 'cool': 1782, 'jay': 1783, 'pedestrians': 1784, 'pulse': 1785, 'excitement': 1786, 'amount': 1787, 'feast': 1788, 'sandhill': 1789, 'teen': 1790, 'bakery': 1791, 'skincare': 1792, 'radiologist': 1793, 'sketch': 1794, 'production': 1795, 'melt': 1796, 'cyclist': 1797, 'redrock': 1798, 'hit': 1799, 'agency': 1800, 'backlit': 1801, 'newly': 1802, 'mold': 1803, 'dreidel': 1804, 'putt': 1805, 'pump': 1806, 'lollipop': 1807, 'shack': 1808, 'scar': 1809, 'crazy': 1810, 'nice': 1811, 'candy': 1812, 'dentistry': 1813, 'glidingof': 1814, 'orbit': 1815, 'areas': 1816, 'finches': 1817, 'human': 1818, 'javelin': 1819, 'thrower': 1820, 'kennedy': 1821, 'yukon': 1822, 'volley': 1823, 'beam': 1824, 'sound': 1825, 'role': 1826, 'yet': 1827, 'marlen': 1828, 'khutsiev': 1829, 'carousel': 1830, 'eternal': 1831, 'barn': 1832, 'girlfriend': 1833, 'environment': 1834, 'motionfpsx': 1835, 'pack': 1836, 'colleague': 1837, 'trick': 1838, 'communicate': 1839, 'till': 1840, 'merry': 1841, 'grove': 1842, 'pair': 1843, 'slop': 1844, 'forklift': 1845, 'facial': 1846, 'cleanse': 1847, 'blaze': 1848, 'patrons': 1849, 'drone': 1850, 'boss': 1851, 'eight': 1852, 'central': 1853, 'splash': 1854, 'ring': 1855, 'treek': 1856, 'joshua': 1857, 'fasten': 1858, 'galilee': 1859, 'sisters': 1860, 'perfect': 1861, 'fit': 1862, 'geese': 1863, 'patio': 1864, 'breakdancer': 1865, 'shrimp': 1866, 'lonely': 1867, 'match': 1868, 'blanket': 1869, 'curl': 1870, 'cyclists': 1871, 'olympic': 1872, 'movement': 1873, 'clownfish': 1874, 'gorilla': 1875, 'roadside': 1876, 'post': 1877, 'overpass': 1878, 'timelapsek': 1879, 'chew': 1880, 'hanukia': 1881, 'cast': 1882, 'airplanes': 1883, 'grater': 1884, 'cry': 1885, 'journalist': 1886, 'crustacean': 1887, 'muesli': 1888, 'romanian': 1889, 'visit': 1890, 'cola': 1891, 'unhealth': 1892, 'theme': 1893, 'painter': 1894, 'chefs': 1895, 'sm': 1896, 'flyby': 1897, 'argue': 1898, 'dune': 1899, 'afternoon': 1900, 'entrance': 1901, 'italy': 1902, 'venetian': 1903, 'bath': 1904, 'monastery': 1905, 'share': 1906, 'apollo': 1907, 'uphill': 1908, 'futuristic': 1909, 'corruption': 1910, 'excavator': 1911, 'seafront': 1912, 'blink': 1913, 'cold': 1914, 'detach': 1915, 'county': 1916, 'sparkle': 1917, 'manual': 1918, 'marina': 1919, 'farmers': 1920, 'exhibition': 1921, 'count': 1922, 'metallic': 1923, 'jefferson': 1924, 'sometimes': 1925, 'xc': 1926, 'vr': 1927, 'metalworking': 1928, 'pre': 1929, 'ipad': 1930, 'dollar': 1931, 'delivery': 1932, 'send': 1933, 'bass': 1934, 'whiteboard': 1935, 'pure': 1936, 'towel': 1937, 'rv': 1938, 'ripple': 1939, 'contractor': 1940, 'straighten': 1941, 'applaud': 1942, 'ca': 1943, 'dad': 1944, 'plantation': 1945, 'turquoise': 1946, 'mechanical': 1947, 'pride': 1948, 'miami': 1949, 'athletic': 1950, 'pattern': 1951, 'lip': 1952, 'liner': 1953, 'lovatnet': 1954, 'spade': 1955, 'defocused': 1956, 'vast': 1957, 'gloves': 1958, 'elevator': 1959, 'bleachers': 1960, 'skateboard': 1961, 'cornflakes': 1962, 'creature': 1963, 'freight': 1964, 'british': 1965, 'columbia': 1966, 'rollers': 1967, 'sneak': 1968, 'barefoot': 1969, 'researcher': 1970, 'support': 1971, 'ashkelon': 1972, 'seawater': 1973, 'reverse': 1974, 'osmosis': 1975, 'medicine': 1976, 'basil': 1977, 'co': 1978, 'granddaughter': 1979, 'historic': 1980, 'sew': 1981, 'repel': 1982, 'easter': 1983, 'roulette': 1984, 'dam': 1985, 'tweet': 1986, 'twig': 1987, 'ruin': 1988, 'petco': 1989, 'charlottenburg': 1990, 'illuminate': 1991, 'smog': 1992, 'translucent': 1993, 'subway': 1994, 'reindeer': 1995, 'tsa': 1996, 'root': 1997, 'lago': 1998, 'roca': 1999, 'axe': 2000, 'dodger': 2001, 'contemporary': 2002, 'foliage': 2003, 'bend': 2004, 'platform': 2005, 'slavery': 2006, 'local': 2007, 'starfish': 2008, 'confident': 2009, 'bo': 2010, 'figure': 2011, 'rooster': 2012, 'muscle': 2013, 'coastal': 2014, 'trackingshowing': 2015, 'hardworker': 2016, 'district': 2017, 'transit': 2018, 'transportation': 2019, 'snowcapped': 2020, 'granddaughters': 2021, 'chairlift': 2022, 'job': 2023, 'organize': 2024, 'arrange': 2025, 'placement': 2026, 'mediterranean': 2027, 'rice': 2028, 'nori': 2029, 'puffy': 2030, 'monkey': 2031, 'lockers': 2032, 'vantage': 2033, 'chanuka': 2034, 'firek': 2035, 'carriage': 2036, 'colomna': 2037, 'song': 2038, 'horseback': 2039, 'tail': 2040, 'dawn': 2041, 'cocoa': 2042, 'migration': 2043, 'yogurt': 2044, 'hay': 2045, 'morninig': 2046, 'plum': 2047, 'asphalt': 2048, 'shell': 2049, 'windowsill': 2050, 'roost': 2051, 'lid': 2052, 'wireless': 2053, 'suburban': 2054, 'nap': 2055, 'sliderof': 2056, 'medication': 2057, 'santas': 2058, 'brow': 2059, 'scarf': 2060, 'strange': 2061, 'pray': 2062, 'mosque': 2063, 'wolf': 2064, 'chirp': 2065, 'system': 2066, 'angular': 2067, 'want': 2068, 'whistle': 2069, 'northern': 2070, 'arizona': 2071, 'trolley': 2072, 'mo': 2073, 'barber': 2074, 'clients': 2075, 'comfortable': 2076, 'bumper': 2077, 'hairstyle': 2078, 'printer': 2079, 'doors': 2080, 'warn': 2081, 'authentic': 2082, 'challenge': 2083, 'maybe': 2084, 'split': 2085, 'knees': 2086, 'fork': 2087, 'moab': 2088, 'turret': 2089, 'spanish': 2090, 'harley': 2091, 'squirrel': 2092, 'bookshelves': 2093, 'literature': 2094, 'copy': 2095, 'strawberry': 2096, 'volga': 2097, 'toboggan': 2098, 'beautyof': 2099, 'carrots': 2100, 'womanfrom': 2101, 'wifes': 2102, 'pregnancy': 2103, 'offof': 2104, 'motorcyclists': 2105, 'mortgage': 2106, 'designers': 2107, 'sailboats': 2108, 'waimanalo': 2109, 'variety': 2110, 'choice': 2111, 'price': 2112, 'sarong': 2113, 'wrangler': 2114, 'fountain': 2115, 'smi': 2116, 'weather': 2117, 'ambulance': 2118, 'pipe': 2119, 'twin': 2120, 'retro': 2121, 'cafek': 2122, 'windmills': 2123, 'striate': 2124, 'guinea': 2125, 'pig': 2126, 'concertk': 2127, 'dof': 2128, 'sack': 2129, 'explosion': 2130, 'coconut': 2131, 'tropics': 2132, 'loader': 2133, 'smith': 2134, 'employee': 2135, 'meadowlands': 2136, 'trumpet': 2137, 'wildebeest': 2138, 'leg': 2139, 'punta': 2140, 'cana': 2141, 'dominican': 2142, 'republic': 2143, 'novemer': 2144, 'smock': 2145, 'canvass': 2146, 'supply': 2147, 'elbow': 2148, 'pennsylvania': 2149, 'pagosa': 2150, 'special': 2151, 'offshowing': 2152, 'control': 2153, 'ears': 2154, 'tip': 2155, 'helicopters': 2156, 'uss': 2157, 'sprayer': 2158, 'daydream': 2159, 'tiny': 2160, 'cereals': 2161, 'juiceink': 2162, 'financial': 2163, 'usa': 2164, 'seas': 2165, 'musical': 2166, 'businesslady': 2167, 'injure': 2168, 'knee': 2169, 'sooth': 2170, 'smokestack': 2171, 'glamorous': 2172, 'advance': 2173, 'treetops': 2174, 'corella': 2175, 'headlights': 2176, 'roast': 2177, 'caribbean': 2178, 'tourism': 2179, 'stall': 2180, 'gadgets': 2181, 'object': 2182, 'dollywoman': 2183, 'epic': 2184, 'wintertime': 2185, 'greet': 2186, 'return': 2187, 'fashion': 2188, 'temple': 2189, 'staircase': 2190, 'second': 2191, 'data': 2192, 'furniture': 2193, 'fatherping': 2194, 'labor': 2195, 'elementary': 2196, 'europe': 2197, 'cart': 2198, 'bermuda': 2199, 'pierpanorama': 2200, 'cardio': 2201, 'motorboat': 2202, 'molt': 2203, 'husband': 2204, 'spiral': 2205, 'ha': 2206, 'rear': 2207, 'newest': 2208, 'submarine': 2209, 'minnesota': 2210, 'underneath': 2211, 'steady': 2212, 'songs': 2213, 'champangne': 2214, 'fireplacek': 2215, 'arabian': 2216, 'gulf': 2217, 'irrigate': 2218, 'greenhouse': 2219, 'beaver': 2220, 'defense': 2221, 'yoghurt': 2222, 'lazy': 2223, 'polish': 2224, 'dust': 2225, 'chamonix': 2226, 'alps': 2227, 'relaxation': 2228, 'cardboard': 2229, 'expect': 2230, 'carpet': 2231, 'honey': 2232, 'lighten': 2233, 'barrell': 2234, 'takeoff': 2235, 'scoreboard': 2236, 'hunter': 2237, 'employ': 2238, 'chopsticks': 2239, 'soy': 2240, 'mascara': 2241, 'gamepads': 2242, 'jetty': 2243, 'handsk': 2244, 'lakeside': 2245, 'meerkat': 2246, 'crayfish': 2247, 'strand': 2248, 'pony': 2249, 'joy': 2250, 'twinkle': 2251, 'sportsman': 2252, 'jibover': 2253, 'forward': 2254, 'unseen': 2255, 'radio': 2256, 'jeweller': 2257, 'tin': 2258, 'ferry': 2259, 'like': 2260, 'spectacles': 2261, 'channel': 2262, 'us': 2263, 'valleys': 2264, 'lap': 2265, 'walkway': 2266, 'beneath': 2267, 'naturalist': 2268, 'combat': 2269, 'bookshelf': 2270, 'graceful': 2271, 'fine': 2272, 'parchment': 2273, 'teddy': 2274, 'swoop': 2275, 'lunar': 2276, 'version': 2277, 'landmarks': 2278, 'cereal': 2279, 'maldives': 2280, 'expedition': 2281, 'antarctica': 2282, 'moor': 2283, 'sledge': 2284, 'unicycle': 2285, 'single': 2286, 'plug': 2287, 'showroom': 2288, 'atlantis': 2289, 'dumbbell': 2290, 'sink': 2291, 'topple': 2292, 'forehand': 2293, 'rugged': 2294, 'spires': 2295, 'brace': 2296, 'impalas': 2297, 'weld': 2298, 'alpine': 2299, 'virgin': 2300, 'de': 2301, 'sparklers': 2302, 'actor': 2303, 'tolstoy': 2304, 'kennedys': 2305, 'gravesite': 2306, 'owl': 2307, 'tallinn': 2308, 'estonia': 2309, 'acupuncture': 2310, 'sill': 2311, 'lax': 2312, 'turkey': 2313, 'vnukovo': 2314, 'huddle': 2315, 'snowboarders': 2316, 'decorations': 2317, 'vaping': 2318, 'wet': 2319, 'roses': 2320, 'easy': 2321, 'october': 2322, 'garland': 2323, 'depict': 2324, 'ear': 2325, 'ww': 2326, 'frustrate': 2327, 'wintery': 2328, 'handicap': 2329, 'leash': 2330, 'valentines': 2331, 'screeming': 2332, 'onion': 2333, 'customers': 2334, 'agents': 2335, 'skyscraper': 2336, 'department': 2337, 'smil': 2338, 'attack': 2339, 'caterpillar': 2340, 'comfort': 2341, 'charleston': 2342, 'finale': 2343, 'bring': 2344, 'frantic': 2345, 'offer': 2346, 'tape': 2347, 'intense': 2348, 'sandy': 2349, 'womanink': 2350, 'seller': 2351, 'headache': 2352, 'oriental': 2353, 'orthopedic': 2354, 'navajo': 2355, 'barista': 2356, 'bandana': 2357, 'simmer': 2358, 'saucepan': 2359, 'mens': 2360, 'shade': 2361, 'active': 2362, 'lecture': 2363, 'piggy': 2364, 'case': 2365, 'karate': 2366, 'longue': 2367, 'seascape': 2368, 'arc': 2369, 'triumph': 2370, 'outisde': 2371, 'barley': 2372, 'slide': 2373, 'form': 2374, 'architectural': 2375, 'bucket': 2376, 'workouts': 2377, 'astronauts': 2378, 'masseuse': 2379, 'dumbbells': 2380, 'sweater': 2381, 'bead': 2382, 'topical': 2383, 'saltwater': 2384, 'anemonefish': 2385, 'massive': 2386, 'draft': 2387, 'accidently': 2388, 'scavenge': 2389, 'loudspeaker': 2390, 'pop': 2391, 'antigua': 2392, 'chiloe': 2393, 'goal': 2394, 'cud': 2395, 'ornament': 2396, 'dropper': 2397, 'latte': 2398, 'misty': 2399, 'fox': 2400, 'flyover': 2401, 'profile': 2402, 'handheldof': 2403, 'bloodpressure': 2404, 'backpacker': 2405, 'moutains': 2406, 'liquid': 2407, 'starry': 2408, 'poolside': 2409, 'grab': 2410, 'express': 2411, 'snowstorm': 2412, 'squid': 2413, 'see': 2414, 'rainbow': 2415, 'trap': 2416, 'flashlights': 2417, 'buggy': 2418, 'el': 2419, 'mingle': 2420, 'formation': 2421, 'cigar': 2422, 'topography': 2423, 'scent': 2424, 'waiter': 2425, 'jerusalem': 2426, 'countries': 2427, 'roadway': 2428, 'paraglider': 2429, 'snowplows': 2430, 'lone': 2431, 'pleasure': 2432, 'windmill': 2433, 'freeway': 2434, 'windy': 2435, 'depart': 2436, 'scroll': 2437, 'hitch': 2438, 'jibas': 2439, 'kirillov': 2440, 'performer': 2441, 'aquamarine': 2442, 'appear': 2443, 'cant': 2444, 'boot': 2445, 'chickens': 2446, 'rubber': 2447, 'heel': 2448, 'crack': 2449, 'coloradok': 2450, 'alligator': 2451, 'ecu': 2452, 'smell': 2453, 'cabinet': 2454, 'daily': 2455, 'routine': 2456, 'university': 2457, 'hispanic': 2458, 'luxuary': 2459, 'scatter': 2460, 'constructor': 2461, 'determine': 2462, 'slot': 2463, 'tripod': 2464, 'directions': 2465, 'hurry': 2466, 'eyebrows': 2467, 'fee': 2468, 'stalk': 2469, 'bug': 2470, 'powell': 2471, 'domestic': 2472, 'scream': 2473, 'travelers': 2474, 'watermelon': 2475, 'batter': 2476, 'pitch': 2477, 'bat': 2478, 'catcher': 2479, 'crouch': 2480, 'kettle': 2481, 'vulture': 2482, 'rinse': 2483, 'campus': 2484, 'chess': 2485, 'chip': 2486, 'promote': 2487, 'elderly': 2488, 'ramp': 2489, 'hush': 2490, 'grapes': 2491, 'trackingfocusing': 2492, 'mechanism': 2493, 'diary': 2494, 'gardenk': 2495, 'sweep': 2496, 'materials': 2497, 'gentleman': 2498, 'aitutaki': 2499, 'english': 2500, 'collect': 2501, 'banknote': 2502, 'ticket': 2503, 'debit': 2504, 'jar': 2505, 'ink': 2506, 'redwood': 2507, 'down': 2508, 'waste': 2509, 'dollythrough': 2510, 'crimson': 2511, 'prey': 2512, 'bounce': 2513, 'sms': 2514, 'shine': 2515, 'baltic': 2516, 'wintry': 2517, 'hurghada': 2518, 'egypt': 2519, 'basin': 2520, 'worry': 2521, 'mosfilm': 2522, 'motor': 2523, 'worship': 2524, 'bow': 2525, 'playingwith': 2526, 'compass': 2527, 'barbell': 2528, 'still': 2529, 'beverage': 2530, 'assemble': 2531, 'warthog': 2532, 'ge': 2533, 'isolate': 2534, 'harbork': 2535, 'ginger': 2536, 'disco': 2537, 'backhoe': 2538, 'baboon': 2539, 'e': 2540, 'stealth': 2541, 'bomber': 2542, 'firework': 2543, 'mexico': 