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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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="c1"># PREVENT MULTIPLE PRE PROCESSING</span>
<span class="n">PRE_PROCESSING_DONE</span> <span class="o">=</span> <span class="kc">False</span>

<span class="n">NUM_CLASSES</span> <span class="o">=</span> <span class="mi">43</span>
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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="s1">&#39;./traffic-signs-data/train.p&#39;</span>
<span class="n">validation_file</span><span class="o">=</span> <span class="s1">&#39;./traffic-signs-data/valid.p&#39;</span>
<span class="n">testing_file</span> <span class="o">=</span> <span class="s1">&#39;./traffic-signs-data/test.p&#39;</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>

<span class="c1"># print(train);</span>
<span class="c1"># print(X_train);</span>
<span class="c1"># print(y_train);</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">y_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">y_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">y_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="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="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of training examples =&quot;</span><span class="p">,</span> <span class="n">n_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of validation examples =&quot;</span><span class="p">,</span> <span class="n">n_validation</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of testing examples =&quot;</span><span class="p">,</span> <span class="n">n_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;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>
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<pre>Number of training examples = 34799
Number of validation examples = 4410
Number of testing examples = 12630
Image data shape = (32, 32, 3)
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
<span class="kn">import</span> <span class="nn">csv</span>


<span class="c1">#Import traffic signs class names</span>
<span class="n">signs_class</span><span class="o">=</span><span class="p">[]</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;signnames.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;rt&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">csvfile</span><span class="p">:</span>
    <span class="n">reader</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">DictReader</span><span class="p">(</span><span class="n">csvfile</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">reader</span><span class="p">:</span>
        <span class="n">signs_class</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;SignName&#39;</span><span class="p">]))</span>
        
        
<span class="n">len_samples</span><span class="o">=</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">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</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="mi">0</span><span class="p">,</span> <span class="n">n_classes</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">10</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">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">x_selected</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">i</span><span class="p">]</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x_selected</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="p">:])</span> <span class="c1">#draw the first image of each class</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">signs_class</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">len_samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x_selected</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="c1">#Plot number of images per class</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">16</span><span class="p">,</span> <span class="mi">4</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="mi">0</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">),</span> <span class="n">len_samples</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;Distribution graph&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Class label&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Number of images&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Minimum number of images for each label =&quot;</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">len_samples</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Maximum number of images for each label =&quot;</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">len_samples</span><span class="p">))</span>
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<pre>Minimum number of images for each label = 180
Maximum number of images for each label = 2010
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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>
<ul>
<li>Neural network architecture (is the network over or underfitting?)</li>
<li>Play around preprocessing techniques (normalization, rgb to grayscale, etc)</li>
<li>Number of examples per label (some have more than others).</li>
<li>Generate fake data.</li>
</ul>
<p>Here is an example of a <a href="http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf">published baseline model on this problem</a>. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.</p>

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<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">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">shuffle</span>

<span class="c1"># 1. Normalize image data X</span>
<span class="c1"># 2. Shuffle data to prevent ordered learning </span>
<span class="c1"># 3. Convert to one hot later ikn the training section with tf.one_hot</span>
<span class="k">def</span> <span class="nf">preprocess_data</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
 
    <span class="c1"># Mean normalizes the pixel and make all image array values fall within the range -1 to 1</span>
    <span class="c1"># Note all values in original images are between 0 and 255, as uint8</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">X</span> <span class="o">-</span> <span class="mf">128.</span><span class="p">)</span> <span class="o">/</span> <span class="mf">128.</span>
    
    <span class="c1"># DATA SHUFFLE</span>
    <span class="n">shuffled_X</span><span class="p">,</span> <span class="n">shuffled_y</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">shuffled_X</span><span class="p">,</span> <span class="n">shuffled_y</span>


<span class="c1"># PRE - PROCESS DATA</span>
<span class="k">if</span> <span class="n">PRE_PROCESSING_DONE</span><span class="o">==</span><span class="kc">False</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">preprocess_data</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">preprocess_data</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">preprocess_data</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">PRE_PROCESSING_DONE</span> <span class="o">=</span> <span class="kc">True</span>


<span class="c1"># SHOW AND PRINT PRE PROCESSED AND SHUFFLED VALUE</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="mi">0</span><span class="p">])</span>

<span class="nb">print</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="n">signs_class</span><span class="p">[</span><span class="n">y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="s1">&#39;- Index: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
</pre></div>

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<pre>[[[-0.57031 -0.53906 -0.47656]
  [-0.61719 -0.59375 -0.53125]
  [-0.61719 -0.60938 -0.60938]
  ..., 
  [-0.44531 -0.48438 -0.44531]
  [-0.375   -0.375   -0.32812]
  [-0.47656 -0.42969 -0.36719]]

 [[-0.60156 -0.60156 -0.55469]
  [-0.625   -0.625   -0.57031]
  [-0.625   -0.625   -0.64062]
  ..., 
  [-0.48438 -0.5     -0.46875]
  [-0.46875 -0.46875 -0.42188]
  [-0.48438 -0.46094 -0.40625]]

 [[-0.64062 -0.64062 -0.59375]
  [-0.64844 -0.64844 -0.60156]
  [-0.625   -0.64062 -0.64062]
  ..., 
  [-0.52344 -0.52344 -0.49219]
  [-0.53906 -0.53906 -0.49219]
  [-0.52344 -0.51562 -0.46875]]

 ..., 
 [[-0.25781 -0.32031 -0.42969]
  [-0.28906 -0.34375 -0.42969]
  [-0.33594 -0.375   -0.47656]
  ..., 
  [-0.23438 -0.23438 -0.20312]
  [-0.23438 -0.22656 -0.17188]
  [-0.23438 -0.21875 -0.16406]]

 [[-0.375   -0.38281 -0.47656]
  [-0.39062 -0.39844 -0.45312]
  [-0.375   -0.375   -0.44531]
  ..., 
  [-0.20312 -0.17188 -0.10938]
  [-0.22656 -0.1875  -0.10938]
  [-0.23438 -0.1875  -0.11719]]

 [[-0.36719 -0.35938 -0.39844]
  [-0.375   -0.36719 -0.375  ]
  [-0.375   -0.36719 -0.375  ]
  ..., 
  [-0.21094 -0.17969 -0.11719]
  [-0.23438 -0.20312 -0.13281]
  [-0.23438 -0.19531 -0.125  ]]] 

