Traffic_Sign_Classifier.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />

<title>Traffic_Sign_Classifier</title>

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html {
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body {
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article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
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audio,
canvas,
progress,
video {
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audio:not([controls]) {
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[hidden],
template {
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a {
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a:hover {
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b,
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dfn {
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h1 {
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small {
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sup {
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sup {
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sub {
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svg:not(:root) {
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figure {
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hr {
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input,
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input[type="submit"] {
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td,
th {
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  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 4.7.0 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=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#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;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.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;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-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 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-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 {
  -ms-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-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.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,
.fa-gratipay: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-pp: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-resistance: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-y-combinator-square:before,
.fa-yc-square:before,
.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";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  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;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* 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;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: 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 {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  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;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#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#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
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;
}
.modal-header {
  cursor: move;
}
@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;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* 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 {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
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 .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.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: text-bottom;
  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;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.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_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.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;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.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;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.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;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-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.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.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);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.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-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* 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;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  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,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@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. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.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. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

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;
}
div.output_area .mglyph > img {
  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: 1px 0 1px 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:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.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;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.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;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
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.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.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;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 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;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  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;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button: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);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.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 .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#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;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -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.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.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;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.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;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#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;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#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;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#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.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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 {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  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;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#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;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.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;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
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;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#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;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#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 */
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<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 */
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.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 */
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.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<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; }

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<style type="text/css">
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<h1 id="Self-Driving-Car-Engineer-Nanodegree">Self-Driving Car Engineer Nanodegree<a class="anchor-link" href="#Self-Driving-Car-Engineer-Nanodegree">&#182;</a></h1><h2 id="Deep-Learning">Deep Learning<a class="anchor-link" href="#Deep-Learning">&#182;</a></h2><h2 id="Project:-Build-a-Traffic-Sign-Recognition-Classifier">Project: Build a Traffic Sign Recognition Classifier<a class="anchor-link" href="#Project:-Build-a-Traffic-Sign-Recognition-Classifier">&#182;</a></h2><h5 id="Author-Ian-Whittal">Author Ian Whittal<a class="anchor-link" href="#Author-Ian-Whittal">&#182;</a></h5><p>The <a href="https://review.udacity.com/#!/rubrics/481/view">rubric</a> contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.</p>

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<h2 id="Import-Libraries">Import Libraries<a class="anchor-link" href="#Import-Libraries">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">scipy.ndimage</span>
<span class="kn">import</span> <span class="nn">cv2</span> 
<span class="o">%</span><span class="k">matplotlib</span> inline
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<h2 id="Load-The-Data">Load The Data<a class="anchor-link" href="#Load-The-Data">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load pickled data</span>
<span class="c1"># import pickle</span>

<span class="c1"># Reference location of saved the training, validation and testing data</span>


<span class="n">training_file</span> <span class="o">=</span> <span class="s2">&quot;../data/train.p&quot;</span>
<span class="n">validation_file</span><span class="o">=</span> <span class="s2">&quot;../data/valid.p&quot;</span>
<span class="n">testing_file</span> <span class="o">=</span> <span class="s2">&quot;../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="c1"># rb -&gt; (r) read only and (b) opens the file in binary mode.</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="c1"># train is a dictionary</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="c1"># valid is a dictionary</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="c1"># test is a dictionary</span>

<span class="c1"># Numpy Arrays</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_valid</span><span class="p">,</span> <span class="n">y_valid</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"># Check to ensure features equals labels for each data set</span>
<span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span>
<span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_valid</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_valid</span><span class="p">))</span>
<span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_test</span><span class="p">))</span>

<span class="c1"># Print shapes of training, validation and test data</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_train shape:&quot;</span><span class="p">,</span> <span class="n">X_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;y_train shape:&quot;</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_valid shape:&quot;</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;y_valid shape:&quot;</span><span class="p">,</span> <span class="n">y_valid</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_test shape:&quot;</span><span class="p">,</span> <span class="n">X_test</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;y_test shape:&quot;</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>X_train shape: (32, 32, 3)
y_train shape: (34799,)
X_valid shape: (32, 32, 3)
y_valid shape: (4410,)
X_test shape: (32, 32, 3)
y_test shape: (12630,)
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<h2 id="Dataset-Summary-&amp;-Exploration">Dataset Summary &amp; Exploration<a class="anchor-link" href="#Dataset-Summary-&amp;-Exploration">&#182;</a></h2><p>The pickled data is a dictionary with 4 key/value pairs:</p>
<ul>
<li><code>'features'</code> is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).</li>
<li><code>'labels'</code> is a 1D array containing the label/class id of the traffic sign. The file <code>signnames.csv</code> contains id -&gt; name mappings for each id.</li>
<li><code>'sizes'</code> is a list containing tuples, (width, height) representing the original width and height the image.</li>
<li><code>'coords'</code> is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. <strong>THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES</strong></li>
</ul>
<p>Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html">pandas shape method</a> might be useful for calculating some of the summary results.</p>

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<h3 id="Basic-Summary-of-the-Data-Set-Using-Python,-Numpy">Basic Summary of the Data Set Using Python, Numpy<a class="anchor-link" href="#Basic-Summary-of-the-Data-Set-Using-Python,-Numpy">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Replace each question mark with the appropriate value. </span>
<span class="c1">### Use python, pandas or numpy methods rather than hard coding the results</span>

<span class="c1"># Number of training examples</span>
<span class="n">n_train</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="c1"># Number of validation examples</span>
<span class="n">n_validation</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_valid</span><span class="p">)</span>

<span class="c1"># Number of testing examples.</span>
<span class="n">n_test</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># hat&#39;s the shape of an traffic sign image?</span>
<span class="n">image_shape</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>

<span class="c1"># How many unique classes/labels there are in the dataset. python np.unique() returns unique in array, use training dataset as it will have all labels</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span> 

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of training examples =&quot;</span><span class="p">,</span> <span class="n">n_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of validation examples =&quot;</span><span class="p">,</span> <span class="n">n_validation</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of testing examples =&quot;</span><span class="p">,</span> <span class="n">n_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape of first training image =&quot;</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;w x&quot;</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;h x &quot;</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="s2">&quot;d&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of classes =&quot;</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
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<pre>Number of training examples = 34799
Number of validation examples = 4410
Number of testing examples = 12630
Shape of first training image = 32 w x 32 h x  3 d
Number of classes = 43
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<h3 id="Exploratory-Visualization-of-the-Dataset">Exploratory Visualization of the Dataset<a class="anchor-link" href="#Exploratory-Visualization-of-the-Dataset">&#182;</a></h3>
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<p>Visualize the German Traffic Signs Dataset using the pickled file(s).  From the visualization we can clearly see this is a large amount of training data, however there is very little validation data.  The test data seems appropriate enough to test our classifier on.</p>
<p>The histogram below shows an overlay of each data set across each of the 43 different sign classes.  Of additional note is that there are limited images available for individual classes, which may make those images harder to classify accurately.  I will want to use data augmentation to create more images overall for training.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Show visualations of dataset</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span> 
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">1</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</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">20</span><span class="p">):</span>
    <span class="n">index</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</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="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="n">index</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Histogram of Image Class Distribution</span>

<span class="n">a</span> <span class="o">=</span> <span class="n">y_train</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">y_valid</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">y_test</span>
<span class="n">bins</span> <span class="o">=</span> <span class="n">n_classes</span>

