traffic-sign-classifier.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>traffic-sign-classifier</title><script src="https://unpkg.com/jupyter-js-widgets@2.0.*/dist/embed.js"></script><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="Classifying-Traffic-Signs-With-a-Convolutional-Neural-Network">Classifying Traffic Signs With a Convolutional Neural Network<a class="anchor-link" href="#Classifying-Traffic-Signs-With-a-Convolutional-Neural-Network">&#182;</a></h1><p>The following code builds off the LeNet lab from the Udacity self driving car nano degree, term one. I have written my own architiecture for a new convolutional neural net following inpiriation from the GoogLeNet paper and source [2], defined below. 
I still have a network with a humble number of layers, though I've added something akin to an inception module as described in source [1], and an extra fully connected layer in the final stages of the network. Additionaly, I added dropout to the weights of the fully connected layers, excluding the logit layer, and l2 regularization penalties on all fully connected layer weights. I tuned all the hyper parameters mannually to get a decent fit, in this case accuracy.</p>
<hr>
<p>Additinally, I added the preprocessing steps of appending an upside-down rotation to of all labeled data, doubling the number of training examples and divided all values of input data by 255, forcing the range to [0, 1] for input features.</p>
<hr>
<p>Before final testing, I tried the network on a few pictures of signs outside of the training set I found online, but were still one of the 43 classes represented in the dataset.</p>
<hr>
<p>Just for fun, I ran myself through the classifier. There's a sixty percent chance I'm a 60 km/h speed limit sign.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Here's a visual of my archetecture, style inspiried by:
<a href="https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf">https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf</a></p>
<p><img src="sign_classifier_2.png" alt="My Architecture"></p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Data-Set-Summary">Data Set Summary<a class="anchor-link" href="#Data-Set-Summary">&#182;</a></h2><h6 id="Data-description-from-class-material:">Data description from class material:<a class="anchor-link" href="#Data-description-from-class-material:">&#182;</a></h6><p>The pickled data is a dictionary with 4 key/value pairs:</p>
<ul>
<li>'features' is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).</li>
<li>'labels' is a 2D array containing the label/class id of the traffic sign. The file signnames.csv contains id -&gt; name mappings for each id.</li>
<li>'sizes' is a list containing tuples, (width, height) representing the the original width and height the image.</li>
<li>'coords' is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Import-Libraries-and-Load-Data">Import Libraries and Load Data<a class="anchor-link" href="#Import-Libraries-and-Load-Data">&#182;</a></h2><p>First thing, I imported all the libraries and functions, and used the code from the class material to import the traffic sign data.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[2]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#============================================================================</span>
<span class="c1"># Load pickled data</span>
<span class="kn">import</span> <span class="nn">pickle</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">tensorflow.contrib.layers</span> <span class="k">import</span> <span class="n">flatten</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">random</span> <span class="k">import</span> <span class="n">randint</span>
<span class="kn">import</span> <span class="nn">copy</span>

<span class="c1"># Visualizations will be shown in the notebook.</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="c1"># import tensorflow</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>

<span class="c1"># sklearn data prep functions </span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">shuffle</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>

<span class="c1"># For visualizing loaded pictures </span>
<span class="kn">import</span> <span class="nn">matplotlib.image</span> <span class="k">as</span> <span class="nn">mpimg</span>
<span class="kn">import</span> <span class="nn">os</span>

<span class="c1"># import read csvpandas</span>
<span class="kn">from</span> <span class="nn">pandas</span> <span class="k">import</span> <span class="n">read_csv</span>
<span class="c1">#===================================================================</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">#============================================================================</span>
<span class="c1"># load data and define train, valid, and test sets </span>
<span class="n">training_file</span> <span class="o">=</span> <span class="s2">&quot;/home/andrewrs/Desktop/udacity/Traffic-Sign-Classifier/data/train.p&quot;</span>
<span class="n">validation_file</span><span class="o">=</span> <span class="s2">&quot;/home/andrewrs/Desktop/udacity/Traffic-Sign-Classifier/data/valid.p&quot;</span>
<span class="n">testing_file</span> <span class="o">=</span> <span class="s2">&quot;/home/andrewrs/Desktop/udacity/Traffic-Sign-Classifier/data/test.p&quot;</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">training_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">validation_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">valid</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">testing_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
<span class="n">X_validation</span><span class="p">,</span> <span class="n">y_validation</span> <span class="o">=</span> <span class="n">valid</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">valid</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
<span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">test</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
<span class="c1">#============================================================================</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="Data-Exploration">Data Exploration<a class="anchor-link" href="#Data-Exploration">&#182;</a></h2><p>I breifly explored the imported data by plotting a histogram of class insances. There is somemclass imbalance, howeer, I will continue without correcting this now. The speed limit signs occur rather frequently compared with other classes, and pedestrians, end of passing, bumpy road, and road narrows on right are examples of the least represented classes. I'm guessing this will translate to higher confidences in speed limit predictions than in some of the less represetned classes.</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"># observe class distribution of data. </span>
<span class="c1"># modifed from the class material</span>
<span class="c1"># histogram of label frequency</span>

<span class="c1"># read in sign class labels</span>
<span class="n">class_names</span> <span class="o">=</span> <span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;/home/andrewrs/Desktop/udacity/Traffic-Sign-Classifier/signnames.csv&#39;</span><span class="p">)</span>

<span class="n">labs</span> <span class="o">=</span> <span class="n">class_names</span><span class="p">[</span><span class="s1">&#39;SignName&#39;</span><span class="p">]</span>

<span class="n">n_classes</span> <span class="o">=</span> <span class="mi">43</span>
<span class="n">hist</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">n_classes</span><span class="p">)</span>
<span class="n">center</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">43</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># build plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">hist</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">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Sign Class&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Number of Instances&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">labs</span><span class="p">)),</span> <span class="n">labs</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">,</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span> 

