RNN_project.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>RNN_project</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 */
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    color: #000 !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
  }
}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
@media (max-width: 991px) {
  #ipython_notebook {
    margin-left: 10px;
  }
}
[dir="rtl"] #ipython_notebook {
  float: right !important;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#login_widget {
  float: right;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  text-align: center;
  vertical-align: middle;
  display: inline;
  opacity: 0;
  z-index: 2;
  width: 12ex;
  margin-right: -12ex;
}
.alternate_upload .btn-upload {
  height: 22px;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
[dir="rtl"] #tabs li {
  float: right;
}
ul#tabs {
  margin-bottom: 4px;
}
[dir="rtl"] ul#tabs {
  margin-right: 0px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons {
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-right {
  padding-top: 1px;
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-left {
  float: right !important;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: baseline;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
#tree-selector {
  padding-right: 0px;
}
[dir="rtl"] #tree-selector a {
  float: right;
}
#button-select-all {
  min-width: 50px;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
[dir="rtl"] #new-menu {
  text-align: right;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
[dir="rtl"] #running .col-sm-8 {
  float: right !important;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
  font-weight: bold;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  border-left-width: 1px;
  padding-left: 5px;
  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
  border-left-color: #90CAF9;
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected {
  border-color: #ababab;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
  div.cell.selected {
    border-color: transparent;
  }
}
div.cell.selected.jupyter-soft-selected {
  border-left-width: 0;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  padding: 0.4em;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
  /* .CodeMirror-lines */
  padding: 0;
  border: 0;
  border-radius: 0;
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  display: none;
}
.command-shortcut:before {
  content: "(command)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>


<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

div#notebook {
  overflow: visible;
  border-top: none;
}

@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Artificial-Intelligence-Nanodegree">Artificial Intelligence Nanodegree<a class="anchor-link" href="#Artificial-Intelligence-Nanodegree">&#182;</a></h1><h2 id="Recurrent-Neural-Network-Projects">Recurrent Neural Network Projects<a class="anchor-link" href="#Recurrent-Neural-Network-Projects">&#182;</a></h2><p>Welcome to the Recurrent Neural Network Project in the Artificial Intelligence Nanodegree! In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with <strong>'Implementation'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!</p>
<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
</blockquote>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation-TODOs-in-this-notebook">Implementation TODOs in this notebook<a class="anchor-link" href="#Implementation-TODOs-in-this-notebook">&#182;</a></h3><p>This notebook contains two problems, cut into a variety of TODOs.  Make sure to complete each section containing a TODO marker throughout the notebook.  For convenience we provide links to each of these sections below.</p>
<p><a href="#TODO_1">TODO #1: Implement a function to window time series</a></p>
<p><a href="#TODO_2">TODO #2: Create a simple RNN model using keras to perform regression</a></p>
<p><a href="#TODO_3">TODO #3: Finish cleaning a large text corpus</a></p>
<p><a href="#TODO_4">TODO #4: Implement a function to window a large text corpus</a></p>
<p><a href="#TODO_5">TODO #5: Create a simple RNN model using keras to perform multiclass classification</a></p>
<p><a href="#TODO_6">TODO #6: Generate text using a fully trained RNN model and a variety of input sequences</a></p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Problem-1:-Perform-time-series-prediction">Problem 1: Perform time series prediction<a class="anchor-link" href="#Problem-1:-Perform-time-series-prediction">&#182;</a></h1><p>In this project you will perform time series prediction using a Recurrent Neural Network regressor.  In particular you will re-create the figure shown in the notes - where the stock price of Apple was forecasted (or predicted) 7 days in advance.  In completing this exercise you will learn how to construct RNNs using Keras, which will also aid in completing the second project in this notebook.</p>
<p>The particular network architecture we will employ for our RNN is known as  <a href="https://en.wikipedia.org/wiki/Long_short-term_memory">Long Term Short Memory (LSTM)</a>, which helps significantly avoid technical problems with optimization of RNNs.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="1.1-Getting-started">1.1 Getting started<a class="anchor-link" href="#1.1-Getting-started">&#182;</a></h2><p>First we must load in our time series - a history of around 140 days of Apple's stock price.  Then we need to perform a number of pre-processing steps to prepare it for use with an RNN model.  First off, it is good practice to normalize time series - by normalizing its range.  This helps us avoid serious numerical issues associated how common activation functions (like tanh) transform very large (positive or negative) numbers, as well as helping us to avoid related issues when computing derivatives.</p>
<p>Here we normalize the series to lie in the range [0,1] <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html">using this scikit function</a>, but it is also commonplace to normalize by a series standard deviation.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Load in necessary libraries for data input and normalization</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="o">%</span><span class="k">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2

<span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="o">*</span>

<span class="o">%</span><span class="k">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2

<span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="o">*</span>

<span class="c1">### load in and normalize the dataset</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">&#39;datasets/normalized_apple_prices.csv&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Lets take a quick look at the (normalized) time series we'll be performing predictions on.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[2]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># lets take a look at our time series</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;time period&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;normalized series value&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt output_prompt">Out[2]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.text.Text at 0x7f58f043f390&gt;</pre>
</div>

</div>

<div class="output_area">
<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZQAAAEKCAYAAAA1qaOTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="1.2--Cutting-our-time-series-into-sequences">1.2  Cutting our time series into sequences<a class="anchor-link" href="#1.2--Cutting-our-time-series-into-sequences">&#182;</a></h2><p>Remember, our time series is a sequence of numbers that we can represent in general mathematically as</p>
$$s_{0},s_{1},s_{2},...,s_{P}$$<p>where $s_{p}$ is the numerical value of the time series at time period $p$ and where $P$ is the total length of the series.  In order to apply our RNN we treat the time series prediction problem as a regression problem, and so need to use a sliding window to construct a set of associated input/output pairs to regress on.  This process is animated in the gif below.</p>
<p><img src="images/timeseries_windowing_training.gif" width=600 height=600/></p>
<p>For example - using a window of size T = 5 (as illustrated in the gif above) we produce a set of input/output pairs like the one shown in the table below</p>
$$\begin{array}{c|c}
\text{Input} & \text{Output}\\
\hline \color{CornflowerBlue} {\langle s_{1},s_{2},s_{3},s_{4},s_{5}\rangle} & \color{Goldenrod}{ s_{6}} \\
\ \color{CornflowerBlue} {\langle s_{2},s_{3},s_{4},s_{5},s_{6} \rangle } & \color{Goldenrod} {s_{7} } \\
\color{CornflowerBlue}  {\vdots} & \color{Goldenrod} {\vdots}\\
\color{CornflowerBlue} { \langle s_{P-5},s_{P-4},s_{P-3},s_{P-2},s_{P-1} \rangle } & \color{Goldenrod} {s_{P}}
\end{array}$$<p>Notice here that each input is a sequence (or vector) of length 5 (and in general has length equal to the window size T) while each corresponding output is a scalar value.  Notice also how given a time series of length P and window size T = 5 as shown above, we created P - 5  input/output pairs.  More generally, for a window size T we create P - T such pairs.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Now its time for you to window the input time series as described above!</p>
<p><a id='TODO_1'></a></p>
<p><strong>TODO:</strong> Implement the function called <strong>window_transform_series</strong> in my_answers.py so that it runs a sliding window along the input series and creates associated input/output pairs.    Note that this function should input a) the series and b) the window length, and return the input/output subsequences.  Make sure to format returned input/output as generally shown in table above (where window_size = 5), and make sure your returned input is a numpy array.</p>
<hr>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>You can test your function on the list of odd numbers given below</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="o">*</span>
<span class="o">%</span><span class="k">reload_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2

<span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="o">*</span>

<span class="n">odd_nums</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">13</span><span class="p">])</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">window_transform_series</span><span class="p">(</span><span class="n">odd_nums</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>[[ 1  3]
 [ 3  5]
 [ 5  7]
 [ 7  9]
 [ 9 11]]
[[ 5]
 [ 7]
 [ 9]
 [11]
 [13]]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Here is a hard-coded solution for odd_nums.  You can compare its results with what you get from your <strong>window_transform_series</strong> implementation.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># run a window of size 2 over the odd number sequence and display the results</span>
<span class="n">window_size</span> <span class="o">=</span> <span class="mi">2</span>

<span class="n">X</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">odd_nums</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">])</span>
<span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">odd_nums</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">])</span>
<span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">odd_nums</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">])</span>
<span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">odd_nums</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">5</span><span class="p">])</span>
<span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">odd_nums</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">])</span>

<span class="n">y</span> <span class="o">=</span> <span class="n">odd_nums</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span>

<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">),</span><span class="mi">1</span><span class="p">))</span> <span class="c1">#optional</span>

<span class="k">assert</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;ndarray&#39;</span><span class="p">)</span>
<span class="k">assert</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;ndarray&#39;</span><span class="p">)</span>
<span class="k">assert</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="k">assert</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span> <span class="ow">in</span> <span class="p">[(</span><span class="mi">5</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">5</span><span class="p">,)])</span>

<span class="c1"># print out input/output pairs --&gt; here input = X, corresponding output = y</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;--- the input X will look like ----&#39;</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="n">X</span><span class="p">)</span>

<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;--- the associated output y will look like ----&#39;</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="n">y</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>--- the input X will look like ----
[[ 1  3]
 [ 3  5]
 [ 5  7]
 [ 7  9]
 [ 9 11]]
--- the associated output y will look like ----
[[ 5]
 [ 7]
 [ 9]
 [11]
 [13]]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Again - you can check that your completed <strong>window_transform_series</strong> function works correctly by trying it on the odd_nums sequence - you should get the above output.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: implement the function window_transform_series in the file my_answers.py</span>
<span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="n">window_transform_series</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>With this function in place apply it to the series in the Python cell below.  We use a window_size = 7 for these experiments.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># window the data using your windowing function</span>
<span class="n">window_size</span> <span class="o">=</span> <span class="mi">7</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span> <span class="o">=</span> <span class="n">window_transform_series</span><span class="p">(</span><span class="n">series</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">,</span><span class="n">window_size</span> <span class="o">=</span> <span class="n">window_size</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="1.3--Splitting-into-training-and-testing-sets">1.3  Splitting into training and testing sets<a class="anchor-link" href="#1.3--Splitting-into-training-and-testing-sets">&#182;</a></h2><p>In order to perform proper testing on our dataset we will lop off the last 1/3 of it for validation (or testing).  This is that once we train our model we have something to test it on (like any regression problem!).  This splitting into training/testing sets is done in the cell below.</p>
<p>Note how here we are <strong>not</strong> splitting the dataset <em>randomly</em> as one typically would do when validating a regression model.  This is because our input/output pairs <em>are related temporally</em>.   We don't want to validate our model by training on a random subset of the series and then testing on another random subset, as this simulates the scenario that we receive new points <em>within the timeframe of our training set</em>.</p>
<p>We want to train on one solid chunk of the series (in our case, the first full 2/3 of it), and validate on a later chunk (the last 1/3) as this simulates how we would predict <em>future</em> values of a time series.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># split our dataset into training / testing sets</span>
<span class="n">train_test_split</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="mi">3</span><span class="p">)))</span>   <span class="c1"># set the split point</span>

<span class="c1"># partition the training set</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="n">train_test_split</span><span class="p">,:]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="n">train_test_split</span><span class="p">]</span>

<span class="c1"># keep the last chunk for testing</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_test_split</span><span class="p">:,:]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">train_test_split</span><span class="p">:]</span>

