CNN3.html

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

<title>CNN3</title>

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html {
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body {
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aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
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summary {
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canvas,
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video {
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[hidden],
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a {
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a:active,
a:hover {
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abbr[title] {
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dfn {
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h1 {
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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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hr {
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samp {
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input,
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button,
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button,
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input[type="submit"] {
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/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
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}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
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<h1 id="A-simple-C++-implementation-of-a-convolutional-neural-network">A simple C++ implementation of a convolutional neural network<a class="anchor-link" href="#A-simple-C++-implementation-of-a-convolutional-neural-network">&#182;</a></h1><h2 id="Table-of-Contents">Table of Contents<a class="anchor-link" href="#Table-of-Contents">&#182;</a></h2><ul>
<li><a href="#intro">Introduction</a></li>
<li><a href="#afeww">A few words on the implementation</a></li>
<li><a href="#ex_MNIST">Example of use on the MNIST dataset</a></li>
<li><a href="#Performance">Performance</a></li>
<li><a href="#concl">Conclusion</a></li>
<li><a href="#app_layers">Appendix 1: Brief description of each layer</a></li>
<li><a href="#app_cifar">Appendix 2: Training the model on the CIFAR-10 dataset</a></li>
</ul>
<h2 id="Introduction-">Introduction <a class="anchor" id="intro" /><a class="anchor-link" href="#Introduction-">&#182;</a></h2><p>Convolutional Neural Networks (CNNs) are among the most widely-used Deep Learning models for image, speech, and language analysis (although newer models like Transformers and more general Xformers are making inroads into these fields). 
Their distinguishing feature is the convolutional layer, which make use of the translation equivariance (roughly speaking, the fact that the nature of an object does not depend of where it appears in an image) and locality properties of a problem to significantly reduce the number of parameters compared with dense layers with the same size. 
These properties make CNNs typically faster to train and more easily generalizable to unseen data.
Among their main domains of applications are image analysis tasks like object detection (they are important ingredients of real-time detection models like <a href="https://arxiv.org/pdf/2004.10934.pdf">YOLO</a>, <a href="https://www.paperswithcode.com/method/retinanet">RetinaNet</a>, and <a href="https://link.springer.com/chapter/10.1007/978-3-319-46448-0_2">SSD</a>) and classification. 
Efficient implementations of CNNs running on either CPU or GPU are available in standard libraries like <a href="https://www.tensorflow.org/">TensorFlow</a> and <a href="https://pytorch.org/">PyTorch</a>, making it straightforward to build, train, and test a network with just a few lines of code.</p>
<p>However, using these libraries may not explain much of how CNNs work beyond the high-level description they provide. 
At least, that was my experience when starting to use them: thanks to the wealth of tutorials available online, I was soon able to build different CNNs, train them on several datasets, evaluate them, and play with the parameters until I got something which worked well while minimizing the training time; however, I felt I did not really understand what a CNN really was. 
I was able to use it and understood the main steps—data acquisition, data cleaning, feature engineering, hyperparameter selection through cross-validation, model training, and testing—and got a bird's eye view of the convolution, feedforward, and gradient descent algorithms, but I wanted to know more precisely what actually happened when data goes through the CNN, why these models are so efficient, and the reasons for some of their limitations.</p>
<p>To me, the best way to understand something is to actually build it. 
So, to understand CNNs, I started to write a simple C++ implementation with handles to access it from Python and experiment with it. 
The present notebook shows some results of this experiment. 
While this implementation is, obviously, much less efficient than TensorFlow or PyTorch ones (in part because it does not really optimise the use of different CPU cores and does not use the GPU at all), I hope its simplicity may help others who, like me when I started this project, feel intimidated by the complexity of these libraries' source code but want to get a deeper understanding of CNNs than just using them can provide. 
If you are in this situation, I strongly encourage you to write your own implementation: writing code is often the best way to learn. 
Feel free to take inspiration from mine, copy the parts you like (but make sure you really understand them!), improve what you would like to be better, and add your own features. 
Many standard features like batch normalization, skip connections, and optimizers are not present in the current version of the code; implementing them would be a very good way for you to deepen your understanding! 
And if you don't have the time to write your own implementation (I know only too well that our time is often limited and one has to choose wisely what to devote it to), I hope the code I have written, which focuses on the core aspects of CNNs without recent improvements which make them both more efficient and more complex, may still help you understand some of the inner workings of CNNs.</p>
<p>In any case, if you have any questions, if something is not clear to you, or if you have spotted a mistake in the C++ code or this notebook, please feel free to reach out!</p>
<h2 id="A-few-words-on-the-implementation-">A few words on the implementation <a class="anchor" id="afeww" /><a class="anchor-link" href="#A-few-words-on-the-implementation-">&#182;</a></h2><p>The code for the neural network can be found in the <code>C++</code> folder. 
As mentioned above (and as the name of the folder indicates), it is written in C++, and makes use of the <a href="https://www.boost.org/doc/libs/1_75_0/libs/python/doc/html/index.html">Boost.Python</a> library for Python interoperability.</p>
<p>The network itself is defined in the file <code>CNN3.cpp</code>, using layers defined in <code>Layers.cpp</code> and in their respective <code>.cpp</code> files. 
It can be divided into three parts:</p>
<ul>
<li>Lines 1 to 32 import the required header and source files and define objects that will be required in the following.</li>
<li>Lines 34 to 312 define the <code>CNN3</code> class.</li>
<li>Lines 314 to 325 define the Python interface. </li>
</ul>
<p>The later is the most relevant for using the CNN from Python. 
It defines a Python class <code>CNN3</code> with 8 member functions:</p>
<ul>
<li>An initializer with 8 arguments: <ul>
<li>the width of input images (int),</li>
<li>the height of input images (int),</li>
<li>the number of input channels (int),</li>
<li>the list of filter sizes for each convolution layer (list of ints),</li>
<li>the list of numbers of filters for each convolution layer (list of ints),</li>
<li>the list of pooling layer strides (list of ints),</li>
<li>the list of numbers of neurons for each dense layer (list of ints),</li>
<li>the number of different labels (int). </li>
</ul>
</li>
<li>An initializer with no argument (it should be used only if loading the model from a file).</li>
<li>A <code>forward</code> function performing a forward pass on an image encoded in a 3D NumPy array and a dropout probability (float), and returning an array of probabilities for the different classes.</li>
<li>A <code>backprop</code> function performing a backward pass taking the derivative of a loss function with respect to the output, encoded in a NumPy array, and a learning rate and updating the network weights accordingly.</li>
<li>A <code>train</code> function taking an image (encoded as a 3D NumPy array), the correct label (int), a learning rate (float) and a dropout probability (float), performing a forward and a backward passes, and returning the result of the former as a Python list.</li>
<li>A <code>save</code> function taking a filename (string) and saving the parameters of the network to the corresponding file.</li>
<li>A <code>load</code> function taking a filename (string) and loading the parameters of the network from the corresponding file.</li>
<li>A <code>forward_la</code> function performing a forward pass on an image encoded in a NumPy array and a label (int) and returning a Python list containing the loss and accuracy (equal to 1 if the image is correctly classified and 0 otherwise).</li>
</ul>
<p>This class directly refers to the eponymous C++ class. 
Its member functions are those of the C++ class, with wrapper functions converting between Python and C++ objects when necessary.</p>
<p>The network is made of a number of convolution blocks followed by hidden fully-connected (also called dense layers; we here use these two terms interchangeably) with ReLU activation function, and by an output dense layer with softmax activation function. 
Each convolution block is made of a convolution layer with valid padding, a ‘ReLU’ layer, and a MaxPool layer. 
(These layers will be briefly described in Appendix 1.)</p>
<p>The loss function is the cross-entropy of the output $y$ of the network relative to the probability distribution equal to $1$ for the correct label and $0$ for the other ones. 
It may be written as:
$$
L = - \log(y_l),
$$
where $L$ is the loss and $l$ is the correct label.</p>
<p>The file <code>Layers.cpp</code> defines some helpful functions and the different layer classes, whose implementations can be found in their respective source files.</p>
<h2 id="Example-of-use-on-the-MNIST-dataset-">Example of use on the MNIST dataset <a class="anchor" id="ex_MNIST" /><a class="anchor-link" href="#Example-of-use-on-the-MNIST-dataset-">&#182;</a></h2><p>In this section I show how to use the CNN on the MNIST handwritten digits dataset, one of the simplest image classification tasks. 
(The dataset contains 70,000 grayscale images of size 28 by 28 with handwritten digits. 
The aim of the network will be to learn to reckognise which digit is written on each image.)
I choose hyperparameters which demonstrate the features of the implementation while keeping the resulting network relatively simple. 
As we will see, this simple network is good enough to learn quite efficiently.
In the next section, I will show the effect of changing some of the hyperparameters on the accuracy.</p>
<p>The CNN implementation can be found in the module <code>CNN3.so</code>. 
We also need to load the <code>mnist</code> module, which contains the MNIST dataset, functions from the <code>numpy</code> and <code>os</code> modules we will use later, and the <code>pyplot</code> module to do plots.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">CNN3</span>
<span class="kn">import</span> <span class="nn">mnist</span> 
<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">newaxis</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">permutation</span> <span class="k">as</span> <span class="n">np_permutation</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">exists</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<p>We now define a few parameters. 
The string <code>save_file_name</code> is the name of the file in which the network structure, weights, and biases will be saved.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">save_file_name</span> <span class="o">=</span> <span class="s1">&#39;MNIST_CNN_1.cnn&#39;</span>
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<p>The following parameters relate to the training and testing phases:</p>
<ul>
<li><code>n_train_images</code> (positive integer) is the number of train images (up to 60,000 for MNIST),</li>
<li><code>n_test_images</code> (positive integer) is the number of test images (up to 10,000 for MNIST),</li>
<li><code>n_epochs</code> (positive integer) is the number of epochs,</li>
<li><code>n_print</code> (positive integer) determines the frequency with which the accuracy is printed during training (it is printed every <code>n_print</code> steps),</li>
<li><code>learn_rate</code> (positive float) is the learning rate.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_train_images</span> <span class="o">=</span> <span class="mi">60000</span> 
<span class="n">n_test_images</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">n_print</span> <span class="o">=</span> <span class="mi">4000</span>
<span class="n">learn_rate</span> <span class="o">=</span> <span class="mf">0.01</span>
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<p>Next, we define the hyperparameters of the network:</p>
<ul>
<li><code>img_w</code> (positive integer): width of the input images,</li>
<li><code>img_h</code> (positive integer): height of the input images,</li>
<li><code>num_labels</code> (positive integer): the number of different labels (10 for MNIST),</li>
<li><code>CL_size_filters</code> (list of positive integers): filter sizes for the convolution layers,</li>
<li><code>CL_num_filters</code> (list of positive integers): number of filters for the convolution layers,</li>
<li><code>MP_size</code> (list of positive integers): size of the maxpool layers</li>
<li><code>FC_size</code> (list of positive integers): number of neurons for the fully-connected layers, excluding the output layer, </li>
<li><code>p_dropout</code>: the dropout probability.</li>
</ul>
<p>The lists <code>CL_size_filters</code>, <code>CL_num_filters</code>, and <code>MP_size</code> must have the same size (equal to the number of convolution layers). 
The length of the list <code>FC_size</code> gives the number of fully-connected layers excluding the output layer. 
(The number of neurons in the output layer is equal to <code>num_labels</code>.)</p>
<p>Here we work with a CNN with two convolution layers and one hidden fully-connected layer:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img_w</span> <span class="o">=</span> <span class="mi">28</span> 
<span class="n">img_h</span> <span class="o">=</span> <span class="mi">28</span> 
<span class="n">num_labels</span> <span class="o">=</span> <span class="mi">10</span> 
<span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">16</span><span class="p">,</span><span class="mi">32</span><span class="p">]</span> 
<span class="n">MP_size</span> <span class="o">=</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="n">FC_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">]</span> 
<span class="n">p_dropout</span> <span class="o">=</span> <span class="mf">0.1</span>
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<p>We now load the training and test images from the MNIST dataset and normalize them to have values between -0.5 and 0.5:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># normalization function</span>
<span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">image</span> <span class="o">/</span> <span class="mi">255</span> <span class="o">-</span> <span class="mf">0.5</span>