<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training data&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation data&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.6</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test data&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper center&#39;</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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tHGjBnD1KlT6dOnDzfddFN1vE2bNixYsIAmTZrwr3/9i9GjR7No0SImTZrElClTmDdvHgC7d++OWU6kvigJSOi8+uqrLFmypPrRkHv27KF9+/YMGjSItWvXcuONNzJ06FAGDhwYdzvbt29nz5491S2Aq666qnqOoL179zJhwgRWrlzJ4Ycfzvr162NuI9FyInVFSUBCx90ZN24cd99990HLSktLefnll3nooYeYM2cOU6dOjbutmmY/uf/++2nfvj1//vOf+frrr2nWrFlK5UTqiqaSltAZMGAAs2fPrn7U444dO/jwww/Ztm0b7s5ll13GnXfeyfLly4Gap4Ru1aoVTZo04e233wYOnta5TZs2mBnTp0+vfoZArGmdY5UTqS9qCUi9qW1IZ305/fTTmThxIgMGDOCbb76hcePGPP744zRq1Ijx48fj7pgZv//97wEYO3YsV199dcwLw0899RRXX301TZs23a/7aMKECQwfPpyZM2cyYMCA6ofF9OjRg3379tG9e3fGjx9fYzmR+qKppA9RuqZZTnSbdUVTSUttNJX0/hrSJJBpnUpaRERyl5KAiEiIKQlInXFcFzobKP3dwkNJQOrMBzu/pnL35/pAaWDcnR07dtCkSZNMV0XqgUYHSZ15eNG/+TnwX823s2bX0ZmujhyCJk2a0K5du9oLSoOnJCB15vO933DP6zuAhjWyQiRM1B0kIhJiSgIiIiGmJCAiEmJKAiIiIaYkICISYok8XnKamW01s1VRsTvM7GMzKwl+hkYtu8XM1pnZWjMbFBUfHMTWmVlx+g9FREQOVSItgaeBwTHif3T3guBnPoCZdSHy7OGuwTqPmlkjM2sEPAIMAboAI4OyIiKSQYk8Y/h1M8tLcHsXAbPcfS+w0czWAb2CZevcfQOAmc0Kyq4+5BqLiEjapHJNYIKZlQbdRS2CWFvgo6gy5UGspnhMZlZkZkvNbOm2bdtSqKKIiMSTbBJ4DDgFKAA2A/cH8VjP2vM48Zjcfaq7F7p7YevWrZOsooiI1CapaSPcfUvVazN7AngxeFsOtI8q2g6oCF7XFBcRkQxJqiVgZm2i3l4CVI0cegEYYWZHmllHoBOwGFgCdDKzjmZ2BJGLxy8kX20REUmHWlsCZjYT6Ae0MrNyYCLQz8wKiHTpbAKuAXD398xsNpELvpXA9e6+L9jOBOAVoBEwzd3fS/vRiIjIIUlkdNDIGOEn45S/B7gnRnw+MP+QaiciInVKdwyLiISYkoCISIgpCYiIhJiSgIhIiCkJiIiEmJKAiEiIKQmIiISYkoCISIgpCYiIhJiSgIhIiCU1i6iI1I+84pdqXLZp0gX1WBPJVWoJiIiEmJKAiEiIqTsoR8XrRhARqaKWgIhIiCkJiIiEWCJPFpsG/BDY6u75Qexe4EfAf4D1wFh332lmecAaYG2w+jvufm2wzhnA08BRRB4uc6O71/iweYnQ6BARqUuJtASeBgYfEFsA5Lt7N+B94JaoZevdvSD4uTYq/hhQROS5w51ibFNEROpZrUnA3V8HPj0g9r/uXhm8fQdoF28bwYPpj3X3t4Nv/88AFydXZRERSZd0jA4aB/wl6n1HM1sBfA7c5u5vAG2B8qgy5UGswcmmUTfZVBcRaZhSSgJmditQCcwIQpuBDu6+I7gGMM/MugIWY/UarweYWRGRriM6dOiQShVFRCSOpEcHmdloIheMr6i6wOvue919R/B6GZGLxqcS+eYf3WXUDqioadvuPtXdC929sHXr1slWUUREapFUEjCzwcDNwIXuvjsq3trMGgWvTyZyAXiDu28GvjCzs83MgFHA8ynXXkREUpLIENGZQD+glZmVAxOJjAY6ElgQ+UyvHgr6feAuM6sE9gHXunvVReXr+HaI6MvBj4iIZFCtScDdR8YIP1lD2TnAnBqWLQXyD6l2IiJSp3THsIhIiCkJiIiEmJKAiEiIaSppOYhuQhMJD7UERERCTElARCTElAREREJMSUBEJMSUBEREQkxJQEQkxJQERERCTElARCTElAREREJMdwxLTop31/OmSRfUY02kruhvnB5qCYiIhJiSgIhIiCkJiIiEWEJJwMymmdlWM1sVFTvezBaYWVnwu0UQNzN7yMzWmVmpmfWMWmd0UL4seFC9iIhkUKItgaeBwQfEioHX3L0T8FrwHmAIkQfMdwKKgMcgkjSIPJ/4LKAXMLEqcYiISGYklATc/XXg0wPCFwHTg9fTgYuj4s94xDtAczNrAwwCFrj7p+7+b2ABBycWERGpR6lcEzjR3TcDBL9PCOJtgY+iypUHsZriIiKSIXVxYdhixDxO/OANmBWZ2VIzW7pt27a0Vk5ERL6Vys1iW8ysjbtvDrp7tgbxcqB9VLl2QEUQ73dA/B+xNuzuU4GpAIWFhTEThYgegymSulRaAi8AVSN8RgPPR8VHBaOEzgY+C7qLXgEGmlmL4ILwwCAmIiIZklBLwMxmEvkW38rMyomM8pkEzDaz8cCHwGVB8fnAUGAdsBsYC+Dun5rZ3cCSoNxd7n7gxWYREalHCSUBdx9Zw6L+Mco6cH0N25kGTEu4diIiUqd0x7CISIgpCYiIhJiSgIhIiCkJiIiEmJKAiEiIKQmIiISYkoCISIgpCYiIhJiSgIhIiCkJiIiEmJKAiEiIKQmIiISYkoCISIgpCYiIhJiSgIhIiCkJiIiEmJKAiEiIJZ0EzOw0MyuJ+vnczH5hZneY2cdR8aFR69xiZuvMbK2ZDUrPIYiISLISerxkLO6+FigAMLNGwMfAXCLPFP6ju98XXd7MugAjgK7Ad4BXzexUd9+XbB1ERCQ16eoO6g+sd/cP4pS5CJjl7nvdfSORB9H3StP+RUQkCelKAiOAmVHvJ5hZqZlNM7MWQawt8FFUmfIgJiIiGZJ0d1AVMzsCuBC4JQg9BtwNePD7fmAcYDFW9xq2WQQUAXTo0CHVKh6yI0/6a43L9n4yrB5rIiJSt9LREhgCLHf3LQDuvsXd97n7N8ATfNvlUw60j1qvHVARa4PuPtXdC929sHXr1mmoooiIxJKOJDCSqK4gM2sTtewSYFXw+gVghJkdaWYdgU7A4jTsX0REkpRSd5CZHQ38ALgmKvwHMysg0tWzqWqZu79nZrOB1UAlcH0mRwbF6/KR5MQ/pxfUWz1EJHEpJQF33w20PCB2VZzy9wD3pLJPERFJH90xLCISYimPDpJwSbYbLa/4pRqXbZqkrqKGLlf+vmHs0lRLQEQkxJQERERCTN1BadSQbjJrSHUVkbqjloCISIgpCYiIhJi6g+pJGEcdiEj2U0tARCTElAREREJM3UEikjG5cpNZQ6aWgIhIiCkJiIiEmJKAiEiIKQmIiISYkoCISIhpdJBknEaIJEfnTdIh5ZaAmW0ys3fNrMTMlgax481sgZmVBb9bBHEzs4fMbJ2ZlZpZz1T3LyIiyUtXd9B57l7g7oXB+2LgNXfvBLwWvAcYQuQB852AIuCxNO1fRESSUFfXBC4CpgevpwMXR8Wf8Yh3gOZm1qaO6iAiIrVIRxJw4H/NbJmZFQWxE919M0Dw+4Qg3hb4KGrd8iC2HzMrMrOlZrZ027ZtaaiiiIjEko4Lw33cvcLMTgAWmNm/4pS1GDE/KOA+FZgKUFhYeNByERFJj5STgLtXBL+3mtlcoBewxczauPvmoLtna1C8HGgftXo7oCLVOsjBkn0gvGSXungCnEYVSbSUuoPMrKmZHVP1GhgIrAJeAEYHxUYDzwevXwBGBaOEzgY+q+o2EhGR+pdqS+BEYK6ZVW3rWXf/m5ktAWab2XjgQ+CyoPx8YCiwDtgNjE1x/yIikoKUkoC7bwC6x4jvAPrHiDtwfSr7zLT67mZRt05uUBeMZCtNGyEiEmJKAiIiIaYkICISYppATg6i6xAi4aGWgIhIiCkJiIiEmLqDJCfF79LSkMxkxBvmmgn6G6eHWgIiIiGmJCAiEmLqDsoC8ZrZR55UjxXJQvHPjUYx5bLaup/C/n8jXdQSEBEJMSUBEZEQU3eQSJpk2+iZMNOEfYlTS0BEJMSUBEREQkzdQSJR1I0gyWjI/26SbgmYWXszW2hma8zsPTO7MYjfYWYfm1lJ8DM0ap1bzGydma01s0HpOAAREUleKi2BSuBX7r48eM7wMjNbECz7o7vfF13YzLoAI4CuwHeAV83sVHffl0IdREQkBUkngeAB8ZuD11+Y2RqgbZxVLgJmufteYKOZrQN6AW8nWwdpOOLd2LX3k2H1WJPkafRPzcIwj0+u/v3TcmHYzPKAHsCiIDTBzErNbJqZtQhibYGPolYrJ37SEBGROpZyEjCzZsAc4Bfu/jnwGHAKUECkpXB/VdEYq3sN2ywys6VmtnTbtm2pVlFERGqQ0uggM2tMJAHMcPe/Arj7lqjlTwAvBm/LgfZRq7cDKmJt192nAlMBCgsLYyaKVP2k0WtJrffsvv5prknuiHdOdd5qlk1zR+Vql4fULJXRQQY8Caxx9wei4m2iil0CrApevwCMMLMjzawj0AlYnOz+RUQkdam0BPoAVwHvmllJEPstMNLMCoh09WwCrgFw9/fMbDawmsjIous1MkhEJLNSGR30JrH7+efHWece4J5k95ntku1ieor6Hx2TbNdNsscYTy6MHMo22dStk+x04Jn428f7953s/9Nk/xb1dZOZpo0QEQkxJQERkRDL6bmD4jXDxuoOhdDKpq6SVGRbV0pDoc+F/aklICISYkoCIiIhltPdQXWhvkfH1MX+apOJfdYkVx4mn2zXTV3cgJdsXXLlbyH7U0tARCTElAREREJM3UHSYGXbzXnqLkmv2s5n3K6yOHMuaeTU/tQSEBEJMSUBEZEQUxIQEQkxXROQetFQnt+QiX79bLqWkGxdkp14rSE9gyLZc5P8sFtNICciInVMSUBEJMRyujsom5rZ8WTTHbrZJgx3aCfb7ZHsEMn6fpZEXXQxpbJuvHPTUPaXTmoJiIiEWL0nATMbbGZrzWydmRXX9/5FRORb5u71tzOzRsD7wA+AcmAJMNLdV9e0TmFhoS9dujSp/d35cF5S64mIZNrEn29Kel0zW+buhYmUre+WQC9gnbtvcPf/ALOAi+q5DiIiEqjvJNAW+CjqfXkQExGRDKjv0UEWI3ZQf5SZFQFFwdtdZrY2yf21ArYnuW4Y6PzUTucoPp2f2iV1ju64IdbHZcL+K9GC9Z0EyoH2Ue/bARUHFnL3qcDUVHdmZksT7RcLI52f2ukcxafzU7tsP0f13R20BOhkZh3N7AhgBPBCPddBREQC9doScPdKM5sAvAI0Aqa5+3v1WQcREflWvd8x7O7zgfn1tLuUu5RynM5P7XSO4tP5qV1Wn6N6vU9ARESyi6aNEBEJsZxMApqa4mBmNs3MtprZqqjY8Wa2wMzKgt8tMlnHTDKz9ma20MzWmNl7ZnZjENc5CphZEzNbbGYrg3N0ZxDvaGaLgnP0l2DQR2iZWSMzW2FmLwbvs/r85FwSCKameAQYAnQBRppZl8zWKis8DQw+IFYMvObunYDXgvdhVQn8yt07A2cD1wf/bnSOvrUXON/duwMFwGAzOxv4PfDH4Bz9GxifwTpmgxuBNVHvs/r85FwSQFNTxOTurwOfHhC+CJgevJ4OXFyvlcoi7r7Z3ZcHr78g8p+4LTpH1TxiV/C2cfDjwPnAc0E81OfIzNoReSTY/wTvjSw/P7mYBDQ1ReJOdPfNEPkQBE7IcH2ygpnlAT2ARegc7Sfo6igBtgILgPXATnevDIqE/f/bZOD/Ad8E71uS5ecnF5NAQlNTiMRiZs2AOcAv3P3zTNcn27j7PncvIHK3fy+gc6xi9Vur7GBmPwS2uvuy6HCMoll1fnLxyWIJTU0hAGwxszbuvtnM2hD5dhdaZtaYSAKY4e5Vj8PSOYrB3Xea2T+IXD9pbmaHB992w/z/rQ9woZkNBZoAxxJpGWT1+cnFloCmpkjcC8Do4PVo4PkM1iWjgr7bJ4E17v5A1CKdo4CZtTaz5sHro4ABRK6dLASGB8VCe47c/RZ3b+fueUQ+d/7u7leQ5ecnJ28WCzLxZL6dmuKeDFcp48xsJtCPyIyGW4CJwDxgNtAB+BC4zN0PvHgcCmbWF3gDeJdv+3N/S+S6gM4RYGbdiFzYbETkC+R8ACcmAAAAW0lEQVRsd7/LzE4mMgDjeGAFcKW7781cTTPPzPoBv3b3H2b7+cnJJCAiIonJxe4gERFJkJKAiEiIKQmIiISYkoCISIgpCYiIhJiSgIhIiCkJiIiEmJKAiEiI/X88eDlsQJAHYwAAAABJRU5ErkJggg==
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<h2 id="Design-and-Test-a-Model-Architecture">Design and Test a Model Architecture<a class="anchor-link" href="#Design-and-Test-a-Model-Architecture">&#182;</a></h2><p>Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the <a href="http://benchmark.ini.rub.de/?section=gtsrb&amp;subsection=dataset">German Traffic Sign Dataset</a>.</p>
<p>The LeNet-5 implementation shown in the <a href="https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81">classroom</a> at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!</p>
<p>With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.</p>
<p>There are various aspects to consider when thinking about this problem:</p>
<ul>
<li>Neural network architecture (is the network over or underfitting?)</li>
<li>Play around preprocessing techniques (normalization, rgb to grayscale, etc)</li>
<li>Number of examples per label (some have more than others).</li>
<li>Generate fake data.</li>
</ul>
<p>Here is an example of a <a href="http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf">published baseline model on this problem</a>. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.</p>

