Image Classifier Project.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>Image Classifier 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://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/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="Developing-an-AI-application">Developing an AI application<a class="anchor-link" href="#Developing-an-AI-application">&#182;</a></h1><p>Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.</p>
<p>In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using <a href="http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html">this dataset</a> of 102 flower categories, you can see a few examples below.</p>
<p><img src='assets/Flowers.png' width=500px></p>
<p>The project is broken down into multiple steps:</p>
<ul>
<li>Load and preprocess the image dataset</li>
<li>Train the image classifier on your dataset</li>
<li>Use the trained classifier to predict image content</li>
</ul>
<p>We'll lead you through each part which you'll implement in Python.</p>
<p>When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.</p>
<p>First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Imports here</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;

<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">os.path</span>
<span class="kn">import</span> <span class="nn">PIL</span>  
<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">optim</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="k">import</span> <span class="n">Variable</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">models</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="Load-the-data">Load the data<a class="anchor-link" href="#Load-the-data">&#182;</a></h2><p>Here you'll use <code>torchvision</code> to load the data (<a href="http://pytorch.org/docs/0.3.0/torchvision/index.html">documentation</a>). The data should be included alongside this notebook, otherwise you can <a href="https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz">download it here</a>. The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.</p>
<p>The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.</p>
<p>The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's <code>[0.485, 0.456, 0.406]</code> and for the standard deviations <code>[0.229, 0.224, 0.225]</code>, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1.</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="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;flowers&#39;</span>
<span class="n">train_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/train&#39;</span>
<span class="n">valid_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/valid&#39;</span>
<span class="n">test_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/test&#39;</span>
</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;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Define your transforms for the training, validation, and testing sets</span>
<span class="n">data_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomRotation</span><span class="p">(</span><span class="mi">30</span><span class="p">),</span>
                                       <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                       <span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
                                       <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                       <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span> 
                                                            <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>

<span class="n">test_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span> 
                                                           <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>


<span class="n">validation_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span> 
                                                           <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>


<span class="c1"># TODO: Load the datasets with ImageFolder</span>
<span class="n">train_data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">train_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">data_transforms</span><span class="p">)</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">test_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">test_transforms</span><span class="p">)</span>
<span class="n">validate_data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">valid_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">validation_transforms</span><span class="p">)</span>


<span class="c1"># TODO: Using the image datasets and the trainforms, define the dataloaders</span>
<span class="n">trainloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">testloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>

