raman.shinde15@gmail.com_25.html

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}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
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    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
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.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
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.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

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<h1 id="CNN-on-CIFR-Assignment:">CNN on CIFR Assignment:<a class="anchor-link" href="#CNN-on-CIFR-Assignment:">&#182;</a></h1>
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<ol>
<li>Please visit this link to access the state-of-art DenseNet code for reference - DenseNet - cifar10 notebook link</li>
<li>You need to create a copy of this and "retrain" this model to achieve 90+ test accuracy. </li>
<li>You cannot use Dense Layers (also called fully connected layers), or DropOut.</li>
<li>You MUST use Image Augmentation Techniques.</li>
<li>You cannot use an already trained model as a beginning points, you have to initilize as your own</li>
<li>You cannot run the program for more than 300 Epochs, and it should be clear from your log, that you have only used 300 Epochs</li>
<li>You cannot use test images for training the model.</li>
<li>You cannot change the general architecture of DenseNet (which means you must use Dense Block, Transition and Output blocks as mentioned in the code)</li>
<li>You are free to change Convolution types (e.g. from 3x3 normal convolution to Depthwise Separable, etc)</li>
<li>You cannot have more than 1 Million parameters in total</li>
<li>You are free to move the code from Keras to Tensorflow, Pytorch, MXNET etc. </li>
<li>You can use any optimization algorithm you need. </li>
<li>You can checkpoint your model and retrain the model from that checkpoint so that no need of training the model from first if you lost at any epoch while training. You can directly load that model and Train from that epoch. </li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># ! pip install tensorflow==1.13.1</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.regularizers</span> <span class="k">import</span> <span class="n">l2</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span><span class="n">AveragePooling2D</span><span class="p">,</span><span class="n">BatchNormalization</span><span class="p">,</span><span class="n">Conv2D</span><span class="p">,</span><span class="n">Input</span><span class="p">,</span><span class="n">Flatten</span><span class="p">,</span><span class="n">Activation</span><span class="p">,</span><span class="n">concatenate</span><span class="p">,</span><span class="n">Dropout</span><span class="p">,</span><span class="n">GlobalAveragePooling2D</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.models</span> <span class="k">import</span> <span class="n">Model</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Hyperparameters</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">num_classes</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">num_filter</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">compression</span> <span class="o">=</span> <span class="mf">0.5</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load CIFAR10 Data</span>
<span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="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">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">img_height</span><span class="p">,</span> <span class="n">img_width</span><span class="p">,</span> <span class="n">channel</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
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<pre>Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 3s 0us/step
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># from sklearn.model_selection import train_test_split</span>
<span class="c1"># from sklearn.utils import resample</span>
<span class="c1"># X_train,y_train = resample(X_train,y_train,n_samples = 30000)</span>
<span class="c1"># X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, test_size=5/30, random_state=42)</span>
<span class="c1"># X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=5/25, random_state=42)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">resample</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_cv</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_cv</span> <span class="o">=</span> <span class="n">train_test_split</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">test_size</span><span class="o">=</span><span class="mi">10</span><span class="o">/</span><span class="mi">50</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># convert to one hot encoing </span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
<span class="n">y_cv</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">y_cv</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">y_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">X_cv</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">y_cv</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(40000, 32, 32, 3) (40000, 10)
(10000, 32, 32, 3) (10000, 10)
(10000, 32, 32, 3) (10000, 10)
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<h2 id="Model-Architechture">Model Architechture<a class="anchor-link" href="#Model-Architechture">&#182;</a></h2>
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<p>Implemented <strong>DenseNet-BC(k= 12, L = 100) with 0.76M parameters</strong> with cifar-10 using augmentation.</p>

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<p><img src="data:image/jpeg;base64, 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" alt="densenet.jfif"></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Convolution</span><span class="p">():</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Before entering the first dense vlock, a convolution with 16 (or twice the </span>
<span class="sd">    growth rate for BC type) output channels is performed on the input images</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">growthRate</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">1E-4</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">growthRate</span> <span class="o">=</span> <span class="n">growthRate</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span> <span class="o">=</span> <span class="n">weight_decay</span>
        
    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        
        <span class="c1">#x = Activation(&#39;relu&#39;)(x)</span>
        <span class="n">op</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">growthRate</span><span class="p">),</span><span class="mi">3</span><span class="p">,</span><span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span><span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">bias_initializer</span><span class="o">=</span><span class="s1">&#39;zeros&#39;</span><span class="p">,</span><span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="n">op</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test code</span>
<span class="n">in_</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="n">op</span> <span class="o">=</span> <span class="n">Convolution</span><span class="p">()(</span><span class="n">in_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">op</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</pre></div>

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<pre>WARNING: Logging before flag parsing goes to stderr.
W0813 16:36:52.858003 140186611292032 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
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<pre>(?, 32, 32, 24)
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<h3 id="Class-BottleNeck">Class BottleNeck<a class="anchor-link" href="#Class-BottleNeck">&#182;</a></h3>
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<p><img src="data:image/jpeg;base64, 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" alt="bottleneck.jfif"></p>

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<div class="prompt input_prompt">In&nbsp;[0]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Bottelneck</span><span class="p">():</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This class implements H(l) as mentioned in paper.</span>
<span class="sd">    BN-ReLU-Conv(1x1)-BN-ReLU-Conv(3x3) version of H.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">growthrate</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span><span class="n">weight_decay</span><span class="o">=</span><span class="mf">1E-4</span><span class="p">):</span>
        
        <span class="bp">self</span><span class="o">.</span><span class="n">growthrate</span> <span class="o">=</span> <span class="n">growthrate</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span> <span class="o">=</span> <span class="n">weight_decay</span>
        
        
    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        This is where logic lives.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">(</span><span class="n">beta_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">),</span><span class="n">gamma_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s2">&quot;relu&quot;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="mi">4</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">growthrate</span><span class="p">),</span><span class="mi">1</span><span class="p">,</span><span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span><span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">kernel_initializer</span><span class="o">=</span><span class="s2">&quot;he_uniform&quot;</span><span class="p">,</span><span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">(</span><span class="n">beta_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">),</span><span class="n">gamma_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s2">&quot;relu&quot;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">growthrate</span><span class="p">),</span><span class="mi">3</span><span class="p">,</span><span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span><span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">kernel_initializer</span><span class="o">=</span><span class="s2">&quot;he_uniform&quot;</span><span class="p">,</span><span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="n">x</span>
</pre></div>

    </div>
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<h3 id="Class-Transition">Class Transition<a class="anchor-link" href="#Class-Transition">&#182;</a></h3>
</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Transition</span><span class="p">():</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This class implements transition layer as per paper.</span>
<span class="sd">    BN-Con-pooling</span>
<span class="sd">    &quot;&quot;&quot;</span>  
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">compression</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span><span class="n">growthrate</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span><span class="n">weight_decay</span><span class="o">=</span><span class="mf">1E-4</span><span class="p">):</span>
        
        <span class="bp">self</span><span class="o">.</span><span class="n">compression</span> <span class="o">=</span> <span class="n">compression</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">growthrate</span> <span class="o">=</span> <span class="n">growthrate</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span> <span class="o">=</span> <span class="n">weight_decay</span>
        
    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
        
        <span class="n">nChannels</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
        
        <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">(</span><span class="n">beta_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">),</span><span class="n">gamma_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s2">&quot;relu&quot;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">compression</span> <span class="o">*</span> <span class="n">nChannels</span><span class="p">),</span><span class="mi">1</span><span class="p">,</span><span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span><span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">AveragePooling2D</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="n">x</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="n">x</span>
</pre></div>

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<h3 id="Class-DenseNet">Class DenseNet<a class="anchor-link" href="#Class-DenseNet">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Densenet</span><span class="p">():</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This layer implements densenet.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">depth</span><span class="p">,</span><span class="n">growthrate</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span><span class="n">bottleneck</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
        
        <span class="n">nDenseLayers</span> <span class="o">=</span> <span class="p">(</span><span class="n">depth</span><span class="o">-</span><span class="mi">4</span><span class="p">)</span> <span class="o">//</span> <span class="mi">3</span>
        <span class="k">if</span> <span class="n">bottleneck</span><span class="p">:</span>
            <span class="n">nDenseLayers</span> <span class="o">//=</span> <span class="mi">2</span>
        
        <span class="bp">self</span><span class="o">.</span><span class="n">nDenseLayers</span> <span class="o">=</span> <span class="n">nDenseLayers</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">growthrate</span> <span class="o">=</span> <span class="n">growthrate</span>
        
            
    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
        
        <span class="c1"># Dense Block logic</span>
        
        <span class="c1"># convolution block</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Convolution</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="c1"># 1st dense block</span>
        <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nDenseLayers</span><span class="p">)):</span>
            <span class="n">bt</span> <span class="o">=</span> <span class="n">Bottelneck</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
            <span class="n">x</span> <span class="o">=</span> <span class="n">concatenate</span><span class="p">([</span><span class="n">x</span><span class="p">,</span><span class="n">bt</span><span class="p">])</span>
            
        <span class="c1"># transition layer</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Transition</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
            
        <span class="c1"># 2nd dense block</span>
        <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nDenseLayers</span><span class="p">)):</span>
            <span class="n">bt</span> <span class="o">=</span> <span class="n">Bottelneck</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
            <span class="n">x</span> <span class="o">=</span> <span class="n">concatenate</span><span class="p">([</span><span class="n">x</span><span class="p">,</span><span class="n">bt</span><span class="p">])</span>
            
        <span class="c1"># transition layer</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">Transition</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>

        <span class="c1"># 3rd dense block</span>
        <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nDenseLayers</span><span class="p">)):</span>
            <span class="n">bt</span> <span class="o">=</span> <span class="n">Bottelneck</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
            <span class="n">x</span> <span class="o">=</span> <span class="n">concatenate</span><span class="p">([</span><span class="n">x</span><span class="p">,</span><span class="n">bt</span><span class="p">])</span>

        <span class="k">return</span> <span class="n">x</span>
</pre></div>

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<h3 id="Class-Output">Class Output<a class="anchor-link" href="#Class-Output">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Output</span><span class="p">():</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This layer implements output layer.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">num_classes</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span><span class="n">weight_decay</span><span class="o">=</span><span class="mf">1E-4</span><span class="p">):</span>
        
        <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="n">num_classes</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span> <span class="o">=</span> <span class="n">weight_decay</span>
        
    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
      
        <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">(</span><span class="n">beta_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">),</span><span class="n">gamma_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">GlobalAveragePooling2D</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
        <span class="c1">#avg = AveragePooling2D(pool_size=(8,8))(x)</span>
        <span class="c1">#flt = Flatten()(avg)</span>
        <span class="n">op</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s2">&quot;softmax&quot;</span><span class="p">,</span><span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">),</span> <span class="n">bias_regularizer</span><span class="o">=</span><span class="n">l2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="n">op</span>
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<h3 id="Model-Building">Model Building<a class="anchor-link" href="#Model-Building">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># # The DenseNet used in our experiments has three dense blocks that each has an equal number of layers.</span>

<span class="c1"># </span>

<span class="c1"># # First convolution</span>
<span class="c1"># cnv = Convolution()(in_)</span>

<span class="c1"># # For dense block  1</span>
<span class="c1"># op = Densenet(depth=100)(cnv)</span>
<span class="c1"># op = Transition()(op)</span>

<span class="c1"># # For dense block  2</span>
<span class="c1"># op = Densenet(depth=100)(op)</span>
<span class="c1"># op = Transition()(op)</span>


<span class="n">in_</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>

<span class="c1"># densenet</span>
<span class="n">op</span> <span class="o">=</span> <span class="n">Densenet</span><span class="p">(</span><span class="mi">100</span><span class="p">)(</span><span class="n">in_</span><span class="p">)</span>