<span class="c1"># print the plot </span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA/oAAALCCAYAAABuuKjVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Data-preprocessing">Data preprocessing<a class="anchor-link" href="#Data-preprocessing">&#182;</a></h2><p>I preprocessed the data with a few simple steps:</p>
<ul>
<li>Combining training and validation sets</li>
<li>Copying the combined set and rotating all images 180 degrees to an upside-down orientation </li>
<li>Appending the original and rotated</li>
<li>Dividing the resulting dataset by 255 to force input range to [0, 1]</li>
<li>Separating the data into 80% of the labeled data into the training set, and 20% into the validation set </li>
</ul>
<p>I repeated the transformitive preprocessing operatinons on the test set</p>
<p>Additinally, I selected 9 examples from the original training set, and 9 from the rotated set before dividing by 255 and plotted them.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#============================================================================</span>
<span class="c1"># Data exploration, visualization, and prep</span>
<span class="c1"># reload shuffle, masked by later imports </span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">shuffle</span>
<span class="c1"># select a few example images from a few classes from train\</span>
<span class="k">def</span> <span class="nf">rand_subset</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="mi">9</span><span class="p">):</span>
    <span class="c1">#selected_classes = []</span>
    <span class="c1">#for i in range(0, 5):</span>
        <span class="c1">#selected_classes.append(randint(train[&quot;labels&quot;].min(), train[&quot;labels&quot;].max()))</span>

    <span class="c1"># grab a subset of classes</span>
    <span class="c1">#train_subset = {&quot;features&quot;: train[&quot;features&quot;] for label in selected_classes}</span>

    <span class="c1"># get a few random selections </span>
    <span class="n">selected_index</span> <span class="o">=</span> <span class="p">[]</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="mi">0</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
        <span class="n">selected_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
    
    <span class="c1"># final subset </span>
    <span class="n">train_subset</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">selected_index</span><span class="p">,:,:,:]</span>
    
    <span class="k">return</span> <span class="n">train_subset</span>
<span class="c1">#============================================================================</span>

<span class="c1">#============================================================================</span>
<span class="c1"># combine for one processing of training</span>
<span class="n">X_both</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">X_validation</span><span class="p">,</span> <span class="n">X_train</span><span class="p">])</span>
<span class="n">y_both</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">y_validation</span><span class="p">,</span> <span class="n">y_train</span><span class="p">])</span>
<span class="c1">#============================================================================</span>


<span class="c1">#============================================================================</span>
<span class="c1"># define the rotation function. rotates to the left 90 degrees*times</span>
<span class="k">def</span> <span class="nf">rotate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">times</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</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="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]):</span>
        <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">rot90</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">times</span><span class="p">)</span>
        <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">rot90</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">times</span><span class="p">)</span>
        <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">rot90</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:,</span><span class="mi">2</span><span class="p">],</span> <span class="n">times</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">x</span>
<span class="c1">#============================================================================</span>

<span class="c1">#============================================================================</span>
<span class="c1"># put the data prep steps in a function for easy repeat on test data</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span>
<span class="k">def</span> <span class="nf">data_prep</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">vis_num</span> <span class="o">=</span> <span class="mi">3</span><span class="p">):</span>
    <span class="c1"># get an upside down version </span>
    <span class="n">cpy</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="n">X_c</span> <span class="o">=</span> <span class="n">rotate</span><span class="p">(</span><span class="n">cpy</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
    <span class="n">X_big</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">X_c</span><span class="p">,</span> <span class="n">X</span><span class="p">])</span>
    <span class="n">X_01</span> <span class="o">=</span> <span class="n">X_big</span> <span class="o">/</span> <span class="mi">255</span>
    
    <span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="c1"># double up the labels and </span>
        <span class="n">y_01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">y</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span>
        <span class="c1"># shuffle</span>
        <span class="n">X_01</span><span class="p">,</span> <span class="n">y_01</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">X_01</span><span class="p">,</span> <span class="n">y_01</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">X_01</span>  <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">X_01</span><span class="p">)</span>
        <span class="n">y_01</span> <span class="o">=</span> <span class="kc">None</span>

    <span class="c1"># the final has very small values, so wont look like anything look at the rotation</span>
    <span class="n">x_sub</span> <span class="o">=</span> <span class="n">rand_subset</span><span class="p">(</span><span class="n">X_c</span><span class="p">)</span>

    <span class="c1"># the originals</span>
    <span class="n">train_subset</span> <span class="o">=</span> <span class="n">rand_subset</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">vis_num</span><span class="p">)</span>
    
    <span class="n">tplot</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">tplot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">train_subset</span><span class="p">,</span> <span class="n">x_sub</span><span class="p">])</span>
    
    <span class="k">return</span> <span class="n">X_01</span><span class="p">,</span> <span class="n">y_01</span><span class="p">,</span> <span class="n">tplot</span>
<span class="c1">#============================================================================</span>
  
<span class="c1">#============================================================================</span>
<span class="c1"># run prep function on training and validation combo data</span>
<span class="n">X_01</span><span class="p">,</span> <span class="n">y_01</span><span class="p">,</span> <span class="n">tplot</span> <span class="o">=</span> <span class="n">data_prep</span><span class="p">(</span><span class="n">X_both</span><span class="p">,</span> <span class="n">y_both</span><span class="p">,</span> <span class="n">vis_num</span> <span class="o">=</span> <span class="mi">9</span><span class="p">)</span>

<span class="c1"># split into train and validation again </span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_validation</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_validation</span><span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_01</span><span class="p">,</span> <span class="n">y_01</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span> <span class="mf">0.2</span><span class="p">)</span>

<span class="c1"># just do the division on the test data</span>
<span class="c1">#X_test, y_test, plot_test = data_prep(X_test, y_test)</span>
<span class="n">X_test_process</span> <span class="o">=</span> <span class="n">X_test</span> <span class="o">/</span> <span class="mi">255</span>
<span class="c1">#============================================================================</span>