<span class="c1"># training images and labels</span>
<span class="n">train_images</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">train_images</span><span class="p">()[:</span><span class="n">n_train_images</span><span class="p">])</span>
<span class="n">train_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">train_labels</span><span class="p">()[:</span><span class="n">n_train_images</span><span class="p">]</span>

<span class="c1"># test images and labels</span>
<span class="n">test_images</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">test_images</span><span class="p">()[:</span><span class="n">n_test_images</span><span class="p">])</span>
<span class="n">test_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_labels</span><span class="p">()[:</span><span class="n">n_test_images</span><span class="p">]</span>
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<p>If no file with the name <code>save_file_name</code> exists, we build it and train it. 
If it exists, we skip this step.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_CNN</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">):</span>
    
    <span class="n">nsteps_l</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># number of steps elapsed </span>
    <span class="n">loss_l</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># loss function</span>
    <span class="n">acc_l</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># accuracy</span>

    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">— Start of training —&#39;</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">n_epochs</span><span class="p">):</span>
        <span class="n">nsteps_l</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>
        <span class="n">loss_l</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>
        <span class="n">acc_l</span><span class="o">.</span><span class="n">append</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">— Epoch </span><span class="si">{:d}</span><span class="s1"> of </span><span class="si">{:d}</span><span class="s1"> —</span><span class="se">\n</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="n">n_epochs</span><span class="p">))</span>

        <span class="c1"># shuffle the training data</span>
        <span class="n">permutation</span> <span class="o">=</span> <span class="n">np_permutation</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_images</span><span class="p">))</span>
        <span class="n">train_images</span> <span class="o">=</span> <span class="n">train_images</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span>
        <span class="n">train_labels</span> <span class="o">=</span> <span class="n">train_labels</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span>

        <span class="c1"># set the loss and number of correctly predicted labels to 0</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
        <span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="c1"># for each image in the training set, train the CNN and update the loss </span>
        <span class="c1"># and number of correctly-predicted labels</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">)):</span> 
            <span class="n">results</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_images</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">newaxis</span><span class="p">,:],</span> <span class="nb">int</span><span class="p">(</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> 
                                <span class="n">learn_rate</span><span class="p">,</span> <span class="n">p_dropout</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">num_correct</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
            <span class="c1"># if i is a multiple of n_print, print the loss and accuracy            </span>
            <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="n">n_print</span> <span class="o">==</span> <span class="n">n_print</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">nsteps_l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
                <span class="n">loss_l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span> <span class="o">/</span> <span class="n">n_print</span><span class="p">)</span>
                <span class="n">acc_l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">num_correct</span> <span class="o">/</span> <span class="n">n_print</span><span class="p">)</span>
                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Step </span><span class="si">{:d}</span><span class="s1"> — average loss: </span><span class="si">{:.10f}</span><span class="s1">, 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">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">loss</span><span class="o">/</span><span class="n">n_print</span><span class="p">,</span> <span class="n">num_correct</span><span class="o">*</span><span class="mf">100.</span><span class="o">/</span><span class="n">n_print</span><span class="p">))</span>
                <span class="c1"># reset the loss and number of correctly-preducted labels</span>
                <span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
                <span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>

    <span class="c1"># save the CNN</span>
    <span class="n">CNN</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">save_file_name</span><span class="p">)</span>

    <span class="c1"># plot the evolution of the loss function</span>
    <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">):</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">nsteps_l</span><span class="p">[</span><span class="n">e</span><span class="p">],</span> <span class="n">loss_l</span><span class="p">[</span><span class="n">e</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;epoch </span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">e</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_train_images</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="sa">r</span><span class="s1">&#39;$n_{\mathrm</span><span class="si">{steps}</span><span class="s1">}$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{loss}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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 the evolution of the accuracy</span>
    <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">):</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">nsteps_l</span><span class="p">[</span><span class="n">e</span><span class="p">],</span> <span class="n">acc_l</span><span class="p">[</span><span class="n">e</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;epoch </span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">e</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_train_images</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="sa">r</span><span class="s1">&#39;$n_{\mathrm</span><span class="si">{steps}</span><span class="s1">}$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{accuracy}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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 the evolution of the loss function - epoch to epoch</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="p">[</span><span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">loss_l</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_epochs</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">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{epoch}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{loss}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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 the evolution of the accuracy  - epoch to epoch</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="p">[</span><span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">acc_l</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_epochs</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">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{epoch}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{accuracy}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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="k">if</span> <span class="ow">not</span> <span class="n">exists</span><span class="p">(</span><span class="n">save_file_name</span><span class="p">):</span>