<span class="c1"># output </span>
<span class="n">op</span> <span class="o">=</span> <span class="n">Output</span><span class="p">()(</span><span class="n">op</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">inputs</span> <span class="o">=</span> <span class="n">in_</span><span class="p">,</span><span class="n">outputs</span> <span class="o">=</span> <span class="n">op</span><span class="p">)</span>
<span class="n">sgd</span> <span class="o">=</span> <span class="n">tf</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">SGD</span><span class="p">(</span><span class="n">lr</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span><span class="n">momentum</span> <span class="o">=</span> <span class="mf">0.9</span><span class="p">,</span><span class="n">nesterov</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">sgd</span><span class="p">,</span><span class="n">loss</span><span class="o">=</span><span class="s2">&quot;categorical_crossentropy&quot;</span><span class="p">,</span><span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></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;model&#34;
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_2 (InputLayer)            [(None, 32, 32, 3)]  0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 32, 32, 24)   648         input_2[0][0]                    
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 32, 32, 24)   96          conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation (Activation)         (None, 32, 32, 24)   0           batch_normalization[0][0]        
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 32, 32, 48)   1152        activation[0][0]                 
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 32, 32, 48)   192         conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 32, 32, 48)   0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 32, 32, 12)   5184        activation_1[0][0]               
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 32, 32, 36)   0           conv2d_1[0][0]                   
                                                                 conv2d_3[0][0]                   
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 32, 32, 36)   144         concatenate[0][0]                
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 32, 32, 36)   0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 32, 32, 48)   1728        activation_2[0][0]               
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 32, 32, 48)   192         conv2d_4[0][0]                   
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 32, 32, 48)   0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 32, 32, 12)   5184        activation_3[0][0]               
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 32, 32, 48)   0           concatenate[0][0]                
                                                                 conv2d_5[0][0]                   
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 32, 32, 48)   192         concatenate_1[0][0]              
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 32, 32, 48)   0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 32, 32, 48)   2304        activation_4[0][0]               
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 32, 32, 48)   192         conv2d_6[0][0]                   
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 32, 32, 48)   0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 32, 32, 12)   5184        activation_5[0][0]               
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, 32, 32, 60)   0           concatenate_1[0][0]              
                                                                 conv2d_7[0][0]                   
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 32, 32, 60)   240         concatenate_2[0][0]              
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 32, 32, 60)   0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 32, 32, 48)   2880        activation_6[0][0]               
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 32, 32, 48)   192         conv2d_8[0][0]                   
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 32, 32, 48)   0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 32, 32, 12)   5184        activation_7[0][0]               
__________________________________________________________________________________________________
concatenate_3 (Concatenate)     (None, 32, 32, 72)   0           concatenate_2[0][0]              
                                                                 conv2d_9[0][0]                   
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 32, 32, 72)   288         concatenate_3[0][0]              
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 32, 32, 72)   0           batch_normalization_8[0][0]      
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 32, 32, 48)   3456        activation_8[0][0]               
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 32, 32, 48)   192         conv2d_10[0][0]                  
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 32, 32, 48)   0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 32, 32, 12)   5184        activation_9[0][0]               
__________________________________________________________________________________________________
concatenate_4 (Concatenate)     (None, 32, 32, 84)   0           concatenate_3[0][0]              
                                                                 conv2d_11[0][0]                  
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 32, 32, 84)   336         concatenate_4[0][0]              
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 32, 32, 84)   0           batch_normalization_10[0][0]     
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 32, 32, 48)   4032        activation_10[0][0]              
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 32, 32, 48)   192         conv2d_12[0][0]                  
__________________________________________________________________________________________________
activation_11 (Activation)      (None, 32, 32, 48)   0           batch_normalization_11[0][0]     
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 32, 32, 12)   5184        activation_11[0][0]              
__________________________________________________________________________________________________
concatenate_5 (Concatenate)     (None, 32, 32, 96)   0           concatenate_4[0][0]              
                                                                 conv2d_13[0][0]                  
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 32, 32, 96)   384         concatenate_5[0][0]              
__________________________________________________________________________________________________
activation_12 (Activation)      (None, 32, 32, 96)   0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 32, 32, 48)   4608        activation_12[0][0]              
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 32, 32, 48)   192         conv2d_14[0][0]                  
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 32, 32, 48)   0           batch_normalization_13[0][0]     
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 32, 32, 12)   5184        activation_13[0][0]              
__________________________________________________________________________________________________
concatenate_6 (Concatenate)     (None, 32, 32, 108)  0           concatenate_5[0][0]              
                                                                 conv2d_15[0][0]                  
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 32, 32, 108)  432         concatenate_6[0][0]              
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 32, 32, 108)  0           batch_normalization_14[0][0]     
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 32, 32, 48)   5184        activation_14[0][0]              
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 32, 32, 48)   192         conv2d_16[0][0]                  
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 32, 32, 48)   0           batch_normalization_15[0][0]     
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 32, 32, 12)   5184        activation_15[0][0]              
__________________________________________________________________________________________________
concatenate_7 (Concatenate)     (None, 32, 32, 120)  0           concatenate_6[0][0]              
                                                                 conv2d_17[0][0]                  
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 32, 32, 120)  480         concatenate_7[0][0]              
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 32, 32, 120)  0           batch_normalization_16[0][0]     
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 32, 32, 48)   5760        activation_16[0][0]              
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 32, 32, 48)   192         conv2d_18[0][0]                  
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 32, 32, 48)   0           batch_normalization_17[0][0]     
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 32, 32, 12)   5184        activation_17[0][0]              
__________________________________________________________________________________________________
concatenate_8 (Concatenate)     (None, 32, 32, 132)  0           concatenate_7[0][0]              
                                                                 conv2d_19[0][0]                  
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 32, 32, 132)  528         concatenate_8[0][0]              
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 32, 32, 132)  0           batch_normalization_18[0][0]     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 32, 32, 48)   6336        activation_18[0][0]              
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 32, 32, 48)   192         conv2d_20[0][0]                  
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 32, 32, 48)   0           batch_normalization_19[0][0]     
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 32, 32, 12)   5184        activation_19[0][0]              
__________________________________________________________________________________________________
concatenate_9 (Concatenate)     (None, 32, 32, 144)  0           concatenate_8[0][0]              
                                                                 conv2d_21[0][0]                  
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 32, 32, 144)  576         concatenate_9[0][0]              
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 32, 32, 144)  0           batch_normalization_20[0][0]     
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 32, 32, 48)   6912        activation_20[0][0]              
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 32, 32, 48)   192         conv2d_22[0][0]                  
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 32, 32, 48)   0           batch_normalization_21[0][0]     
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 32, 32, 12)   5184        activation_21[0][0]              
__________________________________________________________________________________________________
concatenate_10 (Concatenate)    (None, 32, 32, 156)  0           concatenate_9[0][0]              
                                                                 conv2d_23[0][0]                  
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 32, 32, 156)  624         concatenate_10[0][0]             
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 32, 32, 156)  0           batch_normalization_22[0][0]     
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 32, 32, 48)   7488        activation_22[0][0]              
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 32, 32, 48)   192         conv2d_24[0][0]                  
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 32, 32, 48)   0           batch_normalization_23[0][0]     
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 32, 32, 12)   5184        activation_23[0][0]              
__________________________________________________________________________________________________
concatenate_11 (Concatenate)    (None, 32, 32, 168)  0           concatenate_10[0][0]             
                                                                 conv2d_25[0][0]                  
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 32, 32, 168)  672         concatenate_11[0][0]             
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 32, 32, 168)  0           batch_normalization_24[0][0]     
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, 32, 32, 48)   8064        activation_24[0][0]              
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 32, 32, 48)   192         conv2d_26[0][0]                  
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 32, 32, 48)   0           batch_normalization_25[0][0]     
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, 32, 32, 12)   5184        activation_25[0][0]              
__________________________________________________________________________________________________
concatenate_12 (Concatenate)    (None, 32, 32, 180)  0           concatenate_11[0][0]             
                                                                 conv2d_27[0][0]                  
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 32, 32, 180)  720         concatenate_12[0][0]             
__________________________________________________________________________________________________
activation_26 (Activation)      (None, 32, 32, 180)  0           batch_normalization_26[0][0]     
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, 32, 32, 48)   8640        activation_26[0][0]              
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 32, 32, 48)   192         conv2d_28[0][0]                  
__________________________________________________________________________________________________
activation_27 (Activation)      (None, 32, 32, 48)   0           batch_normalization_27[0][0]     
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, 32, 32, 12)   5184        activation_27[0][0]              
__________________________________________________________________________________________________
concatenate_13 (Concatenate)    (None, 32, 32, 192)  0           concatenate_12[0][0]             
                                                                 conv2d_29[0][0]                  
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 32, 32, 192)  768         concatenate_13[0][0]             
__________________________________________________________________________________________________
activation_28 (Activation)      (None, 32, 32, 192)  0           batch_normalization_28[0][0]     
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, 32, 32, 48)   9216        activation_28[0][0]              
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 32, 32, 48)   192         conv2d_30[0][0]                  
__________________________________________________________________________________________________
activation_29 (Activation)      (None, 32, 32, 48)   0           batch_normalization_29[0][0]     
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, 32, 32, 12)   5184        activation_29[0][0]              
__________________________________________________________________________________________________
concatenate_14 (Concatenate)    (None, 32, 32, 204)  0           concatenate_13[0][0]             
                                                                 conv2d_31[0][0]                  
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 32, 32, 204)  816         concatenate_14[0][0]             
__________________________________________________________________________________________________
activation_30 (Activation)      (None, 32, 32, 204)  0           batch_normalization_30[0][0]     
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, 32, 32, 48)   9792        activation_30[0][0]              
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 32, 32, 48)   192         conv2d_32[0][0]                  
__________________________________________________________________________________________________
activation_31 (Activation)      (None, 32, 32, 48)   0           batch_normalization_31[0][0]     
__________________________________________________________________________________________________
conv2d_33 (Conv2D)              (None, 32, 32, 12)   5184        activation_31[0][0]              
__________________________________________________________________________________________________
concatenate_15 (Concatenate)    (None, 32, 32, 216)  0           concatenate_14[0][0]             
                                                                 conv2d_33[0][0]                  
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 32, 32, 216)  864         concatenate_15[0][0]             
__________________________________________________________________________________________________
activation_32 (Activation)      (None, 32, 32, 216)  0           batch_normalization_32[0][0]     
__________________________________________________________________________________________________
conv2d_34 (Conv2D)              (None, 32, 32, 108)  23328       activation_32[0][0]              
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 16, 16, 108)  0           conv2d_34[0][0]                  
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 16, 16, 108)  432         average_pooling2d[0][0]          
__________________________________________________________________________________________________
activation_33 (Activation)      (None, 16, 16, 108)  0           batch_normalization_33[0][0]     
__________________________________________________________________________________________________
conv2d_35 (Conv2D)              (None, 16, 16, 48)   5184        activation_33[0][0]              
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 16, 16, 48)   192         conv2d_35[0][0]                  
__________________________________________________________________________________________________
activation_34 (Activation)      (None, 16, 16, 48)   0           batch_normalization_34[0][0]     
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, 16, 16, 12)   5184        activation_34[0][0]              
__________________________________________________________________________________________________
concatenate_16 (Concatenate)    (None, 16, 16, 120)  0           average_pooling2d[0][0]          
                                                                 conv2d_36[0][0]                  
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 16, 16, 120)  480         concatenate_16[0][0]             
__________________________________________________________________________________________________
activation_35 (Activation)      (None, 16, 16, 120)  0           batch_normalization_35[0][0]     
__________________________________________________________________________________________________
conv2d_37 (Conv2D)              (None, 16, 16, 48)   5760        activation_35[0][0]              
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 16, 16, 48)   192         conv2d_37[0][0]                  
__________________________________________________________________________________________________
activation_36 (Activation)      (None, 16, 16, 48)   0           batch_normalization_36[0][0]     
__________________________________________________________________________________________________
conv2d_38 (Conv2D)              (None, 16, 16, 12)   5184        activation_36[0][0]              
__________________________________________________________________________________________________
concatenate_17 (Concatenate)    (None, 16, 16, 132)  0           concatenate_16[0][0]             
                                                                 conv2d_38[0][0]                  
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 16, 16, 132)  528         concatenate_17[0][0]             
__________________________________________________________________________________________________
activation_37 (Activation)      (None, 16, 16, 132)  0           batch_normalization_37[0][0]     
__________________________________________________________________________________________________
conv2d_39 (Conv2D)              (None, 16, 16, 48)   6336        activation_37[0][0]              
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 16, 16, 48)   192         conv2d_39[0][0]                  
__________________________________________________________________________________________________
activation_38 (Activation)      (None, 16, 16, 48)   0           batch_normalization_38[0][0]     
__________________________________________________________________________________________________
conv2d_40 (Conv2D)              (None, 16, 16, 12)   5184        activation_38[0][0]              
__________________________________________________________________________________________________
concatenate_18 (Concatenate)    (None, 16, 16, 144)  0           concatenate_17[0][0]             
                                                                 conv2d_40[0][0]                  
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 16, 16, 144)  576         concatenate_18[0][0]             
__________________________________________________________________________________________________
activation_39 (Activation)      (None, 16, 16, 144)  0           batch_normalization_39[0][0]     
__________________________________________________________________________________________________
conv2d_41 (Conv2D)              (None, 16, 16, 48)   6912        activation_39[0][0]              
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 16, 16, 48)   192         conv2d_41[0][0]                  
__________________________________________________________________________________________________
activation_40 (Activation)      (None, 16, 16, 48)   0           batch_normalization_40[0][0]     
__________________________________________________________________________________________________
conv2d_42 (Conv2D)              (None, 16, 16, 12)   5184        activation_40[0][0]              
__________________________________________________________________________________________________
concatenate_19 (Concatenate)    (None, 16, 16, 156)  0           concatenate_18[0][0]             
                                                                 conv2d_42[0][0]                  
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 16, 16, 156)  624         concatenate_19[0][0]             
__________________________________________________________________________________________________
activation_41 (Activation)      (None, 16, 16, 156)  0           batch_normalization_41[0][0]     
__________________________________________________________________________________________________
conv2d_43 (Conv2D)              (None, 16, 16, 48)   7488        activation_41[0][0]              
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 16, 16, 48)   192         conv2d_43[0][0]                  
__________________________________________________________________________________________________
activation_42 (Activation)      (None, 16, 16, 48)   0           batch_normalization_42[0][0]     
__________________________________________________________________________________________________
conv2d_44 (Conv2D)              (None, 16, 16, 12)   5184        activation_42[0][0]              
__________________________________________________________________________________________________
concatenate_20 (Concatenate)    (None, 16, 16, 168)  0           concatenate_19[0][0]             
                                                                 conv2d_44[0][0]                  
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 16, 16, 168)  672         concatenate_20[0][0]             
__________________________________________________________________________________________________
activation_43 (Activation)      (None, 16, 16, 168)  0           batch_normalization_43[0][0]     
__________________________________________________________________________________________________
conv2d_45 (Conv2D)              (None, 16, 16, 48)   8064        activation_43[0][0]              
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 16, 16, 48)   192         conv2d_45[0][0]                  
__________________________________________________________________________________________________
activation_44 (Activation)      (None, 16, 16, 48)   0           batch_normalization_44[0][0]     
__________________________________________________________________________________________________
conv2d_46 (Conv2D)              (None, 16, 16, 12)   5184        activation_44[0][0]              
__________________________________________________________________________________________________
concatenate_21 (Concatenate)    (None, 16, 16, 180)  0           concatenate_20[0][0]             
                                                                 conv2d_46[0][0]                  
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 16, 16, 180)  720         concatenate_21[0][0]             
__________________________________________________________________________________________________
activation_45 (Activation)      (None, 16, 16, 180)  0           batch_normalization_45[0][0]     
__________________________________________________________________________________________________
conv2d_47 (Conv2D)              (None, 16, 16, 48)   8640        activation_45[0][0]              
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 16, 16, 48)   192         conv2d_47[0][0]                  
__________________________________________________________________________________________________
activation_46 (Activation)      (None, 16, 16, 48)   0           batch_normalization_46[0][0]     
__________________________________________________________________________________________________
conv2d_48 (Conv2D)              (None, 16, 16, 12)   5184        activation_46[0][0]              
__________________________________________________________________________________________________
concatenate_22 (Concatenate)    (None, 16, 16, 192)  0           concatenate_21[0][0]             
                                                                 conv2d_48[0][0]                  
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 16, 16, 192)  768         concatenate_22[0][0]             
__________________________________________________________________________________________________
activation_47 (Activation)      (None, 16, 16, 192)  0           batch_normalization_47[0][0]     
__________________________________________________________________________________________________
conv2d_49 (Conv2D)              (None, 16, 16, 48)   9216        activation_47[0][0]              
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 16, 16, 48)   192         conv2d_49[0][0]                  
__________________________________________________________________________________________________
activation_48 (Activation)      (None, 16, 16, 48)   0           batch_normalization_48[0][0]     
__________________________________________________________________________________________________
conv2d_50 (Conv2D)              (None, 16, 16, 12)   5184        activation_48[0][0]              
__________________________________________________________________________________________________
concatenate_23 (Concatenate)    (None, 16, 16, 204)  0           concatenate_22[0][0]             
                                                                 conv2d_50[0][0]                  
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 16, 16, 204)  816         concatenate_23[0][0]             
__________________________________________________________________________________________________
activation_49 (Activation)      (None, 16, 16, 204)  0           batch_normalization_49[0][0]     
__________________________________________________________________________________________________
conv2d_51 (Conv2D)              (None, 16, 16, 48)   9792        activation_49[0][0]              
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 16, 16, 48)   192         conv2d_51[0][0]                  
__________________________________________________________________________________________________
activation_50 (Activation)      (None, 16, 16, 48)   0           batch_normalization_50[0][0]     
__________________________________________________________________________________________________
conv2d_52 (Conv2D)              (None, 16, 16, 12)   5184        activation_50[0][0]              
__________________________________________________________________________________________________
concatenate_24 (Concatenate)    (None, 16, 16, 216)  0           concatenate_23[0][0]             
                                                                 conv2d_52[0][0]                  
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 16, 16, 216)  864         concatenate_24[0][0]             
__________________________________________________________________________________________________
activation_51 (Activation)      (None, 16, 16, 216)  0           batch_normalization_51[0][0]     
__________________________________________________________________________________________________
conv2d_53 (Conv2D)              (None, 16, 16, 48)   10368       activation_51[0][0]              
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 16, 16, 48)   192         conv2d_53[0][0]                  
__________________________________________________________________________________________________
activation_52 (Activation)      (None, 16, 16, 48)   0           batch_normalization_52[0][0]     
__________________________________________________________________________________________________
conv2d_54 (Conv2D)              (None, 16, 16, 12)   5184        activation_52[0][0]              
__________________________________________________________________________________________________
concatenate_25 (Concatenate)    (None, 16, 16, 228)  0           concatenate_24[0][0]             
                                                                 conv2d_54[0][0]                  
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 16, 16, 228)  912         concatenate_25[0][0]             
__________________________________________________________________________________________________
activation_53 (Activation)      (None, 16, 16, 228)  0           batch_normalization_53[0][0]     
__________________________________________________________________________________________________
conv2d_55 (Conv2D)              (None, 16, 16, 48)   10944       activation_53[0][0]              
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 16, 16, 48)   192         conv2d_55[0][0]                  
__________________________________________________________________________________________________
activation_54 (Activation)      (None, 16, 16, 48)   0           batch_normalization_54[0][0]     
__________________________________________________________________________________________________
conv2d_56 (Conv2D)              (None, 16, 16, 12)   5184        activation_54[0][0]              
__________________________________________________________________________________________________
concatenate_26 (Concatenate)    (None, 16, 16, 240)  0           concatenate_25[0][0]             
                                                                 conv2d_56[0][0]                  
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 16, 16, 240)  960         concatenate_26[0][0]             
__________________________________________________________________________________________________
activation_55 (Activation)      (None, 16, 16, 240)  0           batch_normalization_55[0][0]     
__________________________________________________________________________________________________
conv2d_57 (Conv2D)              (None, 16, 16, 48)   11520       activation_55[0][0]              
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 16, 16, 48)   192         conv2d_57[0][0]                  
__________________________________________________________________________________________________
activation_56 (Activation)      (None, 16, 16, 48)   0           batch_normalization_56[0][0]     
__________________________________________________________________________________________________
conv2d_58 (Conv2D)              (None, 16, 16, 12)   5184        activation_56[0][0]              
__________________________________________________________________________________________________
concatenate_27 (Concatenate)    (None, 16, 16, 252)  0           concatenate_26[0][0]             
                                                                 conv2d_58[0][0]                  
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 16, 16, 252)  1008        concatenate_27[0][0]             
__________________________________________________________________________________________________
activation_57 (Activation)      (None, 16, 16, 252)  0           batch_normalization_57[0][0]     
__________________________________________________________________________________________________
conv2d_59 (Conv2D)              (None, 16, 16, 48)   12096       activation_57[0][0]              
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 16, 16, 48)   192         conv2d_59[0][0]                  
__________________________________________________________________________________________________
activation_58 (Activation)      (None, 16, 16, 48)   0           batch_normalization_58[0][0]     
__________________________________________________________________________________________________
conv2d_60 (Conv2D)              (None, 16, 16, 12)   5184        activation_58[0][0]              
__________________________________________________________________________________________________
concatenate_28 (Concatenate)    (None, 16, 16, 264)  0           concatenate_27[0][0]             
                                                                 conv2d_60[0][0]                  
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 16, 16, 264)  1056        concatenate_28[0][0]             
__________________________________________________________________________________________________
activation_59 (Activation)      (None, 16, 16, 264)  0           batch_normalization_59[0][0]     
__________________________________________________________________________________________________
conv2d_61 (Conv2D)              (None, 16, 16, 48)   12672       activation_59[0][0]              
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 16, 16, 48)   192         conv2d_61[0][0]                  
__________________________________________________________________________________________________
activation_60 (Activation)      (None, 16, 16, 48)   0           batch_normalization_60[0][0]     
__________________________________________________________________________________________________
conv2d_62 (Conv2D)              (None, 16, 16, 12)   5184        activation_60[0][0]              
__________________________________________________________________________________________________
concatenate_29 (Concatenate)    (None, 16, 16, 276)  0           concatenate_28[0][0]             
                                                                 conv2d_62[0][0]                  
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 16, 16, 276)  1104        concatenate_29[0][0]             
__________________________________________________________________________________________________
activation_61 (Activation)      (None, 16, 16, 276)  0           batch_normalization_61[0][0]     
__________________________________________________________________________________________________
conv2d_63 (Conv2D)              (None, 16, 16, 48)   13248       activation_61[0][0]              
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 16, 16, 48)   192         conv2d_63[0][0]                  
__________________________________________________________________________________________________
activation_62 (Activation)      (None, 16, 16, 48)   0           batch_normalization_62[0][0]     
__________________________________________________________________________________________________
conv2d_64 (Conv2D)              (None, 16, 16, 12)   5184        activation_62[0][0]              
__________________________________________________________________________________________________
concatenate_30 (Concatenate)    (None, 16, 16, 288)  0           concatenate_29[0][0]             
                                                                 conv2d_64[0][0]                  
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 16, 16, 288)  1152        concatenate_30[0][0]             
__________________________________________________________________________________________________
activation_63 (Activation)      (None, 16, 16, 288)  0           batch_normalization_63[0][0]     
__________________________________________________________________________________________________
conv2d_65 (Conv2D)              (None, 16, 16, 48)   13824       activation_63[0][0]              
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 16, 16, 48)   192         conv2d_65[0][0]                  
__________________________________________________________________________________________________
activation_64 (Activation)      (None, 16, 16, 48)   0           batch_normalization_64[0][0]     
__________________________________________________________________________________________________
conv2d_66 (Conv2D)              (None, 16, 16, 12)   5184        activation_64[0][0]              
__________________________________________________________________________________________________
concatenate_31 (Concatenate)    (None, 16, 16, 300)  0           concatenate_30[0][0]             
                                                                 conv2d_66[0][0]                  
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 16, 16, 300)  1200        concatenate_31[0][0]             
__________________________________________________________________________________________________
activation_65 (Activation)      (None, 16, 16, 300)  0           batch_normalization_65[0][0]     
__________________________________________________________________________________________________
conv2d_67 (Conv2D)              (None, 16, 16, 150)  45000       activation_65[0][0]              
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 8, 8, 150)    0           conv2d_67[0][0]                  
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 8, 8, 150)    600         average_pooling2d_1[0][0]        
__________________________________________________________________________________________________
activation_66 (Activation)      (None, 8, 8, 150)    0           batch_normalization_66[0][0]     
__________________________________________________________________________________________________
conv2d_68 (Conv2D)              (None, 8, 8, 48)     7200        activation_66[0][0]              
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 8, 8, 48)     192         conv2d_68[0][0]                  
__________________________________________________________________________________________________
activation_67 (Activation)      (None, 8, 8, 48)     0           batch_normalization_67[0][0]     
__________________________________________________________________________________________________
conv2d_69 (Conv2D)              (None, 8, 8, 12)     5184        activation_67[0][0]              
__________________________________________________________________________________________________
concatenate_32 (Concatenate)    (None, 8, 8, 162)    0           average_pooling2d_1[0][0]        
                                                                 conv2d_69[0][0]                  
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 8, 8, 162)    648         concatenate_32[0][0]             
__________________________________________________________________________________________________
activation_68 (Activation)      (None, 8, 8, 162)    0           batch_normalization_68[0][0]     
__________________________________________________________________________________________________
conv2d_70 (Conv2D)              (None, 8, 8, 48)     7776        activation_68[0][0]              
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 8, 8, 48)     192         conv2d_70[0][0]                  
__________________________________________________________________________________________________
activation_69 (Activation)      (None, 8, 8, 48)     0           batch_normalization_69[0][0]     
__________________________________________________________________________________________________
conv2d_71 (Conv2D)              (None, 8, 8, 12)     5184        activation_69[0][0]              
__________________________________________________________________________________________________
concatenate_33 (Concatenate)    (None, 8, 8, 174)    0           concatenate_32[0][0]             
                                                                 conv2d_71[0][0]                  
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 8, 8, 174)    696         concatenate_33[0][0]             
__________________________________________________________________________________________________
activation_70 (Activation)      (None, 8, 8, 174)    0           batch_normalization_70[0][0]     
__________________________________________________________________________________________________
conv2d_72 (Conv2D)              (None, 8, 8, 48)     8352        activation_70[0][0]              
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 8, 8, 48)     192         conv2d_72[0][0]                  
__________________________________________________________________________________________________
activation_71 (Activation)      (None, 8, 8, 48)     0           batch_normalization_71[0][0]     
__________________________________________________________________________________________________
conv2d_73 (Conv2D)              (None, 8, 8, 12)     5184        activation_71[0][0]              
__________________________________________________________________________________________________
concatenate_34 (Concatenate)    (None, 8, 8, 186)    0           concatenate_33[0][0]             
                                                                 conv2d_73[0][0]                  
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 8, 8, 186)    744         concatenate_34[0][0]             
__________________________________________________________________________________________________
activation_72 (Activation)      (None, 8, 8, 186)    0           batch_normalization_72[0][0]     
__________________________________________________________________________________________________
conv2d_74 (Conv2D)              (None, 8, 8, 48)     8928        activation_72[0][0]              
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 8, 8, 48)     192         conv2d_74[0][0]                  
__________________________________________________________________________________________________
activation_73 (Activation)      (None, 8, 8, 48)     0           batch_normalization_73[0][0]     
__________________________________________________________________________________________________
conv2d_75 (Conv2D)              (None, 8, 8, 12)     5184        activation_73[0][0]              
__________________________________________________________________________________________________
concatenate_35 (Concatenate)    (None, 8, 8, 198)    0           concatenate_34[0][0]             
                                                                 conv2d_75[0][0]                  
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 8, 8, 198)    792         concatenate_35[0][0]             
__________________________________________________________________________________________________
activation_74 (Activation)      (None, 8, 8, 198)    0           batch_normalization_74[0][0]     
__________________________________________________________________________________________________
conv2d_76 (Conv2D)              (None, 8, 8, 48)     9504        activation_74[0][0]              
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 8, 8, 48)     192         conv2d_76[0][0]                  
__________________________________________________________________________________________________
activation_75 (Activation)      (None, 8, 8, 48)     0           batch_normalization_75[0][0]     
__________________________________________________________________________________________________
conv2d_77 (Conv2D)              (None, 8, 8, 12)     5184        activation_75[0][0]              
__________________________________________________________________________________________________
concatenate_36 (Concatenate)    (None, 8, 8, 210)    0           concatenate_35[0][0]             
                                                                 conv2d_77[0][0]                  
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 8, 8, 210)    840         concatenate_36[0][0]             
__________________________________________________________________________________________________
activation_76 (Activation)      (None, 8, 8, 210)    0           batch_normalization_76[0][0]     
__________________________________________________________________________________________________
conv2d_78 (Conv2D)              (None, 8, 8, 48)     10080       activation_76[0][0]              
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 8, 8, 48)     192         conv2d_78[0][0]                  
__________________________________________________________________________________________________
activation_77 (Activation)      (None, 8, 8, 48)     0           batch_normalization_77[0][0]     
__________________________________________________________________________________________________
conv2d_79 (Conv2D)              (None, 8, 8, 12)     5184        activation_77[0][0]              
__________________________________________________________________________________________________
concatenate_37 (Concatenate)    (None, 8, 8, 222)    0           concatenate_36[0][0]             
                                                                 conv2d_79[0][0]                  
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 8, 8, 222)    888         concatenate_37[0][0]             
__________________________________________________________________________________________________
activation_78 (Activation)      (None, 8, 8, 222)    0           batch_normalization_78[0][0]     
__________________________________________________________________________________________________
conv2d_80 (Conv2D)              (None, 8, 8, 48)     10656       activation_78[0][0]              
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 8, 8, 48)     192         conv2d_80[0][0]                  
__________________________________________________________________________________________________
activation_79 (Activation)      (None, 8, 8, 48)     0           batch_normalization_79[0][0]     
__________________________________________________________________________________________________
conv2d_81 (Conv2D)              (None, 8, 8, 12)     5184        activation_79[0][0]              
__________________________________________________________________________________________________
concatenate_38 (Concatenate)    (None, 8, 8, 234)    0           concatenate_37[0][0]             
                                                                 conv2d_81[0][0]                  
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 8, 8, 234)    936         concatenate_38[0][0]             
__________________________________________________________________________________________________
activation_80 (Activation)      (None, 8, 8, 234)    0           batch_normalization_80[0][0]     
__________________________________________________________________________________________________
conv2d_82 (Conv2D)              (None, 8, 8, 48)     11232       activation_80[0][0]              
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 8, 8, 48)     192         conv2d_82[0][0]                  
__________________________________________________________________________________________________
activation_81 (Activation)      (None, 8, 8, 48)     0           batch_normalization_81[0][0]     
__________________________________________________________________________________________________
conv2d_83 (Conv2D)              (None, 8, 8, 12)     5184        activation_81[0][0]              
__________________________________________________________________________________________________
concatenate_39 (Concatenate)    (None, 8, 8, 246)    0           concatenate_38[0][0]             
                                                                 conv2d_83[0][0]                  
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 8, 8, 246)    984         concatenate_39[0][0]             
__________________________________________________________________________________________________
activation_82 (Activation)      (None, 8, 8, 246)    0           batch_normalization_82[0][0]     
__________________________________________________________________________________________________
conv2d_84 (Conv2D)              (None, 8, 8, 48)     11808       activation_82[0][0]              
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 8, 8, 48)     192         conv2d_84[0][0]                  
__________________________________________________________________________________________________
activation_83 (Activation)      (None, 8, 8, 48)     0           batch_normalization_83[0][0]     
__________________________________________________________________________________________________
conv2d_85 (Conv2D)              (None, 8, 8, 12)     5184        activation_83[0][0]              
__________________________________________________________________________________________________
concatenate_40 (Concatenate)    (None, 8, 8, 258)    0           concatenate_39[0][0]             
                                                                 conv2d_85[0][0]                  
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, 8, 8, 258)    1032        concatenate_40[0][0]             
__________________________________________________________________________________________________
activation_84 (Activation)      (None, 8, 8, 258)    0           batch_normalization_84[0][0]     
__________________________________________________________________________________________________
conv2d_86 (Conv2D)              (None, 8, 8, 48)     12384       activation_84[0][0]              
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 8, 8, 48)     192         conv2d_86[0][0]                  
__________________________________________________________________________________________________
activation_85 (Activation)      (None, 8, 8, 48)     0           batch_normalization_85[0][0]     
__________________________________________________________________________________________________
conv2d_87 (Conv2D)              (None, 8, 8, 12)     5184        activation_85[0][0]              
__________________________________________________________________________________________________
concatenate_41 (Concatenate)    (None, 8, 8, 270)    0           concatenate_40[0][0]             
                                                                 conv2d_87[0][0]                  
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 8, 8, 270)    1080        concatenate_41[0][0]             
__________________________________________________________________________________________________
activation_86 (Activation)      (None, 8, 8, 270)    0           batch_normalization_86[0][0]     
__________________________________________________________________________________________________
conv2d_88 (Conv2D)              (None, 8, 8, 48)     12960       activation_86[0][0]              
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 8, 8, 48)     192         conv2d_88[0][0]                  
__________________________________________________________________________________________________
activation_87 (Activation)      (None, 8, 8, 48)     0           batch_normalization_87[0][0]     
__________________________________________________________________________________________________
conv2d_89 (Conv2D)              (None, 8, 8, 12)     5184        activation_87[0][0]              
__________________________________________________________________________________________________
concatenate_42 (Concatenate)    (None, 8, 8, 282)    0           concatenate_41[0][0]             
                                                                 conv2d_89[0][0]                  
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 8, 8, 282)    1128        concatenate_42[0][0]             
__________________________________________________________________________________________________
activation_88 (Activation)      (None, 8, 8, 282)    0           batch_normalization_88[0][0]     
__________________________________________________________________________________________________
conv2d_90 (Conv2D)              (None, 8, 8, 48)     13536       activation_88[0][0]              
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 8, 8, 48)     192         conv2d_90[0][0]                  
__________________________________________________________________________________________________
activation_89 (Activation)      (None, 8, 8, 48)     0           batch_normalization_89[0][0]     
__________________________________________________________________________________________________
conv2d_91 (Conv2D)              (None, 8, 8, 12)     5184        activation_89[0][0]              
__________________________________________________________________________________________________
concatenate_43 (Concatenate)    (None, 8, 8, 294)    0           concatenate_42[0][0]             
                                                                 conv2d_91[0][0]                  
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 8, 8, 294)    1176        concatenate_43[0][0]             
__________________________________________________________________________________________________
activation_90 (Activation)      (None, 8, 8, 294)    0           batch_normalization_90[0][0]     
__________________________________________________________________________________________________
conv2d_92 (Conv2D)              (None, 8, 8, 48)     14112       activation_90[0][0]              
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 8, 8, 48)     192         conv2d_92[0][0]                  
__________________________________________________________________________________________________
activation_91 (Activation)      (None, 8, 8, 48)     0           batch_normalization_91[0][0]     
__________________________________________________________________________________________________
conv2d_93 (Conv2D)              (None, 8, 8, 12)     5184        activation_91[0][0]              
__________________________________________________________________________________________________
concatenate_44 (Concatenate)    (None, 8, 8, 306)    0           concatenate_43[0][0]             
                                                                 conv2d_93[0][0]                  
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 8, 8, 306)    1224        concatenate_44[0][0]             
__________________________________________________________________________________________________
activation_92 (Activation)      (None, 8, 8, 306)    0           batch_normalization_92[0][0]     
__________________________________________________________________________________________________
conv2d_94 (Conv2D)              (None, 8, 8, 48)     14688       activation_92[0][0]              
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, 8, 8, 48)     192         conv2d_94[0][0]                  
__________________________________________________________________________________________________
activation_93 (Activation)      (None, 8, 8, 48)     0           batch_normalization_93[0][0]     
__________________________________________________________________________________________________
conv2d_95 (Conv2D)              (None, 8, 8, 12)     5184        activation_93[0][0]              
__________________________________________________________________________________________________
concatenate_45 (Concatenate)    (None, 8, 8, 318)    0           concatenate_44[0][0]             
                                                                 conv2d_95[0][0]                  
__________________________________________________________________________________________________
batch_normalization_94 (BatchNo (None, 8, 8, 318)    1272        concatenate_45[0][0]             
__________________________________________________________________________________________________
activation_94 (Activation)      (None, 8, 8, 318)    0           batch_normalization_94[0][0]     
__________________________________________________________________________________________________
conv2d_96 (Conv2D)              (None, 8, 8, 48)     15264       activation_94[0][0]              
__________________________________________________________________________________________________
batch_normalization_95 (BatchNo (None, 8, 8, 48)     192         conv2d_96[0][0]                  
__________________________________________________________________________________________________
activation_95 (Activation)      (None, 8, 8, 48)     0           batch_normalization_95[0][0]     
__________________________________________________________________________________________________
conv2d_97 (Conv2D)              (None, 8, 8, 12)     5184        activation_95[0][0]              
__________________________________________________________________________________________________
concatenate_46 (Concatenate)    (None, 8, 8, 330)    0           concatenate_45[0][0]             
                                                                 conv2d_97[0][0]                  
__________________________________________________________________________________________________
batch_normalization_96 (BatchNo (None, 8, 8, 330)    1320        concatenate_46[0][0]             
__________________________________________________________________________________________________
activation_96 (Activation)      (None, 8, 8, 330)    0           batch_normalization_96[0][0]     
__________________________________________________________________________________________________
conv2d_98 (Conv2D)              (None, 8, 8, 48)     15840       activation_96[0][0]              
__________________________________________________________________________________________________
batch_normalization_97 (BatchNo (None, 8, 8, 48)     192         conv2d_98[0][0]                  
__________________________________________________________________________________________________
activation_97 (Activation)      (None, 8, 8, 48)     0           batch_normalization_97[0][0]     
__________________________________________________________________________________________________
conv2d_99 (Conv2D)              (None, 8, 8, 12)     5184        activation_97[0][0]              
__________________________________________________________________________________________________
concatenate_47 (Concatenate)    (None, 8, 8, 342)    0           concatenate_46[0][0]             
                                                                 conv2d_99[0][0]                  
__________________________________________________________________________________________________
batch_normalization_98 (BatchNo (None, 8, 8, 342)    1368        concatenate_47[0][0]             
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 342)          0           batch_normalization_98[0][0]     
__________________________________________________________________________________________________
dense (Dense)                   (None, 10)           3430        global_average_pooling2d[0][0]   
==================================================================================================
Total params: 793,150
Trainable params: 769,162
Non-trainable params: 23,988
__________________________________________________________________________________________________
</pre>
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<div class="input">
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<div class="inner_cell">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># sgd = tf.keras.optimizers.SGD(momentum=0.9,nesterov=True)</span>