<span class="c1">#============================================================================</span>
<span class="c1"># plot seected examples from original and rotated sets</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">18</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">image</span> <span class="ow">in</span>  <span class="nb">enumerate</span><span class="p">(</span><span class="n">tplot</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="o">+</span><span class="n">i</span><span class="p">)</span>
    <span class="n">plt</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="c1">#============================================================================</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,iVBORw0KGgoAAAANSUhEUgAAA3YAAAP1CAYAAAAtiX37AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Model-Archetecture">Model Archetecture<a class="anchor-link" href="#Model-Archetecture">&#182;</a></h2><p>The model is a combination of the class lab on LeNet, my code, and inspiration from these sources:</p>
<p>source [1] <a href="https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf">https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf</a></p>
<p>source [2] <a href="https://hacktilldawn.com/2016/09/25/inception-modules-explained-and-implemented/">https://hacktilldawn.com/2016/09/25/inception-modules-explained-and-implemented/</a></p>
<hr>
<p>The archetecture is as follows:</p>
<ul>
<li>Input   : 32x32x3</li>
</ul>
<ul>
<li><p>Layer  1:</p>
<ul>
<li><p>3x3x6 filter Convolution</p>
</li>
<li><p>2x2x6 filter Max pool</p>
</li>
</ul>
</li>
</ul>
<ul>
<li>Layer  2: 5x5x12 filter Convolution</li>
</ul>
<ul>
<li><p>Layer  3: Inception module concatonating:</p>
<ul>
<li><p>1x1x12 filter convolution</p>
</li>
<li><p>1x1x12 filter convolution -&gt; 5x5x12 filter convolution</p>
</li>
<li><p>3x3x12 filter Max pool -&gt; 1x1x12 filter convolution</p>
</li>
<li><p>1x1x12 filter convlution -&gt; 3x3x12 filter convolution</p>
</li>
</ul>
</li>
</ul>
<ul>
<li><p>Layer  4:</p>
<ul>
<li><p>7x7x60 filter convolution</p>
</li>
<li><p>2x2x60 filter Max pool</p>
</li>
</ul>
</li>
</ul>
<ul>
<li><p>Layer  5: Fully connected with dropout 240x500</p>
</li>
<li><p>Layer  6: Fully connected with dropout 500x250</p>
</li>
<li><p>Layer  7: Fully connected with dropout 250x125</p>
</li>
<li><p>Layer  8: Fully connected 125x43</p>
</li>
</ul>
<ul>
<li>Output  : Softmax layer put in loss function</li>
</ul>
<hr>
<p>Loss function includes l2 regularization on all weights in fully connected layers.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#=============================================================================</span>
<span class="c1"># New net as described above. In addition to logits, returns the fully connected </span>
<span class="c1">#  layer&#39;s weights for l2 penalty in loss function.  </span>
<span class="k">def</span> <span class="nf">new_net</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>    
    <span class="c1">#=============================================================================</span>
    <span class="c1"># hyper params</span>
    <span class="n">mu</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">sigma</span> <span class="o">=</span> <span class="mf">0.1</span>
    <span class="c1">#keep_prob = 1</span>
    
    <span class="c1">#=============================================================================</span>

    <span class="c1">#=============================================================================</span>
    <span class="c1"># conv Layer 1</span>
    <span class="n">conv1_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv1_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">6</span><span class="p">))</span>
    <span class="n">conv1</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">conv1_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv1_b</span>
    <span class="n">conv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv1</span><span class="p">)</span>

    <span class="c1"># pooling 1</span>
    <span class="n">conv1_pool</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span>
    <span class="c1">#=============================================================================</span>

    <span class="c1">#=============================================================================</span>
    <span class="c1"># conv Layer 2</span>
    <span class="n">conv2_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv2_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span>
    <span class="n">conv2</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv1_pool</span><span class="p">,</span> <span class="n">conv2_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv2_b</span>
    <span class="n">conv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv2</span><span class="p">)</span>
    <span class="c1">#=============================================================================</span>
    
    <span class="c1">#=============================================================================</span>
    <span class="c1"># layer 3: try inception-like layer </span>
    <span class="n">incept1_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">incept1_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span>
    <span class="n">incept1</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">incept1_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">incept1_b</span>
    <span class="n">incept1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">incept1</span><span class="p">)</span>
    
    <span class="n">incept2_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">incept2_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span>
    <span class="n">incept2</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">incept2_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">incept2_b</span>
    <span class="n">incept2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">incept2</span><span class="p">)</span>
    
    <span class="n">incept3_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">incept3_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span>
    <span class="n">incept3</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">incept2</span><span class="p">,</span> <span class="n">incept3_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">incept3_b</span>
    <span class="n">incept3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">incept3</span><span class="p">)</span>
    
    <span class="n">incept4</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</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">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span>

    <span class="n">incept5_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">incept5_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span>
    <span class="n">incept5</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">incept4</span><span class="p">,</span> <span class="n">incept5_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">incept5_b</span>
    <span class="n">incept5</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">incept5</span><span class="p">)</span>
    
    <span class="n">incept6_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">incept6_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span>
    <span class="n">incept6</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">incept6_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">incept6_b</span>
    <span class="n">incept6</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">incept6</span><span class="p">)</span>
    
    <span class="n">incept7_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">incept7_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span>
    <span class="n">incept7</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">incept6</span><span class="p">,</span> <span class="n">incept7_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">incept7_b</span>
    <span class="n">incept7</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">incept7</span><span class="p">)</span>
    
    <span class="n">concat_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="mi">3</span><span class="p">,[</span><span class="n">incept1</span><span class="p">,</span> <span class="n">incept3</span><span class="p">,</span> <span class="n">incept5</span><span class="p">,</span> <span class="n">incept7</span><span class="p">])</span>
    <span class="n">concat_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">concat_layer</span><span class="p">)</span>
    <span class="c1">#=============================================================================</span>
    