    <span class="c1"># build the CNN </span>
    <span class="n">CNN</span> <span class="o">=</span> <span class="n">CNN3</span><span class="o">.</span><span class="n">CNN3</span><span class="p">(</span><span class="n">img_w</span><span class="p">,</span> <span class="n">img_h</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">MP_size</span><span class="p">,</span> 
                    <span class="n">FC_size</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;MNIST CNN initialized&#39;</span><span class="p">)</span> 

    <span class="c1"># train the CNN</span>
    <span class="n">train_CNN</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">)</span>
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<p>We then load the CNN:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CNN</span> <span class="o">=</span> <span class="n">CNN3</span><span class="o">.</span><span class="n">CNN3</span><span class="p">()</span>
<span class="n">CNN</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">save_file_name</span><span class="p">)</span>
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<p>We then test the CNN on the training set and print the loss and accuracy:</p>

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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;</span><span class="se">\n\n</span><span class="s1">— Testing the CNN —</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)):</span>
    <span class="n">results</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">forward_la</span><span class="p">(</span><span class="n">im</span><span class="p">[</span><span class="n">newaxis</span><span class="p">,:],</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">))</span>
    <span class="n">loss</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">num_correct</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Loss:&#39;</span><span class="p">,</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">n_test_images</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Accuracy:&#39;</span><span class="p">,</span> <span class="n">num_correct</span> <span class="o">/</span> <span class="n">n_test_images</span><span class="p">)</span>
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<pre>

— Testing the CNN —

Test Loss: 0.04846670674257006
Test Accuracy: 0.9891
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<p>The accuracy is larger than 98.9%, which seems decent. 
Let us further train the model to see if the accuracy improves:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># number of epochs</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">5</span>

<span class="c1"># learning rate</span>
<span class="n">learn_rate</span> <span class="o">=</span> <span class="mf">0.01</span>
 
<span class="n">train_CNN</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">)</span>
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— Start of training —

— Epoch 1 of 5 —

Step 4000 — average loss: 0.0307272000, accuracy: 98.950%
Step 8000 — average loss: 0.0294517234, accuracy: 99.150%
Step 12000 — average loss: 0.0274015628, accuracy: 99.175%
Step 16000 — average loss: 0.0445786455, accuracy: 98.625%
Step 20000 — average loss: 0.0350710463, accuracy: 98.750%
Step 24000 — average loss: 0.0253533431, accuracy: 99.125%
Step 28000 — average loss: 0.0389144270, accuracy: 98.825%
Step 32000 — average loss: 0.0377908384, accuracy: 98.875%
Step 36000 — average loss: 0.0368412406, accuracy: 98.825%
Step 40000 — average loss: 0.0249421296, accuracy: 99.150%
Step 44000 — average loss: 0.0311605531, accuracy: 99.150%
Step 48000 — average loss: 0.0374812832, accuracy: 98.800%
Step 52000 — average loss: 0.0346062218, accuracy: 99.025%
Step 56000 — average loss: 0.0388803127, accuracy: 98.750%
Step 60000 — average loss: 0.0387342689, accuracy: 98.925%

— Epoch 2 of 5 —

Step 4000 — average loss: 0.0237115158, accuracy: 99.325%
Step 8000 — average loss: 0.0267077304, accuracy: 99.150%
Step 12000 — average loss: 0.0256059961, accuracy: 99.075%
Step 16000 — average loss: 0.0326340652, accuracy: 98.925%
Step 20000 — average loss: 0.0322652615, accuracy: 98.975%
Step 24000 — average loss: 0.0432634243, accuracy: 98.650%
Step 28000 — average loss: 0.0416451138, accuracy: 98.725%
Step 32000 — average loss: 0.0546298358, accuracy: 98.425%
Step 36000 — average loss: 0.0299816773, accuracy: 98.900%
Step 40000 — average loss: 0.0585306243, accuracy: 98.300%
Step 44000 — average loss: 0.0411108882, accuracy: 98.575%
Step 48000 — average loss: 0.0393134589, accuracy: 98.900%
Step 52000 — average loss: 0.0413355809, accuracy: 98.875%
Step 56000 — average loss: 0.0519371748, accuracy: 98.450%
Step 60000 — average loss: 0.0487349757, accuracy: 98.625%

— Epoch 3 of 5 —

Step 4000 — average loss: 0.0339770834, accuracy: 99.000%
Step 8000 — average loss: 0.0377071679, accuracy: 98.775%
Step 12000 — average loss: 0.0463572447, accuracy: 98.625%
Step 16000 — average loss: 0.0413088599, accuracy: 98.825%
Step 20000 — average loss: 0.0278019374, accuracy: 99.025%
Step 24000 — average loss: 0.0362220987, accuracy: 98.800%
Step 28000 — average loss: 0.0347637974, accuracy: 98.900%
Step 32000 — average loss: 0.0369160245, accuracy: 98.750%
Step 36000 — average loss: 0.0442302745, accuracy: 98.425%
Step 40000 — average loss: 0.0311949380, accuracy: 99.025%
Step 44000 — average loss: 0.0433941445, accuracy: 98.700%
Step 48000 — average loss: 0.0507242626, accuracy: 98.675%
Step 52000 — average loss: 0.0317074810, accuracy: 99.025%
Step 56000 — average loss: 0.0397422903, accuracy: 98.850%
Step 60000 — average loss: 0.0386780191, accuracy: 99.000%

— Epoch 4 of 5 —

Step 4000 — average loss: 0.0395523119, accuracy: 98.850%
Step 8000 — average loss: 0.0356448765, accuracy: 98.925%
Step 12000 — average loss: 0.0461691804, accuracy: 98.775%
Step 16000 — average loss: 0.0258937349, accuracy: 99.125%
Step 20000 — average loss: 0.0458036035, accuracy: 98.675%
Step 24000 — average loss: 0.0322282153, accuracy: 98.950%
Step 28000 — average loss: 0.0360580984, accuracy: 98.975%
Step 32000 — average loss: 0.0305760022, accuracy: 99.000%
Step 36000 — average loss: 0.0409128920, accuracy: 98.925%
Step 40000 — average loss: 0.0406076217, accuracy: 98.875%
Step 44000 — average loss: 0.0381256006, accuracy: 98.750%
Step 48000 — average loss: 0.0429783680, accuracy: 98.850%
Step 52000 — average loss: 0.0432177341, accuracy: 98.650%
Step 56000 — average loss: 0.0386228061, accuracy: 98.775%
Step 60000 — average loss: 0.0588488618, accuracy: 98.525%

— Epoch 5 of 5 —

Step 4000 — average loss: 0.0332806244, accuracy: 99.125%
Step 8000 — average loss: 0.0298940349, accuracy: 99.125%
Step 12000 — average loss: 0.0231992029, accuracy: 99.100%
Step 16000 — average loss: 0.0471978900, accuracy: 98.975%
Step 20000 — average loss: 0.0436086540, accuracy: 98.800%
Step 24000 — average loss: 0.0449378003, accuracy: 98.775%
Step 28000 — average loss: 0.0545749438, accuracy: 98.400%
Step 32000 — average loss: 0.0376516229, accuracy: 98.775%
Step 36000 — average loss: 0.0356222544, accuracy: 98.975%
Step 40000 — average loss: 0.0411271246, accuracy: 98.800%
Step 44000 — average loss: 0.0450796454, accuracy: 98.875%
Step 48000 — average loss: 0.0304666208, accuracy: 99.200%
Step 52000 — average loss: 0.0222555889, accuracy: 99.325%
Step 56000 — average loss: 0.0423603894, accuracy: 98.750%
Step 60000 — average loss: 0.0333730003, accuracy: 98.850%
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
"
>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">— Testing the CNN —</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)):</span>
    <span class="n">results</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">forward_la</span><span class="p">(</span><span class="n">im</span><span class="p">[</span><span class="n">newaxis</span><span class="p">,:],</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">))</span>
    <span class="n">loss</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">num_correct</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Loss:&#39;</span><span class="p">,</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">n_test_images</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Accuracy:&#39;</span><span class="p">,</span> <span class="n">num_correct</span> <span class="o">/</span> <span class="n">n_test_images</span><span class="p">)</span>
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<pre>