<span class="c1"># NOTE: to use keras&#39;s RNN LSTM module our input must be reshaped to [samples, window size, stepsize] </span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">window_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">window_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><a id='TODO_2'></a></p>
<h2 id="1.4--Build-and-run-an-RNN-regression-model">1.4  Build and run an RNN regression model<a class="anchor-link" href="#1.4--Build-and-run-an-RNN-regression-model">&#182;</a></h2><p>Having created input/output pairs out of our time series and cut this into training/testing sets, we can now begin setting up our RNN.  We use Keras to quickly build a two hidden layer RNN of the following specifications</p>
<ul>
<li>layer 1 uses an LSTM module with 5 hidden units (note here the input_shape = (window_size,1))</li>
<li>layer 2 uses a fully connected module with one unit</li>
<li>the 'mean_squared_error' loss should be used (remember: we are performing regression here)</li>
</ul>
<p>This can be constructed using just a few lines - see e.g., the <a href="https://keras.io/getting-started/sequential-model-guide/">general Keras documentation</a> and the <a href="https://keras.io/layers/recurrent/">LSTM documentation in particular</a> for examples of how to quickly use Keras to build neural network models.  Make sure you are initializing your optimizer given the <a href="https://keras.io/optimizers/">keras-recommended approach for RNNs</a></p>
<p>(given in the cell below).  (remember to copy your completed function into the script <em>my_answers.py</em> function titled <em>build_part1_RNN</em> before submitting your project)</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: create required RNN model</span>
<span class="c1"># import keras network libraries</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">LSTM</span>
<span class="kn">import</span> <span class="nn">keras</span>

<span class="c1"># given - fix random seed - so we can all reproduce the same results on our default time series</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>


<span class="c1"># TODO: implement build_part1_RNN in my_answers.py</span>
<span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="n">build_part1_RNN</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">build_part1_RNN</span><span class="p">(</span><span class="n">window_size</span><span class="p">)</span>

<span class="c1"># build model using keras documentation recommended optimizer initialization</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">RMSprop</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>