Keep right - Index: 38
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<h2 id="Tensorflow-Setup-and-GPU-Check">Tensorflow Setup and GPU Check<a class="anchor-link" href="#Tensorflow-Setup-and-GPU-Check">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># PRE- REQUISITES :  Download carnd-term1 with gpu enabled , </span>
<span class="c1"># Remove existing and install with condo with command : conda env create -f environment-gpu.yml</span>

<span class="c1"># TENSORFLOW RELEASE INFORMATION OFFICIAL</span>
<span class="c1"># All our prebuilt binaries have been built with CUDA 8 and cuDNN 6.</span>
<span class="c1"># We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.</span>

<span class="c1"># https://devtalk.nvidia.com/default/topic/1026198/cuda-9-0-importerror-libcublas-so-8-0/</span>

<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="c1"># Check for a GPU</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">():</span>
    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;No GPU found. Please use a GPU to train your neural network.&#39;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Default GPU Device: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">()))</span>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">AttributeError</span>                            Traceback (most recent call last)
<span class="ansi-green-fg">&lt;ipython-input-229-b1fe6d710a93&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span><span class="ansi-blue-fg">()</span>
<span class="ansi-green-intense-fg ansi-bold">     10</span> <span class="ansi-green-fg">import</span> tensorflow <span class="ansi-green-fg">as</span> tf
<span class="ansi-green-intense-fg ansi-bold">     11</span> <span class="ansi-red-fg"># Check for a GPU</span>
<span class="ansi-green-fg">---&gt; 12</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">if</span> <span class="ansi-green-fg">not</span> tf<span class="ansi-blue-fg">.</span>test<span class="ansi-blue-fg">.</span>gpu_device_name<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold">     13</span>     warnings<span class="ansi-blue-fg">.</span>warn<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">&#39;No GPU found. Please use a GPU to train your neural network.&#39;</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold">     14</span> <span class="ansi-green-fg">else</span><span class="ansi-blue-fg">:</span>

<span class="ansi-red-fg">AttributeError</span>: module &#39;tensorflow.python.platform.test&#39; has no attribute &#39;gpu_device_name&#39;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="nb">print</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>

<span class="c1"># Creates a graph.</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;a&#39;</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
<span class="n">c</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">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="c1"># Creates a session with log_device_placement set to True.</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">ConfigProto</span><span class="p">(</span><span class="n">log_device_placement</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="c1"># Runs the op.</span>
<span class="nb">print</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">c</span><span class="p">))</span>
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<h2 id="Model-Architecture">Model Architecture<a class="anchor-link" href="#Model-Architecture">&#182;</a></h2>
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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">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>

<span class="c1">#----------------------------------- With LeNet1------------------------------------------------</span>
<span class="c1"># Accuracy below is dependent on shuffling as well</span>

<span class="c1">#Model Parameters Set</span>
<span class="c1"># EPOCHS = 15</span>
<span class="c1"># BATCH_SIZE = 128</span>
<span class="c1"># learn_rate = 0.001</span>
<span class="c1"># # accuracy of 94.6</span>

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

<span class="c1"># EPOCHS = 15</span>
<span class="c1"># BATCH_SIZE = 64</span>
<span class="c1"># # accuracy of 94.2</span>

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

<span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">40</span>
<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">64</span>
<span class="c1"># accuracy of 96.1</span>
<span class="c1"># Test accuracy of 95.1</span>
<span class="c1"># --------------------------------------------------------------------------------------------------</span>

<span class="n">LEARN_RATE</span> <span class="o">=</span> <span class="mf">0.001</span>
<span class="n">MEAN</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">STD_DEV</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="n">DROPOUT</span> <span class="o">=</span> <span class="mf">0.5</span>
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<h2 id="LeNet-Architecture">LeNet Architecture<a class="anchor-link" href="#LeNet-Architecture">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Function for Initializing Random TF Variables</span>
<span class="k">def</span> <span class="nf">trun_var</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="p">[],</span> <span class="n">mean</span><span class="o">=</span><span class="n">MEAN</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="n">STD_DEV</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="k">return</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">dim</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">stddev</span><span class="o">=</span><span class="n">std</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">LeNet</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>    

    <span class="c1">#Initializing Weights and Biases</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="p">{</span>    <span class="s1">&#39;conv1&#39;</span><span class="p">:</span> <span class="n">trun_var</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">name</span><span class="o">=</span><span class="s1">&#39;weights_conv1&#39;</span><span class="p">),</span>
                   <span class="s1">&#39;conv2&#39;</span><span class="p">:</span> <span class="n">trun_var</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">name</span><span class="o">=</span><span class="s1">&#39;weights_conv2&#39;</span><span class="p">),</span>
                   <span class="s1">&#39;full_1&#39;</span><span class="p">:</span> <span class="n">trun_var</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">name</span><span class="o">=</span><span class="s1">&#39;weights_full_1&#39;</span><span class="p">),</span>
                   <span class="s1">&#39;full_2&#39;</span><span class="p">:</span> <span class="n">trun_var</span><span class="p">([</span><span class="mi">120</span><span class="p">,</span><span class="mi">83</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;weights_full_2&#39;</span><span class="p">),</span>
                   <span class="s1">&#39;out&#39;</span><span class="p">:</span> <span class="n">trun_var</span><span class="p">([</span><span class="mi">83</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;weights_out&#39;</span><span class="p">)}</span>

    <span class="n">biases</span> <span class="o">=</span> <span class="p">{</span>     <span class="s1">&#39;conv1&#39;</span><span class="p">:</span> <span class="n">trun_var</span><span class="p">([</span><span class="mi">6</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;biases_conv1&#39;</span><span class="p">),</span>
                   <span class="s1">&#39;conv2&#39;</span><span class="p">:</span> <span class="n">trun_var</span><span class="p">([</span><span class="mi">16</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;biases_conv2&#39;</span><span class="p">),</span>
                   <span class="s1">&#39;full_1&#39;</span><span class="p">:</span> <span class="n">trun_var</span><span class="p">([</span><span class="mi">120</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;biases_full_1&#39;</span><span class="p">),</span>
                   <span class="s1">&#39;full_2&#39;</span><span class="p">:</span> <span class="n">trun_var</span><span class="p">([</span><span class="mi">83</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;biases_full_2&#39;</span><span class="p">),</span>
                   <span class="s1">&#39;out&#39;</span><span class="p">:</span> <span class="n">trun_var</span><span class="p">([</span><span class="n">n_classes</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;biases_out&#39;</span><span class="p">)}</span>