<span class="c1"># from sklearn.metrics import roc_auc_score</span>
<span class="c1"># # https://stackoverflow.com/questions/41032551/how-to-compute-receiving-operating-characteristic-roc-and-auc-in-keras</span>
    
<span class="c1"># def auroc(y_true, y_pred):</span>
<span class="c1">#     return tf.py_func(roc_auc_score, (y_true, y_pred), tf.double)</span>
</pre></div>

    </div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[0]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#callbacks</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">datetime</span>


<span class="kn">from</span> <span class="nn">tensorflow.python.keras.callbacks</span> <span class="k">import</span> <span class="n">TensorBoard</span>

<span class="n">filepath</span> <span class="o">=</span> <span class="s2">&quot;weights.</span><span class="si">{epoch:02d}</span><span class="s2">-</span><span class="si">{val_loss:.2f}</span><span class="s2">.hdf5&quot;</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">History</span><span class="p">()</span>

<span class="c1"># tensorboard</span>
<span class="n">tensorboard</span> <span class="o">=</span> <span class="n">TensorBoard</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="s2">&quot;model_logs/</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="p">()))</span>

<span class="n">filepath</span> <span class="o">=</span> <span class="s2">&quot;weights.</span><span class="si">{epoch:02d}</span><span class="s2">-</span><span class="si">{val_loss:.2f}</span><span class="s2">.hdf5&quot;</span>
<span class="n">learning_rate_reduction</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span><span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_acc&#39;</span><span class="p">,</span> 
                                            <span class="n">patience</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> 
                                            <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> 
                                            <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> 
                                            <span class="n">min_lr</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">)</span>
<span class="n">checkpoint_save</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_acc&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;max&#39;</span><span class="p">)</span>

<span class="n">callbacks_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">checkpoint_save</span><span class="p">,</span><span class="n">learning_rate_reduction</span><span class="p">,</span><span class="n">history</span><span class="p">,</span><span class="n">tensorboard</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.preprocessing.image</span> <span class="k">import</span> <span class="n">ImageDataGenerator</span>

<span class="n">train_datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span>
    <span class="n">zoom_range</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
    <span class="n">rotation_range</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
    <span class="n">horizontal_flip</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
    <span class="n">rescale</span><span class="o">=</span><span class="mf">1.</span><span class="o">/</span><span class="mi">255</span><span class="p">,</span>
    <span class="n">fill_mode</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span>

<span class="n">cv_datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">rescale</span><span class="o">=</span><span class="mf">1.</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span>
<span class="n">test_datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">rescale</span><span class="o">=</span><span class="mf">1.</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span>
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<pre>Using TensorFlow backend.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_datagen</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">cv_datagen</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_cv</span><span class="p">)</span>
<span class="n">test_datagen</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># # using tensorboard instance for callbacks</span>
<span class="c1"># from time import time</span>
<span class="c1"># from datetime import datetime</span>
<span class="c1"># from tensorflow.python.keras.callbacks import TensorBoard</span>

<span class="c1"># filepath = &quot;weights.{epoch:02d}-{val_loss:.2f}.hdf5&quot;</span>
<span class="c1"># history = tf.keras.callbacks.History()</span>

<span class="c1"># # tensorboard</span>
<span class="c1"># tensorboard = TensorBoard(log_dir=&quot;model_logs/{}&quot;.format(time()))</span>

<span class="c1"># # best model saving</span>
<span class="c1"># model_check = tf.keras.callbacks.ModelCheckpoint(filepath, monitor=&#39;val_loss&#39;, verbose=1, save_best_only=True, save_weights_only=False, mode=&#39;auto&#39;, period=1)</span>

<span class="c1"># # reduce learning rate</span>
<span class="c1"># reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor=&#39;val_loss&#39;, factor=0.1,patience=3, min_lr=0.0001)</span>
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<pre>W0813 09:44:20.662464 139758047876992 callbacks.py:875] `period` argument is deprecated. Please use `save_freq` to specify the frequency in number of samples seen.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">val_batch_size</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">steps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="o">//</span><span class="n">batch_size</span>
<span class="n">val_steps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_cv</span><span class="p">)</span><span class="o">//</span><span class="n">val_batch_size</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">train_datagen</span><span class="o">.</span><span class="n">flow</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">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">),</span><span class="n">steps_per_epoch</span><span class="o">=</span><span class="n">steps</span><span class="p">,</span>
                    <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span><span class="n">callbacks</span><span class="o">=</span><span class="n">callbacks_list</span><span class="p">,</span>
                    <span class="n">validation_data</span><span class="o">=</span><span class="n">cv_datagen</span><span class="o">.</span><span class="n">flow</span><span class="p">(</span><span class="n">X_cv</span><span class="p">,</span><span class="n">y_cv</span><span class="p">,</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">),</span><span class="n">validation_steps</span> <span class="o">=</span> <span class="n">val_steps</span><span class="p">)</span>
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<pre>Epoch 1/100
624/625 [============================&gt;.] - ETA: 0s - loss: 3.2604 - acc: 0.4124
Epoch 00001: val_acc improved from -inf to 0.38642, saving model to weights.01-3.60.hdf5
625/625 [==============================] - 226s 362ms/step - loss: 3.2598 - acc: 0.4125 - val_loss: 3.6044 - val_acc: 0.3864
Epoch 2/100
624/625 [============================&gt;.] - ETA: 0s - loss: 2.4873 - acc: 0.5839
Epoch 00002: val_acc improved from 0.38642 to 0.48528, saving model to weights.02-2.66.hdf5
625/625 [==============================] - 159s 255ms/step - loss: 2.4870 - acc: 0.5839 - val_loss: 2.6554 - val_acc: 0.4853
Epoch 3/100
624/625 [============================&gt;.] - ETA: 0s - loss: 2.0091 - acc: 0.6760
Epoch 00003: val_acc improved from 0.48528 to 0.57592, saving model to weights.03-2.20.hdf5
625/625 [==============================] - 160s 256ms/step - loss: 2.0088 - acc: 0.6762 - val_loss: 2.1968 - val_acc: 0.5759
Epoch 4/100
624/625 [============================&gt;.] - ETA: 0s - loss: 1.7002 - acc: 0.7203
Epoch 00004: val_acc improved from 0.57592 to 0.68239, saving model to weights.04-1.78.hdf5
625/625 [==============================] - 161s 257ms/step - loss: 1.6999 - acc: 0.7203 - val_loss: 1.7836 - val_acc: 0.6824
Epoch 5/100
624/625 [============================&gt;.] - ETA: 0s - loss: 1.4817 - acc: 0.7533
Epoch 00005: val_acc improved from 0.68239 to 0.74399, saving model to weights.05-1.45.hdf5
625/625 [==============================] - 161s 257ms/step - loss: 1.4818 - acc: 0.7533 - val_loss: 1.4527 - val_acc: 0.7440
Epoch 6/100
624/625 [============================&gt;.] - ETA: 0s - loss: 1.3299 - acc: 0.7657
Epoch 00006: val_acc did not improve from 0.74399
625/625 [==============================] - 160s 256ms/step - loss: 1.3300 - acc: 0.7656 - val_loss: 1.8016 - val_acc: 0.6072
Epoch 7/100
624/625 [============================&gt;.] - ETA: 0s - loss: 1.2035 - acc: 0.7833
Epoch 00007: val_acc did not improve from 0.74399
625/625 [==============================] - 160s 256ms/step - loss: 1.2036 - acc: 0.7833 - val_loss: 1.3199 - val_acc: 0.7306
Epoch 8/100
624/625 [============================&gt;.] - ETA: 0s - loss: 1.1205 - acc: 0.7885
Epoch 00008: val_acc did not improve from 0.74399

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.05000000074505806.
625/625 [==============================] - 161s 257ms/step - loss: 1.1204 - acc: 0.7885 - val_loss: 1.4653 - val_acc: 0.6929
Epoch 9/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.9555 - acc: 0.8318
Epoch 00009: val_acc improved from 0.74399 to 0.79928, saving model to weights.09-1.03.hdf5
625/625 [==============================] - 160s 257ms/step - loss: 0.9554 - acc: 0.8318 - val_loss: 1.0333 - val_acc: 0.7993
Epoch 10/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.8989 - acc: 0.8386
Epoch 00010: val_acc did not improve from 0.79928
625/625 [==============================] - 160s 256ms/step - loss: 0.8990 - acc: 0.8386 - val_loss: 1.3260 - val_acc: 0.7337
Epoch 11/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.8688 - acc: 0.8398
Epoch 00011: val_acc did not improve from 0.79928
625/625 [==============================] - 160s 256ms/step - loss: 0.8691 - acc: 0.8396 - val_loss: 0.9800 - val_acc: 0.7991
Epoch 12/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.8360 - acc: 0.8446
Epoch 00012: val_acc improved from 0.79928 to 0.79998, saving model to weights.12-1.00.hdf5
625/625 [==============================] - 161s 257ms/step - loss: 0.8360 - acc: 0.8446 - val_loss: 1.0038 - val_acc: 0.8000
Epoch 13/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.8073 - acc: 0.8487
Epoch 00013: val_acc did not improve from 0.79998
625/625 [==============================] - 160s 256ms/step - loss: 0.8073 - acc: 0.8487 - val_loss: 1.1439 - val_acc: 0.7578
Epoch 14/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.7871 - acc: 0.8514
Epoch 00014: val_acc improved from 0.79998 to 0.80970, saving model to weights.14-0.94.hdf5
625/625 [==============================] - 161s 257ms/step - loss: 0.7871 - acc: 0.8514 - val_loss: 0.9397 - val_acc: 0.8097
Epoch 15/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.7736 - acc: 0.8508
Epoch 00015: val_acc did not improve from 0.80970
625/625 [==============================] - 160s 255ms/step - loss: 0.7736 - acc: 0.8507 - val_loss: 0.9367 - val_acc: 0.8097
Epoch 16/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.7573 - acc: 0.8535
Epoch 00016: val_acc did not improve from 0.80970
625/625 [==============================] - 159s 255ms/step - loss: 0.7572 - acc: 0.8534 - val_loss: 0.9727 - val_acc: 0.7969
Epoch 17/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.7424 - acc: 0.8548
Epoch 00017: val_acc did not improve from 0.80970

Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.02500000037252903.
625/625 [==============================] - 159s 255ms/step - loss: 0.7422 - acc: 0.8549 - val_loss: 0.9129 - val_acc: 0.7983
Epoch 18/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.6451 - acc: 0.8870
Epoch 00018: val_acc improved from 0.80970 to 0.86008, saving model to weights.18-0.73.hdf5
625/625 [==============================] - 160s 256ms/step - loss: 0.6453 - acc: 0.8869 - val_loss: 0.7349 - val_acc: 0.8601
Epoch 19/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.6214 - acc: 0.8913
Epoch 00019: val_acc improved from 0.86008 to 0.87230, saving model to weights.19-0.68.hdf5
625/625 [==============================] - 160s 256ms/step - loss: 0.6215 - acc: 0.8912 - val_loss: 0.6831 - val_acc: 0.8723
Epoch 20/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.6040 - acc: 0.8932
Epoch 00020: val_acc did not improve from 0.87230
625/625 [==============================] - 159s 254ms/step - loss: 0.6042 - acc: 0.8932 - val_loss: 0.7740 - val_acc: 0.8456
Epoch 21/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.5881 - acc: 0.8952
Epoch 00021: val_acc did not improve from 0.87230
625/625 [==============================] - 159s 254ms/step - loss: 0.5879 - acc: 0.8953 - val_loss: 0.8960 - val_acc: 0.8137
Epoch 22/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.5812 - acc: 0.8980
Epoch 00022: val_acc improved from 0.87230 to 0.87350, saving model to weights.22-0.67.hdf5
625/625 [==============================] - 160s 255ms/step - loss: 0.5813 - acc: 0.8980 - val_loss: 0.6677 - val_acc: 0.8735
Epoch 23/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.5720 - acc: 0.8986
Epoch 00023: val_acc did not improve from 0.87350
625/625 [==============================] - 159s 254ms/step - loss: 0.5718 - acc: 0.8987 - val_loss: 0.6725 - val_acc: 0.8632
Epoch 24/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.5679 - acc: 0.8977
Epoch 00024: val_acc did not improve from 0.87350
625/625 [==============================] - 159s 254ms/step - loss: 0.5679 - acc: 0.8978 - val_loss: 0.7070 - val_acc: 0.8615
Epoch 25/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.5623 - acc: 0.8991
Epoch 00025: val_acc did not improve from 0.87350

Epoch 00025: ReduceLROnPlateau reducing learning rate to 0.012500000186264515.
625/625 [==============================] - 159s 255ms/step - loss: 0.5621 - acc: 0.8991 - val_loss: 0.7965 - val_acc: 0.8350
Epoch 26/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4951 - acc: 0.9204
Epoch 00026: val_acc improved from 0.87350 to 0.88822, saving model to weights.26-0.60.hdf5
625/625 [==============================] - 160s 256ms/step - loss: 0.4951 - acc: 0.9204 - val_loss: 0.6030 - val_acc: 0.8882
Epoch 27/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4688 - acc: 0.9282
Epoch 00027: val_acc improved from 0.88822 to 0.89032, saving model to weights.27-0.62.hdf5
625/625 [==============================] - 160s 256ms/step - loss: 0.4688 - acc: 0.9282 - val_loss: 0.6220 - val_acc: 0.8903
Epoch 28/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4652 - acc: 0.9285
Epoch 00028: val_acc did not improve from 0.89032
625/625 [==============================] - 159s 255ms/step - loss: 0.4652 - acc: 0.9286 - val_loss: 0.6632 - val_acc: 0.8759
Epoch 29/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4564 - acc: 0.9291
Epoch 00029: val_acc did not improve from 0.89032
625/625 [==============================] - 159s 254ms/step - loss: 0.4565 - acc: 0.9291 - val_loss: 0.6051 - val_acc: 0.8899
Epoch 30/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4526 - acc: 0.9296
Epoch 00030: val_acc improved from 0.89032 to 0.89944, saving model to weights.30-0.55.hdf5
625/625 [==============================] - 159s 255ms/step - loss: 0.4526 - acc: 0.9295 - val_loss: 0.5517 - val_acc: 0.8994
Epoch 31/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4445 - acc: 0.9303
Epoch 00031: val_acc improved from 0.89944 to 0.91086, saving model to weights.31-0.54.hdf5
625/625 [==============================] - 159s 255ms/step - loss: 0.4446 - acc: 0.9303 - val_loss: 0.5391 - val_acc: 0.9109
Epoch 32/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4431 - acc: 0.9315
Epoch 00032: val_acc did not improve from 0.91086
625/625 [==============================] - 159s 254ms/step - loss: 0.4430 - acc: 0.9315 - val_loss: 0.6024 - val_acc: 0.8887
Epoch 33/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4406 - acc: 0.9301
Epoch 00033: val_acc did not improve from 0.91086
625/625 [==============================] - 159s 254ms/step - loss: 0.4407 - acc: 0.9300 - val_loss: 0.6307 - val_acc: 0.8817
Epoch 34/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.4316 - acc: 0.9332
Epoch 00034: val_acc did not improve from 0.91086

Epoch 00034: ReduceLROnPlateau reducing learning rate to 0.0062500000931322575.
625/625 [==============================] - 159s 254ms/step - loss: 0.4317 - acc: 0.9332 - val_loss: 0.6349 - val_acc: 0.8808
Epoch 35/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3892 - acc: 0.9489
Epoch 00035: val_acc improved from 0.91086 to 0.91346, saving model to weights.35-0.51.hdf5
625/625 [==============================] - 160s 256ms/step - loss: 0.3890 - acc: 0.9490 - val_loss: 0.5112 - val_acc: 0.9135
Epoch 36/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3741 - acc: 0.9522
Epoch 00036: val_acc improved from 0.91346 to 0.91556, saving model to weights.36-0.51.hdf5
625/625 [==============================] - 159s 255ms/step - loss: 0.3740 - acc: 0.9522 - val_loss: 0.5088 - val_acc: 0.9156
Epoch 37/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3655 - acc: 0.9536
Epoch 00037: val_acc improved from 0.91556 to 0.92578, saving model to weights.37-0.47.hdf5
625/625 [==============================] - 160s 255ms/step - loss: 0.3655 - acc: 0.9535 - val_loss: 0.4690 - val_acc: 0.9258
Epoch 38/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3687 - acc: 0.9518
Epoch 00038: val_acc did not improve from 0.92578
625/625 [==============================] - 159s 254ms/step - loss: 0.3687 - acc: 0.9519 - val_loss: 0.4885 - val_acc: 0.9195
Epoch 39/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3539 - acc: 0.9551
Epoch 00039: val_acc did not improve from 0.92578
625/625 [==============================] - 159s 255ms/step - loss: 0.3538 - acc: 0.9551 - val_loss: 0.5563 - val_acc: 0.8995
Epoch 40/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3540 - acc: 0.9558
Epoch 00040: val_acc did not improve from 0.92578

Epoch 00040: ReduceLROnPlateau reducing learning rate to 0.0031250000465661287.
625/625 [==============================] - 159s 254ms/step - loss: 0.3541 - acc: 0.9558 - val_loss: 0.4972 - val_acc: 0.9164
Epoch 41/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3330 - acc: 0.9620
Epoch 00041: val_acc improved from 0.92578 to 0.92738, saving model to weights.41-0.47.hdf5
625/625 [==============================] - 160s 255ms/step - loss: 0.3331 - acc: 0.9620 - val_loss: 0.4677 - val_acc: 0.9274
Epoch 42/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3233 - acc: 0.9670
Epoch 00042: val_acc improved from 0.92738 to 0.92748, saving model to weights.42-0.46.hdf5
625/625 [==============================] - 160s 256ms/step - loss: 0.3234 - acc: 0.9670 - val_loss: 0.4644 - val_acc: 0.9275
Epoch 43/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3186 - acc: 0.9681
Epoch 00043: val_acc did not improve from 0.92748
625/625 [==============================] - 159s 254ms/step - loss: 0.3186 - acc: 0.9681 - val_loss: 0.4770 - val_acc: 0.9222
Epoch 44/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3136 - acc: 0.9691
Epoch 00044: val_acc did not improve from 0.92748
625/625 [==============================] - 159s 255ms/step - loss: 0.3139 - acc: 0.9689 - val_loss: 0.4715 - val_acc: 0.9267
Epoch 45/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3106 - acc: 0.9690
Epoch 00045: val_acc improved from 0.92748 to 0.92929, saving model to weights.45-0.46.hdf5
625/625 [==============================] - 161s 257ms/step - loss: 0.3105 - acc: 0.9689 - val_loss: 0.4577 - val_acc: 0.9293
Epoch 46/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3056 - acc: 0.9703
Epoch 00046: val_acc did not improve from 0.92929
625/625 [==============================] - 160s 256ms/step - loss: 0.3056 - acc: 0.9703 - val_loss: 0.4708 - val_acc: 0.9260
Epoch 47/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3038 - acc: 0.9715
Epoch 00047: val_acc did not improve from 0.92929
625/625 [==============================] - 161s 257ms/step - loss: 0.3039 - acc: 0.9714 - val_loss: 0.4649 - val_acc: 0.9287
Epoch 48/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.3028 - acc: 0.9706
Epoch 00048: val_acc did not improve from 0.92929