2544, 'vail': 2545, 'bokeh': 2546, 'newborn': 2547, 'wrap': 2548, 'wipe': 2549, 'verge': 2550, 'ideas': 2551, 'worldwide': 2552, 'brightly': 2553, 'lovek': 2554, 'computers': 2555, 'walker': 2556, 'cellphones': 2557, 'remote': 2558, 'strength': 2559, 'stomach': 2560, 'hanger': 2561, 'craft': 2562, 'best': 2563, 'frutillar': 2564, 'mountainside': 2565, 'rejoice': 2566, 'pub': 2567, 'nestle': 2568, 'result': 2569, 'osprey': 2570, 'refuel': 2571, 'tide': 2572, 'bruschettas': 2573, 'pastry': 2574, 'smilin': 2575, 'far': 2576, 'burgers': 2577, 'record': 2578, 'treat': 2579, 'reception': 2580, 'sunflower': 2581, 'jesus': 2582, 'eyeglasses': 2583, 'discussion': 2584, 'haired': 2585, 'workplace': 2586, 'hunt': 2587, 'vegetation': 2588, 'tongs': 2589, 'flutter': 2590, 'amsterdam': 2591, 'holland': 2592, 'netherl': 2593, 'bark': 2594, 'viewk': 2595, 'bottom': 2596, 'alcoholic': 2597, 'pierce': 2598, 'diver': 2599, 'scenes': 2600, 'honor': 2601, 'procedure': 2602, 'c': 2603, 'swimsuits': 2604, 'xmas': 2605, 'bacon': 2606, 'monica': 2607, 'icy': 2608, 'wade': 2609, 'orthopaedist': 2610, 'propeller': 2611, 'guide': 2612, 'puddle': 2613, 'mykonos': 2614, 'chi': 2615, 'kittiwakes': 2616, 'clap': 2617, 'lumber': 2618, 'temples': 2619, 'protect': 2620, 'assort': 2621, 'cave': 2622, 'ham': 2623, 'cutter': 2624, 'thai': 2625, 'neighborhood': 2626, 'hollywood': 2627, 'rusty': 2628, 'teeth': 2629, 'rhinoceros': 2630, 'navigation': 2631, 'disc': 2632, 'fat': 2633, 'droplets': 2634, 'discovery': 2635, 'vineyard': 2636, 'oral': 2637, 'hygiene': 2638, 'reenactor': 2639, 'coastline': 2640, 'oceanarium': 2641, 'burger': 2642, 'musket': 2643, 'sh': 2644, 'racket': 2645, 'sitter': 2646, 'alsatian': 2647, 'aisen': 2648, 'chacabuco': 2649, 'addict': 2650, 'assembly': 2651, 'springtime': 2652, 'hoover': 2653, 'conveyor': 2654, 'barren': 2655, 'hartebeest': 2656, 'stroller': 2657, 'bound': 2658, 'schloss': 2659, 'upward': 2660, 'berlin': 2661, 'germa': 2662, 'baskets': 2663, 'pound': 2664, 'fencepost': 2665, 'intersectionk': 2666, 'north': 2667, 'nordkapp': 2668, 'pat': 2669, 'downk': 2670, 'glare': 2671, 'coal': 2672, 'protrude': 2673, 'erode': 2674, 'sheik': 2675, 'beyond': 2676, 'schedule': 2677, 'aiport': 2678, 'foggy': 2679, 'vermilion': 2680, 'flycatcher': 2681, 'leafy': 2682, 'mate': 2683, 'geometrical': 2684, 'rapidswith': 2685, 'abseil': 2686, 'vertical': 2687, 'barbershop': 2688, 'dominate': 2689, 'chilean': 2690, 'nose': 2691, 'hanky': 2692, 'ill': 2693, 'smartphoneis': 2694, 'enclose': 2695, 'lioness': 2696, 'ethereal': 2697, 'drawshoot': 2698, 'nursery': 2699, 'plasticine': 2700, 'childern': 2701, 'bobcat': 2702, 'classic': 2703, 'electricians': 2704, 'transformer': 2705, 'blueptints': 2706, 'weave': 2707, 'mussels': 2708, 'cuisine': 2709, 'dusty': 2710, 'silhouetterifle': 2711, 'wheatfield': 2712, 'column': 2713, 'confer': 2714, 'donats': 2715, 'handshake': 2716, 'dissolve': 2717, 'toronto': 2718, 'deli': 2719, 'rink': 2720, 'bryant': 2721, 'christm': 2722, 'drag': 2723, 'waling': 2724, 'sunbeam': 2725, 'underground': 2726, 'beachk': 2727, 'happily': 2728, 'humorous': 2729, 'portraitof': 2730, 'schoolbus': 2731, 'continental': 2732, 'downof': 2733, 'administer': 2734, 'fractional': 2735, 'resurface': 2736, 'rejuvenate': 2737, 'wag': 2738, 'rustic': 2739, 'tranquillity': 2740, 'kittwakes': 2741, 'package': 2742, 'aubergine': 2743, 'upset': 2744, 'problem': 2745, 'flatten': 2746, 'mazefield': 2747, 'cyan': 2748, 'emracing': 2749, 'expectation': 2750, 'fell': 2751, 'anhinga': 2752, 'offcapturing': 2753, 'reel': 2754, 'sterilizer': 2755, 'cosmetological': 2756, 'roadwork': 2757, 'gunfight': 2758, 'squat': 2759, 'canada': 2760, 'honk': 2761, 'sailboard': 2762, 'rotation': 2763, 'steel': 2764, 'raspberries': 2765, 'babyboy': 2766, 'pyongshong': 2767, 'pyongyang': 2768, 'celebrations': 2769, 'lumberjack': 2770, 'lupines': 2771, 'england': 2772, 'bunch': 2773, 'handle': 2774, 'dab': 2775, 'barracuda': 2776, 'chill': 2777, 'quickly': 2778, 'guitars': 2779, 'sids': 2780, 'inflatable': 2781, 'mattress': 2782, 'houseboat': 2783, 'footbridge': 2784, 'vultures': 2785, 'birch': 2786, 'atd': 2787, 'easily': 2788, 'tech': 2789, 'trend': 2790, 'expo': 2791, 'roadk': 2792, 'anonymous': 2793, 'sprout': 2794, 'wildebeests': 2795, 'dolphins': 2796, 'honeymoon': 2797, 'abraham': 2798, 'lincoln': 2799, 'emancipation': 2800, 'liteiny': 2801, 'coffin': 2802, 'citycan': 2803, 'content': 2804, 'reject': 2805, 'let': 2806, 'winek': 2807, 'trim': 2808, 'structure': 2809, 'expertise': 2810, 'character': 2811, 'personality': 2812, 'dreadlocks': 2813, 'circular': 2814, 'saw': 2815, 'legionary': 2816, 'trampoline': 2817, 'amusement': 2818, 'imitation': 2819, 'bolt': 2820, 'steadycome': 2821, 'plasma': 2822, 'dismount': 2823, 'staticand': 2824, 'panningtracking': 2825, 'greek': 2826, 'dofk': 2827, 'launchk': 2828, 'mouse': 2829, 'girs': 2830, 'pavilion': 2831, 'grandparents': 2832, 'facewash': 2833, 'spark': 2834, 'ignite': 2835, 'fringe': 2836, 'wasabi': 2837, 'instal': 2838, 'parabolic': 2839, 'antennae': 2840, 'booster': 2841, 'charmer': 2842, 'tempanos': 2843, 'tampanos': 2844, 'iceberg': 2845, 'sarcastic': 2846, 'handlebar': 2847, 'drugin': 2848, 'pharmacy': 2849, 'afterwards': 2850, 'identicalwith': 2851, 'stss': 2852, 'nut': 2853, 'antenna': 2854, 'ii': 2855, 'pinguin': 2856, 'mineral': 2857, 'bubbler': 2858, 'breather': 2859, 'herbs': 2860, 'elaborate': 2861, 'hermit': 2862, 'caw': 2863, 'zoomi': 2864, 'scissor': 2865, 'stub': 2866, 'drivingk': 2867, 'kinds': 2868, 'greenfieldfrom': 2869, 'oceans': 2870, 'octopus': 2871, 'crawl': 2872, 'creatures': 2873, 'tablefood': 2874, 'dingle': 2875, 'urchins': 2876, 'eco': 2877, 'transport': 2878, 'kickitat': 2879, 'smov': 2880, 'hippo': 2881, 'source': 2882, 'dairy': 2883, 'every': 2884, 'emergency': 2885, 'vehicles': 2886, 'cofa': 2887, 'latin': 2888, 'poncho': 2889, 'pannin': 2890, 'closi': 2891, 'tiramisu': 2892, 'daddy': 2893, 'mojito': 2894, 'chemicals': 2895, 'product': 2896, 'geiranger': 2897, 'itchy': 2898, 'shiny': 2899, 'loft': 2900, 'stereo': 2901, 'stewardess': 2902, 'rancher': 2903, 'ease': 2904, 'joint': 2905, 'glove': 2906, 'flashlight': 2907, 'force': 2908, 'marry': 2909, 'brainwave': 2910, 'depend': 2911, 'mood': 2912, 'bacalar': 2913, 'figher': 2914, 'midway': 2915, 'nevada': 2916, 'steep': 2917, 'wooded': 2918, 'crevice': 2919, 'firefight': 2920, 'dragonfly': 2921, 'footpath': 2922, 'gondola': 2923, 'youg': 2924, 'cupsp': 2925, 'flurry': 2926, 'cherries': 2927, 'tnen': 2928, 'pigeon': 2929, 'coconuts': 2930, 'tandem': 2931, 'voter': 2932, 'responsibilities': 2933, 'havana': 2934, 'cuba': 2935, 'cuban': 2936, 'souvenirs': 2937, 'handicrafts': 2938, 'posters': 2939, 'cheek': 2940, 'account': 2941, 'pod': 2942, 'cameraman': 2943, 'workday': 2944, 'freelance': 2945, 'geek': 2946, 'compute': 2947, 'grunge': 2948, 'cloths': 2949, 'parquetcolor': 2950, 'disable': 2951, 'storage': 2952, 'lose': 2953, 'atm': 2954, 'pectoral': 2955, 'wellness': 2956, 'kiwi': 2957, 'overwork': 2958, 'raindrops': 2959, 'dads': 2960, 'verticalof': 2961, 'sparlking': 2962, 'settle': 2963, 'skiiers': 2964, 'murder': 2965, 'lad': 2966, 'aside': 2967, 'woolly': 2968, 'lemonade': 2969, 'soldierin': 2970, 'tank': 2971, 'seabirds': 2972, 'mannequin': 2973, 'situps': 2974, 'abdominal': 2975, 'commercial': 2976, 'doha': 2977, 'capital': 2978, 'qatar': 2979, 'decorative': 2980, 'rag': 2981, 'diagram': 2982, 'toe': 2983, 'aegean': 2984, 'mont': 2985, 'blanc': 2986, 'haute': 2987, 'savoie': 2988, 'hakafot': 2989, 'sokkot': 2990, 'prestige': 2991, 'neatly': 2992, 'platfrom': 2993, 'chainsawing': 2994, 'stingray': 2995, 'intenet': 2996, 'kitten': 2997, 'avb': 2998, 'westernpistolsfiring': 2999, 'cosmeticians': 3000, 'issue': 3001, 'supermarket': 3002, 'creepy': 3003, 'hollow': 3004, 'rome': 3005, 'colosseum': 3006, 'appetizing': 3007, 'upwards': 3008, 'topless': 3009, 'expression': 3010, 'neighbour': 3011, 'acupuncturist': 3012, 'shannon': 3013, 'banagher': 3014, 'harbour': 3015, 'knossos': 3016, 'propylaea': 3017, 'palace': 3018, 'pee': 3019, 'clink': 3020, 'sportswoman': 3021, 'riverside': 3022, 'loo': 3023, 'robotic': 3024, 'vet': 3025, 'staten': 3026, 'keypad': 3027, 'target': 3028, 'chant': 3029, 'organic': 3030, 'insecticide': 3031, 'thiscould': 3032, 'topic': 3033, 'application': 3034, 'foilage': 3035, 'duckling': 3036, 'hitchhike': 3037, 'nothing': 3038, 'dome': 3039, 'fishermanfrom': 3040, 'culvert': 3041, 'whistler': 3042, 'goat': 3043, 'waterskiing': 3044, 'magnify': 3045, 'puffer': 3046, 'tetraodontidae': 3047, 'lily': 3048, 'loon': 3049, 'adu': 3050, 'intimateof': 3051, 'paintbrush': 3052, 'beak': 3053, 'splittig': 3054, 'teenage': 3055, 'snowfallk': 3056, 'piont': 3057, 'electronical': 3058, 'protest': 3059, 'grandmothers': 3060, 'entertain': 3061, 'panningcapturing': 3062, 'cane': 3063, 'chiron': 3064, 'ushuaia': 3065, 'wint': 3066, 'dummy': 3067, 'mono': 3068, 'van': 3069, 'transmit': 3070, 'events': 3071, 'broadcast': 3072, 'grenade': 3073, 'extend': 3074, 'amazon': 3075, 'mission': 3076, 'shipwreck': 3077, 'cameraon': 3078, 'found': 3079, 'fotographer': 3080, 'pincers': 3081, 'boardroom': 3082, 'waimea': 3083, 'wristwatch': 3084, 'sunblock': 3085, 'shrine': 3086, 'cascada': 3087, 'iconography': 3088, 'lourdes': 3089, 'grotto': 3090, 'prune': 3091, 'welcome': 3092, 'winkle': 3093, 'peek': 3094, 'alexey': 3095, 'taxi': 3096, 'moma': 3097, 'spiti': 3098, 'himachal': 3099, 'pradesh': 3100, 'womens': 3101, 'thanksgiving': 3102, 'sneaky': 3103, 'batteries': 3104, 'vine': 3105, 'lime': 3106, 'shilouetted': 3107, 'steamboat': 3108, 'breakfastink': 3109, 'storefront': 3110, 'eyesight': 3111, 'pelmeni': 3112, 'catfish': 3113, 'defocusedof': 3114, 'visible': 3115, 'unable': 3116, 'tender': 3117, 'asana': 3118, 'sandpipers': 3119, 'preschooler': 3120, 'zoo': 3121, 'plaka': 3122, 'editorial': 3123, 'weed': 3124, 'pistoltwirl': 3125, 'businessmeeting': 3126, 'dnipro': 3127, 'ukraine': 3128, 'closeof': 3129, 'con': 3130, 'skilled': 3131, 'muff': 3132, 'fisheye': 3133, 'peach': 3134, 'billboards': 3135, 'jumbotrons': 3136, 'icelandic': 3137, 'hyper': 3138, 'carrot': 3139, 'sportwoman': 3140, 'elate': 3141, 'joke': 3142, 'flute': 3143, 'bluff': 3144, 'hydrangeas': 3145, 'birdcage': 3146, 'access': 3147, 'things': 3148, 'cafeteria': 3149, 'name': 3150, 'aurelia': 3151, 'gossip': 3152, 'reprimand': 3153, 'furious': 3154, 'tongue': 3155, 'granfather': 3156, 'thoughful': 3157, 'garlic': 3158, 'boardfrom': 3159, 'jelly': 3160, 'shock': 3161, 'exepnsive': 3162, 'skrimmaging': 3163, 'pokermoneybillfold': 3164, 'hula': 3165, 'tallin': 3166, 'rout': 3167, 'malnourish': 3168, 'virus': 3169, 'program': 3170, 'cyber': 3171, 'chic': 3172, 'balkony': 3173, 'official': 3174, 'parachuters': 3175, 'bayonet': 3176, 'enlighting': 3177, 'layer': 3178, 'navyfighters': 3179, 'teens': 3180, 'mary': 3181, 'christ': 3182, 'flank': 3183, 'net': 3184, 'sockeye': 3185, 'avoid': 3186, 'doorway': 3187, 'povof': 3188, 'taxidermy': 3189, 'siamese': 3190, 'startle': 3191, 'alert': 3192, 'patagonia': 3193, 'associate': 3194, 'middleof': 3195, 'moo': 3196, 'peer': 3197, 'ct': 3198, 'laso': 3199, 'propose': 3200, 'react': 3201, 'horror': 3202, 'ind': 3203, 'danger': 3204, 'berry': 3205, 'beetle': 3206, 'date': 3207, 'machinegunbeltammo': 3208, 'mat': 3209, 'azure': 3210, 'getaway': 3211, 'paradise': 3212, 'sardines': 3213, 'backyard': 3214, 'stairway': 3215, 'bengal': 3216, 'migraine': 3217, 'fife': 3218, 'dispenser': 3219, 'lower': 3220, 'footstep': 3221, 'orthotics': 3222, 'continuous': 3223, 'parkour': 3224, 'choke': 3225, 'bureau': 3226, 'reclamationk': 3227, 'carcass': 3228, 'stroll': 3229, 'espresso': 3230, 'sparkly': 3231, 'physician': 3232, 'atop': 3233, 'flake': 3234, 'leisure': 3235, 'vs': 3236, 'businesscouple': 3237, 'moray': 3238, 'well': 3239, 'whiskey': 3240, 'hearts': 3241, 'fishers': 3242, 'erriff': 3243, 'aasleagh': 3244, 'curb': 3245, 'melon': 3246, 'backgroun': 3247, 'regular': 3248, 'ledge': 3249, 'zero': 3250, 'gravity': 3251, 'peace': 3252, 'chimney': 3253, 'retriever': 3254, 'rehabilitate': 3255, 'snorkeler': 3256, 'cardigan': 3257, 'hazy': 3258, 'nail': 3259, 'pedicure': 3260, 'floatation': 3261, 'devices': 3262, 'lush': 3263, 'copyspace': 3264, 'automobile': 3265, 'reconstruction': 3266, 'competition': 3267, 'puzzle': 3268, 'wider': 3269, 'cargo': 3270, 'excursion': 3271, 'spiny': 3272, 'steak': 3273, 'ozernaya': 3274, 'blog': 3275, 'hangar': 3276, 'antelope': 3277, 'waterbuck': 3278, 'signal': 3279, 'contract': 3280, 'molly': 3281, 'malone': 3282, 'grafton': 3283, 'dublin': 3284, 'tranquil': 3285, 'driveway': 3286, 'cappuccino': 3287, 'invoice': 