<span class="c1"># compile the model</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mean_squared_error&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>With your model built you can now fit the model by activating the cell below!  Note: the number of epochs (np_epochs) and batch_size are preset (so we can all produce the same results).  You can choose to toggle the verbose parameter - which gives you regular updates on the progress of the algorithm - on and off by setting it to 1 or 0 respectively.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># run your model!</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>Epoch 1/1000
88/88 [==============================] - 5s - loss: 0.1422     
Epoch 2/1000
88/88 [==============================] - 0s - loss: 0.1343     
Epoch 3/1000
88/88 [==============================] - 0s - loss: 0.1290     
Epoch 4/1000
88/88 [==============================] - 0s - loss: 0.1245     
Epoch 5/1000
88/88 [==============================] - 0s - loss: 0.1205     
Epoch 6/1000
88/88 [==============================] - 0s - loss: 0.1169     
Epoch 7/1000
88/88 [==============================] - 0s - loss: 0.1138     
Epoch 8/1000
88/88 [==============================] - 0s - loss: 0.1106     
Epoch 9/1000
88/88 [==============================] - 0s - loss: 0.1079     
Epoch 10/1000
88/88 [==============================] - 0s - loss: 0.1051     
Epoch 11/1000
88/88 [==============================] - 0s - loss: 0.1021     
Epoch 12/1000
88/88 [==============================] - 0s - loss: 0.0994     
Epoch 13/1000
88/88 [==============================] - 0s - loss: 0.0967     
Epoch 14/1000
88/88 [==============================] - 0s - loss: 0.0944     
Epoch 15/1000
88/88 [==============================] - 0s - loss: 0.0917     
Epoch 16/1000
88/88 [==============================] - 0s - loss: 0.0893     
Epoch 17/1000
88/88 [==============================] - 0s - loss: 0.0869     
Epoch 18/1000
88/88 [==============================] - 0s - loss: 0.0846     
Epoch 19/1000
88/88 [==============================] - 0s - loss: 0.0823     
Epoch 20/1000
88/88 [==============================] - 0s - loss: 0.0802     
Epoch 21/1000
88/88 [==============================] - 0s - loss: 0.0781     
Epoch 22/1000
88/88 [==============================] - 0s - loss: 0.0762     
Epoch 23/1000
88/88 [==============================] - 0s - loss: 0.0740     
Epoch 24/1000
88/88 [==============================] - 0s - loss: 0.0721     
Epoch 25/1000
88/88 [==============================] - 0s - loss: 0.0702     
Epoch 26/1000
88/88 [==============================] - 0s - loss: 0.0685     
Epoch 27/1000
88/88 [==============================] - 0s - loss: 0.0667     
Epoch 28/1000
88/88 [==============================] - 0s - loss: 0.0651     
Epoch 29/1000
88/88 [==============================] - 0s - loss: 0.0635     
Epoch 30/1000
88/88 [==============================] - 0s - loss: 0.0621     
Epoch 31/1000
88/88 [==============================] - 0s - loss: 0.0608     
Epoch 32/1000
88/88 [==============================] - 0s - loss: 0.0592     
Epoch 33/1000
88/88 [==============================] - 0s - loss: 0.0580     
Epoch 34/1000
88/88 [==============================] - 0s - loss: 0.0568     
Epoch 35/1000
88/88 [==============================] - 0s - loss: 0.0557     
Epoch 36/1000
88/88 [==============================] - 0s - loss: 0.0546     
Epoch 37/1000
88/88 [==============================] - 0s - loss: 0.0536     
Epoch 38/1000
88/88 [==============================] - 0s - loss: 0.0528     
Epoch 39/1000
88/88 [==============================] - 0s - loss: 0.0518     
Epoch 40/1000
88/88 [==============================] - 0s - loss: 0.0511     
Epoch 41/1000
88/88 [==============================] - 0s - loss: 0.0504     
Epoch 42/1000
88/88 [==============================] - 0s - loss: 0.0498     
Epoch 43/1000
88/88 [==============================] - 0s - loss: 0.0491     
Epoch 44/1000
88/88 [==============================] - 0s - loss: 0.0486     
Epoch 45/1000
88/88 [==============================] - 0s - loss: 0.0481     
Epoch 46/1000
88/88 [==============================] - 0s - loss: 0.0477     
Epoch 47/1000
88/88 [==============================] - 0s - loss: 0.0473     
Epoch 48/1000
88/88 [==============================] - 0s - loss: 0.0469     
Epoch 49/1000
88/88 [==============================] - 0s - loss: 0.0466     
Epoch 50/1000
88/88 [==============================] - 0s - loss: 0.0465     
Epoch 51/1000
88/88 [==============================] - 0s - loss: 0.0462     
Epoch 52/1000
88/88 [==============================] - 0s - loss: 0.0457     
Epoch 53/1000
88/88 [==============================] - 0s - loss: 0.0456     
Epoch 54/1000
88/88 [==============================] - 0s - loss: 0.0452     
Epoch 55/1000
88/88 [==============================] - 0s - loss: 0.0451     
Epoch 56/1000
88/88 [==============================] - 0s - loss: 0.0448     
Epoch 57/1000
88/88 [==============================] - 0s - loss: 0.0447     
Epoch 58/1000
88/88 [==============================] - 0s - loss: 0.0443     
Epoch 59/1000
88/88 [==============================] - 0s - loss: 0.0443     
Epoch 60/1000
88/88 [==============================] - 0s - loss: 0.0441     
Epoch 61/1000
88/88 [==============================] - 0s - loss: 0.0438     
Epoch 62/1000
88/88 [==============================] - 0s - loss: 0.0436     
Epoch 63/1000
88/88 [==============================] - 0s - loss: 0.0434     
Epoch 64/1000
88/88 [==============================] - 0s - loss: 0.0432     
Epoch 65/1000
88/88 [==============================] - 0s - loss: 0.0429     
Epoch 66/1000
88/88 [==============================] - 0s - loss: 0.0427     
Epoch 67/1000
88/88 [==============================] - 0s - loss: 0.0426     
Epoch 68/1000
88/88 [==============================] - 0s - loss: 0.0423     
Epoch 69/1000
88/88 [==============================] - 0s - loss: 0.0421     
Epoch 70/1000
88/88 [==============================] - 0s - loss: 0.0419     
Epoch 71/1000
88/88 [==============================] - 0s - loss: 0.0417     
Epoch 72/1000
88/88 [==============================] - 0s - loss: 0.0414     
Epoch 73/1000
88/88 [==============================] - 0s - loss: 0.0413     
Epoch 74/1000
88/88 [==============================] - 0s - loss: 0.0411     
Epoch 75/1000
88/88 [==============================] - 0s - loss: 0.0408     
Epoch 76/1000
88/88 [==============================] - 0s - loss: 0.0406     
Epoch 77/1000
88/88 [==============================] - 0s - loss: 0.0404     
Epoch 78/1000
88/88 [==============================] - 0s - loss: 0.0403     
Epoch 79/1000
88/88 [==============================] - 0s - loss: 0.0400     
Epoch 80/1000
88/88 [==============================] - 0s - loss: 0.0399     
Epoch 81/1000
88/88 [==============================] - 0s - loss: 0.0396     
Epoch 82/1000
88/88 [==============================] - 0s - loss: 0.0393     
Epoch 83/1000
88/88 [==============================] - 0s - loss: 0.0392     
Epoch 84/1000
88/88 [==============================] - 0s - loss: 0.0391     
Epoch 85/1000
88/88 [==============================] - 0s - loss: 0.0389     
Epoch 86/1000
88/88 [==============================] - 0s - loss: 0.0387     
Epoch 87/1000
88/88 [==============================] - 0s - loss: 0.0384     
Epoch 88/1000
88/88 [==============================] - 0s - loss: 0.0383     
Epoch 89/1000
88/88 [==============================] - 0s - loss: 0.0380     
Epoch 90/1000
88/88 [==============================] - 0s - loss: 0.0382     
Epoch 91/1000
88/88 [==============================] - 0s - loss: 0.0376     
Epoch 92/1000
88/88 [==============================] - 0s - loss: 0.0376     
Epoch 93/1000
88/88 [==============================] - 0s - loss: 0.0375     
Epoch 94/1000
88/88 [==============================] - 0s - loss: 0.0372     
Epoch 95/1000
88/88 [==============================] - 0s - loss: 0.0374     
Epoch 96/1000
88/88 [==============================] - 0s - loss: 0.0370     
Epoch 97/1000
88/88 [==============================] - 0s - loss: 0.0370     
Epoch 98/1000
88/88 [==============================] - 0s - loss: 0.0367     
Epoch 99/1000
88/88 [==============================] - 0s - loss: 0.0367     
Epoch 100/1000
88/88 [==============================] - 0s - loss: 0.0364     
Epoch 101/1000
88/88 [==============================] - 0s - loss: 0.0363     
Epoch 102/1000
88/88 [==============================] - 0s - loss: 0.0361     
Epoch 103/1000
88/88 [==============================] - 0s - loss: 0.0360     
Epoch 104/1000
88/88 [==============================] - 0s - loss: 0.0358     
Epoch 105/1000
88/88 [==============================] - 0s - loss: 0.0357     
Epoch 106/1000
88/88 [==============================] - ETA: 0s - loss: 0.037 - 0s - loss: 0.0355     
Epoch 107/1000
88/88 [==============================] - 0s - loss: 0.0354     
Epoch 108/1000
88/88 [==============================] - 0s - loss: 0.0352     
Epoch 109/1000
88/88 [==============================] - 0s - loss: 0.0350     
Epoch 110/1000
88/88 [==============================] - 0s - loss: 0.0350     
Epoch 111/1000
88/88 [==============================] - 0s - loss: 0.0346     
Epoch 112/1000
88/88 [==============================] - 0s - loss: 0.0345     
Epoch 113/1000
88/88 [==============================] - 0s - loss: 0.0343     
Epoch 114/1000
88/88 [==============================] - ETA: 0s - loss: 0.038 - 0s - loss: 0.0341     
Epoch 115/1000
88/88 [==============================] - 0s - loss: 0.0342     
Epoch 116/1000
88/88 [==============================] - ETA: 0s - loss: 0.039 - 0s - loss: 0.0339     
Epoch 117/1000
88/88 [==============================] - 0s - loss: 0.0338     
Epoch 118/1000
88/88 [==============================] - 0s - loss: 0.0336     
Epoch 119/1000
88/88 [==============================] - 0s - loss: 0.0336     
Epoch 120/1000
88/88 [==============================] - 0s - loss: 0.0332     
Epoch 121/1000
88/88 [==============================] - 0s - loss: 0.0332     
Epoch 122/1000
88/88 [==============================] - 0s - loss: 0.0329     
Epoch 123/1000
88/88 [==============================] - ETA: 0s - loss: 0.033 - 0s - loss: 0.0328     
Epoch 124/1000
88/88 [==============================] - 0s - loss: 0.0326     
Epoch 125/1000
88/88 [==============================] - 0s - loss: 0.0325     
Epoch 126/1000
88/88 [==============================] - 0s - loss: 0.0323     
Epoch 127/1000
88/88 [==============================] - 0s - loss: 0.0322     
Epoch 128/1000
88/88 [==============================] - ETA: 0s - loss: 0.030 - 0s - loss: 0.0320     
Epoch 129/1000
88/88 [==============================] - 0s - loss: 0.0320     
Epoch 130/1000
88/88 [==============================] - 0s - loss: 0.0318     
Epoch 131/1000
88/88 [==============================] - 0s - loss: 0.0315     
Epoch 132/1000
88/88 [==============================] - 0s - loss: 0.0313     
Epoch 133/1000
88/88 [==============================] - 0s - loss: 0.0314     
Epoch 134/1000
88/88 [==============================] - 0s - loss: 0.0315     
Epoch 135/1000
88/88 [==============================] - 0s - loss: 0.0309     
Epoch 136/1000
88/88 [==============================] - 0s - loss: 0.0308     
Epoch 137/1000
88/88 [==============================] - 0s - loss: 0.0308     
Epoch 138/1000
88/88 [==============================] - 0s - loss: 0.0306     
Epoch 139/1000
88/88 [==============================] - 0s - loss: 0.0304     
Epoch 140/1000
88/88 [==============================] - 0s - loss: 0.0303     
Epoch 141/1000
88/88 [==============================] - 0s - loss: 0.0301     
Epoch 142/1000
88/88 [==============================] - 0s - loss: 0.0301     
Epoch 143/1000
88/88 [==============================] - 0s - loss: 0.0299     
Epoch 144/1000
88/88 [==============================] - 0s - loss: 0.0297     
Epoch 145/1000
88/88 [==============================] - 0s - loss: 0.0296     
Epoch 146/1000
88/88 [==============================] - 0s - loss: 0.0294     
Epoch 147/1000
88/88 [==============================] - 0s - loss: 0.0295     
Epoch 148/1000
88/88 [==============================] - 0s - loss: 0.0291     
Epoch 149/1000
88/88 [==============================] - 0s - loss: 0.0290     
Epoch 150/1000
88/88 [==============================] - 0s - loss: 0.0289     
Epoch 151/1000
88/88 [==============================] - 0s - loss: 0.0287     
Epoch 152/1000
88/88 [==============================] - 0s - loss: 0.0286     
Epoch 153/1000
88/88 [==============================] - 0s - loss: 0.0287     
Epoch 154/1000
88/88 [==============================] - 0s - loss: 0.0284     
Epoch 155/1000
88/88 [==============================] - 0s - loss: 0.0282     
Epoch 156/1000
88/88 [==============================] - 0s - loss: 0.0280     
Epoch 157/1000
88/88 [==============================] - 0s - loss: 0.0280     
Epoch 158/1000
88/88 [==============================] - 0s - loss: 0.0278     
Epoch 159/1000
88/88 [==============================] - 0s - loss: 0.0276     
Epoch 160/1000
88/88 [==============================] - 0s - loss: 0.0277     
Epoch 161/1000
88/88 [==============================] - 0s - loss: 0.0275     
Epoch 162/1000
88/88 [==============================] - 0s - loss: 0.0275     
Epoch 163/1000
88/88 [==============================] - 0s - loss: 0.0272     
Epoch 164/1000
88/88 [==============================] - 0s - loss: 0.0270     
Epoch 165/1000
88/88 [==============================] - 0s - loss: 0.0270     
Epoch 166/1000
88/88 [==============================] - 0s - loss: 0.0269     
Epoch 167/1000
88/88 [==============================] - 0s - loss: 0.0268     
Epoch 168/1000
88/88 [==============================] - 0s - loss: 0.0266     
Epoch 169/1000
88/88 [==============================] - 0s - loss: 0.0265     
Epoch 170/1000
88/88 [==============================] - 0s - loss: 0.0265     
Epoch 171/1000
88/88 [==============================] - 0s - loss: 0.0263     
Epoch 172/1000
88/88 [==============================] - 0s - loss: 0.0264     
Epoch 173/1000
88/88 [==============================] - 0s - loss: 0.0262     
Epoch 174/1000
88/88 [==============================] - 0s - loss: 0.0259     
Epoch 175/1000
88/88 [==============================] - 0s - loss: 0.0259     
Epoch 176/1000
88/88 [==============================] - 0s - loss: 0.0258     
Epoch 177/1000
88/88 [==============================] - 0s - loss: 0.0256     
Epoch 178/1000
88/88 [==============================] - 0s - loss: 0.0256     
Epoch 179/1000
88/88 [==============================] - 0s - loss: 0.0254     
Epoch 180/1000
88/88 [==============================] - 0s - loss: 0.0254     
Epoch 181/1000
88/88 [==============================] - 0s - loss: 0.0253     
Epoch 182/1000
88/88 [==============================] - 0s - loss: 0.0251     
Epoch 183/1000
88/88 [==============================] - 0s - loss: 0.0252     
Epoch 184/1000
88/88 [==============================] - 0s - loss: 0.0249     
Epoch 185/1000
88/88 [==============================] - 0s - loss: 0.0248     
Epoch 186/1000
88/88 [==============================] - 0s - loss: 0.0249     
Epoch 187/1000
88/88 [==============================] - 0s - loss: 0.0245     
Epoch 188/1000
88/88 [==============================] - 0s - loss: 0.0244     
Epoch 189/1000
88/88 [==============================] - 0s - loss: 0.0244     
Epoch 190/1000
88/88 [==============================] - 0s - loss: 0.0242     
Epoch 191/1000
88/88 [==============================] - 0s - loss: 0.0241     
Epoch 192/1000
88/88 [==============================] - 0s - loss: 0.0240     
Epoch 193/1000
88/88 [==============================] - ETA: 0s - loss: 0.016 - 0s - loss: 0.0239     
Epoch 194/1000
88/88 [==============================] - 0s - loss: 0.0241     
Epoch 195/1000
88/88 [==============================] - 0s - loss: 0.0238     
Epoch 196/1000
88/88 [==============================] - 0s - loss: 0.0237     
Epoch 197/1000
88/88 [==============================] - 0s - loss: 0.0235     
Epoch 198/1000
88/88 [==============================] - 0s - loss: 0.0234     
Epoch 199/1000
88/88 [==============================] - 0s - loss: 0.0233     
Epoch 200/1000
88/88 [==============================] - 0s - loss: 0.0231     
Epoch 201/1000
88/88 [==============================] - 0s - loss: 0.0231     
Epoch 202/1000
88/88 [==============================] - 0s - loss: 0.0229     
Epoch 203/1000
88/88 [==============================] - 0s - loss: 0.0229     
Epoch 204/1000
88/88 [==============================] - 0s - loss: 0.0228     
Epoch 205/1000
88/88 [==============================] - 0s - loss: 0.0228     
Epoch 206/1000
88/88 [==============================] - ETA: 0s - loss: 0.019 - 0s - loss: 0.0226     
Epoch 207/1000
88/88 [==============================] - 0s - loss: 0.0224     
Epoch 208/1000
88/88 [==============================] - 0s - loss: 0.0223     
Epoch 209/1000
88/88 [==============================] - 0s - loss: 0.0222     
Epoch 210/1000
88/88 [==============================] - 0s - loss: 0.0223     
Epoch 211/1000
88/88 [==============================] - 0s - loss: 0.0221     
Epoch 212/1000
88/88 [==============================] - 0s - loss: 0.0219     
Epoch 213/1000
88/88 [==============================] - 0s - loss: 0.0220     
Epoch 214/1000
88/88 [==============================] - ETA: 0s - loss: 0.024 - 0s - loss: 0.0217     
Epoch 215/1000
88/88 [==============================] - 0s - loss: 0.0219     
Epoch 216/1000
88/88 [==============================] - 0s - loss: 0.0216     
Epoch 217/1000
88/88 [==============================] - ETA: 0s - loss: 0.019 - 0s - loss: 0.0216     
Epoch 218/1000
88/88 [==============================] - 0s - loss: 0.0217     
Epoch 219/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0215     
Epoch 220/1000
88/88 [==============================] - ETA: 0s - loss: 0.022 - 0s - loss: 0.0213     
Epoch 221/1000
88/88 [==============================] - 0s - loss: 0.0213     
Epoch 222/1000
88/88 [==============================] - 0s - loss: 0.0211     
Epoch 223/1000
88/88 [==============================] - 0s - loss: 0.0211     
Epoch 224/1000
88/88 [==============================] - 0s - loss: 0.0211     
Epoch 225/1000
88/88 [==============================] - 0s - loss: 0.0209     
Epoch 226/1000
88/88 [==============================] - 0s - loss: 0.0212     
Epoch 227/1000
88/88 [==============================] - 0s - loss: 0.0208     
Epoch 228/1000
88/88 [==============================] - 0s - loss: 0.0207     
Epoch 229/1000
88/88 [==============================] - 0s - loss: 0.0207     
Epoch 230/1000
88/88 [==============================] - 0s - loss: 0.0206     
Epoch 231/1000
88/88 [==============================] - 0s - loss: 0.0205     
Epoch 232/1000
88/88 [==============================] - 0s - loss: 0.0205     
Epoch 233/1000
88/88 [==============================] - 0s - loss: 0.0203     
Epoch 234/1000
88/88 [==============================] - 0s - loss: 0.0206     
Epoch 235/1000
88/88 [==============================] - 0s - loss: 0.0202     
Epoch 236/1000
88/88 [==============================] - 0s - loss: 0.0201     
Epoch 237/1000
88/88 [==============================] - 0s - loss: 0.0201     
Epoch 238/1000
88/88 [==============================] - 0s - loss: 0.0200     
Epoch 239/1000
88/88 [==============================] - 0s - loss: 0.0199     
Epoch 240/1000
88/88 [==============================] - 0s - loss: 0.0202     
Epoch 241/1000
88/88 [==============================] - 0s - loss: 0.0198     
Epoch 242/1000
88/88 [==============================] - ETA: 0s - loss: 0.020 - 0s - loss: 0.0197     
Epoch 243/1000
88/88 [==============================] - 0s - loss: 0.0198     
Epoch 244/1000
88/88 [==============================] - 0s - loss: 0.0196     
Epoch 245/1000
88/88 [==============================] - 0s - loss: 0.0197     
Epoch 246/1000
88/88 [==============================] - 0s - loss: 0.0196     
Epoch 247/1000
88/88 [==============================] - 0s - loss: 0.0195     
Epoch 248/1000
88/88 [==============================] - 0s - loss: 0.0196     
Epoch 249/1000
88/88 [==============================] - 0s - loss: 0.0194     
Epoch 250/1000
88/88 [==============================] - 0s - loss: 0.0196     
Epoch 251/1000
88/88 [==============================] - 0s - loss: 0.0192     
Epoch 252/1000
88/88 [==============================] - 0s - loss: 0.0192     
Epoch 253/1000
88/88 [==============================] - 0s - loss: 0.0192     
Epoch 254/1000
88/88 [==============================] - 0s - loss: 0.0191     
Epoch 255/1000
88/88 [==============================] - 0s - loss: 0.0193     
Epoch 256/1000
88/88 [==============================] - ETA: 0s - loss: 0.022 - 0s - loss: 0.0190     
Epoch 257/1000
88/88 [==============================] - 0s - loss: 0.0190     
Epoch 258/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0190     
Epoch 259/1000
88/88 [==============================] - 0s - loss: 0.0189     
Epoch 260/1000
88/88 [==============================] - ETA: 0s - loss: 0.023 - 0s - loss: 0.0188     
Epoch 261/1000
88/88 [==============================] - 0s - loss: 0.0191     
Epoch 262/1000
88/88 [==============================] - 0s - loss: 0.0190     
Epoch 263/1000
88/88 [==============================] - ETA: 0s - loss: 0.022 - 0s - loss: 0.0187     
Epoch 264/1000
88/88 [==============================] - 0s - loss: 0.0187     
Epoch 265/1000
88/88 [==============================] - 0s - loss: 0.0186     
Epoch 266/1000
88/88 [==============================] - ETA: 0s - loss: 0.016 - 0s - loss: 0.0188     
Epoch 267/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0186     
Epoch 268/1000
88/88 [==============================] - 0s - loss: 0.0188     
Epoch 269/1000
88/88 [==============================] - 0s - loss: 0.0186     
Epoch 270/1000
88/88 [==============================] - 0s - loss: 0.0184     
Epoch 271/1000
88/88 [==============================] - 0s - loss: 0.0187     
Epoch 272/1000
88/88 [==============================] - 0s - loss: 0.0184     
Epoch 273/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0184     
Epoch 274/1000
88/88 [==============================] - 0s - loss: 0.0183     
Epoch 275/1000
88/88 [==============================] - 0s - loss: 0.0183     
Epoch 276/1000
88/88 [==============================] - ETA: 0s - loss: 0.020 - 0s - loss: 0.0183     
Epoch 277/1000
88/88 [==============================] - 0s - loss: 0.0186     
Epoch 278/1000
88/88 [==============================] - ETA: 0s - loss: 0.021 - 0s - loss: 0.0182     
Epoch 279/1000
88/88 [==============================] - 0s - loss: 0.0183     
Epoch 280/1000
88/88 [==============================] - 0s - loss: 0.0181     
Epoch 281/1000
88/88 [==============================] - ETA: 0s - loss: 0.021 - 0s - loss: 0.0181     
Epoch 282/1000
88/88 [==============================] - 0s - loss: 0.0183     
Epoch 283/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0183     
Epoch 284/1000
88/88 [==============================] - 0s - loss: 0.0181     
Epoch 285/1000
88/88 [==============================] - 0s - loss: 0.0180     
Epoch 286/1000
88/88 [==============================] - 0s - loss: 0.0182     
Epoch 287/1000
88/88 [==============================] - 0s - loss: 0.0180     
Epoch 288/1000
88/88 [==============================] - 0s - loss: 0.0180     
Epoch 289/1000
88/88 [==============================] - 0s - loss: 0.0180     
Epoch 290/1000
88/88 [==============================] - 0s - loss: 0.0180     
Epoch 291/1000
88/88 [==============================] - 0s - loss: 0.0180     
Epoch 292/1000
88/88 [==============================] - 0s - loss: 0.0179     
Epoch 293/1000
88/88 [==============================] - 0s - loss: 0.0179     
Epoch 294/1000
88/88 [==============================] - 0s - loss: 0.0179     
Epoch 295/1000
88/88 [==============================] - 0s - loss: 0.0179     
Epoch 296/1000
88/88 [==============================] - 0s - loss: 0.0180     
Epoch 297/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0179     
Epoch 298/1000
88/88 [==============================] - 0s - loss: 0.0179     
Epoch 299/1000
88/88 [==============================] - 0s - loss: 0.0177     
Epoch 300/1000
88/88 [==============================] - 0s - loss: 0.0177     
Epoch 301/1000
88/88 [==============================] - 0s - loss: 0.0178     
Epoch 302/1000
88/88 [==============================] - 0s - loss: 0.0177     
Epoch 303/1000
88/88 [==============================] - 0s - loss: 0.0179     
Epoch 304/1000
88/88 [==============================] - 0s - loss: 0.0176     
Epoch 305/1000
88/88 [==============================] - 0s - loss: 0.0177     
Epoch 306/1000
88/88 [==============================] - ETA: 0s - loss: 0.015 - 0s - loss: 0.0178     
Epoch 307/1000
88/88 [==============================] - 0s - loss: 0.0178     
Epoch 308/1000
88/88 [==============================] - 0s - loss: 0.0176     
Epoch 309/1000
88/88 [==============================] - 0s - loss: 0.0176     
Epoch 310/1000
88/88 [==============================] - 0s - loss: 0.0178     
Epoch 311/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 312/1000
88/88 [==============================] - 0s - loss: 0.0176     
Epoch 313/1000
88/88 [==============================] - ETA: 0s - loss: 0.021 - 0s - loss: 0.0176     
Epoch 314/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 315/1000
88/88 [==============================] - ETA: 0s - loss: 0.013 - 0s - loss: 0.0176     
Epoch 316/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 317/1000
88/88 [==============================] - ETA: 0s - loss: 0.015 - 0s - loss: 0.0178     
Epoch 318/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 319/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 320/1000
88/88 [==============================] - 0s - loss: 0.0177     
Epoch 321/1000
88/88 [==============================] - 0s - loss: 0.0177     
Epoch 322/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 323/1000
88/88 [==============================] - ETA: 0s - loss: 0.021 - 0s - loss: 0.0175     
Epoch 324/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0174     
Epoch 325/1000
88/88 [==============================] - 0s - loss: 0.0179     
Epoch 326/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 327/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 328/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0175     
Epoch 329/1000
88/88 [==============================] - ETA: 0s - loss: 0.012 - 0s - loss: 0.0175     
Epoch 330/1000
88/88 [==============================] - 0s - loss: 0.0176     
Epoch 331/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 332/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 333/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 334/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 335/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 336/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 337/1000
88/88 [==============================] - 0s - loss: 0.0176     
Epoch 338/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 339/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 340/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 341/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 342/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 343/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 344/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 345/1000
88/88 [==============================] - 0s - loss: 0.0176     
Epoch 346/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 347/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 348/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 349/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 350/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 351/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 352/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 353/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 354/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 355/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 356/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 357/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 358/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 359/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 360/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 361/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 362/1000
88/88 [==============================] - 0s - loss: 0.0174     
Epoch 363/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 364/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 365/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 366/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 367/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 368/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 369/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 370/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 371/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 372/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 373/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 374/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 375/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 376/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 377/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 378/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 379/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 380/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 381/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 382/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 383/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 384/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 385/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 386/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 387/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 388/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 389/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 390/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 391/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 392/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 393/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 394/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 395/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 396/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 397/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 398/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 399/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 400/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 401/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 402/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 403/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 404/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 405/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 406/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 407/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 408/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 409/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 410/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 411/1000
88/88 [==============================] - 0s - loss: 0.0175     
Epoch 412/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 413/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 414/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 415/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 416/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 417/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 418/1000
88/88 [==============================] - 0s - loss: 0.0173     
Epoch 419/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 420/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 421/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 422/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 423/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 424/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 425/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 426/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 427/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 428/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 429/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 430/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 431/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 432/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 433/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 434/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 435/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 436/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 437/1000
88/88 [==============================] - 0s - loss: 0.0176     
Epoch 438/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 439/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 440/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 441/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 442/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 443/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 444/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 445/1000
88/88 [==============================] - ETA: 0s - loss: 0.020 - 0s - loss: 0.0170     