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<h2 id="Data-Augmentation-(Rotation)">Data Augmentation (Rotation)<a class="anchor-link" href="#Data-Augmentation-(Rotation)">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Data augmentation technique: Rotate training images</span>
<span class="n">degrees</span> <span class="o">=</span> <span class="mi">10</span>

<span class="c1"># Rotating images</span>
<span class="n">degrees_positive</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">X_train_rotated_positive</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="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)):</span>
    <span class="n">rotated_image</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">ndimage</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">degrees_positive</span><span class="p">)</span>
    <span class="n">X_train_rotated_positive</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rotated_image</span><span class="p">)</span>
    
<span class="n">degrees_negative</span> <span class="o">=</span> <span class="mi">350</span>
<span class="n">X_train_rotated_negative</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="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)):</span>
    <span class="n">rotated_image</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">ndimage</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">degrees_negative</span><span class="p">)</span>
    <span class="n">X_train_rotated_negative</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rotated_image</span><span class="p">)</span>    

<span class="c1"># Crop image, due to other image size after rotation. Attention: it&#39;s not automated! It fit&#39;s currently to the 10° rotation.</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train_rotated_positive</span><span class="p">)):</span>
    <span class="n">X_train_rotated_positive</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">X_train_rotated_positive</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="mi">34</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="mi">34</span><span class="p">]</span>   <span class="c1"># box=(y:y+crop, x:x+crop)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train_rotated_negative</span><span class="p">)):</span>
    <span class="n">X_train_rotated_negative</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">X_train_rotated_negative</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="mi">34</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="mi">34</span><span class="p">]</span>   <span class="c1"># box=(y:y+crop, x:x+crop)</span>
    