<span class="n">validateloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">validate_data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</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">
<h3 id="Label-mapping">Label mapping<a class="anchor-link" href="#Label-mapping">&#182;</a></h3><p>You'll also need to load in a mapping from category label to category name. You can find this in the file <code>cat_to_name.json</code>. It's a JSON object which you can read in with the <a href="https://docs.python.org/2/library/json.html"><code>json</code> module</a>. This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers.</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="kn">import</span> <span class="nn">json</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;cat_to_name.json&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">cat_to_name</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</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">
<h1 id="Building-and-training-the-classifier">Building and training the classifier<a class="anchor-link" href="#Building-and-training-the-classifier">&#182;</a></h1><p>Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from <code>torchvision.models</code> to get the image features. Build and train a new feed-forward classifier using those features.</p>
<p>We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students! You can also ask questions on the forums or join the instructors in office hours.</p>
<p>Refer to <a href="https://review.udacity.com/#!/rubrics/1663/view">the rubric</a> for guidance on successfully completing this section. Things you'll need to do:</p>
<ul>
<li>Load a <a href="http://pytorch.org/docs/master/torchvision/models.html">pre-trained network</a> (If you need a starting point, the VGG networks work great and are straightforward to use)</li>
<li>Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout</li>
<li>Train the classifier layers using backpropagation using the pre-trained network to get the features</li>
<li>Track the loss and accuracy on the validation set to determine the best hyperparameters</li>
</ul>
<p>We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!</p>
<p>When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.</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: Build and train your network</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</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_stderr output_text">
<pre>Downloading: &#34;https://download.pytorch.org/models/densenet121-a639ec97.pth&#34; to /root/.torch/models/densenet121-a639ec97.pth
100%|██████████| 32342954/32342954 [00:01&lt;00:00, 21993897.33it/s]
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt output_prompt">Out[5]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>DenseNet(
  (features): Sequential(
    (conv0): Conv2d (3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    (relu0): ReLU(inplace)
    (pool0): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))
    (denseblock1): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm.1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm.1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm.1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm.1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm.1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm.1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition1): _Transition(
      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU(inplace)
      (conv): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)
    )
    (denseblock2): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm.1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm.1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm.1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm.1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm.1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm.1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm.1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm.1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm.1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm.1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm.1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm.1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition2): _Transition(
      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU(inplace)
      (conv): Conv2d (512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)
    )
    (denseblock3): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm.1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm.1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm.1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm.1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm.1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm.1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm.1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm.1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm.1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm.1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm.1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm.1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm.1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm.1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm.1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm.1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer17): _DenseLayer(
        (norm.1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer18): _DenseLayer(
        (norm.1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer19): _DenseLayer(
        (norm.1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer20): _DenseLayer(
        (norm.1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer21): _DenseLayer(
        (norm.1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer22): _DenseLayer(
        (norm.1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer23): _DenseLayer(
        (norm.1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer24): _DenseLayer(
        (norm.1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition3): _Transition(
      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU(inplace)
      (conv): Conv2d (1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)
    )
    (denseblock4): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm.1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm.1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm.1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm.1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm.1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm.1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm.1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm.1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm.1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm.1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm.1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm.1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm.1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm.1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm.1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm.1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True)
  )
  (classifier): Linear(in_features=1024, out_features=1000)
)</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;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Freeze parameters so we don&#39;t backprop through them</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
    <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">False</span>

<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">OrderedDict</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">([</span>
                          <span class="p">(</span><span class="s1">&#39;fc1&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">500</span><span class="p">)),</span>
                          <span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()),</span>
                          <span class="p">(</span><span class="s1">&#39;dropout&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">()),</span>
                          <span class="p">(</span><span class="s1">&#39;fc2&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="mi">102</span><span class="p">)),</span>
                          <span class="p">(</span><span class="s1">&#39;output&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
                          <span class="p">]))</span>
    
<span class="n">model</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
</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;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">parameters</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">cudayouthere</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="n">cudayouthere</span><span class="p">)</span>

<span class="n">model</span><span class="o">.</span><span class="n">cuda</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>True
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt output_prompt">Out[7]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>DenseNet(
  (features): Sequential(
    (conv0): Conv2d (3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    (relu0): ReLU(inplace)
    (pool0): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))
    (denseblock1): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm.1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm.1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm.1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm.1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm.1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm.1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition1): _Transition(
      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU(inplace)
      (conv): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)
    )
    (denseblock2): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm.1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm.1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm.1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm.1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm.1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm.1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm.1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm.1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm.1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm.1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm.1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm.1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition2): _Transition(
      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU(inplace)
      (conv): Conv2d (512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)
    )
    (denseblock3): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm.1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm.1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm.1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm.1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm.1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm.1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm.1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm.1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm.1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm.1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm.1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm.1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm.1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm.1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm.1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm.1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer17): _DenseLayer(
        (norm.1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer18): _DenseLayer(
        (norm.1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer19): _DenseLayer(
        (norm.1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer20): _DenseLayer(
        (norm.1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer21): _DenseLayer(
        (norm.1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer22): _DenseLayer(
        (norm.1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer23): _DenseLayer(
        (norm.1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer24): _DenseLayer(
        (norm.1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition3): _Transition(
      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True)
      (relu): ReLU(inplace)
      (conv): Conv2d (1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)
    )
    (denseblock4): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm.1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm.1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm.1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm.1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm.1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm.1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm.1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm.1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm.1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm.1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm.1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm.1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm.1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm.1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm.1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm.1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True)
        (relu.1): ReLU(inplace)
        (conv.1): Conv2d (992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
        (relu.2): ReLU(inplace)
        (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True)
  )
  (classifier): Sequential(
    (fc1): Linear(in_features=1024, out_features=500)
    (relu): ReLU()
    (dropout): Dropout(p=0.5)
    (fc2): Linear(in_features=500, out_features=102)
    (output): LogSoftmax()
  )
)</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;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">steps</span> <span class="o">=</span> <span class="mi">0</span> 
<span class="n">running_loss</span> <span class="o">=</span> <span class="mi">0</span> 
<span class="n">print_every</span> <span class="o">=</span> <span class="mi">40</span>
<span class="n">epochs</span> <span class="o">=</span> <span class="mi">3</span>