Epoch 446/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 447/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 448/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 449/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 450/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 451/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 452/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 453/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 454/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 455/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 456/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 457/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 458/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 459/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 460/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0169     
Epoch 461/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 462/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 463/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 464/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 465/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 466/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 467/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 468/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 469/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 470/1000
88/88 [==============================] - ETA: 0s - loss: 0.012 - 0s - loss: 0.0169     
Epoch 471/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 472/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 473/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 474/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 475/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 476/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 477/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 478/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 479/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 480/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 481/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 482/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 483/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 484/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 485/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 486/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 487/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0168     
Epoch 488/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 489/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 490/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 491/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 492/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 493/1000
88/88 [==============================] - 0s - loss: 0.0172     
Epoch 494/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 495/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 496/1000
88/88 [==============================] - ETA: 0s - loss: 0.015 - 0s - loss: 0.0167     
Epoch 497/1000
88/88 [==============================] - ETA: 0s - loss: 0.012 - 0s - loss: 0.0169     
Epoch 498/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 499/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 500/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 501/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0168     
Epoch 502/1000
88/88 [==============================] - ETA: 0s - loss: 0.015 - 0s - loss: 0.0169     
Epoch 503/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 504/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 505/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 506/1000
88/88 [==============================] - ETA: 0s - loss: 0.020 - 0s - loss: 0.0168     
Epoch 507/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 508/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 509/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 510/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 511/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 512/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 513/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 514/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0168     
Epoch 515/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 516/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 517/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 518/1000
88/88 [==============================] - ETA: 0s - loss: 0.016 - 0s - loss: 0.0167     
Epoch 519/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 520/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 521/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 522/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 523/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 524/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 525/1000
88/88 [==============================] - ETA: 0s - loss: 0.015 - 0s - loss: 0.0167     
Epoch 526/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 527/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 528/1000
88/88 [==============================] - ETA: 0s - loss: 0.016 - 0s - loss: 0.0167     
Epoch 529/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 530/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 531/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 532/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 533/1000
88/88 [==============================] - 0s - loss: 0.0170     
Epoch 534/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 535/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0168     
Epoch 536/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 537/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 538/1000
88/88 [==============================] - 0s - loss: 0.0171     
Epoch 539/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0167     
Epoch 540/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 541/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 542/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 543/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 544/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 545/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 546/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 547/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 548/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 549/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0167     
Epoch 550/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 551/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 552/1000
88/88 [==============================] - ETA: 0s - loss: 0.016 - 0s - loss: 0.0167     
Epoch 553/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 554/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 555/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 556/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 557/1000
88/88 [==============================] - ETA: 0s - loss: 0.021 - 0s - loss: 0.0167     
Epoch 558/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 559/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 560/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 561/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 562/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 563/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0166     
Epoch 564/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 565/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 566/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 567/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 568/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 569/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 570/1000
88/88 [==============================] - ETA: 0s - loss: 0.013 - 0s - loss: 0.0165     
Epoch 571/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 572/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 573/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 574/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 575/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 576/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 577/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0166     
Epoch 578/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 579/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 580/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 581/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 582/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 583/1000
88/88 [==============================] - ETA: 0s - loss: 0.019 - 0s - loss: 0.0166     
Epoch 584/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0165     
Epoch 585/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0166     
Epoch 586/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 587/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 588/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 589/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 590/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 591/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 592/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 593/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 594/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 595/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 596/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 597/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0165     
Epoch 598/1000
88/88 [==============================] - ETA: 0s - loss: 0.012 - 0s - loss: 0.0165     
Epoch 599/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 600/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0166     
Epoch 601/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 602/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 603/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 604/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 605/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 606/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 607/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 608/1000
88/88 [==============================] - ETA: 0s - loss: 0.013 - 0s - loss: 0.0165     
Epoch 609/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 610/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 611/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 612/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 613/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 614/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 615/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 616/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0165     
Epoch 617/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 618/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 619/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 620/1000
88/88 [==============================] - ETA: 0s - loss: 0.019 - 0s - loss: 0.0165     
Epoch 621/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0165     
Epoch 622/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 623/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 624/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 625/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 626/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0165     
Epoch 627/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 628/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 629/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 630/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 631/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 632/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 633/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 634/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 635/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 636/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 637/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 638/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 639/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 640/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 641/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 642/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 643/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 644/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 645/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 646/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 647/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 648/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 649/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 650/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 651/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 652/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 653/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 654/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 655/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 656/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 657/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 658/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 659/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 660/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 661/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 662/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 663/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 664/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 665/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 666/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 667/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 668/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 669/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 670/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 671/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 672/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 673/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 674/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 675/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 676/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 677/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 678/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 679/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 680/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 681/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 682/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 683/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 684/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 685/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 686/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 687/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 688/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 689/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 690/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 691/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 692/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 693/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 694/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 695/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 696/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 697/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 698/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 699/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 700/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 701/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 702/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 703/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 704/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 705/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 706/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 707/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 708/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 709/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 710/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 711/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 712/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 713/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 714/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 715/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 716/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 717/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 718/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 719/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 720/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 721/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 722/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 723/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 724/1000
88/88 [==============================] - 0s - loss: 0.0169     
Epoch 725/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 726/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 727/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 728/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 729/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 730/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 731/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 732/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 733/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 734/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 735/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 736/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 737/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 738/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0165     
Epoch 739/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 740/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0165     
Epoch 741/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 742/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 743/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 744/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 745/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 746/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 747/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0163     
Epoch 748/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 749/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 750/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 751/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 752/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 753/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 754/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 755/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 756/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 757/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 758/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 759/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 760/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 761/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 762/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 763/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 764/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 765/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 766/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 767/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 768/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 769/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 770/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 771/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 772/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 773/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 774/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 775/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 776/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 777/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 778/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 779/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 780/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 781/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 782/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 783/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 784/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 785/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 786/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 787/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 788/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 789/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 790/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 791/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 792/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 793/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 794/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 795/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 796/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 797/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 798/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 799/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 800/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 801/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 802/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 803/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 804/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 805/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 806/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 807/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 808/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 809/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 810/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 811/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 812/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 813/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 814/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 815/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 816/1000
88/88 [==============================] - 0s - loss: 0.0167     
Epoch 817/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 818/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 819/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 820/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 821/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 822/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 823/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 824/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 825/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 826/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 827/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 828/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 829/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 830/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 831/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 832/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 833/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 834/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 835/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 836/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 837/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 838/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 839/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 840/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 841/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 842/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 843/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 844/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 845/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 846/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 847/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 848/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 849/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 850/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 851/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 852/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 853/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 854/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 855/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 856/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0163     
Epoch 857/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 858/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 859/1000
88/88 [==============================] - ETA: 0s - loss: 0.011 - 0s - loss: 0.0164     
Epoch 860/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 861/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 862/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 863/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 864/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 865/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 866/1000
88/88 [==============================] - ETA: 0s - loss: 0.013 - 0s - loss: 0.0165     
Epoch 867/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 868/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 869/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 870/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 871/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 872/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 873/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 874/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 875/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0163     
Epoch 876/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 877/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 878/1000
88/88 [==============================] - 0s - loss: 0.0168     
Epoch 879/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 880/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 881/1000
88/88 [==============================] - ETA: 0s - loss: 0.016 - 0s - loss: 0.0162     
Epoch 882/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 883/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 884/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0161     
Epoch 885/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 886/1000
88/88 [==============================] - ETA: 0s - loss: 0.017 - 0s - loss: 0.0162     
Epoch 887/1000
88/88 [==============================] - ETA: 0s - loss: 0.016 - 0s - loss: 0.0161     
Epoch 888/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 889/1000
88/88 [==============================] - ETA: 0s - loss: 0.020 - 0s - loss: 0.0164     
Epoch 890/1000
88/88 [==============================] - ETA: 0s - loss: 0.018 - 0s - loss: 0.0163     
Epoch 891/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 892/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 893/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 894/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 895/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 896/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 897/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 898/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 899/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 900/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 901/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 902/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 903/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 904/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 905/1000
88/88 [==============================] - ETA: 0s - loss: 0.014 - 0s - loss: 0.0161     
Epoch 906/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 907/1000
88/88 [==============================] - ETA: 0s - loss: 0.015 - 0s - loss: 0.0162     
Epoch 908/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 909/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 910/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 911/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 912/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 913/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 914/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 915/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 916/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 917/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 918/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 919/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 920/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 921/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 922/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 923/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 924/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 925/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 926/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 927/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 928/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 929/1000
88/88 [==============================] - 0s - loss: 0.0165     
Epoch 930/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 931/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 932/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 933/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 934/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 935/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 936/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 937/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 938/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 939/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 940/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 941/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 942/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 943/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 944/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 945/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 946/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 947/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 948/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 949/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 950/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 951/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 952/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 953/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 954/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 955/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 956/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 957/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 958/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 959/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 960/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 961/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 962/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 963/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 964/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 965/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 966/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 967/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 968/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 969/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 970/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 971/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 972/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 973/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 974/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 975/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 976/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 977/1000
88/88 [==============================] - 0s - loss: 0.0163     
Epoch 978/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 979/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 980/1000
88/88 [==============================] - 0s - loss: 0.0166     
Epoch 981/1000
88/88 [==============================] - 0s - loss: 0.0164     
Epoch 982/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 983/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 984/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 985/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 986/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 987/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 988/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 989/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 990/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 991/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 992/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 993/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 994/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 995/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 996/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 997/1000
88/88 [==============================] - 0s - loss: 0.0160     
Epoch 998/1000
88/88 [==============================] - 0s - loss: 0.0161     
Epoch 999/1000
88/88 [==============================] - 0s - loss: 0.0162     
Epoch 1000/1000
88/88 [==============================] - 0s - loss: 0.0162     
</pre>
</div>
</div>