    <span class="c1">#=============================================================================</span>
    <span class="c1"># layer 4: conv 2d</span>
    <span class="n">conv3_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">60</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv3_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">60</span><span class="p">))</span>
    <span class="n">conv3</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">concat_layer</span><span class="p">,</span> <span class="n">conv3_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</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="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv3_b</span>
    <span class="n">conv3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv3</span><span class="p">)</span>
    
    <span class="c1"># pooling 2</span>
    <span class="n">conv3_pool</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">conv3</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span>
    <span class="c1">#=============================================================================</span>
      
    <span class="c1">#=============================================================================</span>
    <span class="c1"># flatten out for fc layer </span>
    <span class="n">fc0</span>   <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv3_pool</span><span class="p">)</span>
    <span class="c1">#=============================================================================</span>
    
    <span class="c1">#=============================================================================</span>
    <span class="c1"># fully connected layers with dropout</span>
    <span class="c1"># layer 5: fully connected with dropout, relu</span>
    <span class="n">fc1_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">240</span><span class="p">,</span> <span class="mi">500</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">fc1_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">500</span><span class="p">))</span>
    <span class="n">fc1</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">fc0</span><span class="p">,</span> <span class="n">fc1_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">fc1_b</span>
    <span class="n">fc1</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">fc1</span><span class="p">)</span>
    <span class="n">fc1</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
    
    <span class="c1"># Layer 6: Fully Connected with dropout, relu</span>
    <span class="n">fc2_W</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="mi">250</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">fc2_b</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">250</span><span class="p">))</span>
    <span class="n">fc2</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span> <span class="n">fc2_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">fc2_b</span>
    <span class="n">fc2</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">fc2</span><span class="p">)</span>
    <span class="n">fc2</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
    
    <span class="c1"># Layer 7: Fully Connected with dropout, relu</span>
    <span class="n">fc3_W</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">250</span><span class="p">,</span> <span class="mi">125</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">fc3_b</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">125</span><span class="p">))</span>
    <span class="n">fc3</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span> <span class="n">fc3_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">fc3_b</span>
    <span class="n">fc3</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">fc3</span><span class="p">)</span>
    <span class="n">fc3</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc3</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>

    <span class="c1"># Layer 8: Fully Connected relu</span>
    <span class="n">fc4_W</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">125</span><span class="p">,</span> <span class="mi">43</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">fc4_b</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">43</span><span class="p">))</span>
    <span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">fc3</span><span class="p">,</span> <span class="n">fc4_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">fc4_b</span>
    <span class="c1">#=============================================================================</span>

    <span class="c1"># return logits before softmax and fc weights</span>
    <span class="k">return</span> <span class="n">logits</span><span class="p">,</span> <span class="n">fc1_W</span><span class="p">,</span> <span class="n">fc2_W</span><span class="p">,</span> <span class="n">fc3_W</span><span class="p">,</span> <span class="n">fc4_W</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="Define-remainder-of-model-and-training-gradient">Define remainder of model and training gradient<a class="anchor-link" href="#Define-remainder-of-model-and-training-gradient">&#182;</a></h2><ul>
<li>Define place holders</li>
<li>Define learning rate input to Adam optimizer </li>
<li>Define l2 loss constant (ended up being 0.01, rather high in my oppinion</li>
<li>Evaluation function, set to return accuracy, metric chosen from the lab material</li>
<li>Set epochs</li>
<li>Set batch size</li>
</ul>
<p>I tried a few different batch sizes, including a large one, though the 128 seemed to be the fastest. Through manual hyper parameter tuning, I found about 40 epochs and a l2 loss constant of 0.01 to get a pretty good score. Additinoally, I tuned the keep_prob rate definined in the new_net function to 0.5 through trial and error. Additionally adadelta was used as an alternative optimization scheme, though adam proved to be better.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#==========================================================================</span>
<span class="c1"># Define placeholders </span>
<span class="n">x</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">))</span>
<span class="n">one_hot_y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">one_hot</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">43</span><span class="p">)</span>
<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">))</span>
<span class="c1">#==========================================================================</span>

<span class="c1">#==========================================================================</span>
<span class="c1"># rate into Adam optimizer</span>
<span class="n">rate</span> <span class="o">=</span> <span class="mf">0.001</span> 
<span class="c1"># hyper parameter: l2 loss constant, 1*10^-4 (my experience, kind of big.)</span>
<span class="n">loss_const</span> <span class="o">=</span> <span class="mf">0.01</span> <span class="c1">#0.0000411</span>
<span class="c1">#==========================================================================</span>

<span class="c1">#==========================================================================</span>
<span class="c1"># get outputs, calcualte loss and define training gradient</span>
<span class="n">logits</span><span class="p">,</span> <span class="n">fc1_W</span><span class="p">,</span> <span class="n">fc2_W</span><span class="p">,</span> <span class="n">fc3_W</span><span class="p">,</span> <span class="n">fc4_W</span> <span class="o">=</span> <span class="n">new_net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">cross_entropy_l2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="n">one_hot_y</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">)</span>

<span class="c1"># define this for predictins later</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Print</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="p">[</span><span class="n">preds</span><span class="p">])</span>

<span class="c1"># calcualte the l2 loss penalty</span>
<span class="n">loss_penalty</span> <span class="o">=</span> <span class="n">loss_const</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">l2_loss</span><span class="p">(</span><span class="n">fc1_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">loss_const</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">l2_loss</span><span class="p">(</span><span class="n">fc2_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">loss_const</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">l2_loss</span><span class="p">(</span><span class="n">fc3_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">loss_const</span><span class="o">*</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">l2_loss</span><span class="p">(</span><span class="n">fc4_W</span><span class="p">)</span>

<span class="c1"># feed it into the mean </span>
<span class="n">loss_operation</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">cross_entropy_l2</span> <span class="o">+</span> <span class="n">loss_penalty</span><span class="p">)</span>

