Traffic_Sign_Classifier.html

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<title>Traffic_Sign_Classifier</title><script src="https://unpkg.com/jupyter-js-widgets@2.0.*/dist/embed.js"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<style type="text/css">
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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:active,
a:hover {
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b,
strong {
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dfn {
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h1 {
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mark {
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small {
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sup {
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sup {
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sub {
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img {
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svg:not(:root) {
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figure {
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input,
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button {
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button,
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  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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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><p>In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.</p>
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \n",
    "<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
</blockquote>
<p>In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a <a href="https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md">write up template</a> that can be used to guide the writing process. Completing the code template and writeup template will cover all of the <a href="https://review.udacity.com/#!/rubrics/481/view">rubric points</a> for this project.</p>
<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="Step-0:-Load-The-Data">Step 0: Load The Data<a class="anchor-link" href="#Step-0:-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="kn">import</span> <span class="nn">pickle</span>

<span class="c1"># TODO: Fill this in based on where you saved the training and testing data</span>

<span class="n">training_file</span> <span class="o">=</span> <span class="s2">&quot;./traffic-signs-data/train.p&quot;</span>
<span class="n">validation_file</span> <span class="o">=</span> <span class="s2">&quot;./traffic-signs-data/valid.p&quot;</span>
<span class="n">testing_file</span> <span class="o">=</span> <span class="s2">&quot;./traffic-signs-data/test.p&quot;</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">training_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">validation_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">valid</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">testing_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
<span class="n">X_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>
</pre></div>

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<h2 id="Step-1:-Dataset-Summary-&amp;-Exploration">Step 1: Dataset Summary &amp; Exploration<a class="anchor-link" href="#Step-1:-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="Provide-a-Basic-Summary-of-the-Data-Set-Using-Python,-Numpy-and/or-Pandas">Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas<a class="anchor-link" href="#Provide-a-Basic-Summary-of-the-Data-Set-Using-Python,-Numpy-and/or-Pandas">&#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"># TODO: 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"># TODO: 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"># TODO: 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"># TODO: What&#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="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>

<span class="c1"># TODO: How many unique classes/labels there are in the dataset.</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</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 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;Image data shape =&quot;</span><span class="p">,</span> <span class="n">image_shape</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>
<span class="nb">print</span><span class="p">()</span>
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<pre>Number of training examples = 34799
Number of testing examples = 12630
Image data shape = (32, 32)
Number of classes = 43

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<h3 id="Include-an-exploratory-visualization-of-the-dataset">Include an exploratory visualization of the dataset<a class="anchor-link" href="#Include-an-exploratory-visualization-of-the-dataset">&#182;</a></h3>
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<p>Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.</p>
<p>The <a href="http://matplotlib.org/">Matplotlib</a> <a href="http://matplotlib.org/examples/index.html">examples</a> and <a href="http://matplotlib.org/gallery.html">gallery</a> pages are a great resource for doing visualizations in Python.</p>
<p><strong>NOTE:</strong> It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Data exploration visualization code goes here.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># Visualizations will be shown in the notebook.</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
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<div class=" highlight hl-ipython3"><pre><span></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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">signnames_csv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;./signnames.csv&#39;</span><span class="p">)</span>
<span class="n">signnames</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">signnames_csv</span><span class="p">[</span><span class="s1">&#39;SignName&#39;</span><span class="p">])</span>
<span class="n">examples_idx</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">num</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
    <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">sign_num</span> <span class="ow">in</span> <span class="n">y_train</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">sign_num</span> <span class="o">==</span> <span class="n">num</span><span class="p">:</span>
            <span class="n">examples_idx</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
            <span class="k">break</span>
        <span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">16</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">examples_idx</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">9</span><span class="p">,</span> <span class="mi">5</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">plt</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="n">examples_idx</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
    <span class="n">plt</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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</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="s2">&quot;. &quot;</span> <span class="o">+</span> <span class="n">signnames</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span> <span class="o">=</span> <span class="mf">0.99</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">counts</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">num</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
    <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">sign_num</span> <span class="ow">in</span> <span class="n">y_train</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">sign_num</span> <span class="o">==</span> <span class="n">num</span><span class="p">:</span>
            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="n">counts</span><span class="p">[</span><span class="n">num</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">),</span> <span class="p">[</span><span class="n">counts</span><span class="p">[</span><span class="n">count</span><span class="p">]</span> <span class="k">for</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">counts</span><span class="p">],</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">),</span> <span class="n">signnames</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Counts (training set)&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<h2 id="Step-2:-Design-and-Test-a-Model-Architecture">Step 2: Design and Test a Model Architecture<a class="anchor-link" href="#Step-2:-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>
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<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>
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<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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<h3 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></h3>
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<p>Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, <code>(pixel - 128)/ 128</code> is a quick way to approximately normalize the data and can be used in this project.</p>
<p>Other pre-processing steps are optional. You can try different techniques to see if it improves performance.</p>
<p>Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include </span>
<span class="c1">### converting to grayscale, etc.</span>
<span class="c1">### Feel free to use as many code cells as needed.</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="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</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">shuffle</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>