<div class="output_area">
<div class="prompt output_prompt">Out[9]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;keras.callbacks.History at 0x7f58ed91ad30&gt;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="1.5--Checking-model-performance">1.5  Checking model performance<a class="anchor-link" href="#1.5--Checking-model-performance">&#182;</a></h2><p>With your model fit we can now make predictions on both our training and testing sets.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># generate predictions for training</span>
<span class="n">train_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">test_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>In the next cell we compute training and testing errors using our trained model - you should be able to achieve at least</p>
<p><em>training_error</em> &lt; 0.02</p>
<p>and</p>
<p><em>testing_error</em> &lt; 0.02</p>
<p>with your fully trained model.</p>
<p>If either or both of your accuracies are larger than 0.02 re-train your model - increasing the number of epochs you take (a maximum of around 1,000 should do the job) and/or adjusting your batch_size.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># print out training and testing errors</span>
<span class="n">training_error</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;training error = &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">training_error</span><span class="p">))</span>

<span class="n">testing_error</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;testing error = &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">testing_error</span><span class="p">))</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>training error = 0.0160044291141
testing error = 0.013983733064
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Activating the next cell plots the original data, as well as both predictions on the training and testing sets.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Plot everything - the original series as well as predictions on training and testing sets</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="c1"># plot original series</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span><span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>

<span class="c1"># plot training set prediction</span>
<span class="n">split_pt</span> <span class="o">=</span> <span class="n">train_test_split</span> <span class="o">+</span> <span class="n">window_size</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">window_size</span><span class="p">,</span><span class="n">split_pt</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span><span class="n">train_predict</span><span class="p">,</span><span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;b&#39;</span><span class="p">)</span>

<span class="c1"># plot testing set prediction</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">split_pt</span><span class="p">,</span><span class="n">split_pt</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_predict</span><span class="p">),</span><span class="mi">1</span><span class="p">),</span><span class="n">test_predict</span><span class="p">,</span><span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>

<span class="c1"># pretty up graph</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;day&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;(normalized) price of Apple stock&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;original series&#39;</span><span class="p">,</span><span class="s1">&#39;training fit&#39;</span><span class="p">,</span><span class="s1">&#39;testing fit&#39;</span><span class="p">],</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;center left&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAgAAAAEKCAYAAABkC+0BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Note:</strong> you can try out any time series for this exercise!  If you would like to try another see e.g., <a href="https://datamarket.com/data/list/?q=provider%3Atsdl">this site containing thousands of time series</a> and pick another one!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Problem-2:-Create-a-sequence-generator">Problem 2: Create a sequence generator<a class="anchor-link" href="#Problem-2:-Create-a-sequence-generator">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="2.1--Getting-started">2.1  Getting started<a class="anchor-link" href="#2.1--Getting-started">&#182;</a></h2><p>In this project you will implement a popular Recurrent Neural Network (RNN) architecture to create an English language sequence generator capable of building semi-coherent English sentences from scratch by building them up character-by-character.  This will require a substantial amount amount of parameter tuning on a large training corpus (at least 100,000 characters long).  In particular for this project we will be using a complete version of Sir Arthur Conan Doyle's classic book The Adventures of Sherlock Holmes.</p>
<p>How can we train a machine learning model to generate text automatically, character-by-character?  <em>By showing the model many training examples so it can learn a pattern between input and output.</em>  With this type of text generation each input is a string of valid characters like this one</p>
<p><em>dogs are grea</em></p>
<p>while the corresponding output is the next character in the sentence - which here is 't' (since the complete sentence is 'dogs are great').  We need to show a model many such examples in order for it to make reasonable predictions.</p>
<p><strong>Fun note:</strong> For those interested in how text generation is being used check out some of the following fun resources:</p>
<ul>
<li><p><a href="http://www.cs.toronto.edu/~ilya/rnn.html">Generate wacky sentences</a> with this academic RNN text generator</p>
</li>
<li><p>Various twitter bots that tweet automatically generated text like<a href="http://tweet-generator-alex.herokuapp.com/">this one</a>.</p>
</li>
<li><p>the <a href="https://github.com/NaNoGenMo/2016">NanoGenMo</a> annual contest to automatically produce a 50,000+ novel automatically</p>
</li>
<li><p><a href="https://github.com/genekogan/RobotShakespeare">Robot Shakespeare</a> a text generator that automatically produces Shakespear-esk sentences</p>
</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="2.2--Preprocessing-a-text-dataset">2.2  Preprocessing a text dataset<a class="anchor-link" href="#2.2--Preprocessing-a-text-dataset">&#182;</a></h2><p>Our first task is to get a large text corpus for use in training, and on it we perform a several light pre-processing tasks.  The default corpus we will use is the classic book Sherlock Holmes, but you can use a variety of others as well - so long as they are fairly large (around 100,000 characters or more).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># read in the text, transforming everything to lower case</span>
<span class="n">text</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;datasets/holmes.txt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;our original text has &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">text</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39; characters&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>our original text has 581864 characters
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Next, lets examine a bit of the raw text.  Because we are interested in creating sentences of English words automatically by building up each word character-by-character, we only want to train on valid English words.  In other words - we need to remove all of the other characters that are not part of English words.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[25]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### print out the first 1000 characters of the raw text to get a sense of what we need to throw out</span>
<span class="n">text</span><span class="p">[:</span><span class="mi">2000</span><span class="p">]</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt output_prompt">Out[25]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&#34;\ufeffproject gutenberg&#39;s the adventures of sherlock holmes, by arthur conan doyle\n\nthis ebook is for the use of anyone anywhere at no cost and with\nalmost no restrictions whatsoever.  you may copy it, give it away or\nre-use it under the terms of the project gutenberg license included\nwith this ebook or online at www.gutenberg.net\n\n\ntitle: the adventures of sherlock holmes\n\nauthor: arthur conan doyle\n\nposting date: april 18, 2011 [ebook #1661]\nfirst posted: november 29, 2002\n\nlanguage: english\n\n\n*** start of this project gutenberg ebook the adventures of sherlock holmes ***\n\n\n\n\nproduced by an anonymous project gutenberg volunteer and jose menendez\n\n\n\n\n\n\n\n\n\nthe adventures of sherlock holmes\n\nby\n\nsir arthur conan doyle\n\n\n\n   i. a scandal in bohemia\n  ii. the red-headed league\n iii. a case of identity\n  iv. the boscombe valley mystery\n   v. the five orange pips\n  vi. the man with the twisted lip\n vii. the adventure of the blue carbuncle\nviii. the adventure of the speckled band\n  ix. the adventure of the engineer&#39;s thumb\n   x. the adventure of the noble bachelor\n  xi. the adventure of the beryl coronet\n xii. the adventure of the copper beeches\n\n\n\n\nadventure i. a scandal in bohemia\n\ni.\n\nto sherlock holmes she is always the woman. i have seldom heard\nhim mention her under any other name. in his eyes she eclipses\nand predominates the whole of her sex. it was not that he felt\nany emotion akin to love for irene adler. all emotions, and that\none particularly, were abhorrent to his cold, precise but\nadmirably balanced mind. he was, i take it, the most perfect\nreasoning and observing machine that the world has seen, but as a\nlover he would have placed himself in a false position. he never\nspoke of the softer passions, save with a gibe and a sneer. they\nwere admirable things for the observer--excellent for drawing the\nveil from men&#39;s motives and actions. but for the trained reasoner\nto admit such intrusions into his own delicate and finely\nadjusted temperament was to introduce a dist&#34;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Wow - there's a lot of junk here (i.e., weird uncommon character combinations - as this first character chunk contains the title and author page, as well as table of contents)!  To keep things simple, we want to train our RNN on a large chunk of more typical English sentences - we don't want it to start thinking non-english words or strange characters are valid! - so lets clean up the data a bit.</p>
<p>First, since the dataset is so large and the first few hundred characters contain a lot of junk, lets cut it out.  Lets also find-and-replace those newline tags with empty spaces.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[26]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### find and replace &#39;\n&#39; and &#39;\r&#39; symbols - replacing them </span>
<span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="p">[</span><span class="mi">1302</span><span class="p">:]</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)</span>    <span class="c1"># replacing &#39;\n&#39; with &#39;&#39; simply removes the sequence</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\r</span><span class="s1">&#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Lets see how the first 1000 characters of our text looks now!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[27]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### print out the first 1000 characters of the raw text to get a sense of what we need to throw out</span>
<span class="n">text</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt output_prompt">Out[27]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&#34;is eyes she eclipses and predominates the whole of her sex. it was not that he felt any emotion akin to love for irene adler. all emotions, and that one particularly, were abhorrent to his cold, precise but admirably balanced mind. he was, i take it, the most perfect reasoning and observing machine that the world has seen, but as a lover he would have placed himself in a false position. he never spoke of the softer passions, save with a gibe and a sneer. they were admirable things for the observer--excellent for drawing the veil from men&#39;s motives and actions. but for the trained reasoner to admit such intrusions into his own delicate and finely adjusted temperament was to introduce a distracting factor which might throw a doubt upon all his mental results. grit in a sensitive instrument, or a crack in one of his own high-power lenses, would not be more disturbing than a strong emotion in a nature such as his. and yet there was but one woman to him, and that woman was the late irene ad&#34;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><a id='TODO_3'></a></p>
<h4 id="TODO:-finish-cleaning-the-text">TODO: finish cleaning the text<a class="anchor-link" href="#TODO:-finish-cleaning-the-text">&#182;</a></h4><p>Lets make sure we haven't left any other atypical characters (commas, periods, etc., are ok) lurking around in the depths of the text.  You can do this by enumerating all the text's unique characters, examining them, and then replacing any unwanted characters with empty spaces!  Once we find all of the text's unique characters, we can remove all of the atypical ones in the next cell.  Note: don't remove the punctuation marks given in my_answers.py.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[28]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: implement cleaned_text in my_answers.py</span>
<span class="k">def</span> <span class="nf">cleaned_text</span><span class="p">(</span><span class="n">text</span><span class="p">):</span>
    <span class="n">punctuation</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;!&#39;</span><span class="p">,</span> <span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="s1">&#39;;&#39;</span><span class="p">,</span> <span class="s1">&#39;?&#39;</span><span class="p">]</span>
    <span class="n">text</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">text</span><span class="p">]</span>
    <span class="n">text</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
    <span class="n">counts</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
    