<span class="c1"># appending rotated images to training set</span>

<span class="c1"># Convert the data into list type to use the method &quot;append&quot;</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>

<span class="c1"># combine the Lists</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train_rotated_positive</span><span class="p">)):</span>
    <span class="n">X_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">X_train_rotated_positive</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">X_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">X_train_rotated_negative</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    
<span class="c1">#Convert the data back to a np.array</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>

<span class="c1"># New number of training examples (after data augmentation)</span>
<span class="n">new_n_train</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;New number of training samples after data augmentation =&quot;</span><span class="p">,</span> <span class="n">new_n_train</span><span class="p">)</span>


<span class="c1"># do the same for the labels y_train</span>
<span class="c1"># Convert the data into list type to use the method &quot;append&quot;</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>

<span class="c1"># lengthen the list</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">)):</span>
    <span class="n">y_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">y_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    
<span class="c1">#Convert the data back to a np.array</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>

<span class="c1"># New length of ground truth labels</span>
<span class="n">new_n_train_y</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;New length of ground truth labels =&quot;</span><span class="p">,</span> <span class="n">new_n_train_y</span><span class="p">)</span>
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<pre>New number of training samples after data augmentation = 104397
New length of ground truth labels = 104397
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Display an example for rotation</span>

<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">,</span> <span class="n">ax3</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="mi">2018</span><span class="p">])</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Original&#39;</span><span class="p">)</span>    
<span class="n">ax1</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;ON&#39;</span><span class="p">)</span>  <span class="c1"># clear x- and y-axes</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train_rotated_positive</span><span class="p">[</span><span class="mi">2018</span><span class="p">])</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Rotated +10°&#39;</span><span class="p">)</span>    
<span class="n">ax2</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;ON&#39;</span><span class="p">)</span>  <span class="c1"># clear x- and y-axes</span>
<span class="n">ax3</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train_rotated_negative</span><span class="p">[</span><span class="mi">2018</span><span class="p">])</span>
<span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Rotated -10°&#39;</span><span class="p">)</span>   
<span class="n">ax3</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;ON&#39;</span><span class="p">)</span>  <span class="c1"># clear x- and y-axes</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;./examples/Figure_original_rotated.jpg&#39;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="Pre-process-the-Data-Set-(normalization,-grayscale,-etc.)">Pre-process the Data Set (normalization, grayscale, etc.)<a class="anchor-link" href="#Pre-process-the-Data-Set-(normalization,-grayscale,-etc.)">&#182;</a></h2>
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<h3 id="Shuffle">Shuffle<a class="anchor-link" href="#Shuffle">&#182;</a></h3><p>Shuffle the training data.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">shuffle</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="nb">print</span><span class="p">(</span><span class="s1">&#39;shuffle complete&#39;</span><span class="p">)</span>
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<pre>shuffle complete
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<h3 id="Normalizing">Normalizing<a class="anchor-link" href="#Normalizing">&#182;</a></h3><p>Normalizing helps the network to converge faster. It makes it a lot easier for the optimizer to proceed numerically. It is required to normalize the image data so that the data has mean zero and equal variance. Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, <code>(pixel - 127.5)/ 255</code> will take the pixel  is a quick way to approximately normalize the data and can be used in this project. A well conditioned image allows the optimizer to be more efficient to find a solution.</p>
<h3 id="Single-channel-images-(e.g.-grayscale)">Single-channel images (e.g. grayscale)<a class="anchor-link" href="#Single-channel-images-(e.g.-grayscale)">&#182;</a></h3><p>As Pierre Sermanet and Yann LeCun mentioned in their <a href="http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf">paper</a>, using color channels didn't seem to improve things a lot.  Therefore, I will only use a single channel in my model, e.g. grayscale images instead of color RGB.</p>
<h3 id="Histogram-Equalization">Histogram Equalization<a class="anchor-link" href="#Histogram-Equalization">&#182;</a></h3><p>This method usually increases the global contrast of many images, especially when the usable data of the image is represented by close contrast values. Through this adjustment, the intensities can be better distributed on the histogram. This allows for areas of lower local contrast to gain a higher contrast. Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># # Normalizing Images for zero mean and equal variance to improve convergence rate</span>
<span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="n">normal</span> <span class="o">=</span> <span class="p">(</span><span class="n">image</span> <span class="o">-</span> <span class="mf">127.5</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">normal</span><span class="p">,</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">1</span><span class="p">))</span>

<span class="c1"># Convert RGB to grayscale</span>
<span class="k">def</span> <span class="nf">grayscale</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_RGB2GRAY</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">gray</span><span class="p">,</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">1</span><span class="p">))</span>

<span class="c1"># Histogram equalization to improve contrast</span>
<span class="k">def</span> <span class="nf">histogram_equalize</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="n">equal</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">equalizeHist</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">equal</span><span class="p">,</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">1</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># training set data preprocessing</span>
<span class="n">X_train_normalized</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">X_train</span><span class="p">:</span>
    <span class="n">gray_image</span> <span class="o">=</span> <span class="n">grayscale</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">equal_image</span> <span class="o">=</span> <span class="n">histogram_equalize</span><span class="p">(</span><span class="n">gray_image</span><span class="p">)</span>
    <span class="n">normalized_image</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">equal_image</span><span class="p">)</span>
    <span class="n">X_train_normalized</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">normalized_image</span><span class="p">)</span>

<span class="c1"># validation set data preprocessing</span>
<span class="n">X_valid_normalized</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">X_valid</span><span class="p">:</span>
    <span class="n">gray_image</span> <span class="o">=</span> <span class="n">grayscale</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">equal_image</span> <span class="o">=</span> <span class="n">histogram_equalize</span><span class="p">(</span><span class="n">gray_image</span><span class="p">)</span>
    <span class="n">normalized_image</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">equal_image</span><span class="p">)</span>
    <span class="n">X_valid_normalized</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">normalized_image</span><span class="p">)</span>
    
<span class="c1"># test set data preprocessing</span>
<span class="n">X_test_normalized</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">X_test</span><span class="p">:</span>
    <span class="n">gray_image</span> <span class="o">=</span> <span class="n">grayscale</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">equal_image</span> <span class="o">=</span> <span class="n">histogram_equalize</span><span class="p">(</span><span class="n">gray_image</span><span class="p">)</span>
    <span class="n">normalized_image</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">equal_image</span><span class="p">)</span>
    <span class="n">X_test_normalized</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">normalized_image</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;completed&#39;</span><span class="p">)</span>
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<pre>completed
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<h3 id="Model-Architecture-(Deep-Learning-Model)-Based-on-LeNet-Architecture">Model Architecture (Deep Learning Model) Based on LeNet Architecture<a class="anchor-link" href="#Model-Architecture-(Deep-Learning-Model)-Based-on-LeNet-Architecture">&#182;</a></h3>
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<p><img src="lenet.png" alt="LeNet Architecture">
Source: Yan LeCun</p>
<h4 id="Parameters">Parameters<a class="anchor-link" href="#Parameters">&#182;</a></h4><p>To calculate the number of neurons in each layer on our CNN:</p>
<p>Given:</p>
<ul>
<li>Input Layer has a Width <code>W</code> and a Height <code>H</code>  </li>
<li>Convolution Layer has a Filter size <code>F</code></li>
<li>Stride of <code>S</code></li>
<li>Padding <code>P</code>  Note no padding was neccessary for image library (images are 32x32x3)</li>
<li>Number of Filters <code>K</code></li>
</ul>
<p>Formula:</p>
<ul>
<li>Width of Next Layer <code>W_out = [(W - F + 2P) / S] + 1</code></li>
<li>Height of Next Layer <code>H_out = [(H - F + 2P) / S] + 1</code></li>
<li>Output Depth <code>D_out = K</code> Number of filters</li>
<li>Output Volume <code>V_out = W_out * H_out * D_out</code>  </li>
</ul>
<p>With parameter sharing, each neuron in an output channel shares it's weights with every other neuron in that channel.  So the number of parameters is equal to the number of neurons in the filter, plus a bias neuron, all multiplied by the number of channels in the output layer.</p>
<p><strong>Remember with weight sharing we use the same filter for an entire depth slice!</strong></p>
<p>If you have N inputs, &amp; K outputs you have (N+1)K parameters to use.</p>