Epoch 00048: ReduceLROnPlateau reducing learning rate to 0.0015625000232830644.
625/625 [==============================] - 162s 258ms/step - loss: 0.3027 - acc: 0.9706 - val_loss: 0.4879 - val_acc: 0.9237
Epoch 49/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2926 - acc: 0.9745
Epoch 00049: val_acc improved from 0.92929 to 0.93239, saving model to weights.49-0.45.hdf5
625/625 [==============================] - 162s 259ms/step - loss: 0.2926 - acc: 0.9745 - val_loss: 0.4547 - val_acc: 0.9324
Epoch 50/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2860 - acc: 0.9757
Epoch 00050: val_acc did not improve from 0.93239
625/625 [==============================] - 161s 258ms/step - loss: 0.2860 - acc: 0.9757 - val_loss: 0.4502 - val_acc: 0.9321
Epoch 51/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2845 - acc: 0.9767
Epoch 00051: val_acc did not improve from 0.93239
625/625 [==============================] - 161s 258ms/step - loss: 0.2845 - acc: 0.9767 - val_loss: 0.4542 - val_acc: 0.9304
Epoch 52/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2836 - acc: 0.9759
Epoch 00052: val_acc improved from 0.93239 to 0.93319, saving model to weights.52-0.45.hdf5
625/625 [==============================] - 162s 259ms/step - loss: 0.2836 - acc: 0.9759 - val_loss: 0.4459 - val_acc: 0.9332
Epoch 53/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2817 - acc: 0.9770
Epoch 00053: val_acc did not improve from 0.93319
625/625 [==============================] - 162s 259ms/step - loss: 0.2816 - acc: 0.9771 - val_loss: 0.4455 - val_acc: 0.9323
Epoch 54/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2796 - acc: 0.9770
Epoch 00054: val_acc did not improve from 0.93319
625/625 [==============================] - 162s 260ms/step - loss: 0.2796 - acc: 0.9770 - val_loss: 0.4443 - val_acc: 0.9328
Epoch 55/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2788 - acc: 0.9771
Epoch 00055: val_acc did not improve from 0.93319

Epoch 00055: ReduceLROnPlateau reducing learning rate to 0.0007812500116415322.
625/625 [==============================] - 162s 259ms/step - loss: 0.2788 - acc: 0.9771 - val_loss: 0.4601 - val_acc: 0.9291
Epoch 56/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2705 - acc: 0.9808
Epoch 00056: val_acc did not improve from 0.93319
625/625 [==============================] - 162s 260ms/step - loss: 0.2706 - acc: 0.9808 - val_loss: 0.4507 - val_acc: 0.9327
Epoch 57/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2692 - acc: 0.9806
Epoch 00057: val_acc improved from 0.93319 to 0.93389, saving model to weights.57-0.44.hdf5
625/625 [==============================] - 163s 261ms/step - loss: 0.2692 - acc: 0.9806 - val_loss: 0.4450 - val_acc: 0.9339
Epoch 58/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2687 - acc: 0.9810
Epoch 00058: val_acc improved from 0.93389 to 0.93440, saving model to weights.58-0.44.hdf5
625/625 [==============================] - 163s 261ms/step - loss: 0.2688 - acc: 0.9810 - val_loss: 0.4426 - val_acc: 0.9344
Epoch 59/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2679 - acc: 0.9816
Epoch 00059: val_acc did not improve from 0.93440
625/625 [==============================] - 163s 261ms/step - loss: 0.2680 - acc: 0.9816 - val_loss: 0.4425 - val_acc: 0.9343
Epoch 60/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2657 - acc: 0.9819
Epoch 00060: val_acc did not improve from 0.93440
625/625 [==============================] - 163s 261ms/step - loss: 0.2657 - acc: 0.9819 - val_loss: 0.4443 - val_acc: 0.9334
Epoch 61/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2647 - acc: 0.9822
Epoch 00061: val_acc improved from 0.93440 to 0.93530, saving model to weights.61-0.44.hdf5
625/625 [==============================] - 164s 262ms/step - loss: 0.2649 - acc: 0.9821 - val_loss: 0.4443 - val_acc: 0.9353
Epoch 62/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2647 - acc: 0.9819
Epoch 00062: val_acc did not improve from 0.93530
625/625 [==============================] - 163s 260ms/step - loss: 0.2646 - acc: 0.9819 - val_loss: 0.4464 - val_acc: 0.9337
Epoch 63/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2637 - acc: 0.9821
Epoch 00063: val_acc did not improve from 0.93530
625/625 [==============================] - 163s 261ms/step - loss: 0.2637 - acc: 0.9821 - val_loss: 0.4413 - val_acc: 0.9349
Epoch 64/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2628 - acc: 0.9822
Epoch 00064: val_acc did not improve from 0.93530

Epoch 00064: ReduceLROnPlateau reducing learning rate to 0.0003906250058207661.
625/625 [==============================] - 163s 262ms/step - loss: 0.2628 - acc: 0.9822 - val_loss: 0.4404 - val_acc: 0.9350
Epoch 65/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2587 - acc: 0.9840
Epoch 00065: val_acc improved from 0.93530 to 0.93560, saving model to weights.65-0.44.hdf5
625/625 [==============================] - 164s 263ms/step - loss: 0.2588 - acc: 0.9840 - val_loss: 0.4414 - val_acc: 0.9356
Epoch 66/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2619 - acc: 0.9828
Epoch 00066: val_acc did not improve from 0.93560
625/625 [==============================] - 164s 262ms/step - loss: 0.2619 - acc: 0.9828 - val_loss: 0.4400 - val_acc: 0.9356
Epoch 67/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2617 - acc: 0.9829
Epoch 00067: val_acc improved from 0.93560 to 0.93570, saving model to weights.67-0.44.hdf5
625/625 [==============================] - 164s 262ms/step - loss: 0.2618 - acc: 0.9829 - val_loss: 0.4406 - val_acc: 0.9357
Epoch 68/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2569 - acc: 0.9845
Epoch 00068: val_acc did not improve from 0.93570
625/625 [==============================] - 163s 261ms/step - loss: 0.2568 - acc: 0.9845 - val_loss: 0.4432 - val_acc: 0.9346
Epoch 69/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2575 - acc: 0.9843
Epoch 00069: val_acc did not improve from 0.93570
625/625 [==============================] - 164s 262ms/step - loss: 0.2575 - acc: 0.9843 - val_loss: 0.4443 - val_acc: 0.9350
Epoch 70/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2590 - acc: 0.9831
Epoch 00070: val_acc did not improve from 0.93570

Epoch 00070: ReduceLROnPlateau reducing learning rate to 0.00019531250291038305.
625/625 [==============================] - 163s 261ms/step - loss: 0.2590 - acc: 0.9831 - val_loss: 0.4449 - val_acc: 0.9347
Epoch 71/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2572 - acc: 0.9838
Epoch 00071: val_acc did not improve from 0.93570
625/625 [==============================] - 163s 261ms/step - loss: 0.2571 - acc: 0.9839 - val_loss: 0.4426 - val_acc: 0.9345
Epoch 72/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2568 - acc: 0.9838
Epoch 00072: val_acc did not improve from 0.93570
625/625 [==============================] - 163s 261ms/step - loss: 0.2568 - acc: 0.9838 - val_loss: 0.4415 - val_acc: 0.9351
Epoch 73/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2574 - acc: 0.9838
Epoch 00073: val_acc did not improve from 0.93570

Epoch 00073: ReduceLROnPlateau reducing learning rate to 0.0001.
625/625 [==============================] - 163s 262ms/step - loss: 0.2574 - acc: 0.9838 - val_loss: 0.4427 - val_acc: 0.9347
Epoch 74/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2582 - acc: 0.9835
Epoch 00074: val_acc did not improve from 0.93570
625/625 [==============================] - 163s 261ms/step - loss: 0.2581 - acc: 0.9836 - val_loss: 0.4429 - val_acc: 0.9350
Epoch 75/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2553 - acc: 0.9844
Epoch 00075: val_acc did not improve from 0.93570
625/625 [==============================] - 163s 261ms/step - loss: 0.2553 - acc: 0.9844 - val_loss: 0.4435 - val_acc: 0.9350
Epoch 76/100
624/625 [============================&gt;.] - ETA: 0s - loss: 0.2547 - acc: 0.9853
Epoch 00076: val_acc did not improve from 0.93570
625/625 [==============================] - 163s 261ms/step - loss: 0.2548 - acc: 0.9852 - val_loss: 0.4422 - val_acc: 0.9354
Epoch 77/100
 62/625 [=&gt;............................] - ETA: 2:18 - loss: 0.2558 - acc: 0.9846</pre>
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<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">KeyboardInterrupt</span>                         Traceback (most recent call last)
<span class="ansi-green-fg">&lt;ipython-input-23-75f3d03121f9&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span><span class="ansi-blue-fg">()</span>
<span class="ansi-green-intense-fg ansi-bold">      1</span> model.fit_generator(train_datagen.flow(X_train, y_train, batch_size=64),steps_per_epoch=steps,
<span class="ansi-green-intense-fg ansi-bold">      2</span>                     epochs<span class="ansi-blue-fg">=</span><span class="ansi-cyan-fg">100</span><span class="ansi-blue-fg">,</span>callbacks<span class="ansi-blue-fg">=</span>callbacks_list<span class="ansi-blue-fg">,</span>
<span class="ansi-green-fg">----&gt; 3</span><span class="ansi-red-fg">                     validation_data=cv_datagen.flow(X_cv,y_cv,batch_size=64),validation_steps = val_steps)
</span>
<span class="ansi-green-fg">/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py</span> in <span class="ansi-cyan-fg">fit_generator</span><span class="ansi-blue-fg">(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)</span>
<span class="ansi-green-intense-fg ansi-bold">   1431</span>         shuffle<span class="ansi-blue-fg">=</span>shuffle<span class="ansi-blue-fg">,</span>
<span class="ansi-green-intense-fg ansi-bold">   1432</span>         initial_epoch<span class="ansi-blue-fg">=</span>initial_epoch<span class="ansi-blue-fg">,</span>
<span class="ansi-green-fg">-&gt; 1433</span><span class="ansi-red-fg">         steps_name=&#39;steps_per_epoch&#39;)
</span><span class="ansi-green-intense-fg ansi-bold">   1434</span> 
<span class="ansi-green-intense-fg ansi-bold">   1435</span>   def evaluate_generator(self,

<span class="ansi-green-fg">/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_generator.py</span> in <span class="ansi-cyan-fg">model_iteration</span><span class="ansi-blue-fg">(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold">    262</span> 
<span class="ansi-green-intense-fg ansi-bold">    263</span>       is_deferred <span class="ansi-blue-fg">=</span> <span class="ansi-green-fg">not</span> model<span class="ansi-blue-fg">.</span>_is_compiled
<span class="ansi-green-fg">--&gt; 264</span><span class="ansi-red-fg">       </span>batch_outs <span class="ansi-blue-fg">=</span> batch_function<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">*</span>batch_data<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold">    265</span>       <span class="ansi-green-fg">if</span> <span class="ansi-green-fg">not</span> isinstance<span class="ansi-blue-fg">(</span>batch_outs<span class="ansi-blue-fg">,</span> list<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold">    266</span>         batch_outs <span class="ansi-blue-fg">=</span> <span class="ansi-blue-fg">[</span>batch_outs<span class="ansi-blue-fg">]</span>

<span class="ansi-green-fg">/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py</span> in <span class="ansi-cyan-fg">train_on_batch</span><span class="ansi-blue-fg">(self, x, y, sample_weight, class_weight, reset_metrics)</span>
<span class="ansi-green-intense-fg ansi-bold">   1173</span>       self<span class="ansi-blue-fg">.</span>_update_sample_weight_modes<span class="ansi-blue-fg">(</span>sample_weights<span class="ansi-blue-fg">=</span>sample_weights<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold">   1174</span>       self<span class="ansi-blue-fg">.</span>_make_train_function<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">-&gt; 1175</span><span class="ansi-red-fg">       </span>outputs <span class="ansi-blue-fg">=</span> self<span class="ansi-blue-fg">.</span>train_function<span class="ansi-blue-fg">(</span>ins<span class="ansi-blue-fg">)</span>  <span class="ansi-red-fg"># pylint: disable=not-callable</span>
<span class="ansi-green-intense-fg ansi-bold">   1176</span> 
<span class="ansi-green-intense-fg ansi-bold">   1177</span>     <span class="ansi-green-fg">if</span> reset_metrics<span class="ansi-blue-fg">:</span>

<span class="ansi-green-fg">/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py</span> in <span class="ansi-cyan-fg">__call__</span><span class="ansi-blue-fg">(self, inputs)</span>
<span class="ansi-green-intense-fg ansi-bold">   3290</span> 
<span class="ansi-green-intense-fg ansi-bold">   3291</span>     fetched = self._callable_fn(*array_vals,
<span class="ansi-green-fg">-&gt; 3292</span><span class="ansi-red-fg">                                 run_metadata=self.run_metadata)
</span><span class="ansi-green-intense-fg ansi-bold">   3293</span>     self<span class="ansi-blue-fg">.</span>_call_fetch_callbacks<span class="ansi-blue-fg">(</span>fetched<span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">-</span>len<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">.</span>_fetches<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span><span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold">   3294</span>     output_structure = nest.pack_sequence_as(

<span class="ansi-green-fg">/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py</span> in <span class="ansi-cyan-fg">__call__</span><span class="ansi-blue-fg">(self, *args, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold">   1456</span>         ret = tf_session.TF_SessionRunCallable(self._session._session,
<span class="ansi-green-intense-fg ansi-bold">   1457</span>                                                self<span class="ansi-blue-fg">.</span>_handle<span class="ansi-blue-fg">,</span> args<span class="ansi-blue-fg">,</span>
<span class="ansi-green-fg">-&gt; 1458</span><span class="ansi-red-fg">                                                run_metadata_ptr)
</span><span class="ansi-green-intense-fg ansi-bold">   1459</span>         <span class="ansi-green-fg">if</span> run_metadata<span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold">   1460</span>           proto_data <span class="ansi-blue-fg">=</span> tf_session<span class="ansi-blue-fg">.</span>TF_GetBuffer<span class="ansi-blue-fg">(</span>run_metadata_ptr<span class="ansi-blue-fg">)</span>

<span class="ansi-red-fg">KeyboardInterrupt</span>: </pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># https://stackoverflow.com/questions/50127257/is-there-any-way-to-stop-training-a-model-in-keras-after-a-certain-accuracy-has</span>

<span class="c1"># callbacks = [</span>
<span class="c1">#     EarlyStoppingByAccuracy(monitor=&#39;accuracy&#39;, value=0.98, verbose=1),</span>
<span class="c1">#     ModelCheckpoint(kfold_weights_path, monitor=&#39;val_loss&#39;, save_best_only=True, verbose=0),</span>
<span class="c1"># ]</span>
<span class="c1"># model.fit(X_train.astype(&#39;float32&#39;), Y_train, batch_size=batch_size, nb_epoch=nb_epoch,</span>
<span class="c1">#       shuffle=True, verbose=1, validation_data=(X_valid, Y_valid),</span>
<span class="c1">#       callbacks=callbacks)</span>
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<h2 id="Loss-And-Accuracy-Plots">Loss And Accuracy Plots<a class="anchor-link" href="#Loss-And-Accuracy-Plots">&#182;</a></h2>
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<h4 id="Download-tensorboard-logs">Download tensorboard logs<a class="anchor-link" href="#Download-tensorboard-logs">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>zip -r /content/logs.zip /content/model_logs
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<pre>  adding: content/model_logs/ (stored 0%)
  adding: content/model_logs/1565714363.7803748/ (stored 0%)
  adding: content/model_logs/1565714363.7803748/train/ (stored 0%)
  adding: content/model_logs/1565714363.7803748/train/events.out.tfevents.1565714551.4683528c7227.137.14507.v2 (deflated 95%)
  adding: content/model_logs/1565714363.7803748/validation/ (stored 0%)
  adding: content/model_logs/1565714363.7803748/validation/events.out.tfevents.1565714781.4683528c7227.137.28198.v2 (deflated 65%)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
<span class="o">!</span>unzip ngrok-stable-linux-amd64.zip
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<pre>--2019-08-13 20:42:35--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
Resolving bin.equinox.io (bin.equinox.io)... 34.196.237.103, 52.45.111.123, 52.201.75.180, ...
Connecting to bin.equinox.io (bin.equinox.io)|34.196.237.103|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 13607069 (13M) [application/octet-stream]
Saving to: ‘ngrok-stable-linux-amd64.zip’

ngrok-stable-linux- 100%[===================&gt;]  12.98M  31.6MB/s    in 0.4s    

2019-08-13 20:42:35 (31.6 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [13607069/13607069]