    <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="k">if</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">punctuation</span><span class="p">:</span>
            <span class="k">pass</span>
        <span class="k">elif</span> <span class="nb">ord</span><span class="p">(</span><span class="n">l</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">97</span> <span class="ow">and</span> <span class="nb">ord</span><span class="p">(</span><span class="n">l</span><span class="p">)</span> <span class="o">&lt;=</span><span class="mi">122</span><span class="p">:</span>
            <span class="k">pass</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">l</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)</span>           

    <span class="k">return</span> <span class="n">text</span>


<span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="n">cleaned_text</span>

<span class="n">text</span> <span class="o">=</span> <span class="n">cleaned_text</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>

<span class="c1"># shorten any extra dead space created above</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;  &#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>With your chosen characters removed print out the first few hundred lines again just to double check that everything looks good.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[29]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### print out the first 2000 characters of the raw text to get a sense of what we need to throw out</span>
<span class="n">text</span><span class="p">[:</span><span class="mi">2000</span><span class="p">]</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt output_prompt">Out[29]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&#39;is eyes she eclipses and predominates the whole of her sex. it was not that he felt any emotion akin to love for irene adler. all emotions, and that one particularly, were abhorrent to his cold, precise but admirably balanced mind. he was, i take it, the most perfect reasoning and observing machine that the world has seen, but as a lover he would have placed himself in a false position. he never spoke of the softer passions, save with a gibe and a sneer. they were admirable things for the observer excellent for drawing the veil from men s motives and actions. but for the trained reasoner to admit such intrusions into his own delicate and finely adjusted temperament was to introduce a distracting factor which might throw a doubt upon all his mental results. grit in a sensitive instrument, or a crack in one of his own high power lenses, would not be more disturbing than a strong emotion in a nature such as his. and yet there was but one woman to him, and that woman was the late irene adler, of dubious and questionable memory. i had seen little of holmes lately. my marriage had drifted us away from each other. my own complete happiness, and the home centred interests which rise up around the man who first finds himself master of his own establishment, were sufficient to absorb all my attention, while holmes, who loathed every form of society with his whole bohemian soul, remained in our lodgings in baker street, buried among his old books, and alternating from week to week between cocaine and ambition, the drowsiness of the drug, and the fierce energy of his own keen nature. he was still, as ever, deeply attracted by the study of crime, and occupied his immense faculties and extraordinary powers of observation in following out those clues, and clearing up those mysteries which had been abandoned as hopeless by the official police. from time to time i heard some vague account of his doings: of his summons to odessa in the case of the trepoff murder, of his clearing up o&#39;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Now that we have thrown out a good number of non-English characters/character sequences lets print out some statistics about the dataset - including number of total characters and number of unique characters.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[30]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># count the number of unique characters in the text</span>
<span class="n">chars</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">text</span><span class="p">)))</span>

<span class="c1"># print some of the text, as well as statistics</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;this corpus has &quot;</span> <span class="o">+</span>  <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">text</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot; total number of characters&quot;</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;this corpus has &quot;</span> <span class="o">+</span>  <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">chars</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot; unique characters&quot;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>this corpus has 573681 total number of characters
this corpus has 33 unique characters
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="2.3--Cutting-data-into-input/output-pairs">2.3  Cutting data into input/output pairs<a class="anchor-link" href="#2.3--Cutting-data-into-input/output-pairs">&#182;</a></h2><p>Now that we have our text all cleaned up, how can we use it to train a model to generate sentences automatically?  First we need to train a machine learning model - and in order to do that we need a set of input/output pairs for a model to train on.  How can we create a set of input/output pairs from our text to train on?</p>
<p>Remember in part 1 of this notebook how we used a sliding window to extract input/output pairs from a time series?  We do the same thing here!  We slide a window of length $T$ along our giant text corpus - everything in the window becomes one input while the character following becomes its corresponding output.  This process of extracting input/output pairs is illustrated in the gif below on a small example text using a window size of T = 5.</p>
<p><img src="images/text_windowing_training.gif" width=400 height=400/></p>
<p>Notice one aspect of the sliding window in this gif that does not mirror the analogous gif for time series shown in part 1 of the notebook - we do not need to slide the window along one character at a time but can move by a fixed step size $M$ greater than 1 (in the gif indeed $M = 1$).  This is done with large input texts (like ours which has over 500,000 characters!) when sliding the window along one character at a time we would create far too many input/output pairs to be able to reasonably compute with.</p>
<p>More formally lets denote our text corpus - which is one long string of characters - as follows</p>
$$s_{0},s_{1},s_{2},...,s_{P}$$<p>where $P$ is the length of the text (again for our text $P \approx 500,000!$).  Sliding a window of size T = 5 with a step length of M = 1 (these are the parameters shown in the gif above) over this sequence produces the following list of input/output pairs</p>
$$\begin{array}{c|c}
\text{Input} & \text{Output}\\
\hline \color{CornflowerBlue} {\langle s_{1},s_{2},s_{3},s_{4},s_{5}\rangle} & \color{Goldenrod}{ s_{6}} \\
\ \color{CornflowerBlue} {\langle s_{2},s_{3},s_{4},s_{5},s_{6} \rangle } & \color{Goldenrod} {s_{7} } \\
\color{CornflowerBlue}  {\vdots} & \color{Goldenrod} {\vdots}\\
\color{CornflowerBlue} { \langle s_{P-5},s_{P-4},s_{P-3},s_{P-2},s_{P-1} \rangle } & \color{Goldenrod} {s_{P}}
\end{array}$$<p>Notice here that each input is a sequence (or vector) of 5 characters (and in general has length equal to the window size T) while each corresponding output is a single character.  We created around P total number of input/output pairs  (for general step size M we create around ceil(P/M) pairs).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><a id='TODO_4'></a></p>
<p>Now its time for you to window the input time series as described above!</p>
<p><strong>TODO:</strong> Create a function that runs a sliding window along the input text and creates associated input/output pairs.  A skeleton function has been provided for you.  Note that this function should input a) the text  b) the window size and c) the step size, and return the input/output sequences.  Note: the return items should be <em>lists</em> - not numpy arrays.</p>
<p>(remember to copy your completed function into the script <em>my_answers.py</em> function titled <em>window_transform_text</em> before submitting your project)</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[31]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: implement window_transform_series in my_answers.py</span>
<span class="k">def</span> <span class="nf">window_transform_text</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">window_size</span><span class="p">,</span> <span class="n">step_size</span><span class="p">):</span>
    <span class="c1"># containers for input/output pairs</span>
    <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">outputs</span> <span class="o">=</span> <span class="p">[]</span>
    
    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">text</span><span class="p">)</span><span class="o">-</span><span class="n">window_size</span><span class="p">,</span> <span class="n">step_size</span><span class="p">):</span>
        <span class="c1">#print(idx)</span>
        <span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">text</span><span class="p">[</span><span class="n">idx</span><span class="p">:</span><span class="n">idx</span><span class="o">+</span><span class="n">window_size</span><span class="p">])</span>
        <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">text</span><span class="p">[</span><span class="n">idx</span><span class="o">+</span><span class="n">window_size</span><span class="p">])</span>

    <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span><span class="n">outputs</span>

<span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="n">window_transform_text</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>With our function complete we can now use it to produce input/output pairs!  We employ the function in the next cell, where the window_size = 50 and step_size = 5.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[32]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># run your text window-ing function </span>
<span class="n">window_size</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">step_size</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">window_transform_text</span><span class="p">(</span><span class="n">text</span><span class="p">,</span><span class="n">window_size</span><span class="p">,</span><span class="n">step_size</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Lets print out a few input/output pairs to verify that we have made the right sort of stuff!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[33]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># print out a few of the input/output pairs to verify that we&#39;ve made the right kind of stuff to learn from</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;input = &#39;</span> <span class="o">+</span> <span class="n">inputs</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;output = &#39;</span> <span class="o">+</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;--------------&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;input = &#39;</span> <span class="o">+</span> <span class="n">inputs</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;output = &#39;</span> <span class="o">+</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>input = e eclipses and predominates the whole of her sex. it was not that he felt any emotion akin to love f
output = o
--------------
input = er excellent for drawing the veil from men s motives and actions. but for the trained reasoner to ad
output = m
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Looks good!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="2.4--Wait,-what-kind-of-problem-is-text-generation-again?">2.4  Wait, what kind of problem is text generation again?<a class="anchor-link" href="#2.4--Wait,-what-kind-of-problem-is-text-generation-again?">&#182;</a></h2><p>In part 1 of this notebook we used the same pre-processing technique - the sliding window - to produce a set of training input/output pairs to tackle the problem of time series prediction <em>by treating the problem as one of regression</em>.  So what sort of problem do we have here now, with text generation?  Well, the time series prediction was a regression problem because the output (one value of the time series) was a continuous value.  Here - for character-by-character text generation - each output is a <em>single character</em>.  This isn't a continuous value - but a distinct class - therefore <strong>character-by-character text generation is a classification problem</strong>.</p>
<p>How many classes are there in the data?  Well, the number of classes is equal to the number of unique characters we have to predict!  How many of those were there in our dataset again?  Lets print out the value again.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[34]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># print out the number of unique characters in the dataset</span>
<span class="n">chars</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">text</span><span class="p">)))</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;this corpus has &quot;</span> <span class="o">+</span>  <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">chars</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot; unique characters&quot;</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;and these characters are &#39;</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="n">chars</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>this corpus has 33 unique characters
and these characters are 
[&#39; &#39;, &#39;!&#39;, &#39;,&#39;, &#39;.&#39;, &#39;:&#39;, &#39;;&#39;, &#39;?&#39;, &#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;i&#39;, &#39;j&#39;, &#39;k&#39;, &#39;l&#39;, &#39;m&#39;, &#39;n&#39;, &#39;o&#39;, &#39;p&#39;, &#39;q&#39;, &#39;r&#39;, &#39;s&#39;, &#39;t&#39;, &#39;u&#39;, &#39;v&#39;, &#39;w&#39;, &#39;x&#39;, &#39;y&#39;, &#39;z&#39;]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Rockin' - so we have a multiclass classification problem on our hands!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="2.5--One-hot-encoding-characters">2.5  One-hot encoding characters<a class="anchor-link" href="#2.5--One-hot-encoding-characters">&#182;</a></h2><p>The last issue we have to deal with is representing our text data as numerical data so that we can use it as an input to a neural network. One of the conceptually simplest ways of doing this is via a 'one-hot encoding' scheme.  Here's how it works.</p>
<p>We transform each character in our inputs/outputs into a vector with length equal to the number of unique characters in our text.  This vector is all zeros except one location where we place a 1 - and this location is unique to each character type.  e.g., we transform 'a', 'b', and 'c' as follows</p>
$$a\longleftarrow\left[\begin{array}{c}
1\\
0\\
0\\
\vdots\\
0\\
0
\end{array}\right]\,\,\,\,\,\,\,b\longleftarrow\left[\begin{array}{c}
0\\
1\\
0\\
\vdots\\
0\\
0
\end{array}\right]\,\,\,\,\,c\longleftarrow\left[\begin{array}{c}
0\\
0\\
1\\
\vdots\\
0\\
0 
\end{array}\right]\cdots$$<p>where each vector has 32 entries (or in general: number of entries = number of unique characters in text).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>The first practical step towards doing this one-hot encoding is to form a dictionary mapping each unique character to a unique integer, and one dictionary to do the reverse mapping.  We can then use these dictionaries to quickly make our one-hot encodings, as well as re-translate (from integers to characters) the results of our trained RNN classification model.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[35]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># this dictionary is a function mapping each unique character to a unique integer</span>
<span class="n">chars_to_indices</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">((</span><span class="n">c</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">chars</span><span class="p">))</span>  <span class="c1"># map each unique character to unique integer</span>