<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>

    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>        

    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">trainloader</span><span class="p">):</span>
        <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="n">Variable</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
        <span class="n">steps</span> <span class="o">=</span> <span class="n">steps</span> <span class="o">+</span> <span class="mi">1</span>

        <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>

        <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">labels</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

        
        <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
        
        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
        <span class="n">running_loss</span> <span class="o">=</span> <span class="n">running_loss</span> <span class="o">+</span> <span class="n">loss</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

        <span class="k">if</span> <span class="n">steps</span> <span class="o">%</span> <span class="n">print_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
            <span class="n">accuracy</span> <span class="o">=</span> <span class="mi">0</span>
            <span class="n">test_loss</span> <span class="o">=</span> <span class="mi">0</span>
            <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">validateloader</span><span class="p">):</span>

                <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">labels</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
                <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="n">Variable</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
                <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
                <span class="n">test_loss</span> <span class="o">+=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

                <span class="n">ps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output</span><span class="p">)</span><span class="o">.</span><span class="n">data</span>
                <span class="n">equality</span> <span class="o">=</span> <span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">data</span> <span class="o">==</span> <span class="n">ps</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
                
                <span class="n">accuracy</span> <span class="o">+=</span> <span class="n">equality</span><span class="o">.</span><span class="n">type_as</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">())</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>

            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Epoch: </span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">.. &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">e</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">epochs</span><span class="p">),</span>
                  <span class="s2">&quot;Training Loss: </span><span class="si">{:.3f}</span><span class="s2">.. &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">running_loss</span><span class="o">/</span><span class="n">print_every</span><span class="p">),</span>
                  <span class="s2">&quot;Validation Loss: </span><span class="si">{:.3f}</span><span class="s2">.. &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">validateloader</span><span class="p">)),</span>
                  <span class="s2">&quot;Validation Accuracy: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">validateloader</span><span class="p">)))</span>
            <span class="n">running_loss</span> <span class="o">=</span> <span class="mi">0</span>
            <span class="n">model</span><span class="o">.</span><span class="n">train</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: 1/3..  Training Loss: 4.293..  Validation Loss: 3.692..  Validation Accuracy: 0.270
Epoch: 1/3..  Training Loss: 3.394..  Validation Loss: 2.519..  Validation Accuracy: 0.533
Epoch: 2/3..  Training Loss: 2.481..  Validation Loss: 1.717..  Validation Accuracy: 0.635
Epoch: 2/3..  Training Loss: 2.025..  Validation Loss: 1.268..  Validation Accuracy: 0.728
Epoch: 2/3..  Training Loss: 1.710..  Validation Loss: 1.035..  Validation Accuracy: 0.804
Epoch: 3/3..  Training Loss: 1.418..  Validation Loss: 0.861..  Validation Accuracy: 0.818
Epoch: 3/3..  Training Loss: 1.303..  Validation Loss: 0.736..  Validation Accuracy: 0.834
</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;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;2 + 2 is 4 - 1 thats 3 | quick mafs&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>2 + 2 is 4 - 1 thats 3 | quick mafs
</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="Testing-your-network">Testing your network<a class="anchor-link" href="#Testing-your-network">&#182;</a></h2><p>It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well.</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="n">accuracy</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">test_loss</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">testloader</span><span class="p">):</span>