— Testing the CNN —

Test Loss: 0.07904200086080525
Test Accuracy: 0.9857
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<p>The accuracy and loss have actually got worse, although by a small amount only. 
This indicates that the model has learned essentially as well as it can. 
It may, however, be possible to reach higher performance using a smaller learning rate.</p>
<p>Let us check that by dividing the learning rate by 10.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># re-load the previous CNN</span>
<span class="n">CNN</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">save_file_name</span><span class="p">)</span>

<span class="c1"># number of epochs</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">5</span>

<span class="c1"># learning rate</span>
<span class="n">learn_rate</span> <span class="o">=</span> <span class="mf">0.001</span>

<span class="n">train_CNN</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">)</span>
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— Start of training —

— Epoch 1 of 5 —

Step 4000 — average loss: 0.0284546070, accuracy: 99.175%
Step 8000 — average loss: 0.0173090840, accuracy: 99.350%
Step 12000 — average loss: 0.0130150627, accuracy: 99.550%
Step 16000 — average loss: 0.0153547525, accuracy: 99.425%
Step 20000 — average loss: 0.0166134855, accuracy: 99.625%
Step 24000 — average loss: 0.0170233095, accuracy: 99.450%
Step 28000 — average loss: 0.0095192004, accuracy: 99.725%
Step 32000 — average loss: 0.0149739833, accuracy: 99.550%
Step 36000 — average loss: 0.0106585518, accuracy: 99.650%
Step 40000 — average loss: 0.0096159887, accuracy: 99.625%
Step 44000 — average loss: 0.0103893115, accuracy: 99.550%
Step 48000 — average loss: 0.0095242103, accuracy: 99.700%
Step 52000 — average loss: 0.0104614388, accuracy: 99.700%
Step 56000 — average loss: 0.0085243403, accuracy: 99.750%
Step 60000 — average loss: 0.0195945641, accuracy: 99.400%

— Epoch 2 of 5 —

Step 4000 — average loss: 0.0089791589, accuracy: 99.625%
Step 8000 — average loss: 0.0087372946, accuracy: 99.625%
Step 12000 — average loss: 0.0099045334, accuracy: 99.650%
Step 16000 — average loss: 0.0140164992, accuracy: 99.550%
Step 20000 — average loss: 0.0080687922, accuracy: 99.700%
Step 24000 — average loss: 0.0140491146, accuracy: 99.575%
Step 28000 — average loss: 0.0113379342, accuracy: 99.675%
Step 32000 — average loss: 0.0113591735, accuracy: 99.600%
Step 36000 — average loss: 0.0112698131, accuracy: 99.625%
Step 40000 — average loss: 0.0080424645, accuracy: 99.700%
Step 44000 — average loss: 0.0078099665, accuracy: 99.725%
Step 48000 — average loss: 0.0075131179, accuracy: 99.800%
Step 52000 — average loss: 0.0147044498, accuracy: 99.525%
Step 56000 — average loss: 0.0111653621, accuracy: 99.525%
Step 60000 — average loss: 0.0095129849, accuracy: 99.675%

— Epoch 3 of 5 —

Step 4000 — average loss: 0.0099757673, accuracy: 99.625%
Step 8000 — average loss: 0.0054635886, accuracy: 99.800%
Step 12000 — average loss: 0.0118061674, accuracy: 99.725%
Step 16000 — average loss: 0.0092392565, accuracy: 99.675%
Step 20000 — average loss: 0.0069483667, accuracy: 99.700%
Step 24000 — average loss: 0.0095017751, accuracy: 99.800%
Step 28000 — average loss: 0.0110368968, accuracy: 99.575%
Step 32000 — average loss: 0.0066447907, accuracy: 99.800%
Step 36000 — average loss: 0.0088598323, accuracy: 99.700%
Step 40000 — average loss: 0.0066628688, accuracy: 99.825%
Step 44000 — average loss: 0.0113461069, accuracy: 99.700%
Step 48000 — average loss: 0.0064956515, accuracy: 99.825%
Step 52000 — average loss: 0.0078597971, accuracy: 99.775%
Step 56000 — average loss: 0.0044614422, accuracy: 99.825%
Step 60000 — average loss: 0.0059588175, accuracy: 99.800%

— Epoch 4 of 5 —

Step 4000 — average loss: 0.0081367303, accuracy: 99.750%
Step 8000 — average loss: 0.0069863149, accuracy: 99.775%
Step 12000 — average loss: 0.0092531732, accuracy: 99.650%
Step 16000 — average loss: 0.0053124411, accuracy: 99.875%
Step 20000 — average loss: 0.0048535012, accuracy: 99.825%
Step 24000 — average loss: 0.0052289106, accuracy: 99.775%
Step 28000 — average loss: 0.0053026283, accuracy: 99.750%
Step 32000 — average loss: 0.0068437622, accuracy: 99.800%
Step 36000 — average loss: 0.0081047616, accuracy: 99.700%
Step 40000 — average loss: 0.0101764882, accuracy: 99.700%
Step 44000 — average loss: 0.0108141094, accuracy: 99.725%
Step 48000 — average loss: 0.0090181646, accuracy: 99.775%
Step 52000 — average loss: 0.0070909436, accuracy: 99.800%
Step 56000 — average loss: 0.0089845890, accuracy: 99.725%
Step 60000 — average loss: 0.0055930989, accuracy: 99.850%

— Epoch 5 of 5 —

Step 4000 — average loss: 0.0048180501, accuracy: 99.875%
Step 8000 — average loss: 0.0075927879, accuracy: 99.725%
Step 12000 — average loss: 0.0062344536, accuracy: 99.800%
Step 16000 — average loss: 0.0048489262, accuracy: 99.825%
Step 20000 — average loss: 0.0084449512, accuracy: 99.750%
Step 24000 — average loss: 0.0072631738, accuracy: 99.825%
Step 28000 — average loss: 0.0074281710, accuracy: 99.725%
Step 32000 — average loss: 0.0040472485, accuracy: 99.900%
Step 36000 — average loss: 0.0122228256, accuracy: 99.675%
Step 40000 — average loss: 0.0069409176, accuracy: 99.750%
Step 44000 — average loss: 0.0073869387, accuracy: 99.775%
Step 48000 — average loss: 0.0056657061, accuracy: 99.775%
Step 52000 — average loss: 0.0095016442, accuracy: 99.675%
Step 56000 — average loss: 0.0072628303, accuracy: 99.800%
Step 60000 — average loss: 0.0072177516, accuracy: 99.800%
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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;</span><span class="se">\n\n</span><span class="s1">— Testing the CNN —</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)):</span>
    <span class="n">results</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">forward_la</span><span class="p">(</span><span class="n">im</span><span class="p">[</span><span class="n">newaxis</span><span class="p">,:],</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">))</span>
    <span class="n">loss</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">num_correct</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Loss:&#39;</span><span class="p">,</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">n_test_images</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Accuracy:&#39;</span><span class="p">,</span> <span class="n">num_correct</span> <span class="o">/</span> <span class="n">n_test_images</span><span class="p">)</span>
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<pre>

— Testing the CNN —

Test Loss: 0.04633375078692688
Test Accuracy: 0.9913
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<p>The test accuracy is now above 99.1%. 
The test loss is an order of magnitude above the train loss, suggesting that the model overfits the training data and can not be significantly improved further by simply training on more epochs.</p>
<h2 id="Performance-">Performance <a class="anchor" id="Performance" /><a class="anchor-link" href="#Performance-">&#182;</a></h2><h3 id="Choice-of-hyperparameters">Choice of hyperparameters<a class="anchor-link" href="#Choice-of-hyperparameters">&#182;</a></h3><p>Let us now try to change some of the hyperparameters of the model, like the number of layers, neurons, and the size of filters, to see their effects. 
Proper hyperparameter optimization would require keeping track of the validation accuracy to ensure the models are trained over enough epochs and possibly add batch normalization and a learning rate optimizer, which we do not include here to keep the model as simple as possible. 
More modestly, our aim is to compare a few sets of parameters and look, on specific examples, at the effects of changing one of them. 
The results shown below should thus be seen as illustrations on a specific case rather than generalizable facts, and could be a first step to a more precise study.</p>
<p>We first define a function training a model over 20 epochs, the last 10 ones being performed with a learning rate divided by 10, and returning the test accuracy.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">test_params</span><span class="p">(</span><span class="n">CNN_params</span><span class="p">,</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="n">learn_rate</span><span class="p">,</span> <span class="n">p_dropout</span><span class="p">,</span>
               <span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">,</span> <span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">):</span>