    <span class="c1">#LeNet CNN Model Implementation</span>


    <span class="c1">#Convolutional Layer 1</span>
    <span class="c1">#INPUT 32x32x1</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">weights</span><span class="p">[</span><span class="s1">&#39;conv1&#39;</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="c1">#28x28x6</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">bias_add</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="n">biases</span><span class="p">[</span><span class="s1">&#39;conv1&#39;</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">relu</span><span class="p">(</span><span class="n">conv1</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">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;SAME&#39;</span><span class="p">)</span>    
    <span class="c1">#14x14x6</span>

    <span class="c1">#Convolutional Layer 2</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">weights</span><span class="p">[</span><span class="s1">&#39;conv2&#39;</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="c1">#10x10x16</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">bias_add</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">biases</span><span class="p">[</span><span class="s1">&#39;conv2&#39;</span><span class="p">])</span>
    
    <span class="c1"># USED IN OPTIONAL 5 SECTION</span>
    <span class="n">CONV2_BIAS</span> <span class="o">=</span> <span class="n">conv2</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="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;SAME&#39;</span><span class="p">)</span>    
    <span class="c1">#5x5x16</span>

    <span class="c1">#Flatten</span>
    <span class="n">conv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">layers</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">#400</span>

    <span class="c1">#Fully Connected Layer 1</span>
    <span class="n">full_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">weights</span><span class="p">[</span><span class="s1">&#39;full_1&#39;</span><span class="p">]),</span> <span class="n">biases</span><span class="p">[</span><span class="s1">&#39;full_1&#39;</span><span class="p">])</span>    
    <span class="c1">#120</span>
    <span class="n">full_1</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">full_1</span><span class="p">)</span>
    <span class="n">full_1</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">full_1</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>

    <span class="c1">#Fully Connected Layer 2</span>
    <span class="n">full_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">full_1</span><span class="p">,</span> <span class="n">weights</span><span class="p">[</span><span class="s1">&#39;full_2&#39;</span><span class="p">]),</span> <span class="n">biases</span><span class="p">[</span><span class="s1">&#39;full_2&#39;</span><span class="p">])</span>    
    <span class="c1">#83</span>
    <span class="n">full_2</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">full_2</span><span class="p">)</span>
    <span class="n">full_2</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">full_2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>

    <span class="c1">#Output Layer</span>
    <span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">full_2</span><span class="p">,</span> <span class="n">weights</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]),</span> <span class="n">biases</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">])</span>  
    <span class="k">return</span> <span class="n">logits</span><span class="p">,</span> <span class="n">CONV2_BIAS</span>
    <span class="c1">#N_CLASSES = 43</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO</span>

<span class="c1"># def LeNet2(x, keep_prob):    </span>

<span class="c1">#     #Initializing Weights and Biases</span>
<span class="c1">#     weights = {    &#39;conv1&#39;: trun_var([3,3,3,12], name=&#39;weights_conv1&#39;),</span>
<span class="c1">#                    &#39;conv2&#39;: trun_var([3,3,12,24], name=&#39;weights_conv2&#39;),</span>
<span class="c1">#                    &#39;full_1&#39;: trun_var([600,353], name=&#39;weights_full_1&#39;),</span>
<span class="c1">#                    &#39;full_2&#39;: trun_var([353,83], name=&#39;weights_full_2&#39;),</span>
<span class="c1">#                    &#39;out&#39;: trun_var([83, n_classes], name=&#39;weights_out&#39;)}</span>

<span class="c1">#     biases = {     &#39;conv1&#39;: trun_var([12], name=&#39;biases_conv1&#39;),</span>
<span class="c1">#                    &#39;conv2&#39;: trun_var([24], name=&#39;biases_conv2&#39;),</span>
<span class="c1">#                    &#39;full_1&#39;: trun_var([353], name=&#39;biases_full_1&#39;),</span>
<span class="c1">#                    &#39;full_2&#39;: trun_var([83], name=&#39;biases_full_2&#39;),</span>
<span class="c1">#                    &#39;out&#39;: trun_var([n_classes], name=&#39;biases_out&#39;)}</span>

<span class="c1">#     #LeNet CNN Model Implementation</span>


<span class="c1">#     #Convolutional Layer 1</span>
<span class="c1">#     #INPUT 32x32x1</span>
<span class="c1">#     conv1 = tf.nn.conv2d(x, weights[&#39;conv1&#39;], strides=[1,1,1,1], padding=&#39;VALID&#39;) </span>
<span class="c1">#     #28x28x12</span>
<span class="c1">#     conv1 = tf.nn.bias_add(conv1, biases[&#39;conv1&#39;])</span>
<span class="c1">#     conv1 = tf.nn.relu(conv1)</span>
<span class="c1">#     conv1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding=&#39;SAME&#39;)    </span>
<span class="c1">#     #14x14x24</span>

<span class="c1">#     #Convolutional Layer 2</span>
<span class="c1">#     conv2 = tf.nn.conv2d(conv1, weights[&#39;conv2&#39;], strides=[1,1,1,1], padding=&#39;VALID&#39;)</span>
<span class="c1">#     #10x10x24</span>
<span class="c1">#     conv2 = tf.nn.bias_add(conv2, biases[&#39;conv2&#39;])</span>

<span class="c1">#     # USED IN OPTIONAL 5 SECTION</span>
<span class="c1">#     CONV2_BIAS = conv2</span>
    
<span class="c1">#     conv2 = tf.nn.relu(conv2)</span>
<span class="c1">#     conv2 = tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding=&#39;SAME&#39;)    </span>
<span class="c1">#     #5x5x24</span>

<span class="c1">#     #Flatten</span>
<span class="c1">#     conv2 = tf.contrib.layers.flatten(conv2)</span>
<span class="c1">#     #600</span>

<span class="c1">#     #Fully Connected Layer 1</span>
<span class="c1">#     full_1 = tf.add(tf.matmul(conv2, weights[&#39;full_1&#39;]), biases[&#39;full_1&#39;])    </span>
<span class="c1">#     #353</span>
<span class="c1">#     full_1 = tf.nn.relu(full_1)</span>
<span class="c1">#     full_1 = tf.nn.dropout(full_1, keep_prob)</span>

<span class="c1">#     #Fully Connected Layer 2</span>
<span class="c1">#     full_2 = tf.add(tf.matmul(full_1, weights[&#39;full_2&#39;]), biases[&#39;full_2&#39;])    </span>
<span class="c1">#     #83</span>
<span class="c1">#     full_2 = tf.nn.relu(full_2)</span>
<span class="c1">#     full_2 = tf.nn.dropout(full_2, keep_prob)</span>