<span class="n">normalized_X_train</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">sign</span> <span class="ow">in</span> <span class="n">X_train</span><span class="p">:</span>
    <span class="n">normalized_X_train</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">sign</span> <span class="o">-</span> <span class="mf">128.</span><span class="p">)</span> <span class="o">/</span> <span class="mf">128.</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">normalized_X_train</span>
<span class="n">normalized_X_valid</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">sign</span> <span class="ow">in</span> <span class="n">X_valid</span><span class="p">:</span>
    <span class="n">normalized_X_valid</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">sign</span> <span class="o">-</span> <span class="mf">128.</span><span class="p">)</span> <span class="o">/</span> <span class="mf">128.</span><span class="p">)</span>
<span class="n">X_valid</span> <span class="o">=</span> <span class="n">normalized_X_valid</span>
<span class="n">normalized_X_test</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">sign</span> <span class="ow">in</span> <span class="n">X_test</span><span class="p">:</span>
    <span class="n">normalized_X_test</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">sign</span> <span class="o">-</span> <span class="mf">128.</span><span class="p">)</span> <span class="o">/</span> <span class="mf">128.</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">normalized_X_test</span>
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<pre>/root/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/sklearn/utils/fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.
  _nan_object_mask = _nan_object_array != _nan_object_array
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<h3 id="Model-Architecture">Model Architecture<a class="anchor-link" href="#Model-Architecture">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Define your architecture here.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>

<span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">128</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="c1"># Arguments 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="n">sigma</span> <span class="o">=</span> <span class="mf">0.1</span>
    
    <span class="c1"># Layer 1: Convolutional. Input = 32x32x3. 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">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv1_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">6</span><span class="p">))</span>
    <span class="n">conv1</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">conv1_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv1_b</span>

    <span class="c1"># Activation.</span>
    <span class="n">conv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv1</span><span class="p">)</span>

    <span class="c1"># Pooling. Input = 28x28x6. Output = 14x14x6.</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"># 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="c1"># Activation.</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"># 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="c1"># Flatten. Input = 5x5x16. 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="c1"># Activation.</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"># Dropout</span>
    <span class="n">fc1</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>

    <span class="c1"># Layer 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="c1"># Activation.</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"># Dropout</span>
    <span class="n">fc2</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
    
    <span class="c1"># Layer 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="k">return</span> <span class="n">logits</span>
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<h3 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></h3>
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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>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Train your model here.</span>
<span class="c1">### Calculate and report the accuracy on the training and validation set.</span>
<span class="c1">### Once a final model architecture is selected, </span>
<span class="c1">### the accuracy on the test set should be calculated and reported as well.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>

<span class="c1"># Features and Labels</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">))</span>
<span class="n">one_hot_y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">one_hot</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">43</span><span class="p">)</span>
<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>

<span class="c1"># Training Pipeline</span>
<span class="n">rate</span> <span class="o">=</span> <span class="mf">0.001</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">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="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="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="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"># Model Evaluation</span>
<span class="n">correct_prediction</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">one_hot_y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">accuracy_operation</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_prediction</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>

<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">X_data</span><span class="p">,</span> <span class="n">y_data</span><span class="p">):</span>
    <span class="n">num_examples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_data</span><span class="p">)</span>
    <span class="n">total_accuracy</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">get_default_session</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">offset</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
        <span class="n">batch_x</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">X_data</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">offset</span><span class="o">+</span><span class="n">BATCH_SIZE</span><span class="p">],</span> <span class="n">y_data</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">offset</span><span class="o">+</span><span class="n">BATCH_SIZE</span><span class="p">]</span>
        <span class="n">accuracy</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy_operation</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">batch_y</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
        <span class="n">total_accuracy</span> <span class="o">+=</span> <span class="p">(</span><span class="n">accuracy</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch_x</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">total_accuracy</span> <span class="o">/</span> <span class="n">num_examples</span>

<span class="c1"># Train the Model</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="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">offset</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
            <span class="n">end</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">+</span> <span class="n">BATCH_SIZE</span>
            <span class="n">batch_x</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">end</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
            <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">training_operation</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">batch_y</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">})</span>
        
        <span class="n">training_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
        <span class="n">validation_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</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;Training Accuracy = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">training_accuracy</span><span class="p">))</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Validation Accuracy = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">validation_accuracy</span><span class="p">))</span>
        <span class="nb">print</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="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 ...
Training Accuracy = 0.654
Validation Accuracy = 0.588