Archive:  ngrok-stable-linux-amd64.zip
  inflating: ngrok                   
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">LOG_DIR</span> <span class="o">=</span> <span class="s1">&#39;/content/model_logs&#39;</span>
<span class="n">get_ipython</span><span class="p">()</span><span class="o">.</span><span class="n">system_raw</span><span class="p">(</span>
    <span class="s1">&#39;tensorboard --logdir </span><span class="si">{}</span><span class="s1"> --host 0.0.0.0 --port 6006 &amp;&#39;</span>
    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">)</span>
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<span class="n">get_ipython</span><span class="p">()</span><span class="o">.</span><span class="n">system_raw</span><span class="p">(</span><span class="s1">&#39;./ngrok http 6006 &amp;&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>curl -s http://localhost:4040/api/tunnels <span class="p">|</span> python3 -c <span class="err">\</span>
    <span class="s2">&quot;import sys, json; print(json.load(sys.stdin)[&#39;tunnels&#39;][0][&#39;public_url&#39;])&quot;</span>
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<pre>https://aacd44cd.ngrok.io
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<h3 id="Train-and-Test-Loss">Train and Test Loss<a class="anchor-link" href="#Train-and-Test-Loss">&#182;</a></h3>
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iVBORw0KGgoAAAANSUhEUgAABUQAAAFRCAIAAAAzSjA7AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAHVoSURBVHhe7d17dBzneed5/z0TTXzyR846Z2ftP3aczZnNOnZ2xzPOyZHPOpNMYtlKZke2Y0WSM7FlxYkSwY6ti617ZFOSrTtt05ZNURJpW6IokuZVvIIERQIEAQgESIEkwAsuJEAQAEECBEFCvU/386JQqG40uqv7edld+H5OHZ5+qxtd6Gb3D+9TVf30+1IAAAAAAKCqUMwDAAAAAFBlKOYBAAAAAKgyFPMAAAAAAFSZa1/MHzlyZNGiRfKvGxdm3bp1P/rRjy5cuODGAAAAAAAsGNe+mK+rq3vf+94n/7pxYR555JE/+7M/GxwcdGMAAAAAABYMinkAAAAAAKoMxTwAXANHn/mERN8nnjnqxgAAAEAxchfzJ0+eXLx48aJFi5YuXRoUzEeOHHn66ad7e3vffvvtJ598Uq7duXPn1NSUXquCH5R/5bJbm/Hee+8dOnToqaeeitxtUMwH165Zs2ZiYkKvnUt2MZ9n03Jvb731llwl1q1bd/HiRXdF3qsAwE5Zinm6hwAAACxY0WJeSu5f/OIXv/Vbv3XDDTfcc889v//7v//xj39cymy5SqvuT3ziEx/96Ee/+tWvXn/99TKUGvjKlSvhH/zUpz717W9/+4//+I8/+MEPvvHGG7JerpXbPP/887/927/9l3/5l3K3cp8f+chHGhoagrv9m7/5m9/7vd/7p3/6J/lxGT7xxBNXr16Va+cSLubzb1rq86997Wuy5u8z5MJNN9105syZ/FcBwBw23C4hdfsGN7qmOEcJQDIVELTszQSAaDHf1tYmRfVLL72kh9ylsr3xxhulxp6YmNCqW+aOethc6nOp5D/wgQ/U19fL8J133pEfDGp7uc13v/tdqdjfffddGe7fv/8//If/sHz5ci2wR0dHv/zlL3/pS1+SclrvVmppWal3++ijj37yk5/s7e2V4VzCU9j8m16zZo1suqmpKf1jqdSBAwfkxi+++GL+qwBgDhTzAGCsgKAlAAEgWsw/9dRTf/Inf9LX1+fGqdSyZcs+8YlPnDhxQqtu+dddkUp1dXV9/OMflx+Ry1I/RypwvfbHP/6xFPBSn/+3//bfzp49665LpWpra7/whS8cP35c71aG7opU6tVXXw1K8bmEEzzPpuXyL3/5y3DFPjU1NTQ0pPtx81wFANn03PgQd5p8Zr1MOzPTTxHMQKdXqFkT08hUNT1Mj0I/Mf85+MxlASRTJCFzIQABYFYxPz4+/tWvfvWP/uiPpPZeNO0f//EftbTOLubHxsa+/OUv33vvvVIAyw8KuQd33fS1NTU1IyMj2dcGsu+2qGJef+fInQebvnz58pkzZ2666aYPfvCDd9555+bNm6VcdzfKnHcw11UAMIccc8ygyA+vTq8M1eOZH4uOZxfzadO30Lv8u7xT2RxzWbqHAEgCinkAKECOYl6K2z/5kz/505DPfe5znZ2d2VW33v5rX/ualMGZgnpWRR2U2efOndML4WsDFsV8ZKX8K7X6bbfdJg/tuuuu+853vhNMVfNcBQC5zFnMz3MkPXOjmdvkKuZn3WtmzVd+TfcQAAtPrGKevZkAFppZxfzly5dramq+/OUvj42NuVUh2VW31PCf/vSnJUz1B2+55Rb93LuKXFv43RZVzOfftBtPu3Llytq1a2Wq+vzzz7tV0/JcBQAhcxXz89TyBRTzs+/h6DP/7n3v+8OH9rhhLuG5LN1DAFSUTMhFgnF28GVGAfZmAkCxop+ZlwmcTONkUujGmWPXev65Vt0bNsxkbX19/Qc+8AGZCMplSczID27btk0ycevWrXI5uz6XNXN9FL+oYl4u59n0pUuXJKPDpf758+e/8IUvyI/nuUqHAJDL7MloxlzFvB6xDyuqmP+373vfR+/b7Ia5hJOQ7iEAKksm5WblWij30vEYui5628z4b16hFzIA5BMt5gcGBv7qr/7qj/7oj7Zv3y75KCF46623fu5zn5PLWnVLMm7cuFGGUsn/6Z/+qdxYfkR+UCZ/N910k6ypra2Va6WclkmkVMt6tpLerWhubpbLa9eulfvR758rvZjPv+nVq1f/9m//tuR7d3f3mTNnfvazn0lqy8r8VwHAHEKz0Wk5i/no3LT4I/OFF/ORzxYpuocAuKai0ZgjPQO5EvLzPyl0KsjeTAALU7SYF6dPn/7KV75y3XXXSYzKv3JZ1sh6rbqfeuqpG264QS4IuXDkyBH9KXHu3Lm7775bf/C3fuu37rvvPlnjrkulTp069bd/+7d6rcwaf/jDH05kvuKu9GJe5Nm05PIbb7zx+7//+3KV+N3f/d0VK1bojts8VwHAHHJMR3MV81k3817MR1bKv3QPAeBTJvaCnMuOuZASivlI1in2ZgJYCHIU80qyT/PRjUNV93vvvTeSoZ9EitAflH/deDa5QwnKQgpmuRPJaNliRJ46P8+m9XeWTUsB71ZNy3MVAGTJUbnnKuYz62bPYkVkqlquYl5yj+4hACpOuJrPSjlNybBIQpZSzEdWyr/szQSQPHMW89myD6GbmpiY2Lt3744su3fvDs9WAcC3mRmom3nmKuZFeKYq16aHkalquYp5uUz3EACVJxODmaSLhFwmAkPjSIwWU8yzNxPAglW5xTwALHB0DwFQ9dJluVTzuWr5md2ZJRXzcpm9mQAWpiKK+SNHjshsL/wheQCAHbqHAKh+mWr+di3pA3riUrAmU7uXUMyzNxPAwlREMQ8A8ExmpTJbzcxyZ6F7CIBq4Sr1cC2fpvW8kio+PYxdzAv2ZgJYgCjmAaBy0T0EwILF3kwAyI9iHgAAABWHvZkAkB/FPAAAAAAAVYZiHgAAAACAKkMxDwAAAABAlaGYBwAAAACgylDMAwAAAABQZSjmAQAAAACoMhTzAAAAAABUGYp5AAAAAACqTFUW8yMjI8eznASALC41AAAAgGRJSDG/devWNWvWuMm7F11dXUuWLHn55ZfdWJw4frqhVpaG/c1vrd+cuutTsnzymz97391b9rQdc7cp2fbt21taWtzAl7feequ9vd0NvJCnd9OmTR0dHW7shWxONiqbdmMv5ImVp9cNfJGXkLyQ3MCXxsbG3bt3u4Eve/fulX9dcAAAAAAJkpDT7N/OcAMvJicnpZhfsWKFG6tTHbL0dfc2tndpMX/Xv3xXivkdnUPuBiU7dOjQ2bNn3cCXd9555/z5827gxdWrV5ubm8fHx93YC9mcbFQ27cZeyBMrT68b+CIvIXkhuYEvfX19x44dcwNftKR3AwAAACBBKOZjmqeY7zk/dtefSTH/0DcelGJ+aWOvu0HJKObtUMybopgHAAAAyohiPqbcxXx/txTzo93HpZgfffxrUszv/PrfSjH/4JZOd4OSUczboZg3RTFfdnQPAVAglxoAgGShmI9p3mJ++NlvSTG/71t/J8X8ba+1uRuUjGLeDsW8KYr5sqvw7iE9hw/273HdQ+SCu7ZM6B5ih+4hpugeUi7szQRQIJcaCUUxH1OeYn6su0uK+d5fLdFZrBTz1y9pdDcoGcW8HYp5UxTzHlRKEmY+cJQ6P5g6d1pjMLVzpbuqTEhCOyShKZKwXCp0b2ZGw6HOdY0dssjl728+KPNAWeiFXCz2Zppib2ZiUMzHlHsKK5PXzCw2XcyveTUo5j+0qM7doGRMYe0whTXFFNaDSknCoJgXWsxvXJa5omxIQjskoSmS0E6lBGAq1Xt+QuaBssjlHZ1DWszTC7lYBKApAjAxKOZjmreYP3bggM5i9dvp3A1KRoLbIcFNkeAeVEoSZs5RSv8rnrg9nYTP12SuKBuS0A5JaIoktFMpAZhKDY5NajE/Nnn1+NC4FvP0Qi4WAWiKAEwMivmYcif46JAW8809Ix2Nrpj/y28ulhCXNHe3KQ0JbocEN0WCe1ApSRgu5qWMp5iPiyQ0RRImTKUEoEwGJ65qMS8XZKjFPL2Qi0UAmiIAE4NiPqbcCT4xrsV8R/fZQ/0XtZjXb6cr1+lVJLgdEtwUCe5BpSShFvNnTqQvr1qsSZi5omxIQjskoSmS0E6lBGComB8evyLDDy7aLfNAeiEXiwA0RQAmBsV8TPMW8xLiqXs+K1PYZ75xt4T407vL8zIiwe2Q4KZIcA8qJQmnP3CUvrxxGcV8bCShKZIwYSolADO0mO89PyGXr1/SKPNAeiEXiwA0RQAmBsV8TLkTfHJCp7DHu8+ki/nM+aXl/ap5EtwOCW6KBPegUpIwXMwfbXHFvFwoH5LQDkloiiS0UykBmBEu5m97rU3mgfRCLhYBaIoATIzKKuaHh0duveWOj330elnkggzdFfOpoATPTGH7unslxMdeeVKmsN3f+P8kxG9cVp6JLAluhwQ3RYJ7UClJON09JDU1NVPMt5ZtIitIQjskoSmS0E6lBGBG6+kLMg88Nph+8z64pVPmgbLoVaUjAO0QgKYIQAsVVMxrJb969XodyoXC6/kKSvBQMT+67mWdxUqCl+v0KhLcDgluigT3oFKScPoDR+kLQov5sn47HUlohyQ0RRLaqZQAzOg4OybzQPlXLi9t7NVinl7IRSEATRGAiVFBxXxjY0u4etfaXlbqML8KSvC+LpnCDnafkhAfbqzTWewnv/mz9z+0w92gNCS4HRLcFAnuQaUkYaSYz3QPSXfCKx+S0A5JaIoktFMpAZhxbHBc5oHpXsgGXzVPANohAE0RgBYq9zPz1VrMZ9o4j3YflxDv7ejQYv5L//KUhLi7QWlIcDskuCkS3INKScLp7iGpi5ncMPh2OpLQDkloiiS0UykBmNF7fkLmgbLI5eCr5umFXBQC0BQBmBiVW8xLGf/pv/hcV1fmy43mU0EJHi7mz09oMa/fTtfcO+puUwIS3A4JbooEL5zuykxA95B0Jzyx/PF0Ej5yc+aK8iAJ7ZCEpkhCOxUUgLOLeaHFPL2Qi0IAmiIAE6NCi3mp4aWSDz4/HzGVZU+GG3gxMTGhCe7GgbN9UyffvXKyo+HUyOH+C6knviKzWG1ov/3ooLtNCdra2vr7+93Al5aWluHhYTfwQv5ANjU1Xbx40Y29kM3JRmXTbuyFPLHy9LqBL/ISkheSG/jS09Nz5MgRN/Dl+PHjVZfgWskno3uIK+YNvp2Ouawd5rKmmMvOKwl7M+VRjF/RYn5sMv1W0q+apxdyUQhAUwRgYlRiMa+V/HPPLnHjLCMjI42zbdiw4dVXX5W3nzeyUUnwpUuXuvG0tj21HVt/LcuKbftX72oc+cE/yRT2eM1/lxD/l1++7W5UgtWrV+/cudMNfFm5cmVdXZ0beHHgwIHXX3993759buyFbE42Kpt2Yy/kiZWn1w18kZeQvJDcwJetW7euW7fODXzZtGlT1SV4krqHpM6dTl9udd1DyvjtdMxl7TCXNcVcNj9NvKrfm5lKjU5c1WJeLsiwvF81TwDaIQBNEYAWKq6Yn7eSFxez7Nq1a8eOHWMeyRtPEnz58uVuPG387JlLR96RpelYb8Pxs5NrX9RZrIT4fRvedTcqQVNT06lTp9zAl/r6+jNnzriBF6Ojo3v37h0cHHRjL2RzslHZtBt7IU+sPL1u4Iu8hOSF5Aa+dHZ2trW1uYEv7777brUneLUW85kPHKX/FT3HXDFfvylzXRkwl7XDXNYUc9n8ErI3M5Uam3TF/PD4FRnqV83TC7koBKApAjAxKquYz392fR4VlODTbZw7us9KiAeHpD75zZ+V5fQqEtwOCW6KBI9HZrHV2z3EFfNCi/nyfTsdSWiHJDRFEhaleot5ocV8un1Sub9qngC0QwCaIgAtVFAxr5Edo5IXFZTgU1NazOtXzQeHpL70L0+V5fQqEtwOCW6KBI+h2ruHTHW7LgnaPST1fI0OS0f3EDuyObqH2KF7SFGqdW9mRriYD75qnl7IhWMqaIqpYGJUUDEvE1btdxJeCqztKyvBQ8V8+rNSmWL+oW88WJbTq0hwOyS4KRK8WPN+5qgquofoULuHXP7OTTosHd1D7NA9xBTdQwpXxXszM9pOj2ovZLm8/eigFvP0Qi4cezNNsTczMSqxAV4MlVXMh76dLl3MP3G7zGK1ob27QQko5u1QzJuimC/KvJW8kDlHRAV2DxkbGZJh0D1Ery2dTLboHmKE7iGm6B5SoKrem6lW72rUXshyeX1dugGeLPRCLhx7M02xNzMxKOZjmreYH+vukmJ+cGwy9eL9MoXVhvaln15FMW+HYt4UxXzh8h+PyqOCknC6e0j6gpjuHlKuhvYkoR2S0BRJWIhq35upDvacqzvW33D8rA61mKcXcuHYm2mKvZmJQTEfU75i/vygzmLdZ6Wmv2NZQnxH55C7TVxMYe0whTXFFLZACese4r5qvtwN7UlCOyShKZJwXknYm5khM0CZB8qiwzJ+1TwBaIcANEUAWqCYjylfgo8O6Sy2tWcoXcxPH5L6g2+9+vTuUl9MJLgdEtwUCV4gmcJGWofIUuC8trKSMFzMCy3my9TQniS0QxKaIgnzS8jezIxIMX99+b5qngC0QwCaIgAtUMzHlC/BQ99O13F2LDgk9ZffXPzglk53m7hIcDskuCkS3IPKSsLIt9Nluoeknq9xw9KQhHZIQlMkYX7J2ZuZSg2PX9FiPt0+qaxfNU8A2iEATRGAFijmY8qX4JMTWswf7z6TLuZFpph/6BsP/sGz+yTcMzeKiQS3Q4KbIsE9qKwk1GL+zPR3SmW6h6QeudkNS0MS2iEJTZGEdiorAFMpqeHDxXwZv2qeALRDAJoiAC1QzMeUP8G1mO/r7m09fSE9fPiLMotd/fU7JMSlni+lDR4JbocEN0WCe1BZSTjdPcQNp7uHuGFpSEI7JKEpktBOZQVgKjU26Yr5dC/ksn7VPAFohwA0RQBaoJiPaZ5ivq9LprDD3SclxNPD52tkCnvy27dqjr//oR2xo5wEt0OCmyLBPaisJJzuHpK6kp7IzjS07ynDy4AktEMSmiIJ7VRWAGZoMZ9un5RK7egc0kkgvZALRACaIgATg2I+pnkSPPRV8+nh9CGpf17bEdTzb7YNZG5aHBLcDgluigT3oLKSMPLtdEFDe6nqS0YS2iEJTZGEdiorADPCxfzxoXGdAdILuUAEoCkCMDEo5mOaJ8GHB2QKe/XUUQnx9Gel6je5WWzPseA8q9tea3M3LgYJbocEN0WCe1BZSTjdPSR1cTo9NAbL0dCeJLRDEpoiCe1UVgBmtPdflHmga58kc+7M9I9eyAUiAE0RgIlBMR/TPAke+qr5dDF/tMXNYjOHpPTrSf7g2X1626KQ4HZIcFMkuAcVl4RazAffTpfpHpLuhFcyktAOSWiKJLRTcQGYSkkZHy7m9avm6YVcIALQFAGYGBTzMc2T4OMXdBbb0X1WG5+ED0mV0tGUBLdDgpsiwT2ouCTMdA9Jne11w0z3kNSTt7thCUhCOyShKZLQTsUFYObUeinmXS/kVOrGZS06/aMXciEIQFMEYGJQzMc0T4JPf1j0WPeAflYqfEjqzbYBTfMYTVBIcDskuCkS3IOKS8LIV82Xr6E9SWiHJDRFEtqpuABMpWQGKMW8LG48/W3zstALeV4EoCkCMDGqspgfGRk5Pdv27ds3bNjQ71Fvb68k+CuvvOLGWc69s0+Wtnfamo/1yHBi8bdkCnv1wS/I5b0dpzTKH93YnrltEerq6g4fPuwGvuzcuVPee27ghbzb5f9U3ntu7IVsTjYqm3ZjL+SJlafXDXyRl5C8kNzAl4MHDzY0NLiBL01NTclO8IiKm8tmuofMtK8PdQ9xa+JiLmuHuawp5rJ2Ki4AcxXzgl7IBSIATRGAiVGtxbxUI2GbN29+/fXXOzySN4Ak+LJly9w4y4k9207WbWnYs2dTQ7sMB199SmexR1tbVu05qDl+68v79MaF27RpU319vRv4sm7dOimK3MAL+T9du3Zta2urG3shm5ONyqbd2At5YuXpdQNf5CUkLyQ38GXPnj3btm1zA19qa2sXwhQ2UHFz2chXzc/uHlIK5rJ2mMuaYi5rp+ICMJUanXBfNZ9unxRCL+RCEICmCMDE4DT7mOZN8ODb6Vzjk+lZ7NUjLRLrH3uuXkL8+iWNmZsWgQS3Q4KbIsE9qLgknO4e4r6dTmgxX3JDe5LQDkloiiS0U3EBOHcxL+iFPC8C0BQBmBgU8zHNX8yf7ZUp7OXuY819mc9EyaQ2M4sdXfeyxPpfr3AH5zM3LQIJbocEN0WCe1BxSRh81bwEoCpTQ3uS0A5JaIoktFNxAZhKXb46pcW864UcQi/keRGApgjAxKCYj2n+Yj707XRuTWYWO/yTh2VNEOLFtj8hwe2Q4KZI8LKrou4hZzvf1WG4e0gp6B5iRzZH9xA7dA+xU4lTwVRKi3nXCzmEXsjzYipoiqlgYlDMxzR/gl88r8V8a8/Q2GQmFF68X2axvQ9/RWJ9RfNpDfGljdNf2lQYEtwOCW6KBC+7Kuoe0nnA9QcJdw/RNfHQPcQO3UNM0T2kXKpib6Z4q/WELNoLOYxeyPNib6YpeQmxNzMZKOZjmr+Ynz6/tKP7rPus1M6VMoU9VfNXje1dUs9riD+4pTNz60JRzNuhmDdFMe9BJSZh5Nvpgh54cqEEJKEdktAUSVguVbE3U2xqaF+156D2Qg6jF/K85P+UvZl22JuZGBTzMc0/hb0yqcX8qe7T/Rcup9dkZrEdNTc17twlxfyHn9wjIV5sDzymsHaYwppiCutBJSZhpntIqq/LDae7h5TYA48ktEMSmiIJ7VRiAKZSHWfHZMrneiFPuzr1nqykF3J+BKApAjAxKOZjKiTBtZjv6+6d+ayUFvOvrZAQv+GlZgnx9z+0w11VGBLcDgluigT3oBKTMPLtdKIcPfBIQjskoSmS0E4lBqDMBEcuyZTP9UKepl3u6YWcHwFoigBMDIr5mAoq5s+ckCnszLfTiSdub6y5ufEHD0mIf31dh4b48aEicooEt0OCmyLBPajEJJzuHpI+WUlluoekHrnZDWMhCe2QhKZIQjuVGICpVO/5CZnyyeLGGcPjV2QNvZDzIwBNEYCJQTEfU0HFfObDomPdXTPF/PLH08X8vekeeEvqezTE32wbcNcWgAS3Q4KbIsE9qMQkDL6dLviq+Uz3kPRy7rRbUzyS0A5JaIoktFOJAThdt8vieiFnaIVPL+T8CEBTBGBiUMzHVFAxn/XtdKNb30gX87LE7YFHgtshwU2R4B5UYhJOdw9JjU5//VLQA6+1zq0pHklohyQ0RRLaqcQAnD6jXhbXCzlDz72PNw8UBKAdAtAUAWiBYj6mgor58Qs6i+3odpk7eqjZFfOZHnj/63d3SYjfuKyIrs4kuB0S3BQJ7kGFJqEW8+cH3VBoMV9CDzyS0A5JaIoktFOZAai97mRxvZAztCueLPRCzoMANEUAJobvYn54eOTOO+/u6jrhxllWr17/sY9eL8un/+JzeW4WUZkJHm5or3tk0ydWZYr59jeWS4h/6qcHJMQ/tKiIw1MkuB0S3BQJ7kGFJmGme8jMt9OJJ25PF/PP17hh8UhCOyShKZLQToUGYCqldftML+RQMU8v5DwIQFMEYGJ4Lealkr/1ljvyVOnPPbtEbiA3k8tS1QeX51WxCf5ez1GZxfZ398wU89+9W4r5jqe/IyH+j2velRCXZXj8it5+XiS4HRLcFAnuQYUmoX7VvJT0geWPp4v5e290w+KRhHZIQlMkoZ0KDcBUqr3/okz5wt9Op5W8LPRCzoMANEUAJoa/Yr6xseVjH73+llvukCVnMS8rpc6Xm+lQK/9gmF/FJvh7/adkFjvafVz3yB4bHG988fn2ms933HubhPjLTX0a4js6pz9NOh8S3A4JbooE96BCkzD72+lK7oFHEtohCU2RhHYqNABzfdV8UMzTCzkPAtAUAZgYnor5S5cu3Xffo1KZS8U+VzEv10r1XuCh+IiKTfDU8IDOYrWYTwf65s0dNTf11tzY2NQuNbyG+G2vtenN50WC2yHBTZHgHlRoEk53D5lpaF9yDzyS0A5JaIoktFOhARhqd6fDoCWeLjoPpBdyNgLQFAGYGL4/M5+nmF+9er0U/HW79ybnM/Ni+juWT/Snd1I0941KDX+85r+ni/nNmyXEr1/SqDle4LeMkuB2SHBTJHjY8ILsHjLT0F5oMR+3Bx5JaIckNEUS2qnQAMz6qvlIMU8v5LkQgKYIwMSorGJeZq7PPbskGBY+i63YBE9NTugs9vTp9AlUGtxSyffXfLbxlZ/J5WODY1rM/8Gz+/Qn8iPB7ZDgpkjwgH6GaEF1D0n1HEsn4XDoPNLSeuCRhHZIQlMkoVhYezPl8U5/1fxM+6TMUD9LTy/kuRCApgjAxKi4I/OXLl3SoZ6ZH9T2YSMjI62zbdy48Ze//GW7R7JRSfCXXnrJjefWWbtJlr21uw680/barhZZuhfdOXrXn7/9cI1cbmhp+9qKfVrP372ywf3M3NavX79nzx438GXNmjUNDfP/bmUkT++bb77Z1NTkxl7I5mSjsmk39kKeWHl63cAXeQnJC8kNfKmtrd28ebMb+LJt27ZKS/CF2T3E9cALN7QvrQcec1k7zGVNMZddgHszg0PxkWJeP0tPL+S5EICmCMDEqNxiXkigz1XMD862c+dOqRPOedTf3y8J/sorr7jx3AYOt55v29/T9s7J02e3t3fLMvD6T6SYb6y5eXvzEVnZ1Tfw7x+rlRz/zQe2y2X3Y3PYt2/f0aNH3cCX3bt3nzhxwg28GBgYkNqvt7fXjb2QzclGZdNu7IU8sfL0uoEv8hKSF5Ib+CJ/Mw4cOOAGvrS2tlZUgtM9xA1F/SZ3pn1PnL/rzGXtMJc1tcDnsgtzb2bwVfMzvZAzh+W1mKcX8lwIQFMEYGJUUDEvK++88+5gCquzXqnwdZhfxSa4uDzQJ1PYq6eOHh9Kx7cslzuax+76s/QXzu/cpTtiC++ER4LbIcFNkeBheZJQd2smtXtI+pNHSmp4Lealqi8eSWiHJDS1kJNw4e7NDD5lGfRCzhyW10P09EKeCwFoigBMjAoq5kX4ULzMaAufxVZygk8Mn9NZbOdAOsplSa+961PpYv6Vn2myixuXtWia5++ER4LbIcFNkeBheZJQok9q+KQl4XT3kHRVH9BiftViNywGSWiHJDRFEor8AZjAvZmzv2o+3Qu55/zxofHgfHt6IedEAJoiABPjGhfz2YffZQqbsARPfxtTZhZ7rHtAItsl9fM16WL+e3cHxbzEukZ5/q8nIcHtkOCmSPCwSBKG6Vw2+MCRhuRcHzhqna3yu4ccaZhp+TH2+FelmL/06K1uXAy6h9ihe4gpuoeI/AGYwL2Zs79qXgt4mf71X7isl+mFnBNTQVNMBRPDdzFvpJITPC1TzPd19wZRntq4rLXmC1LPH+8bzNwi7YOLdkuU5/96EhLcDgluigQPK7yYFzKvnauYd11DplVy95CRIwfPt+2Xf9343LnxVT/Wg/NDvafcqoLRPcQO3UNMSfbSPaTwAEzM3sxtDa3aAjnohbyn6WBDi7tML+Sc5Ollb6Yd9mYmBsV8TEUV8xM9x6WYH+0+ntn/milQj7Z01NwkxXxH3e7MTdL0TPv8X09CMW+HYt4UxXxYnrmsrExk95DUudPp3ZrhdndHW9yZ9q1FfCeTIgntkISmSEJReDEvkrE3s/3kGW2BHPRClgvhy/RCzsbeTFPszUwMivmYiirmz/f2yCz26qmjemKVrnTF/Iqf6lA8uKVT98vm+XoSprB2mMKaYgoblmcuK8KT18ScZZoaHdJzlFJXJt0aEfdj8yShHZLQFEko8gSgrEzk3szgq+ZneiFfnRqbdF9ZRy/knAhAUwRgYlDMx1RUMT860K+z2Oaekf4Ll3Xl8Wfuk2K+9Xvf1KEIcjzP15OQ4HZIcFMkeFhkLps9YZViPqndQ1LjF9wa8XxNuph/8nY3LBhJaIckNEUSikgARiRyb2bwVfP64XlZdL1ephdyTgSgKQIwMSjmYyqqmB8cGtFZbEf3WQl0Xdm75tV0D7yam4Op7fD4FQ3xPD3wSHA7JLgpEtyDCk9CV8yfn2kUktq4zB2cD1f4BSAJ7ZCEpkhCESnmF8TezOm6XZegVtchvZBzIgBNEYCJQTEfU1EJfuHSZZ3F9nX3zhTz7zS5Yj70edHffHCHhHieM6xIcDskuCkS3IMKT8LUmRPpJOzvdkMR92PzJKEdktAUSWinwgMwqORlcb2QU6nW0xdkKDW8DgW9kAMEoCkCMDEo5mMqKsGlgL/cfUxmscPdMy+m9AeoMsX82MofulWplH7XaJ7vJiHB7ZDgpkhwDyo8CXP0wBNazBf5sXmS0A5JaIoktFPhARicYC+L64U8+yvrFL2QAwSgKQIwMSjmYypuCptKDXafklns1VNH3Vg/QPWDh6SYH33ia25VqAeeG2chwe2Q4KZIcA8qPQnPD6aLeVmmptwaEetj8yShHZLQFElop8IDMFzMz/RCzirm6YUcIABNEYCJQTEfU7HF/PHuM24WO+kSPF3Mv7YiXczf9eep8QvpYc/579ee1BCfqwceCW6HBDdFgntQ6UkY9MCTC4FYH5snCe2QhKZIQjsVHoBSwAfF/Ewv5Exz+9bTM+lHL+QAAWiKAEwMivmYii3mh0anZ7HTzZ+uTr3XuLdRivn+ms+mWutOjVySQF97aEBD/OnduV92JLgdEtwUCe5BpSfh1FQkBtNifWyeJLRDEpoiCe1UeACGi/mZ9knTK3Uo6IUcIABNEYCJQTEfU7HFfJo2f5J/p6UTvObm3pobU8sf1yYoslz3QL4eeCS4HRLcFAnuQRUkYV9XOgbP9rqh0mK+mI/Nk4R2SEJTJKGdCg9APQFTlzzFvKAXsiIATRGAiUExH1OcYn54wB2Vmv68aDrBFz8uxfzle/+7prks/+mFegnxuXrgkeB2SHBTJLgHVZCEOXvgFf+xeZLQDkloiiS0U+EBGC7m3SrthZxZMzY5846jF7IiAE0RgIlBMR9TnGI++Lzo9EdD041PNm/uqLmpv+azjU3tGuj/uOZdCXFZ9DYRJLgdEtwUCe5BFSThxfMuBqe7h6QV/7F5ktAOSWiKJLRT+QGo0zxZ3DhU4QfH6gW9kBUBaIoATIyqLOZHstTW1m7dulXeDN6cO3dOEvzVV19148JcPNwky4WTR3V44Hj/rtZOKeaP1fyPvT//cW1HnyyLth7REF/f2q03C9u/f39XV5cb+CJ/HXt6etzAi6Ghobq6uv7+fjf2QjYnG5VNu7EX8sTK0+sGvshLSF5IbuDLkSNHWlpa3MCX9vb2hTCFDWSmspVdzF+ZdMV8aR+bZy5rh7msKeaydio/ALVuD/eujxTzOqQXsiIATRGAiVGtxXzTbBs3bly+fLm8E7yRjUqCL1261I0Lc7hu25Ht6zt2btLh2rqmX+w40PzdbzTW3Lz3238vl2VZtCZ9mr0s3/zVXr1Z2Nq1a3ft2uUGvqxatUr+QLqBF5KkK1eubGhocGMvZHOyUdm0G3shT6w8vW7gi7yE5IXkBr5s3759w4YNbuDLW2+9tRCmsIHMVLbiz1HK6h6SpsX88sfdcD7MZe0wlzXFXNZO5Qfg4Nhk7/kJ+deN5T0+9Z4W89rfnl7IYQSgKQIwMTjNPqY4U1gxOuSOSl1JR7lrfLLmTSnmZWlvPayZriH+z2s79IfCSHA7JLgpEtyD6kjCrO4haS/eny7m773RDedDEtohCU2RhHaqIwCz6MRPJoRymV7IYQSgKQIwMSjmY4qZ4JMTbhYrVX1QzLd3aTE/+NqPNcT/+Mf7JcSvX9KoPxRGgtshwU2R4B5URxJmdQ9Jq9/kDs5HeuPNgSS0QxKaIgntVEcAZtGJn0wIL1+d0suy0AtZEICmCMDEoJiPKX6Ch76Zqf/CZRfc3727o+am0Se+psO/f3POHngkuB0S3BQJXnbaMSSsSruHiNH+Pi3mL//yabcqL7qH2KF7iCm6h9jJzASrr5hP90LOfJB+Zk5IL+QMpoKmmAomBsV8TPETXL+ZSZbw95SsebO/5rNjd/1Z+ih9z/nFe09piMvyySWND23p3DndBIUEt0OCmyLBy06qd20aEqjS7iHq4qLb08X8d25y47zoHmKH7iGm6B5SLroHM6xa9mZGHDjeX9vRJ/+2nBjQRsj0QlbszTTF3szEoJiPKX4xP37BFfMT4zPFfHuXVPIykU1/fr7n/FtHzgXFfLAMj1+Rn6aYt0Mxb4pi3oOqScLZ3UOcnSv14HwhZ9qThHZIQlMkYblI9a47MQPVsjczQnsh67/BQi9kIUHE3kw77M1MDIr5mOIX81NTbhY7PDA26Yr51tMXUg9/MV3Mf+9uGfaenzg+NL60sfe219o+8sw+DXQZyk8zhbXDFNYUU1gPqiYJZ3cPcc6ddsX8qsVuzdxIQjskoSmS0E7VBOBsrn3S9NLef1Ev6NyPXsgEoBECMDEo5mMqKcH1m5n6uuSiRraU7un5612faq35QmN717HBmeTqv3D5N+7fLoH+xV8clCEJbocEN0WCe1BNSRjqHjLjifSZ9qknb3fDuZGEdkhCUyShnWoKwJBIMT84NqkX6IVMAJoiABODYj6mkhI8OMV0Mv1NJE7PMZnFdtTc1LjmzY6zY25lJuVveKlZAv03H9ohQxLcDgluigT3oJqSMNQ9ZEZwpr1cmxdJaIckNEUS2qmmAAwJ972T6V/wAUx6IROApgjAxKCYj6mkBL8y6WaxwwNujXr4i+lifvHj4WJeLj+4pVMDfUfnEAluhwQ3RYJ7UE1JGOoe4taI4Ex7qerzIgntkISmSEI71RSAITPtk3rOS2EffACTXsgEoCkCMDEo5mMqNcFDZ9rPWP54b82N6e+c75mJy0P9F6WG1zT/57UdJLgdEtwUCe5BNSVhqHuIW6My3UPmPdOeJLRDEpoiCe1UUwCGhIt5qeRljV6mFzIBaIoATAyK+ZhKTfCcZ9q31rlifucut2Y60z/85B4J8T94dh8JbocEN0WCe1BlSZhzn2ame8i8Z9qThHZIQlMkoZ0qC8Bps3ohZ+iQXsgEoCkCMDF8F/PDwyN33nl3V9cJN55Nrr31ljs+9tHrZZELMnRXzKf6EjznmfbjFwZrbkgX86/87PLVKVkh/2qm3/7GIc3xLQ0HSXAjJLgpEtyDKkvCubuHzHumPXNZOyShKZLQTpUFYIjO9NK9kDOkqpchvZAJQFMEYGJ4Lea1Vv/0X3wuZzGv165evV6HckGGBdbzVZnguY5KjT73zXQx/9A/jU6kcyQ4+WpF82kt5h9b20iCGyHBTZHgHlRZEs7dPSRdzL94vxvmwlzWDkloiiS0U2UBOLeOs2My8aMXMgFoigBMDH/FfGNjy8c+ev0tt9whS85iXm4Qrt61tpeVOsyvKhM811Gp0a1vpIv5mptHT6fPpAr3ONWdsv/vC7tJcCMkuCkS3IPqS8I5uoe4g/NzYy5rhyQ0RRLaqb4AnEN2MU8vZG8IQFMEoAVPxfylS5fuu+9RqcyljJ+rmI9IfjGf66jU1bN9Wsz3bl4lw/C3j+pO2evu30aCGyHBTZHgHlRfEs7RPcQV83JhDsxl7ZCEpkhCO9UXgHMI5n5uLIlHL2RfCEBTBKAF35+ZL7yYlzJ+rhPys1Vrguc6KtX44J3pYv6Ze+Sy7p3VJdgpu7rpuN7SGxLcDgluqmITfJjuIWqO7iGumF+12K3JwlzWDkloiiS0U30BOIfsYl6H9EL2gAA0RQBaqNBiXm4glXzw+fl5VWuC5zoq1fzqi1LMn6r5K5nRHuq/KPHdnvk32Cl7+68KOluhjEhwOyS4qcpMcK3V6R7i5DzT/vmadDH/yM1umIW5rB2S0BRJKCTQ2JuZx+DYpFbv9EImAO0QgIlRicW8VvLPPbvEjbOMjIwcnW3Lli2rVq3q9OjIkSOS4MuWLXPjWLqOdPTs3S7LyeYGt6qzc8+aN6WY76i56cyWN9bUH5Jl78EjeuGjT+2SEP+dR7a5m/qyceNGSRk38EKe3vXr17e3t7uxF7I52ahs2o29kCdWnl438KWxsVHeMm7gy759+3bu3OkGvtTV1VVagjfSPSQi55n2O1e6g/NzfEEdxbwd5rKmmMtqprE3M4+g+TG9kAlAOwRgYlRcMT9vJS+kmO+ebdu2bevWrev1SF4WkuAvv/yyG8fVf/DAQNMeWfpOndQ1u9uPazE//NOHNjZ3ytLWeWpba5dcuOuNFs3xlfVH9MZ+yNPb0dHhBl6cOnVKq0039kI2JxuVTbuxF/LEytPrBr60tbXt2rXLDXyRv4579+51A1/2799fUQlO95Accp5pLzW8FvNzfEEdxbwd5rKmFvhclr2ZhYgU8/RCdmMvCEBTFPMWKquYl/VFnV0fqOIEnxh3E9npA1CnRi41Ln68ueavRx/+UpDm+uH5/d3nr7t/m+T4hxbN2RfKAgluhwQ3VbEJnj8Jw2QWO9chrGzVmoQ5z7TXL6h7vsYNZ6OYt0MSmlrIScjezAJdnXpPp3+959PnK9EL2Y29IABNMRW0UEHFvEZ2jEpeVHeC93e7ev7KpIzSqZ05076/5rONTe2S3RLrxwbH5ULr6Qv/sGKfHpxf2pj+7jo/SHA7JLipik3wAueycoOi9m9WaxLmPNN+1WJ3cH78glsTQjFvhyQ0RRKKAgNQLIi9mblo6a7FPL2Q3dgLAtAUAWjhGhfzuptWp6ryr/Y7CS8FzmKrO8FnH5xPF/PtXdoDr/GVn0l2u5WZHJc33r9/rFZy/EOL6obHr2R+3hwJbocEN1WxCV7IXFauzf+Zo2R3Dzmz5Q0t5uWCWxUij7SxsdENfKF7iB15YukeYqfSuocUEoBCMzD5ezNzae4dlSnfqZFLcpleyG7sBVNBU0wFLfgu5o1UfYKHDs67z0otflzq+cZ7v9Jxdix9/fQnpt5pa39+R4dG+YNbOvWnxyavyk/JvzosOxLcDgluqmITfN65rM5iF3L3EKHF/NiLD7pxyK5du9ra2tzAF7qH2KF7iKlK6x4ybwCKeTMwMXszc9ra9O6a+kPyr1ymF7Ibe8HeTFPszbRAMR9TmYv50MF5V8xv3pwu5mtuPrZ9k1zvVvac3/9Ou1RiH3kmfbL9+x/aMTx+ZWfn0NfePPyHz9U/vNXV9mVHMW+HYt5UupSvwmJeZ7GFH48KVHESZnUPSXvx/nQ9n+sL6uQFLC9jN/CFJLRDEpqqtCTMH4BCM3Dh7M3Mtrv9+MbmTvn3+KkeeiG7sReyOfZm2mFvpgWK+ZjKf27V9MH5yxPTbUvv+bIU873P3itXjk26Yr6+pU3mH8F5VuHluge291+4rHdWXkxh7TCFNZWZwVZZMU/3EO0ekhZ8QV1P9JVDMW+HJDRFEor8xbxW8gtrb2aWdC/knvPNfaPBsRy5QC9kDwhAUwSgBYr5mMqf4MGBqdEhDe70B+Zrbh6suUEPVenK2gMHdQp7/ZLGcCWvy90bj2buq8xIcDskuKmKTfDIXJbuIWnZB+flghbzqxa7NdMo5u2QhKZIQpGnmF+4ezNnCzolBZ+ypBeyHwSgKQLQAsV8TCYJ3teVnsie7dXgTreyr7l59K4/T21cJldqN5Tt+10xv6Nz6CPP7PvntR2L3+6Wyx97rl5i/d89uOP4UPlTjwS3Q4KbSnyCR1R9EmYfnNcz7e+90Q2nUczbIQlNkYSCvZnzCop5PUQvS3ilvG7phWyEADRFAFqgmI/JJMHPnU7PYnuOzXwNyXfvvnrXf9WPjOrKDftaw1PY4MtIl9T36G7a215rc9eVDwluhwQ3lfgEj6j6JMw+OF+/yR2clwshFPN2SEJTJKGdqg/AkODsel3ohewNAWiKALRAMR+TSYJfPK8T2RP9Iy7BN292E9nWOi3mf713VjE/ODapt2w9fSE48b65d9RdXSYkuB0S3FTiEzwiCUmYfXD+ns+mM/CFr7thBsW8HZLQFEloJwkBOC1SzB8bTIdAsJJeyHYIQFMEoAWK+ZhMElwmr5lZ7Ln+Ac3rju6zbiK7/HE9ver13e+Ep7DHh9IfoJLqvf/C5bWHBrSY/+SSRnd1mZDgdkhwU4lP8IgkJGGoe4hbs2qx26cZHK6nmLdEEpoiCe0kIQCnXb46pfNAXWQGKCvphewBAWiKALRAMR+TVYJnPjZ/6XS35nV6X+zyx3Ui23syvVKK+TP9A+7GqVRzX/qD9FLS6/7av15xUNNcIt7dohxIcDskuKnEJ3hEQpJwunuIG/Ycc8V8pnuIopi3QxKaIgntJCQAp+k8UJfBMXemkg7phWyHADRFAFqgmI/JKsEzH5uf6j6qeZ3eF3u0RSeyo698X9ZIMX+8t19vG5xwpSkvF6SGv+6BHRLl739oRxlPtifB7ZDgphKf4BEJScLp7iFuKJ64PR2DoS+cp5i3QxKaIgntJCQAp+kETxeZ7+lKeiFbIwBNEYAWKOZjskrw6Y/Nd3Sflbx2+2Iz/ZxH7/rzxvaucDEf9Di9OvWeDNv7L8rl7+04rrtmy9jjlAS3Q4KbSnyCRyQkCadjMDWZPrM0LWiD1+q+V5li3g5JaIoktJOQAJw20wt5epoXrKQXsh0C0BQBaIFiPiarBJ/+2PzoQH/v+YnLV6fSK8+5L1tufOphKeYPHu/L3DTVevqCZLf2OBVBbf/glk5N8z94dl9Z6nkS3A4JbirxCR6RkCScjsGZj82PXwi6h+gKink7JKEpktBOQgJwWriYd6umV9IL2Q4BaIoAtEAxH5NhguvnRfu73VBlPjnfWHPzr1f9Wov5oDlK0OkkSPOxyau3vdZWxnqeBLdDgptKfIJHJCcJIx+bF9PdQ7QNHsW8HZLQFEloJzkBmKEdjmUJjtkIeiFbIwBNEYAWqrKYv5ylrq5u165dbuDF2NiYJPjy5cvduHwmz5ya7GqXxY3VyFDqns+213x+12P37G4/fmH80uHTI3uPn5NFLrubXLyka/qGL8rwll+0aqDL8tmXml6sP9V/fkxvWazGxkZ5z7uBFxKmDQ0NIyMjbuyFbE42Kpt2Yy/kiZWn1w186e3tlT8bbuCLJOnhw4fdwBf5m5HsBI9Izlw2+2Pz091D9OA8xbwd5rKmmMvaSU4AZmjdLot+L50Kinl6IRshAE0RgBaqspiXokvqn7ANGza8+uqr8vbzRjYqCb506VI3Lp/W+rc7tv5alnca9rlVGaeXPt5Rc1Njzc2rf7Lklzv2r9iWXl7fud9dnaErN+05IJd31Td9+HvpZnjh5Zu/eltvWZSVK1fW1dW5gRcHDhx4/fXX9+2b9QxYk83JRmXTbuyFPLHy9LqBLzt37ly9erUb+LJ169Z169a5gS+bNm1aCFPYQHLmssHH5idC07hM9xA9OE8xb4e5rCnmsnaSE4AZQTEvF9yq6c7H9EK2QwCaIgAtcGQ+Jrsj85cvXdIj85NnT7s1amTo1D1flGK+4f5/2Ptuz97j5w6fHgkOy6u2vvThevnXjS9ffr2l75ZftP5v39sd1POffamp2EP0jRyZNyNPrDy9buALR+aTKjlz2eBj8+cH3Rox3T0k9cLXKebtMJc1xVzWTnICMGN4/Eq4SlfZxTy9kMuLADRFAFrgM/MxmSZ46syJ9Cw28rH5VKq3dpMU87K0//y54HtKwoJMd+NpY5NXf9rQ84HHaoNwL2pnLQluhwQ3lfgEj0hUEs7dPUSWE1vXUMwbIQlNkYR2EhWAmdZIvecnZHG9kKfJHI9eyHYIQFMEoAWK+Zhsi/nhAXdUajaJ473fvXuw5ob0dHb6K5rCwj3w3KrMT+nnqXZ0Dn1yus1pUSdfkeB2SHBTyU5wd/pByALpHiIBeGHRV3t7e91KXzhHyY48sZyjZEeyN8FJGEjaVHAOMp0Linl6IZcdU0FTTAUtUMzHZJvg4xdcMR98M1PG1an3DjUfeO/uz6SL+Xtv1K7OYZLdGuLBSVlBrAfLfZuOargX/jWkJLgdEtxUshN8wXYP0YPz7778rFvlC91D7NA9xFQiu4e4HRUhifrE5dxaeoZ/seMAvZCNsDfTlAQRezPLjmI+JttifmrKnWIqy+RM4xMhNdj5ug06l0298HW3NiRSuuvS3DsqVX1w8tVNy124Hx8qqFpuanlnaHjEDbygmDdFMZ8M7s9UyALpHqIH568++Hm57FZ6wVzWDnNZU4mcyyZ7b2Yeq3c1/mD52hfX7qAXsgX2Zppib6YFivmYzM+tkhpei3mp6qW2nyY1mEx6UqsWu3p+50p3xbSOs2NBDa+LVPJ61v3Vqfe0M0rwNaQFHpyvbWjuPVvOLziZF8W8KYr5pEpaEs7RPSRVv8kF4Ion3Bov5K3KOUpGSEJTiUxCt6MiZIEcme8cGP3FjgMbmzv1CDy9kMuLvZmmJIjYm1l2FPMx+fig1OiQq+fP9ro1QTE/fiH18BfddFbmtSGXr04Nj1/RpimyHB8aD7dOCc7D/39eqJc0f/9DOwr5GNW6PU0dvaGe0vaYwppiCptUSUvCObqHiLHHv+oCMFf3ECMU83ZIQlMLJAmTFoBzkKnd67vf2dp2sr3/Ir2Qy44ANEUAWqCYj8lTgksZr3PZ6Q/Pu2Je9BzTc03TH56XywWTPwMS8UvqezTNH9zS6a6Yw+WrU7/YceDgqQE39oIEN0WCJ1XSknCO7iGioyVf9xAjzGXtkISmFkgSJi0A5zA8fmVV3Tsdp+bMPXohl4IANEUAWqCYj8lTgoc/PJ/5vuWZYl601rljU0XW85LdEugfX9ygUZ7/4Hz/hctSzDd0nnFjL0hwUyR4UiUtCUvoHmKBuawdktDUAknCpAXgHK5OvXewPTQVzEIv5FIQgKYIQAsU8zH5S/CJcTeXleXMicMHW2clePDZUannxy+4lfPRz9U/tr1Lo/zp3fle4of6L0oxX9vhvtHUDxLcFAmeVAlMwrjdQywwl7VDEppaIEmYwACcgwvAuYXr9mChF3IhCEBTBKAFivmYvCa4TGe1EdSpjq7db5071eXWK5nF6nT2ydsLPD6vZ9rL8sFF6W4oH1o054dO9StMtZgPn69ljQQ3RYInVTKTMFb3EAsU83ZIQlMLJAmTGYC5zFvM0ws5NgLQFAFogWI+Jt8JPjWlvaC6dm0ebq1PXw5b7r54ucDz7Ucn3ClYi/ee0iif6+B8/4XLcjMt5oPztTwgwU2R4EmV2CQ06B4SA8W8HZLQ1AJJwsQGYJZ5i3l6IcdGAJoiAC34K+a7uk58+i8+97GPXi/L6tXr3doszz27RG9z6y13DBd8Ps9CSfDxC12730oX8zKpPXMifNJpauMyV8/LUsARKs3xUyOX9OD8XFF+KLP7Vot5+Xvg1tojwU2R4EmV2CS06R5SLIp5OyShqQWShIkNwCzzFvPz0jM06YWcjQA0RQBa8FTMS1kuxbnW8FrVNza26FVhcoOghpeq/r77Hr106ZJeld/CSfDDB1uHDre4Sa1MW8NNoYLPz8vy5g/zf4ReT8GSf5c29s4V5XqOvSx3vPr2+tYeubG7wh4JbooET6okJ6FB95BiUczbIQlNLZAkTHIAzlZ6MS/ohZwTAWiKALTgqZiXKj1cmUeGSoayMjhor/V/zpo/28JK8P7+9FcxBfPazHEqJzjjVJZHbk4dnfPZC38N6fXTX1ISaYKi59hLkS9X/fHi+ua+Qr+/JHw2VzwkuCkSPKkSnoTl7h5SLIp5OyShqQWShAkPwJCyFPP0Qs6JADRFAFrwVMw/9+wSWdwgc3D+zjvvjpxFTzFfiJkEv3h+pp6XCW5wiF7q/Odr3KRWljkO0Q+PX9FifnTi6o7OIY3ySBOU1tMXtJLX5fu1J69Oveeuy+v4UEHnU+RBgpsiwZMq+UlY1u4hxaKYt0MSmlogSZj8AJxWlmJez7SXhV7IYQSgKQLQgo9iPlKlCynmb7nlDvnXjafJbaSA1yI/fHleCzTBQ8ep0sv5wZlP0e9cOXOIXua1m5ZFSnopyzXE9ZPwUsZrxS6Fvd5A8jpcycty3QPbu0fm/9i8dtcrsOyfCwluigS/hugeUgbl6x5SFIp5OyShqQpMQgsLJQDLVMzTCzknAtAUAWihsop5ITfTKWz2efiBkZGR7tm2bdu2bt26Xo/kZSEJ/vLLL7uxL7t27Wpra3ODjDMd7QNNe9zSvPfMoXf6Tp1Mrz/0zuUf/EMwr7364OeHN/9Kf0TtPNi1sblzd/txubzv8AmN8v/4/d3fWNWysuHo8rc7dM1192/7q8Vb9fIXX96vP5uH3KHc7eGuU24cy6lTp7Zs2dLZ2enGXsjmZKOyaTf2oqOjQ169buCLvITkheQGvshfx71797qBL/v376+oBNcTjjQMtaqne0g85eoeUhSKeTvMZU1lprIVkYTszSyLshTzQot5eiGHEYCmKOYtVFAxrzcLzsaXOa7keM6ZrhTzR2eTGmzVqlVSjHlz5MgRSfBly5a5sS/ySBsbG91gWlfH4VMH3u7Zuz1YTjXtO9He2nnsaN+uDZf/9bZganvlgc8NrHv1+KGD8lO1LR1r6g+tazisd/JPr7lPzkeWJza2bNy48T8+vkOHK3a36e1zan33qNynLHsPHnGrYpGnd/369e3t7W7shWxONiqbdmMvJL7l6XUDX+QlpPtKfNq3b9/OnTvdwJe6urqKSnCJwXBlHhmqSGBq/c8HjiLSc9lydA8pCsW8HeaypipkLsvezHIpVzFPL+RsBKApinkLFfSZecn0IL5V5KfyIMHTJsZT/d0zU1tdzvamP11fvyn18BeDkl5PvB8cm9SA1k9ADY9fuXFZi+6dDZZn69KvfomYX7d2/8b922XNhxbV5Wl5enxoXO+zxLgnwU2R4NdKIUlIMV+ImSQsrXtIUSjm7ZCEpiokCSO7LyNDRQAWIt9UsBj0Qs5GAJpiKmjBUzFfSIJTzBdi/gSXkl5msT3HZia4sshweCD19rpwSX/5jR9qiEtSu5/NkBxfsq/n9jcOSXZrFkvEdPQOfn2dO/H+S6+16y0jgt23shQe9zmR4KZI8GsiMkkVOc9REnKbIAzDl+e1QJOwhO4hRaGYt0MSmqqQJGRvZrnMPxUsDL2QsxGAppgKWvBUzGscazpLfOc8t0oTPAh6ucFcp9lnI8FzkDmrFPCRql6mvLWrUg//tc5uW7dskhA/NjgrKIPeeMF6iZhT/UOy5sNP7tE0z1nP62F5CfoPf3+P/FUopeUpCW6KBL8mCi/mhdxMPzKavd8zQPcQN8iI3T2kcPL0dnR0uIEXdA8xRfcQn9ibWUblKubphZyNqaAppoIWPBXzQmt4nZ4GaR4Jdx3qbeTGOee4OZHg+Vw8nz7ZPlzSt72duvsGmdceq/kfjU3tradnHaoKvq1EsljXSMScGRyWNWsPDfzHp/ZqoH9ySWP4fPvLV6ck/T/zUoteKxdKaXlKgpsiwa+JAueyerNCdmvSPcQNpsXrHlK4jRs3yrvVDbyge4gpuof4VHgxL+RmOhWUH5EfdGtnY2+mG5SGXsgRsjn2ZtpZsHszTfkr5k1RzM/vymRqdCjV1+Xq+QPbZEbbX/PZxnu/0tjeFRxFvzr1XnPfqNTt4Q+9yzRLak6t8NvOXPzIM/s00P/g2X2r2wf041VvHTn34e+74/a6LNnXoz8eA8W8KYr5a6WQs0ylbo8ciYr8VB4kYVqR3UPcTxVAk9ANvCAJTZGEPrE3s4xy7s2Mh17IEezNNEUvZAsU8zFV8blVkxOupN/86thdf9ZYc3Pj9+4+dHJAz4bSTieyhI+66xQ26HoqVwXnYuny/od2XPdAuj2eLNcvadTL1z24PdJGpXBMYU0xhb1WZCIbPtAUGSqK+ULMn4QFdw9JrVrsfmQ+FPN2SEJTFZKEkShjb2ZsZZgKTqMXcgQBaIqpoAWK+ZiqO8GD/s+/erq35kat54/1pu+29fQFyd/Iifc6hdV0DjrbRep5Xe7ddFSu+tl+9x0nn1zSqDcuFgluigS/VmSGKvNUPTYlE1m6h8RWRBIW0D0kfcS+AJqEbuAFSWiKJPSMvZnlUp6pYEbQupheyIoANMVU0ALFfExVn+A6tZUZ7fLH05+cnz4+r/kb+bi7TmGDg/Ya62pH59C/buu64aXmjz1X/1Jjn64cHr/y1ysOatw/vTvO+4cEN0WCX0Naw+vHQbWqF1rAR4Z6G7qH5BQnCefuHpJeJBLno0noBl6QhKZIQs/Ym1kuZZsKZuhRHHohKwLQFFNBCxTzMVV9gp8fdNPZifGrrz7RXvN5reez++EJncJqb1JZgsZ44viQ+5JSWYJzruRvgCT4Bx6r1bh/JnOCVlFIcFMkeFKRhPPL1T0kvdx7Y/q0/Lw0Cd3AC5LQFEnoH3szy6JsU8EMKddlCkcvZEUAmmIqaIFiPqaqT3CZ0epcNjN/HXvlyeaaL6br+Xu/0vtOk94koFPYYDetRLxclnzXT9HL0tw7Gtn5Kn8VVjSf1jTXuC/q8/MkuCkSPKlIwiKEuoe4ev7J2/N/C70moRt4QRKaIgkThgCMJzjvkl7IggA0RQBaoJiPKQkJLmW81vNS2KdSo698X4p5Kemv3vVfIx2egymsnosVWdr7L4ZPvFe6o/fn+3uDNirvf2jHXb/ueGhLpyx3re342f5ed9NcSHBTJHhSkYTFCXUPKaSeD5LQG5LQFEmYMARgPFLD63TuUP9FKeNlDb2QCUAjBKAFivmYkpDgkxNuInt+UFf079rUe890RyiZ1E6fdBpMYYND8cEiGa3RHxH8JZAL/7zWtUuJLH/8o/07OofcD8xGgpsiwZOKJCxaqHvIvPV8kITekISmSMKEIQBjC06q10/I65GbyIn3GoD6kXh6IZcXAWgq8QFIMR9TQhJcv41ZprOZg/Piavex1BO3u0nt9JcwB1PYwbFJCXoJfSnRRyeuhj88HyFX6R8G3a0rRXvkm06C5UuvtWfvtSXBTZHgSUUSFi3UPWRWPZ+rH16QhN6QhKZIwoQhAEuhJ1TKcqj/ol6gF7I3BKCpxAcgxXxMCUnw4CxTWYYHUlOZaB6/kP7iZZ3UyvLIzZ2b34gxhdW4PzUy860zQbe8XV3Dt79xKKjn3//Qjsgh+rkSfGzyat/5CVk6zo7J0nr6QimNUiNIcFMkuAckYdFmdw+ZqefvvTF1NNpAm2LeDkloaoEkIQFYiqtT77VPl/GyRA7LCw3A4FBN+HAOvZBLRACaSnwAUszHlJwEDz45L0vPsfRxKi3pZSL78BeDkv7KM/+cPbXNT/8qSMktly9fnQqyXtZLxB8bHF97aOCGl5qDkn5p48yn6HMmeLA/OFhWNJ8O7ywoEQluigT3gCSMY3b3kJl6XpY5uod4w1zWFEmYMARgicYmrzb3pvveydJ7fsKtnaYBKPO34AZyWUr64AOY9EKOjQA0lfgApJiPKVEJPjnhzrcPSvpzZ9IrxcZlqXs+OzO1feTmVO0b+Rs+B6TM1nwP77LVSl6ulX/1b8bDWzt/88EdGvR3/bpDfzaS4JevTgV/LeQPwzfWd3zqpwf0Rx7d1qW3KR0JbooE94AkjCOre0iqftNM7uXqHuINc1lTJGHCEICl0wPvMj3TqVpYEID6ifrIQi/kUhCAphIfgBTzMSUvwdMlevDdy7qcOZE+D/9s79AL97x3z2dmSnpZfvZAqmFz/qp+cGwyHPSySEEe/vMQnKz1xsH+oJ7XHbfhBJf7ae4b3dE5JNH/f/zgbb1ZeFlS3613WCIS3BQJ7gFJGFNW95D05bm7h3jDXNYUSZgwBGBZ9F+4nH1YXgQBGBxcCRZ6IZeIADSV+ACsymJ+ZGSkabaNGzcuX75c3gneyEYlwZcuXerGvqxdu3bXrl1uYOBgY8Phum1Htq+fWXZs2LRi2YGtG/peefryd/7HTD2fWS4s+uqZZU8c27zK/XxIY3PLL3Yc0GXj2017D7S4K0I2723WG7y0tfF3F7l6/rr7t97xyp6VK1c2NDTsqG/+5q/2Xv9c7b/5ztYg+mX5wCPbP/Pj3f/m227lI2/Wu3ssgWxONioh7sZeyMxj1aocz54peQnJC8kNfNm+ffuGDRvcwJe33nprIUxhA8xlY7LsHlIK5rKmmMsmDAFYLjkrc3mHagDSC7nsCEBTiQ/Aai3mI2pra7du3SpvBm/OnTsnCf7qq6+6sS/79+/v6upyAztD50Z7Tlw40nrxcJMs76xf2X+gTi9f2rnqypJvv3f3DTNz3Onl6qIvTy577NLm5Rc7WvRu9h47ffDUwNmhER3m1NB5prajT5Z3Tgz8/UrX+FSWDzy89f/8fl0w1OXff3fXPesOyX3qz/6q8eS/vX+bXvXD3Z26Mrb+/v66urqhoSE39qKnp0cmH27gi7yE5IXkBr4cOXKkpcW9MLxpb29fCFPYAHPZ+My6h5SCuawp5rIJQwCaCor5omgxTy/k/AhAU4kPQE6zj2mhJPj4hdTZ3iPb10sZPzPT1WX7a6mXH0s99qVgmjtrWfGEu4f5BB+el0XiWKL8I8/sC8I9WG5c1vJm24D7mWmDY5Mrmk//xv3b9TZ3/bojaKOaX84dySS4KRLcA5KwJDbdQ0pBMW+KJEwYAtBUvGKeXsiFIABNJT4AKeZjWmgJPnr2THrmen5QavvoR+uPtqQL+189lfr+HalvhY7YhxpH5Sc1vCZy6+kLenLXA5uP/i+PbPvYM3tvf+PQz/b3Dl6c9WWnAbmx5vh1D7h6/v0P7ciu+SPkz0lz32h22U+CmyLBPSAJy0CCrqzdQ0pBMW+KJEwYAtBUvGKeXsiFIABNJT4AKeZjWugJfmUyPbvV2r6/e9bhrLa3U4/d5ma6996Yaq1zP5JXsKtV6nlJ/MGLE6t3Ne7pHJA1eT6OJbRX6vp3z16/pFEzXZa/fLklzyF6/RGp5yOdV0lwUyR42Y3QPcRMGbuHlEISSbuHuLEXdA8xRfcQO0wFTckLKUYxTy/kQjAVNJXsqaCgmI+JBM9tYjxd4UtJ//JjMzPdN39YyJErCe4g6xtODq/Ytl+K+XlPnQr+Tkj1vrSxN/hL8KFFdc29o+5GIcGfDVn0vK8ACW6KBC877RgSRveQMitT95DYhoaG6urq+vv73dgLuoeYontIuUji6U7MAHszTa1atUrepG5QMHohF0KeWPZm2kn83kyK+Zgo5vM5cyJdz29/bdZ3NffMU8hdnXpPW6RKma3F/P4Tg+HdtznJDbQy1z6oUtLfuKwlCPpn6ma9e+XGracvLKnv+eSSxtvfOPST+p7+C5fddRTzxijmPSAJrYybdw/JKXxgyhuS0BRJWC66BzOMvZmm5I9LT0+PGxSDXsjzYm+mqcT3QqaYj4liPp/JCTfNPdKUer5mZl5bWMuodFV/cWJ9XePASEGfRA1Omx/NfEXK2OTVp3efDEJf6nYp6fUo/cqD/X/4XH1wlSzXPbD9My81v9TYJ9cyhTXFFNYDktCUvFVNu4dko5g3RRImDAFoquipYDFk4kcvZDfwhQBMDIr5mEjweQwPuAnuxfPpRtDBvPbeG1Obls1b0hc1hc3+RJYU9ntPjsz1daY5lw8tqntyx7Fd9U0kuBES3AOS0FSOJCx395AIinlTJGHCEICm4kwFi0EvZDfwhQBMDIr5mEjweUxNucNWPcfSB+rl39B3NaentiueyHPifVFTWM3xyCIZ3Xf+8m2vtUVK+t+4f/t9m45JfMvydN3JG15qDvbmynLd/Vtv+9XB1e0DBe7WnZf8cdK/SXMhwU0lPsEjSEJTRSRhCd1DwijmTZGECUMAmoozFSwSvZB9IgATg2I+JhJ8fjJtDQ5SDQ+ky/v6TbPOupflkZvTE9ysqr7YKaxksZ5jL0vwxyDY59p25sJ9m45KxN/+xqG2Mxf1R1TH2bEdnUMPbun8ncd2BX8AdPnkkkZZ7vp1x0NbOu/ffOyZupM7O4dk0Q/nzyv4DpVD/RcjfyrCSPD83jjY/6XX2v/riwe++IuDLzelPw1RlMQneARJaKroJIzVPSSMYt4Uc9mEIQBNxZwKFoleyN4QgIlBMR8TCV4QPTalS19X+nzUifHUofrUT78zU8/rcu+N6W9srn1DZ7olTmHDJ963nr4Qvhw5Ti5D3Tsry/e2Hf3E0zv/3fTfgDyL/J0I6nxZvr3p2DfWHZHlWxuO/nhfz+aOwbvWdsgNdAfBdQ9s/7+fr79v09HV7QPZOwJI8DykgA8/7bLIs3rxchF/YhOf4BEkoamik7C07iGCYt4Uc9mEIQBNxZ8KFkNmZfRC9oMATAyK+ZhI8EJdmZz1OdJgOdqSWvdi6nv/c2aCG1ree77m9EuPX27ckToWM90iH6Rv7h3tPT8x198DyXG5wZ7OAfmzIX88vl978q9XHPzYc/WyBH8MyrjI348vvdauOwLuXXfo0Tfrd3YO5dx5HHH56tSpkUt95yfyn282r6pI8K+uOhx53nT5q5db3C0KkPgEjyAJTcVJQo/dQ8qFuawp5rJ2CEBTfor5QLqqpxeyJQIwMfwV811dJz79F5/72Eevl2X16vVu7RyGh0duveWOxsZCZ+0kuKkyJPjoUPqQe1DJh5e2t9Nz3J98O/XATTMz3cgiE98Xvp4+dC/TX1mkwi+gO7SkuQR36+kLecr4gOR+08lBLeYjewGOD43LPdSfGlnefPrlpr6vr+u44aXmDz+5J/yXIHv5ne/ukr8ND27p/MKK1g8/+Xbk2jzL//XM3j/+0X5ZZCv3bTqqy7c2HP3Ll9+RPz96tF/+lcv/6YX6/7y44RM/bPj00vQtv73p2ENbOoOPA+RZ1jQd/+nWpo3vng2WzR2DBX58oBDyZOpfU130yS8qwQ8PXAw/J5Fl69Fz7nbzSXyCR5CEpuIkocfuIeXCXNYUc1k7BKApz8W8KCoA6YVcLAIwMTwV81qcaw2vVX3+Qv25Z5dIzU8xn626E1zmshPjrjXU8ID7QGl40cL+5cfee+zWmflunuXJ21MNm92d55Lnw+rZ5A/Gnv1Nw2OX5U+CVO+ySFHqrgtJ7y2ePgdMFrnQf+Gybkhu33bmwsqD/d0jE3pjJev1nK6vr+vQY/4feKw28iek8pff+8Hb/+mF9B6EyPKHz9V/+Pvz7Noo7/Lglk73zM4n8QkeQRKaipmEHruHlAXFvCnmsnYIQFMVXszL3Cwo44OFXsh5EICJ4amYlzL+vvsevXTJdbCIDCOkhr/lljvmLfjDSHBTtgkelPeynDut5+RPnTjcsfXXl3euct/b/JNvp7+6Ofiep8gi8+C8JX2Bip3CSjTnT+cwuaXuIDg+NN5xdkyXY4PjHb2Dy7c3vtzU9/DWztvfOCSLnt4vS+Tg/288sP0/L274zEvN97/V+Uc/2v9fftiQLqSfz3HLxC/fWHfEPa3zSXyCR5CEpuIn4TXqHhIPxbwp5rJ2CEBTFV7Mi8v0Qi5GFQUgvZDz81TMP/fsElncIHNw/s477x4eHnHjELlKKnkp42/lNPtcFkqCXxp7p2Hfpf4eV+TPKvXfTb29LrX6R+nvfPrWDTOT4JJL+ms+hQ3/HQovuvNYb5OTntauJxQc7h9bebA///LzPUdf2Hxg/btng2VN+8DDWzr/+dcd/7jm3bmW8NH48PKfFzf8lx82fH3dkfBy76ajwaJrvvqrpr97tV4v37n2XVn0cs7ls6HWNdnLC293u0c+n8QneARJaKqkJLxG3UNioJg3RTFvhwA0dQ2mgqXtzQyfeE8v5GzVEoD0Qp6Xj2L+0qVL9933aPhz8lqxy79uPC24JZ+ZnwsJ7ujBfJkH/+qp1N2hkl7bShXwifpsTGFNFZvgkewOL32jsz7FkEcFJrh+zojuISWq1iS8Ft1DikUSmqKYt0MAmqrEqeB8Ih+kb6YXckhVBCC9kAtRWcV8cPp9/insyMjI4Gw7d+7cvHnzOY/6+/slwV955RU39mXfvn1Hjx51A19279594sQJN/BiYGCgtra2t7fXjecw1HPifNv+8027Lv3qmakHPh+e9V7+0T0Xt68a6j3lbloA2ZxsVDbtxl7IEytPrxv4Ii8heSG5gS/yN+PAgQNuUIClbx+LZLcuT259192iAK2trRWV4HQPKZfqnst67x5SFIp5Uwu8mGdvZllQzBdOKl56Iedc6IWcGBVUzIdXzlvMyxw9bOPGjb/85S/bPZKNSoK/9NJLbuzL+vXr9+zZ4wa+rFmzpqGhwQ28kKf3zTffbGpqcuO5vXugobN2U3rZ9daZX74wtuj2yDT34uN3nHvx0d43lx7fusb9zBxkc7JR2bQbeyFPrDy9buCLvITkheQGvtTW1m7evNkNCvPCpgP/+/dmTnX7nUe2f3ftfnddYbZt21ZRCR7sr8w5jJAAlEike0hOCZzLBuX9zEeKqqN7SFlQzJuqkLksezPLhWK+KHk+rJ5NNkcv5DwLvZArUAV9Zl7yXXfWhpfwT+VBgpuq9AS/eH7WQa2W2tQvf5C65zPRma4uL3zdnZ6ahSmsqdhT2AM9o+sOn913KkeLjXlVWoIXkoRKd27KLDbPbs1sJKGpa5CEC7J7iDcU856xN7NcmAraKTYApaqf94B/QG6pOwjohVz6Qi/kgKdivqgEF5xbNRcSPLfxC6mzvbNKelm2v5ZuFv2Dv5+Z5kYW/QDqiifSc9/xC0xhTS3wk0uFJJ7knqSfG88+HSksuCVJOBeS0KF7SAlIQs/Ym1kuBKCdax6A9EIOFnohF8hTMa9TUp3FSkbPu6uVKexcSPB8rkym+0tlfwBVlgPb0rX9y4/NeYbqXZ+aevwrp196fKqjyd2bF0xhTVVUghdezAe7O/MnId1D3MAXuofYoXuIqdYK6B7C3swyYipoh72ZpoqdCkYK+PBS1b2Qy8tTMS+0hteT54M0zw53RYLPhQQv1MR4urDX81GD01P1q6GOtsz6AGr4DFVdgq96tv86KBLcVEUleIFz2fDKeYt5maOH0T3EFN1D7NA9xFQldA8pvJiX28gt5fbszZwLezPtsDfTVLF7MxPZC7ns/BXzpijmTSVqd6wevZeqPlj6utIfPX35sal/vSUy/XXLk7enT8W3+SIoinlTmVq+ghK8kLNMZSIbNA0JlvBP5UESmqr0JKR7SJFIQp/Ym1lG7M20w95MUzH2ZiavF3LZUczHxBTWlGExn5NMgvu6Jo62Ht24cmrD0nm+6lkWmQfrVFiP3vfEn5kxhTVVCVPYsOCIU85hNs5RmgtJmBvdQ4pBEnrG3sxyIQDtEICmYgdgknohlx3FfEwkuKlrkuCXBs8cqn1r6sRhNwNue9udjZ/zVPycy3N3pZ6vSZ+iL3W+LjI5zns8nwQ3VWkJrsW5HpuSiSzdQ2IjCfOhe0hhSELP2JtZLgSgHYp5U0wFLVDMx0SCm7rGCa4Nos/2upPwdRJ8tGXmi6DyfNVznuXJ27OP55Pg8zvUkNq3Mb1v5XKhzU4CFZjgWsPr4Sat6kX2CaiKuexcSMJC0T1kbsxlPWNvZrkQgHYo5k1RzFugmI+JBDdVcQk+NZWeE2uRL8vwgPu8/eGGdIW/cVl6QqxFviz5T9HPWsYfviV9SF+Wn347tf7F1IalMwV/sJT1s/pVk+D1myLPVWrnSndVYRKf4BEkoalEJSHdQ5jLXgvszSwLAtAOxbwpinkLFPMxkeCmqizBZVqspb4sF8/PHP6S5fD+1P4t6eP5erp+jOP5OZdn/yld/C9/fNbJ/MEyn+pI8H0boo9aly1FvOkSn+ARJKGp5Cch3UPsMZe1QwCaYipohwA0lfgApJiPiQQ3lcAE16P6erJr29vpI2CZE1wvv/L4+KO3uUP65Sr1s5dF/zP1zJ3BcuX7fz/+vS+nlj2aWvPjmWXdT9PnBYSXph2po82zdhPIcrhh1lLwAbriEnzycvQhhJfBPnez+SQ+wSNIQlMLJAnpHmKKuawdAtAUU0E7BKCpxAcgxXxMJLipBZHgmeP5o2fPHGxsmDmwL8voULrs7z+ZatmVXpp3po/tr3sxtfJ5t7z8XVf8FzixvubLPZ9JPf0Pk0/cful7fycXClry79co+GT7xCd4BEloasEloe6CpHtIWcWfyyare4gFAtAUU0E7BKCpxAcgxXxMJLgpEnweemK/nskvy4nD6YJfl92rZ8r+YNHif3q5sugrl/71S1WzLyB7kUKiMIlP8AiS0BRJSPeQ0sWZy9I9pDAEoCkC0A7FvKnEByDFfEwkuCkS3FTuBNc5eng5VJ9qrXMnCASLrAyWtr3pOeWbP4zuOwiWVYvT1y7+l9RTX5t8/CvpI/NPfa2g5ZGbo5PX8LL5Vfc7zyfxCR5BEpoiCedB95ACFD2XpXtIwQhAUwSgHYp5U4kPQIr5mEhwUyS4qSpI8HOnozPX8NLV5m42n8QneARJaIokLAOp86W2p3tIgegeUgwC0BQBaIepoKnEByDFfEwkuCkS3FR1JPjaJdGZqy7LH3c3KEDiEzyCJDRFEpqge0iehe4hxSAATRGAdpgKmkp8AFLMx0SCmyLBTVVNgq98Ljp5ffkxd1VhEp/gESShKZLQTswkpHtIYRZIEhKApghAO0wFTSU+AKuymB8ZGTk82+bNm19//fUOj+QNIAm+bNkyN/Zl06ZN9fX1buDLunXrmpqa3MAL+T9du3Zta2urG3shm5ONyqbd2At5YuXpdQNf5CUkLyQ38GXPnj3btm1zg4Kd2rn+3LLHz//w3qGfP9az9U23tmC1tbULYQobYC5rirmsnQqay4bPDtCF7iFVggA0RQDaoZg3lfgArNZi/vRs27dv37BhQ79Hvb29kuCvvPKKG/tSV1cn1aYb+LJz505577mBF/Jul/9Tee+5sReyOdmobNqNvZAnVp5eN/BFXkLyQnIDXw4ePNjQ0OAGvjQ1NSU7wSOYy5piLmuHuWxudA8pBgFoigC0QwCaSnwAcpp9TCS4KRLcFAmeVCShKZLQDkk4J7qHFIwANEUA2iEATSU+ACnmYyLBTZHgpkjwpCIJTZGEdkjCfOgeUhgC0BQBaIcANJX4AKSYj4kEN0WCmyLBk4HuIW7gC91D7NA9JD+6hxSCqaAppoJ2mAqaSvZUUFDMx0SCmyLBTZHgyUD3EDfwhe4hduSJpXuInUR2D2Fvphv4wt5MO+zNNJX4vZkU8zFRzJuimDdFMZ9UJKEpktAOSWgqkUnI3kw38IW9mXbYm2kqkXszwyjmY2IKa4oprCmmsElFEpoiCe2QhKYWSBISgKYIQDsEoKnEByDFfEwkuCkS3BQJnlQkoSmS0A5JaGqBJCEBaIoAtEMAmkp8AFLMx0SCmyLBTZHgSUUSmiIJ7ZCEphZIEhKApghAOwSgqcQHIMV8TCS4KRLcFAmeVCShKZLQDkloaoEkIQFoigC0QwCaSnwAUszHRIKbIsFNkeBJRRKaIgntkISmFkgSEoCmCEA7BKCpxAcgxXxMJLgpEtwUCZ5UJKEpktAOSWhqgSQhAWiKALRDAJpKfABWXDH/3LNLPvbR62W59ZY7hodH3Nr5kOCmSHA7JLipxCd4BEloiiS0QxKaWiBJSACaIgDtEICmEh+AlVXMr169Pqjhpaq/775HL126pFflR4KbIsHtkOCmEp/gESShKZLQDkloaoEkIQFoigC0QwCaSnwAVlAxLzW8VPKNjS05h/mR4KZIcDskuKnEJ3gESWiKJLRDEppaIElIAJoiAO0QgKYSH4AVVMx3dZ245ZY75F83zhycX716vRvkRYKbIsHtkOCmEp/gESShKZLQDkloaoEkIQFoigC0QwCaSnwAVlAx39jYcuvsz8lLMS+LG+RFgpsiwe2Q4KaqN8HpHpIfSWiHJDRFEtohAE0RgHYIQFOJD8CqLOYvZtm1a9frr7++x6Pdu3dLgv/85z93Y1/Wrl27detWN/Bl9erVO3bscAMv5Ol98803a2tr3dgL2ZxsVDbtxl7IEytPrxv4Ii8heSG5gS+bN29ev369G/iycePGakxwuofMi7msHeayppjLFoK9mfkRgHYIQFMEoIWqLOZHRkYaZ9uwYYPkKQBEVF2CSwZKEtI9JD/msnaYy5piLjsv9mbOiwC0QwCaIgAtVOVn5qey7NmzZ/369R0eyRtAEnzZsmVu7MuOHTsOHDjgBr5s3bq1paXFDbx49913t2zZ0tbW5sZeyOZko7JpN/ZCnlh5et3AF3kJyQvJDXypr6/fvXu3G/giE7uqS3C6hxSCuawd5rKmmMvmx97MQhCAdghAUwSghQoq5knwQpDgdkhwUyR4gST0JPr0qJSa6xylbCShKZLQDkloqoqSkL2ZhSAA7RCApghACxVUzAvOrZoXCW6HBDdFgheo8GLetQwJoXuIKbqH2KF7iKkq6h7C3sxCMBW0w1TQFFNBC5VVzAuJbLqe5EGC2yHBTZHgBSp8Lkv3EAAFqpYkZG9mIdibaYe9mabYm2mh4or5eCjmTVHM26GYN5Wp5asswQs/y9S1DAmRP1p0D7EjUx+6hxihe4gpmSBVSxKyNxNA2VXdVLAoFPMxUcybopg3RTFfyWQWK3NZmdHmHOZHEpoiCe2QhKaqKAnZm1kI9mbaYW+mKfZmWqCYj4kprCmmsKaYwlY4uofMiyS0QxKaIgnzY29mIQhAOwSgKQLQAsV8TCS4KRLcFAle+aSGp3tIHiShHZLQFEk4L/ZmzosAtEMAmiIALVDMx0SCmyLBTZHgSUUSmiIJ7ZCEpqouCdmbmR8BaIcANEUAWqCYj4kEN0WCmyLBk4okNEUS2iEJTS2QJCQATRGAdghAU4kPQIr5mEhwUyS4KRI8qUhCUyShHZLQ1AJJQgLQFAFohwA0lfgApJiPiQQ3RYKbIsGTiiQ0RRLaIQlNLZAkJABNEYB2CEBTiQ9AivmYSHBTJLgpEjypSEJTJKEdktDUAklCAtAUAWiHADSV+ACkmI+JBDdFgpsiwZOKJDRFEtohCU0tkCQkAE0RgHYIQFOJD0CK+ZhIcFMkuCkSPKlIQlMkoR2S0NQCSUIC0BQBaIcANJX4AKSYj4kEN0WCmyLBk4okNEUS2iEJTS2QJCQATRGAdghAU4kPQIr5mEhwUyS4KRI8qUhCUyShHZLQ1AJJQgLQFAFohwA0lfgArMpifmRk5PhsW7du3bVrV5tHBw8e3LRpk2zXjX3Zu3dvU1OTG/iyZ88eiTY38EKe3rq6Ook2N/ZCNicblU27sRfyxMrT6wa+yEtIXkhu4EtjY2N9fb0b+NLQ0LAQprAB5rKmmMvaYS5rKvFzWUUAmiIA7RCAphIfgAkp5qUAk/eem7/7IhVRS0uLG/hy4MABecO7gS9ShrW2trqBL7JRz3W1bE426ga+yBPrf6PyEpIXkhv4Im8W//uhJBYkH1xwLADMZU0xl7XDXNZU4ueyigA0RQDaIQBNJT4AE3KavZb0buDLkSNHent73cAXqU8GBgbcwBepwUZGRtzAiytXrkiJ6z/BZaOyaTf2Qp5YeXrdwBd5CckLyQ18kTeLvGXcwJdrkgzXEHNZU8xl7TCXNZX4uawiAE0RgHYIQFOJD0CK+fgo5u1QzJuimE8q5rKmmMvaYS5rKvFzWUUAmiIA7RCAphIfgBTz8VHM26GYN0Uxnwzy4tEHGKB7iCm6h9iRJ5buIXYWSPcQinlTFPN2KOZNUcxXB53IuoEvFPN2KOZNUcwnQ3YxLwWY52pTyAuY7iF2ZKOe62rZnGzUDXyhe4gpiQXJBxccScHeTDfwhb2ZduSJZW+mncTvzaSYj49i3g7FvCmK+aS6Jo+XJLRDEpoiCatadjEvBRh7M+1ILLA30wh7M00lcm9mWEKK+YkMN/CFjdqReaT/jeoWPU9hdaNu4AsbTSqd0bqBLxTzdijmTVHMJ8w1eZgEoB0C0BQBmBgJKeYBANfkLxZzWTvMZU0xl02Ya/IwCUA7BKApAjAxKOYBICGuyV8s5rJ2mMuaYi6bMNfkYRKAdghAUwRgYlDMA0BCXJO/WMxl7TCXNcVcNmGuycMkAO0QgKYIwMSgmAeAhLgmPQLYqB2ZUPrfqG7R81xWN+oGvrDRhLkmU3aKeTsU86Yo5hODYh4AAADV7Zrss2CjdtibaYqNJgbFPAAAAAAAVYZiPo5Lly7dd9+jH/vo9bLIBRm6K2wMD4/ceefdXV0n3Nj4F3ju2SV6z7I0Nra4tcYblQ3pPX/6Lz4XfqRyWdboVatXr3dry0qe3ltvuSP8SO02Gr5nWcIPNngGZAn/MmUhjyJ7i8Joo8HmwkvwTAYvMHna5cnXlQAAAACKQjEfh1QjWs1qfStDd4UBLTUjNZjdLxDcs1zWyjOo8ew2KpsIHqBcDmo8fexaBEZ+mTKSByKFZXDPphsNP7qw8Ibk3+DZKAt5gMFG5XEFl003GhbeaPhy+MUGAAAAoCgU80WTgueWW+4Iyp7IsLykxJI6U+4/zxbL+AtIiSWFllZ3SsotLdrtNipkE1o8C91ToL+DrAwXe5FhWciG5IGEK3bTjc51b8HzrCLDUsj/UfjRhf+L7TYaFv4FIi+w7NcbAAAAgAJRzBdNao/g0KKQwkzKM4uCJLjnSOXs7RcQQYHn+VFL0SuXI+WlPAN33nl38DuUTp9YfWjBYzHdaOTOVfghK/llZI2sd+MSRP7jAqYbDehWgocceSULuSr8OwAAAAAoEMV80SIHV7OLorKLlEDefgGpAKUO1Hv2tlG5Ty0+szeRXQqWIrh/fZhazJtuVO9cPy4uS3Bae/gXUHNV4DHIY5GN1u3e63OjAbnPYIs6jGwi594NVIvwS1ouyNBdYUNeOXQPkSUcUGWUnQl2Gw3fsyyRlAjWh3+ZspBHkb1FYbTRYHPhJXgmgxdY2YMXALBwUMwXTf4ShydwOrEr70QnQuYc/ot5vdtgQx42Gkyn9G6zNxF5HkoUPKLwFNZ0o7qhoHaVLeqEMnsOLZfLNb2TxyJPabBRGXrYaEC2G2xaZG8icgNUF/m/0zeRvnFM/yv1FRupwex+geCe5bJsUbYbvFnsNiqbCB5g+M2ij11zKfLLlJE8EMmK4J5NNxp+dGHhDcm/wbNRFvIAg43K4woum240LLzR8OXwiw1VRBNA5y0e/gfl1cLeTFk0lMpOnl55S4Yfqd1Gw/csS/jBBs+ALOFfpizkUWRvURhtNNhceAmeyeAFFiQhYqOYL5q8EMP5pblW3vd5hLzlPBfzep/hN5i3R633LG/y7E1EnodShO8qnOCmG40IHmn2nxC5XK50y/kfZ71RVcgm5DeRxQ1QVSJvDbt3ipBXjvzJl/vPs8Uy/gLZL93ghWq3USGbCMJH36r6O0TexZFhWciG5IHIDC941KYbneveIoEQGZZC/o/Cjy78X2y30bDwLxB5gWW/3lAV5HWiL2MhFyxeNgF9kURqMLtfILhnuRx579htVDYRPEC5LI9XHrVc1scuoSGXI79MGckDkb8ywT2bbjT86MLCG5J/g2ejLOQBBhuVxxVcNt1oWHij4cvhFxvioZgvWuRNqHGmbwMj8r4KzxetfwG9w/AmhM9HHWxL3uHhvxPyDJTr4+uSI7pHMLzotuw2mk23pU+m/s1Q8gzIGlnvxiWQu43clYeNqshrRkReyUJ+k/DvgCoS+f/VV5Ss1GEZBfccef14+wWEvmvkgudHre+OYOuq7KGkT6w+tOCxmG40cucq/JCV/DKyRta7cQki/3EB040GdCvBQ468koVcRRJWl8h/Yvb/aRnJa1KmKHL/ebZYxl9A3ibhKBDBG9ZuoyL8LtC3jP4OsjL8lowMy0I2JA8kXLGbbnSuewueZxUZlkL+j8KPLvxfbLfRsPAvEHmBZb/eUCyK+aJFwisytJB/i5FhiSRcJGKyU8Zuo7pFiTY3Ds26TMM0EMkRu41GnrTwA7cLU9lceAruZ6Mq+6kjwZMk8v8bfmkZibyDvP0C+kLVe/a2UblPjcHsTUSehxIF9x9+P5puVO882IsaHAjKDgS5rE+CG5dAHotslO4hKJfIf6K+qsMvpHIJ7jnyHvT2C4jg9en5UWsERd4d8gywN7NYkf+4gOlGA7qV4CFHXslCrgr/DigWxXwc8rLT13rkBWok5+ve6BcI7tmNQ+w2Ku/hyNRK39Xhy3JtsFevvHQrwT3bbTTyvMkmgmwNb0j+DU/7SidbDG80uHPTjYrwdgPhRy3XzvViQ+WT/8rwf5++vPWNY0Ren+Ek9PML6N0GG/KwUXkzasGpd5u9icjzUKLgEYWT0HSjuqEgHILwCf8CSi4HcVEieSzylGYnoelGA5EwzN5E5AaofMEbR4cWURAReQ96+wX0PaL37G2jcp/6HsneROR5KFFw/+EoMN2o3rmGvCzB1Cv8C6gyZpE8FtkoezOTimI+jvBbUS7I0F1hIztEjH4B2YS83/RugyW4f9NHLUETbDESoMGvFF5fRtlZZrfR8HMYyTL5BXS9LOFfpiwkJfWew3kq7DaqjzTnsxf8MmX/gwGf5D83nAN5/sfLRV66nueyep/hF6q3R633LG+W7E1EnodShO8qnISmG40IHml2FMvlcqVEzv84642qQjYhv4ksboBq4C0KApH3oJ9fQO822JCHjcq7Q2cIerfZmyhvFgWPKPw+Nd2obih4v8sWdWJWSFDEJo9FntJgozL0sNFAJN+yNxG5AYpFMQ8AKFrk77HOfsJzgrKLTKesfwG9w8icw+ejDrYVmejIM1CuEz51hhdZdFt2G82m28qeQMszIGtkvRuXQO42clceNqoirxkReSUL+U3CvwMqX+QVlf1CKrvIy8bDL6D3GX71envUes8536HZb5/Ywnclj1EeqYa56UYjgkca/gVUdnTElvM/znqjqpBNyG8iixugeBTzAICiRSY3dnOdQP4tRoYl0rlOePaj7DaqWwxPH4MZT2QeFhmWS2TKZbfRyJMWfuCRKV0ZZ3iyObqHoIwiBYm+okz/EyNvHOtfQO8wvAnh81EH24q8JSPv5VLIezOyK1MW3ZbdRrPptvTJ1FBS8gzIGlnvxiXITiEPG1WR14yIvJKF/Cbh3wHFopgHAMQhf4D1r75OCGTorrCRcwZg9AsE9+zGIXYbldlM8EEYmfrIBEjnN+HLcq3cxmL2rFsJ7tluo5HnTTYRTPXCG5J/g2ejLGSL4Y0Gd266URHebiD8qOXauV5sqFjyIgnHUWRoIf8WI8MS6Zs0+2Vpt1HdogaOkjejvkdkZfg3iQzLJRKAdhuNPGnhBx7JipzREY9sjr2ZCUYxDwCIQycEejSjXBOdPLInjka/gGxCKjq922AJ7t/0Ucu8J9iizrRU+FcKry+j7BmV3UbDz6HO190VmRm8rpel7NM7maTqPUcqdruN6iPN+ewFv0zkGUC1kP9BTQD9Xy5vCZRNXrGRALT7BYJ7duMQu43K2yR4Y2oc6RsnfFlDqezJICIBaLfRyPMmmwgSILwh+TcSUyWSLYY3Gty56UZFeLuB8KOWa+d6saFAFPMAAABAEbQq0z0yHqoRKbHYmxlZX0aRYl7YbTT8HAY1rZJfQNfLEv5lykLKZr3nSMVut1F9pDmfveCXiTwDiIFiHgAAAACAKkMxDwAAAABAlaGYBwAAAACgylDMAwAAAABQZSjmAQAAAACoMhTzAAAAAABUGYp5AAAAAACqDMU8AAAAAABVhmIeAAAAAIAqQzEPAAAAAECVoZgHAAAAAKDKUMwDAAAAAFBlKOYBAAAAAKgyFPMAAAAAAFQZinkAAAAAAKoMxTwAAAAAAFWGYh4AAAAAgCpDMQ8AAAAAQJWhmAcAAAAAoMpQzAMAAAAAUGUo5gEAAAAAqDIU8wAAAAAAVBmKeQAAAAAAqgzFPAAAAAAAVYZiHgAAAACAKkMxDwAAAABAlaGYBwAAAACgylDMAwAAAABQZSjmAQAAAACoMhTzAAAAAABUGYp5AAAAAACqDMU8AAAAAABVhmIeAAAAAIAqQzEPAAAAAECVoZgHAAAAAKDKUMwD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<h3 id="Train-and-Test-Accuracy">Train and Test Accuracy<a class="anchor-link" href="#Train-and-Test-Accuracy">&#182;</a></h3>
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" alt="acc.png"></p>