    <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">labels</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
    <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="n">Variable</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
    <span class="n">test_loss</span> <span class="o">+=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">ps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output</span><span class="p">)</span><span class="o">.</span><span class="n">data</span> 
    <span class="n">equality</span> <span class="o">=</span> <span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">data</span> <span class="o">==</span> <span class="n">ps</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
                
    <span class="n">accuracy</span> <span class="o">+=</span> <span class="n">equality</span><span class="o">.</span><span class="n">type_as</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">())</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test Accuracy: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">testloader</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>Test Accuracy: 0.060
Test Accuracy: 0.126
Test Accuracy: 0.188
Test Accuracy: 0.243
Test Accuracy: 0.315
Test Accuracy: 0.374
Test Accuracy: 0.444
Test Accuracy: 0.504
Test Accuracy: 0.576
Test Accuracy: 0.650
Test Accuracy: 0.715
Test Accuracy: 0.774
Test Accuracy: 0.831
</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="Save-the-checkpoint">Save the checkpoint<a class="anchor-link" href="#Save-the-checkpoint">&#182;</a></h2><p>Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: <code>image_datasets['train'].class_to_idx</code>. You can attach this to the model as an attribute which makes inference easier later on.</p>
<p><code>model.class_to_idx = image_datasets['train'].class_to_idx</code></p>
<p>Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, <code>optimizer.state_dict</code>. You'll likely want to use this trained model in the next part of the project, so best to save it now.</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"># TODO: Save the checkpoint</span>
<span class="k">def</span> <span class="nf">find_classes</span><span class="p">(</span><span class="nb">dir</span><span class="p">):</span>
    <span class="n">classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="nb">dir</span><span class="p">)</span> <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">dir</span><span class="p">,</span> <span class="n">d</span><span class="p">))]</span>
    <span class="n">classes</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
    <span class="n">class_to_idx</span> <span class="o">=</span> <span class="p">{</span><span class="n">classes</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span> <span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">classes</span><span class="p">))}</span>
    <span class="k">return</span> <span class="n">class_to_idx</span>

<span class="n">state</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <span class="n">epochs</span><span class="p">,</span>
    <span class="s1">&#39;state_dict&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
    <span class="s1">&#39;optimizer&#39;</span><span class="p">:</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
    <span class="s1">&#39;class_to_idx&#39;</span><span class="p">:</span> <span class="n">find_classes</span><span class="p">(</span><span class="n">train_dir</span><span class="p">)</span>
<span class="p">}</span>

<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="s2">&quot;checkpoint.pth&quot;</span><span class="p">)</span>
<span class="c1"># torch.save(model, &quot;checkpoint.pth&quot;)</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="Loading-the-checkpoint">Loading the checkpoint<a class="anchor-link" href="#Loading-the-checkpoint">&#182;</a></h2><p>At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network.</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"># TODO: Write a function that loads a checkpoint and rebuilds the model</span>
<span class="c1"># Model in training mode, dropout is on</span>
<span class="k">def</span> <span class="nf">load_checkpoint</span><span class="p">(</span><span class="n">filepath</span><span class="p">):</span>
    <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">filepath</span><span class="p">)</span>
    <span class="n">state_dict</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s2">&quot;state_dict&quot;</span><span class="p">]</span>
    <span class="n">class_to_idx</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s2">&quot;class_to_idx&quot;</span><span class="p">]</span>
    <span class="n">optimizer_dict</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s2">&quot;optimizer&quot;</span><span class="p">]</span>


    <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">class_to_idx</span> <span class="o">=</span> <span class="n">class_to_idx</span>
    <span class="n">optimizer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">optimizer_dict</span><span class="p">)</span> 
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s2">&quot;epochs&quot;</span><span class="p">]</span>
    
    <span class="k">return</span> <span class="n">epochs</span><span class="p">,</span> <span class="n">model</span>