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<div class=" highlight hl-ipython3"><pre><span></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="k">def</span> <span class="nf">LeNet</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>    
    <span class="c1"># Hyperparameters for tuning</span>
    
    <span class="c1">#Agruments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer</span>
    <span class="n">mu</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># zero mean</span>
    <span class="n">sigma</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="c1"># variance</span>
    
    <span class="c1"># Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.</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">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="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="c1">#[5,5,1,6] is a 5x5 filter with a input depth of 1 and output depth of 6</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="c1"># strides = [batch, y_direction, x_direction, input_channels]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape after 1st convolutional layer: &quot;</span><span class="p">,</span> <span class="n">conv1</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    
    
    <span class="c1"># Activation Function: Rectified Linear Units (ReLU)</span>
    <span class="sd">&#39;&#39;&#39;The rectified linear activation function or ReLU for short is a piecewise linear function that will output the </span>
<span class="sd">    input directly if it is positive, otherwise, it will output zero. The sigmoid and hyperbolic tangent activation </span>
<span class="sd">    functions cannot be used in networks with many layers due to the vanishing gradient problem.</span>
<span class="sd">    The rectified linear activation function overcomes the vanishing gradient problem, allowing models to learn faster </span>
<span class="sd">    and perform better.&#39;&#39;&#39;</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"># Subsampling - Pooling - Max Pooling. Input = 28x28x6. Output = 14x14x6.</span>
    <span class="sd">&#39;&#39;&#39;A pooling layer is generally used to:</span>
<span class="sd">        * Decrease the size of the output</span>
<span class="sd">        * Prevent Overfitting&#39;&#39;&#39;</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">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"># 2x2 Kernal. 2x2 Stride</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape after 1st pooling: &quot;</span><span class="p">,</span> <span class="n">conv1</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>

    <span class="c1"># Layer 2: Convolutional. Output = 10x10x16.</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">16</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">16</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</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="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape after 2nd convolutional layer: &quot;</span><span class="p">,</span> <span class="n">conv2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    
    <span class="c1"># Activation Function: Rectified Linear Units (ReLU)</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"># Subsampling - Pooling - Max Pooling. Input = 10x10x16. Output = 5x5x16</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">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">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="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape after 2nd pooling: &quot;</span><span class="p">,</span> <span class="n">conv2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>

    <span class="c1"># Flatten. Flattens to a vector Input = 5x5x16 = 400, therefore Output = 400.</span>
    <span class="n">fc0</span>   <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv2</span><span class="p">)</span>
    
    <span class="c1"># Layer 3: Fully Connected. Input = 400. Output = 120.</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">400</span><span class="p">,</span> <span class="mi">120</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">120</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="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape after 1st fully connected layer: &quot;</span><span class="p">,</span> <span class="n">fc1</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    
    <span class="c1"># Activation Function: Rectified Linear Units (ReLU)</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="c1"># Layer 4: Fully Connected. Input = 120. Output = 84.</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">120</span><span class="p">,</span> <span class="mi">84</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">84</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="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape after 2nd fully connected layer: &quot;</span><span class="p">,</span> <span class="n">fc2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    
    <span class="c1"># Activation Function: Rectified Linear Units (ReLU)</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="c1"># Regularization: Dropout</span>
    <span class="sd">&#39;&#39;&#39;Dropout is a regularization technique for reducing overfitting.  The technique temporarily drops units (artificial neurons) from</span>
<span class="sd">    from the network, along with all of those units incoming and outgoing connections&#39;&#39;&#39;</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="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape after dropout: &quot;</span><span class="p">,</span> <span class="n">fc2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    
    <span class="c1"># Layer 5: Fully Connected. Input = 84. Output = 43.</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">84</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">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">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">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="nb">print</span><span class="p">(</span><span class="s2">&quot;Shape of Logits after 3rd fully connected layer: &quot;</span><span class="p">,</span> <span class="n">logits</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">logits</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;completed&#39;</span><span class="p">)</span>
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<pre>completed
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<h2 id="Train,-Validate-and-Test-the-Model">Train, Validate and Test the Model<a class="anchor-link" href="#Train,-Validate-and-Test-the-Model">&#182;</a></h2>
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<p>A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation
sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting.</p>
<h3 id="Setup-Hyperparameters">Setup Hyperparameters<a class="anchor-link" href="#Setup-Hyperparameters">&#182;</a></h3><p><code>keep_prob</code> Dropout is a regularization technique for reducing overfitting.  The technique temporarily drops units (artificial neurons) from the network, along with all of those units incoming and outgoing connections.  From Nitish Srivastava <a href="https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf">paper</a> *"Dropout probability p independent of other units, where p can be chosen using a validation set or can simply be set at 0.5, which seems to be close to optimal for a wide range of networks and tasks."</p>
<p><code>EPOCHS</code> tells TensorFlow how many times to run our training data through the network in general the more epochs, the better our model will train but also the longer training will take.</p>
<p><code>BATCH_SIZE</code> tells TensorFlow how many training images to run through the network at a time the larger the batch size, the faster our model will train, but our processor may have a memory limit on how large a batch it can run.</p>
<p><code>rate</code> learning rate tells TensorFlow how quickly to update the network's weights; 0.001 is a good default value but can be experimented with.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">keep_prob</span> <span class="o">=</span> <span class="mf">1.0</span>  <span class="c1">#0.5 for Training/Valdation, 1.0 for Test</span>

<span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">40</span> 

<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">100</span> 

<span class="n">rate</span> <span class="o">=</span> <span class="mf">0.001</span> 
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<h3 id="Features-and-Labels">Features and Labels<a class="anchor-link" href="#Features-and-Labels">&#182;</a></h3><p>Train our modified LeNet CNN to classify training data.</p>
<p><code>x</code> is a placeholder for a batch of input images. Initalize the batch size to <code>None</code> which allows the placeholder to later accept a batch of any size.</p>
<p><code>y</code> is a placeholder for a batch of output labels. Initially our <code>y</code> labels come through with sparce variables, which means they are integers and not <code>one-hot</code> encoded yet.</p>
<p><code>one_hot_y</code> is a one-hot-label, a 1D list that is 'n' length of the classes</p>

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<div class=" highlight hl-ipython3"><pre><span></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">1</span><span class="p">))</span> <span class="c1"># images</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="c1"># labels</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="c1"># One-hot label encode each of the 43 classes</span>
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<h3 id="Training-Pipeline">Training Pipeline<a class="anchor-link" href="#Training-Pipeline">&#182;</a></h3><p>Training pipeline that uses the model to classify the "training" traffic sign data.</p>
<p><code>LeNet</code> pass input date to the LeNet function to calculate our <code>logits</code></p>
<p>The learning <code>rate</code> tells TensorFlow how quickly to update the network's weights.</p>
<p>A <code>logits</code> function, also known as the log-odds function, is a function that represents probability values from 0 to 1, and negative infinity to infinity. The function is an inverse to the sigmoid function that limits values between 0 and 1 across the Y-axis, rather than the X-axis.</p>
<p>Cross Entropy - After determining our <code>logits</code> we need to assign a probability to each label, which can be used to classify the data.  The <code>softmax function</code> turns logits into probabilities.  Cross-entropy is just a measure of how different the logits are from the ground truth training labels.</p>
<p>Loss Operation - The <code>tf.reduce_mean</code> function averages the cross entropy from all of the training images.</p>
<p>Optimizer - <code>AdamOptimizer</code> is a replacement optimization algorithm for stochastic gradient descent (SGD) for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems.  We use our hyperparamter <code>rate</code> to tune the learning rate here.</p>
<p>Training Operation - The <code>minimize</code> function is used on the optimizer which uses backpropagation to update the network and minimize our training loss</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Pipeline</span>

<span class="n">logits</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span> <span class="c1"># pass the input data to the LeNet function to calculate our logits</span>