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<h2 id="Test-Accuracy">Test Accuracy<a class="anchor-link" href="#Test-Accuracy">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># evaluate on test data</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate_generator</span><span class="p">(</span><span class="n">test_datagen</span><span class="o">.</span><span class="n">flow</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">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<pre>157/157 [==============================] - 10s 64ms/step - loss: 0.4492 - acc: 0.9269
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test loss:&#39;</span><span class="p">,</span> <span class="n">score</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy:&#39;</span><span class="p">,</span> <span class="n">score</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
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<pre>Test loss: 0.4492105321519694
Test accuracy: 0.9269
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<h2 id="Confusion-Matrix">Confusion Matrix<a class="anchor-link" href="#Confusion-Matrix">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="c1"># Look at confusion matrix </span>
<span class="c1">#Note, this code is taken straight from the SKLEARN website, an nice way of viewing confusion matrix.</span>
<span class="k">def</span> <span class="nf">plot_confusion_matrix</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span>
                          <span class="n">normalize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                          <span class="n">title</span><span class="o">=</span><span class="s1">&#39;Confusion matrix&#39;</span><span class="p">,</span>
                          <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Blues</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This function prints and plots the confusion matrix.</span>
<span class="sd">    Normalization can be applied by setting `normalize=True`.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
    <span class="n">tick_marks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">classes</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">tick_marks</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">tick_marks</span><span class="p">,</span> <span class="n">classes</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">normalize</span><span class="p">:</span>
        <span class="n">cm</span> <span class="o">=</span> <span class="n">cm</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="o">/</span> <span class="n">cm</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>