<span class="n">epochs</span><span class="p">,</span> <span class="n">model</span> <span class="o">=</span> <span class="n">load_checkpoint</span><span class="p">(</span><span class="s2">&quot;checkpoint.pth&quot;</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">
<h1 id="Inference-for-classification">Inference for classification<a class="anchor-link" href="#Inference-for-classification">&#182;</a></h1><p>Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called <code>predict</code> that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like</p>
<div class="highlight"><pre><span></span><span class="n">probs</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
<span class="o">&gt;</span> <span class="p">[</span> <span class="mf">0.01558163</span>  <span class="mf">0.01541934</span>  <span class="mf">0.01452626</span>  <span class="mf">0.01443549</span>  <span class="mf">0.01407339</span><span class="p">]</span>
<span class="o">&gt;</span> <span class="p">[</span><span class="s1">&#39;70&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;45&#39;</span><span class="p">,</span> <span class="s1">&#39;62&#39;</span><span class="p">,</span> <span class="s1">&#39;55&#39;</span><span class="p">]</span>
</pre></div>
<p>First you'll need to handle processing the input image such that it can be used in your network.</p>
<h2 id="Image-Preprocessing">Image Preprocessing<a class="anchor-link" href="#Image-Preprocessing">&#182;</a></h2><p>You'll want to use <code>PIL</code> to load the image (<a href="https://pillow.readthedocs.io/en/latest/reference/Image.html">documentation</a>). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training.</p>
<p>First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the <a href="http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail"><code>thumbnail</code></a> or <a href="http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail"><code>resize</code></a> methods. Then you'll need to crop out the center 224x224 portion of the image.</p>
<p>Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so <code>np_image = np.array(pil_image)</code>.</p>
<p>As before, the network expects the images to be normalized in a specific way. For the means, it's <code>[0.485, 0.456, 0.406]</code> and for the standard deviations <code>[0.229, 0.224, 0.225]</code>. You'll want to subtract the means from each color channel, then divide by the standard deviation.</p>
<p>And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using <a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html"><code>ndarray.transpose</code></a>. The color channel needs to be first and retain the order of the other two dimensions.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">process_image</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Scales, crops, and normalizes a PIL image for a PyTorch model,</span>
<span class="sd">        returns an Numpy array</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">h</span> <span class="o">=</span> <span class="mi">112</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    
    <span class="k">if</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">&gt;=</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
        <span class="n">img</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">10000</span><span class="p">,</span><span class="mi">256</span><span class="p">))</span> 
    <span class="k">else</span><span class="p">:</span> 
        <span class="n">img</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">256</span><span class="p">,</span><span class="mi">10000</span><span class="p">))</span>

    <span class="n">hw</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    <span class="n">hh</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">crop</span><span class="p">(</span> <span class="p">(</span><span class="n">hw</span> <span class="o">-</span> <span class="n">h</span><span class="p">,</span> <span class="n">hh</span> <span class="o">-</span> <span class="n">h</span><span class="p">,</span> <span class="n">hw</span> <span class="o">+</span> <span class="n">h</span><span class="p">,</span> <span class="n">hh</span> <span class="o">+</span> <span class="n">h</span><span class="p">)</span> <span class="p">)</span>

    <span class="n">img</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="n">img</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
    <span class="n">img</span> <span class="o">=</span> <span class="p">(</span> <span class="n">img</span> <span class="o">-</span> <span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span> <span class="o">/</span> <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]</span> 

    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    
    
<span class="n">img</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/test&#39;</span> <span class="o">+</span> <span class="s1">&#39;/41/&#39;</span> <span class="o">+</span> <span class="s1">&#39;/image_02257.jpg&#39;</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">img</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>&lt;class &#39;torch.DoubleTensor&#39;&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">
<p>To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your <code>process_image</code> function works, running the output through this function should return the original image (except for the cropped out portions).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Imshow for Tensor.&quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
    
    <span class="c1"># PyTorch tensors assume the color channel is the first dimension</span>
    <span class="c1"># but matplotlib assumes is the third dimension</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
    
    <span class="c1"># Undo preprocessing</span>
    <span class="n">mean</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="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span>
    <span class="n">std</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="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">std</span> <span class="o">*</span> <span class="n">image</span> <span class="o">+</span> <span class="n">mean</span>
    