EPOCH 2 ...
Training Accuracy = 0.861
Validation Accuracy = 0.810

EPOCH 3 ...
Training Accuracy = 0.920
Validation Accuracy = 0.882

EPOCH 4 ...
Training Accuracy = 0.948
Validation Accuracy = 0.917

EPOCH 5 ...
Training Accuracy = 0.964
Validation Accuracy = 0.933

EPOCH 6 ...
Training Accuracy = 0.968
Validation Accuracy = 0.941

EPOCH 7 ...
Training Accuracy = 0.972
Validation Accuracy = 0.942

EPOCH 8 ...
Training Accuracy = 0.978
Validation Accuracy = 0.939

EPOCH 9 ...
Training Accuracy = 0.979
Validation Accuracy = 0.945

EPOCH 10 ...
Training Accuracy = 0.983
Validation Accuracy = 0.953

Model saved
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Evaluate the Model</span>
<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s1">&#39;.&#39;</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</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test Accuracy = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_accuracy</span><span class="p">))</span>
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
Test Accuracy = 0.930
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<h2 id="Step-3:-Test-a-Model-on-New-Images">Step 3: Test a Model on New Images<a class="anchor-link" href="#Step-3:-Test-a-Model-on-New-Images">&#182;</a></h2><p>To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.</p>
<p>You may find <code>signnames.csv</code> useful as it contains mappings from the class id (integer) to the actual sign name.</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="c1">### Load the images and plot them here.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="n">signs</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;web-traffic-signs/*.png&quot;</span><span class="p">)</span>
<span class="n">signs_arr</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">sign</span> <span class="ow">in</span> <span class="n">signs</span><span class="p">:</span>
    <span class="n">cv_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="n">sign</span><span class="p">)</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">cv_image</span><span class="p">[</span><span class="o">...</span><span class="p">,::</span><span class="o">-</span><span class="mi">1</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">resize</span><span class="p">(</span><span class="n">image</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="n">arr</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="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="n">signs_arr</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;binary&#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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<h3 id="Predict-the-Sign-Type-for-Each-Image">Predict the Sign Type for Each Image<a class="anchor-link" href="#Predict-the-Sign-Type-for-Each-Image">&#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="n">normalized_signs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">sign</span> <span class="ow">in</span> <span class="n">signs_arr</span><span class="p">:</span>
    <span class="n">normalized_signs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">sign</span> <span class="o">-</span> <span class="mf">128.</span><span class="p">)</span> <span class="o">/</span> <span class="mf">128.</span><span class="p">)</span>
<span class="n">signs_arr</span> <span class="o">=</span> <span class="n">normalized_signs</span>

<span class="n">predictions</span> <span class="o">=</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">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="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">signs_arr</span><span class="p">)):</span>
        <span class="n">predict_logits</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">logits</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">signs_arr</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
        <span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predict_logits</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">signs</span><span class="p">)):</span>
    <span class="n">cv_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="n">signs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">cv_image</span><span class="p">[</span><span class="o">...</span><span class="p">,::</span><span class="o">-</span><span class="mi">1</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">resize</span><span class="p">(</span><span class="n">image</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="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Predicted: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">predictions</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;. &quot;</span> <span class="o">+</span> <span class="n">signnames</span><span class="p">[</span><span class="n">predictions</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;binary&#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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<pre>INFO:tensorflow:Restoring parameters from ./lenet
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<h3 id="Analyze-Performance">Analyze Performance<a class="anchor-link" href="#Analyze-Performance">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Calculate the accuracy for these 5 new images. </span>
<span class="c1">### For example, if the model predicted 1 out of 5 signs correctly, it&#39;s 20% accurate on these new images.</span>
<span class="n">true_labels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">33</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">12</span><span class="p">]</span>
<span class="n">correct</span> <span class="o">=</span> <span class="mi">0</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">predictions</span><span class="p">)):</span>
    <span class="k">if</span> <span class="p">(</span><span class="n">predictions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">true_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]):</span>
        <span class="n">correct</span> <span class="o">+=</span> <span class="mi">1</span>

<span class="n">accuracy</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">correct</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">true_labels</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy of predictions: </span><span class="si">%f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">accuracy</span> <span class="o">*</span> <span class="mf">100.0</span><span class="p">))</span>
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<pre>Accuracy of predictions: 60.000000%
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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="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">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="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">signs_arr</span><span class="p">)):</span>
        <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">signs_arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">),</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">predict_logits</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">logits</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">img</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
        <span class="n">softmax_prob</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">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="o">=</span><span class="n">predict_logits</span><span class="p">))</span>
        <span class="n">top_5</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">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">constant</span><span class="p">(</span><span class="n">softmax_prob</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="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">top_5</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Probability&#39;</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Top 5 Softmax Probabilities&quot;</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="n">signnames</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">top_5</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">flatten</span><span class="p">()],</span> <span class="n">rotation</span><span class="o">=</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>
        <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Top 5 prediction probabilities: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">top_5</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>  
        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<pre>INFO:tensorflow:Restoring parameters from ./lenet
Top 5 prediction probabilities: [[ 0.69675237  0.1765514   0.11955417  0.00399985  0.00222583]]