<span class="c1"># keep adam as optimizer for now</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="n">rate</span><span class="p">)</span>

<span class="c1"># run minimize </span>
<span class="n">training_operation</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">loss_operation</span><span class="p">)</span>
<span class="c1">#==========================================================================</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;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#============================================================================</span>
<span class="c1"># define the performance operations </span>
<span class="n">correct_prediction</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">one_hot_y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">accuracy_operation</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_prediction</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
<span class="c1">#============================================================================</span>

<span class="c1">#============================================================================</span>
<span class="c1"># evaluate the model from the default session and input X and y. </span>
<span class="c1"># use this equation for testing and on non-training data</span>
<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">X_data</span><span class="p">,</span> <span class="n">y_data</span><span class="p">):</span>
    <span class="n">num_examples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_data</span><span class="p">)</span>
    <span class="n">total_accuracy</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">get_default_session</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">offset</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
        <span class="n">batch_x</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">X_data</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">offset</span><span class="o">+</span><span class="n">BATCH_SIZE</span><span class="p">],</span> <span class="n">y_data</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">offset</span><span class="o">+</span><span class="n">BATCH_SIZE</span><span class="p">]</span>
        <span class="n">accuracy</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy_operation</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">batch_y</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mi">1</span><span class="p">})</span>
        <span class="n">total_accuracy</span> <span class="o">+=</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">batch_x</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">total_accuracy</span> <span class="o">/</span> <span class="n">num_examples</span>
<span class="c1">#============================================================================</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="Train-the-Model">Train the Model<a class="anchor-link" href="#Train-the-Model">&#182;</a></h2><p>Run the training data through the training pipeline to train the model. This function from the class material.</p>
<p>Brief summary:</p>
<ul>
<li><p>Before each epoch, shuffles the training set.</p>
</li>
<li><p>After each epoch, measures the loss and accuracy of the validation set.</p>
</li>
<li><p>prints out training and validation metrics at each epoch</p>
</li>
<li><p>Saves the model after training</p>
</li>
</ul>
<hr>
<p>The result, ~98% accuracy on train, ~97% accuracy on validation suggest that the model is not overfit, though, its worth exploring further. I used the training code from the example, but modified it to print out training and validation metrics to check for overfitting during training.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#============================================================================</span>
<span class="c1"># Train the model on X_train and y_train from the data_prep function</span>
<span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">50</span>
<span class="c1"># small batch size seems to run faster</span>
<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">128</span>
<span class="c1">#============================================================================</span>

<span class="c1">#============================================================================</span>
<span class="c1"># Tensor flow session</span>
<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
    <span class="n">num_examples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training...&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epoch,train_acc,valid_acc&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">EPOCHS</span><span class="p">):</span>
        <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">offset</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
            <span class="n">end</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">+</span> <span class="n">BATCH_SIZE</span>
            <span class="n">batch_x</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">end</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
            <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">training_operation</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">batch_y</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">})</span>
           
        <span class="n">training_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
        <span class="n">validation_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_validation</span><span class="p">,</span> <span class="n">y_validation</span><span class="p">)</span>
        <span class="c1"># edited for easy copy-paste to visualize training history </span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</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">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;,</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">training_accuracy</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;,</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">validation_accuracy</span><span class="p">))</span> 

    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="s2">&quot;./train_50&quot;</span><span class="p">,</span> <span class="n">global_step</span> <span class="o">=</span> <span class="n">EPOCHS</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model saved&quot;</span><span class="p">)</span>  
<span class="c1">#============================================================================</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Training...

epoch,train_acc,valid_acc
1,0.465,0.461
2,0.627,0.622
3,0.759,0.749
4,0.823,0.812
5,0.856,0.852
6,0.890,0.884
7,0.892,0.885
8,0.910,0.898
9,0.912,0.907
10,0.928,0.915
11,0.925,0.916
12,0.945,0.935
13,0.946,0.938
14,0.953,0.944
15,0.950,0.938
16,0.950,0.940
17,0.957,0.944
18,0.962,0.953
19,0.959,0.950
20,0.951,0.944
21,0.959,0.950
22,0.964,0.951
23,0.971,0.961
24,0.967,0.957
25,0.957,0.945
26,0.970,0.962
27,0.969,0.959
28,0.966,0.955
29,0.971,0.958
30,0.968,0.958
31,0.974,0.960
32,0.974,0.965
33,0.971,0.960
34,0.974,0.963
35,0.979,0.965
36,0.980,0.968
37,0.977,0.966
38,0.979,0.968
39,0.982,0.973
40,0.975,0.963
41,0.981,0.971
42,0.979,0.969
43,0.982,0.972
44,0.981,0.972
45,0.980,0.967
46,0.978,0.967
47,0.976,0.966
48,0.979,0.968
49,0.970,0.956
50,0.985,0.973

Model saved
</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="New-Images-Outside-of-Dataset">New Images Outside of Dataset<a class="anchor-link" href="#New-Images-Outside-of-Dataset">&#182;</a></h2><p>For further testing, I classified some new images that did not come with the data set. These images were found through an online image search, and preprocessed to 32x32 RGB pixel images with similar contraints as seen in the dataset. Each new image is a picutre of one of the sign classes included in the training data.</p>
<p>The purpose of this exersize is to seen how the classifier does with actual new pictures, before trying the hold-out test data. In addition, it may help to gain insight into how the model works and what its strengths and weaknesses are.</p>
<p>The left turn ahead and stop sign are both front facing, though the left turn sign has some odd coloring. I chose a left turn because it has fewer examples in the training data and I was interested to see how a new sign of this class would be classified. The pedestrain sign is an at an more extreme angle compared to much of the training data, snow covers a portion of the yeild sign, and for an experiment, the 30 kph speed limit sign has been vandalized to say "130." Such a instance could be encountered in actual implementation, and humans have no problem reading the sign as "30" but, I wanted to see if the classifier could.</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="o">%</span><span class="k">matplotlib</span> inline