<span class="c1">#     #Output Layer</span>
<span class="c1">#     logits = tf.add(tf.matmul(full_2, weights[&#39;out&#39;]), biases[&#39;out&#39;])  </span>
<span class="c1">#     return logits, CONV2_BIAS</span>
<span class="c1">#     #N_CLASSES = 43</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Placeholders for Network</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="c1">#Colored</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="n">n_classes</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">one_hot_y</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>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Training</span>

<span class="n">logits</span><span class="p">,</span> <span class="n">CONV2_BIAS</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">keep_prob</span><span class="p">)</span>
<span class="c1"># logits = LeNet2(x,keep_prob)</span>


<span class="c1">#Cost Function</span>
<span class="n">cost</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">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">)</span>    

<span class="c1">#Adam Optimizer for Back Propagation</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">LEARN_RATE</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Prediction and Accuracy</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">labels</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">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>

<span class="n">accuracy</span> <span class="o">=</span> <span class="n">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>
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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="n">init</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">()</span>

<span class="c1"># To save the model</span>
<span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
<span class="n">save_file</span> <span class="o">=</span> <span class="s1">&#39;./lenet_traffic.ckpt&#39;</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Running LeNet and Training on Training Set</span>
<span class="c1"># Measuring Accuracy on Validation Set</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">init</span><span class="p">)</span>
            
    <span class="k">for</span> <span class="n">epoch</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="k">for</span> <span class="n">start_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_train</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
            <span class="n">end_i</span> <span class="o">=</span> <span class="n">start_i</span> <span class="o">+</span> <span class="n">BATCH_SIZE</span>
            <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_train</span><span class="p">[</span><span class="n">start_i</span><span class="p">:</span><span class="n">end_i</span><span class="p">],</span>
                                           <span class="n">y</span><span class="p">:</span> <span class="n">y_train</span><span class="p">[</span><span class="n">start_i</span><span class="p">:</span><span class="n">end_i</span><span class="p">]</span> <span class="p">,</span>
                                           <span class="n">keep_prob</span><span class="p">:</span> <span class="n">DROPOUT</span><span class="p">})</span>
        
        <span class="n">valid_acc</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</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_valid</span><span class="p">,</span>
                                                  <span class="n">y</span><span class="p">:</span> <span class="n">y_valid</span><span class="p">,</span>
                                                  <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.</span><span class="p">})</span>
        
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;EPOCH: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</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="s1">&#39;Validation Accuracy = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">valid_acc</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="n">save_file</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>EPOCH: 1
Validation Accuracy = 0.6090703010559082
EPOCH: 2
Validation Accuracy = 0.8038548827171326
EPOCH: 3
Validation Accuracy = 0.8553287982940674
EPOCH: 4
Validation Accuracy = 0.8888888955116272
EPOCH: 5
Validation Accuracy = 0.9140589833259583
EPOCH: 6
Validation Accuracy = 0.9163265228271484
EPOCH: 7
Validation Accuracy = 0.9260770678520203
EPOCH: 8
Validation Accuracy = 0.9292517304420471
EPOCH: 9
Validation Accuracy = 0.9276643991470337
EPOCH: 10
Validation Accuracy = 0.9319728016853333
EPOCH: 11
Validation Accuracy = 0.9276643991470337
EPOCH: 12
Validation Accuracy = 0.9453514814376831
EPOCH: 13
Validation Accuracy = 0.9310657382011414
EPOCH: 14
Validation Accuracy = 0.9467120170593262
EPOCH: 15
Validation Accuracy = 0.9560090899467468
EPOCH: 16
Validation Accuracy = 0.9514739513397217
EPOCH: 17
Validation Accuracy = 0.9566893577575684
EPOCH: 18
Validation Accuracy = 0.9485260844230652
EPOCH: 19
Validation Accuracy = 0.9535147547721863
EPOCH: 20
Validation Accuracy = 0.9473922848701477
EPOCH: 21
Validation Accuracy = 0.9535147547721863
EPOCH: 22
Validation Accuracy = 0.9560090899467468
EPOCH: 23
Validation Accuracy = 0.9526076912879944
EPOCH: 24
Validation Accuracy = 0.9575963616371155
EPOCH: 25
Validation Accuracy = 0.9566893577575684
EPOCH: 26
Validation Accuracy = 0.9630385637283325
EPOCH: 27
Validation Accuracy = 0.960544228553772
EPOCH: 28
Validation Accuracy = 0.9485260844230652
EPOCH: 29
Validation Accuracy = 0.958276629447937
EPOCH: 30
Validation Accuracy = 0.9571428298950195
EPOCH: 31
Validation Accuracy = 0.9560090899467468
EPOCH: 32
Validation Accuracy = 0.9575963616371155
EPOCH: 33
Validation Accuracy = 0.9609977602958679
EPOCH: 34
Validation Accuracy = 0.9526076912879944
EPOCH: 35
Validation Accuracy = 0.9598639607429504
EPOCH: 36
Validation Accuracy = 0.960090696811676
EPOCH: 37
Validation Accuracy = 0.962358295917511
EPOCH: 38
Validation Accuracy = 0.9519274234771729
EPOCH: 39
Validation Accuracy = 0.9609977602958679
EPOCH: 40
Validation Accuracy = 0.9607709646224976
Model saved
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<h2 id="Running-on-Test-Set">Running on Test Set<a class="anchor-link" href="#Running-on-Test-Set">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Test Set Accuracy</span>

<span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>
<span class="n">test_data_size</span> <span class="o">=</span>  <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

<span class="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">save_file</span><span class="p">)</span>
    <span class="n">total_test_acc</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">start_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">test_data_size</span> <span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
        <span class="n">end_i</span> <span class="o">=</span> <span class="n">start_i</span> <span class="o">+</span> <span class="n">BATCH_SIZE</span>
        <span class="n">X_batch_test</span><span class="p">,</span> <span class="n">y_batch_test</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[</span><span class="n">start_i</span><span class="p">:</span><span class="n">end_i</span><span class="p">],</span> <span class="n">y_test</span><span class="p">[</span><span class="n">start_i</span><span class="p">:</span><span class="n">end_i</span><span class="p">]</span>
        <span class="n">test_acc</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</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_batch_test</span><span class="p">,</span>
                                                 <span class="n">y</span><span class="p">:</span> <span class="n">y_batch_test</span><span class="p">,</span>
                                                 <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.</span><span class="p">})</span>
        <span class="n">total_test_acc</span> <span class="o">+=</span> <span class="n">test_acc</span> <span class="o">*</span> <span class="n">X_batch_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    
    <span class="n">total_test_acc</span> <span class="o">/=</span> <span class="n">test_data_size</span>