    <span class="c1"># build the CNN </span>
    <span class="n">CNN</span> <span class="o">=</span> <span class="n">CNN3</span><span class="o">.</span><span class="n">CNN3</span><span class="p">(</span><span class="o">*</span><span class="n">CNN_params</span><span class="p">)</span>

    <span class="c1"># train the CNN</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">n_epochs</span><span class="p">):</span>

        <span class="c1"># shuffle the training data</span>
        <span class="n">permutation</span> <span class="o">=</span> <span class="n">np_permutation</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_images</span><span class="p">))</span>
        <span class="n">train_images</span> <span class="o">=</span> <span class="n">train_images</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span>
        <span class="n">train_labels</span> <span class="o">=</span> <span class="n">train_labels</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span>

        <span class="c1"># train the CNN for each image in the training set</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">)):</span> 
            <span class="n">CNN</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_images</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">newaxis</span><span class="p">,:],</span> <span class="nb">int</span><span class="p">(</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">learn_rate</span><span class="p">,</span> <span class="n">p_dropout</span><span class="p">)</span>
    
    <span class="c1"># divide the learning rate by 10</span>
    <span class="n">learn_rate</span> <span class="o">/=</span> <span class="mi">10</span>
    
    <span class="c1"># re-train the CNN</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">n_epochs</span><span class="p">):</span>

        <span class="c1"># shuffle the training data</span>
        <span class="n">permutation</span> <span class="o">=</span> <span class="n">np_permutation</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_images</span><span class="p">))</span>
        <span class="n">train_images</span> <span class="o">=</span> <span class="n">train_images</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span>
        <span class="n">train_labels</span> <span class="o">=</span> <span class="n">train_labels</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span>

        <span class="c1"># train the CNN for each image in the training set</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">)):</span> 
            <span class="n">CNN</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_images</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">newaxis</span><span class="p">,:],</span> <span class="nb">int</span><span class="p">(</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">learn_rate</span><span class="p">,</span> <span class="n">p_dropout</span><span class="p">)</span>

    <span class="c1"># test the CNN</span>
    <span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)):</span>
        <span class="n">results</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">forward_la</span><span class="p">(</span><span class="n">im</span><span class="p">[</span><span class="n">newaxis</span><span class="p">,:],</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">))</span>
        <span class="n">num_correct</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

    <span class="k">return</span> <span class="n">num_correct</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_images</span><span class="p">)</span>
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<p>Let us first define the parameters common to all the networks:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_train_images</span> <span class="o">=</span> <span class="mi">60000</span> <span class="c1"># number of train images</span>
<span class="n">n_test_images</span> <span class="o">=</span> <span class="mi">10000</span> <span class="c1"># number of test images</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># number of epochs in each training stage</span>
<span class="n">n_print</span> <span class="o">=</span> <span class="mi">4000</span> <span class="c1"># period of the result printing</span>
<span class="n">learn_rate</span> <span class="o">=</span> <span class="mf">0.01</span> <span class="c1"># initial learning rate</span>
<span class="n">img_w</span> <span class="o">=</span> <span class="mi">28</span> <span class="c1"># image width</span>
<span class="n">img_h</span> <span class="o">=</span> <span class="mi">28</span> <span class="c1"># image height</span>
<span class="n">num_labels</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># number of different labels</span>
<span class="n">p_dropout</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="c1"># dropout probability</span>
<span class="n">MP_size</span> <span class="o">=</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="c1"># size of the maxpool layers</span>

<span class="c1"># normalization function</span>
<span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">image</span> <span class="o">/</span> <span class="mi">255</span> <span class="o">-</span> <span class="mf">0.5</span>

<span class="c1"># training images and labels</span>
<span class="n">train_images</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">train_images</span><span class="p">()[:</span><span class="n">n_train_images</span><span class="p">])</span>
<span class="n">train_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">train_labels</span><span class="p">()[:</span><span class="n">n_train_images</span><span class="p">]</span>

<span class="c1"># test images and labels</span>
<span class="n">test_images</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">test_images</span><span class="p">()[:</span><span class="n">n_test_images</span><span class="p">])</span>
<span class="n">test_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_labels</span><span class="p">()[:</span><span class="n">n_test_images</span><span class="p">]</span>
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<p>We now define a function taking as parameters the lists of fiter sizes, numbers of filters, and numbers of neurons in the fully-connected layers and training the corresponding network:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">test_network</span><span class="p">(</span><span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">):</span>
    <span class="n">CNN_params</span> <span class="o">=</span> <span class="p">[</span><span class="n">img_w</span><span class="p">,</span> <span class="n">img_h</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> 
                  <span class="n">MP_size</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">test_params</span><span class="p">(</span><span class="n">CNN_params</span><span class="p">,</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="n">learn_rate</span><span class="p">,</span> <span class="n">p_dropout</span><span class="p">,</span>
               <span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">,</span> <span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)</span>
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<p>We first perform a test with a small number of filters and no dense layer:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</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="n">FC_size</span> <span class="o">=</span> <span class="p">[]</span> 
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">accuracy</span><span class="p">))</span>
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<pre>Accuracy: 0.9475
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<p>The accuracy is close to 94.8%.
Let us add a dense layer with 32 neurons, like in the previous section:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</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="n">FC_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">]</span> 
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">accuracy</span><span class="p">))</span>
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<pre>Accuracy: 0.9569
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<p>The accuracy has increased to 95.7%. 
The dense layer thus seems to help a little with the learning, although the reduced number of filters still hampers it.
Let us multiply by 4 the number of filters in each layer:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span> 
<span class="n">FC_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">]</span> 
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">accuracy</span><span class="p">))</span>
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<pre>Accuracy: 0.9893
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<p>The accuracy is close to 98.9%. 
It seems that (as one could expect), increasing the number of filters leads to better results.</p>
<p>Let us now try with only one convolution layer with the same total number of filters (the following cell takes longer to run than the previous ones):</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">12</span><span class="p">]</span> 
<span class="n">FC_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">]</span> 
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">accuracy</span><span class="p">))</span>
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<pre>Accuracy: 0.9842
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<p>The accuracy is close to 98.4%, a bit lower than the previous one. 
While the difference in accuracy is not sinificant, this network took significantly longer (by a factor larger than 2) to train than the previous one. 
This illustrates the well-known fact that, typically, “deep” neural networks (with many layers) tend to be more efficient than “wide” ones (with less layers but more neurons per layer) for a given number of connections. 
(Although the above experiment is not exactly an apple-to-apple comparison because the image size is reduced between two convolution layers.)</p>
<p>Let us now look at the effect of the dense layer. 
We first try to reduce the number of neurons:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span> 
<span class="n">FC_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">16</span><span class="p">]</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">accuracy</span><span class="p">))</span>
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<pre>Accuracy: 0.9847
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<p>The accuracy is very close to the one we got with 32 neurons, which suggests that, for this number and size of convolution filters, a dense layer with 16 neurons is large enough.</p>
<p>Let us now try with no dense layer at all:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span> 
<span class="n">FC_size</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">accuracy</span><span class="p">))</span>
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<pre>Accuracy: 0.9852
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<p>The accuracy is very close to the previous one, confirming that the dense layer does not play an important role in this network</p>
<p>Let us finally look at the effect of the filter size by increasing it to 5:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span> 
<span class="n">CL_num_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span> 
<span class="n">FC_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">16</span><span class="p">]</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">accuracy</span><span class="p">))</span>
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<pre>Accuracy: 0.9852
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<p>The accuracy is very slightly above the one obtained with smaller filters, everything else being equal. 
This small increase may well not be statistically significant, though (<em>i.e.</em>, running the above cells several times can lead to different results because of the randomness in the initialization of the weights and biases). 
Increasing the filter size thus may help a little, but increasing the number of filters is more advantageous.</p>
<p>All in all, the main observation from these experiments is that changing the hyperparameters does not affect the resulting accuracy by a huge amount: all the models we have tried in this section have an accuracy between 94.7% (with all models except the simplest one exceeding 95%) and 98.6%. 
Moreover, it is relatively easy to get above 98%, by imply adding more filters.</p>
<p>The reason is that the problem we are testing these models on, handwritten digits classification on the MNIST dataset, is a relatively simple one, as the different classes can be separated by detecting some simple features such as straight or curved lines, and the images are small and of relatively good quality. Therefore, even very simple models with only a few filters and neurons perform decently well. More complex models are required when trying to increase the accuracy above 99%, as such a model needs to classify correctly even the few “hard” images where these features may not be as clear cut, when working on more complex problems such as blood cell classification, or when the dataset has a higher level of noise.</p>
<h3 id="Effect-of-dropout">Effect of dropout<a class="anchor-link" href="#Effect-of-dropout">&#182;</a></h3><p>In the previous experiments we trained networks with dropout, <em>i.e.</em>, randomly cutting connections between neurons in dense layers during training. 
Dropout can reduce the propension of a network to overfit on the training data, leading to better generalization to the test data. 
However, it can also make training slower. 
Let us briefly investigate the effect it has on our network by re-training it without dropout:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">16</span><span class="p">,</span><span class="mi">32</span><span class="p">]</span> 
<span class="n">FC_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">]</span>
<span class="n">CNN_params</span> <span class="o">=</span> <span class="p">[</span><span class="n">img_w</span><span class="p">,</span> <span class="n">img_h</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">MP_size</span><span class="p">,</span> <span class="n">FC_size</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">]</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">test_params</span><span class="p">(</span><span class="n">CNN_params</span><span class="p">,</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="n">learn_rate</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span>
               <span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">,</span> <span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">accuracy</span><span class="p">))</span>
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<pre>Accuracy: 0.9926
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<p>The accuracy is quite close to what we obtained with dropout, although very slightly higher. 
The dropout thus does not seem to have a big effect in this particular case. 
(The difference between the two results is not large enough to tell for sure whether it is detrimental.)</p>
<h3 id="Comparison-with-a-TensorFlow-implementation">Comparison with a TensorFlow implementation<a class="anchor-link" href="#Comparison-with-a-TensorFlow-implementation">&#182;</a></h3><p>Let us compare the accuracy of our model with a simple CNN built using the Keras library with the TensorFlow backend.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import the keras library</span>
<span class="kn">import</span> <span class="nn">keras</span>
<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span>