3288, 'brass': 3289, 'raven': 3290, 'backflip': 3291, 'sprint': 3292, 'ferris': 3293, 'insect': 3294, 'broom': 3295, 'shennandoah': 3296, 'shredder': 3297, 'melodrama': 3298, 'hillk': 3299, 'alight': 3300, 'technologist': 3301, 'smear': 3302, 'specimen': 3303, 'diagnosis': 3304, 'objective': 3305, 'lenses': 3306, 'climate': 3307, 'mid': 3308, 'harleys': 3309, 'outboard': 3310, 'handsmov': 3311, 'camouflage': 3312, 'anenome': 3313, 'orleans': 3314, 'marker': 3315, 'veggies': 3316, 'preikestolen': 3317, 'prekestolen': 3318, 'scooters': 3319, 'soda': 3320, 'wathing': 3321, 'bridal': 3322, 'headscarf': 3323, 'zipper': 3324, 'quarell': 3325, 'landline': 3326, 'peak': 3327, 'utahk': 3328, 'businessmann': 3329, 'freep': 3330, 'presentations': 3331, 'staircases': 3332, 'stairways': 3333, 'quote': 3334, 'engrave': 3335, 'gaucho': 3336, 'spectator': 3337, 'estancia': 3338, 'tamper': 3339, 'necktie': 3340, 'gengrations': 3341, 'hippos': 3342, 'corkscrew': 3343, 'terlingua': 3344, 'iii': 3345, 'stabilize': 3346, 'formula': 3347, 'blackboard': 3348, 'gauge': 3349, 'wink': 3350, 'outfit': 3351, 'reaction': 3352, 'alarm': 3353, 'calbuco': 3354, 'volcano': 3355, 'loom': 3356, 'christian': 3357, 'swimsuit': 3358, 'surfboard': 3359, 'demonstration': 3360, 'daisy': 3361, 'algae': 3362, 'pocket': 3363, 'geometric': 3364, 'elevate': 3365, 'djemaa': 3366, 'fna': 3367, 'marrakech': 3368, 'marrakesh': 3369, 'morocco': 3370, 'n': 3371, 'mushroom': 3372, 'ranch': 3373, 'skinny': 3374, 'crusteacean': 3375, 'copper': 3376, 'plain': 3377, 'recess': 3378, 'cheerfully': 3379, 'herbed': 3380, 'tear': 3381, 'loaf': 3382, 'crunch': 3383, 'meadowwith': 3384, 'passport': 3385, 'mark': 3386, 'lesson': 3387, 'punk': 3388, 'stuntman': 3389, 'cling': 3390, 'jerk': 3391, 'mediumangled': 3392, 'monuments': 3393, 'leonabelle': 3394, 'turnbull': 3395, 'meringues': 3396, 'mitzpe': 3397, 'ramon': 3398, 'crater': 3399, 'rig': 3400, 'intently': 3401, 'arena': 3402, 'actors': 3403, 'rehearse': 3404, 'onstage': 3405, 'hear': 3406, 'breeze': 3407, 'motionx': 3408, 'ascend': 3409, 'ticker': 3410, 'cabbage': 3411, 'soil': 3412, 'exoticm': 3413, 'yorknight': 3414, 'kirillo': 3415, 'belozersky': 3416, 'largest': 3417, 'russiaon': 3418, 'sulphur': 3419, 'crest': 3420, 'tightof': 3421, 'january': 3422, 'cube': 3423, 'underwaterof': 3424, 'jib': 3425, 'coop': 3426, 'beachy': 3427, 'piranha': 3428, 'colossoma': 3429, 'macropomum': 3430, 'jasmine': 3431, 'skyward': 3432, 'premise': 3433, 'removal': 3434, 'pro': 3435, 'animas': 3436, 'durango': 3437, 'amuse': 3438, 'solutions': 3439, 'beakers': 3440, 'gravel': 3441, 'action': 3442, 'chanukia': 3443, 'recreationk': 3444, 'pann': 3445, 'spacious': 3446, 'dentists': 3447, 'scary': 3448, 'playfully': 3449, 'pallets': 3450, 'superheroes': 3451, 'territory': 3452, 'alberca': 3453, 'education': 3454, 'pilot': 3455, 'maintain': 3456, 'scooter': 3457, 'photograph': 3458, 'overshining': 3459, 'banner': 3460, 'formations': 3461, 'vest': 3462, 'pelicans': 3463, 'mow': 3464, 'scythe': 3465, 'cambrils': 3466, 'bodyof': 3467, 'filmmakers': 3468, 'dslr': 3469, 'success': 3470, 'cameras': 3471, 'noodle': 3472, 'bell': 3473, 'thirsty': 3474, 'centre': 3475, 'insects': 3476, 'speedboat': 3477, 'boarder': 3478, 'bartenders': 3479, 'pint': 3480, 'lager': 3481, 'motionbarman': 3482, 'loud': 3483, 'oregon': 3484, 'squids': 3485, 'biologists': 3486, 'tab': 3487, 'matal': 3488, 'toolbox': 3489, 'scoop': 3490, 'feature': 3491, 'lick': 3492, 'terrain': 3493, 'timek': 3494, 'unidentifiable': 3495, 'tickle': 3496, 'offaly': 3497, 'shave': 3498, 'fton': 3499, 'enjoyment': 3500, 'craftsman': 3501, 'lathe': 3502, 'uproad': 3503, 'social': 3504, 'network': 3505, 'pulloutas': 3506, 'hamburgers': 3507, 'freckle': 3508, 'grapefruit': 3509, 'hanuka': 3510, 'boom': 3511, 'mic': 3512, 'schoolgirl': 3513, 'analyse': 3514, 'overview': 3515, 'ein': 3516, 'gedi': 3517, 'halibut': 3518, 'agencies': 3519, 'germantrench': 3520, 'kitcken': 3521, 'choreograph': 3522, 'g': 3523, 'meerkats': 3524, 'draftsmanship': 3525, 'sungla': 3526, 'degree': 3527, 'deer': 3528, 'doe': 3529, 'headstones': 3530, 'lagoon': 3531, 'mount': 3532, 'chaning': 3533, 'lizard': 3534, 'player': 3535, 'nectar': 3536, 'politics': 3537, 'hundred': 3538, 'usingd': 3539, 'cornfield': 3540, 'sellers': 3541, 'soft': 3542, 'motorcade': 3543, 'bathtub': 3544, 'fireweed': 3545, 'denim': 3546, 'neon': 3547, 'slope': 3548, 'emts': 3549, 'butcher': 3550, 'pork': 3551, 'ballot': 3552, 'secretary': 3553, 'blast': 3554, 'automate': 3555, 'electronics': 3556, 'manufacture': 3557, 'unfinished': 3558, 'enormous': 3559, 'multimedia': 3560, 'povzooming': 3561, 'onboard': 3562, 'bananas': 3563, 'corgi': 3564, 'reed': 3565, 'builders': 3566, 'headsets': 3567, 'progect': 3568, 'virtuald': 3569, 'simulator': 3570, 'interrior': 3571, 'rosella': 3572, 'amid': 3573, 'pounce': 3574, 'selfiemade': 3575, 'synchronize': 3576, 'competitionjudging': 3577, 'bungee': 3578, 'out': 3579, 'buoy': 3580, 'runners': 3581, 'gril': 3582, 'mourn': 3583, 'simple': 3584, 'deckchair': 3585, 'skype': 3586, 'forge': 3587, 'ahead': 3588, 'doolin': 3589, 'clare': 3590, 'malachite': 3591, 'vatican': 3592, 'speter': 3593, 'induction': 3594, 'hob': 3595, 'latticed': 3596, 'lot': 3597, 'mug': 3598, 'livingroom': 3599, 'fd': 3600, 'falcon': 3601, 'cockpit': 3602, 'pyrenees': 3603, 'unshaven': 3604, 'mist': 3605, 'kfps': 3606, 'icefields': 3607, 'parkway': 3608, 'jasper': 3609, 'knit': 3610, 'episode': 3611, 'fim': 3612, 'narrate': 3613, 'chekhov': 3614, 'direct': 3615, 'wreaths': 3616, 'breathe': 3617, 'email': 3618, 'seville': 3619, 'guadalquivir': 3620, 'potomac': 3621, 'tummy': 3622, 'marine': 3623, 'winsunrise': 3624, 'clang': 3625, 'childrens': 3626, 'walkingfeet': 3627, 'bomb': 3628, 'longingly': 3629, 'hammerhead': 3630, 'snap': 3631, 'punter': 3632, 'sailboarding': 3633, 'lotus': 3634, 'kof': 3635, 'expressions': 3636, 'nekresi': 3637, 'kakheti': 3638, 'georgia': 3639, 'nightpanning': 3640, 'dollyshowing': 3641, 'murramarang': 3642, 'kangaroo': 3643, 'flipchart': 3644, 'prescriptions': 3645, 'outcrop': 3646, 'toulon': 3647, 'warth': 3648, 'mass': 3649, 'melancholy': 3650, 'cabin': 3651, 'crocodile': 3652, 'ox': 3653, 'adults': 3654, 'seafood': 3655, 'heave': 3656, 'glitch': 3657, 'whiteman': 3658, 'romania': 3659, 'crime': 3660, 'aftermath': 3661, 'couscous': 3662, 'kasha': 3663, 'tunnel': 3664, 'solve': 3665, 'freelancer': 3666, 'gregorian': 3667, 'sauna': 3668, 'dollyslow': 3669, 'hvita': 3670, 'gullfoss': 3671, 'livorno': 3672, 'busk': 3673, 'broccoli': 3674, 'seminary': 3675, 'cupola': 3676, 'aboveof': 3677, 'examination': 3678, 'goldfish': 3679, 'bully': 3680, 'passover': 3681, 'cypress': 3682, 'swamp': 3683, 'surge': 3684, 'depression': 3685, 'lamb': 3686, 'linen': 3687, 'days': 3688, 'kimball': 3689, 'brainstorm': 3690, 'corporate': 3691, 'corral': 3692, 'nineteenth': 3693, 'portmagee': 3694, 'xray': 3695, 'platek': 3696, 'dollyhealthy': 3697, 'courgette': 3698, 'galicia': 3699, 'visitors': 3700, 'cucumbers': 3701, 'mad': 3702, 'install': 3703, 'sim': 3704, 'errors': 3705, 'must': 3706, 'king': 3707, 'cew': 3708, 'bathrobe': 3709, 'trash': 3710, 'somebody': 3711, 'aerialk': 3712, 'macrocheira': 3713, 'kaempferi': 3714, 'nimitz': 3715, 'overheadof': 3716, 'steeple': 3717, 'chil': 3718, 'wildflower': 3719, 'phablet': 3720, 'actual': 3721, 'innovative': 3722, 'humanity': 3723, 'sanibel': 3724, 'florida': 3725, 'tak': 3726, 'mayan': 3727, 'observatory': 3728, 'frame': 3729, 'bungalow': 3730, 'somersault': 3731, 'acrobatic': 3732, 'skills': 3733, 'ascension': 3734, 'beat': 3735, 'approve': 3736, 'five': 3737, 'boxer': 3738, 'abandon': 3739, 'word': 3740, 'pram': 3741, 'cranberries': 3742, 'hump': 3743, 'bye': 3744, 'wideshot': 3745, 'merchandise': 3746, 'girraffes': 3747, 'employees': 3748, 'liberty': 3749, 'portraits': 3750, 'panorig': 3751, 'finch': 3752, 'quaint': 3753, 'task': 3754, 'calculator': 3755, 'positive': 3756, 'jfk': 3757, 'cu': 3758, 'mather': 3759, 'ceremonyk': 3760, 'retreat': 3761, 'billiard': 3762, 'triangle': 3763, 'coffe': 3764, 'pur': 3765, 'feeder': 3766, 'rejoin': 3767, 'payload': 3768, 'gloved': 3769, 'summerk': 3770, 'usage': 3771, 'slip': 3772, 'pterois': 3773, 'radiata': 3774, 'melbourne': 3775, 'yarra': 3776, 'h': 3777, 'calculate': 3778, 'nutrician': 3779, 'judge': 3780, 'gavel': 3781, 'barge': 3782, 'tend': 3783, 'haul': 3784, 'tidal': 3785, 'disposable': 3786, 'tiltingand': 3787, 'cheerleaders': 3788, 'kale': 3789, 'sandcastle': 3790, 'poolsde': 3791, 'unloadinggun': 3792, 'priestly': 3793, 'bless': 3794, 'horsehead': 3795, 'sightsee': 3796, 'reflector': 3797, 'lolly': 3798, 'netbook': 3799, 'wrestle': 3800, 'zoomin': 3801, 'coney': 3802, 'cutouts': 3803, 'lookin': 3804, 'barb': 3805, 'degrees': 3806, 'fiddle': 3807, 'favorite': 3808, 'rhythms': 3809, 'receptionist': 3810, 'kayakwith': 3811, 'trolleys': 3812, 'bob': 3813, 'hustle': 3814, 'unite': 3815, 'supreme': 3816, 'court': 3817, 'nude': 3818, 'conquer': 3819, 'heights': 3820, 'strret': 3821, 'wonderful': 3822, 'rate': 3823, 'cello': 3824, 'await': 3825, 'personal': 3826, 'stronger': 3827, 'massagist': 3828, 'spirit': 3829, 'canteloupes': 3830, 'lemaire': 3831, 'tutor': 3832, 'solubility': 3833, 'tester': 3834, 'affection': 3835, 'tenderness': 3836, 'flats': 3837, 'bookcase': 3838, 'macrowith': 3839, 'rhinos': 3840, 'benefit': 3841, 'sandals': 3842, 'ssn': 3843, 'efforts': 3844, 'ajman': 3845, 'uae': 3846, 'ut': 3847, 'aspen': 3848, 'whittier': 3849, 'smokestacks': 3850, 'abstract': 3851, 'tuna': 3852, 'dallas': 3853, 'non': 3854, 'fizzy': 3855, 'vampire': 3856, 'amtrak': 3857, 'goblin': 3858, 'grin': 3859, 'beachgoers': 3860, 'holidaymakers': 3861, 'ominously': 3862, 'fusilli': 3863, 'cookingk': 3864, 'cocktails': 3865, 'sheremetyevo': 3866, 'hoboken': 3867, 'fisrt': 3868, 'galway': 3869, 'menuk': 3870, 'dollylifestyle': 3871, 'cracker': 3872, 'reflectionslow': 3873, 'embankment': 3874, 'balloonsk': 3875, 'tale': 3876, 'crazily': 3877, 'naval': 3878, 'grungy': 3879, 'hippie': 3880, 'inscription': 3881, 'scrunch': 3882, 'fur': 3883, 'stepmill': 3884, 'surronded': 3885, 'overhead': 3886, 'evergreen': 3887, 'dreamy': 3888, 'wonderland': 3889, 'sceene': 3890, 'reload': 3891, 'sweat': 3892, 'reveal': 3893, 'infant': 3894, 'accompany': 3895, 'freshly': 3896, 'modernfamily': 3897, 'silky': 3898, 'stew': 3899, 'ladder': 3900, 'conesk': 3901, 'effective': 3902, 'ledges': 3903, 'bistro': 3904, 'beverages': 3905, 'biotech': 3906, 'precision': 3907, 'heidelberg': 3908, 'daybreak': 3909, 'cavalrymen': 3910, 'crayon': 3911, 'advantage': 3912, 'crispy': 3913, 'arrival': 3914, 'language': 3915, 'indoor': 3916, 'buenos': 3917, 'skater': 3918, 'bryan': 3919, 'picketts': 3920, 'fighter': 3921, 'flightk': 3922, 'dollyand': 3923, 'unrecognisable': 3924, 'sleeves': 3925, 'businessmans': 3926, 'poolk': 3927, 'need': 3928, 'recover': 3929, 'contunuing': 3930, 'biochemists': 3931, 'preservative': 3932, 'monk': 3933, 'master': 3934, 'tai': 3935, 'scaly': 3936, 'choir': 3937, 'islandk': 3938, 'clunker': 3939, 'season': 3940, 'median': 3941, 'backwards': 3942, 'sittin': 3943, 'chrysler': 3944, 'z': 3945, 'aerialat': 3946, 'list': 3947, 'depress': 3948, 'refuse': 3949, 'skipper': 3950, 'cambodian': 3951, 'apsara': 3952, 'angkor': 3953, 'wat': 3954, 'siem': 3955, 'reap': 3956, 'cambod': 3957, 'sunscreen': 3958, 'harmful': 3959, 'uv': 3960, 'june': 3961, 'folk': 3962, 'musicians': 3963, 'dancer': 3964, 'colomenskoe': 3965, 'unload': 3966, 'flippers': 3967, 'clumsy': 3968, 'straddle': 3969, 'savers': 3970, 'hereford': 3971, 'tl': 3972, 'blender': 3973, 'cottonwood': 3974, 'canadian': 3975, 'sias': 3976, 'cheery': 3977, 'monorail': 3978, 'ostankino': 3979, 'overalls': 3980, 'helmets': 3981, 'pause': 3982, 'coworking': 3983, 'hild': 3984, 'nest': 3985, 'cathedral': 3986, 'amateur': 3987, 'persons': 3988, 'ontop': 3989, 'members': 3990, 'pistol': 3991, 'duel': 3992, 'caress': 3993, 'habitat': 3994, 'mitts': 3995, 'forth': 3996, 'aransas': 3997, 'trucker': 3998, 'dashboard': 3999, 'kori': 4000, 'bustard': 4001, 'slam': 4002, 'dunk': 4003, 'server': 4004, 'continent': 4005, 'pursuit': 4006, 'lanterns': 4007, 'rough': 4008, 'italian': 4009, 'checker': 4010, 'stationary': 4011, 'buisiness': 4012, 'balance': 4013, 'console': 4014, 'tricolored': 4015, 'tinker': 4016, 'purchase': 4017, 'blazer': 4018, 'shedk': 4019, 'mode': 4020, 'harriet': 4021, 'tubman': 4022, 'tackle': 4023, 'vab': 4024, 'dozers': 4025, 'buss': 4026, 'macaw': 4027, 'without': 4028, 'martinique': 4029, 'fort': 4030, 'clapper': 4031, 'douse': 4032, 'spawn': 4033, 'upstream': 4034, 'explode': 4035, 'masseur': 4036, 'distortion': 