<span class="n">cross_entropy</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"># softmax function, compare those</span>
<span class="c1"># logits to the ground truth labels and calculate the cross entropy.</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</span><span class="p">)</span> <span class="c1"># averages the CE from all of the training images</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"># uses Adam algorithm to minimize the loss function similarly to SGD.</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"># run the minimize function on the optimzier</span>
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<pre>Shape after 1st convolutional layer:  (?, 28, 28, 6)
Shape after 1st pooling:  (?, 14, 14, 6)
Shape after 2nd convolutional layer:  (?, 10, 10, 16)
Shape after 2nd pooling:  (?, 5, 5, 16)
Shape after 1st fully connected layer:  (?, 120)
Shape after 2nd fully connected layer:  (?, 84)
Shape after dropout:  (?, 84)
Shape of Logits after 3rd fully connected layer:  (?, 43)
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<h3 id="Model-Evaluation">Model Evaluation<a class="anchor-link" href="#Model-Evaluation">&#182;</a></h3><p>Evaluate how well the loss and accuracy of the model for a given dataset.</p>
<p><code>correct_prediction</code> is to measure whether a given prediction is correct by comparing the <code>logits</code> prediction to the <code>one_hot_y</code> ground truth label.</p>
<p><code>accuracy_operation</code> is to calculate the model's overall accuracy by averaging the individual prediction accuracies.</p>
<p><code>evaluate</code> function takes a dataset as input, sets intial variables <code>num_examples</code> and <code>total_accuracy</code> and batches to data set to run it through the evaluation pipeline.  The function averages each batch to calculate the total accuracy of the model.</p>

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<div class=" highlight hl-ipython3"><pre><span></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="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="c1"># batch the dataset</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="c1"># accuracy = 1 or 0</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>
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<h3 id="Train-the-Model">Train the Model<a class="anchor-link" href="#Train-the-Model">&#182;</a></h3><p>Run the training data through the training pipeline to train the model.</p>
<p>Before each <code>EPOCHS</code>, <code>shuffle</code> the training set.</p>
<p>After each <code>EPOCHS</code>, measure the loss and accuracy of the validation set.</p>
<p>Save the model after training.</p>

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<div class=" highlight hl-ipython3"><pre><span></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="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="c1"># EPOCHS is a set hyperparameter</span>
        <span class="n">X_train_normalized</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_normalized</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="c1"># shuffle to prevent training data bias</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="c1"># range(start, stop, step)</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="o">=</span> <span class="n">X_train_normalized</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="c1"># break training data into batches</span>
            <span class="n">batch_y</span> <span class="o">=</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="c1"># train the model on each batch</span>
            
        <span class="c1"># at the end of each epoch, we evaluate the model on our validation data</span>
        <span class="n">validation_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_valid_normalized</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
        
        <span class="c1"># also evaluate the model on training data to see if the model is over- or underfitted</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_normalized</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
        
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;EPOCH </span><span class="si">{}</span><span class="s2"> ...&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="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy on the validation set = </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="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy on the training set = </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="nb">print</span><span class="p">()</span>
        
    <span class="k">try</span><span class="p">:</span>
        <span class="n">saver</span>
    <span class="k">except</span> <span class="ne">NameError</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="s1">&#39;lenet&#39;</span><span class="p">)</span> <span class="c1"># save the model to be able to load it up later and modify it or evaluate it on test dataset</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model saved&quot;</span><span class="p">)</span>
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<pre>Training...

EPOCH 1 ...
Accuracy on the validation set = 0.807
Accuracy on the training set = 0.833

EPOCH 2 ...
Accuracy on the validation set = 0.863
Accuracy on the training set = 0.906

EPOCH 3 ...
Accuracy on the validation set = 0.903
Accuracy on the training set = 0.935

EPOCH 4 ...
Accuracy on the validation set = 0.914
Accuracy on the training set = 0.955

EPOCH 5 ...
Accuracy on the validation set = 0.925
Accuracy on the training set = 0.959

EPOCH 6 ...
Accuracy on the validation set = 0.935
Accuracy on the training set = 0.970

EPOCH 7 ...
Accuracy on the validation set = 0.932
Accuracy on the training set = 0.975

EPOCH 8 ...
Accuracy on the validation set = 0.943
Accuracy on the training set = 0.981

EPOCH 9 ...
Accuracy on the validation set = 0.939
Accuracy on the training set = 0.981

EPOCH 10 ...
Accuracy on the validation set = 0.941
Accuracy on the training set = 0.982

EPOCH 11 ...
Accuracy on the validation set = 0.944
Accuracy on the training set = 0.985

EPOCH 12 ...
Accuracy on the validation set = 0.949
Accuracy on the training set = 0.987

EPOCH 13 ...
Accuracy on the validation set = 0.953
Accuracy on the training set = 0.990

EPOCH 14 ...
Accuracy on the validation set = 0.956
Accuracy on the training set = 0.989

EPOCH 15 ...
Accuracy on the validation set = 0.951
Accuracy on the training set = 0.990

EPOCH 16 ...
Accuracy on the validation set = 0.952
Accuracy on the training set = 0.989

EPOCH 17 ...
Accuracy on the validation set = 0.951
Accuracy on the training set = 0.991

EPOCH 18 ...
Accuracy on the validation set = 0.960
Accuracy on the training set = 0.992

EPOCH 19 ...
Accuracy on the validation set = 0.957
Accuracy on the training set = 0.994

EPOCH 20 ...
Accuracy on the validation set = 0.952
Accuracy on the training set = 0.993

EPOCH 21 ...
Accuracy on the validation set = 0.952
Accuracy on the training set = 0.991

EPOCH 22 ...
Accuracy on the validation set = 0.958
Accuracy on the training set = 0.993

EPOCH 23 ...
Accuracy on the validation set = 0.962
Accuracy on the training set = 0.995

EPOCH 24 ...
Accuracy on the validation set = 0.957
Accuracy on the training set = 0.994

EPOCH 25 ...
Accuracy on the validation set = 0.961
Accuracy on the training set = 0.996

EPOCH 26 ...
Accuracy on the validation set = 0.950
Accuracy on the training set = 0.992

EPOCH 27 ...
Accuracy on the validation set = 0.955
Accuracy on the training set = 0.993

EPOCH 28 ...
Accuracy on the validation set = 0.959
Accuracy on the training set = 0.995

EPOCH 29 ...
Accuracy on the validation set = 0.958
Accuracy on the training set = 0.995

EPOCH 30 ...
Accuracy on the validation set = 0.964
Accuracy on the training set = 0.997

EPOCH 31 ...
Accuracy on the validation set = 0.961
Accuracy on the training set = 0.995

EPOCH 32 ...
Accuracy on the validation set = 0.962
Accuracy on the training set = 0.996

EPOCH 33 ...
Accuracy on the validation set = 0.966
Accuracy on the training set = 0.996

EPOCH 34 ...
Accuracy on the validation set = 0.962
Accuracy on the training set = 0.994

EPOCH 35 ...
Accuracy on the validation set = 0.961
Accuracy on the training set = 0.996

EPOCH 36 ...
Accuracy on the validation set = 0.959
Accuracy on the training set = 0.997

EPOCH 37 ...
Accuracy on the validation set = 0.961
Accuracy on the training set = 0.997

EPOCH 38 ...
Accuracy on the validation set = 0.961
Accuracy on the training set = 0.997

EPOCH 39 ...
Accuracy on the validation set = 0.959
Accuracy on the training set = 0.994

EPOCH 40 ...
Accuracy on the validation set = 0.958
Accuracy on the training set = 0.995

Model saved
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<h3 id="Test-Log">Test Log<a class="anchor-link" href="#Test-Log">&#182;</a></h3><p>2021-02-25</p>
<ul>
<li>Test 1 - 94.4 % 
preprocessing: shuffle, normalization<br>
model: original LeNet, batch size: 128, epochs: 10, rate: 0.001, mu: 0, sigma: 0.1</li>
</ul>
<ul>
<li>Test 2 - 90.3 %
preprocessing: shuffle, normalization 
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 10, rate: 0.001, mu: 0, sigma: 0.1</li>
</ul>
<ul>
<li>Test 3 - 89.0 % 
preprocessing: shuffle, normalization, added grayscale
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 10, rate: 0.001, mu: 0, sigma: 0.1  </li>
</ul>
<ul>
<li>Test 4 - 98.2 %
preprocessing: shuffle, normalization, grayscale
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 50, rate: 0.001, mu: 0, sigma: 0.1
Notes: Changing the number of Epochs improved accuracy greatly, validation accuracy is only 78.3% (overfitting)  </li>
</ul>
<ul>
<li>Test 5 - 98.3 % 
preprocessing: shuffle, normalization, grayscale
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 50, rate: 0.0005, mu: 0, sigma: 0.1
Notes: Changing the learning rate did not improve accuracy, validation accuracy is only 74.5% (overfitting)  </li>
</ul>
<ul>
<li>Test 6 - 98.3 %- Added Data Augmentation to add additional training images (rotated)
preprocessing: shuffle, normalization, grayscale
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 50, rate: 0.001, mu: 0, sigma: 0.1
Notes: Changing the learning rate did not improve accuracy, validation accuracy is only 82.5% (overfitting)  </li>
</ul>
<ul>
<li>Test 7 - 98.0 % - Added Data Augmentation training images (rotated)
preprocessing: shuffle, normalization, grayscale
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 50, rate: 0.001, mu: 0, sigma: 0.1 keep_prob: 0.5 for Dropout
Notes: From Nitish Srivastava <a href="https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf">paper</a> "<em>Dropout probability p independent of other units, where p can be chosen using a validation set or can simply be set at 0.5, which seems to be close to optimal for a wide range of networks and tasks."</em>  </li>
</ul>
<ul>
<li>Test 8 - 99.5% !! Training Accuracy / 94.2 % Validation Accuracy !!!
Added Data Augmentation training images (rotated)
preprocessing: shuffle, normalization (changed normalization to -1 to 1), grayscale
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 50, rate: 0.001, mu: 0, sigma: 0.1 keep_prob: 0.5 for Dropout
Notes: From Nitish Srivastava <a href="https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf">paper</a> "<i>Dropout probability p independent of other units, where p can be chosen using a validation set or can simply be set at 0.5, which seems to be close to optimal for a wide range of networks and tasks."  </li>
</ul>
<ul>
<li>Test 9 - 99.7% !! Training Accuracy / 94.8 % Validation Accuracy !!!<br>
Same as Test 8, reduced Batch size to 100.  Slight improvement.  </li>
</ul>
<ul>
<li><p>Test 10 - 99.6% Training Accuracy / Validation Accuracy = 95.9
Added histogram equalization to pre-processing.  Added Data Augmentation training images (rotated)
preprocessing: shuffle, normalization (changed normalization to -1 to 1), grayscale
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 50, rate: 0.001, mu: 0, sigma: 0.1 keep_prob: 0.5 for Dropout</p>
</li>
<li><p>Test 11 - **99.5% Training Accuracy / Validation Accuracy = 95.8 %
Added histogram equalization to pre-processing.  Added Data Augmentation training images (rotated)
preprocessing: shuffle, normalization (changed normalization to -1 to 1), grayscale
model: original LeNet plus added a dropout before final 3rd fully connected layer, 
batch size: 128, epochs: 40, rate: 0.001, mu: 0, sigma: 0.1 keep_prob: 0.5 for Dropout</p>
</li>
</ul>

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<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">&#182;</a></h3><p>Load our <code>LeNet</code> model saved from training and run on test data.</p>

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<div class=" highlight hl-ipython3"><pre><span></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">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">import_meta_graph</span><span class="p">(</span><span class="s1">&#39;./lenet.meta&#39;</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="s2">&quot;./lenet&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_normalized</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;Accuracy on the Test set = </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>
    
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
Accuracy on the Test set = 0.931
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<hr>
<h2 id="Test-a-Model-on-New-Images">Test a Model on New Images<a class="anchor-link" href="#Test-a-Model-on-New-Images">&#182;</a></h2><p>To test my model I downloaded (5) German road signs at random.  I wanted to try really unique photos, so I tried to find road signs that are toxic as I read about in the following paper <a href="https://arxiv.org/pdf/1802.06430.pdf">DARTS: Deceiving Autonomous Cars with Toxic Signs</a>.  I wanted to ensure the pictures had some graffiti, poor lighting, I also selected a few images that had a limited training data for that class.</p>

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<h3 id="Load-and-Output-the-Images">Load and Output the Images<a class="anchor-link" href="#Load-and-Output-the-Images">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</span>


<span class="c1"># Load images from .png files to` NumPy array named X_web</span>
<span class="n">X_web</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="mi">0</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">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span> <span class="c1"># numpy.empty(shape, dtype); shape &gt; int or tuple of int; </span>

<span class="c1"># loading the images into a file list (no numpy array! only a list)</span>
<span class="n">web_file_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;signs/&quot;</span> <span class="o">+</span> <span class="s1">&#39;*.png&#39;</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">1</span><span class="p">,</span><span class="mi">6</span><span class="p">):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">&#39;../CarND-Traffic-Sign-Classifier-Project/signs/example_&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;.png&#39;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;original image has shape: &quot;</span><span class="p">,</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">img_rgb</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">)</span>
    <span class="n">image_resized</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">img_rgb</span><span class="p">,</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="nb">print</span><span class="p">(</span><span class="s2">&quot;resized image has shape:  &quot;</span><span class="p">,</span> <span class="n">image_resized</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">cv2</span><span class="o">.</span><span class="n">imwrite</span><span class="p">(</span><span class="s1">&#39;../CarND-Traffic-Sign-Classifier-Project/signs/cv2_&#39;</span><span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">+</span><span class="s1">&#39;.png&#39;</span><span class="p">,</span> <span class="n">image_resized</span><span class="p">)</span>
    <span class="n">X_web</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">X_web</span><span class="p">,</span> <span class="p">[</span><span class="n">image_resized</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
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<pre>original image has shape:  (2110, 2445, 3)
resized image has shape:   (32, 32, 3)
original image has shape:  (126, 130, 3)
resized image has shape:   (32, 32, 3)
original image has shape:  (380, 382, 3)
resized image has shape:   (32, 32, 3)
original image has shape:  (317, 326, 3)
resized image has shape:   (32, 32, 3)
original image has shape:  (1059, 1204, 3)
resized image has shape:   (32, 32, 3)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Enter correct labels (groundtruth) for the images found on the web</span>

<span class="n">y_web</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
    <span class="mi">14</span><span class="p">,</span> <span class="c1"># &quot;example_1&quot; Stop</span>
    <span class="mi">17</span><span class="p">,</span> <span class="c1"># &quot;example_2&quot; No entry</span>
    <span class="mi">4</span><span class="p">,</span> <span class="c1"># &quot;example_3&quot; Speed limit (70km/h) </span>
    <span class="mi">19</span><span class="p">,</span> <span class="c1"># &quot;example_4&quot; Dangerous curve to the left     </span>
    <span class="mi">0</span><span class="p">,</span> <span class="c1"># &quot;example_5&quot; Speed limit (20km/h) </span>
<span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot original images from the web</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">left</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">right</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="n">bottom</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="n">hspace</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span> <span class="n">wspace</span> <span class="o">=</span> <span class="mf">0.05</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">1</span><span class="p">,</span><span class="mi">6</span><span class="p">):</span>
    <span class="n">axis</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</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="n">xticks</span><span class="o">=</span><span class="p">[],</span> <span class="n">yticks</span><span class="o">=</span><span class="p">[])</span>
    <span class="n">web_image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">&#39;../CarND-Traffic-Sign-Classifier-Project/signs/example_&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;.png&#39;</span><span class="p">)</span>
    <span class="n">axis</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">web_image</span><span class="p">)</span>
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<h2 id="Pre-Process-Data-(Normalize-&amp;-Grayscale-Image)">Pre-Process Data (Normalize &amp; Grayscale Image)<a class="anchor-link" href="#Pre-Process-Data-(Normalize-&amp;-Grayscale-Image)">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Normalizing Images for zero mean and equal variance.</span>
<span class="n">X_web</span> <span class="o">=</span> <span class="p">(</span><span class="n">X_web</span> <span class="o">-</span> <span class="mf">127.5</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span> <span class="c1">#pixel range from 0 to 1</span>

<span class="c1"># Convert RGB image to grayscale</span>
<span class="n">X_web</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">X_web</span><span class="o">/</span><span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>