<span class="c1">#===================================================================</span>
<span class="c1"># change working directory</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;/home/andrewrs/Desktop/udacity/Traffic-Sign-Classifier/pics/test_signs/&quot;</span><span class="p">)</span>
<span class="c1"># file names of the exra pics</span>
<span class="n">images</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">()</span>

<span class="c1">#===================================================================</span>
<span class="c1">#read them in </span>
<span class="n">extra_images</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">images</span><span class="p">:</span>
    <span class="n">read_in</span> <span class="o">=</span> <span class="p">(</span><span class="n">mpimg</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img</span><span class="p">))</span>
    <span class="c1">#slice off the alpha channel </span>
    <span class="n">slice_in</span> <span class="o">=</span> <span class="n">read_in</span><span class="p">[:,:,</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
    <span class="n">extra_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">slice_in</span><span class="p">)</span>
    
<span class="n">look</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">extra_images</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1">#===================================================================</span>

<span class="c1">#===================================================================</span>
<span class="c1"># make the data like X_train </span>
<span class="c1">#X_wild, y_empty, plot_pics = data_prep(X = look_slice[0:3], y = None, vis_num = 2)</span>
<span class="n">X_wild</span> <span class="o">=</span> <span class="n">look</span>
<span class="c1">#===================================================================</span>

<span class="c1">#============================================================================</span>
<span class="c1"># plot seected examples from original and rotated sets</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</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">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">image</span> <span class="ow">in</span>  <span class="nb">enumerate</span><span class="p">(</span><span class="n">look</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="o">+</span><span class="n">i</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</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,iVBORw0KGgoAAAANSUhEUgAAAXQAAAE3CAYAAAC6r7qRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Classifying-New-Images">Classifying New Images<a class="anchor-link" href="#Classifying-New-Images">&#182;</a></h2><p>Next, I ran the new images through the classifier outputting the softmax probabilities for all classes, and the most probable class. In all but one case, the most probable class was the correct one. The grafitied 30 kph speed limit sign was incorrectly classifeid as a general caution sign. After exploring outputs a bit, I found that the general caution class is often predicted when the network incorrectly classifies something. This may be due to class balance. However, the correct label, the 30 kph sign, was the second most probable class.</p>
<p>Summary of Results:</p>
<table>
<thead><tr>
<th>Sign</th>
<th>Predicted Classes (decreasing probability left-to-right)</th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<td>Left turn</td>
<td>Left turn: 40%</td>
<td>No passing for vehicles over 3.5 metric tons: 21%</td>
<td>Double Curve: 8%</td>
<td>Go staight or left: 6%</td>
<td>Priority road: 5%</td>
</tr>
<tr>
<td>Stop</td>
<td>Stop: 100%</td>
<td>Turn left ahead: NA</td>
<td>Bumpy road: NA</td>
<td>Vehicles over 3.5 metric tons prohibited: NA</td>
<td>Dangerous curve to the right: NA</td>
</tr>
<tr>
<td>Pedestrians</td>
<td>Pedestrians: 99%</td>
<td>Road work :0.2%</td>
<td>Keep left: 0.07%</td>
<td>Beware of ice/snow: 0.06%</td>
<td>Road work: 0.06%</td>
</tr>
<tr>
<td>Yield</td>
<td>Yield: 95%</td>
<td>Bumpy Road: 3%</td>
<td>Bicycles crossing: 0.4%</td>
<td>Traffic signals: 0.4%</td>
<td>Wild animals crossing: 0.1%</td>
</tr>
<tr>
<td>30 km/h Speed Limit</td>
<td>General caution: 66%</td>
<td>30 km/h Speed Limit: 26%</td>
<td>Speed limit (50km/h): 6%</td>
<td>Speed limit (70km/h): 0.5%</td>
<td>Priority road: 0.3%</td>
</tr>
</tbody>
</table>
<p>Given these results, I chose to continue to model testing.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[55]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># change back to model file path</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;/home/andrewrs/Desktop/udacity/Traffic-Sign-Classifier/&quot;</span><span class="p">)</span>