4037, 'engage': 4038, 'backpackers': 4039, 'restourant': 4040, 'competitions': 4041, 'belt': 4042, 'violence': 4043, 'arccapturing': 4044, 'sons': 4045, 'charismatic': 4046, 'developers': 4047, 'drill': 4048, 'jack': 4049, 'russell': 4050, 'terrier': 4051, 'trough': 4052, 'mustache': 4053, 'ashore': 4054, 'various': 4055, 'species': 4056, 'pullout': 4057, 'coin': 4058, 'concern': 4059, 'reason': 4060, 'deadline': 4061, 'rent': 4062, 'crosswalk': 4063, 'koalas': 4064, 'sognefjord': 4065, 'sognefjorden': 4066, 'flam': 4067, 'demonstrative': 4068, 'farewell': 4069, 'flowery': 4070, 'enduro': 4071, 'hatcher': 4072, 'drainage': 4073, 'poor': 4074, 'bask': 4075, 'lanscape': 4076, 'concentrate': 4077, 'banners': 4078, 'warmly': 4079, 'agains': 4080, 'recreational': 4081, 'ethnician': 4082, 'observe': 4083, 'sale': 4084, 'goods': 4085, 'rothenburg': 4086, 'ob': 4087, 'der': 4088, 'tauber': 4089, 'franconia': 4090, 'bavaria': 4091, 'blood': 4092, 'pressure': 4093, 'weapon': 4094, 'seattle': 4095, 'commons': 4096, 'parlor': 4097, 'holy': 4098, 'sepulchre': 4099, 'grocery': 4100, 'mutton': 4101, 'actively': 4102, 'groggy': 4103, 'wing': 4104, 'greenbrier': 4105, 'watery': 4106, 'sundance': 4107, 'photovoltaic': 4108, 'convert': 4109, 'renewable': 4110, 'resources': 4111, 'nutrition': 4112, 'nab': 4113, 'rangeland': 4114, 'hut': 4115, 'clothesline': 4116, 'mombassa': 4117, 'crusher': 4118, 'warthogs': 4119, 'clutch': 4120, 'enginek': 4121, 'headig': 4122, 'houseplant': 4123, 'keep': 4124, 'trippy': 4125, 'waterfront': 4126, 'wagon': 4127, 'mechanics': 4128, 'skateboarder': 4129, 'halfpipe': 4130, 'snowboard': 4131, 'skiis': 4132, 'lurk': 4133, 'tress': 4134, 'violet': 4135, 'lighthouse': 4136, 'handful': 4137, 'obama': 4138, 'officant': 4139, 'sausages': 4140, 'parrott': 4141, 'maternity': 4142, 'payphone': 4143, 'biotechnology': 4144, 'machinery': 4145, 'lisbon': 4146, 'portugal': 4147, 'brunet': 4148, 'someones': 4149, 'pebbles': 4150, 'trout': 4151, 'struggle': 4152, 'nypd': 4153, 'playful': 4154, 'flowerpot': 4155, 'beekeeper': 4156, 'roomkx': 4157, 'rzr': 4158, 'atvx': 4159, 'bazaar': 4160, 'tvindefossen': 4161, 'aerialover': 4162, 'stray': 4163, 'ravine': 4164, 'torch': 4165, 'entertainment': 4166, 'mountainous': 4167, 'syringe': 4168, 'hope': 4169, 'better': 4170, 'can': 4171, 'scoot': 4172, 'peaces': 4173, 'half': 4174, 'boiler': 4175, 'cultivate': 4176, 'german': 4177, 'settlers': 4178, 'monarch': 4179, 'bungy': 4180, 'softball': 4181, 'playk': 4182, 'thee': 4183, 'snowbank': 4184, 'dimensional': 4185, 'chalten': 4186, 'trek': 4187, 'rappel': 4188, 'lovemodern': 4189, 'carefree': 4190, 'militaryprison': 4191, 'scrap': 4192, 'windshield': 4193, 'usvi': 4194, 'lessons': 4195, 'carskx': 4196, 'sensitive': 4197, 'glamour': 4198, 'cloudless': 4199, 'piraeus': 4200, 'athensk': 4201, 'lace': 4202, 'minimalistic': 4203, 'barbecue': 4204, 'speedway': 4205, 'tan': 4206, 'sip': 4207, 'sharply': 4208, 'tulips': 4209, 'cost': 4210, 'tailpipe': 4211, 'emissions': 4212, 'kness': 4213, 'hacker': 4214, 'animate': 4215, 'goalpost': 4216, 'peel': 4217, 'illuminator': 4218, 'cure': 4219, 'salami': 4220, 'patch': 4221, 'barcuttinglemons': 4222, 'highways': 4223, 'alexandria': 4224, 'looki': 4225, 'surgical': 4226, 'spaghetti': 4227, 'loaves': 4228, 'include': 4229, 'longues': 4230, 'banjos': 4231, 'zealand': 4232, 'tekapo': 4233, 'bodybuilder': 4234, 'stabilizer': 4235, 'camerais': 4236, 'workboat': 4237, 'swimmers': 4238, 'pelican': 4239, 'programmer': 4240, 'paw': 4241, 'radioactive': 4242, 'tentacles': 4243, 'bee': 4244, 'casino': 4245, 'gamble': 4246, 'moustache': 4247, 'goofy': 4248, 'miss': 4249, 'forestk': 4250, 'inflate': 4251, 'lilo': 4252, 'hairdresser': 4253, 'straight': 4254, 'weary': 4255, 'adjust': 4256, 'toll': 4257, 'cartoon': 4258, 'base': 4259, 'seaweed': 4260, 'mustang': 4261, 'interlocutor': 4262, 'pelvic': 4263, 'logic': 4264, 'upthrough': 4265, 'courier': 4266, 'parcel': 4267, 'feline': 4268, 'suppression': 4269, 'fighters': 4270, 'ornate': 4271, 'druck': 4272, 'outkmov': 4273, 'impress': 4274, 'unconditional': 4275, 'surrender': 4276, 'mole': 4277, 'windowsk': 4278, 'tahoe': 4279, 'protector': 4280, 'banana': 4281, 'letterboxed': 4282, 'crate': 4283, 'gyro': 4284, 'stabilizin': 4285, 'gimbal': 4286, 'director': 4287, 'operation': 4288, 'victorias': 4289, 'secret': 4290, 'rnyk': 4291, 'ac': 4292, 'advice': 4293, 'longof': 4294, 'handheldshowing': 4295, 'unknown': 4296}\n",
            "\n",
            "First five captions:\n",
            "[[678, 1, 196, 317], [1984, 66, 230, 170, 68, 17], [121, 301], [679, 924, 1042, 152], [7, 85, 493, 221, 17]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kWjFZqNkrX3W",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Sequence Length and Padding\n",
        " Outlined in section 4.2 of the report"
      ]
    },
    {
      "metadata": {
        "id": "I6heG7tBZXKU",
        "colab_type": "code",
        "outputId": "4f1b0b17-5b33-4063-cfc0-287d69bae3c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "cell_type": "code",
      "source": [
        "#breakdown of number of words per captions\n",
        "#important for deciding the sequence length later\n",
        "%matplotlib inline\n",
        "cap_len = [len(x) for x in captions_int]\n",
        "pd.Series(cap_len).hist()\n",
        "plt.ylabel('Sequence Count')\n",
        "plt.xlabel('Length of Sequence')\n",
        "plt.title('Distribution of Sequence Lengths')\n",
        "plt.show()"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FOXiYmVlhDh5mZlaYg4eZmRXm4GFmZoU5eJiZWWEOHmZmVpiDh5mZFebgYWZm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\nYWZmhTl4mJlZYf8LWgSj4icKE/YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ws4wzol6Zbr0",
        "colab_type": "code",
        "outputId": "4f7753ca-ebc1-4bdd-c3b8-21d92809e639",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "cell_type": "code",
      "source": [
        "# pad the caption sequences with 0's so that they're all the same length for the NN\n",
        "def pad_captions(captions_int, seq_length):\n",
        "    sequences = np.zeros((len(captions_int), seq_length), dtype = int)\n",
        "    \n",
        "    for i, caption in enumerate(captions_int):\n",
        "        cap_len = len(caption)\n",
        "        \n",
        "        if cap_len <= seq_length:\n",
        "            zeroes = list(np.zeros(seq_length-cap_len))\n",
        "            new = zeroes+caption\n",
        "        elif cap_len > seq_length:\n",
        "            new = caption[0:seq_length]\n",
        "        \n",
        "        sequences[i,:] = np.array(new)\n",
        "    \n",
        "    return sequences\n",
        "\n",
        "# sequence length of 10 was decided from the graph above\n",
        "sequence_length = 10\n",
        "sequences = pad_captions(captions_int, sequence_length)\n",
        "print(sequences[0:5])"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[   0    0    0    0    0    0  678    1  196  317]\n",
            " [   0    0    0    0 1984   66  230  170   68   17]\n",
            " [   0    0    0    0    0    0    0    0  121  301]\n",
            " [   0    0    0    0    0    0  679  924 1042  152]\n",
            " [   0    0    0    0    0    7   85  493  221   17]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fQN9YrrkrgIm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Splitting and Batching\n",
        " Outlined in section 4.3 of the report"
      ]
    },
    {
      "metadata": {
        "id": "qo4c0IzolSXm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#split into training, validation and test data\n",
        "len_feat = len(sequences)\n",
        "\n",
        "#training set size\n",
        "split_frac = 0.8\n",
        "\n",
        "train_x = sequences[0:int(split_frac*len_feat)]\n",
        "train_y = ground_truth[0:int(split_frac*len_feat)]\n",
        "\n",
        "remaining_x = sequences[int(split_frac*len_feat):]\n",
        "remaining_y = ground_truth[int(split_frac*len_feat):]\n",
        "\n",
        "valid_x = remaining_x[0:int(len(remaining_x)*0.5)]\n",
        "valid_y = remaining_y[0:int(len(remaining_y)*0.5)]\n",
        "\n",
        "test_x = remaining_x[int(len(remaining_x)*0.5):]\n",
        "test_y = remaining_y[int(len(remaining_y)*0.5):]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xxdA6eP_d81P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#set seed for consistent accuracy prediction\n",
        "seed = 0\n",
        "torch.manual_seed(seed)\n",
        "torch.cuda.manual_seed(seed)\n",
        "\n",
        "# create Tensor datasets\n",
        "train_data = TensorDataset(torch.from_numpy(train_x), torch.from_numpy(train_y))\n",
        "valid_data = TensorDataset(torch.from_numpy(valid_x), torch.from_numpy(valid_y))\n",
        "test_data = TensorDataset(torch.from_numpy(test_x), torch.from_numpy(test_y))\n",
        "\n",
        "# use DataLoaders for each set with batch size 50\n",
        "batch_size = 50\n",
        "train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)\n",
        "valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size)\n",
        "test_loader = DataLoader(test_data, shuffle=True, batch_size=batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rKU8EQ6UrtL2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Defining the Model and Training\n",
        " Outlined in section 4.4 of the report"
      ]
    },
    {
      "metadata": {
        "id": "CO8bitNSlUtO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_on_gpu = torch.cuda.is_available()\n",
        "\n",
        "#define model class\n",
        "class LSTM_RNN(nn.Module):\n",
        "\n",
        "    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5, ):\n",
        "\n",
        "        super().__init__()\n",
        "\n",
        "        self.output_size = output_size\n",
        "        self.n_layers = n_layers\n",
        "        self.hidden_dim = hidden_dim\n",
        "        \n",
        "        # embedding layer\n",
        "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
        "        \n",
        "        #LSTM layer - 200 layers deep\n",
        "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, \n",
        "                            dropout=drop_prob, batch_first=True)\n",
        "        \n",
        "        # dropout layer\n",
        "        self.dropout = nn.Dropout(0.3)\n",
        "        \n",
        "        # fully connected layer for 2 outputs\n",
        "        self.fc1 = nn.Linear(hidden_dim, output_size)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_size)\n",
        "        \n",
        "        #squash to 0 and 1\n",
        "        self.sig = nn.Sigmoid()\n",
        "        \n",
        "\n",
        "    def forward(self, x, hidden):\n",
        "        \n",
        "        batch_size = x.size(0)\n",
        "\n",
        "        # embeddings and lstm_out\n",
        "        embeds = self.embedding(x)\n",
        "\n",
        "        lstm_out, hidden = self.lstm(embeds, hidden)\n",
        "    \n",
        "        # stack up lstm outputs\n",
        "        lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim)\n",
        "        \n",
        "        # drop 30% of the outputs of the lstm layer\n",
        "        out = self.dropout(lstm_out)\n",
        "        \n",
        "        # fully connected layer - call for each output\n",
        "        #short mem\n",
        "        out1 = self.fc1(out)\n",
        "        #long mem\n",
        "        out2 = self.fc2(out)\n",
        "        \n",
        "        # sigmoid function\n",
        "        sig_out1 = self.sig(out1)\n",
        "        sig_out2 = self.sig(out2)\n",
        "        \n",
        "        # reshape to be batch_size first\n",
        "        sig_out1 = sig_out1.view(batch_size, -1)\n",
        "        sig_out1 = sig_out1[:, -1] # get last batch of labels\n",
        "        \n",
        "        # reshape to be batch_size first\n",
        "        sig_out2 = sig_out2.view(batch_size, -1)\n",
        "        sig_out2 = sig_out2[:, -1] # get last batch of labels\n",
        "            \n",
        "        # return last sigmoid outputs and hidden state\n",
        "        return sig_out1, sig_out2, hidden\n",
        "    \n",
        "    \n",
        "    def init_hidden(self, batch_size):\n",
        "        # Create two new tensors with sizes n_layers x batch_size x hidden_dim,\n",
        "        # initialized to zero, for hidden state and cell state of LSTM\n",