    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Accuracy = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">total_test_acc</span><span class="p">))</span>
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<pre>Test Accuracy = 0.9427553443897365
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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">os</span>
<span class="kn">import</span> <span class="nn">matplotlib.image</span> <span class="k">as</span> <span class="nn">mpimg</span>


<span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;./web_images_test/&#39;</span>

<span class="n">imgs</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;1.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;11.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;12.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;13.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;22.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;27.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;30.jpg&#39;</span><span class="p">]</span>

<span class="n">test_labels</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">imgs</span><span class="p">:</span>
    <span class="n">test_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">img</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))</span>

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

<span class="n">num_images</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_labels</span><span class="p">)</span>
<span class="n">test_imgs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">num_images</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>


<span class="c1">#Loading and Previewing Images Downloaded from the Internet</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">test_imgs</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="c1">#     DO NOT USE cv2.imread() : DOES NOT LOAD IN THE SAME FORMAT</span>
<span class="c1">#     test_imgs[i] = cv2.imread(path + imgs[i])</span>
    <span class="n">test_imgs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpimg</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">path</span> <span class="o">+</span> <span class="n">imgs</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">figure</span><span class="p">(</span><span class="mi">1</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">20</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">img</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">test_imgs</span><span class="p">)):</span>
        <span class="n">sub_img</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span> <span class="n">img</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">sub_img</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">signs_class</span><span class="p">[</span><span class="n">test_labels</span><span class="p">[</span><span class="n">img</span><span class="p">]])</span>
        <span class="n">sub_img</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">imshow</span><span class="p">(</span><span class="n">test_imgs</span><span class="p">[</span><span class="n">img</span><span class="p">])</span>
        
<span class="c1"># Print shape of test image        </span>
<span class="nb">print</span><span class="p">(</span><span class="n">test_imgs</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>
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<pre>[1, 11, 12, 13, 22, 27, 30]
(32, 32, 3)
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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"># Pre process the images</span>

<span class="n">X_web_test</span><span class="p">,</span> <span class="n">y_web_test</span> <span class="o">=</span> <span class="n">preprocess_data</span><span class="p">(</span><span class="n">test_imgs</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)</span>


<span class="c1">#Loading and Previewing Preprocessed Images</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</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">20</span><span class="p">))</span>
<span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_web_test</span><span class="p">)):</span>
    <span class="n">sub_img</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span> <span class="n">img</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">sub_img</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">signs_class</span><span class="p">[</span><span class="n">y_web_test</span><span class="p">[</span><span class="n">img</span><span class="p">]])</span>
    <span class="n">sub_img</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">imshow</span><span class="p">(</span><span class="n">X_web_test</span><span class="p">[</span><span class="n">img</span><span class="p">])</span>
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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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</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">save_file</span><span class="p">)</span>
    
    <span class="n">results</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">X_web_test</span><span class="p">,</span>
                                <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.</span><span class="p">})</span>
    <span class="n">results</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="n">y_web_test</span><span class="p">)</span>
<span class="c1">#     result_dict = [str(i) for i in results]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Image</span><span class="se">\t</span><span class="s1">Prediction</span><span class="se">\t</span><span class="s1">Actual Label</span><span class="se">\t</span><span class="s1">  Label Predicted&#39;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">50</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">results</span><span class="p">)):</span>
        <span class="k">if</span> <span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">y_web_test</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
            <span class="n">prediction</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\u2713</span><span class="s1">&#39;</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">prediction</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\u274C</span><span class="s1">&#39;</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="se">\t</span><span class="s1">   </span><span class="si">{}</span><span class="se">\t</span><span class="s1"> </span><span class="se">\t</span><span class="si">{}</span><span class="se">\t\t</span><span class="si">{}</span><span class="s1">  </span><span class="si">{}</span><span class="se">\t</span><span class="s1">&#39;</span>
              <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">signs_class</span><span class="p">[</span><span class="n">y_web_test</span><span class="p">[</span><span class="n">i</span><span class="p">]][:</span><span class="mi">6</span><span class="p">],</span> <span class="n">prediction</span><span class="p">,</span> <span class="n">signs_class</span><span class="p">[</span><span class="n">results</span><span class="p">[</span><span class="n">i</span><span class="p">]]))</span>
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<pre>[1, 11, 22, 30, 27, 12, 13]
Image	Prediction	Actual Label	  Label Predicted
--------------------------------------------------
0	   1	 	Speed 		✓  Speed limit (30km/h)	
1	   11	 	Right-		✓  Right-of-way at the next intersection	
2	   22	 	Bumpy 		✓  Bumpy road	
3	   30	 	Beware		✓  Beware of ice/snow	
4	   24	 	Pedest		❌  Road narrows on the right	
5	   12	 	Priori		✓  Priority road	
6	   13	 	Yield		✓  Yield	
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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="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">save_file</span><span class="p">)</span>
    
    <span class="n">test_img_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</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_web_test</span><span class="p">,</span>
                                                      <span class="n">y</span><span class="p">:</span> <span class="n">y_web_test</span><span class="p">,</span>
                                                      <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.</span><span class="p">})</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Images Accuracy = </span><span class="si">{:.3f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_img_accuracy</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
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<pre>Test Images Accuracy = 85.714%
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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="n">softmax</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
<span class="n">top_5</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">softmax</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">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>

<span class="n">highest</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">correct</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
    <span class="n">highest</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">signs_class</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">y_web_test</span><span class="p">:</span>
    <span class="n">correct</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">signs_class</span><span class="p">[</span><span class="n">i</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">save_file</span><span class="p">)</span>
    <span class="n">results</span><span class="p">,</span> <span class="n">results_index</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">top_5</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_web_test</span><span class="p">,</span>
                                <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.</span><span class="p">})</span>
    <span class="n">sched</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">results</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="n">highest</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Top 5 Probabilites</span><span class="se">\n</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">results</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Top 5 Indices</span><span class="se">\n</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">results_index</span><span class="p">))</span>
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<pre>Top 5 Probabilites
[[  9.99932e-01   5.98368e-05   8.07525e-06   7.81070e-08   4.83744e-08]
 [  1.00000e+00   3.21550e-08   6.58909e-13   5.38661e-14   3.75090e-20]
 [  1.00000e+00   1.05207e-19   1.74571e-22   5.24722e-23   1.88789e-28]
 [  6.03363e-01   3.96587e-01   4.67132e-05   2.79825e-06   2.94332e-07]
 [  8.93050e-01   8.97164e-02   1.72306e-02   1.79787e-06   1.60737e-06]
 [  1.00000e+00   1.00287e-15   9.23921e-16   4.63679e-17   2.99104e-17]
 [  1.00000e+00   5.29809e-25   1.02840e-33   5.37218e-34   4.52818e-34]]