<span class="c1"># define the model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
    <span class="p">[</span>
        <span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">input_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span> <span class="c1"># convolution layer</span>
        <span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</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="c1"># MaxPool layer</span>
        <span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</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">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span> <span class="c1"># convolution layer</span>
        <span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</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="c1"># MaxPool layer</span>
        <span class="n">keras</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="c1"># flatten the output</span>
        <span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span> <span class="c1"># dense layer</span>
        <span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span> <span class="c1"># dropout layer</span>
        <span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span> <span class="c1"># output layer</span>
    <span class="p">]</span>
<span class="p">)</span>

<span class="c1"># compile the model</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
    <span class="n">optimizer</span><span class="o">=</span><span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(),</span>  <span class="c1"># Optimizer</span>
    <span class="c1"># Loss function to minimize</span>
    <span class="n">loss</span><span class="o">=</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">categorical_crossentropy</span><span class="p">,</span>
    <span class="c1"># List of metrics to monitor</span>
    <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">],</span>
<span class="p">)</span>

<span class="c1"># print a summary of the model</span>
<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>Model: &#34;sequential&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 26, 26, 16)        160       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 16)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 32)        4640      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 32)          0         
_________________________________________________________________
flatten (Flatten)            (None, 800)               0         
_________________________________________________________________
dense (Dense)                (None, 32)                25632     
_________________________________________________________________
dropout (Dropout)            (None, 32)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                330       
=================================================================
Total params: 30,762
Trainable params: 30,762
Non-trainable params: 0
_________________________________________________________________
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<p>Let us train the model on 30 epochs:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># fit the model on the training images after converting the labels to </span>
<span class="c1"># one-hot format</span>

<span class="k">def</span> <span class="nf">to_one_hot</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
    <span class="n">res</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">l</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
        <span class="n">res</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="k">return</span> <span class="n">array</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>

<span class="c1"># the newaxis is needed as Keras uses four-dimensional arrays as input</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_images</span><span class="p">[:,:,:,</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">to_one_hot</span><span class="p">(</span><span class="n">train_labels</span><span class="p">),</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
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<pre>Epoch 1/30
1875/1875 [==============================] - 14s 7ms/step - loss: 0.2984 - accuracy: 0.9075
Epoch 2/30
1875/1875 [==============================] - 14s 7ms/step - loss: 0.1031 - accuracy: 0.9688
Epoch 3/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0763 - accuracy: 0.9779
Epoch 4/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0628 - accuracy: 0.9808
Epoch 5/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0541 - accuracy: 0.9829
Epoch 6/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0472 - accuracy: 0.9853
Epoch 7/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0411 - accuracy: 0.9872
Epoch 8/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0372 - accuracy: 0.9878
Epoch 9/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0319 - accuracy: 0.9898
Epoch 10/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0316 - accuracy: 0.9896
Epoch 11/30
1875/1875 [==============================] - 17s 9ms/step - loss: 0.0283 - accuracy: 0.9908
Epoch 12/30
1875/1875 [==============================] - 16s 9ms/step - loss: 0.0272 - accuracy: 0.9908
Epoch 13/30
1875/1875 [==============================] - 16s 9ms/step - loss: 0.0251 - accuracy: 0.9919
Epoch 14/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0225 - accuracy: 0.9926
Epoch 15/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0228 - accuracy: 0.9926
Epoch 16/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0209 - accuracy: 0.9931
Epoch 17/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0191 - accuracy: 0.9935
Epoch 18/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0190 - accuracy: 0.9939
Epoch 19/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0184 - accuracy: 0.9940
Epoch 20/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0161 - accuracy: 0.9941
Epoch 21/30
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0163 - accuracy: 0.9948
Epoch 22/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0165 - accuracy: 0.9945
Epoch 23/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0145 - accuracy: 0.9949
Epoch 24/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0146 - accuracy: 0.9948
Epoch 25/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0146 - accuracy: 0.9952
Epoch 26/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0149 - accuracy: 0.9951
Epoch 27/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0143 - accuracy: 0.9953
Epoch 28/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0131 - accuracy: 0.9957
Epoch 29/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0126 - accuracy: 0.9960
Epoch 30/30
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0140 - accuracy: 0.9953
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<pre>&lt;tensorflow.python.keras.callbacks.History at 0x7f94e7d68f70&gt;</pre>
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<p>Let us test the model:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_images</span><span class="p">[:,:,:,</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">to_one_hot</span><span class="p">(</span><span class="n">test_labels</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;test loss, test accuracy:&#39;</span><span class="p">,</span> <span class="n">results</span><span class="p">)</span>
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<pre>313/313 [==============================] - 1s 3ms/step - loss: 0.0468 - accuracy: 0.9912
test loss, test accuracy: [0.04680200666189194, 0.9911999702453613]
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<p>The accuracy is slightly above 99%, which is close to what we obtained with the previous model. 
(This is not surprizing as the models are essentially the same. 
The main difference is that the Keras implementation uses an optimizer which automatically adjusts the learning rate during the training, while we had to to it manually on the custom implementation.)</p>
<p>This implementation is typically faster to train (by how much depends on the hardware). 
This is because TensorFlow makes use of many optimisations, including (but surely not limited to) a better use of multi-core architectures and of the GPU if set-up to run on it. 
(Running a large model is generally much more efficient on a GPU than a CPU, because many operations can be performed in parallel, making full use of the GPU architecture. 
However, since the model we run here is relatively small, with only a few tens of thousands of parameters, running it on a CPU or GPU should not make a huge difference. 
Which one is more efficient depends, of course, of the specific CPU and GPU.)</p>