        "        weight = next(self.parameters()).data\n",
        "        \n",
        "        if (train_on_gpu):\n",
        "            hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda(),\n",
        "                  weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda())\n",
        "        else:\n",
        "            hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(),\n",
        "                      weight.new(self.n_layers, batch_size, self.hidden_dim).zero_())\n",
        "        \n",
        "        return hidden"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jYZ-4fSRenGO",
        "colab_type": "code",
        "outputId": "fdd5e334-7af9-48a4-da95-5a08c48c0d7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "cell_type": "code",
      "source": [
        "# Instantiate the model with hyperparams\n",
        "vocab_size = len(vocab_to_int)+1 # +1 for the 0 padding\n",
        "output_size = 2\n",
        "embedding_dim = 400\n",
        "hidden_dim = 200\n",
        "n_layers = 2\n",
        "net = LSTM_RNN(vocab_size, output_size, embedding_dim, hidden_dim, n_layers)\n",
        "print(net)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LSTM_RNN(\n",
            "  (embedding): Embedding(4297, 400)\n",
            "  (lstm): LSTM(400, 200, num_layers=2, batch_first=True, dropout=0.5)\n",
            "  (dropout): Dropout(p=0.3)\n",
            "  (fc1): Linear(in_features=200, out_features=2, bias=True)\n",
            "  (fc2): Linear(in_features=200, out_features=2, bias=True)\n",
            "  (sig): Sigmoid()\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TOSOc4_ufXDN",
        "colab_type": "code",
        "outputId": "3cab0e83-eec4-4ff0-d590-c9a7da35e098",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "# loss and optimization functions\n",
        "lr=0.001\n",
        "\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
        "\n",
        "# training params\n",
        "\n",
        "epochs = 4 # 3-4 is approx where I noticed the validation loss stop decreasing\n",
        "\n",
        "print_every = 100\n",
        "clip=5 # gradient clipping\n",
        "\n",
        "# move model to GPU, if available\n",
        "if(train_on_gpu):\n",
        "    net.cuda()\n",
        "\n",
        "net.train()\n",
        "\n",
        "def train(loader):\n",
        "  counter = 0\n",
        "  \n",
        "  # train for some number of epochs\n",
        "  for e in range(epochs):\n",
        "    \n",
        "      # initialize hidden state\n",
        "      h = net.init_hidden(batch_size)\n",
        "\n",
        "      # batch loop\n",
        "      for inputs, labels in loader:\n",
        "          counter += 1\n",
        "\n",
        "          if(train_on_gpu):\n",
        "              inputs, labels = inputs.cuda(), labels.cuda()\n",
        "\n",
        "          h = tuple([each.data for each in h])\n",
        "\n",
        "          # zero accumulated gradients\n",
        "          net.zero_grad()\n",
        "\n",
        "          # get the output from the model\n",
        "          inputs = inputs.type(torch.LongTensor)\n",
        "          output1, output2, h = net(inputs, h)\n",
        "\n",
        "          # calculate the loss and perform backprop\n",
        "\n",
        "          # calculate loss for both short and long mem and add loss together\n",
        "          loss1 = criterion(output1, labels[:,0].float())\n",
        "          loss2 = criterion(output2, labels[:,1].float())\n",
        "          loss = loss1 + loss2\n",
        "          loss.backward()\n",
        "          \n",
        "          # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n",
        "          nn.utils.clip_grad_norm_(net.parameters(), clip)\n",
        "          optimizer.step()\n",
        "\n",
        "          # loss stats\n",
        "          if counter % print_every == 0:\n",
        "              # Get validation loss\n",
        "              val_h = net.init_hidden(batch_size)\n",
        "              val_losses = []\n",
        "              net.eval()\n",
        "              for inputs, labels in valid_loader:\n",
        "\n",
        "                  val_h = tuple([each.data for each in val_h])\n",
        "\n",
        "                  if(train_on_gpu):\n",
        "                      inputs, labels = inputs.cuda(), labels.cuda()\n",
        "\n",
        "                  inputs = inputs.type(torch.LongTensor)\n",
        "                  val_output1, val_output2, val_h = net(inputs, val_h)\n",
        "                  \n",
        "                  # calculate val loss for both short and long mem and add loss together\n",
        "                  val_loss1 = criterion(val_output1, labels[:,0].float())\n",
        "                  val_loss2 = criterion(val_output2, labels[:,1].float())\n",
        "                  val_loss = val_loss1 + val_loss2\n",
        "                  #loss.backward()\n",
        "\n",
        "                  val_losses.append(val_loss.item())\n",
        "\n",
        "              net.train()\n",
        "              print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
        "                    \"Step: {}...\".format(counter),\n",
        "                    \"Loss: {:.6f}...\".format(loss.item()),\n",
        "                    \"Val Loss: {:.6f}\".format(np.mean(val_losses)))\n",
        "              \n",
        "              \n",
        "train(train_loader)\n",
        "torch.save(net.state_dict(), 'trained_model.t7')"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 2/4... Step: 100... Loss: 0.033241... Val Loss: 0.027199\n",
            "Epoch: 3/4... Step: 200... Loss: 0.029681... Val Loss: 0.025975\n",
            "Epoch: 4/4... Step: 300... Loss: 0.023471... Val Loss: 0.027591\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "S1NiT9DCtshs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "net = LSTM_RNN(vocab_size, output_size, embedding_dim, hidden_dim, n_layers)\n",
        "net.load_state_dict(torch.load('trained_model.t7'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9V4uthQFsDmt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Evaluation\n",
        " Outlined in section 5 of the report"
      ]
    },
    {
      "metadata": {
        "id": "wYAyDyKKGdKW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Create a function that takes in x's and y's\n",
        "def spearmans_rank_correlation(xs, ys):\n",
        "    \n",
        "    # Calculate the rank of x's\n",
        "    xranks = pd.Series(xs).rank()\n",
        "    \n",
        "    # Caclulate the ranking of the y's\n",
        "    yranks = pd.Series(ys).rank()\n",
        "    \n",
        "    # Calculate Pearson's correlation coefficient on the ranked versions of the data\n",
        "    return scipy.stats.pearsonr(xranks, yranks)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "40yBAppjghm0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "7018aa7b-ca96-46f6-aadc-8c6e8605b684"
      },
      "cell_type": "code",
      "source": [
        "# Get test data loss and accuracy\n",
        "xs1 = []\n",
        "ys1 = []\n",
        "xs2 = []\n",
        "ys2 = []\n",
        "\n",
        "def test(loader):\n",
        "  test_losses = [] # track loss\n",
        "  num_correct = 0\n",
        "\n",
        "  # init hidden state\n",
        "  h = net.init_hidden(batch_size)\n",
        "\n",
        "  net.eval()\n",
        "  # iterate over test data\n",
        "  for inputs, labels in loader:\n",
        "\n",
        "      h = tuple([each.data for each in h])\n",
        "\n",
        "      if(train_on_gpu):\n",
        "          inputs, labels = inputs.cuda(), labels.cuda()\n",
        "\n",
        "      # get predicted outputs\n",
        "      inputs = inputs.type(torch.LongTensor)\n",
        "      output1, output2, h = net(inputs, h)\n",
        "\n",
        "      # calculate loss\n",
        "      # calculate test loss for both short and long mem and add losses together\n",
        "      test_loss1 = criterion(output1, labels[:,0].float())\n",
        "      test_loss2 = criterion(output2, labels[:,1].float())\n",
        "      test_loss = test_loss1 + test_loss2\n",
        "      test_losses.append(test_loss.item())\n",
        "\n",
        "      pred1 = output1.squeeze()\n",
        "      pred2 = output2.squeeze()\n",
        "\n",
        "      xs1.extend(pred1.detach().numpy())\n",
        "      xs2.extend(pred2.detach().numpy())\n",
        "      ys1.extend(labels[:,0].detach().numpy())\n",
        "      ys2.extend(labels[:,1].detach().numpy())\n",
        "      \n",
        "\n",
        "test(test_loader)\n",
        "\n",
        "print('Spearman result for short term memorability scores:')\n",
        "result = spearmans_rank_correlation(xs1,ys1)  \n",
        "print(result[0])\n",
        "\n",
        "print('Spearman result for long term memorability scores:')\n",
        "result2 = spearmans_rank_correlation(xs2,ys2)  \n",
        "print(result2[0])\n",
        "\n"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Spearman result for short term memorability scores:\n",
            "0.3844844775881982\n",
            "Spearman result for long term memorability scores:\n",
            "0.1521178922549887\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_PQGaonOBInj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "outputId": "a68825d4-4a40-4b33-fa5b-e6b5311b9c63"
      },
      "cell_type": "code",
      "source": [
        "#load test captions\n",
        "test_captions = list(open('test-set-1_video-captions.txt', 'r'))\n",
        "test_captions = [i.split('\\t') for i in test_captions]\n",
        "test_captions = [[a, b.strip()] for a, b in test_captions]\n",
        "test_captions = pd.DataFrame(test_captions, columns=['video', 'caption'])\n",
        "\n",
        "for index, row in test_captions.iterrows():\n",
        "  test_captions.at[index, 'caption'] = norm_lemm_corpus(row.loc['caption'])\n",
        "\n",
        "test_captions.head(5)\n",
        "\n",
        "test_captions_list = np.asarray(test_captions['caption'])\n",
        "test_caption_counts = get_counts(test_captions_list)\n",
        "test_vocab_to_int = assign_ints(test_caption_counts)\n",
        "test_captions_int = caption_text_to_int(test_captions_list,test_vocab_to_int)\n",
        "\n",
        "test_sequences = pad_captions(test_captions_int, 10)"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counts of words in order:\n",
            "[('woman', 303), ('young', 233), ('man', 169), ('slow', 133), ('motion', 116), ('girl', 108), ('couple', 107), ('sit', 106), ('park', 93), ('walk', 88), ('close', 86), ('happy', 85), ('shoot', 83), ('work', 77), ('smile', 76), ('use', 74), ('look', 72), ('camera', 70), ('beach', 70), ('talk', 61), ('tablet', 58), ('tree', 58), ('view', 58), ('water', 58), ('people', 57), ('two', 53), ('videoblocks', 53), ('run', 51), ('sunset', 49), ('hand', 47), ('eat', 47), ('computer', 47), ('city', 47), ('beautiful', 46), ('play', 45), ('female', 45), ('take', 42), ('little', 42), ('field', 40), ('family', 39), ('drink', 38), ('laptop', 37), ('phone', 37), ('fish', 37), ('light', 37), ('green', 35), ('business', 35), ('fly', 35), ('swim', 35), ('father', 34), ('background', 34), ('pan', 33), ('relax', 33), ('snow', 32), ('grass', 32), ('outdoors', 32), ('lie', 32), ('garden', 31), ('child', 31), ('farm', 31), ('national', 30), ('street', 30), ('night', 30), ('mountain', 30), ('mother', 30), ('doctor', 29), ('modern', 29), ('steadicamof', 29), ('businessman', 29), ('closeup', 29), ('son', 29), ('steadicam', 29), ('american', 28), ('zoom', 28), ('blue', 28), ('sun', 27), ('african', 27), ('stand', 27), ('portrait', 27), ('boy', 27), ('home', 27), ('sea', 27), ('cute', 26), ('time', 26), ('rock', 25), ('black', 25), ('white', 25), ('christmas', 25), ('office', 25), ('sunny', 25), ('train', 24), ('hold', 24), ('read', 24), ('cellphone', 24), ('sky', 24), ('around', 24), ('new', 24), ('group', 23), ('women', 23), ('daughter', 23), ('restaurant', 23), ('table', 23), ('friends', 22), ('summer', 22), ('landscape', 22), ('baby', 22), ('dolly', 22), ('asian', 22), ('outside', 21), ('winter', 21), ('kitchen', 21), ('war', 21), ('road', 21), ('food', 21), ('cafe', 20), ('upof', 20), ('day', 20), ('steadycam', 20), ('flower', 20), ('civil', 20), ('prepare', 20), ('put', 20), ('near', 20), ('pool', 20), ('car', 19), ('dog', 19), ('concept', 19), ('fun', 