Top 5 Indices
[[ 1  2  0  4  6]
 [11 27 30 21  1]
 [22 17 29 26 15]
 [30 23 28 24 19]
 [24 27 26 25 12]
 [12 38 10 41 26]
 [13  9 10 39 35]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Table Showing Highest Five Percentages / Test Image </span><span class="se">\</span>
<span class="s1">      </span><span class="se">\n</span><span class="s1">#Labels shown represent the highest prediction&#39;</span><span class="p">)</span>
<span class="n">pd</span><span class="o">.</span><span class="n">options</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">float_format</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:,.3f}</span><span class="s1"> %&#39;</span><span class="o">.</span><span class="n">format</span>
<span class="n">sched</span>
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<pre>Table Showing Highest Five Percentages / Test Image       
#Labels shown represent the highest prediction
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
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      <th></th>
      <th>1</th>
      <th>2</th>
      <th>3</th>
      <th>4</th>
      <th>5</th>
    </tr>
  </thead>
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    <tr>
      <th>Speed limit (30km/h)</th>
      <td>99.993 %</td>
      <td>0.006 %</td>
      <td>0.001 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
    </tr>
    <tr>
      <th>Right-of-way at the next intersection</th>
      <td>100.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
    </tr>
    <tr>
      <th>Bumpy road</th>
      <td>100.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
    </tr>
    <tr>
      <th>Beware of ice/snow</th>
      <td>60.336 %</td>
      <td>39.659 %</td>
      <td>0.005 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
    </tr>
    <tr>
      <th>Road narrows on the right</th>
      <td>89.305 %</td>
      <td>8.972 %</td>
      <td>1.723 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
    </tr>
    <tr>
      <th>Priority road</th>
      <td>100.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
    </tr>
    <tr>
      <th>Yield</th>
      <td>100.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
      <td>0.000 %</td>
    </tr>
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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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<hr>
<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" />
 <figcaption>
 <p></p> 
 <p style="text-align: center;"> Your output should look something like this (above)</p> 
 </figcaption>
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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="n">image_input</span> <span class="o">=</span> <span class="n">image_input</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
    <span class="n">image_input</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">image_input</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    
    <span class="c1"># Note: x should be the same name as your network&#39;s tensorflow data placeholder variable</span>
    <span class="c1"># If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function</span>
    <span class="n">activation</span> <span class="o">=</span> <span class="n">tf_activation</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">session</span><span class="o">=</span><span class="n">sess</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span> <span class="p">:</span> <span class="n">image_input</span><span class="p">})</span>
    <span class="n">featuremaps</span> <span class="o">=</span> <span class="n">activation</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">plt_num</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">featuremap</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">featuremaps</span><span class="p">):</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span> <span class="n">featuremap</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># sets the number of feature maps to show on each row and column</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;FeatureMap &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">featuremap</span><span class="p">))</span> <span class="c1"># displays the feature map number</span>
        <span class="k">if</span> <span class="n">activation_min</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">&amp;</span> <span class="n">activation_max</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
            <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">activation</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:,</span> <span class="n">featuremap</span><span class="p">],</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="n">vmin</span> <span class="o">=</span><span class="n">activation_min</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">activation_max</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">activation_max</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
            <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">activation</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:,</span> <span class="n">featuremap</span><span class="p">],</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">activation_max</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">activation_min</span> <span class="o">!=-</span><span class="mi">1</span><span class="p">:</span>
            <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">activation</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:,</span> <span class="n">featuremap</span><span class="p">],</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">activation_min</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">activation</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,:,</span> <span class="n">featuremap</span><span class="p">],</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="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">save_file</span><span class="p">)</span>
    <span class="n">outputFeatureMap</span><span class="p">(</span><span class="n">X_web_test</span><span class="p">,</span> <span class="n">CONV2_BIAS</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[263]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Image Resembling the Activations / Feature Map&#39;</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_web_test</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
<span class="n">y_web_test</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
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<pre>Test Image Resembling the Activations / Feature Map
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<div class="prompt output_prompt">Out[263]:</div>