    <span class="c1"># Image needs to be clipped between 0 and 1 or it looks like noise when displayed</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    
    <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">ax</span>

<span class="n">imshow</span><span class="p">(</span><span class="n">img</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[14]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7efe0d6c7390&gt;</pre>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
width=259
height=251
>
</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="Class-Prediction">Class Prediction<a class="anchor-link" href="#Class-Prediction">&#182;</a></h2><p>Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.</p>
<p>To get the top $K$ largest values in a tensor use <a href="http://pytorch.org/docs/master/torch.html#torch.topk"><code>x.topk(k)</code></a>. This method returns both the highest <code>k</code> probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using <code>class_to_idx</code> which hopefully you added to the model or from an <code>ImageFolder</code> you used to load the data (<a href="#Save-the-checkpoint">see here</a>). Make sure to invert the dictionary so you get a mapping from index to class as well.</p>
<p>Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.</p>
<div class="highlight"><pre><span></span><span class="n">probs</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
<span class="o">&gt;</span> <span class="p">[</span> <span class="mf">0.01558163</span>  <span class="mf">0.01541934</span>  <span class="mf">0.01452626</span>  <span class="mf">0.01443549</span>  <span class="mf">0.01407339</span><span class="p">]</span>
<span class="o">&gt;</span> <span class="p">[</span><span class="s1">&#39;70&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;45&#39;</span><span class="p">,</span> <span class="s1">&#39;62&#39;</span><span class="p">,</span> <span class="s1">&#39;55&#39;</span><span class="p">]</span>
</pre></div>

</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="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Predict the class (or classes) of an image using a trained deep learning model.</span>
<span class="sd">    &#39;&#39;&#39;</span>

    <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">volatile</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
    <span class="n">ps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
    
    <span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">ps</span><span class="p">,</span> <span class="n">topk</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">result</span>


<span class="n">img2</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">img3</span> <span class="o">=</span> <span class="n">img2</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="n">img3</span> <span class="o">=</span> <span class="n">img3</span><span class="o">.</span><span class="n">resize_</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">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="n">img3</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>

<span class="n">result</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">img3</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="n">classes</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">class_to_idx</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">class_to_idx</span>
<span class="n">inv_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">class_to_idx</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>


<span class="n">probs</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">probs</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>Variable containing:
 0.7386  0.0784  0.0734  0.0448  0.0375
[torch.FloatTensor of size 1x5]

</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="Sanity-Checking">Sanity Checking<a class="anchor-link" href="#Sanity-Checking">&#182;</a></h2><p>Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use <code>matplotlib</code> to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:</p>
<p><img src='assets/inference_example.png' width=300px></p>
<p>You can convert from the class integer encoding to actual flower names with the <code>cat_to_name.json</code> file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the <code>imshow</code> function defined above.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[16]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Display an image along with the top 5 classes</span>
<span class="k">def</span> <span class="nf">chartload</span><span class="p">(</span><span class="n">ps</span><span class="p">):</span>
    <span class="n">ps</span> <span class="o">=</span> <span class="n">ps</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>

    <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">cat_to_name</span><span class="p">[</span><span class="n">inv_map</span><span class="p">[</span><span class="n">classes</span><span class="p">[</span><span class="n">x</span><span class="p">]]]</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span> <span class="p">]</span>
    <span class="n">f</span><span class="p">,</span> <span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">barh</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="nb">len</span><span class="p">(</span><span class="n">classes</span><span class="p">)),</span> <span class="n">ps</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;pink&#39;</span><span class="p">)</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">set_yticks</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="nb">len</span><span class="p">(</span><span class="n">classes</span><span class="p">)))</span> 
    <span class="n">ax2</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">y</span><span class="p">);</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Class Probability&#39;</span><span class="p">)</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
    
</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;[17]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
<span class="n">chartload</span><span class="p">(</span><span class="n">probs</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,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
"
width=259
height=251
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
width=454
height=426
>
</div>

</div>

</div>
</div>

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

 
</html>