19), ('mobile', 19), ('river', 19), ('build', 19), ('back', 18), ('shop', 18), ('watch', 18), ('move', 18), ('attractive', 18), ('jog', 18), ('businesswoman', 18), ('window', 17), ('men', 17), ('bed', 17), ('book', 17), ('film', 17), ('even', 17), ('wave', 17), ('aerial', 17), ('listen', 17), ('crowd', 17), ('mountains', 16), ('room', 16), ('bike', 16), ('together', 16), ('resort', 16), ('forest', 16), ('smartphone', 16), ('music', 16), ('focus', 16), ('bright', 16), ('jump', 16), ('airport', 16), ('horse', 16), ('open', 16), ('south', 16), ('boat', 16), ('show', 16), ('ice', 16), ('khr', 16), ('glass', 16), ('eagle', 15), ('male', 15), ('sunrise', 15), ('house', 15), ('cream', 15), ('red', 14), ('enjoy', 14), ('sport', 14), ('laugh', 14), ('ball', 14), ('make', 14), ('moscow', 14), ('paint', 14), ('bicycle', 14), ('three', 14), ('africa', 14), ('space', 14), ('race', 13), ('board', 13), ('hotel', 13), ('timelapse', 13), ('get', 13), ('floor', 13), ('romantic', 13), ('lake', 13), ('patient', 13), ('bridge', 13), ('reef', 13), ('line', 13), ('coffee', 13), ('school', 13), ('across', 13), ('nature', 13), ('hair', 13), ('bench', 13), ('russia', 12), ('bald', 12), ('station', 12), ('bird', 12), ('motionof', 12), ('silhouette', 12), ('cook', 12), ('rid', 12), ('fire', 12), ('desert', 12), ('dance', 12), ('exercise', 12), ('low', 12), ('love', 12), ('outdoor', 12), ('ocean', 12), ('colorful', 12), ('face', 12), ('massage', 12), ('pour', 12), ('children', 11), ('terrace', 11), ('bubble', 11), ('front', 11), ('selfie', 11), ('bath', 11), ('eye', 11), ('moose', 11), ('snowy', 11), ('fall', 11), ('center', 11), ('sunbed', 11), ('skyline', 11), ('traffic', 11), ('bear', 11), ('cat', 11), ('yellow', 11), ('leave', 11), ('spring', 11), ('warm', 11), ('artist', 11), ('lettuce', 11), ('gym', 10), ('canyon', 10), ('chair', 10), ('type', 10), ('feet', 10), ('small', 10), ('kiss', 10), ('sand', 10), ('underwater', 10), ('texting', 10), ('student', 10), ('potter', 10), ('caucasian', 10), ('throw', 10), ('discuss', 10), ('hot', 10), ('large', 10), ('mix', 9), ('place', 9), ('finish', 9), ('swing', 9), ('box', 9), ('adult', 9), ('go', 9), ('healthy', 9), ('suit', 9), ('elephant', 9), ('oil', 9), ('check', 9), ('brunette', 9), ('bar', 9), ('chef', 9), ('angle', 9), ('sleep', 9), ('parent', 9), ('utah', 9), ('wheel', 9), ('girls', 9), ('autumn', 9), ('wear', 9), ('plane', 9), ('tourists', 9), ('along', 9), ('cut', 9), ('push', 9), ('dress', 9), ('strong', 9), ('cage', 9), ('kid', 9), ('cover', 9), ('construction', 9), ('free', 9), ('nyc', 9), ('fry', 9), ('crane', 9), ('deep', 9), ('game', 9), ('hike', 8), ('call', 8), ('draw', 8), ('chat', 8), ('ray', 8), ('runner', 8), ('bag', 8), ('cheerful', 8), ('guy', 8), ('teach', 8), ('ride', 8), ('childhood', 8), ('truck', 8), ('aquarium', 8), ('color', 8), ('orange', 8), ('style', 8), ('air', 8), ('head', 8), ('cruise', 8), ('lady', 8), ('clothe', 8), ('tilt', 8), ('stadium', 8), ('march', 8), ('away', 8), ('senior', 8), ('island', 8), ('rotate', 8), ('lift', 8), ('arch', 8), ('morning', 8), ('western', 8), ('parade', 8), ('perform', 8), ('chop', 8), ('lapse', 8), ('tropical', 8), ('meet', 8), ('scene', 8), ('tea', 8), ('vintage', 8), ('cloud', 8), ('punch', 8), ('businesspeople', 8), ('wall', 8), ('point', 8), ('produce', 8), ('shin', 7), ('headphones', 7), ('decorate', 7), ('legs', 7), ('storm', 7), ('vacation', 7), ('scientist', 7), ('balcony', 7), ('middle', 7), ('spa', 7), ('slave', 7), ('next', 7), ('hill', 7), ('lifestyle', 7), ('aerialof', 7), ('dark', 7), ('fitness', 7), ('wideof', 7), ('trendy', 7), ('york', 7), ('ship', 7), ('big', 7), ('july', 7), ('square', 7), ('washington', 7), ('statue', 7), ('balloon', 7), ('note', 7), ('come', 7), ('crash', 7), ('pretty', 7), ('fight', 7), ('worker', 7), ('helmet', 7), ('meadow', 7), ('meal', 7), ('panningof', 7), ('friend', 7), ('top', 7), ('workers', 7), ('image', 7), ('model', 7), ('document', 7), ('sad', 7), ('valley', 7), ('wheelbarrow', 7), 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('christi', 1), ('owl', 1), ('beyond', 1), ('inflatable', 1), ('mattress', 1)]\n",
            "\n",
            "Words with assigned ints:\n",
            "{'woman': 1, 'young': 2, 'man': 3, 'slow': 4, 'motion': 5, 'girl': 6, 'couple': 7, 'sit': 8, 'park': 9, 'walk': 10, 'close': 11, 'happy': 12, 'shoot': 13, 'work': 14, 'smile': 15, 'use': 16, 'look': 17, 'camera': 18, 'beach': 19, 'talk': 20, 'tablet': 21, 'tree': 22, 'view': 23, 'water': 24, 'people': 25, 'two': 26, 'videoblocks': 27, 'run': 28, 'sunset': 29, 'hand': 30, 'eat': 31, 'computer': 32, 'city': 33, 'beautiful': 34, 'play': 35, 'female': 36, 'take': 37, 'little': 38, 'field': 39, 'family': 40, 'drink': 41, 'laptop': 42, 'phone': 43, 'fish': 44, 'light': 45, 'green': 46, 'business': 47, 'fly': 48, 'swim': 49, 'father': 50, 'background': 51, 'pan': 52, 'relax': 53, 'snow': 54, 'grass': 55, 'outdoors': 56, 'lie': 57, 'garden': 58, 'child': 59, 'farm': 60, 'national': 61, 'street': 62, 'night': 63, 'mountain': 64, 'mother': 65, 'doctor': 66, 'modern': 67, 'steadicamof': 68, 'businessman': 69, 'closeup': 70, 'son': 71, 'steadicam': 72, 'american': 73, 'zoom': 74, 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891, 'health': 892, 'unhealth': 893, 'theme': 894, 'indoor': 895, 'trailer': 896, 'homosexual': 897, 'gay': 898, 'sex': 899, 'javelin': 900, 'spear': 901, 'executive': 902, 'architect': 903, 'urban': 904, 'development': 905, 'electronic': 906, 'oculus': 907, 'cape': 908, 'buffalo': 909, 'stream': 910, 'bedroom': 911, 'shoe': 912, 'chonchi': 913, 'environment': 914, 'angleof': 915, 'first': 916, 'lamp': 917, 'service': 918, 'customer': 919, 'murky': 920, 'creek': 921, 'candle': 922, 'astronaut': 923, 'outer': 924, 'kite': 925, 'cinemagraph': 926, 'seamless': 927, 'loop': 928, 'castro': 929, 'plaza': 930, 'wed': 931, 'labrador': 932, 'colleagues': 933, 'italian': 934, 'cars': 935, 'taxi': 936, 'sailboat': 937, 'hospital': 938, 'float': 939, 'divers': 940, 'chinese': 941, 'kick': 942, 'graze': 943, 'hipster': 944, 'handsome': 945, 'country': 946, 'lane': 947, 'reenactors': 948, 'macro': 949, 'toddler': 950, 'bad': 951, 'clown': 952, 'many': 953, 'band': 954, 'short': 955, 'via': 956, 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1139, 'wooded': 1140, 'rush': 1141, 'load': 1142, 'tap': 1143, 'trunk': 1144, 'support': 1145, 'workshop': 1146, 'cold': 1147, 'snowfall': 1148, 'slice': 1149, 'bazylia': 1150, 'mint': 1151, 'zioa': 1152, 'cookie': 1153, 'desktop': 1154, 'ladder': 1155, 'sunshine': 1156, 'acupuncture': 1157, 'easel': 1158, 'sniff': 1159, 'argentina': 1160, 'comfortable': 1161, 'umbrella': 1162, 'distance': 1163, 'raft': 1164, 'shelf': 1165, 'champagne': 1166, 'grade': 1167, 'raw': 1168, 'uhd': 1169, 'ultra': 1170, 'la': 1171, 'sauce': 1172, 'windy': 1173, 'musicians': 1174, 'runway': 1175, 'cave': 1176, 'los': 1177, 'angeles': 1178, 'bo': 1179, 'staff': 1180, 'cozy': 1181, 'safety': 1182, 'gallery': 1183, 'various': 1184, 'overlook': 1185, 'london': 1186, 'funny': 1187, 'tv': 1188, 'girlfriend': 1189, 'convention': 1190, 'barren': 1191, 'tuscany': 1192, 'seaside': 1193, 'post': 1194, 'tierra': 1195, 'del': 1196, 'fuego': 1197, 'excavator': 1198, 'shape': 1199, 'reach': 1200, 'freep': 1201, 'roar': 1202, 'designer': 1203, 'strawberry': 1204, 'helicopters': 1205, 'conduct': 1206, 'switch': 1207, 'tent': 1208, 'mossy': 1209, 'oaks': 1210, 'florida': 1211, 'lips': 1212, 'lipstick': 1213, 'shotink': 1214, 'gently': 1215, 'shoulder': 1216, 'shadow': 1217, 'bourbon': 1218, 'cast': 1219, 'contact': 1220, 'espresso': 1221, 'flap': 1222, 'display': 1223, 'basketball': 1224, 'lesbian': 1225, 'lesbians': 1226, 'leather': 1227, 'palette': 1228, 'coney': 1229, 'consultant': 1230, 'patio': 1231, 'cartoon': 1232, 'shoreline': 1233, 'muscle': 1234, 'capture': 1235, 'bass': 1236, 'concrete': 1237, 'oregon': 1238, 'twist': 1239, 'comb': 1240, 'atlantis': 1241, 'macroof': 1242, 'cow': 1243, 'coast': 1244, 'nol': 1245, 'glacier': 1246, 'symbol': 1247, 'monitor': 1248, 'fishermen': 1249, 'medal': 1250, 'honor': 1251, 'gate': 1252, 'north': 1253, 'wheelchair': 1254, 'ok': 1255, 'assort': 1256, 'army': 1257, 'slo': 1258, 'chart': 1259, 'jeans': 1260, 'thai': 1261, 'tractor': 1262, 'haul': 1263, 'therapy': 1264, 'bus': 1265, 'pedestrians': 1266, 'staticof': 1267, 'quay': 1268, 'tropics': 1269, 'panningshowing': 1270, 'hard': 1271, 'sandy': 1272, 'bull': 1273, 'past': 1274, 'hall': 1275, 'cattle': 1276, 'scooter': 1277, 'f': 1278, 'receive': 1279, 'unpack': 1280, 'confederate': 1281, 'teenager': 1282, 'grave': 1283, 'grandmother': 1284, 'seashore': 1285, 'cao': 1286, 'dai': 1287, 'animal': 1288, 'shelter': 1289, 'dusk': 1290, 'string': 1291, 'jam': 1292, 'ripple': 1293, 'cub': 1294, 'haircut': 1295, 'calf': 1296, 'conveyer': 1297, 'bunny': 1298, 'glide': 1299, 'rattlesnake': 1300, 'fit': 1301, 'penguins': 1302, 'terminal': 1303, 'burn': 1304, 'farmer': 1305, 'geese': 1306, 'flock': 1307, 'yarra': 1308, 'range': 1309, 'foreground': 1310, 'hunter': 1311, 'provide': 1312, 'hit': 1313, 'trap': 1314, 'mesa': 1315, 'canyonlands': 1316, 'narrow': 1317, 'deck': 1318, 'multiracial': 1319, 'mascara': 1320, 'add': 1321, 'complex': 1322, 'gettysburg': 1323, 'disco': 1324, 'vote': 1325, 'germany': 1326, 'cameraman': 1327, 'mud': 1328, 'emergency': 1329, 'several': 1330, 'mustang': 1331, 'tomato': 1332, 'banana': 1333, 'saw': 1334, 'drum': 1335, 'chip': 1336, 'another': 1337, 'pencil': 1338, 'venice': 1339, 'california': 1340, 'plain': 1341, 'way': 1342, 'repel': 1343, 'cliff': 1344, 'brick': 1345, 'brooklyn': 1346, 'strawberries': 1347, 'motionx': 1348, 'relaxation': 1349, 'moments': 1350, 'therapist': 1351, 'norway': 1352, 'hluhluwe': 1353, 'imfolozi': 1354, 'technician': 1355, 'saint': 1356, 'may': 1357, 'scaly': 1358, 'tobacco': 1359, 'blanket': 1360, 'francisco': 1361, 'leaf': 1362, 'overview': 1363, 'metro': 1364, 'rift': 1365, 'pharmacist': 1366, 'drug': 1367, 'angry': 1368, 'mushroom': 1369, 'steer': 1370, 'serious': 1371, 'track': 1372, 'picnic': 1373, 'retro': 1374, 'paintbrush': 1375, 'personal': 1376, 'exterior': 1377, 'practice': 1378, 'tombstones': 1379, 'poppies': 1380, 'engrave': 1381, 'tennis': 1382, 'insideof': 1383, 'central': 1384, 'up': 1385, 'purple': 1386, 'jacket': 1387, 'zebra': 1388, 'rocket': 1389, 'luxury': 1390, 'vinyl': 1391, 'playerwith': 1392, 'blackmagic': 1393, 'ursa': 1394, 'mini': 1395, 'crop': 1396, 'caribbean': 1397, 'solar': 1398, 'panel': 1399, 'policeman': 1400, 'forward': 1401, 'unseen': 1402, 'sphygmomanometer': 1403, 'blood': 1404, 'physical': 1405, 'necessary': 1406, 'photographer': 1407, 'patriotic': 1408, 'straight': 1409, 'rotary': 1410, 'polisher': 1411, 'paintwork': 1412, 'conversation': 1413, 'kettle': 1414, 'presentation': 1415, 'technologies': 1416, 'candy': 1417, 'handrail': 1418, 'cosy': 1419, 'coat': 1420, 'afternoon': 1421, 'sprinter': 1422, 'preparate': 1423, 'recreational': 1424, 'boil': 1425, 'swimsuite': 1426, 'knees': 1427, 'dialog': 1428, 'majestic': 1429, 'snowcapped': 1430, 'alps': 1431, 'glaciers': 1432, 'bare': 1433, 'pregnancy': 1434, 'exit': 1435, 'airlock': 1436, 'easter': 1437, 'egg': 1438, 'rope': 1439, 'loader': 1440, 'waste': 1441, 'hairstylist': 1442, 'summertime': 1443, 'parenthood': 1444, 'swarm': 1445, 'lose': 1446, 'topical': 1447, 'saltwater': 1448, 'clownfish': 1449, 'anemonefish': 1450, 'lumber': 1451, 'mill': 1452, 'operation': 1453, 'recipe': 1454, 'treatment': 1455, 'garra': 1456, 'rufa': 1457, 'daily': 1458, 'cleanse': 1459, 'corella': 1460, 'pop': 1461, 'rowboat': 1462, 'closek': 1463, 'grey': 1464, 'jay': 1465, 'arrival': 1466, 'cafeteria': 1467, 'circular': 1468, 'jaccuzzi': 1469, 'tub': 1470, 'concern': 1471, 'shoulderwith': 1472, 'reporter': 1473, 'ask': 1474, 'impact': 1475, 'siberian': 1476, 'husky': 1477, 'snowshoe': 1478, 'vehicle': 1479, 'centrek': 1480, 'alaskan': 1481, 'bunch': 1482, 'fleas': 1483, 'crazy': 1484, 'sexy': 1485, 'glamour': 1486, 'rice': 1487, 'nori': 1488, 'funnel': 1489, 'stalk': 1490, 'automatic': 1491, 'ravine': 1492, 'rural': 1493, 'welness': 1494, 'platfrom': 1495, 'fishermanfrom': 1496, 'ibis': 1497, 'kfps': 1498, 'jasper': 1499, 'chicks': 1500, 'rarotongo': 1501, 'islands': 1502, 'moion': 1503, 'east': 1504, 'bridal': 1505, 'veil': 1506, 'provo': 1507, 'chinatown': 1508, 'bucket': 1509, 'sing': 1510, 'humorous': 1511, 'song': 1512, 'arsonist': 1513, 'playpen': 1514, 'bloodpressure': 1515, 'canister': 1516, 'automotive': 1517, 'brook': 1518, 'interest': 1519, 'george': 1520, 'emotionally': 1521, 'newspaper': 1522, 'reenactme': 1523, 'windsurfer': 1524, 'freelancer': 1525, 'lollipop': 1526, 