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JqxTp19IPDfwI0UbQVTJf2r7QtHel05s+q8jn0nDXPuPmOJJSIi6jPWqbM/CbzE9hIASU8HLgBGTAoREbF2GevU2Xe0EkJpKXBHDfFERESDRut99E/l04WS5gHnULQpHE7RkBwREX1ktOqjg9qe3w68uHy+HNi0logiIqIxo/U+OrpXgURERPPG2vtoA+AY4FnABq39tv+lprgiIqIBY21oPotixPH+wGUUo5NX1BVUREQ0Y6xJYTvb7wXuK+dDOhDYub6wIiKiCWNNCg+WP++WtBOwCTCllogiIqIxYx28NlvSpsB7gbkUK7G9t7aoIiKiEWNKCra/XD69DHhafeFERESTxlR9JOnJkj4n6WpJV0n6tKQn1x1cRET01ljbFOZQTGtxKHAY8Gfg7LqCioiIZoy1TeFJtv+rbfsDkl5RR0AREdGcsZYULpE0U9I65eNVFLOkRkREHxltQrwVFBPgCTgB+Hp5aB3gf4H31RpdRET01GhzH03sVSAREdG8sbYpIOlgYO9y81LbP6gnpIiIaMpYu6R+BHgzsKh8vLncFxERfWSsJYWXA8+x/TCApDOBa4AT6wosIiJ6b6y9jwCe2PZ8k7G8QNIMSYslLZH0qAQi6VhJ10u6VtJPJU1bjXgiIqLLxlpS+DBwjaRLKHoi7Q28c6QXSJoAnArsBywD5kuaa3tR22nftP3f5fkHA6cAM1bvI0RERLeMmhQkCfgp8Dxgd4qk8A7bt43y0j2AJbaXlteZAxxC0SYBgO17287fkKL7a0RENGTUpGDbks63vRvFDKljtRVwc9v2MmDPzpMkvYliDMT6wL6rcf2IiOiysbYpXClp99W8tobY96iSgO1TbT8deAfwniEvJM2StEDSguXLl69mGBERMVZjTQovoUgMN0q6rmwcvm6U1ywDtmnb3hq4ZYTz5wBDzqdke7bt6banT5o0aYwhR0TE6hprQ/MBa3Dt+cD2kqYCfwJmAq9pP0HS9rZ/V24eCPyOiIhozGhzH20AHAtsB1wPnG575VgubHulpOOAi4AJwBm2F0o6GVhgey5wnKSXUiz3eRdw1Jp/lIiIGK/RSgpnUnxhX05RWphGMbJ5TGzPA+Z17Dup7fmYrxUREfUbLSlMs70zgKTTgV/WH1JERDRltIbmB1tPxlptFBERa6/RSgq7SGoNMBPw+HJbFEMYNq41uoiI6KnR1lOY0KtAIiKieaszIV5ERPS5JIWIiKgkKURERCVJISIiKkkKERFRSVKIiIhKkkJERFSSFCIiopKkEBERlSSFiIioJClEREQlSSEiIipJChERUUlSiIiISpJCRERUkhQiIqKSpBAREZVak4KkGZIWS1oi6cQhjp8gaZGk6yT9P0nb1hlPRESMrLakIGkCcCpwADANOELStI7TrgGm23428G3gY3XFExERo6uzpLAHsMT2UtsPAHOAQ9pPsH2J7b+Wm1cCW9cYT0REjKLOpLAVcHPb9rJy33COAS6sMZ6IiBjFujVeW0Ps85AnSq8FpgMvHub4LGAWwOTJk7sVX0REdKizpLAM2KZte2vgls6TJL0UeDdwsO37h7qQ7dm2p9uePmnSpFqCjYiIepPCfGB7SVMlrQ/MBOa2nyBpV+CLFAnhjhpjiYiIMagtKdheCRwHXATcAJxje6GkkyUdXJ72cWAj4FxJ10qaO8zlIiKiB+psU8D2PGBex76T2p6/tM73j4iI1ZMRzRERUUlSiIiISq3VR/HYMuXEC5oOoStu+siBTYcQ0bdSUoiIiEqSQkREVJIUIiKikqQQERGVJIWIiKgkKURERCVJISIiKkkKERFRSVKIiIhKkkJERFSSFCIiopKkEBERlSSFiIioJClEREQlSSEiIipJChERUckiOzEQ+mWBIcgiQ1GvlBQiIqJSa1KQNEPSYklLJJ04xPG9JV0taaWkw+qMJSIiRldbUpA0ATgVOACYBhwhaVrHaX8EXg98s644IiJi7OpsU9gDWGJ7KYCkOcAhwKLWCbZvKo89XGMcERExRnVWH20F3Ny2vazct9okzZK0QNKC5cuXdyW4iIh4tDqTgobY5zW5kO3Ztqfbnj5p0qRxhhUREcOpMyksA7Zp294auKXG94uIiHGqMynMB7aXNFXS+sBMYG6N7xcREeNUW1KwvRI4DrgIuAE4x/ZCSSdLOhhA0u6SlgGHA1+UtLCueCIiYnS1jmi2PQ+Y17HvpLbn8ymqlSIi4jEgI5ojIqKSpBAREZUkhYiIqCQpREREJUkhIiIqSQoREVFJUoiIiEqSQkREVJIUIiKikqQQERGVJIWIiKgkKURERCVJISIiKkkKERFRSVKIiIhKkkJERFSSFCIiopKkEBERlSSFiIioJClEREQlSSEiIiq1JgVJMyQtlrRE0olDHH+cpLPL47+QNKXOeCIiYmS1JQVJE4BTgQOAacARkqZ1nHYMcJft7YBPAR+tK56IiBjdujVeew9gie2lAJLmAIcAi9rOOQT4z/L5t4HPS5Jt1xhXxMCZcuIFTYfQFTd95MDVfk2/fHZYs8+/ulTX96+kw4AZtt9Qbr8O2NP2cW3n/Lo8Z1m5fWN5zp87rjULmFVuPgNYXEvQ3bMZ8OdRz+pP+eyDa5A//9rw2be1PWm0k+osKWiIfZ0ZaCznYHs2MLsbQfWCpAW2pzcdRxPy2Qfzs8Ngf/5++ux1NjQvA7Zp294auGW4cyStC2wC/KXGmCIiYgR1JoX5wPaSpkpaH5gJzO04Zy5wVPn8MODHaU+IiGhObdVHtldKOg64CJgAnGF7oaSTgQW25wKnA2dJWkJRQphZVzw9ttZUddUgn31wDfLn75vPXltDc0RErH0yojkiIipJChERUUlSiIg1IunNY9kXa5e0KYyDpLsYYlxFi+0n9TCcRkj6DnAGcKHth5uOpxckXcPIf/fn9jCcxki6uvOzSrrG9q5NxRTjV+fgtUGwGcUAvPcBy4Gzyu0jgSc0GFcvnQYcDXxW0rnAV23/puGY6nZY+fNYip51Z5XbRwIrGomohyQdAbwGmCqpvZv5RODOZqLqLUkrePSNwT3AAuDfW9P7rI1SUugCSb+wvWfHvittP6+pmHpN0ibAEcC7gZuBLwFft/1go4HVSNIVtl842r5+I2lbYCrwYaB99uMVwHW2VzYSWA9Jej/FYNxvUtwIzgS2oJiC5//Y3qe56MYnJYXusKRXA+fYbj0fGJKeDLwWeB1wDfANYC+KgYn7NBdZ7TaS9DzbVwJI2hPYqOGYamf7D8AfgOc3HUuDZnTcCM4ubwRPlvSuxqLqgiSF7ngN8DngNEkPA1dSVCX0PUnnATtSVKEcZPvW8tDZkhY0F1lPvAH4iqQNyu2/Af/SYDw9JemfKKa7fwrF3bIA29640cB642FJr6KY3RkeqVKEEdqb1gapPopxkbSv7R83HUeTypIStgeiPr2lnIngINs3NB1Lr0l6GvAZitKSKW4E3wr8CdjN9k8bDG9ckhS6QNLjgNcDzwJad43YnjXca9Z25V3isGyf16tYmiRpfx79d/9QcxH1ziC0nwyiVB91x9eApcA/Ah+kqE5a2GhE9TtohGMG+j4pSPoC8ERgb+ArwKEUd4x9re2GYIGks4HzgftbxwfhhkDSJOCNwBTavkdtr/XVhykpdEGrb7ak62w/W9J6wEW29206tqhP29/7V7Z3kTQR+I7tlzUdW50kfWWEw+6HL8bRSPoZcDlwFfBQa7/t7zQWVJekpNAdrW6Xd0t6JnA7sG2D8fRMWXV2KI++Yzq5qZh66G/lz79L2oKij/6U5sLpDdtHNx3DY8ATbL+j6SDqkKTQHadL2pRiENtFFAPX3tdsSD3zPYpBO1fRVoUwIC6U9ETgE8C1FHeMX2s2pN6R9Nkhdt9DMTX+93odT4/9QNLLbc9rOpBuS/VRjIukX9veqek4mibp8cDjbQ/MyoGSZlN0Rz633HUoRVvaNsBS229pKra6lSOaN6S4EXqQPuqOm5JCF5SNTh8AtrL9j5KmAXvY/mqzkfXEzyTtbPv6pgPptTIRvIViQfRjJW0laU/bFzYdW49sB+zbGsEs6TTgh8B+QF//e7A9sekY6pJZUrvjq8BlPLIm9e+Af28smt7aC7hK0mJJ10m6XtJ1TQfVI2dQ3CHuVW7fAgxEd9TSVhR3yy0bAk+1/RB9WpUoacfy53OHejQdXzekpNAdT7H9TUlvB7D9oKSHRntRnzig6QAatL3tIyQdDmD7r5LUdFA99DHgWkmXUiTHvYEPSdoQuLjJwGp0AjAL+OQQxwys9T0OkxS64z5JT6Ic3i5pdwZgtkwo5sGRtAvwonLX5bZ/1WRMPfRAOcVF6+8+FXig2ZB6x/bpkuYBe1AkhXfZvqU8/PbmIqtPa0Cq7Zc0HUtdUn3UHW8Dvg88TdJlwLeAf2s2pN4oF1X5BsX8N08Bvi5pID47cDLwP8DWks4ELgHe2WxI9WuvQgG2pJgV94/AFv1ShTIaSYeX41KQ9B5J50nqi3Uk0vuoSyStDzyT4o5pke2BuGMs2w+eb/u+cntD4Oe2n91sZL1RdjJ4AcXf/We272g4pNpJmm17lqRLhjjsQRi02TZwcS+KKcQ/QVFS2nOUlz7mpfqoe57DIwO4pknC9jebDaknRNuIzvL5INWrTwBupfi7bydpO9s/azimWpUJYR3gPbavaDqehrT+zR8InGb7e5L+s8F4uiZJoQskfRWYxiMDmKCoZx6EpPAV4BeSvltuvwI4vcF4ekbShyjWkbgBaC1FauDljQXVI7YflvQJBndNhT9J+iLwUuCj5cj+vqiOT/VRF0j6DTBtUNYo7lTWI+9FUUL4ie1rGg6pJyQtBnax/femY2lCufrYdcB5HrAvEklPAGYA19v+naQtgZ1t/7Dh0MYtJYXuWEixXnPf1ycPxfbVwNVNx9GA39Mnd4dr6ASKsQkrJf2dPhrVOwZbAhfYvl/SPsCz6ZMpTlJSGIeyysTAxsCuFNMmt08hPOKaA7F2kvQpir/7NhRfBhez6t/9hIZCix6RdC0wnaId8SJgLvAM22t91WFKCuPz+aYDiEb8uvy5kKJLagyeh22vLNeW+LTtz0nqi2rTJIVxsP3/mo4hes/2QDSkx4gelHQE8M88suDUeg3G0zVJCl1QjmD+HMU4hcdR1K3e3891q+UskcPWPfbzZ2+R9HSKlfamsepynDs0FlT0ytHAscAHbf++HM3+9YZj6oq0KXSBpPkUXRPnUAz5fz2wje2TmoyrFySdDNwGnEWRDI8EJtr+WKOB9YCkyylmx/0ERVfcoymqFQbh774OcN0gT5teDlht3QAstv3gSOevLQa550Q3rWN7MbCu7Qdtf4mi//Ig2N/2F2yvsH2v7dMo5tUfBE+wfRGA7Rttvwfo2zlx2pXdr38laXLTsTSh7HH0O+BU4AvAbyXt3WhQXZLqo+64r7xr+FU5oOlWYKOGY+qVhyQdSVFKMnAEq45w7mf3l7Oi3ijpWOBPFPM/DYotgYWSfgnc19pp++DmQuqZTwIvK28GkbQDxZxnuzUaVRek+qgLJD2NYi79DSjWUdgE+Lzt3zYaWA9ImgJ8BnghRVK4AniL7Zuai6o3JO0JLAI2pWhb2Bj42KBM/SDpxUPtt31Zr2PptdbcR6PtWxslKUTEGpO0ObB7ufnLQZgQEEDSGRQ3QWeVu46kqD4+urmouiNJIcalXE/gGOBZrNoD518aCyp6QtKrgI8Dl1J0MngR8Hbb324yrl4o5zp6E23TuwBfsL3WrziXpBDjIulc4DfAayjWFzgSuMH2mxsNLGon6VfAfq3SQTmN+MW2d2k2snpJmgCcafu1TcdSh/Q+6oJyVOOo+/rUdrbfC9xn+0yKqYR3bjimnpD0vLHs62PrdFQX3ckAfKeUa1BPKjuX9J30PuqO9wDndex79xD7+lGrb/bdknaiGLMwpblweuoLQOdKY6fSBz1Qxuh/JF1E0esG4NXAvAbj6aWbgCskzWXVnlenNBZRlyQpjIOk/Smmz91KUvs/ho15ZH79fjdb0qbAeykmBdsI6OvBW2UdysUhAAAK70lEQVSvo+dR3C0e33ZoY/pkqoOxsP32skTcqlefbfu7o7ysX9xSPtYBJjYcS1clKYzPHRSTo/2dYnK0lhXAiY1E1GO2v1w+vQx4WpOx9NATKKZKXxeY1LZ/BXB4IxE1xPZ5DEaJeBW23990DHVJQ3MXSNpggBda2Rz4EPBU2wdImkaxZnPfThon6au2Xy/peNufbTqe6D1JPwIOt313ub0pMMf2/s1GNn593yjUI8+RdKGkRZJ+K+l3kvp+4FrpqxTzyT+13P4t8JbGoumNPSRtBRwjaaKkjdsfTQcXPTGplRAAbN9Fn4xmT1Lojq9QNDq+lKKv9l7lz0Gwme1zKNtQbK+k/6e5+DJF3/wdKUY0L2x7/Hr4l/UXSY/qdjzUvj71UPu8T5K2ZYRZg9cmaVPojnttf7/pIBpyn6QnU/6HKLtk3tNsSPUqe5icIulLtt/YdDwNOopiipN2rx9iXz96N/BTSa0pPfYGZjUYT9ekTWEcJLXmOTmi/Hkeqy7LeF3Pg+oxSc+lWEtiJ4q75EnAYYPw2aFKgjvY/pqkJwEb2f5j03HVqVxc5jUUJeLL2w5NBB6yPRAzBEvajKIXmoCf2/5zwyF1RZLCOJTz6Q/HtvtiKt3RSFoXeAbFf46+mVd+NJLeQzER4NNt71C2M5xte6+GQ6tVWVUyFfgwq/ayW0GxxsLKRgKLrkhSiHGT9AKKAWtVdaTtrzUWUI+Ui7fvClxte9dyX1/MlBmDK20KXdAxgKnlHuAq233d8CjpLODpwLU80sBsoO+TAsWSq5bUak95QtMB9VI5cO2jFL1uVD48CEux9rMkhe54AcX0wT8ot18O/BJ4s6Rv2P5kY5HVbzowzYNZ5DxP0qnAJpKOppgt9oyGY+qljwEH2b6h6UCaUE6MtzmrlpDX+vakVB91QTn/y2G2V5TbE4FzKJalXGB7WpPx1amcJfV427c2HUsTJB0AvIziLvki2xc2HFLPSLrC9gubjqMJkv4NeB9wO49MaeN+qDpMUugCSTcAO7ca2FpLc9p+pqRrWvXN/UTS9ymqiSYCz6EoGbX3vBqEJRkHUtsMwC8GtgDOZ9W/fd9PeyFpCbCn7TubjqXbUn3UHecAP5d0frl9MHCOpA2Bxc2FVatPNB1AUyTdxdADlVp16k/qcUi9dlDb879SlJRazGDMhXQzfToeJyWFLilnzmzNFvlT21c2HFIjJL0QeI3tNzUdS13KuuRhlfPtRx+TdDpFN+wLWLWUlKmzB5mkDW3fV853c0P5aB3b2Pa9zUXXO5KeQzGY6VXA74HvNBtRvfKlX5A01GSA91C0o32v1/H02B/Lx/rlo2+kpDAOki4sZwa9mVWrE1rVCJOHeelaT9IOwEyK0dx3AmcDb7O9baOBRc9Imk0x/9O55a5DKeZ/2gZYarvfJ0asbgybjqObkhTGSZKALW3f0nQsvSTpYYopDo6xvaTct9T2oKypMPAk/Rh4WVsHi3WBHwL7Adf3ea+75wOnU0xrMlnSLsC/2v6/DYc2bpkldZzK/vmDOBneoRRLb14i6UuS/oGihBSDYytgw7btDSnW1XiItnr2PvVpYH+KUjK2f0UxKd5aL0mhO35ZTgw3MGx/1/arKaoPLgXeCmwu6TRJLxvxxX1C0iGSbpB0j6R7Ja2QNBDtSKWPAddK+oqkrwLXAJ8oe91d3GhkPWD75o5dfdHWlOqjLpB0PfBM4EaKRbxbbQoDlSjKWUIPB15te9+m46lb2Vf9lbavbzqWpkjaEtiD4t/8LwelGlXSt4FTgM9TzJR6PDDd9sxGA+uCJIUukPT0ofbbvrHXsUTvDOqIXkk72v7NcKVj21f3OqZeK6fN/gzFwlqiaEt5cz8MZktSiFhDkj5NsX5E54jeuY0F1QOSZtueJemSIQ6730uJ5TiV421/qulY6pCkELGGyhliO9n2P/c8mOgpSZfa3qfpOOqQwWsRa6C8W5xve6gBXAND0k7ANGCD1r5BWEsDuELS5ynG51TjFPqh6iwlhYg11M93i2Mh6X3APhRJYR5wAMUUL4c1GVcv9HPVWZJCF0g6BPgI8FSy2MjAkPQBilli57Dq3eKgrE99PbALcI3tXSRtDnzZ9kGjvDQew1J91B2fZMC7Jg6oF5c/23vhmD4ZxDQGf7P9sKSV5fxfdwB9PaJd0gkjHc+EeNFyexLC4LH9oqZjaNgCSU8EvgRcBfwvxboa/Wxi+fMZFKsttnqaHQT8pJGIuizVR10wqF0TB52kdw213/aHeh1Lr5Vzfm3dGtUraQqw8QBVnf0QOLRjtcVzbc9oNrLxS0mhO55MsSRf+2pj5pG7iOhP7dMabAAcSDFLaN+z7XJRqd3K7ZuajajnJgMPtG0/AExpJpTuSlIYp3RNHFy2P9q+LemjFKXFQXGlpN1tz286kAacRTHn2XcpbgBfCfRFV9xUH3XBoHdNjIKkTSgWmNm+6Vh6QdIiYAfgD6w659dav3j9WJTTfLTalX5i+5om4+mWJIUuGPSuiYNG0rq2V0q6hkcWV5oAbAl8yPanm4uudyQNuaCS7T/0OpZeKSd9HJbtv/QqlrokKXSBpMuH2G3bg9I1caBIutr2czsmQlwJ3Ga739cRqEg6y/brRtvXTyT9nuJGoLV2SOsLtFVKWuu75KZNoQvSNXHgCDILLvCs9o2yfW23hmLpCdtTm46hbkkKXTDIXRMH1KSRBjH1wwCmkUh6J/Au4PFtiwqJogfO7MYC64FBmDY8SaE7BrZr4oCaAGzEgC4/avvDwIclfdj2O5uOp8dOAGZRzGLQyUDmPopHk7QBcH4/DGSJR2u1KTQdR9MkvRC41vZ9kl5LMd3HZ/q5oXkQpKRQj8cBQ67GFn1hIEsIQzgN2EXSLsB/AKdT9NV/8Yiv6hOSXkAxYK36Hu2HacOTFMZhtK6JzUUWNfuHpgN4jFhZjmw+hKKEcLqko5oOqhfKBZaeDlzLI9XHpg8GsCUpjM8vKYrM7fPHD1zXxEHTD33Ru2RF2ej8WmDvsvfReg3H1CvTgWnuw/r3JIXxSdfEGGSvBl4DHGP7NkmTgY83HFOv/BrYAri16UC6LQ3N4yBpGTBs98N+75oYMWgkfZ+immgi8ByK2oL2mZEPHuala42UFMZnoLsmRgygTzQdQN2SFMbnVtsnNx1ERPTMn4DNbV/RvlPS3uWxtd46TQewlksJIQaapPUl7VQ+BqGR+dPAiiH2/7U8ttZLm8I4SHpSeqLEoJK0D3AmcBPFDdI2wFG2+2JZyqFI+rXtnYY5dr3tnXsdU7el+mgckhBiwH0SeJntxQCSdgC+RX9PirfBCMce37MoapTqo4hYU+u1EgKA7d/S/+MU5kt6Y+dOSccAVzUQT9el+igi1oikMyi6Z55V7joSWNf20c1FVS9JmwPfpZgRtpUEpgPrA6+0fVtTsXVLkkJErBFJjwPeBOxF0abwE+ALgzCaX9JLgFbbwkLbP24ynm5KUoiIiEraFCJitUg6RNKb2rZ/IWlp+Ti8ydhi/JIUImJ1/Qcwt237ccDuwD7AsU0EFN2TLqkRsbrWt31z2/ZPbd8J3Clpw6aCiu5ISSEiVtem7Ru2j2vbnNTjWKLLkhQiYnX9Ypi++v9KMWtorMXS+ygiVoukpwDnU0wZfXW5ezeKtoVX2L69qdhi/JIUImKNSNoXeFa52Vd99QdZkkJERFTSphAREZUkhYiIqCQpREREJUkhIiIqSQoREVH5/0pZNcPZwTI8AAAAAElFTkSuQmCC
"
>
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<pre>Top 5 prediction probabilities: [[  9.98000562e-01   1.63300370e-03   2.06789497e-04   8.11524951e-05
    3.09713541e-05]]