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<h2 id="Conclusion-">Conclusion <a class="anchor" id="concl" /><a class="anchor-link" href="#Conclusion-">&#182;</a></h2><p>To summarize, we have implemented a simple convolutional neural network in C++ with wrappers for easy access from Python, and tested it on the MNIST dataset of hand-written digits. 
We found that, with well chosen parameters, this model can reach an accuracy above 99%, and we briefly explored the effects of changing the number of convolution layers, filter sizes, and number of neurons in the hidden dense layer. 
The accuracy of the model is similar to that of a TensorFlow implementation, but the later is faster to train due to optimizations not present in our simple C++ implementation.</p>
<p>One point to keep in mind is that the MNIST problem is a rather “easy” one for a neural network, and may thus not accurately reflect its performance on more difficult problems. 
The results shown here thus have an illustrative purpose only. 
A case-by-case analysis would be required to see which of them holds on harder problems, which arises often in real-world applications of Machine Learning.</p>
<h2 id="Appendix-1:-Brief-description-of-each-layer-">Appendix 1: Brief description of each layer <a class="anchor" id="app_layers" /><a class="anchor-link" href="#Appendix-1:-Brief-description-of-each-layer-">&#182;</a></h2><p>The layers used to build the network are defined in the file <code>C++/Layers.cpp</code>, with implementations in their respective <code>.cpp</code> files. 
Each layer has a <code>save</code> method saving its weights and parameters to a file and a <code>load</code> method loading them from a file (we use a plaintext format to make saving and loading easier, although a binary format would be more space efficient), as well as <code>forward</code> and <code>backprop</code> methods performing, respectively, the forward and backward propagation.</p>
<p>In the rest of this appendix, indices without an explicit range are understood to describe the set of their possible values.</p>
<h3 id="Convolutional-layer">Convolutional layer<a class="anchor-link" href="#Convolutional-layer">&#182;</a></h3><p>The implementation of this layer can be found in <code>ConvLayer.cpp</code>. 
Each convolution layer has two parameters: the number of filters $n$ and size $s$ of the filters. 
It takes a 3D array with fixed shape $(c,w,h)$ as input and returns a 3D array with shape $(n,w-s+1,h-s+1)$. 
Each filter is a 3D array with shape $(c,s,s)$, where $c$ is the number of channels in the input.</p>
<p>Let $X$ denote the input array. 
The coefficient $(i,j,k)$ of the output array $Y$ is given by: 
$$
Y_{i,j,k} = \sum_{l = 1}^n \sum_{m = 1}^c \sum_{a = 1}^s \sum_{b = 1}^s 
    F^{(l)}_{m,a,b} X_{m,i+a-1,j+b-1},
$$
where $F^{(l)}$ denotes the $l$th filter.</p>
<p>In the backpropagation stage, the gradient of the loss function is propagated using the formulae
$$
\frac{\partial Y_{i,j,k}}{\partial X_{m,p,q}} = 
    \sum_{l = 1}^n  
    F^{(l)}_{m,p+1-i,q+1-j},
$$
where $F^{(l)}_{m,p+1-i,q+1-i}$ is understood to be equal to 0 if one of its last two indices is smaller than $1$ or larger than $s$, and 
$$
\frac{\partial Y_{i,j,k}}{\partial F^{(l)}_{m,a,b}} = X_{m,i+a-1,j+b-1}.
$$</p>
<h3 id="ReLU-layer">ReLU layer<a class="anchor-link" href="#ReLU-layer">&#182;</a></h3><p>The implementation of this layer can be found in <code>ReLU.cpp</code> and <code>ReLU_v.cpp</code>. 
It comes in two variants: <code>ReLU</code> and <code>ReLU_v</code>, differing only by their inputs (a 3D array for the former, to be used in a convolution block, or a vector for the latter, to be used after a dense layer).</p>
<p>The forward pass simply applies the ReLU function to each element of the input, <em>i.e.</em>, returning its value if it is positive and 0 if it is not. 
The input is cached during the forward propagation. 
In the backpropagation, the derivative of the loss function is returned identically if the input was non-negative or replaced by 0 otherwise.</p>
<h3 id="Maxpool-layer">Maxpool layer<a class="anchor-link" href="#Maxpool-layer">&#182;</a></h3><p>The implementation of this layer can be found in <code>MaxPool.cpp</code>. 
It takes a 3D array as input and splits each of its layers into $n$ by $n$ sub-arrays, where $n$ is the size (or stride) of the MaxPool layer. 
Only the maximum element of each sub-array is retained, thus dividing the number of elements by $n^2$. 
During the forward pass, the layer also retains the positons of the values which are kept.</p>
<p>During the backpropagation stage, the gradient of the loss function is propagated identically for the values which were kept and set to 0 for those which were not kept.</p>
<h3 id="Fully-connected-layer">Fully-connected layer<a class="anchor-link" href="#Fully-connected-layer">&#182;</a></h3><p>The implementation of this layer can be found in <code>FullCon.cpp</code>. 
Each fully-connected layer takes a vector as input and outputs another vector; it has a weight matrix $W$ with size $N$ by $I$ and a bias vector $B$ with size $N$, where $N$ is the number of neurons and $I$ the input size. 
The output $Y$ (of size $N$) corresponding to the input $X$ (of size $I$) is: $Y = W \cdot X + B$, where $\cdot$ denotes matrix-vector multiplication.</p>
<p>The gradient of the loss function can be propagated backward using the formulae:
$$
\frac{\partial Y_i}{\partial X_j} = W_{i,j},
$$
$$
\frac{\partial Y_i}{\partial B_j} = \delta_{i,j},
$$
where $\delta_{i,j}$ is the Kronecker symbol equal to $1$ if $i=j$ and $0$ otherwise, and 
$$
\frac{\partial Y_i}{\partial W_{j,k}} = \delta_{i,j} X_k.
$$</p>
<h3 id="Softmax-layer">Softmax layer<a class="anchor-link" href="#Softmax-layer">&#182;</a></h3><p>The implementation of this layer can be found in <code>SoftMax.cpp</code>. 
It is simply a fully-connected layer with a softmax activation function, <em>i.e.</em>, if $Y$ denotes the (vector) output of the dense layer, the output of the softmax layer is the vector $Z$ with same size whose components are given by: 
$$
Z_i = \frac{\exp(Y_i)}{\sum_{k} \exp(Y_k)},
$$
where the sum in the denominator is performed over all the components of $Y$.</p>
<p>In the backpropagation stage, we use that 
$$
\frac{\partial Z_i}{\partial Y_j} = (\delta_{i,j} - Z_j) Z_i, 
$$
where $\delta_{i,j}$ is the Kronecker symbol equal to $1$ if $i=j$ and $0$ otherwise, that $\frac{\partial L}{\partial Z_i} = 0$ if $i$ is not the correct label, and that $\frac{\partial L}{\partial Z_i} = - \frac{1}{Z_i}$ if $i$ is the correct label (where $L$ is the loss function).</p>
<h2 id="Appendix-2:-Training-the-model-on-the-CIFAR-10-dataset-">Appendix 2: Training the model on the CIFAR-10 dataset <a class="anchor" id="app_cifar" /><a class="anchor-link" href="#Appendix-2:-Training-the-model-on-the-CIFAR-10-dataset-">&#182;</a></h2><p>As mentioned in the Conclusion, handwritten digits classification on the MNIST dataset is a relatively easy problem, which does not necessarily tell much about the performance of a model. 
A more challenging dataset to work with is <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10</a>. 
Let us train our model on this dataset.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># parameters</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">n_print</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">learn_rate</span> <span class="o">=</span> <span class="mf">0.01</span>
<span class="n">img_w</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">img_h</span> <span class="o">=</span> <span class="mi">32</span> 
<span class="n">num_labels</span> <span class="o">=</span> <span class="mi">10</span> 
<span class="n">CL_size_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> 
<span class="n">CL_num_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">16</span><span class="p">,</span><span class="mi">32</span><span class="p">]</span> 
<span class="n">MP_size</span> <span class="o">=</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="n">FC_size</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">]</span> 
<span class="n">p_dropout</span> <span class="o">=</span> <span class="mf">0.2</span> 

<span class="c1"># normalization function</span>
<span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">image</span> <span class="o">/</span> <span class="mi">255</span> <span class="o">-</span> <span class="mf">0.5</span>

<span class="c1"># load the dataset</span>
<span class="kn">from</span> <span class="nn">keras.datasets.cifar10</span> <span class="kn">import</span> <span class="n">load_data</span>
<span class="kn">from</span> <span class="nn">keras.backend</span> <span class="kn">import</span> <span class="n">set_image_data_format</span>
<span class="n">set_image_data_format</span><span class="p">(</span><span class="s1">&#39;channels_first&#39;</span><span class="p">)</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="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">load_data</span><span class="p">()</span>

<span class="c1"># convert to float16 to reduce memory usage</span>
<span class="n">x_train</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float16&#39;</span><span class="p">)</span>
<span class="n">x_test</span> <span class="o">=</span> <span class="n">x_test</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float16&#39;</span><span class="p">)</span>

<span class="c1"># normalize the images</span>
<span class="n">x_train</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mi">255</span> <span class="o">-</span> <span class="mf">0.5</span>
<span class="n">x_test</span> <span class="o">=</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mi">255</span> <span class="o">-</span> <span class="mf">0.5</span>