<span class="c1"># Print out all soft max class predictions of new signs. </span>
<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="c1"># load the saved model from training </span>
    <span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span>
    <span class="c1">#saver = tf.train.import_meta_graph(&quot;train_45.meta&quot;)</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))</span>
    <span class="n">get</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_wild</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mi">1</span><span class="p">})</span>
    <span class="n">get_class</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">get</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span><span class="mi">1</span> <span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">get_prob</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">get</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span><span class="mi">1</span> <span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">choose</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">get</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>  
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;All Classes In Order of Decending Probability:&#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">get_class</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="s1">&#39;Ordered Probabilities&#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">get_prob</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="s1">&#39;Most Probable:&#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">choose</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">session</span><span class="o">=</span><span class="n">sess</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>All Classes In Order of Decending Probability: 
 [[34 10 21 37 12 19 11 17 40 14  4 18  9 42 30 23 41 38  1  2  7 15 31 13
  36  8 35 33  6 16 26 24 22  5  0 25 20 27 32  3 39 29 28]
 [14 36 22 16 20  3  6 17  0 32  5  1 25 19 37 12 34 10 41 33 30 28 42  9
  11 23 13 21 15 38 39 24 35 40  2  4 26  7 18 31 29 27  8]
 [27 26 39 30 25 31 24 28 11 21 18 33 20 40 37  6 29 42  1 22 13 19 35 23
  32  4  0 38 41 17 14 16 10  5 12 15  7 36  2  8 34  3  9]
 [13 22 29 26 31 24 20 38 19 25 23 28 39 27 30 40  0 15 21  3  2  1 37  4
   9 36 35  8 34 18 14 16 32 42  5 41 33 17  6 10  7 12 11]
 [18  1  2  4 12 27 26 11 21  6 15 31  0  5 35 32 33 13 25 37 10 24 42 17
  34 14 22  8  3 36 28 30 40  7 41 19 20  9 39 29 16 38 23]] 
 Ordered Probabilities 
 [[  4.03088480e-01   2.05377102e-01   7.62632191e-02   5.61101884e-02
    4.92416397e-02   4.59231250e-02   3.70742902e-02   3.10007986e-02
    2.08798200e-02   1.37996459e-02   1.18618011e-02   7.43942708e-03
    7.39999954e-03   7.35042198e-03   5.00052515e-03   2.99792248e-03
    2.89097987e-03   2.71380623e-03   1.95463374e-03   1.94727059e-03
    1.66974054e-03   1.40452327e-03   1.12685782e-03   9.35905089e-04
    7.89815676e-04   4.14387934e-04   3.91337613e-04   3.26920563e-04
    3.25361994e-04   3.14458186e-04   2.82920082e-04   2.82691239e-04
    2.65654031e-04   2.08975092e-04   1.91328538e-04   1.75148074e-04
    1.60005191e-04   1.37010138e-04   1.29631517e-04   4.99478629e-05
    4.76210589e-05   3.84998857e-05   1.62121723e-05]
 [  1.00000000e+00   1.05889624e-17   9.04410000e-18   3.85806137e-18
    1.66937559e-18   2.46243574e-20   1.08276165e-20   5.46281628e-21
    5.18433753e-21   1.16327383e-21   4.14030080e-23   7.89831677e-24
    4.59645707e-26   2.09322546e-27   8.49402802e-28   2.87941645e-28
    9.66376697e-29   4.32666895e-30   6.69496131e-31   4.52794488e-32
    2.80695194e-33   1.55510941e-33   1.60656145e-34   1.29423168e-34
    1.05459797e-34   6.59517649e-35   5.86215819e-35   3.89896610e-35
    2.76320847e-36   1.64857069e-37   7.62916434e-38   1.58985354e-38
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00]
 [  9.94329393e-01   2.49491958e-03   7.51598796e-04   6.61459344e-04
    6.13657641e-04   5.54032507e-04   2.46326148e-04   1.42748206e-04
    9.25498971e-05   3.34573633e-05   2.91132856e-05   2.04426378e-05
    9.79019569e-06   5.02706598e-06   4.56144699e-06   3.97597432e-06
    1.54879012e-06   1.24851533e-06   1.23054758e-06   1.04665241e-06
    4.52774771e-07   3.52023932e-07   3.06923624e-07   2.90881388e-07
    1.78520438e-07   6.57961792e-08   6.56853345e-08   5.95921961e-08
    2.30971153e-08   1.18766836e-08   7.27477856e-09   5.22644150e-09
    4.97808639e-09   4.19162527e-09   3.63029695e-09   2.93165048e-09
    2.26064278e-09   1.65858749e-09   1.11908016e-09   8.02023725e-10
    5.85606952e-10   9.14446852e-11   2.39770235e-11]
 [  9.51531231e-01   3.43194902e-02   4.77286382e-03   4.02191887e-03
    1.63548556e-03   1.53411680e-03   1.21617119e-03   2.24504809e-04
    2.21759794e-04   1.87937316e-04   1.46446153e-04   1.02275480e-04
    4.33274363e-05   3.25630681e-05   7.78908634e-06   8.19969898e-07
    4.46045931e-07   4.13681732e-07   3.23306466e-07   5.91695581e-08
    1.81027140e-08   1.47550381e-08   1.34983145e-08   6.14319440e-09
    5.45318812e-09   5.41539702e-09   4.23980051e-09   3.96072730e-09
    3.64426511e-09   2.71315126e-09   2.25135444e-09   1.35448330e-09
    1.33101408e-09   5.38186717e-10   4.10364354e-10   2.89976876e-10
    2.57138116e-10   2.34667452e-10   1.13264127e-10   1.00771100e-10
    3.98143879e-11   2.33932648e-11   1.39110069e-11]
 [  6.58596635e-01   2.61552006e-01   5.65061122e-02   4.97330632e-03
    3.20893223e-03   3.19817057e-03   3.00083100e-03   1.79226091e-03
    1.41573872e-03   1.25774625e-03   8.89384770e-04   5.75476675e-04
    5.01745148e-04   4.36488219e-04   4.10875742e-04   2.38830733e-04
    2.25621712e-04   1.89905215e-04   1.67970313e-04   1.08627930e-04
    9.72849812e-05   7.75730805e-05   7.55806541e-05   6.87993888e-05
    5.73699544e-05   5.45027542e-05   5.15134197e-05   4.75183406e-05
    4.09825188e-05   3.64830521e-05   3.63895779e-05   2.61812274e-05
    1.74140478e-05   1.65236033e-05   1.51404547e-05   1.29878435e-05
    5.67748157e-06   4.59389867e-06   4.37659901e-06   3.38060931e-06
    1.64456503e-06   1.19026333e-06   3.58872683e-07]] 
 Most Probable: 
 [34 14 27 13 18]
</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="Evaluate-the-Model">Evaluate the Model<a class="anchor-link" href="#Evaluate-the-Model">&#182;</a></h2><p>Post-training, and new sign check, I tested the model on a decent amount of hold-out data for a "one-and-done" test evaluation. The result is a test accuracy of about 92%, 5% lower than the final validation number. Thats not awesome, but it will do for this project.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[64]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># redefine batch sze (if loading from saved)</span>
<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">128</span>

<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))</span>
    <span class="n">test_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_test_process</span><span class="p">,</span> <span class="n">y_test</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">test_accuracy</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 = 0.940
</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="Test-Results">Test Results<a class="anchor-link" href="#Test-Results">&#182;</a></h2><p>The holdout test data was predicted with an accuracy of about 94%. This is comparable to the correctly classfied out-of-data-set images. Not breaking any records, but it will do for this project.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<h2 id="Just-for-Fun">Just for Fun<a class="anchor-link" href="#Just-for-Fun">&#182;</a></h2><p>I want to see what the network thinks I am.</p>
<p><img src="./pics/me_big.jpg" style="width: 200px;"/></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="o">%</span><span class="k">matplotlib</span> inline