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<pre>30</pre>
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9w5Bzc1JqlfTbVbBhzxaI+Uy3BpZTm4thXI8c/DA2NuGfBoL5cVN4xmqW17K2/XdcBD2lrzEq8HF9t/wDne55GQkuri9RMH2nl9wsxt7pqGALC83p2Y9NCrrdQnvJLX1Xvm8SeprXqAt/J6osk9jwd2c8mu0MuTcEJlXN68Y8e3qQ3giWbV5FBdu55LwceHBpzjg2+extT7etESe54DsN4x/l1VbSj+mzHwDcO4vJgx+FW1HcB4CeZiGEYJmcs1/z0i0iMie0Xkyos2I8MwSsKFBv/3AVwPoAHAKQDfYU8UkW0i0iUiXQV+2WMYRom5oOBX1XdU9bSqFgD8AECTx3P3qGqjqjYGSppJYBiGFxcU/CKy9Kw/vwyg9+JMxzCMUjHjuVhEfgygBUBCRNIAvgWgRUQaACiAFICvzWZj4UWLUVfX4DYWMtRvfNR9v3Eqz2W0umW8VlxkmLdjCmS5DJhqPeYcrx9MUZ9dUZ4h1lrHpa1Nt/Laf7FHdvPt1bmlrczgIPU5etc91FZ9iMuK+ac85LfN7vqEhxMV1KfhOZ6t2F3OJbaRlfSLJ9pG3MdONsf3c81jz1JbvJy3c6sJ8+va2nqetVrJJM4CPzeHetzyppymLp9gxuBX1a84hn84+00YhnE5Yr/wMwyfYsFvGD7Fgt8wfIoFv2H4FAt+w/ApJf3ZTdVV5Xhku1tWeqKVt2rKkOKHhX5eMDFWyaXD0SRv1XT/M69Q27797qKPmeZm6nNvBW+TFangstfmtmPUtsujGGT7kTbnePly/p5vGx2httY+nrF4+Nm91BYkcww1cHmzLzdFbXfez1uKHV3nyjubJt3q3p8vbr+D+iAc4bZKvo5lU/znLv0neEuxDDlWQwF+bq5IuKXPweHfU59zsTO/YfgUC37D8CkW/IbhUyz4DcOnWPAbhk+x4DcMnzJjAc+LyVXRRfrfmq532nItq6hfX6rPOd44zKWVzR4Zcy9OcKns9UAVtdUfdffqW7NjB/U5vpsXdVzX9wa1jT71A2q79+mnqa1zz1PO8RUruER1ZNTdKw4AhtJjfB4eBTcnJ9zSVluQq8uHSPFRALivzS1hAsCLJ7kciYK7yus6j+ISNX3u/QwAyyv58bGhwZ1RCQDHX2+ntqkqd8ZfRTnPBFxG3lfnm6OYfD930Qp4GobxGcSC3zB8igW/YfgUC37D8CkW/IbhU0p6t39RSPT6cveNyNEIvwNfU+VOgFkV5v26NpJadgCw7zBvQZV/k/cnue23w87xIy3rqM+tjz9GbVmShAMAz9aTWocA2l/lyUdbo+6klBf/kdfiG7yC16Xz6DKFjEebryeecL/v/cNcWWhoWUttd3skH1XfeSu17Vnr3je1ux+hPmNPuBUTAHgpzmsydp7g61HvoT5NjbvfW2aKr1Ug4E6CGh1R5HIXr12XYRifQSz4DcOnWPAbhk+x4DcMn2LBbxg+xYLfMHzKjFKfiFwL4AUAFQAKAPao6vdEJA7gJwCSmG7ZtUVV/+D1Wp/7/Of0S3/+n5225eAttHo63JJY/ZrV1GfcIyFl5FGeGHPYo0bbww3uWn2RdVzquyWVprYOj9p/qTCXPjeuWUNta290S4SJGK8l2J92S5gAEAvyeSQqE9Q2Nu5e/8OHubzZ0Xmc2qo8qk02vH6U2o7ULneOTxzgcun9PTyxZ3OCJ9sci/MWYMsaeeJa5LhbIgyV8XPzQMgt9b2fzuF0tnDRpL48gL9V1S8AWAXgGyKyAsCDAF5T1RsAvFb82zCMTwkzBr+qnlLV7uLj9wC8BeAaADcDeL74tOcBbLpUkzQM4+JzXtf8IpIE8EUAvwRwtaqeAqY/IADwNqqGYVx2zDr4RaQMwMsA7lPVyfPw2yYiXSLSlfuQ/xzXMIzSMqvgF5GFmA78H6nqmc4V74jI0qJ9KQDnD5FVdY+qNqpqY+gKfvPIMIzSMmPwi4gA+CGAt1T18bNMrQBuLz6+HcDPLv70DMO4VMxG6msG8C8A3gD+vx63A9PX/fsBVAMYAvAXqspT4gAsvjKqX/iTJqdtdZCnj4Xhvlw47lEPbveDvHbe2E08C2zgRp5ZdrSn3zm+wUOmHLj7bmrLZrPUVpfhV1bPPPscte3c587e20fGAeDRbXyOyeQ11HbLzv9ObS0tLc7x/bv4frl1yxZq64/wFlrRAL+cXPHwLuf48Wic+tSv5BmVlScHqK3zvvuo7ZF73G3qAGBjlft22QjJ9gOA3oR7PbJv/x6FDz6aldQ3Y68+Ve0AwF7sT2ezEcMwLj/sF36G4VMs+A3Dp1jwG4ZPseA3DJ9iwW8YPqWkBTwDAdFQ2C0cVHpkRKVG3ZLY9rs2U59EB2/lVfHMi9S2/977qe2OY+6WS2NP8izB9mqeTVeRcbe0AoCVUxlqyxW4SDNIsvAOHTpEfdYtq6a2jZtvobanXjlMbStWrHCO1+S5LJfPcVtPjP96PBvmx86Wbnem4OSNK6lP265d1Lb+KC/+enAtl4kP9fdQWzWZ/uQUl4InYm4nncxC8xcvq88wjM8gFvyG4VMs+A3Dp1jwG4ZPseA3DJ9iwW8YPqWkUl8sEtLmWnffvaE4z7Lq7XTLJJs8Ck8+l+Ofa99u4sUUb3qAlyJc9aI7M260jstG3R4frx2jKWob4YmCCIV4XYSKsLvvXtYjS3AwdZLayuO8SGdZhXtfAkCOyHZlWS5hRkNcssuQ9wUAWY9+gk1T7vddeYBnOe67axu1pQN8jusO8aKgGw9yefmmne5swJYWXuA1O+lexwMv/wyj775rUp9hGBwLfsPwKRb8huFTLPgNw6dY8BuGT5mxjNfFJBwKYkXSfff4cAdvkdTW2uocD5bXUJ+XolFqWz/Ba6MtO8FbRk3csdU5nsvzz9Bxj+SM+JOPUlvsjtuorSzIt7cy604WGk4PUZ9w1t36CQAiPV3UhgFeVy/Ekm08tjWZ4nPsa+NtvsoCfD3ayPmt3OP14vW8hl9g2FmkGgDQ9cIz1Hbwv/Kknzv/4TnneMbjOI1PuNuh/VNo9iFtZ37D8CkW/IbhUyz4DcOnWPAbhk+x4DcMn2LBbxg+ZUZdQESuBfACgApMt+vao6rfE5FdAP4awLvFp+5QVV7UDcCChQtRVkFqsfF8D+y+351ss8FDGhpYs4baqhq5lPPYsWPUlmt323jKCdAb5nJYM5EOASDkUc+us8NdSxAAhk+5pajE52LU55WObmqryvE