<span class="c1"># build the CNN </span>
<span class="n">CNN</span> <span class="o">=</span> <span class="n">CNN3</span><span class="o">.</span><span class="n">CNN3</span><span class="p">(</span><span class="n">img_w</span><span class="p">,</span> <span class="n">img_h</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">CL_size_filters</span><span class="p">,</span> <span class="n">CL_num_filters</span><span class="p">,</span> <span class="n">MP_size</span><span class="p">,</span> 
                <span class="n">FC_size</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">)</span>

<span class="c1"># define the train function</span>
<span class="k">def</span> <span class="nf">train_CNN</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">,</span> <span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">):</span>
    
    <span class="n">nsteps_l</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># number of steps elapsed </span>
    <span class="n">loss_l</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># loss function</span>
    <span class="n">acc_l</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># accuracy</span>

    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">— Start of training —&#39;</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">n_epochs</span><span class="p">):</span>
        <span class="n">nsteps_l</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>
        <span class="n">loss_l</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>
        <span class="n">acc_l</span><span class="o">.</span><span class="n">append</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">— Epoch </span><span class="si">{:d}</span><span class="s1"> of </span><span class="si">{:d}</span><span class="s1"> —</span><span class="se">\n</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="n">n_epochs</span><span class="p">))</span>

        <span class="c1"># shuffle the training data</span>
        <span class="n">permutation</span> <span class="o">=</span> <span class="n">np_permutation</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_images</span><span class="p">))</span>
        <span class="n">train_images</span> <span class="o">=</span> <span class="n">train_images</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span>
        <span class="n">train_labels</span> <span class="o">=</span> <span class="n">train_labels</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span>

        <span class="c1"># set the loss and number of correctly predicted labels to 0</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
        <span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="c1"># for each image in the training set, train the CNN and update the loss </span>
        <span class="c1"># and number of correctly-predicted labels</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">)):</span> 
            <span class="n">results</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_images</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float&#39;</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> 
                                <span class="n">learn_rate</span><span class="p">,</span> <span class="n">p_dropout</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">num_correct</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
            <span class="c1"># if i is a multiple of n_print, print the loss and accuracy            </span>
            <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="n">n_print</span> <span class="o">==</span> <span class="n">n_print</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">nsteps_l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
                <span class="n">loss_l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span> <span class="o">/</span> <span class="n">n_print</span><span class="p">)</span>
                <span class="n">acc_l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">num_correct</span> <span class="o">/</span> <span class="n">n_print</span><span class="p">)</span>
                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Step </span><span class="si">{:d}</span><span class="s1"> — average loss: </span><span class="si">{:.10f}</span><span class="s1">, 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">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">loss</span><span class="o">/</span><span class="n">n_print</span><span class="p">,</span> <span class="n">num_correct</span><span class="o">*</span><span class="mf">100.</span><span class="o">/</span><span class="n">n_print</span><span class="p">))</span>
                <span class="c1"># reset the loss and number of correctly-preducted labels</span>
                <span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
                <span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
    
    <span class="c1"># plot the evolution of the loss function - epoch to epoch</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="p">[</span><span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">loss_l</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_epochs</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">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{epoch}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{loss}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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 the evolution of the accuracy  - epoch to epoch</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="p">[</span><span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">acc_l</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_epochs</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">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{epoch}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\mathrm</span><span class="si">{accuracy}</span><span class="s1">$&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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="s1">&#39;MNIST CNN initialized&#39;</span><span class="p">)</span> 

<span class="c1"># train the CNN</span>
<span class="n">train_CNN</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_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>

<span class="c1"># test the CNN</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">— Testing the CNN —</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</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">results</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">forward_la</span><span class="p">(</span><span class="n">im</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float&#39;</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">))</span>
    <span class="n">loss</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">num_correct</span> <span class="o">+=</span> <span class="n">results</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Loss:&#39;</span><span class="p">,</span> <span class="n">loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_test</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Accuracy:&#39;</span><span class="p">,</span> <span class="n">num_correct</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_test</span><span class="p">))</span>
</pre></div>

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<pre>MNIST CNN initialized


— Start of training —

— Epoch 1 of 10 —

Step 10000 — average loss: 2.0278260217, accuracy: 23.300%
Step 20000 — average loss: 1.6911183880, accuracy: 37.790%
Step 30000 — average loss: 1.5627992138, accuracy: 43.020%
Step 40000 — average loss: 1.4915701281, accuracy: 45.640%
Step 50000 — average loss: 1.4493458588, accuracy: 47.560%

— Epoch 2 of 10 —

Step 10000 — average loss: 1.3809965986, accuracy: 50.750%
Step 20000 — average loss: 1.3797888040, accuracy: 50.720%
Step 30000 — average loss: 1.3688063220, accuracy: 50.890%
Step 40000 — average loss: 1.3816442430, accuracy: 51.620%
Step 50000 — average loss: 1.3481644251, accuracy: 52.590%

— Epoch 3 of 10 —

Step 10000 — average loss: 1.3085939403, accuracy: 53.770%
Step 20000 — average loss: 1.3268691163, accuracy: 52.900%
Step 30000 — average loss: 1.3296960501, accuracy: 52.780%
Step 40000 — average loss: 1.3044005584, accuracy: 53.690%
Step 50000 — average loss: 1.3322393142, accuracy: 53.560%

— Epoch 4 of 10 —

Step 10000 — average loss: 1.2630855456, accuracy: 55.520%
Step 20000 — average loss: 1.2787858052, accuracy: 55.760%
Step 30000 — average loss: 1.3035260042, accuracy: 54.520%
Step 40000 — average loss: 1.2826271081, accuracy: 55.270%
Step 50000 — average loss: 1.3115521079, accuracy: 54.690%

— Epoch 5 of 10 —

Step 10000 — average loss: 1.2377642070, accuracy: 57.190%
Step 20000 — average loss: 1.2711255835, accuracy: 55.740%
Step 30000 — average loss: 1.2757151694, accuracy: 56.050%
Step 40000 — average loss: 1.2911463100, accuracy: 55.490%
Step 50000 — average loss: 1.2970345249, accuracy: 55.430%

— Epoch 6 of 10 —

Step 10000 — average loss: 1.2215555549, accuracy: 58.230%
Step 20000 — average loss: 1.2593275028, accuracy: 56.930%
Step 30000 — average loss: 1.2885576139, accuracy: 56.060%
Step 40000 — average loss: 1.2901561174, accuracy: 55.840%
Step 50000 — average loss: 1.2968022068, accuracy: 55.970%

— Epoch 7 of 10 —

Step 10000 — average loss: 1.2409884248, accuracy: 57.340%
Step 20000 — average loss: 1.2740938224, accuracy: 56.870%
Step 30000 — average loss: 1.2885728133, accuracy: 56.770%
Step 40000 — average loss: 1.3064955306, accuracy: 55.340%
Step 50000 — average loss: 1.2814027590, accuracy: 56.550%

— Epoch 8 of 10 —

Step 10000 — average loss: 1.2317805780, accuracy: 58.880%
Step 20000 — average loss: 1.2900525470, accuracy: 55.820%
Step 30000 — average loss: 1.3015727606, accuracy: 56.640%
Step 40000 — average loss: 1.2964784947, accuracy: 55.900%
Step 50000 — average loss: 1.3095397602, accuracy: 56.080%

— Epoch 9 of 10 —

Step 10000 — average loss: 1.2701621735, accuracy: 56.890%
Step 20000 — average loss: 1.2805136836, accuracy: 57.460%
Step 30000 — average loss: 1.2984125704, accuracy: 56.680%
Step 40000 — average loss: 1.2913106180, accuracy: 57.180%
Step 50000 — average loss: 1.3217139886, accuracy: 56.380%

— Epoch 10 of 10 —

Step 10000 — average loss: 1.2365865606, accuracy: 59.450%
Step 20000 — average loss: 1.2856257239, accuracy: 56.650%
Step 30000 — average loss: 1.2922060406, accuracy: 57.170%
Step 40000 — average loss: 1.3160398198, accuracy: 56.520%
Step 50000 — average loss: 1.3488513162, accuracy: 54.920%
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
"
>
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<div class="output_area">

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


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

— Testing the CNN —

Test Loss: 1.3958687668410223
Test Accuracy: 0.601
</pre>
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<p>The test accuracy is just above 60%, which is far from the state of the art (several very deep networks achieve <a href="https://arxiv.org/pdf/1912.11370.pdf">more</a> <a href="https://github.com/danhdoan/cifar10-end2end-mxnet">than 99%</a>), but decent for such a simple model.</p>

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