<span class="c1"># this dictionary is a function mapping each unique integer back to a unique character</span>
<span class="n">indices_to_chars</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">chars</span><span class="p">))</span>  <span class="c1"># map each unique integer back to unique character</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Now we can transform our input/output pairs - consisting of characters - to equivalent input/output pairs made up of one-hot encoded vectors.  In the next cell we provide a function for doing just this: it takes in the raw character input/outputs and returns their numerical versions.  In particular the numerical input is given as $\bf{X}$, and numerical output is given as the $\bf{y}$</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[36]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># transform character-based input/output into equivalent numerical versions</span>
<span class="k">def</span> <span class="nf">encode_io_pairs</span><span class="p">(</span><span class="n">text</span><span class="p">,</span><span class="n">window_size</span><span class="p">,</span><span class="n">step_size</span><span class="p">):</span>
    <span class="c1"># number of unique chars</span>
    <span class="n">chars</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">text</span><span class="p">)))</span>
    <span class="n">num_chars</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">chars</span><span class="p">)</span>
    
    <span class="c1"># cut up text into character input/output pairs</span>
    <span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">window_transform_text</span><span class="p">(</span><span class="n">text</span><span class="p">,</span><span class="n">window_size</span><span class="p">,</span><span class="n">step_size</span><span class="p">)</span>
    
    <span class="c1"># create empty vessels for one-hot encoded input/output</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="n">window_size</span><span class="p">,</span> <span class="n">num_chars</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">bool</span><span class="p">)</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="n">num_chars</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">bool</span><span class="p">)</span>
    
    <span class="c1"># loop over inputs/outputs and transform and store in X/y</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sentence</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">inputs</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">char</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sentence</span><span class="p">):</span>
            <span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">chars_to_indices</span><span class="p">[</span><span class="n">char</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">chars_to_indices</span><span class="p">[</span><span class="n">outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]]]</span> <span class="o">=</span> <span class="mi">1</span>
        
    <span class="k">return</span> <span class="n">X</span><span class="p">,</span><span class="n">y</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Now run the one-hot encoding function by activating the cell below and transform our input/output pairs!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[37]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># use your function</span>
<span class="n">window_size</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">step_size</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span> <span class="o">=</span> <span class="n">encode_io_pairs</span><span class="p">(</span><span class="n">text</span><span class="p">,</span><span class="n">window_size</span><span class="p">,</span><span class="n">step_size</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><a id='TODO_5'></a></p>
<h2 id="2.6-Setting-up-our-RNN">2.6 Setting up our RNN<a class="anchor-link" href="#2.6-Setting-up-our-RNN">&#182;</a></h2><p>With our dataset loaded and the input/output pairs extracted / transformed we can now begin setting up our RNN for training.  Again we will use Keras to quickly build a single hidden layer RNN - where our hidden layer consists of LSTM modules.</p>
<p>Time to get to work: build a 3 layer RNN model of the following specification</p>
<ul>
<li>layer 1 should be an LSTM module with 200 hidden units --&gt; note this should have input_shape = (window_size,len(chars)) where len(chars) = number of unique characters in your cleaned text</li>
<li>layer 2 should be a linear module, fully connected, with len(chars) hidden units --&gt; where len(chars) = number of unique characters in your cleaned text</li>
<li>layer 3 should be a softmax activation ( since we are solving a <em>multiclass classification</em>)</li>
<li>Use the <strong>categorical_crossentropy</strong> loss </li>
</ul>
<p>This network can be constructed using just a few lines - as with the RNN network you made in part 1 of this notebook.  See e.g., the <a href="https://keras.io/getting-started/sequential-model-guide/">general Keras documentation</a> and the <a href="https://keras.io/layers/recurrent/">LSTM documentation in particular</a> for examples of how to quickly use Keras to build neural network models.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[38]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### necessary functions from the keras library</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">LSTM</span>
<span class="kn">from</span> <span class="nn">keras.optimizers</span> <span class="k">import</span> <span class="n">RMSprop</span>
<span class="kn">from</span> <span class="nn">keras.utils.data_utils</span> <span class="k">import</span> <span class="n">get_file</span>
<span class="kn">import</span> <span class="nn">keras</span>
<span class="kn">import</span> <span class="nn">random</span>

<span class="c1"># TODO implement build_part2_RNN in my_answers.py</span>
<span class="kn">from</span> <span class="nn">my_answers</span> <span class="k">import</span> <span class="n">build_part2_RNN</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">build_part2_RNN</span><span class="p">(</span><span class="n">window_size</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">chars</span><span class="p">))</span>

<span class="c1"># initialize optimizer</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">RMSprop</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>

<span class="c1"># compile model --&gt; make sure initialized optimizer and callbacks - as defined above - are used</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="2.7--Training-our-RNN-model-for-text-generation">2.7  Training our RNN model for text generation<a class="anchor-link" href="#2.7--Training-our-RNN-model-for-text-generation">&#182;</a></h2><p>With our RNN setup we can now train it!  Lets begin by trying it out on a small subset of the larger version.  In the next cell we take the first 10,000 input/output pairs from our training database to learn on.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[39]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># a small subset of our input/output pairs</span>
<span class="n">Xsmall</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="mi">10000</span><span class="p">,:,:]</span>
<span class="n">ysmall</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="mi">10000</span><span class="p">,:]</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Now lets fit our model!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[40]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># train the model</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xsmall</span><span class="p">,</span> <span class="n">ysmall</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span><span class="n">verbose</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>

<span class="c1"># save weights</span>
<span class="n">model</span><span class="o">.</span><span class="n">save_weights</span><span class="p">(</span><span class="s1">&#39;model_weights/best_RNN_small_textdata_weights.hdf5&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>Epoch 1/40
10000/10000 [==============================] - 4s - loss: 3.0242     
Epoch 2/40
10000/10000 [==============================] - 4s - loss: 2.8826     
Epoch 3/40
10000/10000 [==============================] - 4s - loss: 2.8587     
Epoch 4/40
10000/10000 [==============================] - 4s - loss: 2.8267     
Epoch 5/40
10000/10000 [==============================] - 4s - loss: 2.7663     
Epoch 6/40
10000/10000 [==============================] - 4s - loss: 2.6847     
Epoch 7/40
10000/10000 [==============================] - 4s - loss: 2.6082     
Epoch 8/40
10000/10000 [==============================] - 4s - loss: 2.5394     
Epoch 9/40
10000/10000 [==============================] - 4s - loss: 2.4810     
Epoch 10/40
10000/10000 [==============================] - 4s - loss: 2.4317     
Epoch 11/40
10000/10000 [==============================] - 4s - loss: 2.3898     
Epoch 12/40
10000/10000 [==============================] - 4s - loss: 2.3526     
Epoch 13/40
10000/10000 [==============================] - 4s - loss: 2.3204     
Epoch 14/40
10000/10000 [==============================] - 4s - loss: 2.2926     
Epoch 15/40
10000/10000 [==============================] - 4s - loss: 2.2672     
Epoch 16/40
10000/10000 [==============================] - 4s - loss: 2.2463     
Epoch 17/40
10000/10000 [==============================] - 4s - loss: 2.2176     
Epoch 18/40
10000/10000 [==============================] - 4s - loss: 2.1960     
Epoch 19/40
10000/10000 [==============================] - 4s - loss: 2.1784     
Epoch 20/40
10000/10000 [==============================] - 4s - loss: 2.1578     
Epoch 21/40
10000/10000 [==============================] - 4s - loss: 2.1388     
Epoch 22/40
10000/10000 [==============================] - 4s - loss: 2.1230     
Epoch 23/40
10000/10000 [==============================] - 4s - loss: 2.1032     
Epoch 24/40
10000/10000 [==============================] - 4s - loss: 2.0796     
Epoch 25/40
10000/10000 [==============================] - 4s - loss: 2.0658     
Epoch 26/40
10000/10000 [==============================] - 4s - loss: 2.0499     
Epoch 27/40
10000/10000 [==============================] - 4s - loss: 2.0287     
Epoch 28/40
10000/10000 [==============================] - 4s - loss: 2.0121     
Epoch 29/40
10000/10000 [==============================] - 4s - loss: 1.9897     
Epoch 30/40
10000/10000 [==============================] - 4s - loss: 1.9669     
Epoch 31/40
10000/10000 [==============================] - 4s - loss: 1.9499     
Epoch 32/40
10000/10000 [==============================] - 4s - loss: 1.9297     
Epoch 33/40
10000/10000 [==============================] - 4s - loss: 1.9065     
Epoch 34/40
10000/10000 [==============================] - 4s - loss: 1.8861     
Epoch 35/40
10000/10000 [==============================] - 4s - loss: 1.8622     
Epoch 36/40
10000/10000 [==============================] - 4s - loss: 1.8440     
Epoch 37/40
10000/10000 [==============================] - 4s - loss: 1.8110     
Epoch 38/40
10000/10000 [==============================] - 4s - loss: 1.7910     
Epoch 39/40
10000/10000 [==============================] - 4s - loss: 1.7633     
Epoch 40/40
10000/10000 [==============================] - 4s - loss: 1.7385     
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>How do we make a given number of predictions (characters) based on this fitted model?</p>
<p>First we predict the next character after following any chunk of characters in the text of length equal to our chosen window size.  Then we remove the first character in our input sequence and tack our prediction onto the end.  This gives us a slightly changed sequence of inputs that still has length equal to the size of our window.  We then feed in this updated input sequence into the model to predict the another character.  Together then we have two predicted characters following our original input sequence.  Repeating this process N times gives us N predicted characters.</p>
<p>In the next Python cell we provide you with a completed function that does just this - it makes predictions when given a) a trained RNN model, b) a subset of (window_size) characters from the text, and c) a number of characters to predict (to follow our input subset).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[41]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># function that uses trained model to predict a desired number of future characters</span>
<span class="k">def</span> <span class="nf">predict_next_chars</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="n">input_chars</span><span class="p">,</span><span class="n">num_to_predict</span><span class="p">):</span>     
    <span class="c1"># create output</span>
    <span class="n">predicted_chars</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_to_predict</span><span class="p">):</span>
        <span class="c1"># convert this round&#39;s predicted characters to numerical input    </span>
        <span class="n">x_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">window_size</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">chars</span><span class="p">)))</span>
        <span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">char</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">input_chars</span><span class="p">):</span>
            <span class="n">x_test</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">chars_to_indices</span><span class="p">[</span><span class="n">char</span><span class="p">]]</span> <span class="o">=</span> <span class="mf">1.</span>

        <span class="c1"># make this round&#39;s prediction</span>
        <span class="n">test_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span><span class="n">verbose</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>