    <span class="n">thresh</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">/</span> <span class="mf">2.</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="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">cm</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">range</span><span class="p">(</span><span class="n">cm</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])):</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">cm</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="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span>
                 <span class="n">color</span><span class="o">=</span><span class="s2">&quot;white&quot;</span> <span class="k">if</span> <span class="n">cm</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">&gt;</span> <span class="n">thresh</span> <span class="k">else</span> <span class="s2">&quot;black&quot;</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">ylabel</span><span class="p">(</span><span class="s1">&#39;True label&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Predicted label&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># Predict the values from the validation dataset</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;float32&#39;</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">/=</span> <span class="mi">255</span>
</pre></div>

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<div class="prompt input_prompt">In&nbsp;[34]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Convert predictions classes to one hot vectors </span>
<span class="n">Y_pred_classes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">Y_pred</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> 
<span class="c1"># Convert validation observations to one hot vectors</span>
<span class="n">Y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> 
<span class="c1"># compute the confusion matrix</span>
<span class="n">confusion_mtx</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">Y_true</span><span class="p">,</span> <span class="n">Y_pred_classes</span><span class="p">)</span> 
<span class="c1"># plot the confusion matrix</span>
<span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">confusion_mtx</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
</pre></div>

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<h2 id="Heat-Map">Heat Map<a class="anchor-link" href="#Heat-Map">&#182;</a></h2>
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<div class="prompt input_prompt">In&nbsp;[35]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">classification_report</span><span class="p">,</span> <span class="n">confusion_matrix</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">Y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">Y_pred</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
<span class="n">cm</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
                      <span class="n">y_pred</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sn</span>
<span class="kn">import</span> <span class="nn">pandas</span>  <span class="k">as</span> <span class="nn">pd</span>
 
<span class="n">df_cm</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
                  <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
<span class="n">sn</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">font_scale</span><span class="o">=</span><span class="mf">1.4</span><span class="p">)</span><span class="c1">#for label size</span>
<span class="n">sn</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">df_cm</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">annot_kws</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;size&quot;</span><span class="p">:</span> <span class="mi">12</span><span class="p">})</span><span class="c1"># font size</span>
</pre></div>

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<pre>10000/10000 - 8s
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f7fa9532fd0&gt;</pre>
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