'difficulties': 1527, 'motorcyclists': 1528, 'ca': 1529, 'cardigan': 1530, 'steelhead': 1531, 'extremely': 1532, 'grab': 1533, 'surprise': 1534, 'ingredients': 1535, 'entertain': 1536, 'realistic': 1537, 'graphic': 1538, 'ukraine': 1539, 'zaporozhye': 1540, 'september': 1541, 'trot': 1542, 'medium': 1543, 'outpace': 1544, 'buddhist': 1545, 'monk': 1546, 'eyeglasses': 1547, 'salute': 1548, 'join': 1549, 'bodybuilder': 1550, 'barbell': 1551, 'stabilizer': 1552, 'cactus': 1553, 'hello': 1554, 'shopkeeper': 1555, 'tentacles': 1556, 'wildebeest': 1557, 'hole': 1558, 'appomattox': 1559, 'din': 1560, 'settings': 1561, 'swampy': 1562, 'farmhouse': 1563, 'fc': 1564, 'falconth': 1565, 'njang': 1566, 'monuments': 1567, 'gardener': 1568, 'settlers': 1569, 'hug': 1570, 'tulips': 1571, 'settlements': 1572, 'juggle': 1573, 'vast': 1574, 'class': 1575, 'demonstrative': 1576, 'expedition': 1577, 'ushuaia': 1578, 'mannequin': 1579, 'pistol': 1580, 'hop': 1581, 'spot': 1582, 'satellite': 1583, 'orbit': 1584, 'curtain': 1585, 'blissful': 1586, 'dismount': 1587, 'illuminate': 1588, 'cnc': 1589, 'laser': 1590, 'metal': 1591, 'warn': 1592, 'authentic': 1593, 'challenge': 1594, 'grain': 1595, 'maybe': 1596, 'mo': 1597, 'blaze': 1598, 'mineral': 1599, 'pagosa': 1600, 'pank': 1601, 'violinist': 1602, 'raven': 1603, 'beautician': 1604, 'cabinet': 1605, 'smokestack': 1606, 'rifledownbarrel': 1607, 'dollyslow': 1608, 'businesswomen': 1609, 'pensive': 1610, 'guide': 1611, 'tomb': 1612, 'unknowns': 1613, 'scottish': 1614, 'thoughtful': 1615, 'name': 1616, 'paprika': 1617, 'stack': 1618, 'choose': 1619, 'sandpiper': 1620, 'bengal': 1621, 'shade': 1622, 'beside': 1623, 'dollybeautiful': 1624, 'novel': 1625, 'page': 1626, 'daydreamingk': 1627, 'slug': 1628, 'pearl': 1629, 'necklace': 1630, 'bell': 1631, 'ancient': 1632, 'enormous': 1633, 'license': 1634, 'register': 1635, 'photoflashes': 1636, 'pound': 1637, 'lumberjack': 1638, 'hatchet': 1639, 'household': 1640, 'chores': 1641, 'sale': 1642, 'saucepan': 1643, 'ladle': 1644, 'pilot': 1645, 'vision': 1646, 'ch': 1647, 'hercules': 1648, 'airdrop': 1649, 'graph': 1650, 'elaborate': 1651, 'rotation': 1652, 'tiny': 1653, 'seaweed': 1654, 'andrew': 1655, 'jackson': 1656, 'orleans': 1657, 'parish': 1658, 'templemount': 1659, 'timlpase': 1660, 'canoe': 1661, 'ascend': 1662, 'beatiful': 1663, 'bungy': 1664, 'pipe': 1665, 'joyful': 1666, 'grin': 1667, 'scarf': 1668, 'developers': 1669, 'handheldshowing': 1670, 'accordianist': 1671, 'gillin': 1672, 'kauai': 1673, 'symposium': 1674, 'lecture': 1675, 'auditorium': 1676, 'audience': 1677, 'speaker': 1678, 'governors': 1679, 'mansion': 1680, 'soundman': 1681, 'adjust': 1682, 'sound': 1683, 'level': 1684, 'vine': 1685, 'diver': 1686, 'dumplings': 1687, 'pinch': 1688, 'pod': 1689, 'pallet': 1690, 'problem': 1691, 'shiver': 1692, 'nationalities': 1693, 'flash': 1694, 'cells': 1695, 'hes': 1696, 'tavern': 1697, 'parkour': 1698, 'structure': 1699, 'officant': 1700, 'waitress': 1701, 'sausages': 1702, 'luxurious': 1703, 'part': 1704, 'vi': 1705, 'browse': 1706, 'campaign': 1707, 'median': 1708, 'mop': 1709, 'golfer': 1710, 'remove': 1711, 'putt': 1712, 'dollywoman': 1713, 'do': 1714, 'dj': 1715, 'unidentifiable': 1716, 'progress': 1717, 'ellis': 1718, 'launch': 1719, 'atop': 1720, 'spruce': 1721, 'ad': 1722, 'website': 1723, 'garter': 1724, 'snake': 1725, 'coil': 1726, 'kc': 1727, 'tanker': 1728, 'b': 1729, 'bomber': 1730, 'refuel': 1731, 'underwaterof': 1732, 'recline': 1733, 'thoughtfully': 1734, 'zoomedof': 1735, 'tee': 1736, 'course': 1737, 'indistanc': 1738, 'moab': 1739, 'porch': 1740, 'festive': 1741, 'firek': 1742, 'downhill': 1743, 'eatingp': 1744, 'potatoes': 1745, 'departures': 1746, 'emotional': 1747, 'guitars': 1748, 'gyroplan': 1749, 'airlines': 1750, 'aeroflot': 1751, 'cereals': 1752, 'juiceink': 1753, 'tiltingof': 1754, 'gossip': 1755, 'snorkel': 1756, 'fuzzy': 1757, 'caterpillar': 1758, 'jewish': 1759, 'zfat': 1760, 'rim': 1761, 'comedy': 1762, 'carriage': 1763, 'ramp': 1764, 'sharpen': 1765, 'grindstone': 1766, 'twig': 1767, 'edge': 1768, 'plastic': 1769, 'lens': 1770, 'world': 1771, 'skim': 1772, 'operate': 1773, 'pig': 1774, 'spend': 1775, 'marijuana': 1776, 'joint': 1777, 'outcrop': 1778, 'firework': 1779, 'rough': 1780, 'scissor': 1781, 'stormk': 1782, 'uhdx': 1783, 'ferris': 1784, 'tease': 1785, 'waterbucks': 1786, 'grove': 1787, 'osprey': 1788, 'ofrd': 1789, 'maw': 1790, 'battalion': 1791, 'send': 1792, 'mulatto': 1793, 'startup': 1794, 'liner': 1795, 'gearbox': 1796, 'naughty': 1797, 'whittier': 1798, 'horseback': 1799, 'rapid': 1800, 'rafter': 1801, 'manatee': 1802, 'wander': 1803, 'drivers': 1804, 'turtle': 1805, 'communication': 1806, 'rod': 1807, 'peel': 1808, 'wristwatch': 1809, 'barista': 1810, 'captive': 1811, 'hawk': 1812, 'dublin': 1813, 'walkingk': 1814, 'goalpost': 1815, 'players': 1816, 'freestyle': 1817, 'confuse': 1818, 'someone': 1819, 'smith': 1820, 'multi': 1821, 'ethnic': 1822, 'adopt': 1823, 'tight': 1824, 'vibrations': 1825, 'bart': 1826, 'escalator': 1827, 'stir': 1828, 'sour': 1829, 'borsch': 1830, 'operator': 1831, 'mount': 1832, 'axis': 1833, 'gimbal': 1834, 'stabilizator': 1835, 'directly': 1836, 'wsp': 1837, 'toast': 1838, 'champangne': 1839, 'viewk': 1840, 'shenandoah': 1841, 'ridge': 1842, 'cassette': 1843, 'goshawk': 1844, 'memories': 1845, 'beautyof': 1846, 'beets': 1847, 'metlife': 1848, 'chrysler': 1849, 'abdominal': 1850, 'lovers': 1851, 'christ': 1852, 'find': 1853, 'handheldand': 1854, 'homework': 1855, 'dentist': 1856, 'examination': 1857, 'smartph': 1858, 'present': 1859, 'seville': 1860, 'guadalquivir': 1861, 'rigger': 1862, 'investor': 1863, 'accompany': 1864, 'advisers': 1865, 'inspection': 1866, 'command': 1867, 'intend': 1868, 'invest': 1869, 'educational': 1870, 'lobby': 1871, 'trackingfocusing': 1872, 'briefcase': 1873, 'backpackers': 1874, 'wade': 1875, 'peak': 1876, 'overweight': 1877, 'punk': 1878, 'amuse': 1879, 'pareo': 1880, 'banner': 1881, 'illumination': 1882, 'italy': 1883, 'scour': 1884, 'honey': 1885, 'bee': 1886, 'daisy': 1887, 'pave': 1888, 'louisiana': 1889, 'azalea': 1890, 'rosedown': 1891, 'gardenskmov': 1892, 'adults': 1893, 'approach': 1894, 'moray': 1895, 'harmonica': 1896, 'farmers': 1897, 'climate': 1898, 'teen': 1899, 'gush': 1900, 'retreat': 1901, 'nypd': 1902, 'anaesthesiologist': 1903, 'boot': 1904, 'classroom': 1905, 'gnaw': 1906, 'goose': 1907, 'migration': 1908, 'kasba': 1909, 'fez': 1910, 'morocco': 1911, 'slip': 1912, 'slidding': 1913, 'musician': 1914, 'handicap': 1915, 'greet': 1916, 'return': 1917, 'independence': 1918, 'thumb': 1919, 'bokeh': 1920, 'cab': 1921, 'county': 1922, 'sheep': 1923, 'thomas': 1924, 'usvi': 1925, 'massagist': 1926, 'buttock': 1927, 'spiky': 1928, 'captain': 1929, 'quill': 1930, 'financial': 1931, 'windows': 1932, 'multicolor': 1933, 'job': 1934, 'toe': 1935, 'bully': 1936, 'dualpistol': 1937, 'seascape': 1938, 'asana': 1939, 'chaise': 1940, 'lounge': 1941, 'videogames': 1942, 'gamepad': 1943, 'sailboats': 1944, 'surfboard': 1945, 'australian': 1946, 'magpie': 1947, 'seed': 1948, 'downstairs': 1949, 'slide': 1950, 'joy': 1951, 'skilled': 1952, 'rider': 1953, 'twilight': 1954, 'skincare': 1955, 'elderly': 1956, 'sparklers': 1957, 'roam': 1958, 'hermit': 1959, 'crab': 1960, 'bandana': 1961, 'doggy': 1962, 'coaster': 1963, 'montana': 1964, 'pasture': 1965, 'smov': 1966, 'map': 1967, 'falcon': 1968, 'foundation': 1969, 'valentines': 1970, 'proud': 1971, 'organize': 1972, 'arrange': 1973, 'placement': 1974, 'stuff': 1975, 'portable': 1976, 'console': 1977, 'middlewith': 1978, 'panda': 1979, 'still': 1980, 'chill': 1981, 'hut': 1982, 'clothesline': 1983, 'mombassa': 1984, 'fryer': 1985, 'grocery': 1986, 'store': 1987, 'minuteman': 1988, 'concord': 1989, 'baggage': 1990, 'area': 1991, 'preparation': 1992, 'logos': 1993, 'donkeys': 1994, 'overhead': 1995, 'freshly': 1996, 'croissant': 1997, 'comedian': 1998, 'assistant': 1999, 'stage': 2000, 'coyote': 2001, 'ways': 2002, 'chew': 2003, 'teeth': 2004, 'yawn': 2005, 'violet': 2006, 'vivid': 2007, 'result': 2008, 'oranges': 2009, 'upper': 2010, 'teenagers': 2011, 'cultivate': 2012, 'vibrant': 2013, 'shotfps': 2014, 'statuettes': 2015, 'akvamarine': 2016, 'circus': 2017, 'figurines': 2018, 'impress': 2019, 'sprawl': 2020, 'meditate': 2021, 'defocused': 2022, 'vaunatu': 2023, 'camer': 2024, 'formal': 2025, 'hungry': 2026, 'successful': 2027, 'buy': 2028, 'enter': 2029, 'security': 2030, 'code': 2031, 'e': 2032, 'commerce': 2033, 'number': 2034, 'creditcard': 2035, 'th': 2036, 'century': 2037, 'pesidential': 2038, 'motorcade': 2039, 'parachute': 2040, 'lion': 2041, 'streetside': 2042, 'gold': 2043, 'auto': 2044, 'carraige': 2045, 'production': 2046, 'firefighters': 2047, 'containers': 2048, 'chemicals': 2049, 'conveyor': 2050, 'takeaway': 2051, 'arrive': 2052, 'daffodil': 2053, 'petals': 2054, 'slither': 2055, 'keep': 2056, 'gentoo': 2057, 'northern': 2058, 'arizona': 2059, 'electron': 2060, 'naturalist': 2061, 'explorer': 2062, 'binoculars': 2063, 'select': 2064, 'songs': 2065, 'airplanes': 2066, 'unconditional': 2067, 'surrender': 2068, 'mole': 2069, 'turkey': 2070, 'thanksgiving': 2071, 'scan': 2072, 'undrwaterof': 2073, 'singleactioncoltcu': 2074, 'shakedown': 2075, 'money': 2076, 'property': 2077, 'exhibition': 2078, 'domexpo': 2079, 'gostiny': 2080, 'dvor': 2081, 'windsurf': 2082, 'ducklings': 2083, 'communicate': 2084, 'buyer': 2085, 'chief': 2086, 'builder': 2087, 'describe': 2088, 'colleague': 2089, 'unfinished': 2090, 'decorative': 2091, 'motionk': 2092, 'last': 2093, 'pyramid': 2094, 'king': 2095, 'parrot': 2096, 'military': 2097, 'unit': 2098, 'dodger': 2099, 'navajo': 2100, 'dam': 2101, 'spillwayk': 2102, 'halloween': 2103, 'witch': 2104, 'brew': 2105, 'hare': 2106, 'scientists': 2107, 'laptops': 2108, 'pollen': 2109, 'luxuary': 2110, 'rainy': 2111, 'cosmetician': 2112, 'facial': 2113, 'jibof': 2114, 'csa': 2115, 'yoga': 2116, 'trousers': 2117, 'agency': 2118, 'friendly': 2119, 'slot': 2120, 'dollars': 2121, 'beetle': 2122, 'macroon': 2123, 'gravel': 2124, 'vivacious': 2125, 'archway': 2126, 'relay': 2127, 'bullets': 2128, 'drop': 2129, 'lash': 2130, 'main': 2131, 'amsterdam': 2132, 'holland': 2133, 'netherl': 2134, 'yoghurt': 2135, 'dessert': 2136, 'stingrays': 2137, 'alpine': 2138, 'maintain': 2139, 'shovel': 2140, 'pineapple': 2141, 'net': 2142, 'foggy': 2143, 'constructor': 2144, 'protective': 2145, 'uniform': 2146, 'beard': 2147, 'blacksmith': 2148, 'coal': 2149, 'astronauts': 2150, 'womam': 2151, 'sterilizer': 2152, 'public': 2153, 'beneath': 2154, 'barn': 2155, 'sleepy': 2156, 'seller': 2157, 'fischmarkt': 2158, 'every': 2159, 'sunday': 2160, 'racetrack': 2161, 'demolition': 2162, 'swerve': 2163, 'wolf': 2164, 'bone': 2165, 'shell': 2166, 'musical': 2167, 'instrument': 2168, 'frame': 2169, 'osorno': 2170, 'volcano': 2171, 'motor': 2172, 'situations': 2173, 'ministry': 2174, 'opposition': 2175, 'protest': 2176, 'bolotnaya': 2177, 'ambulance': 2178, 'tongs': 2179, 'japanese': 2180, 'teens': 2181, 'greem': 2182, 'sort': 2183, 'port': 2184, 'sesimbra': 2185, 'portugal': 2186, 'timelapsek': 2187, 'warthog': 2188, 'soda': 2189, 'loudspeaker': 2190, 'onion': 2191, 'trainer': 2192, 'holstein': 2193, 'udder': 2194, 'stunt': 2195, 'fran': 2196, 'charm': 2197, 'examine': 2198, 'newly': 2199, 'material': 2200, 'keyboard': 2201, 'wipe': 2202, 'towel': 2203, 'canival': 2204, 'disguise': 2205, 'twin': 2206, 'feather': 2207, 'effect': 2208, 'particles': 2209, 'businesscouple': 2210, 'derby': 2211, 'barrell': 2212, 'research': 2213, 'buggy': 2214, 'wideshot': 2215, 'polarizer': 2216, 'vulture': 2217, 'foothills': 2218, 'aunt': 2219, 'nieces': 2220, 'merry': 2221, 'maryland': 2222, 'gaze': 2223, 'smiley': 2224, 'parchment': 2225, 'cookies': 2226, 'elk': 2227, 'plum': 2228, 'smolder': 2229, 'shoemaker': 2230, 'sew': 2231, 'pedal': 2232, 'haircutes': 2233, 'pigeons': 2234, 'chalk': 2235, 'dad': 2236, 'hairdresser': 2237, 'lovely': 2238, 'homeowner': 2239, 'contractor': 2240, 'pay': 2241, 'upset': 2242, 'experience': 2243, 'mosquitos': 2244, 'bartenders': 2245, 'barman': 2246, 'muesli': 2247, 'iron': 2248, 'flint': 2249, 'defense': 2250, 'escort': 2251, 'presidential': 2252, 'limo': 2253, 'reenactor': 2254, 'decker': 2255, 'fortifications': 2256, 'ground': 2257, 'rhinos': 2258, 'handheldof': 2259, 'downtown': 2260, 'budapest': 2261, 'backyard': 2262, 'youth': 2263, 'stump': 2264, 'gap': 2265, 'durango': 2266, 'downtownk': 2267, 'mediumof': 2268, 'aggressive': 2269, 'carnival': 2270, 'bikinik': 2271, 'muffins': 2272, 'nightmare': 2273, 'unknown': 2274, 'axel': 2275, 'nice': 2276, 'barbecue': 2277, 'bloomy': 2278, 'futuristic': 2279, 'de': 2280, 'armas': 2281, 'silver': 2282, 'mosquito': 2283, 'ecu': 2284, 'base': 2285, 'wallaby': 2286, 'rt': 2287, 'offload': 2288, 'barge': 2289, 'app': 2290, 'gadgetsin': 2291, 'architecture': 2292, 'passageway': 2293, 'slowly': 2294, 'trench': 2295, 'backhoe': 2296, 'frost': 2297, 'panningwith': 2298, 'copy': 2299, 'movie': 2300, 'ind': 2301, 'popcorn': 2302, 'magnify': 2303, 'thoroughly': 2304, 'gymnasium': 2305, 'disk': 2306, 'vultures': 2307, 'foliage': 2308, 'peer': 2309, 'wild': 2310, 'bustle': 2311, 'kinds': 2312, 'desserts': 2313, 'downy': 2314, 'woodpecker': 2315, 'tallinn': 2316, 'estonia': 2317, 'winkle': 2318, 'pickers': 2319, 'brightly': 2320, 'craft': 2321, 'nunchaku': 2322, 'simulator': 2323, 'hign': 2324, 'action': 2325, 'grimace': 2326, 'motionfps': 2327, 'olive': 2328, 'dealer': 2329, 'shuffle': 2330, 'split': 2331, 'native': 2332, 'carrier': 2333, 'stroke': 2334, 'drill': 2335, 'adjustments': 2336, 'mid': 2337, 'puppies': 2338, 'cowboys': 2339, 'roundup': 2340, 'counter': 2341, 'club': 2342, 'whip': 2343, 'jelly': 2344, 'cherries': 2345, 'pine': 2346, 'vehicles': 2347, 'stop': 2348, 'closeupof': 2349, 'cheese': 2350, 'giant': 2351, 'medicine': 2352, 'pharmaceutical': 2353, 'laboratory': 2354, 'test': 2355, 'freeze': 2356, 'wad': 2357, 'dolphins': 2358, 'tail': 2359, 'midtown': 2360, 'streets': 2361, 'footbridge': 2362, 'ants': 2363, 'barcelona': 2364, 'spain': 2365, 'tour': 2366, 'busk': 2367, 'hemisfair': 2368, 'antonio': 2369, 'businessmen': 2370, 'unfurnished': 2371, 'array': 2372, 'tube': 2373, 'supply': 2374, 'copenhagen': 2375, 'denmark': 2376, 'chic': 2377, 'readingnewspaperec': 2378, 'associate': 2379, 'co': 2380, 'mist': 2381, 'fog': 2382, 'nearly': 2383, 'obscure': 2384, 'puffer': 2385, 'tetraodontidae': 2386, 'magazine': 2387, 'paw': 2388, 'cowboy': 2389, 'orchard': 2390, 'elevator': 2391, 'slowmo': 2392, 'reagents': 2393, 'illuminator': 2394, 'dabble': 2395, 'chirp': 2396, 'treetop': 2397, 'backlit': 2398, 'squid': 2399, 'motherhood': 2400, 'strip': 2401, 'continental': 2402, 'treek': 2403, 'ink': 2404, 'yankee': 2405, 'trip': 2406, 'argentine': 2407, 'gauchos': 2408, 'ring': 2409, 'gaucho': 2410, 'knit': 2411, 'warmth': 2412, 'workouts': 2413, 'fatherping': 2414, 'profile': 2415, 'tape': 2416, 'dentistry': 2417, 'row': 2418, 'reveal': 2419, 'semitruck': 2420, 'muscular': 2421, 'physique': 2422, 'cafes': 2423, 'scar': 2424, 'flashlights': 2425, 'prayer': 2426, 'santa': 2427, 'lucia': 2428, 'cabriolet': 2429, 'womens': 2430, 'shrimp': 2431, 'macrobacklit': 2432, 'rehabilitate': 2433, 'seal': 2434, 'sunbeam': 2435, 'beam': 2436, 'fantasy': 2437, 'zealand': 2438, 'auckland': 2439, 'bay': 2440, 'waterfront': 2441, 'gill': 2442, 'hardhat': 2443, 'word': 2444, 'faith': 2445, 'ballet': 2446, 'punta': 2447, 'arenas': 2448, 'southernmost': 2449, 'intricate': 2450, 'fine': 2451, 'selective': 2452, 'bristle': 2453, 'happyp': 2454, 'rear': 2455, 'retail': 2456, 'journey': 2457, 'wagon': 2458, 'backflip': 2459, 'fast': 2460, 'outddor': 2461, 'weather': 2462, 'whiskey': 2463, 'headlights': 2464, 'racers': 2465, 'lookout': 2466, 'poor': 2467, 'tahoe': 2468, 'gloved': 2469, 'release': 2470, 'luck': 2471, 'flipchart': 2472, 'boardroom': 2473, 'alleyway': 2474, 'cheek': 2475, 'uss': 2476, 'midway': 2477, 'girlfriends': 2478, 'toolbelt': 2479, 'dusseldorf': 2480, 'wildebeests': 2481, 'stress': 2482, 'worry': 2483, 'cello': 2484, 'perch': 2485, 'cardboard': 2486, 'lonely': 2487, 'neighborhood': 2488, 'steep': 2489, 'grand': 2490, 'bazaar': 2491, 'meadowby': 2492, 'sword': 2493, 'char': 2494, 'assembly': 2495, 'manufacture': 2496, 'circuit': 2497, 'goods': 2498, 'beaker': 2499, 'director': 2500, 'actor': 2501, 'seagulls': 2502, 'murray': 2503, 'albury': 2504, 'queen': 2505, 'vgetable': 2506, 'asleep': 2507, 'ballerina': 2508, 'confidant': 2509, 'feminine': 2510, 'ladies': 2511, 'abq': 2512, 'fiesta': 2513, 'eventk': 2514, 'steadicamthrough': 2515, 'potato': 2516, 'banjo': 2517, 'strum': 2518, 'carousel': 2519, 'pedestrian': 2520, 'whitewater': 2521, 'porcupine': 2522, 'waddle': 2523, 'jungle': 2524, 'sniper': 2525, 'rifle': 2526, 'dollyshowing': 2527, 'steamboat': 2528, 'ranch': 2529, 'telephone': 2530, 'overdue': 2531, 'husband': 2532, 'reindeer': 2533, 'nordkapp': 2534, 'settle': 2535, 'chairlift': 2536, 'skiier': 2537, 'determine': 2538, 'fishers': 2539, 'erriff': 2540, 'aasleagh': 2541, 'rhinoceros': 2542, 'sample': 2543, 'schoolboy': 2544, 'mark': 2545, 'yes': 2546, 'compare': 2547, 'departure': 2548, 'stun': 2549, 'curious': 2550, 'bermuda': 2551, 'pill': 2552, 'galilee': 2553, 'remote': 2554, 'lobsters': 2555, 'antique': 2556, 'gas': 2557, 'las': 2558, 'hippo': 2559, 'dollythrough': 2560, 'scratch': 2561, 'excitement': 2562, 'amount': 2563, 'grow': 2564, 'shennandoah': 2565, 'lightning': 2566, 'strike': 2567, 'saturn': 2568, 'gleam': 2569, 'tranquil': 2570, 'powell': 2571, 'bedrooms': 2572, 'latin': 2573, 'america': 2574, 'dribble': 2575, 'colony': 2576, 'molt': 2577, 'stony': 2578, 'chase': 2579, 'cuddle': 2580, 'injure': 2581, 'deepwater': 2582, 'playful': 2583, 'carskx': 2584, 'appalachian': 2585, 'ledge': 2586, 'promenade': 2587, 'vegetable': 2588, 'trailers': 2589, 'munch': 2590, 'rub': 2591, 'lotion': 2592, 'doolin': 2593, 'inflate': 2594, 'furniture': 2595, 'bungalows': 2596, 'canon': 2597, 'panni': 2598, 'sportwoman': 2599, 'sportsman': 2600, 'ticker': 2601, 'credit': 2602, 'card': 2603, 'shes': 2604, 'steak': 2605, 'jewellery': 2606, 'adorn': 2607, 'cowgirl': 2608, 'distant': 2609, 'sway': 2610, 'scavenge': 2611, 'riverbank': 2612, 'gumboots': 2613, 'blueberries': 2614, 'sprinkle': 2615, 'sugar': 2616, 'latina': 2617, 'cigar': 2618, 'havana': 2619, 'cuba': 2620, 'folklore': 2621, 'cuban': 2622, 'nail': 2623, 'flame': 2624, 'brushfire': 2625, 'headquarter': 2626, 'confident': 2627, 'pack': 2628, 'con': 2629, 'feeder': 2630, 'revolutionary': 2631, 'glidingof': 2632, 'zebras': 2633, 'headsets': 2634, 'expansion': 2635, 'efforts': 2636, 'alternative': 2637, 'energy': 2638, 'lockers': 2639, 'rollercoaster': 2640, 'pelagic': 2641, 'cormorant': 2642, 'defend': 2643, 'seagull': 2644, 'attack': 2645, 'things': 2646, 'organic': 2647, 'helmut': 2648, 'fetch': 2649, 'snowsuit': 2650, 'commuter': 2651, 'fondue': 2652, 'appetizer': 2653, 'snack': 2654, 'thermal': 2655, 'mug': 2656, 'steady': 2657, 'manhattan': 2658, 'overpass': 2659, 'mobilephone': 2660, 'bound': 2661, 'thrower': 2662, 'ritual': 2663, 'response': 2664, 'date': 2665, 'streamers': 2666, 'fancy': 2667, 'weightlifter': 2668, 'dressready': 2669, 'multimedia': 2670, 'projector': 2671, 'begin': 2672, 'darker': 2673, 'grapes': 2674, 'smilin': 2675, 'sledge': 2676, 'education': 2677, 'toward': 2678, 'straddle': 2679, 'orbitting': 2680, 'heart': 2681, 'sheet': 2682, 'usingd': 2683, 'pen': 2684, 'trinidad': 2685, 'smartophone': 2686, 'sahara': 2687, 'dun': 2688, 'salesman': 2689, 'owners': 2690, 'save': 2691, 'electricity': 2692, 'consumption': 2693, 'emotions': 2694, 'sole': 2695, 'alaska': 2696, 'chiloe': 2697, 'attempt': 2698, 'hunt': 2699, 'waiter': 2700, 'tombstone': 2701, 'hattie': 2702, 'private': 2703, 'charles': 2704, 'thompson': 2705, 'corpus': 2706, 'christi': 2707, 'owl': 2708, 'beyond': 2709, 'inflatable': 2710, 'mattress': 2711}\n",
            "\n",
            "First five captions:\n",
            "[[46, 596, 980, 489, 416, 85], [303, 1, 417, 10, 1401, 149, 29, 351, 76, 1, 17, 132, 304, 1402, 103, 597, 4, 5], [11, 77, 73, 66, 30, 16, 1403, 598, 1404, 981], [4, 5, 3, 16, 490, 246, 982, 1405, 741, 1406, 25, 599], [4, 5, 1407, 61, 9]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R4Py6pqCsQTt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Test Set Predictions"
      ]
    },
    {
      "metadata": {
        "id": "TI8K9xbdBCsE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "07392a84-765a-4543-c6bd-be89ef98d1b4"
      },
      "cell_type": "code",
      "source": [
        "batch_size = 50\n",
        "# test conversion to tensor and pass into your model\n",
        "test_set_data = torch.from_numpy(test_sequences)\n",
        "test_set_loader = DataLoader(test_set_data, shuffle=True, batch_size=batch_size)\n",
        "\n",
        "predictions1 = []\n",
        "predictions2 = []\n",
        "\n",
        "def predict(net, test_set_loader, sequence_length):\n",
        "    \n",
        "    net.eval()\n",
        "    \n",
        "    for inputs in test_set_loader:\n",
        "      \n",
        "      # initialize hidden state\n",
        "      h = net.init_hidden(batch_size)\n",
        "\n",
        "      if(train_on_gpu):\n",
        "          inputs = inputs.cuda()\n",
        "\n",
        "      # get the output from the model\n",
        "      output1, output2, h = net(inputs, h)\n",
        "\n",
        "      # convert output probabilities\n",
        "      pred1 = output1.squeeze()\n",
        "      pred2 = output2.squeeze()\n",
        "      \n",
        "      predictions1.extend(pred1.detach().numpy())\n",
        "      predictions2.extend(pred2.detach().numpy())\n",
        "\n",
        "        \n",
        "predict(net, test_set_loader, 10)\n",
        "print('Short term memorability scores:')\n",
        "print(predictions1)\n",
        "print('')\n",
        "print('Long term memorability scores:')\n",
        "print(predictions2)\n"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Short term memorability scores:\n",
            "[0.78755134, 0.80987084, 0.83669543, 0.8579852, 0.86472464, 0.806046, 0.85237825, 0.85936695, 0.81862307, 0.8268935, 0.86667645, 0.90261394, 0.8913475, 0.8580623, 0.8788206, 0.89102554, 0.8110081, 0.91167, 0.90042746, 0.8766002, 0.9014093, 0.8899993, 0.87956625, 0.77854115, 0.7813344, 0.84553146, 0.8869885, 0.8515446, 0.86584646, 0.83586514, 0.8843447, 0.8845598, 0.8241375, 0.84801733, 0.8704312, 0.88149047, 0.9044567, 0.9018155, 0.8730525, 0.8612203, 0.83323205, 0.83458406, 0.7788179, 0.8749046, 0.81505245, 0.8240797, 0.88372856, 0.9049552, 0.8437859, 0.9062645, 0.85968417, 0.87840563, 0.8378845, 0.8272271, 0.89852446, 0.87443453, 0.8758067, 0.89355415, 0.8618241, 0.89074606, 0.85576487, 0.81493217, 0.8547212, 0.8989951, 0.88204074, 0.87981707, 0.8630298, 0.8391783, 0.8905902, 0.875545, 0.83684194, 0.8690305, 0.8787454, 0.8443849, 0.89252776, 0.8851288, 0.8774054, 0.88068074, 0.8968866, 0.81846285, 0.84504086, 0.8430713, 0.8656201, 0.80977225, 0.8714927, 0.8620838, 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            "\n",
            "Long term memorability scores:\n",
            "[0.6869148, 0.6810528, 0.7121137, 0.81124616, 0.81392103, 0.6767327, 0.78839266, 0.7667974, 0.70199096, 0.747011, 0.7880857, 0.8618489, 0.825145, 0.7470184, 0.79225945, 0.83206266, 0.74726737, 0.8646487, 0.8270462, 0.7941318, 0.87312436, 0.8337935, 0.80200166, 0.64055544, 0.65220225, 0.7655756, 0.8145839, 0.7602531, 0.7454954, 0.7081888, 0.75740504, 0.824726, 0.7256226, 0.7520379, 0.82022375, 0.82423425, 0.83462226, 0.8399762, 0.7765383, 0.7961356, 0.7500151, 0.74314696, 0.6105403, 0.80921084, 0.7167887, 0.72737265, 0.8177286, 0.8471883, 0.75076693, 0.8566004, 0.7957384, 0.8176566, 0.70229423, 0.70907223, 0.8757747, 0.7922851, 0.82054645, 0.8517332, 0.8036945, 0.833805, 0.7833994, 0.76713, 0.77478397, 0.8444123, 0.844137, 0.7816243, 0.77090865, 0.74997324, 0.83705026, 0.8348834, 0.71404237, 0.7977228, 0.7944844, 0.7967016, 0.8172232, 0.79971087, 0.81421643, 0.8266986, 0.8153971, 0.71970385, 0.7024245, 0.7818522, 0.7774815, 0.70035535, 0.7694385, 0.8355985, 0.84692, 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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U6bLHFYxhrR2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# output to CSV\n",
        "ground_truth_template_csv = 'ground_truth_template.csv'\n",
        "ground_truth_template = pd.read_csv(ground_truth_template_csv)\n",
        "ground_truth_template.head()\n",
        "\n",
        "ground_truth_deirdre_corr = ground_truth_template\n",
        "\n",
        "ground_truth_deirdre_corr['short-term_memorability'] \\\n",
        "= [predictions1[index] if math.isnan(mem) else mem for index, mem in enumerate(ground_truth_template['short-term_memorability'])]\n",
        "ground_truth_deirdre_corr['long-term_memorability'] \\\n",
        "= [predictions2[index] if math.isnan(mem) else mem for index, mem in enumerate(ground_truth_template['long-term_memorability'])]\n",
        "ground_truth_deirdre_corr.head(100)\n",
        "ground_truth_deirdre_corr.to_csv('ground_truth_deirdre_corr.csv', sep=',', index=False)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}