</pre>
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<img 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>
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<pre>Top 5 prediction probabilities: [[ 0.21818547  0.201994    0.17728646  0.17581226  0.08255062]]

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
"
>
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<pre>Top 5 prediction probabilities: [[  9.99998927e-01   1.07156905e-06   8.92230842e-13   7.04267238e-14
    5.33231567e-14]]

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>
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<pre>Top 5 prediction probabilities: [[  9.99976754e-01   1.08559016e-05   9.52353003e-06   1.18314608e-06
    9.29785699e-07]]

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<h3 id="Project-Writeup">Project Writeup<a class="anchor-link" href="#Project-Writeup">&#182;</a></h3><p>Once you have completed the code implementation, document your results in a project writeup using this <a href="https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md">template</a> as a guide. The writeup can be in a markdown or pdf file.</p>

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<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
    "<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
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<h2 id="Step-4-(Optional):-Visualize-the-Neural-Network's-State-with-Test-Images">Step 4 (Optional): Visualize the Neural Network's State with Test Images<a class="anchor-link" href="#Step-4-(Optional):-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" />
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 <p style="text-align: center;"> Your output should look something like this (above)</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">### Feel free to use as many code cells as needed.</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">keep_prob</span><span class="p">:</span> <span class="mf">1.0</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">0</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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