6g8vqllNbiLTX6u8bpD6Bjk5q6yX1EwEgGubnsCkifU1O8cypqgjfo+EKvj+Pd/P6fs1LqAmrV9U5x599lq9HJYmjgIfseS6zEQXzAP5WVbtFZAmAX4vIz4u276rqY7PemmEYlw2z6dV3CsCp4uP3ROQtALx7o2EYnwrO65pfRJIAvojpDr0AcI+I9IjIXhG58iLPzTCMS8isg19EygC8DOA+VZ0E8H0A1wNowPQ3g+8Qv20i0iUiXe9n+XWsYRilZVbBLyILMR34P1LVgwCgqu+o6mlVLQD4AYAml6+q7lHVRlVtXBzmFWgMwygtMwa/iAiAHwJ4S1UfP2t86VlP+zIA3iLHMIzLjtnc7f8SgL8C8IaInEm92wHgKyLSAEABpAB8baYXyn6kGBwhBdc86rC9cPCgc/zZO3grqTVR/oKhcV6zriXOZZ5czi0PhUP8M7RmimextdXXUluozt3uCgBGI3y3DSx0zzGU4bXnsh7bmgjw+Xd71BLMB93bC9ZzeTDqIVM1XmACahZE0vN4uZBH/cQ8OQYAoDnMbWNcacVIyp05GY3wY3icbMojjD7BbO72dwBwFQT01PQNw7i8sV/4GYZPseA3DJ9iwW8YPsWC3zB8igW/YfiUkrbrEhFdIO5OQh7qCv2IumvHdurS1MIz9yJRnpmVHuFtraZybvkqm+eSV6TAC3hWBHjGHCbdBRoBIBjkIk0w7M726urmGZCpFC8UGSzj0udtW2+ltrFhd/ZeIDBOfRIVXA+LxKqpbSjF1zFCWoDFyjzW0GN9s3luK+T5Phsd4hl/ra3POcfHJrg8W13rzlo90vrPGB/7vbXrMgyDY8FvGD7Fgt8wfIoFv2H4FAt+w/ApFvyG4VNKKvXV1t6gTz31PadtZITLTWUxtzTX3ccLHKbGe/hEPOSmIOsxB6CjM+0cP3l8mPrUNrmLMwLA8gQvbhIPcckxGODyW2rELUX19vAecwdbj1DboQ5ecPOkR1/DtgNPOsdrl3NRd+Xqemo77iFVdrfz/RkmNSTyeb72ec/UOK9cOO54yyZSuBZAZtK9b9p5zVJUVFY4x3+XHsOH2ZxJfYZhcCz4DcOnWPAbhk+x4DcMn2LBbxg+xYLfMHzKhVVFvEAKWkA2527KNznBpaiRk+5sqZHjXOqL53mRzkSUZ19VJviSrIy7bak6niUYCHIZqpDnGX+FAJeNotEEtR067C6i/MTjD1OfJJ8+pjwy5mIZLkdubko6x+vq+fuazPB9tqmWS2VNIbfsBQBsioUgP++VlXvIch69J1JDfdQWC/FFLquIO8ebm/m2ohH3e/7DmEem6DnYmd8wfIoFv2H4FAt+w/ApFvyG4VMs+A3Dp8x4t19EwgDaAVxRfP4BVf2WiFwHYB+AOIBuAH+lqp5teN8ePIlNN2112rw+hdgkPW5Sg6e+AJvW8Zp7ZR5tvsri7qSUZJQvY/8QT/oZGKAmTHIhAMFgitqYW0V1DfUJeLTJGh3nd48DOf7eqirdh0I4yhN7hsf5mx7yWMd4eBn3S7vVj8plXDGBh9JSKHBb2KPPV98AV7MSFVHneHU1VzHSabcyUvAOwY8xmzP/hwDWqOofY7od93oRWQXg7wB8V1VvAPAHAF+d9VYNw5h3Zgx+neaMOL+w+E8BrAFwoDj+PIBNl2SGhmFcEmZ1zS8iC4odekcB/BzA2wAmVPXMd6A0gGsuzRQNw7gUzCr4VfW0qjYAqALQBOALrqe5fEVkm4h0iYi7D7FhGPPCed3tV9UJAMcArAIQE5Ezd7qqADjvyKjqHlVtVNXGuUzUMIyLy4zBLyJXiUis+HgRgLUA3gLwCwCbi0+7HcDPLtUkDcO4+MwmsWcpgOdFZAGmPyz2q+orItIHYJ+I7AbwfwD8cKYXCocX4brr3JLT8LBHHbx6dx28quVcvjq0fy+1dcVqqW047iHlRN1yTSzORcdRUn8QAAoerbBG+rjsVVPdRG2Z/led4+UrV3EfXrYQ/WM8Makqzs8dY9Ep53hZhK9HKsDbdU3EK7mNtFEDgNGY+xBP1vJtBSL8faV73HUcASCSqKK2sjyfY44UDRwc4tvKF9wZS6fPoybnjMGvqj0AvugYP4np63/DMD6F2C/8DMOnWPAbhk+x4DcMn2LBbxg+xYLfMHxKSdt1ici7AP5v8c8EAF5Mr3TYPD6OzePjfNrm8UeqetVsXrCkwf+xDYt0XQ6/+rN52Dz8Og/72m8YPsWC3zB8ynwG/5553PbZ2Dw+js3j43xm5zFv1/yGYcwv9rXfMHzKvAS/iKwXkd+KyKCIPDgfcyjOIyUib4jIiVIWGxGRvSIyKiK9Z43FReTnIvKvxf+vnKd57BKR3xXX5ISIbCjBPK4VkV+IyFsi8qaI/E1xvKRr4jGPkq6JiIRF5LiI/KY4j/9VHL9ORH5ZXI+fiAivRDsbVLWk/wAswHQZsGUAQgB+A2BFqedRnEsKQGIetrsawEoAvWeNPQrgweLjBwH83TzNYxeAB0q8HksBrCw+XgJgAMCKUq+JxzxKuiYABEBZ8fFCAL/EdAGd/QC2FsefAfD1uWxnPs78TQAGVfWkTpf63gfg5nmYx7yhqu0Azk2UvxnThVCBEhVEJfMoOap6SlW7i4/fw3SxmGtQ4jXxmEdJ0WkuedHc+Qj+awD8+1l/z2fxTwVwRER+LSLb5mkOZ7haVU8B0wchAN4q9tJzj4j0FC8LLvnlx9mISBLT9SN+iXlck3PmAZR4TUpRNHc+gl8cY/MlOXxJVVcC+DMA3xCR1fM0j8uJ7wO4HtM9Gk4B+E6pNiwiZQBeBnCfqvL+36WfR8nXROfqbGDqAAABQElEQVRQNHe2zEfwpwFce9bftPjnpUZVh4v/jwL4Kea3MtE7IrIUAIr/8xYvlxBVfad44BUA/AAlWhMRWYjpgPuRqh4sDpd8TVzzmK81KW77vIvmzpb5CP5fAbiheOcyBGArgNZST0JEFovIkjOPAawD4O7tVBpaMV0IFZjHgqhngq3Il1GCNRERwXQNyLdU9fGzTCVdEzaPUq9JyYrmluoO5jl3Mzdg+k7q2wD+5zzNYRmmlYbfAHizlPMA8GNMf338CNPfhL4K4PMAXgPwr8X/4/M0j38A8AaAHkwH39ISzKMZ019hewCcKP7bUOo18ZhHSdcEQD2mi+L2YPqD5qGzjtnjAAYB/COAK+ayHfuFn2H4FPuFn2H4FAt+w/ApFvyG4VMs+A3Dp1jwG4ZPseA3DJ9iwW8YPsWC3zB8yv8DZcuKP1twvZ4AAAAASUVORK5CYII=
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