        <span class="c1"># translate numerical prediction back to characters</span>
        <span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_predict</span><span class="p">)</span>                           <span class="c1"># predict class of each test input</span>
        <span class="n">d</span> <span class="o">=</span> <span class="n">indices_to_chars</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> 

        <span class="c1"># update predicted_chars and input</span>
        <span class="n">predicted_chars</span><span class="o">+=</span><span class="n">d</span>
        <span class="n">input_chars</span><span class="o">+=</span><span class="n">d</span>
        <span class="n">input_chars</span> <span class="o">=</span> <span class="n">input_chars</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
    <span class="k">return</span> <span class="n">predicted_chars</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><a id='TODO_6'></a></p>
<p>With your trained model try a few subsets of the complete text as input - note the length of each must be exactly equal to the window size.  For each subset use the function above to predict the next 100 characters that follow each input.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[42]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: choose an input sequence and use the prediction function in the previous Python cell to predict 100 characters following it</span>
<span class="c1"># get an appropriately sized chunk of characters from the text</span>
<span class="n">start_inds</span> <span class="o">=</span> <span class="p">[</span><span class="mi">102000</span><span class="p">,</span> <span class="mi">105000</span><span class="p">,</span> <span class="mi">108000</span><span class="p">,</span> <span class="mi">120000</span><span class="p">,</span> <span class="mi">150000</span><span class="p">]</span>

<span class="c1"># load in weights</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;model_weights/best_RNN_small_textdata_weights.hdf5&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">start_inds</span><span class="p">:</span>
    <span class="n">start_index</span> <span class="o">=</span> <span class="n">s</span>
    <span class="n">input_chars</span> <span class="o">=</span> <span class="n">text</span><span class="p">[</span><span class="n">start_index</span><span class="p">:</span> <span class="n">start_index</span> <span class="o">+</span> <span class="n">window_size</span><span class="p">]</span>

    <span class="c1"># use the prediction function</span>
    <span class="n">predict_input</span> <span class="o">=</span> <span class="n">predict_next_chars</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="n">input_chars</span><span class="p">,</span><span class="n">num_to_predict</span> <span class="o">=</span> <span class="mi">100</span><span class="p">)</span>

    <span class="c1"># print out input characters</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;------------------&#39;</span><span class="p">)</span>
    <span class="n">input_line</span> <span class="o">=</span> <span class="s1">&#39;input chars = &#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>  <span class="n">input_chars</span> <span class="o">+</span> <span class="s1">&#39;&quot;&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">input_line</span><span class="p">)</span>

    <span class="c1"># print out predicted characters</span>
    <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;predicted chars = &#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>  <span class="n">predict_input</span> <span class="o">+</span> <span class="s1">&#39;&quot;&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>------------------
input chars = 
epfather, surely, since the name is different.  yes, my stepfather. i call him father, though it sou&#34;

predicted chars = 
ld be to mand doren, ind in the mand the round and the cound doon the seat in was in the ind hit he &#34;

------------------
input chars = 
ess of the firm, but we went, mother and i, with mr. hardy, who used to be our foreman, and it was t&#34;

predicted chars = 
he areand the sing and are the sing had ind the sist on the tound how she bere and the and he the an&#34;

------------------
input chars = 
very neat and plain, but his eyes were weak, just as mine are, and he wore tinted glasses against th&#34;

predicted chars = 
e sis and bere an the sing and ithen the sourd oo the toome and chened ind here and the cound doon t&#34;

------------------
input chars = 
vity was engaging my own attention at the time, and the whole of next day i was busy at the bedside &#34;

predicted chars = 
hed sereng the sing and and the sind hed sere the she had bed and and the pound and the mand ho was &#34;

------------------
input chars = 
ford arms where a room had already been engaged for us.  i have ordered a carriage, said lestrade as&#34;

predicted chars = 
 the and be the and the and and the pouthad and the and dore the is and count of and in the sing and&#34;

</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>This looks ok, but not great.  Now lets try the same experiment with a larger chunk of the data - with the first 100,000 input/output pairs.</p>
<p>Tuning RNNs for a typical character dataset like the one we will use here is a computationally intensive endeavour and thus timely on a typical CPU.  Using a reasonably sized cloud-based GPU can speed up training by a factor of 10.  Also because of the long training time it is highly recommended that you carefully write the output of each step of your process to file.  This is so that all of your results are saved even if you close the web browser you're working out of, as the processes will continue processing in the background but variables/output in the notebook system will not update when you open it again.</p>
<p>In the next cell we show you how to create a text file in Python and record data to it.  This sort of setup can be used to record your final predictions.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[43]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### A simple way to write output to file</span>
<span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;my_test_output.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>              <span class="c1"># create an output file to write too</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;this is only a test &#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>           <span class="c1"># print some output text</span>
<span class="n">x</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;the value of x is &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>    <span class="c1"># record a variable value</span>
<span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>     

<span class="c1"># print out the contents of my_test_output.txt</span>
<span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;my_test_output.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>              <span class="c1"># create an output file to write too</span>
<span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt output_prompt">Out[43]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&#39;this is only a test \nthe value of x is 2\n&#39;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>With this recording devices we can now more safely perform experiments on larger portions of the text.  In the next cell we will use the first 100,000 input/output pairs to train our RNN model.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>First we fit our model to the dataset, then generate text using the trained model in precisely the same generation method applied before on the small dataset.</p>
<p><strong>Note:</strong> your generated words should be - by and large - more realistic than with the small dataset, but you won't be able to generate perfect English sentences even with this amount of data.  A rule of thumb: your model is working well if you generate sentences that largely contain real English words.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[44]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># a small subset of our input/output pairs</span>
<span class="n">Xlarge</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="mi">100000</span><span class="p">,:,:]</span>
<span class="n">ylarge</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="mi">100000</span><span class="p">,:]</span>

<span class="c1"># TODO: fit to our larger dataset</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xlarge</span><span class="p">,</span> <span class="n">ylarge</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># save weights</span>
<span class="n">model</span><span class="o">.</span><span class="n">save_weights</span><span class="p">(</span><span class="s1">&#39;model_weights/best_RNN_large_textdata_weights.hdf5&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>Epoch 1/30
100000/100000 [==============================] - 42s - loss: 2.0284    
Epoch 2/30
100000/100000 [==============================] - 42s - loss: 1.9337    
Epoch 3/30
100000/100000 [==============================] - 42s - loss: 1.8671    
Epoch 4/30
100000/100000 [==============================] - 42s - loss: 1.8125    
Epoch 5/30
100000/100000 [==============================] - 42s - loss: 1.7644    
Epoch 6/30
100000/100000 [==============================] - 42s - loss: 1.7214    
Epoch 7/30
100000/100000 [==============================] - 42s - loss: 1.6822    
Epoch 8/30
100000/100000 [==============================] - 42s - loss: 1.6447    
Epoch 9/30
100000/100000 [==============================] - 42s - loss: 1.6098    
Epoch 10/30
100000/100000 [==============================] - 42s - loss: 1.5761    
Epoch 11/30
100000/100000 [==============================] - 42s - loss: 1.5434    
Epoch 12/30
100000/100000 [==============================] - 42s - loss: 1.5120    
Epoch 13/30
100000/100000 [==============================] - 42s - loss: 1.4815    
Epoch 14/30
100000/100000 [==============================] - 42s - loss: 1.4519    
Epoch 15/30
100000/100000 [==============================] - 42s - loss: 1.4219    
Epoch 16/30
100000/100000 [==============================] - 42s - loss: 1.3927    
Epoch 17/30
100000/100000 [==============================] - 42s - loss: 1.3623    
Epoch 18/30
100000/100000 [==============================] - 42s - loss: 1.3338    
Epoch 19/30
100000/100000 [==============================] - 42s - loss: 1.3051    
Epoch 20/30
100000/100000 [==============================] - 42s - loss: 1.2754    
Epoch 21/30
100000/100000 [==============================] - 42s - loss: 1.2470    
Epoch 22/30
100000/100000 [==============================] - 42s - loss: 1.2176    
Epoch 23/30
100000/100000 [==============================] - 42s - loss: 1.1897    
Epoch 24/30
100000/100000 [==============================] - 42s - loss: 1.1608    
Epoch 25/30
100000/100000 [==============================] - 42s - loss: 1.1319    
Epoch 26/30
100000/100000 [==============================] - 42s - loss: 1.1048    
Epoch 27/30
100000/100000 [==============================] - 42s - loss: 1.0773    
Epoch 28/30
100000/100000 [==============================] - 42s - loss: 1.0506    
Epoch 29/30
100000/100000 [==============================] - 42s - loss: 1.0250    
Epoch 30/30
100000/100000 [==============================] - 42s - loss: 0.9995    
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[45]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: choose an input sequence and use the prediction function in the previous Python cell to predict 100 characters following it</span>
<span class="c1"># get an appropriately sized chunk of characters from the text</span>
<span class="n">start_inds</span> <span class="o">=</span> <span class="p">[</span><span class="mi">102000</span><span class="p">,</span> <span class="mi">105000</span><span class="p">,</span> <span class="mi">108000</span><span class="p">,</span> <span class="mi">120000</span><span class="p">,</span> <span class="mi">150000</span><span class="p">]</span>

<span class="c1"># save output</span>
<span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;text_gen_output/RNN_large_textdata_output.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>  <span class="c1"># create an output file to write too</span>

<span class="c1"># load weights</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;model_weights/best_RNN_large_textdata_weights.hdf5&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">start_inds</span><span class="p">:</span>
    <span class="n">start_index</span> <span class="o">=</span> <span class="n">s</span>
    <span class="n">input_chars</span> <span class="o">=</span> <span class="n">text</span><span class="p">[</span><span class="n">start_index</span><span class="p">:</span> <span class="n">start_index</span> <span class="o">+</span> <span class="n">window_size</span><span class="p">]</span>

    <span class="c1"># use the prediction function</span>
    <span class="n">predict_input</span> <span class="o">=</span> <span class="n">predict_next_chars</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="n">input_chars</span><span class="p">,</span><span class="n">num_to_predict</span> <span class="o">=</span> <span class="mi">100</span><span class="p">)</span>

    <span class="c1"># print out input characters</span>
    <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;-------------------&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
    <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>

    <span class="n">input_line</span> <span class="o">=</span> <span class="s1">&#39;input chars = &#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>  <span class="n">input_chars</span> <span class="o">+</span> <span class="s1">&#39;&quot;&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">input_line</span><span class="p">)</span>
    <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">input_line</span><span class="p">)</span>

    <span class="c1"># print out predicted characters</span>
    <span class="n">predict_line</span> <span class="o">=</span> <span class="s1">&#39;predicted chars = &#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>  <span class="n">predict_input</span> <span class="o">+</span> <span class="s1">&#39;&quot;&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">predict_line</span><span class="p">)</span>
    <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">predict_line</span><span class="p">)</span>
<span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">
<div class="prompt"></div>

<div class="output_subarea output_stream output_stdout output_text">
<pre>-------------------

input chars = 
epfather, surely, since the name is different.  yes, my stepfather. i call him father, though it sou&#34;

predicted chars = 
ld light they freat the led the should be deep come done.  then i see that i should be man in a somp&#34;

-------------------

input chars = 
ess of the firm, but we went, mother and i, with mr. hardy, who used to be our foreman, and it was t&#34;

predicted chars = 
he door of his face, suid he was fiened upon his face, said he in ence of mo home and a books which &#34;

-------------------

input chars = 
very neat and plain, but his eyes were weak, just as mine are, and he wore tinted glasses against th&#34;

predicted chars = 
ere was a rearrad dreach and a shopperion he sempsedvered.  then i see with the state the sectat and&#34;

-------------------

input chars = 
vity was engaging my own attention at the time, and the whole of next day i was busy at the bedside &#34;

predicted chars = 
of the severte clied in as it as the door wathout and lade a with and out of the coronet in the corn&#34;

-------------------

input chars = 
ford arms where a room had already been engaged for us.  i have ordered a carriage, said lestrade as&#34;

predicted chars = 
 the conerd which were found of out with his find, provesed his handsing the edveroction of the fire&#34;

</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span> 
</pre></div>

</div>
</div>
</div>

</div>
    </div>
  </div>
</body>

 
</html>