<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_web[0].shape: &quot;</span><span class="p">,</span> <span class="n">X_web</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_web.shape: &quot;</span><span class="p">,</span> <span class="n">X_web</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>X_web[0].shape:  (32, 32, 1)
X_web.shape:  (5, 32, 32, 1)
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<h3 id="Accuracy">Accuracy<a class="anchor-link" href="#Accuracy">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Run the predictions here and use the model to output the prediction for each image.</span>
<span class="c1">### Make sure to pre-process the images with the same pre-processing pipeline used earlier.</span>
<span class="c1">### Feel free to use as many code cells as needed.</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">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="s1">&#39;lenet.meta&#39;</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="s1">&#39;./&#39;</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_web</span><span class="p">)</span>
    <span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="n">web_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_web</span><span class="p">,</span> <span class="n">y_web</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy on images found on the web = </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">web_accuracy</span><span class="p">))</span>
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
Accuracy on images found on the web = 0.400
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<h3 id="Output-Top-5-Softmax-Probabilities-For-Each-Image-Found-on-the-Web">Output Top 5 Softmax Probabilities For Each Image Found on the Web<a class="anchor-link" href="#Output-Top-5-Softmax-Probabilities-For-Each-Image-Found-on-the-Web">&#182;</a></h3>
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<p>For each of the new images, print out the model's softmax probabilities to show the <strong>certainty</strong> of the model's predictions (limit the output to the top 5 probabilities for each image). <a href="https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k"><code>tf.nn.top_k</code></a> could prove helpful here.</p>
<p>The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.</p>
<p><code>tf.nn.top_k</code> will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.</p>
<p>Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. <code>tf.nn.top_k</code> is used to choose the three classes with the highest probability:</p>

<pre><code># (5, 6) array
a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,
         0.12789202],
       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,
         0.15899337],
       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,
         0.23892179],
       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,
         0.16505091],
       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,
         0.09155967]])</code></pre>
<p>Running it through <code>sess.run(tf.nn.top_k(tf.constant(a), k=3))</code> produces:</p>

<pre><code>TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],
       [ 0.28086119,  0.27569815,  0.18063401],
       [ 0.26076848,  0.23892179,  0.23664738],
       [ 0.29198961,  0.26234032,  0.16505091],
       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],
       [0, 1, 4],
       [0, 5, 1],
       [1, 3, 5],
       [1, 4, 3]], dtype=int32))</code></pre>
<p>Looking just at the first row we get <code>[ 0.34763842,  0.24879643,  0.12789202]</code>, you can confirm these are the 3 largest probabilities in <code>a</code>. You'll also notice <code>[3, 0, 5]</code> are the corresponding indices.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web.  </span>

<span class="n">top_5_probabilities</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">top_k</span><span class="p">(</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">k</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> <span class="c1"># tf.nn.top_k(input, k=?)</span>

<span class="n">keep_prob</span> <span class="o">=</span> <span class="mf">1.0</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">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="s1">&#39;lenet.meta&#39;</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="s1">&#39;./&#39;</span><span class="p">))</span>
    <span class="n">top_5_probabilities_output</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">top_5_probabilities</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_web</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span><span class="n">y_web</span><span class="p">})</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">top_5_probabilities_output</span><span class="p">)</span>
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
TopKV2(values=array([[  9.99999166e-01,   7.14245971e-07,   1.53189191e-07,
          6.08821105e-10,   3.55313945e-10],
       [  1.00000000e+00,   2.60672319e-33,   6.75851166e-34,
          3.30143351e-34,   8.54609289e-36],
       [  1.00000000e+00,   2.71962249e-13,   5.24448454e-15,
          2.43567627e-15,   1.59733645e-16],
       [  9.13184106e-01,   5.63402474e-02,   1.33584142e-02,
          1.28963999e-02,   2.81807827e-03],
       [  9.99728858e-01,   2.71163415e-04,   6.67157163e-11,
          3.48772677e-11,   2.42934266e-11]], dtype=float32), indices=array([[38, 36, 20,  1,  8],
       [17, 18, 14, 38, 26],
       [ 3,  9,  2,  1, 15],
       [28, 32,  3, 35, 36],
       [ 0,  1, 31,  8,  4]], dtype=int32))
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<h2 id="Visualize-the-Neural-Network's-State-with-Test-Images">Visualize the Neural Network's State with Test Images<a class="anchor-link" href="#Visualize-the-Neural-Network's-State-with-Test-Images">&#182;</a></h2><p>This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.</p>
<p>Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the <a href="https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81">LeNet lab's</a> feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.</p>
<p>For an example of what feature map outputs look like, check out NVIDIA's results in their paper <a href="https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/">End-to-End Deep Learning for Self-Driving Cars</a> in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.</p>
<p><figure>
 <img src="visualize_cnn.png" width="380" alt="Combined Image" />
 <figcaption>
 <p></p> 
 <p style="text-align: center;"> Your output should look something like this (above)</p> 
 </figcaption>
</figure>
 <p></p></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Visualize your network&#39;s feature maps here.</span>

<span class="c1"># image_input: the test image being fed into the network to produce the feature maps</span>
<span class="c1"># tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer</span>
<span class="c1"># activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output</span>
<span class="c1"># plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry</span>

<span class="k">def</span> <span class="nf">outputFeatureMap</span><span class="p">(</span><span class="n">image_input</span><span class="p">,</span> <span class="n">tf_activation</span><span class="p">,</span> <span class="n">activation_min</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">activation_max</span><span class="o">=-</span><span class="mi">1</span> <span class="p">,</span><span class="n">plt_num</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="c1"># Here make sure to preprocess your image_input in a way your network expects</span>
    <span class="c1"># with size, normalization, ect if needed</span>
    <span class="c1"># image_input =</span>
    <span class="c1"># Note: x should be the same name as your network&#39;s tensorflow data placeholder variable</span>
    <span class="c1"># If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function</span>
    <span class="n">activation</span> <span class="o">=</span> <span class="n">tf_activation</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><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">image_input</span><span class="p">})</span>
    <span class="n">featuremaps</span> <span class="o">=</span> <span class="n">activation</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">plt_num</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">featuremap</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">featuremaps</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">8</span><span class="p">,</span> <span class="n">featuremap</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># sets the number of feature maps to show on each row and column</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;FeatureMap &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">featuremap</span><span class="p">))</span> <span class="c1"># displays the feature map number</span>
        <span class="k">if</span> <span class="n">activation_min</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">&amp;</span> <span class="n">activation_max</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</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">activation</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:,</span> <span class="n">featuremap</span><span class="p">],</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="n">vmin</span> <span class="o">=</span><span class="n">activation_min</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">activation_max</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">activation_max</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</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">activation</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:,</span> <span class="n">featuremap</span><span class="p">],</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">activation_max</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">activation_min</span> <span class="o">!=-</span><span class="mi">1</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">activation</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:,</span> <span class="n">featuremap</span><span class="p">],</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">activation_min</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
        <span class="k">else</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">activation</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:,</span> <span class="n">featuremap</span><span class="p">],</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#LeNet Model will only accept 32x32 images</span>

<span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="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">img</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">array</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">&#39;../CarND-Traffic-Sign-Classifier-Project/signs/example_2.png&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">resize</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="c1">#normalize</span>
<span class="n">image_input</span> <span class="o">=</span> <span class="p">(</span><span class="n">img</span> <span class="o">-</span> <span class="mf">127.5</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>

<span class="c1">#grayscale</span>

<span class="n">image_input</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">img</span><span class="o">/</span><span class="mi">3</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</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">new_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">import_meta_graph</span><span class="p">(</span><span class="s1">&#39;lenet.meta&#39;</span><span class="p">)</span>
    <span class="n">new_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="s1">&#39;.&#39;</span><span class="p">))</span>
    
    <span class="n">conv_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">get_default_graph</span><span class="p">()</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s2">&quot;Conv2D_1:0&quot;</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">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">image_input</span><span class="p">),</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
    <span class="n">outputFeatureMap</span><span class="p">(</span><span class="n">image_input</span><span class="p">,</span> <span class="n">conv_1</span><span class="p">)</span>
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