<span class="c1">#===================================================================</span>
<span class="kn">import</span> <span class="nn">matplotlib.image</span> <span class="k">as</span> <span class="nn">mpimg</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="c1">#===================================================================</span>

<span class="c1">#===================================================================</span>
<span class="c1"># change working directory</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;/home/andrewrs/Desktop/udacity/Traffic-Sign-Classifier/pics/extra_signs/&quot;</span><span class="p">)</span>
<span class="c1"># file names of the exra pics</span>
<span class="n">images</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">()</span>

<span class="c1">#===================================================================</span>
<span class="c1">#read them in </span>
<span class="n">extra_images</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">images</span><span class="p">:</span>
    <span class="n">read_in</span> <span class="o">=</span> <span class="p">(</span><span class="n">mpimg</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img</span><span class="p">))</span>
    <span class="c1">#slice off the alpha channel </span>
    <span class="n">slice_in</span> <span class="o">=</span> <span class="n">read_in</span><span class="p">[:,:,</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
    <span class="n">extra_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">slice_in</span><span class="p">)</span>
<span class="c1">#===================================================================</span>
  
<span class="c1">#===================================================================</span>
<span class="c1"># make the data like X_train</span>
<span class="n">look</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">extra_images</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>

<span class="n">X_wild</span> <span class="o">=</span> <span class="n">look</span>
<span class="c1">#===================================================================</span>

<span class="c1">#============================================================================</span>
<span class="c1"># plot seected examples from original and rotated sets</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</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">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">image</span> <span class="ow">in</span>  <span class="nb">enumerate</span><span class="p">(</span><span class="n">look</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="o">+</span><span class="n">i</span><span class="p">)</span>
    <span class="n">plt</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="c1">#============================================================================  </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,iVBORw0KGgoAAAANSUhEUgAAAIcAAACFCAYAAABxL+sBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</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="c1">#============================================================================</span>
<span class="c1"># change back to model file path</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;/home/andrewrs/Desktop/udacity/Traffic-Sign-Classifier/&quot;</span><span class="p">)</span>

<span class="c1"># Print out all soft max class predictions of new signs. </span>
<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="c1"># load the saved model from training </span>
    <span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span>
    <span class="c1">#saver = tf.train.import_meta_graph(&quot;train_45.meta&quot;)</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))</span>
    <span class="n">get</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_wild</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mi">1</span><span class="p">})</span>
    <span class="n">get_class</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">get</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span><span class="mi">1</span> <span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">get_prob</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">get</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span><span class="mi">1</span> <span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">choose</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">get</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>  
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;All Classes In Order of Decending Probability:&#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">get_class</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="s1">&#39;Ordered Probabilities&#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">get_prob</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="s1">&#39;Most Probable:&#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">choose</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">session</span><span class="o">=</span><span class="n">sess</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>All Classes In Order of Decending Probability: 
 [[ 3 28  0 20 32 29  8 13 36 22  1  5 41 26  9 30 38 35 14 19 24 15 25  6
  34  2 33 39 16 31 23  4 27 18 42  7 40 21 37 17 11 12 10]] 
 Ordered Probabilities 
 [[  5.92590332e-01   8.63898620e-02   5.73759601e-02   5.31642176e-02
    4.68455628e-02   3.05426717e-02   2.62129586e-02   1.68133955e-02
    1.30065558e-02   1.27024511e-02   8.23739357e-03   8.04786757e-03
    6.57815766e-03   5.30010648e-03   4.60358756e-03   3.23326001e-03
    3.12090060e-03   2.65644747e-03   2.54762755e-03   2.30452395e-03
    2.10725889e-03   1.94484927e-03   1.38308492e-03   1.37641502e-03
    1.25836115e-03   1.21931511e-03   1.12927170e-03   1.01157103e-03
    9.69911460e-04   9.25078057e-04   7.78756978e-04   6.32182811e-04
    4.70920902e-04   4.08573862e-04   3.24861350e-04   3.22271022e-04
    3.21738684e-04   2.61316745e-04   2.49250937e-04   2.46132433e-04
    1.58178751e-04   1.15873925e-04   1.11040659e-04]] 
 Most Probable: 
 [3]
</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="I-am-a-Speed-Limit-Sign">I am a Speed Limit Sign<a class="anchor-link" href="#I-am-a-Speed-Limit-Sign">&#182;</a></h3><p>It turns out, theres a 59% probability I'm a speed limit sign (assuming I am one of the 43 traffic signs in the training data).</p>
<p><img src="./pics/me_big.jpg" style="width: 100px;"/></p>
<p><img src="./pics/60_kph.jpg" style="width: 100px;"/></p>
<p>Although a just-for-fun exercise, feeding strange picutres into the model and trying to interpret the resulting labels and confidence can give you a different look into how the model works and what unexpected problems might arise. This also highlights the limitations of supervised classification with CNNs, as they can only predict one of the training classes for any image, in this case a 59% probability that a human face is a speed limit sign.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#182;</a></h2><p>My convolutional neural was effective in general, even given its simple archetecutre and light data preprocessing. Granted, this data is from a "packaged" data set which is already very clean compared to raw, collected data, and a high score should be achievable. Also, a model with very high accuracy in deployment would be nessesary in an actual self driving car application, this network falls quite short of that.</p>
<p>Training models to classify images in this way is truly amazing, however, it is important to remember that actually implemenating any model is messy, and filled with unexpected problems. Launching is a challenging weakness-highlgtling, surprising test of your whole design, which a neural network will only be a part of. Labeling my face as a speed limit sign is a reminder that supervised classification is inherently limited, and this should be taken into account when building solutions with classifiers.</p>

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

 
</html>